STATE VOCATIONAL INTERESTS, OUTCOMES, AND PREDICTORS OF VARIATION 

“WHAT DO I WANT TO DO TODAY?”: 

 

By 

 

Jacob Bradburn 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

A DISSERTATION 

 

Submitted to 

Michigan State University 

in partial fulfillment of the requirements 

for the degree of 

 

Psychology – Doctor of Philosophy 

 

 

2020 

 

 

 

 

 

 

 

 

 

STATE VOCATIONAL INTERESTS, OUTCOMES, AND PREDICTORS OF VARIATION 

“WHAT DO I WANT TO DO TODAY?”: 

ABSTRACT 

 

 

 

By 

 

Jacob Bradburn 

Current theory and research in the organizational sciences considers vocational interests 

 

 
 

to be stable trait preferences, neglecting any short-term within-person variability that occurs. 

This research project posits that vocational interests display non-trivial state variability, that 

these state vocational interests differ theoretically from other extant interest constructs, and 

details a program of research in which the usefulness and nature of these state vocational 

interests are evaluated. Results suggest that state vocational interests display variability of 

different forms day-to-day, and this variability cannot be attributed to measurement error alone. 

Individuals were found to differ in the degree to which their interests vary, with several 

individual difference variables predicting greater variability. State vocational interest congruence 

was predictive of daily positive affect, intrinsic motivation, engagement, and perseverance. 

However, state vocational interest congruence did not generally predict daily negative affect. 

Tentative support was found for situational interests predicting next day state vocational interests 

and for state vocational interests predicting situational interests, suggesting a positive feedback 

loop between these constructs. Theoretical and practical implications of this research are 

discussed.  

 

 

 

 

 

Copyright by 
JACOB BRADBURN 
2020

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

This dissertation is dedicated to our son, Carson Allen Bradburn.  

Your mother and I love you more that you could ever know. 

iv 

 

 

ACKNOWLEDGEMENTS 

 
 

There are so many people I would like to thank for being a part of my graduate school journey. 

I’d like to thank my cohort, Courtney Bryant, Mike Morrison, Jeffrey Olenick, and Ross Walker, 

for their support as we conquered and commiserated through the various trials of graduate 

school. I’d like to thank the other graduate students in my program, as I truly cherish the 

friendships and memories made during my time at MSU. I’d like to thank the faculty of the 

Organizational Psychology program, and those outside of it with who I had the pleasure to work 

with, for their support and guidance. My personal and professional growth during my time in 

graduate school could not have happened without the advice, guidance, challenge, and 

opportunities they provided. I would also like to thank my dissertation committee, Dr. 

Christopher Nye, Dr. Frederick Leong, and Dr. Jason Huang for their support and assistance 

throughout my dissertation journey, and for their persistence through this lengthy document.  I 

would especially like to thank my advisor, Dr. Ann Marie Ryan. I could not have asked for a 

better dissertation advisor and I truly appreciate the opportunities we had to work together, and 

the ability to learn from such an accomplished and student-dedicated scholar.  

I would like to thank my wife, Brittney Bradburn, for her patience and support during this 

process. I would not be here without her encouragement and support, and I am truly thankful 

than I have her in my life. I would like to thank my parents, Steve and Seneca Bradburn, and my 

sister Taylor for their unwavering support as well. Though my name is on this dissertation, they 

all undertook this journey with me and stood beside me through all the trials and triumphs of 

graduate school. For this, I am truly grateful. 

 

 

 

v 

 

TABLE OF CONTENTS 

 

 
LIST OF TABLES ....................................................................................................................... viii 
 

LIST OF FIGURES ..................................................................................................................... xxi 
 

INTRODUCTION .......................................................................................................................... 1 
Short-Term Within-Person Variability of Individual Differences .............................................. 2 
Perspectives on Interests ............................................................................................................. 5 
Organizational Sciences Perspective ...................................................................................... 5 
Educational Perspective .......................................................................................................... 7 
State Vocational Interests ............................................................................................................ 9 
Variability in Vocation Interests ............................................................................................... 12 
Interests and Whole Trait Theory .......................................................................................... 12 
Theoretical Argument for Variability .................................................................................... 13 
Forms of Variability .............................................................................................................. 15 
Individual Differences in Variability ........................................................................................ 17 
Openness to Experience ......................................................................................................... 18 
Curiosity ................................................................................................................................ 18 
Implicit Interest Theory ......................................................................................................... 19 
Self-Assessment of Variability ............................................................................................... 20 
State Vocational Interests and Prediction.................................................................................. 20 
State Vocational Interests and Interest Development ............................................................... 24 
Situational Interest Driven Change Theory ........................................................................... 25 
Congruence-Incongruence Driven Change Theory .............................................................. 28 

 

METHODOLOGY & RESULTS ................................................................................................. 29 
Study 1....................................................................................................................................... 30 
Sample ................................................................................................................................... 33 
Measures ................................................................................................................................ 34 
Results. ................................................................................................................................... 37 
Study 2....................................................................................................................................... 43 
Pilot Study.............................................................................................................................. 43 
Sample ................................................................................................................................... 43 
Measures. ............................................................................................................................... 46 
Results .................................................................................................................................... 56 

 

DISCUSSION ............................................................................................................................... 89 
Implications for Theory ............................................................................................................. 97 
Implications for Practice ........................................................................................................... 98 
Limitations .............................................................................................................................. 100 
Future Research ....................................................................................................................... 103 
Conclusion ............................................................................................................................... 105 

 

APPENDICES ............................................................................................................................ 106 

vi 
 

 

 

 

APPENDIX A – Tables and Figures ....................................................................................... 107 
APPENDIX B – DSIM Item Pool ........................................................................................... 382 
APPENDIX C - Study 1 Consent Form .................................................................................. 385 
APPENDIX D – Study 1 Debriefing Form ............................................................................. 387 
APPENDIX E – Study 1 Measures ......................................................................................... 389 
APPENDIX F – Study 2 Consent Form .................................................................................. 396 
APPENDIX G – Study 2 – Pre-Survey Measure .................................................................... 398 
APPENDIX H – Study 2 – ESM Morning Survey Measures ................................................. 407 
APPENDIX I – Study 2 – ESM Evening Survey Measures ................................................... 410 
APPENDIX J – Study 2 – Post-Survey Measures .................................................................. 416 
APPENDIX K – Study 2 Debriefing Form ............................................................................. 423 

REFERENCES ........................................................................................................................... 425 

 

vii 

 

 

 

LIST OF TABLES 

 

Table 1: Item level Information for DSIM Developmental Item Pool…………………………107  

Table 2: Mean, Standard Deviation, Intercorrelations, and Alpha for Variables in Study 1…...116  

Table 3: Relationships Between Vocational Interests and Personality…………………………118  

Table 4: Descriptives and Intercorrelations for Between-Person Variables in Study 2.........….120  

Table 5: Descriptives and Intercorrelations for Within-Person Variables in Study 2………….135  

Table 6: Descriptive for State Interest Standard Deviations………………………...………….141  

Table 7: Multilevel Regression Results of State Realistic Interest at T+1 on State Realistic  
 
Interest at T……………………………………………………………………………………..142 

 

Table 8: Multilevel Regression Results of State Investigative Interest at T+1 on State 
 
Investigative Interest at T……………………………………………………………………….143 

 

Table 9: Multilevel Regression Results of State Artistic Interest at T+1 on State Artistic Interest   
at T…………………………………………………………………………………………...…144 

Table 10: Multilevel Regression Results of State Social Interest at T+1 on State Social Interest at  
T………………………………………………...………………………………………………145 

Table 11: Multilevel Regression Results of State Enterprising Interest at T+1 on State 
 
Enterprising Interest at T……………………………………………………………………….146 

 

Table 12: Multilevel Regression Results of State Conventional Interest at T+1 on State 
 
Conventional Interest at T………………………………………………………………………147 

 

Table 13: Spearman Correlations Between Within-Individual Ranked Interests from T and 
 
T+1……………………………………………………………………………………………...148 

 

Table 14: Spearman Correlations Between Days and Averaged Spearman Correlations……...149 

Table 15: Interclass Correlation Coefficient (ICC) Values for State Interests Nested Within 
 
Individuals………………………………………………………………………………………150 

 

Table 16: Multilevel Structural Equation Model Results for Latent Correlations Between State   
 
Interests…………………………………………………………………………………………151 

Table 17: Correlation Between State Interests at T1 and State Interests at T7…………………..… 
…………………………………………………………………………………………………..152 

Table 18: Spearman Correlations Between Within-Individual Ranked Interests from T1 and 
 
T7……………………………………………………………………………………………….153 

 

viii 

 

 

 

 
Table 19: Beta Weights for Relationships Between Individual Differences and Variability of 
State Vocational Interests………………………………………………………………………154 

 

Table 20: Regression Results of Hypothesized Individual Differences Predicting Average 
 
Standard Deviation of State Interests…………………………………………………………...155 

 

Table 21: Results for Polynomial Regression and Related Models for State Realistic Interest            
Predicting Daily Positive Affect………………………………………………………………..156 

Table 22: Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence   
 
Predicting Daily Positive Affect…………………………………………..……………………157 

Table 23: Results for Polynomial Regression and Related Models for State Investigative Interest   
Predicting Daily Positive Affect………………………………………………………………..158 

Table 24: Polynomial Regression Fit Line Coefficients for State Investigative Interest 
 
Congruence Predicting Daily Positive Affect…………………………………………………..159 

 

 
Table 25: Results for Polynomial Regression and Related Models for State Artistic Interest 
Predicting Daily Positive Affect………………………………………………………………..160 

 

Table 26: Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence 
 
Predicting Daily Positive Affect………………………………………………………………..161 

 

Table 27: Results for Polynomial Regression and Related Models for State Social Interest 
 
Predicting Daily Positive Affect………………………………………………………………..162 

 

Table 28: Polynomial Regression Fit Line Coefficients for State Social Interest Congruence 
 
Predicting Daily Positive Affect………………………………………………………………..163 

 

Table 29: Results for Polynomial Regression and Related Models for State Enterprising Interest   
Predicting Daily Positive Affect………………………………………………………………..164 

Table 30: Polynomial Regression Fit Line Coefficients for State Enterprising Interest 
 
Congruence Predicting Daily Positive Affect…………………………………………………..165 

 

Table 31: Results for Polynomial Regression and Related Models for State Conventional Interest  
Predicting Daily Positive Affect………………………………………………………………..166 

Table 32: Polynomial Regression Fit Line Coefficients for State Conventional Interest 
 
Congruence Predicting Daily Positive Affect…………………………………………………..167 

 

Table 33: Results for Polynomial Regression and Related Models for State Realistic Interest 
 
Predicting Daily Negative Affect……………………………………………………………….168 

 

ix 
 

 

 

 
Table 34: Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence   
Predicting Daily Negative Affect……………………………………………………………….169 

Table 35: Results for Polynomial Regression and Related Models for State Investigative Interest   
Predicting Daily Negative Affect………………………………………………………………170 

Table 36: Polynomial Regression Fit Line Coefficients for State Investigative Interest 
 
Congruence Predicting Daily Negative Affect……...………………………………………….171 

 

Table 37: Results for Polynomial Regression and Related Models for State Artistic Interest 
 
Predicting Daily Negative Affect……………………………………………………………….172 

 

Table 38: Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence 
 
Predicting Daily Negative Affect……………………………………………………………….173 

 

Table 39: Results for Polynomial Regression and Related Models for State Social Interest 
 
Predicting Daily Negative Affect……………………………………………………………….174 

 

 
Table 40: Polynomial Regression Fit Line Coefficients for State Social Interest Congruence 
Predicting Daily Negative Affect……………………………………………………………….175 

 

Table 41: Results for Polynomial Regression and Related Models for State Enterprising Interest   
Predicting Daily Negative Affect……………………………………………………………….176 

Table 42: Polynomial Regression Fit Line Coefficients for State Enterprising Interest 
 
Congruence Predicting Daily Negative Affect…………………………………………...…….177 

 

Table 43: Results for Polynomial Regression and Related Models for State Conventional Interest 
Predicting Daily Negative Affect……………………………………………………………….178 

Table 44: Polynomial Regression Fit Line Coefficients for State Conventional Interest 
 
Congruence Predicting Daily Negative Affect…………………………………………………179 

 

Table 45: Results for Polynomial Regression and Related Models for State Realistic Interest 
 
Predicting Daily Intrinsic Motivation…………………………………………………………..180 

 

Table 46: Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence   
 
Predicting Daily Intrinsic Motivation…………………………………………………………..181 

Table 47: Results for Polynomial Regression and Related Models for State Investigative Interest   
Predicting Daily Intrinsic Motivation…………………………………………………………..182 

Table 48: Polynomial Regression Fit Line Coefficients for State Investigative Interest 
 
Congruence Predicting Daily Intrinsic Motivation……………………………………………..183 

 

x 
 

 

 

 
Table 49: Results for Polynomial Regression and Related Models for State Artistic Interest 
Predicting Daily Intrinsic Motivation…………………………………………………………..184 

 

 
Table 50: Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence 
Predicting Daily Intrinsic Motivation…………………………………………………………..185 

 

Table 51: Results for Polynomial Regression and Related Models for State Social Interest 
 
Predicting Daily Intrinsic Motivation…………………………………………………………..186 

 

Table 52: Polynomial Regression Fit Line Coefficients for State Social Interest Congruence 
 
Predicting Daily Intrinsic Motivation…………………………………………………………..187 

 

Table 53: Results for Polynomial Regression and Related Models for State Enterprising Interest   
Predicting Daily Intrinsic Motivation…………………………………………………………..188 

Table 54: Polynomial Regression Fit Line Coefficients for State Enterprising Interest 
 
Congruence Predicting Daily Intrinsic Motivation……………………………………………..189 

 

Table 55: Results for Polynomial Regression and Related Models for State Conventional Interest  
Predicting Daily Intrinsic Motivation…………………………………………………………..190 

 
Table 56: Polynomial Regression Fit Line Coefficients for State Conventional Interest 
Congruence Predicting Daily Intrinsic Motivation……………………………………………..191 

 

Table 57: Results for Polynomial Regression and Related Models for State Realistic Interest 
 
Predicting Daily Engagement…………………………………………………………………..192 

 

Table 58: Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence   
 
Predicting Daily Engagement…………………………………………………………………..193 

Table 59: Results for Polynomial Regression and Related Models for State Investigative Interest   
Predicting Daily Engagement…………………………………………………………………194 

Table 60: Polynomial Regression Fit Line Coefficients for State Investigative Interest 
 
Congruence Predicting Daily Engagement……………………………………………………..195 

 

Table 61: Results for Polynomial Regression and Related Models for State Artistic Interest 
 
Predicting Daily Engagement………………………………………………………………….196 

 

 
Table 62: Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence 
Predicting Daily Engagement…………………………………………………………………..197 

 

Table 63: Results for Polynomial Regression and Related Models for State Social Interest 
 
Predicting Daily Engagement…………………………………………………………………..198 

 

xi 
 

 

 

 
Table 64: Polynomial Regression Fit Line Coefficients for State Social Interest Congruence 
Predicting Daily Engagement…..………………………………………………………………199 

 

Table 65: Results for Polynomial Regression and Related Models for State Enterprising Interest   
Predicting Daily Engagement…………………………………………………………………..200 

Table 66: Polynomial Regression Fit Line Coefficients for State Enterprising Interest 
 
Congruence Predicting Daily Engagement…………………………………………………….201 

 

Table 67: Results for Polynomial Regression and Related Models for State Conventional Interest  
Predicting Daily Engagement…………………………………………………………………202 

Table 68: Polynomial Regression Fit Line Coefficients for State Conventional Interest 

 
Congruence Predicting Daily Engagement……………………………………………………203 

 

Table 69: Results for Polynomial Regression and Related Models for State Realistic Interest 
 
Predicting Daily Perseverance…………………………………………………………………204 

 

 
Table 70: Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence   
Predicting Daily Perseverance…………………………………………………………………205 

Table 71: Results for Polynomial Regression and Related Models for State Investigative Interest   
Predicting Daily Perseverance………………………………………………………………….206 

Table 72: Polynomial Regression Fit Line Coefficients for State Investigative Interest 
 
Congruence Predicting Daily Perseverance…………………………………………………….207 

 

Table 73: Results for Polynomial Regression and Related Models for State Artistic Interest 
 
Predicting Daily Perseverance………………………………………………………………….208 

 

Table 74: Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence 
 
Predicting Daily Perseverance………………………………………………………………….209 

 

Table 75: Results for Polynomial Regression and Related Models for State Social Interest 
 
Predicting Daily Perseverance………………………………………………………………….210 

 

Table 76: Polynomial Regression Fit Line Coefficients for State Social Interest Congruence 
 
Predicting Daily Perseverance………………………………………………………………….211 

 

Table 77: Results for Polynomial Regression and Related Models for State Enterprising Interest   
Predicting Daily Perseverance………………………………………………………………….212 

Table 78: Polynomial Regression Fit Line Coefficients for State Enterprising Interest 
 
Congruence Predicting Daily Perseverance…………………………………………………….213 

 

xii 

 

 

 

Table 79: Results for Polynomial Regression and Related Models for State Conventional Interest  
Predicting Daily Perseverance………………………………………………………………….214 

 
Table 80: Polynomial Regression Fit Line Coefficients for State Conventional Interest 
Congruence Predicting Daily Perseverance…………………………………………………….215 

 

Table 81: Results for Polynomial Regression and Related Models for Trait Realistic Interest 
 
Predicting Daily Positive Affect………………………………………………………………..216 

 

Table 82: Regression Results for Polynomial Trait and State Congruence for Realistic Interest   
 
Predicting Daily Positive Affect………………………………………………………………..217 

Table 83: Results for Polynomial Regression and Related Models for Trait Investigative Interests  
Predicting Daily Positive Affect………………………………………………………………..218 

Table 84: Regression Results for Polynomial Trait and State Congruence for Investigative 
 
Interests Predicting Daily Positive Affect………………………………………………………219 

 

 
Table 85: Results for Polynomial Regression and Related Models for Trait Artistic Interest 
Predicting Daily Positive Affect………………………………………………………………..220 

 

 
Table 86: Regression Results for Polynomial Trait and State Congruence for Artistic Interest 
Predicting Daily Positive Affect………………………………………………………………..221 

 

Table 87: Results for Polynomial Regression and Related Models for Trait Social Interest 
 
Predicting Daily Positive Affect………………………………………………………………..222 

 

Table 88: Regression Results for Polynomial Trait and State Congruence for Social Interest 
 
Predicting Daily Positive Affect………………………………………………………………..223 

 

Table 89: Results for Polynomial Regression and Related Models for Trait Enterprising Interest   
Predicting Daily Positive Affect………………………………………………………………..224 

Table 90: Regression Results for Polynomial Trait and State Congruence for Enterprising 
 
Interest Predicting Daily Positive Affect……………………………………………………….225 

 

Table 91: Results for Polynomial Regression and Related Models for Trait Conventional Interest  
Predicting Daily Positive Affect………………………………………………………………..226 

 
Table 92: Regression Results for Polynomial Trait and State Congruence for Conventional 
Interest Predicting Daily Positive Affect……………………………………………………….227 

 

Table 93: Results for Polynomial Regression and Related Models for Trait Realistic Interest 
 
Predicting Daily Negative Affect……………………………………………………………….228 

 

xiii 

 

 

 

 
Table 94: Regression Results for Polynomial Trait and State Congruence for Realistic Interest   
Predicting Daily Negative Affect……………………………………………………………….229 

Table 95: Results for Polynomial Regression and Related Models for Trait Investigative Interests  
Predicting Daily Negative Affect……………………………………………………………….230 

Table 96: Regression Results for Polynomial Trait and State Congruence for Investigative 
 
Interests Predicting Daily Negative Affect……………………………………………………..231 

 

Table 97: Results for Polynomial Regression and Related Models for Trait Artistic Interest 
 
Predicting Daily Negative Affect……………………………………………………………….232 

 

Table 98: Regression Results for Polynomial Trait and State Congruence for Artistic Interest 
 
Predicting Daily Negative Affect……………………………………………………………….233 

 

Table 99: Results for Polynomial Regression and Related Models for Trait Social Interest 
 
Predicting Daily Negative Affect……………………………………………………………….234 

 

 
Table 100: Regression Results for Polynomial Trait and State Congruence for Social Interest 
Predicting Daily Negative Affect……………………………………………………………….235 

 

Table 101: Results for Polynomial Regression and Related Models for Trait Enterprising Interest  
Predicting Daily Negative Affect……………………………………………………………….236 

Table 102: Regression Results for Polynomial Trait and State Congruence for Enterprising 
 
Interest Predicting Daily Negative Affect………………………………………………………237 

 

Table 103: Results for Polynomial Regression and Related Models for Trait Conventional 
 
Interest Predicting Daily Negative Affect……………………………………………………....238 

 

Table 104: Regression Results for Polynomial Trait and State Congruence for Conventional 
 
Interest Predicting Daily Negative Affect………………………………………………………239 

 

Table 105: Results for Polynomial Regression and Related Models for Trait Realistic Interest 
 
Predicting Daily Intrinsic Motivation…………………………………………………………..240 

 

Table 106: Regression Results for Polynomial Trait and State Congruence for Realistic Interest  
 
Predicting Daily Intrinsic Motivation…………………………………………………………..241 

 
Table 107: Results for Polynomial Regression and Related Models for Trait Investigative 
Interests Predicting Daily Intrinsic Motivation………………………………………………....242 

 

Table 108: Regression Results for Polynomial Trait and State Congruence for Investigative 
 
Interests Predicting Daily Intrinsic Motivation…………………………………………………243 

 

xiv 

 

 

 

 
Table 109: Results for Polynomial Regression and Related Models for Trait Artistic Interest 
Predicting Daily Intrinsic Motivation…………………………………………………………..244 

 

 
Table 110: Regression Results for Polynomial Trait and State Congruence for Artistic Interest   
Predicting Daily Intrinsic Motivation…………………………………………………………..245 

Table 111: Results for Polynomial Regression and Related Models for Trait Social Interest 
 
Predicting Daily Intrinsic Motivation…………………………………………………………..246 

 

Table 112: Regression Results for Polynomial Trait and State Congruence for Social Interest 
 
Predicting Daily Intrinsic Motivation…………………………………………………………..247 

 

Table 113: Results for Polynomial Regression and Related Models for Trait Enterprising Interest  
Predicting Daily Intrinsic Motivation…………………………………………………………..248 

Table 114: Regression Results for Polynomial Trait and State Congruence for Enterprising 
 
Interest Predicting Daily Intrinsic Motivation………………………………………………….249 

 

 
Table 115: Results for Polynomial Regression and Related Models for Trait Conventional 
Interest Predicting Daily Intrinsic Motivation………………………………………………….250 

 

 
Table 116: Regression Results for Polynomial Trait and State Congruence for Conventional 
Interest Predicting Daily Intrinsic Motivation………………………………………………….251 

 

Table 117: Results for Polynomial Regression and Related Models for Trait Realistic Interest 
 
Predicting Daily Engagement…………………………………………………………………..252 

 

Table 118: Regression Results for Polynomial Trait and State Congruence for Realistic Interest  
 
Predicting Daily Engagement…………………………………………………………………..253 

Table 119: Results for Polynomial Regression and Related Models for Trait Investigative 
 
Interests Predicting Daily Engagement………………………………………………………....254 

 

Table 120: Regression Results for Polynomial Trait and State Congruence for Investigative 
 
Interests Predicting Daily Engagement…………………………………………………………255 

 

Table 121: Results for Polynomial Regression and Related Models for Trait Artistic Interest 
 
Predicting Daily Engagement…………………………………………………………………..256 

 

 
Table 122: Regression Results for Polynomial Trait and State Congruence for Artistic Interest   
Predicting Daily Engagement…………………………………………………………………..257 

Table 123: Results for Polynomial Regression and Related Models for Trait Social Interest 
 
Predicting Daily Engagement…………………………………………………………………..258 

 

xv 

 

 

 

 
Table 124: Regression Results for Polynomial Trait and State Congruence for Social Interest 
Predicting Daily Engagement…………………………………………………………………..259 

 

Table 125: Results for Polynomial Regression and Related Models for Trait Enterprising Interest  
Predicting Daily Engagement…………………………………………………………………..260 

Table 126: Regression Results for Polynomial Trait and State Congruence for Enterprising 
 
Interest Predicting Daily Engagement………………………………………………………….261 

 

Table 127: Results for Polynomial Regression and Related Models for Trait Conventional 
 
Interest Predicting Daily Engagement………………………………………………………….262 

 

Table 128: Regression Results for Polynomial Trait and State Congruence for Conventional 
 
Interest Predicting Daily Engagement………………………………………………………….263 

 

Table 129: Results for Polynomial Regression and Related Models for Trait Realistic Interest 
 
Predicting Daily Perseverance………………………………………………………………….264 

 

 
Table 130: Regression Results for Polynomial Trait and State Congruence for Realistic Interest  
Predicting Daily Perseverance………………………………………………………………….265 

 
Table 131: Results for Polynomial Regression and Related Models for Trait Investigative 
Interests Predicting Daily Perseverance………………………………………………………...266 

 

Table 132: Regression Results for Polynomial Trait and State Congruence for Investigative 
 
Interests Predicting Daily Perseverance………………………………………………………...267 

 

Table 133: Results for Polynomial Regression and Related Models for Trait Artistic Interest 
 
Predicting Daily Perseverance………………………………………………………………….268 

 

Table 134: Regression Results for Polynomial Trait and State Congruence for Artistic Interest   
 
Predicting Daily Perseverance………………………………………………………………….269 

Table 135: Results for Polynomial Regression and Related Models for Trait Social Interest 
 
Predicting Daily Perseverance………………………………………………………………….270 

 

Table 136: Regression Results for Polynomial Trait and State Congruence for Social Interest 
 
Predicting Daily Perseverance………………………………………………………………….271 

 

Table 137: Results for Polynomial Regression and Related Models for Trait Enterprising Interest  
Predicting Daily Perseverance………………………………………………………………….272 

Table 138: Regression Results for Polynomial Trait and State Congruence for Enterprising 
 
Interest Predicting Daily Perseverance…………………………………………………………273 

 

xvi 

 

 

 

 
Table 139: Results for Polynomial Regression and Related Models for Trait Conventional 
Interest Predicting Daily Perseverance……………………………………………………...….274 

 

 
Table 140: Regression Results for Polynomial Trait and State Congruence for Conventional 
Interest Predicting Daily Perseverance…………………………………………………………275 

 

Table 141: Results for Polynomial Regression for Full RIASEC Model Congruence Predicting   
 
Daily Positive Affect……………………………………………………………………………276 

Table 142: Results for Polynomial Regression for Full RIASEC Model Congruence Predicting   
 
Daily Negative Affect…………………………………………………………………………..277 

Table 143: Results for Polynomial Regression for Full RIASEC Model Congruence Predicting   
 
Daily Intrinsic Motivation……………………………………………………………………....278 

Table 144: Results for Polynomial Regression for Full RIASEC Model Congruence Predicting   
 
Daily Engagement……………………………………………………………………...……….279 

 
Table 145: Results for Polynomial Regression for Full RIASEC Model Congruence Predicting   
Daily Perseverance……………………………………………………………………………...280 

 
Table 146: Results for Prior Situational Realistic Interest (T) and Related Models Predicting 
Next Day Realistic Interest (T+1)………………………………………………………………281 

 

Table 147: Results for Prior Situational Investigative Interest (T) and Related Models Predicting   
Next Day Investigative Interest (T+1)………………………………………………………….282 

Table 148: Results for Prior Situational Artistic Interest (T) and Related Models Predicting Next   
Day Artistic Interest (T+1)……………………………………………………………………..283 

Table 149: Results for Prior Situational Social Interest (T) and Related Models Predicting Next  
 
Day Social Interest (T+1)………………………………………………………………………284 

Table 150: Results for Prior Situational Enterprising Interest (T) and Related Models Predicting   
Next Day Enterprising Interest (T+1)………………………………………………………….285 

Table 151: Results for Prior Situational Conventional Interest (T) and Related Models Predicting  
Next Day Conventional Interest (T+1)…………………………………………………………286 

 
Table 152: Results for State Realistic Interest and Related Models Predicting Realistic 
Situational Interest……………………………………………………………………………...287 

 

Table 153: Results for State Investigative Interest and Related Models Predicting Investigative   
 
Situational Interest……………………………………………………………………………...288 

xvii 

 

 

 

 
Table 154: Results for State Artistic Interest and Related Models Predicting Artistic Situational  
Interest…………………………………………………………………………………………..289 

 
Table 155: Results for State Social Interest and Related Models Predicting Social Situational 
Interest…………………………………………………………………………………………..290 

 

Table 156: Results for State Enterprising Interest and Related Models Predicting Enterprising 
 
Situational Interest……………………………………………………………………………...291 

 

Table 157: Results for State Conventional Interest and Related Models Predicting Conventional   
Situational Interest……………………………………………………………………………..292 

Table 158: Results for Polynomial Regression and Related Models for State Realistic Interest (T)  
Predicting Next Day Realistic Interest (T+1)…………………………………………………..293 

Table 159: Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence  
 
Predicting Next Day Realistic Interest (T+1)…………………………………………………..294 

 
Table 160: Results for Polynomial Regression and Related Models for State Investigative 
Interest (T) Predicting Next Day Investigative Interest (T+1)………………………………….295 

 

 
Table 161: Polynomial Regression Fit Line Coefficients for State Investigative Interest 
Congruence Predicting Next Day Investigative Interest (T+1)………………………...………296 

 

Table 162: Results for Polynomial Regression and Related Models for State Artistic Interest (T)   
Predicting Next Day Artistic Interest (T+1)…………………………………………………....297 

Table 163: Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence   
 
Predicting Next Day Artistic Interest (T+1)…………………………………………………....298 

Table 164: Results for Polynomial Regression and Related Models for State Social Interest (T)  
 
Predicting Next Day Social Interest (T+1)…………………………………………………..…299 

Table 165: Polynomial Regression Fit Line Coefficients for State Social Interest Congruence 
 
Predicting Next Day Social Interest (T+1)……………………………………………………..300 

 

Table 166: Results for Polynomial Regression and Related Models for State Enterprising Interest  
(T) Predicting Next Day Enterprising Interest (T+1)…………………………………………..301 

 
Table 167: Polynomial Regression Fit Line Coefficients for State Enterprising Interest 
Congruence Predicting Next Day Enterprising Interest (T+1)…………………………………302 

 

Table 168: Results for Polynomial Regression and Related Models for State Conventional 
 
Interest (T) Predicting Next Day Conventional Interest (T+1)…………………………………303 

 

xviii 

 

 

 

 
Table 169: Polynomial Regression Fit Line Coefficients for State Conventional Interest 
Congruence Predicting Next Day Conventional Interest (T+1)………………………………..304 

 

 
Table 170: Bivariate Within-Person Relationships Between State Interests and 
Resources…………………………………………………………………………………….…305 

 

Table 171: Multilevel Regression Results for Relationships Between State Realistic Interests and  
Resources……………………………………………………………………………………….306 

Table 172: Multilevel Regression Results for Relationships Between State Investigative Interests  
and Resources…………………………………………………………………………………..307 

Table 173: Multilevel Regression Results for Relationships Between State Artistic Interests and   
Resources……………………………………………………………………………………….308 

Table 174: Multilevel Regression Results for Relationships Between State Social Interests and   
 
Resources……………………………………………………………………………………….309 

Table 175: Multilevel Regression Results for Relationships Between State Enterprising Interests   
and Resources…………………………………………………………………………………..310 

Table 176: Multilevel Regression Results for Relationships Between State Conventional Interests 
and Resources…………………………………………………………………………………..311 

Table 177: Bivariate Relationships Between Daily Activities and 
 
Resources…………………………………………………………………………………...…..312 

 

Table 178: Multilevel Regression Results for Relationships Between Realistic Daily Activities   
 
and Resources…………………………………………………………………………………..313 

Table 179: Multilevel Regression Results for Relationships Between Investigative Daily 
 
Activities and Resources………………………………………………………………………..314 

 

Table 180: Multilevel Regression Results for Relationships Between Artistic Daily Activities 
 
and Resources…………………………………………………………………………………..315 

 

Table 181: Multilevel Regression Results for Relationships Between Social Daily Activities and   
Resources……………………………………………………………………………………….316 

 
Table 182: Multilevel Regression Results for Relationships Between Enterprising Daily 
Activities and Resources………………………………………………………………………..317 

 

Table 183: Multilevel Regression Results for Relationships Between Conventional Daily 
 
Activities and Resources………………………………………………………………….…….318 

 

xix 

 

 

 

 
Table 184: Within-Person Correlation Between State Interests and Daily 
Activities………………………………………………………………………………………..319 

 

 
Table 185: Multilevel Regression Results for Relationships Between Realistic Daily Activities   
and State Realistic Interest……………………………………………………………………...320 

Table 186: Multilevel Regression Results for Relationships Between Investigative Daily 
 
Activities and State Investigative Interest………………………………………………………321 

 

Table 187: Multilevel Regression Results for Relationships Between Artistic Daily Activities 
 
and State Artistic Interest…………………………………………………………………….…322 

 

Table 188: Multilevel Regression Results for Relationships Between Social Daily Activities and   
State Social Interest……………………………………………………………………………..323 

Table 189: Multilevel Regression Results for Relationships Between Enterprising Daily 
 
Activities and State Enterprising Interest………………………………………………...….…324 

 

 
Table 190: Multilevel Regression Results for Relationships Between Conventional Daily 
Activities and State Conventional Interest……………………………………………………...325 

 

 
Table 191: Correlation Between Pre and Post Trait Interests for DSIM and 
BPDRM………………………………………………………………………………..……….326 

 

Table 192: Correlations of Trait Interest Change from Pre to Post 
 
Survey……………………..........................................................................................................327 

 

 

 

 

xx 

 

 

 

LIST OF FIGURES 

 

Figure 1: Plot of CFA for State Realistic Interest………………………………………………328 

Figure 2: Plot of CFA for State Investigative Interest……………………………………...…..329 

Figure 3: Plot of CFA for State Artistic Interest……………………………………………..…330 

Figure 4: Plot of CFA for State Social Interest…………………………………………………331 

Figure 5: Plot of CFA for State Enterprising Interest…………………………………………..332 

Figure 6: Plot of CFA for State Conventional Interest…………………………………………333 

Figure 7: Plot of CFA for State Interests Replicating Holland Structure………………………334 

Figure 8: Plot of Structural Equation Model for Latent Correlation Between Realistic State 
 
Interests (T) and Next Day Realistic State Interest (T+1)……………………………………...335 

Figure 9: Plot of Structural Equation Model for Latent Correlation Between Investigative State 
 
Interests (T) and Next Day Investigative State Interest (T+1)………………………………….336 

Figure 10: Plot of Structural Equation Model for Latent Correlation Between Artistic State 
 
Interests (T) and Next Day Artistic State Interest (T+1)……………………………………….337 

Figure 11: Plot of Structural Equation Model for Latent Correlation Between Social State  
 
Interests (T) and Next Day Social State Interest (T+1)………………………………………...338 

Figure 12: Plot of Structural Equation Model for Latent Correlation Between Enterprising State 
 
Interests (T) and Next Day Enterprising State Interest (T+1)………………………………….339 

Figure 13: Plot of Structural Equation Model for Latent Correlation Between Conventional State  
Interests (T) and Next Day Conventional State Interest (T+1)…………………………………340 

Figure 14: Example of Response Surface curvature expected for Hypotheses 3a through 3e…341 

Figure 15: Response Plot of Congruence Between State Realistic Interests and Daily Realistic 
 
Activities Predicting Daily Positive Affect……………………………………………………..342 

Figure 16: Response Plot of Congruence Between State Investigative Interests and Daily   
 
Investigative Activities Predicting Daily Positive Affect………………………………………343 

Figure 17: Response Plot of Congruence Between State Artistic Interests and Daily Artistic 
 
Activities Predicting Daily Positive Affect……………………………………………………..344 

Figure 18: Response Plot of Congruence Between State Social Interests and Daily Social   
 
Activities Predicting Daily Positive Affect……………………………………………………..345 

Figure 19: Response Plot of Congruence Between State Enterprising Interests and Daily 
 
Enterprising Activities Predicting Daily Positive Affect………………………………………346 

 

xxi 

 

 

 

 
Figure 20: Response Plot of Congruence Between State Conventional Interests and Daily  
Conventional Activities Predicting Daily Positive Affect……………………………………...347 

Figure 21: Response Plot of Congruence Between State Realistic Interests and Daily Realistic 
 
Activities Predicting Daily Negative Affect……………………………………………………348 

Figure 22: Response Plot of Congruence Between State Investigative Interests and Daily   
 
Investigative Activities Predicting Daily Negative Affect……………………………………..349 

Figure 23: Response Plot of Congruence Between State Artistic Interests and Daily Artistic 
 
Activities Predicting Daily Negative Affect……………………………………………………350 

Figure 24: Response Plot of Congruence Between State Social Interests and Daily Social   
 
Activities Predicting Daily Negative Affect……………………………………………………351 

Figure 25: Response Plot of Congruence Between State Enterprising Interests and Daily 
 
Enterprising Activities Predicting Daily Negative Affect……………………………………...352 

 

Figure 26: Response Plot of Congruence Between State Conventional Interests and Daily  
 
Conventional Activities Predicting Daily Negative Affect…………………………………….353 

Figure 27: Response Plot of Congruence Between State Realistic Interests and Daily Realistic 
 
Activities Predicting Daily Intrinsic Motivation……………………………………………….354 

Figure 28: Response Plot of Congruence Between State Investigative Interests and Daily   
 
Investigative Activities Predicting Daily Intrinsic Motivation…………………………………355 

Figure 29: Response Plot of Congruence Between State Artistic Interests and Daily Artistic 
 
Activities Predicting Daily Intrinsic Motivation……………………………………………….356 

Figure 30: Response Plot of Congruence Between State Social Interests and Daily Social   
 
Activities Predicting Daily Intrinsic Motivation……………………………………………….357 

Figure 31: Response Plot of Congruence Between State Enterprising Interests and Daily 
 
Enterprising Activities Predicting Daily Intrinsic Motivation………………………………….358 

 

Figure 32: Response Plot of Congruence Between State Conventional Interests and Daily  
 
Conventional Activities Predicting Daily Intrinsic Motivation………………………………...359 

Figure 33: Response Plot of Congruence Between State Realistic Interests and Daily Realistic 
 
Activities Predicting Daily Engagement………………………………………………………..360 

Figure 34: Response Plot of Congruence Between State Investigative Interests and Daily   
 
Investigative Activities Predicting Daily Engagement…………………………………………361 

Figure 35: Response Plot of Congruence Between State Artistic Interests and Daily Artistic 
 
Activities Predicting Daily Engagement………………………………………………………..362 

Figure 36: Response Plot of Congruence Between State Social Interests and Daily Social   
 
Activities Predicting Daily Engagement………………………………………………………..363 

xxii 

 

 

 

 
Figure 37: Response Plot of Congruence Between State Enterprising Interests and Daily 
Enterprising Activities Predicting Daily Engagement………………………………………….364 

 

Figure 38: Response Plot of Congruence Between State Conventional Interests and Daily  
 
Conventional Activities Predicting Daily Engagement………………………………………...365 

Figure 39: Response Plot of Congruence Between State Realistic Interests and Daily Realistic 
 
Activities Predicting Daily Perseverance……………………………………………………….366 

Figure 40: Response Plot of Congruence Between State Investigative Interests and Daily   
 
Investigative Activities Predicting Daily Perseverance………………………………………...367 

Figure 41: Response Plot of Congruence Between State Artistic Interests and Daily Artistic 
 
Activities Predicting Daily Perseverance…………………………………………………….....368 

Figure 42: Response Plot of Congruence Between State Social Interests and Daily Social   
 
Activities Predicting Daily Perseverance……………………………………………………….369 

Figure 43: Response Plot of Congruence Between State Enterprising Interests and Daily 
 
Enterprising Activities Predicting Daily Perseverance…………………………………………370 

 

Figure 44: Response Plot of Congruence Between State Conventional Interests and Daily  
 
Conventional Activities Predicting Daily Perseverance………………………………………..371 

Figure 45: Plot of Moderation of Artistic Situational interests and Next Day State Artistic  
 
Interests Relationship by Trait Artistic Interests……………………………………………….372 

Figure 46: Plot of Moderation of Social Situational interests and Next Day State Social Interests   
Relationship by Trait Social Interests…………………………………………………………..373 

Figure 47: Plot of Moderation of State Investigative interests and Same Day Investigative  
 
Situational Interests Relationship by Trait Investigative Interests……………………………..374 

Figure 48: Plot of Moderation of State Social interests and Same Day Social Situational Interests  
Relationship by Trait Social Interests………………………………………………………….375 

Figure 49: Response Plot of Congruence Between State Realistic Interests (T) and Daily   
 
Realistic Activities (T) Predicting Next Day State Realistic Interests (T+1)…………………..376 

Figure 50: Response Plot of Congruence Between State Investigative Interests (T) and Daily 
 
Investigative Activities (T) Predicting Next Day State Investigative Interests (T+1)………….377 

Figure 51: Response Plot of Congruence Between State Artistic Interests (T) and Daily Artistic 
 
Activities (T) Predicting Next Day State Artistic Interests (T+1)……………………………...378 

Figure 52: Response Plot of Congruence Between State Social Interests (T) and Daily Social 
 
Activities (T) Predicting Next Day State Social Interests (T+1)……………………………….379 

Figure 53: Response Plot of Congruence Between State Enterprising Interests (T) and Daily 
 
Enterprising Activities (T) Predicting Next Day State Enterprising Interests (T+1)…………...380 

xxiii 

 

 

 
Figure 54: Response Plot of Congruence Between State Conventional Interests (T) and Daily 
Conventional Activities (T) Predicting Next Day State Conventional Interests (T+1)………...381 

 

 

 

 

 

 

 

xxiv 

 

In recent years, research into vocational interests has experienced a renaissance in the 

INTRODUCTION 

organizational sciences. This reemergence has been primarily driven by recent meta-analytic 

work supportive of the relationship between vocational interests and organizationally relevant 

outcomes (e.g. Nye, Su, Rounds, & Drasgow, 2012; 2017; Van Iddekinge, Roth, Putka, & 

Lanivich, 2011a) and evidence of their incremental validity over other common predictors (e.g. 

Stoll et al., 2017; Van Iddekinge, Putka, & Campbell, 2011b). This reemergence of interest in 

vocational interests in the organizational sciences has led to rapid methodological innovation 

within the domain, including more effective methods of modeling interest congruence (e.g. Nye, 

Prasad, Bradburn, & Elizando, 2018a), development of item response theory-based interest 

inventories (Wetzel & Hell, 2014), and innovations in response scales for interest items (e.g. 

Phan & Rounds, 2018).  

Despite this rapid methodological advancement, authors have lamented a lack of recent 

theoretical development in the interests domain (see Su, Stoll, & Rounds, 2019). One area of 

theory and research lacking is exploration into short-term within-person variability. While other 

individual differences research has expanded into exploring short-term within-person variability 

(e.g. Cervone, 2005), this area in the organizational sciences for vocational interest research is 

nearly non-existent (for the exception, see Phan, Amrhein, Cho, & Rounds, 2017; Phan, 2018).  

The purpose of this dissertation is to detail a research program into short-term within-

person vocational interest variability. More specifically, this paper reviews research on short-

term within-person variability of individual difference constructs, how interests are 

conceptualized in different research domains, and how short-term within-person vocational 

interests differ from extant constructs. Then, theory-based hypotheses and research questions are 

 

1 

 

 

put forward regarding short-term interest variability, predictors of variability at the between 

person level of analysis, usefulness of short-term vocational interests in prediction, and driving 

mechanisms of short-term interest variation. Methodology is described and results are presented 

regarding hypotheses and research questions put forward. Finally, the paper closes with a 

discussion of findings, implications, limitations, and possible future areas of research regarding 

short-term within-person vocational interest variability. Through this, the research project seeks 

to shed light on the potential benefits for the fields of organizational and vocational research, 

theoretically and empirically, from considering the short-term variability of vocational interests 

moving forward. 

Short-Term Within-Person Variability of Individual Differences 

Within individual differences research, a common distinction is made between more 

stable individual differences and more transient characteristics of individuals. This concept is 

referred to as the trait versus state distinction. A trait can be defined as, “A relatively stable, 

consistent, and enduring internal characteristic that is inferred from a pattern of behaviors, 

attitudes, feelings and habits in the individual.” (VandenBos, 2006 via Baumert et al., 2017, pp. 

528). A state, in contrast, refers to, “…how individuals think, feel, or behave in a given situation. 

They are transient and involve change and variability over short periods of time.” (Allemand, 

Steiger, & Hill, 2013, pp. 6). In other words, traits tend to be stable individual characteristics 

while states vary within person over relatively short periods of time. Though traits may change 

or develop over time as well, these changes tend to occur slowly over relatively extended periods 

of time (see Fridhandler, 1986 for further discussion of distinctions). Though differing in 

variability and temporality, state constructs have the same content as the corresponding trait 

construct (e.g. McNiel & Fleeson, 2006). For example, extraversion can be defined as the 

2 
 

 

tendency to be sociable, assertive, talkative, and active (Barrick & Mount, 1991) and regardless 

if conceptualized as a trait or state, extraversion would still refer to these same behavioral 

 

tendencies, albeit for different lengths of time. 

Though not without controversy (e.g. Allen & Potkay, 1981), trait-state research has 

become more commonplace in the individual differences literature in recent years (e.g. Geiser, 

Gӧtz, Preckel, & Preund, 2017; Steyer, Schmitt, & Eid, 1999). For example, considerable theory 

utilizing a trait-state conceptualization has been seen in personality research recently (e.g. 

Fleeson, 2001; 2017). Trait-state research has also made inroads in the organizational sciences 

literature (e.g. Huang & Ryan, 2011; Judge, Simon, Hurst, & Kelley, 2014; Lievens et al., 2018; 

Minbashian, Wood, & Beckmann, 2010). Numerous psychological constructs have been 

conceptualized within the trait-state paradigm, including anger (Deffenbacher et al., 1996), 

hopelessness (Dunn et al., 2014), narcissism (Giacomin & Jordan, 2016), attachment security 

(Haak, Keller, & DeWall, 2017), curiosity (Lydon-Staley, Zurn, & Bassett, In Press; Naylor, 

1981), and need for achievement (Patrick & Zuckerman, 1977).  

This emerging body of research has illustrated the importance of looking at constructs at 

both the trait and state level. Though it might be convenient to consider relationships at the state 

level homologous to relationships at the trait level for the same construct, this cannot be assumed 

to be the case. Research has demonstrated that, in some cases, relationships between variables at 

the state level can be different than those found at the trait level. For example, within the medical 

literature the relationship between exercise and blood pressure has been found to differ 

depending on the level of analysis (Schwartz & Stone, 1998). Between individuals, exercise is 

negatively related to blood pressure in that individuals who exercise more tend to have lower 

blood pressure. However, within individuals exercise is related to higher blood pressure, in that 

3 
 

 

 

individuals have higher blood pressure when exercising. Within the organizational sciences 

literature, self-efficacy is negatively related to performance at the within-person level of analysis 

but positive at the between-person level (Vancouver, Thompson, & Williams, 2001). Level of 

analysis matters (Kozlowski & Klein, 2000), and assuming relationships at the between-person 

level among constructs hold at the within-person level can be problematic.  

In addition to testing the homology of constructs at differing levels, state level constructs 

are useful for a variety of other reasons (e.g. Gabriel et al., 2019). Due to their proximal nature, 

states may be more predictive of day-to-day attitudes and behaviors than more distal traits. For 

example, an individual’s state extraversion at a networking event might more accurately predict 

their networking effectiveness than their trait extraversion. An individual’s state curiosity may 

better predict reading an article than their trait curiosity. State individual differences may be 

better able to predict what an individual will do or how they will feel in a given situation 

compared to more global trait influences. Additionally, information generated from a distribution 

of states (Fleeson & Gallagher, 2009) can be useful in terms of incremental prediction over traits 

(Augustine & Larsen, 2012). Previous research has also demonstrated that it is not uncommon to 

find greater variability in within-person states than between-person traits (e.g. Church et al., 

2013; Fleeson & Gallagher, 2009).  

I agree with previous authors (e.g. Di Blas, Grassi, Carnaghi, Ferrante, & Calarco, 2017) 

in the sentiment that studying traits alone is not enough. If within-person fluctuation is ignored, 

this theoretically and empirically important variation would be missed. We must integrate trait 

and state level constructs within our work to improve our theory and practice. More specifically, 

we must integrate trait and state conceptualizations of vocational interests to better understand 

and utilize vocational interests. 

4 
 

 

Perspectives on Interests 

 

 

Interests, generally, refer to an individual’s preference for specific types of work, 

activities, or contexts (Su et al., 2019). Interest research has had a long and rich history in the 

organizational sciences, with developments in this area being called one of the great 

contributions of applied psychology (Strong, 1943). However, interest in interests has not been 

unique to this field (Fouad & Kozlowski, 2019; Su, 2018). Educational psychology has also 

amassed an impressive body of research on this topic. However, despite a common interest in 

interests and recent efforts to integrate research from these domains (e.g. Su, 2018; Su et al., 

2019; Renninger & Hidi, 2011), research is fragmented across fields. A major gap exists between 

these fields in regard to how interests are conceptualized. 

Organizational Sciences Perspective 

 Within the organizational sciences, interests have been primarily conceptualized as 

“…trait-like preferences to engage in activities, contexts in which activities occur, or outcomes 

associated with preferred activities that motivate goal-oriented behaviors and orient individuals 

toward certain environments” (Round & Su, 2014, pp. 98). In other words, vocational interests 

are thought to be relatively stable preferences that are characterized by engagement, persistence, 

and enjoyment when an individual is engaged with their respective preferred stimuli (Rounds & 

Su, 2014). The organizational sciences have almost exclusively considered vocational interests 

within a trait framework, neglecting any state variation in vocational interests (Henn, 2010; 

Rounds & Su, 2014; Su, 2018).  

 

Though some alternatives have been proposed (e.g. Gati, 1991), the most popular 

structure of vocational interests in the organizational sciences has been Holland’s hexagonal 

5 
 

 

structure (1959; 1997). This model consists of six relatively broad domains of vocational 

interests. The first domain, Realistic, refers to a preference for, “…explicit, ordered, or 

 

systematic manipulation of objects, tools, machines, and animals…” (Holland, 1997, pp. 21). 

Investigative refers to a preference for, “activities that entail observational, symbolic, systematic, 

and creative investigation of physical, biological, and cultural phenomena.” (pp. 22). Artistic 

refers to a preference for, “… ambiguous, free, systemized activities that entail the manipulation 

of physical, verbal, or human materials to create art forms or products…” (pp. 23). Social refers 

to a preference for, “…activities that entail the manipulation of others to inform, train, develop, 

cure, or enlighten…” (pp. 24). Enterprising refers to a preference for, “…manipulating others to 

attain organizational goals or economic gain…” (pp. 25). Finally, Conventional refers to a 

preference for, “…activities that entail the explicit, ordered, systematic manipulation of data…” 

(pp. 26-27). Collectively, these are also known as the RIASEC model of vocational interests. 

These interests form a hexagonal structure in which interests closest to one another are most 

similar and farthest away most dissimilar (Holland, 1997). This structure has been found to 

replicate across racial groups (Day & Rounds, 1998) and gender (Anderson, Tracey, & Rounds, 

1997; Day & Rounds, 1998). 

 

Beyond these six vocational interest types, Holland (1997) also proposed that both 

individuals and contexts could be categorized according to the RIASEC codes. For instance, an 

individual may have a primary interest in Investigative activities while a task, occupation, or 

context may similarly be representative of this type of activity. Holland proposed that an 

individual matched to an environment consistent (i.e. congruent) with his or her interests would 

experience benefits, such as high satisfaction or performance. On the other hand, if an individual 

is placed in an environment incongruent with their vocational interests, they would be more 

6 
 

 

likely to be dissatisfied or perform poorly. These benefits are thought to emerge through 

increased focus on goals, energized action, and sustained effort resulting from interest alignment. 

These principles have been generally supported by research (e.g. Hoff, Wee, Song, Phan, & 

 

Rounds, 2018; Nye et al., 2012; 2017; 2018a). 

Educational Perspective 

Though researchers in the organizational sciences almost exclusively focus on trait 

vocational interests, researchers in education utilize both a trait and state conceptualization of 

interests. Though these constructs are sometimes referred to by different names in the 

educational literature, they are regularly described as individual interests and situational 

interests, respectively. Individual interests are defined as “…relatively stable motivational 

orientation or personal disposition that develops over time in relation to a particular topic or 

domain and is associated with increased knowledge, value, and positive feelings…” (Hidi & 

Harackiewicz, 2000, pp. 152). Research has demonstrated that individuals with individual 

interests in activities tend to pay closer attention to, persist longer at, learn more about, and enjoy 

these activities more than those who do not have interests in those activities (See Hidi & 

Harackiewicz, 2000 for a review). Fundamentally, these individual interest traits are equivalent 

to the organizational sciences’ trait conceptualization of vocational interests both theoretically 

(e.g. Su et al., 2019) and empirically (e.g. Henn, 2010). 

 

Beyond interests as traits, researchers in education also consider a state conceptualization 

of interests, referred to as situational interests. Situational interest can be defined as, “…a short-

term spike in a person’s attention and participation in an activity and it is triggered by features of 

the environment…” (Azevedo, 2018, pp. 109). These interests refer to a relatively transitory 

feeling of captivation and engagement with a specific stimuli or task and are thought to be 

7 
 

 

 

triggered by aspects of the stimuli or task at hand. These interests are relatively agnostic to the 

actual domain content of the stimuli or task and more indicative of presentation style, such as the 

novelty, abstractness, unexpectedness of information, vividness, and meaningfulness (see 

Schraw & Lehman, 2001 for a review). For example, when developing a measure of situational 

interest Linnebrink-Garcia and colleagues (2010) found three factors to emerge pertaining to 

attention captivation, enjoyability/engagement, and perceived importance/value. These three 

factors are relatively agnostic to specific subject matter, and pertain more to reactions to how it is 

presented. 

 

Though some researchers consider situational and individual interests to be on a 

continuum (e.g. Azevedo, 2018; Hidi & Harackiwicz, 2000; Krapp, 2007), these two concepts 

differ theoretically (Hidi & Renninger, 2006; Su et al., 2019) and empirically (Knogler, 

Harackiewicz, Gegenfurtner, & Lewalter, 2015; Linnenbrink-Garcia et al., 2010). According to 

Palmer and colleagues (2017), “Situational interest is a relatively transient reaction to highly 

stimulating factors in the immediate environment, whereas individual interest is a relatively 

long-term preference for a particular subject or activity.” (pp. 731). In other words, situational 

interests refer to short-term effects of a task or stimuli on an individual, while individual interests 

refer to an individual’s enduring preference for the activity or general domain of activities (Hidi 

& Anderson, 1992). The empirical relationship between these two constructs is unclear, with 

some research finding a strong relationship (e.g. Henn, 2010) and some research finding a null 

relationship (Knogler et al., 2015). Though the empirical relationship between these two 

conceptualizations of interest is still ambiguous, it is clear that they theoretically pertain to 

different constructs.  

8 
 

 

State Vocational Interests 

 

 

Despite the prevalence of a state-level interest construct in the educational literature, the 

organizational sciences do not currently, with very few exceptions, utilize a state vocational 

interest construct. This oversight is interesting, as anecdotally one might expect vocational 

interests to fluctuate somewhat over relatively short periods of time. For instance, a researcher’s 

own momentary preference for theoretically oriented (Investigative), data analytic 

(Conventional), and mentoring (Social) activities may fluctuate from day-to-day. 

State vocational interests represent a relatively isomorphic construct analogous to trait 

vocational interests in content. As with other state-level individual difference constructs, state 

vocational interests would have the same affective, behavioral, and cognitive content as the trait 

level constructs, but apply for a shorter duration. For instance, state vocational interests would, in 

that specific moment, refer to a preference for specific types of activities such as Investigative or 

Enterprising tasks. At the trait level, an individual may prefer Social activities but, in the 

moment, their preferences may differ representing their state interests.  

 

At this point, it becomes necessary to distinguish this conceptualization of state 

vocational interests from the state interest conceptualization frequently utilized in educational 

research, as these two conceptualizations represent differing constructs. As previously discussed, 

the state construct of situational interests from educational research can be defined as, “…a 

short-term spike in a person’s attention and participation in an activity and it is triggered by 

features of the environment…” (Azevedo, 2018, pp. 109). At a fundamental level, this 

conceptualization of interests differs from extant trait-level interest constructs in content. 

Situational interests are defined by the effect of the task or target object on the individual, and 

typically function as domain agnostic as opposed to the individual’s preference for this type of 

9 
 

 

activity or another (Hidi & Anderson, 1992; See Linnenbrink-Garcia et al., 2010). By definition 

in the trait-state paradigm, a state must have the same affective, behavioral, and cognitive 

content as the trait is corresponds to. Thus, situational interests cannot represent a state level 

 

conceptualization of vocational interests. 

 

To further illustrate the theoretical similarities and differences between situational 

interests, state vocational interests, and trait vocational interests, parallels, though imperfect and 

limited, can be drawn in regard to the emotions literature (see Gray & Watson, 2001). Within 

this literature, emotions, moods, and temperaments represent related constructs differing in 

temporality and nature in ways that relate to these differing conceptualizations of situational, 

state vocational, and trait vocational interests.  

Emotions are short term, relatively automatic reactions related to a specific event or 

stimuli (Watson & Clark, 1994; Levenson, 1994). This conceptualization is similar to situational 

interests, which are also related to a specific stimulus and short-term in nature (Azevedo, 2018). 

Mood, in contrast, is more diffused than emotions. Where emotions may last mere seconds, 

moods can last hours or even days (Gray & Watson, 2001). Though typically longer, moods still 

refer to a state that fluctuates over time. Where emotions are thought of as response systems that 

are activated by certain stimuli, moods are not so much a reaction to specific stimuli but a 

summary of an individual’s affective states (Gray & Watson, 2001). Like mood, state vocational 

interests are not tied to a specific stimulus but instead are a short-term summary of our 

preference for different activities, contexts, and outcomes. Like moods may be influenced by 

emotions, state vocational interests may be influenced by situational interests (which I expand on 

in the following pages). Though emotions and moods represent state constructs, temperament 

represents a more stable trait-like disposition. Where moods and emotions are transitory, 

10 

 

 

 

temperaments are more stable and can persistent for decades (Gray & Watson, 2001). While 

emotions are primarily stimuli driven, temperaments are a function of enduring individual 

characteristics. Though distinct, mood is substantially related to temperament (Parkinson, 

Totterdell, Briner, & Reynolds, 1996). In a similar way, trait vocational interests are relatively 

enduring (e.g. Low, Loon, Roberts, & Rounds, 2005) and are expected to influence state 

vocational interests.  

 

Though imperfect, this parallel with the emotion literature is helpful in illustrating the 

theoretical differences between situational, state vocational, and trait vocational interests. 

Situational interests are transitory states that are primarily driven by specific immediate 

situations, events, or stimuli. State vocational interests are longer term states driven by recent 

situational interests and trait vocational interests, and trait vocational interests are relatively 

stable preferences. 

 

Though state vocational interests are expected to be longer in duration than situational 

interests and more transitory than trait vocational interests, the lack of research into state 

vocational interests leaves their exact duration an open question. It is worth noting, however, that 

even in the more established emotions literature the length of a mood, a construct which we 

previously drew parallels with in regards to state vocational interests, also remains ambiguous. 

The duration of a mood has been described as hours, days, or even longer (Beedie, Terry, & 

Lane, 2005; Gray & Watson, 2001). Individuals tend to structure their activities daily and their 

preferences for activities may similarly follow this pattern. The natural segmentation of daily 

activities may lead to a similar natural segmentation of preferences. Thus, for the purposes of 

this dissertation I will assume that state vocational interests have a daily duration.  

11 

 

 

 

Variability in Vocation Interests 

Though research into short-term variability in vocational interests is still in its infancy 

due to a general neglect of state interests in the organizational sciences (e.g. Hunn, 2010), 

research into long term changes in vocational interests at the trait level is extensive. Though most 

research suggests that vocational interests at the trait level are mostly stable over time 

(Rottinghaus, Coon, Gaffey, & Zytowski, 2007), there are some normative changes with age 

(Hoff, Briley, Wee & Rounds, 2018; Low et al., 2005). For example, during early adulthood trait 

vocational interests involving people (Artistic, Social, & Enterprising) tend to increase (Hoff et 

al., 2018). 

Interests and Whole Trait Theory 

Though trait vocational interests are mostly stable over time, this does not preclude 

within-individual variability. For instance, though an individual may typically prefer Realistic 

type work, their day-to-day preference for this type of work may fluctuate. One model of this 

state variability around a trait construct is within Whole Trait Theory (WTT) (Fleeson, 2001; 

Fleeson & Jayawickreme, 2015; Jayawickreme, Zachry, & Fleeson, 2019). The central idea of 

this theory is that state and trait levels of a construct can be represented simultaneously utilizing 

a density distribution. A density distribution, within the context of WTT, is a distribution of an 

individual’s state fluctuation on a construct around an individual’s trait (or sometimes aggregated 

state mean) standing on that construct. For instance, an individual may have a relative midpoint 

standing on trait extraversion, but over the course of several weeks may exhibit states of both 

high extraversion and low extraversion. These states form a distribution around this individual’s 

trait standing, indicting how this construct was actually manifested over the period of time. This 

12 

 

 

distribution can fluctuate in location, size, and shape like any density distribution, according to 

how the individual manifests the construct of interest (Fleeson & Jayawickreme, 2015). 

 

Though WTT was conceived within and has been traditionally applied to the Five Factor 

Model of personality (Goldberg, 1990), researchers have discussed the applicability of WTT 

principles to other psychological constructs as well (Jayawickreme et al., 2019). Indeed, this 

density distribution approach has been utilized with other psychological constructs such as 

attachment theory (Haak et al., 2017) and goal orientation (Mihalecz, 2011). Within the limited 

research exploring vocational interest variability, interests have been considered from a density 

distribution perspective (e.g. Phan et al., 2017). This density distribution approach allows for a 

richer illustration of an individual’s vocational interests than a cross-sectional trait approach can 

allow by acknowledging that variation can occur from this point and modeling this variation. 

This WTT approach does not preclude between individual vocational interest trait stability, as 

commonly found, and acknowledges that around this trait variation can occur (i.e. state 

vocational interests). The density distribution framework within WTT is the theoretical trait-state 

model used within this dissertation for trait and state vocational interests. 

Theoretical Argument for Variability 

In order to be considered a state-level construct, state vocational interests would need to 

show meaningful within-person variation over relatively short periods of time (e.g. Allemand et 

al., 2013). Though the empirical research into short-term variability of vocational interests is 

limited, there are theoretical arguments as to why one would expect significant state variation in 

vocational interests. Specifically, within-person variability research within the closely related 

domain of personality and the positive effects of skill variety within jobs provide theoretical 

support for short-term variability of vocational interests. 

13 

 

 

 

Existing individual differences research within the trait-state paradigm provides 

theoretical evidence that vocational interests should display short term within-person variability. 

More and more research within differential psychology is finding considerable state fluctuations 

in traditionally trait concepts (e.g. Neubauer, Voss, & Ditzen, 2018). As previously noted, this 

research has especially flourished in personality research (Fleeson, 2017) where the variability 

within-individuals in personality commonly overshadows variability between-individuals (e.g. 

Fleeson, 2001). Theoretically, vocational interests and personality are closely linked, to the 

extent that Holland (1997) considered vocational interests to be personality constructs. Given the 

close link between personality and vocational interests theoretically (Holland, 1997) and state 

fluctuation found in the personality literature, vocational interests should demonstrate state 

variability.  

Variety is core to the human experience (e.g. Fiske & Maddi, 1961) and this individual 

need for variety has carried over into our theories of job design (e.g. Job Characteristics Model; 

Hackman & Oldham, 1975; 1980). Within the influential Job Characteristics Model, skill variety 

defined as, “…the degree to which a job requires a variety of different activities in carrying out 

the work, which involve the use of a number of different skills and talents of the employee” 

(Cordery & Parker, 2012, pp. 261) has been theorized as an influential variable in the prediction 

of work meaningfulness and a number of important outcomes. Greater skill variety has 

demonstrated relationships with a number of outcomes such as greater motivation, engagement, 

and job satisfaction (Humphrey, Nahrgang, & Morgeson, 2007), while lower skill variety has 

been related to burnout and depression (Karasek & Theorell, 1990; Parker, 2003). Notably many 

of these variables have theoretical links to vocational interest congruence (Holland, 1959; 1997; 

Nye et al., 2012; 2017). This need for variety suggests that vocational interests within-

14 

 

 

individuals may vary over time, in that jobs that provide a wider variety of activities lead to 

better employee outcomes. In other words, these positive outcomes may be related to the variety 

of activities on the job being wide enough to accommodate within-person fluctuations in desire 

for different types of work, driven by state vocational interest fluctuation. 

 

Forms of Variability 

Many different forms of variability exist and may be considered in regard to individual 

differences (Allemand et al., 2013; Tracey & Sodano, 2008). Three forms of stability that may be 

of particular importance for vocational interests are: Absolute stability, relative stability, and 

rank order stability. 

Absolute Stability. Absolute stability refers to changes in individual scores over time and is 

one of the most common and simplest forms of stability (Tracey & Sodano, 2008). Variability in 

this form would refer to changes in an individual’s raw vocational interests score. For instance, 

an individual may, on a five-point scale, score a four on Conventional interests one day and a 

three on the next. For vocational interests to exist at a state level, we would expect variability in 

scores day-to-day where an individual score on interests may vary across repeated 

measurements. 

Relative Stability. Relative stability refers to invariance in the ranking of individuals relative 

to others (Tracey & Sodano, 2008). In other words, relative stability would suggest if individual 

A scored higher than individual B during the first administration, the same would be true on a 

subsequent administration. It is possible to have relative stability without absolute stability. For 

instance, if all individuals decreased in Investigative interests by .5 over time, their scores would 

display absolute variability (i.e. score change) but not relative variability (i.e. ranking of level of 

15 

 

 

Investigative interests relative to others). Thus, it is important to test for variability in both of 

 

these conceptualizations.  

Based on the hypothesized effects driving state vocational interest changes day-to-day, 

discussed later in this dissertation, changes in state vocational interests would not be expected to 

occur uniformly across individuals. For instance, if one individual’s Artistic interest increases 

another individual’s Artistic interest may decrease, stay the same, or increase to a greater or 

lesser extent. Thus, we would expect the rank order of individual in terms of interests to change 

day-to-day. In other words, we would expect state vocational interests to display a lack of 

relative stability.  

Rank Order Stability. Rank order stability refers to stability in the relative ordering of 

interests within individuals (Allemand et al., 2013; Tracy & Sodano, 2008). For example, if state 

vocational interests display rank order stability and Investigative was an individual’s top interest 

one day, it will likely be their dominating interest the next. A lack of rank order stability in 

vocational interests could be of substantial concern given the importance of rank ordering 

vocational interests for congruence indices (e.g. Brown & Gore, 1994; Camp & Chartrand, 

1992). Note that rank order stability is focused on the rank stability of individual interests, which 

differs from profile stability which refers to the stability of an individual’s full profile of interests 

tested simultaneously. 

  Again, based on the hypothesized effects driving state vocational interest change day-to-

day, discussed later in this dissertation, changes in state vocational interests would not be 

expected to occur uniformly across interests within individuals as well. For instance, an 

individual’s Conventional interests may increase from one day to the next while that individual’s 

Enterprising interests may decrease, stay the same, or increase to a greater or lesser extent. Thus, 

16 

 

 

we would not expect rank order stability as relative rankings of interests within individuals may 

 

fluctuate. 

Concerning variability, utilizing only one conceptualization of consistency may be 

deficient for the purposes of capturing the true variable nature of a construct. Differing 

conceptualizations of consistency may be important, as they may be both theoretically and 

empirically independent (Allemand et al., 2013). Based on the outlined rationale, I propose 

variability in daily vocational interests, indicative of state vocational interests, of the following 

forms: 

Hypothesis 1a: State vocational interests will display day-to-day absolute variability. 

Hypothesis 1b: State vocational interests will display day-to-day relative variability. 

Hypothesis 1c: State vocational interests will display day-to-day rank order variability. 

Individual Differences in Variability 

If variability in state vocational interests exists, it may be the case that some individuals 

display greater variability than others (Allemand et al., 2013). For instance, one individual may 

see their interest in Social activities vary considerably over a week’s time, while another 

individual may exhibit little fluctuation in their desire for Social activities, contexts, or outcomes. 

Personality trait-state research conceptualized within the WTT density distribution framework 

has theorized that each individual is likely to have a unique distribution of their personality 

states, including degree of variability (Fleeson, 2001). Indeed, research has found differences in 

personality state variability across individuals (e.g. Fleeson & Law, 2015). If variability in state 

vocational interests exists, it would similarly be expected, based on WTT, that there would be 

individual differences in the degree of variability of interests across individuals.  

17 

 

 

If differences in the degree of variance exists across individuals in state vocational interests, 

it would be useful to know if specific individual differences are related to a greater or lower 

degree of variability. In other words, what individual differences at the between person level 

would predict variability in state vocational interests across individuals? Three individual 

 

differences are hypothesized as predictors of this variance: 

Openness to Experience  

Trait openness to experience is related to an individual’s tendency to be broadminded, 

original, curious, and imaginative (Barrick & Mount, 1991). Individuals high in openness to 

experience tend to be open to new opportunities, activities, and contexts. Due to this tendency, I 

would expect individuals high in openness to display a higher degree of variability in state 

vocational interests. In other words, I would expect individuals higher in openness to desire more 

diverse experiences in the tasks that they do from day-to-day, and this desire to be reflected 

within their variance in state vocational interests. Thus, I hypothesize that: 

 

Hypothesis 2a: Trait openness to experience will be positively related to variability in 

state vocational interests, where a higher degree of openness to experience will be related to 

greater variability in state vocational interests.  

Curiosity  

Trait curiosity has been characterized by exploratory behavior and a drive towards novel 

stimuli (see Loewenstein, 1994). Individuals high in curiosity would be expected to possess 

greater variability in their state vocational interests, due to this propensity towards exploration 

and novelty seeking. For instance, an individual high in curiosity may desire variety in the 

18 

 

 

activities, contexts, and outcomes of their work day-to-day and seek out novel and diverse tasks 

 

to fulfill this need. Thus, I hypothesize that: 

 

Hypothesis 2b: Trait curiosity will be positively related to variability in state vocational 

interests, where a higher degree of curiosity will be related to greater variability in state 

vocational interests. 

Implicit Interest Theory 

Dweck’s concept of implicit fixed versus growth mindset (see Dweck, 1999) has been 

recently extended to the interests literature (O’Keefe, Dweck, & Walton, 2018). Individuals have 

been characterized as having a fixed theory of personal interests, where they believe that 

interests are inherent and relatively unchangeable, or having a growth theory of interests, where 

they believe interests may not be fully formed and may develop over time. Individuals with a 

fixed implicit theory of interests should display less variability in state vocational interest, as 

they see their own interests as unchanging and would see little value in exploration outside of 

their main interests. In contrast, individuals with a growth implicit theory of interests would be 

expected to have a higher degree of variability in state vocational interest, as they may be more 

open to exploring alternative activities, contexts, and domains than their typical interests in the 

spirit of possible interest development. Indeed, in past research individuals with fixed implicit 

theories of interests were less likely to explore interests outside of their existing interests 

(O’Keefe et al., 2018). Thus, I hypothesize that: 

 

Hypothesis 2c: A growth theory of interests will be positively related to variability in 

state vocational interests, where a high degree of growth theory endorsement will be related to 

greater variability in state vocational interests. 

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Self-Assessment of Variability 

 

In addition to these individual differences as predictors of state vocational interest 

variability, some research suggests that individuals may be able to self-report their own 

variability on constructs with some accuracy (e.g. Lievens et al., 2018). It would be useful to 

know if individuals are capable of accurately reporting the degree to which their interests 

fluctuate from day-to-day, in regard to their convergence with actual degree of interest 

fluctuation gauged through repeated measurement of state interests. Some past research has 

found poor convergence between subjective perceptions and objective measures (Edwards, 

Cable, Williamson, Lambert, & Shipp, 2006). Due to conflicting past research (e.g. Edwards et 

al., 2006; Lievens et al., 2018), I put forward a research question: 

Research Question 1: Will self-reported level of variability regarding state vocational 

interests converge with actual degree of state vocational interest variability? 

State Vocational Interests and Prediction 

As mentioned previously, much of the impetus for the resurgence of vocational interest 

research in the organizational sciences was due to meta-analytic evidence supportive of the 

relationship between interests and outcomes (Nye et al., 2012:2017; Van Iddekinge et al., 

2011a), as opposed to older research more critical of their predictive capability (e.g. Ployhart, 

Schneider, & Schmitt, 2006; Schmidt & Hunter, 1998). The view persisted that interests may 

lead to occupational choice, but after that other individual differences take over in terms of 

prediction (Su, 2018). However, new evidence emerged to challenge this viewpoint. This new 

evidence found support for the usefulness of vocational interests in relation to a wide variety of 

outcomes of interest to organizational researchers and practitioners, including job performance 

(Nye et al., 2012, 2017; Van Iddekinge et al., 2011a), training performance (Van Iddekinge et al., 

20 

 

 

2011a), turnover intentions (Van Iddekinge et al., 2011a), turnover (Van Iddekinge et al., 2011a), 

organizational citizenship behaviors (Nye et al., 2012, 2017), persistence (Nye et al., 2012, 

 

2017), and job satisfaction (Hoff et al., 2018). 

Though this evidence is strong support for the criterion validity of trait vocational interests, 

these relationships cannot be generalized to state vocational interests without additional research, 

as relationships between predictors and outcomes at one level may not hold at different levels of 

analyses (i.e. lack of homology). Beyond the existence of relationships, the comparability of 

magnitude across levels for relationships can also be called into question. For example, the 

relationship between personality and exercise differs in strength depending on if the relationship 

is examined at the trait or state level (Mõttus, Epskamp, & Francis, 2017). Based on past 

research alone, it is unclear if state vocational interests will predict outcomes in a similar manner 

as trait vocational interests. 

Trait vocational interests are theorized to predict outcomes due to the enhanced engagement, 

persistence, and enjoyment that comes from interacting with a preferred stimulus (Rounds & Su, 

2014). It’s expected that the same engagement, persistence, and enjoyment would occur when 

state vocational interests are matched to a preferred stimulus, leading to similar relationships 

with outcomes. However, it is important to consider matching of predictors and criteria in terms 

of level, similar to the arguments made in other areas of individual differences research 

(Schneider, Hough, & Dunnette, 1996). For example, an individual might generally prefer a high 

degree of enterprising activities and may be involved in a high number of enterprising activities 

on a particular day, but if their state enterprising interests are low a high degree of state 

satisfaction is unlikely to occur. General (i.e. trait) vocational interests should predict general 

(i.e. global) outcomes, where state vocational interests should predict state outcomes. For 

21 

 

 

example, an individual’s trait vocational interest congruence with their general occupational 

duties should predict an individual’s overall job satisfaction. However, an individual’s state 

vocational interest congruence with their current task should predict their satisfaction with their 

 

job right then (e.g. state job satisfaction).  

Though research in this area is lacking, the relationships between state vocational interest 

congruences and state outcomes are expected to be homologous to trait vocational interest 

congruence and global level outcomes relationships due to similar underlying mechanisms of 

action (e.g. increased engagement, persistence). Based on theory (e.g. Holland, 1959; 1997; 

Rounds & Su, 2014) and empirical results at the trait level (e.g. Hoff et al., 2018; Nye et al., 

2012: 2017; 2018a; Van Iddekinge et al., 2011a), I propose five outcomes related to state 

vocational interest congruence: positive affect, negative affect, intrinsic motivation, engagement, 

and perseverance. 

 At the trait level, interest congruence is theoretically (Rounds & Su, 2014) and 

empirically (e.g. Hoff et al., 2018) related to affect based outcomes, with greater congruence 

related to more positive affective outcomes such as job satisfaction. Mirroring this relationship 

between congruence and positive affective outcomes at the trait level, I propose the following 

hypotheses at the state level: 

Hypothesis 3a: State vocational interest congruence will be positively related to state positive 

affect, where a greater degree of state interest congruence will be related to higher state positive 

affect. 

22 

 

 

Hypothesis 3b: State vocational interest congruence will be negatively related to state 

negative affect, where a greater degree of state interest congruence will be related to lower state 

 

negative affect. 

 

Theory suggests that greater interest congruence leads to a greater degree of motivation 

(Rounds & Su, 2014). Indeed, researchers finding a link between interest congruence at the trait 

level and performance have speculated that may be a result of higher motivation resulting from 

congruence (Nye et al., 2012). Based on these results at the trait level, I hypothesize the 

following relationships between motivation variables and interest congruence at the state level: 

Hypothesis 3c: State vocational interest congruence will be positively related to state intrinsic 

motivation where a greater degree of state interest congruence will be related to greater state 

intrinsic motivation. 

Hypothesis 3d: State vocational interest congruence will be positively related to state 

engagement, where a greater degree of state interest congruence will be related to greater state 

engagement. 

Though related, engagement and intrinsic motivation differ in content warranting separate 

explorations as outcomes of state interest congruence. Intrinsic motivation refers to engaging in 

behaviors for their own sake, in regards to the pleasure or satisfaction derived from performing 

them (Deci, 1971). However, engagement is a broader construct reflecting a state characterized 

by high energy levels, mental resilience, dedication, enthusiasm, and focus (Schaufeli & Bakker, 

2010). For instance, an individual may exhibit enthusiasm, dedication, and focus towards a task 

but not necessarily be intrinsically motivated by that activity and find it enjoyable. Based on 

23 

 

 

theory (Rounds & Su, 2014) and past findings (Nye et al., 2012), both of these motivationally 

related outcomes would be expected from state interest congruence. 

 

Individuals more congruent with a task or environment should persist with that task or 

environment for a longer time than those who are incongruent. This principle is integral to 

Attraction-Selection-Attrition theory (Schneider, 1987). Past research has found that individuals 

with a lower degree of interest congruence at the trait level demonstrate lower persistence and 

are more likely to leave their job (Nye et al., 2012; Van Iddekinge et al., 2011a). At the state 

level, I hypothesize a similar relationship between interest congruence and persistence: 

Hypothesis 3e: State vocational interest congruence will be positively related to state 

perseverance, where a greater degree of state interest congruence will be related to greater state 

perseverance. 

As previously discussed, state vocational interest congruence should better predict state 

outcomes than trait interest congruence. In addition to testing relationships between state 

congruence and these five outcomes, additional analyses will be conducted to determine if state 

congruence displays incremental predictive validity over trait interest congruence with an 

individual’s daily activities. It is expected based on work within other individual differences 

research (Schneider et al., 1996) that state congruence should predict state outcomes over and 

above trait congruence.  

State Vocational Interests and Interest Development 

If state vocational interests are expected and found to vary, a natural question might be 

what would cause them to fluctuate day-to-day. To this end, I put forward two hypothesized 

mechanisms that are predicted to influence these fluctuations. One of these is an extension of the 

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Trait-State Interest Dynamics model of trait vocational interest development (Su et al., 2019) 

based on the influence of situational interests, while the other is based on the congruence or 

incongruence of desired and actual activities leading to subsequent changes in state interests. 

 

Situational Interest Driven Change Theory 

 

Both the fields of organizational science and education have made substantial theoretical 

progress in determining the origins of trait interests (e.g. Hidi & Renniger, 2006; Krapp, 2003; 

Su et al., 2019). Recently, the Trait-State Interests Dynamics model (TSID) (Su et al., 2019) of 

interest development has been introduced. The TSID model (Su et al., 2019) is particularly 

notable as it is a cross-disciplinary framework of trait interest development, integrating interest 

research from organizational science and educational perspectives. Within this model, trait 

interests direct, energize, and sustain individual effort and engagement with tasks in socio-

cultural contexts. As a result of this, trait interests predict outcomes such as performance and 

persistence. These traits then direct individuals towards situations where they experience 

situational interest through affective reactions to the object, task, or context, cognitive appraisals 

of its value or valence, and identification with the object, task, or context. This process then leads 

to increased or decreased interest in the object, task, or context as an abstract concept and iterates 

in a dynamic fashion to develop a person’s trait interests over time. The tenets of this model 

generally find support in the literature (e.g. Palmer et al., 2017; Rotgans & Schmidt, 2017). 

 

Within this model of interest development, situational interest and trait interests interact 

in a dynamic, reciprocal fashion. However, I posit that the concept of state vocational interests 

represents a meso-level construct between these two. This idea of a meso-level construct linking 

situational interest to trait interest development can also be found in Hidi and Renniger’s (2006) 

Four-Phase Model of Interest Development. Within this model, four phases are thought to lead to 

25 

 

 

 

trait interest development: Triggered situational interest, maintained situational interest, 

emerging (less-developed) individual interest, and well-developed interest. State vocational 

interests are most similar to maintained situational interest and emerging individual interest. 

Maintained situational interest within this model refers to, “…a psychological state of interest 

that is subsequent to a triggered state, involves focused attention and persistence over an 

extended episode in time, and/or reoccurs and again persists.” (Hidi & Renniger, 2006, pp. 114). 

Emerging individual interest in this model refers to, “…a psychological state of interests as well 

as to the beginning phases of a relatively enduring predisposition to seek repeated reengagement 

with particular classes of content over time.” (Hidi & Renniger, 2006, pp. 114). The state 

vocational interest concept in regard to interest development refers to a blend of these two 

concepts, borrowing most heavily from emerging interests. State vocational interests are also a 

state, more enduring than situational interests, and are theorized to lead to reengagement with 

types of stimuli over time, but instead of representing less fully formed trait interests the 

construct is more of a transitory state that fluctuates over time and influences the development of 

trait interests. 

Situational interests can be thought of as affective and cognitive reactions to a specific 

stimulus, such as an object, task, or context (Su et al., 2019). These reactions can either be 

positive or negative. A positive reaction can be defined as a positive affective reaction to the 

stimuli, assigning the stimuli positive valence or value, and identifying with the stimuli, as 

represented in the TSID framework (Su et al., 2019). A negative reaction can be considered the 

opposite. I posit that depending on if this reaction is positive or negative, the individual will 

develop either a greater or reduced state vocational interest in the abstract concept the stimuli 

represents. An individual’s standing on the vocational interest trait serves as an anchor for the 

26 

 

 

state interest to fluctuate around (e.g. Fleeson, 2001), where interactions with domain 

representative stimuli act as an additive or subtractive force based on positive or negative 

experiences with the stimuli, respectively, causing state deviation from the trait standing. These 

deviations around the trait represent state vocational interests. 

 

 

For an illustrative example, consider an individual who has a rather moderate interest in 

Realistic activities, such as building or repairing things. However, this individual attends a 

captivating lecture on engineering which sparks their situational interests due to positive feelings 

towards, personal meaning from, and personal identification with the materials. TSID theory 

posits that this individual’s situational interests in this domain would be triggered. I posit that 

state interest in the abstract domain this material pertains to (i.e. Realistic) would be increased, as 

a function of their typical interest in the activity (i.e. trait Realistic standing) and the increase in 

interest derived from their positive reaction to the material (i.e. high situational interests). After 

this lecture, the individual should feel more interested in this type of activity in the moment than 

they did previously. In contrast, if this individual attended a rather dull lecture on engineering 

with little situational interest generated, their state interest in Realistic activities should be 

reduced as a function of their typical trait standing and negative reaction to the experience (i.e. 

low situational interest). Based on the tenets of the TSID theory and rationale described, I 

hypothesize that: 

 

Hypothesis 4: Changes in state vocational interests are predicted by situational interests, 

in that greater or lower situational interests within a domain one day will lead to an increase or 

decrease in state interests within that domain the following day, respectively.  

 

Though situational interests are hypothesized to drive changes in state vocational 

interests, it is plausible that higher state vocational interests in a domain lead to higher situational 

27 

 

 

 

interests in that domain. However, situational interests pertain to an individual’s affective 

reactions to, cognitive appraisal of value towards, and identification with an object, task, or 

context within the TSID model (Su et al., 2019). Just because an individual has high interests in a 

domain, does not necessarily mean they will react in an affectively positive matter to, see value 

in, and identify with activities in that domain. As previously noted, situational interests are 

typically represented as a function of content agnostic components of an activity or environment, 

such a presentation style, as opposed to a reflection of content (Linnebrink-Garcia et al., 2010). 

Though situational interests are expected to drive changes in state vocational interests, reverse 

causality where state vocational interests drive subsequent situational interests will also be 

assessed.  

Congruence-Incongruence Driven Change Theory 

Within the vocational interests literature, individuals seek out activities, contexts, and 

outcomes associated with their interests. In other words, individuals are driven to activities or 

environments that fulfill their vocational interests (Holland, 1997; Rounds & Su, 2014). I posit 

that when an individual’s desire for a specific type of activity or environment is not satisfied, this 

incongruence will lead to a subsequent increase in the desire for that type of activity or 

environment. Alternatively, when an individual experiences more of a type of activity or 

environment than they desire, this will lead to a subsequent decrease in the desire for that type of 

activity or environment. When state vocational interests match activities or environments 

experienced, subsequent state vocational interests would not be expected to change. In other 

words, incongruence of an individual’s daily interests and activities drives subsequent change in 

state vocational interests. 

28 

 

 

 

For an illustrative example, consider a researcher who has a moderate preference for 

Conventional activities. This researcher has focused exclusively on teaching and mentoring 

activities during the day (Social activities), to the point that they have not gotten to fulfill their 

desire for Conventional activities. We would expect this researcher’s state desire for 

Conventional work to increase the next day due to this incongruence. Conversely, consider a 

situation where a manuscript revision has forced this researcher to focus exclusively on data 

analysis (a Conventional activity) for the day, well beyond their preferences. Due to this excess 

of Conventional interests beyond their preferences, the researcher is likely to desire less of this 

type of work the following day. 

Based on these tenets, an individual’s day-to-day interests (i.e. state vocational interests) 

should fluctuate depending on the congruence between the prior day’s state interests and 

activities undertaken. If the prior day’s activities did not meet the desired level for an interest, 

this interest should be enhanced. If the prior day’s activities exceeded the individual’s desire for 

that type of activity, this interest should be reduced. Thus, I hypothesize that: 

 

Hypothesis 5: The congruence between an individual’s state vocational interests and 

activities the previous day will predict an individual’s state vocational interest in the next, such 

that less of an activity than desired will elicit greater state vocational interest in that domain the 

following day and more of an activity than desired will elicit lower state vocational interest in 

that domain the following day. 

METHODOLOGY & RESULTS 

 

In order to test these research hypotheses and questions, a two-study design is necessary. 

The purpose of the first study was to design a state vocational interest inventory, as the current 

29 

 

 

inventories do not fill this need well (discussed below). The purpose of the second study was to 

test research hypotheses and questions put forward utilizing experience sampling methodology 

(ESM), utilizing the validated state vocational interest measure from Study 1.  

 

Study 1 

For the hypotheses and research question outlined, it is necessary to create a vocational 

interest measure which can measure state vocational interests appropriately. This study outlines 

the development and validation of the Daily Short Interest Measure (DSIM) in order to address 

limitations of current inventories and serve as an interest measure suitable for testing research 

hypotheses and questions put forward. The new measure overcomes three limitations of current 

measures. 

 First, the measure is short enough to utilize effectively within an ESM study, like the 

methodology used for Study 2. Many of the extant vocational interest inventories are extensive, 

commonly reaching over 100 items (Chernyshenko, Stark, & Nye, 2019). Even brief scales 

number almost 50 items (e.g. Armstrong, Allison, & Rounds, 2008). 

Most of the existing measures of vocational interests consist of asking individuals to 

indicate their preferences for certain activities. For instance, the Brief Public Domain RIASEC 

Marker Scales (Armstrong et al., 2008) asks for individual preferences regarding activities such 

as “Work on an offshore oil-drilling rig”, “Make a map of the bottom of an ocean”, and 

“Negotiate contracts for professional athletes”. It is unlikely individuals have experience in these 

domains and can accurately compare their preferences with the activities these tasks actual 

consist of, especially undergraduate age populations of which Study 2 utilizes. Instead, the 

inventory developed utilizes overarching principles from these types of work which may be more 

reasonable to utilize as oppose to contextualizing these interests in tasks individuals would likely 

30 

 

 

have little experience with. For example, instead of preference for negotiating contracts for 

professional athletes, an item measuring Enterprising interests may ask for preference for tasks 

 

involving negotiation. 

Interest congruence refers to the match between an individual’s preferred activities, 

contexts, and outcomes with the activities, contexts, and outcomes in their environment (Nye et 

al., 2012). To assess state vocational interests in Study 2 individuals were asked their preferred 

types of activities in the morning, and then asked to report the degree to which they engaged in 

these preferred activities during the day that evening. Utilizing current interest inventories 

contextualized within specific tasks would not have been feasible for this. For instance, asking 

individuals if they preferred to “Lay brick or tile” (Armstrong et al., 2008) that day, then asking 

them in the evening if they “Laid brick or tile” would not likely result in an accurate measure of 

satisfaction of an individual’s Realistic interests. Instead, measuring Realistic interests using a 

more general statement such as if today they’d prefer to “work with their hands” and in the 

evening asking if they had “worked with their hands” during the day would likely represent a 

more accurate measurement of Realistic interest congruence than situating it within highly 

specific tasks. 

Based upon the limitations of current vocational interest inventories and the inability to 

utilize or adapt these measures to measure state vocational interests appropriately to test the 

research hypotheses and question put forward, a new state vocational interest inventory was 

developed and validated in Study 1, outlined below. 

In order to develop items for the DSIM measure, a deductive method was followed based 

on the six vocational interests outlined by Holland (1959;1997). Items were written to 

correspond to each of the six vocational interests outlined by this theory. Specific item content 

31 

 

 

was derived from Holland’s writings (1959; 1997), previous published work on vocational 

interests (e.g. Nye et al., 2012; Rounds & Su, 2014), existing vocational interest inventories (e.g. 

Armstrong et al., 2008), and vocational interest information available from O*NET.  

 

 

For the DSIM, the ideal length of the measure would be five items per vocational interest 

resulting in 30 items total, given the space constraints inherent in ESM methodology and 

potential participant fatigue from a longer measure. Previous authors have recommended four to 

six items for measuring a construct (See Hinkin, 1998 for a discussion). It has been 

recommended that twice the number of items needed for a scale should be pilot tested in 

development, as many items may need to be dropped for the final measure (Hinkin, Tracey, & 

Enz, 1997). Thus, the goal of item generation was to write 60 items to pilot, with 10 items based 

upon each vocational interest. See Appendix B for the 60 items generated for pilot testing.  

For these items, individuals were prompted with, “People may prefer to engage in 

different activities. Please indicate how much you would prefer to do the following activities 

today. ‘Today, I would prefer to do activities that involve…’” and asked to rate these statements 

on a 7-point scale. Research general supports the use of a 5 or 7-point scale based on reliability 

and validity concerns (See Maitland, 2009). Scale point utilized consisted of “Strongly disagree”, 

“Disagree”, “Somewhat disagree”, “Neither disagree or agree”, “Somewhat agree”, “Agree”, and 

“Strongly agree”. Research indicates that these intervals have roughly equal psychological 

distance from one another, which if not the case could attenuate relationships (Casper, Edwards, 

Wallace, Landis, & Fife, 2019). 

Individuals were administered the developmental item pool for the DSIM electronically, 

along with a number of other measures, at a single timepoint to establish appropriate factor 

structure and convergent validity.  

32 

 

 

Sample 

 

433 undergraduate students from Michigan State University’s SONA subject pool were 

administered a survey questionnaire in exchange for course credit. Individuals were given ½ 

hour of SONA credit for their participation. A copy of the consent form and debriefing form for 

this study can be found in Appendix C and D, respectively. 

In regard to data cleaning, responses were removed based on duplicate participant IDs, 

completion percentage, attention checks, and time per page. Previous research has indicated that 

data cleaning based on attention checks and response time can be an effective method for 

removing low quality data (see Curran, 2016; Huang, Curran, Keeney, Poposki, & DeShon, 

2012; Huang, Liu, & Bowling, 2015; Meade & Craig, 2012). If an individual had taken the 

survey more than once, as indicated by duplicate participant IDs, the response where the 

participant progressed the farthest in the survey was retained. If participants completed the same 

proportion of the survey for multiple responses, the most recent response was retained. If an 

individual had completed less than 10% of the survey, their response was removed. If 

respondents missed more than one of the three attention checks (e.g. Choose “Like very much” 

for this statement), their response was removed due to inattentive responding. Finally, responses 

were removed based on response times for each page of the survey containing questions, with the 

exception of the page containing demographic questions. Of these seven survey pages, if 

individuals responded faster than 2 seconds on average to questions on more than two of these 

pages their responses were removed due to likely inattentive responding. These data cleaning 

procedures resulting in a final sample of 341 responses, or 78.8% of the original sample. 

Within the final sample of individuals, 79.8% (272) of respondents identified as female, 

19.9% (68) as male, and less than .01% (1) as non-binary. The average age of participants was 

33 

 

 

 

19.4 years old (SD = 1.74). 70.4% (240) of individuals identified as White, 7.9% (27) identified 

as Black or African American, 7.3% (25) as East Asian, 4.1% (14) as Hispanic or Latinx, 4.1% 

(14) as South Asian, 2.9% (10) as Multiracial, 2.1% (7) as Other, and 1.2% (4) preferred not to 

specify racial or ethnic identity. 31.6% (108) of respondents were undergraduate freshman, 

34.6% (118) were sophomores, 23.2% (79) juniors, and 10.6% (36) seniors. Fifty-two majors 

were represented within these data, with the most common being Psychology (29.6%), Human 

Biology (7.9%), Kinesiology (6.5%), Neuroscience (5.3%), and Advertisement Management 

(4.1%). 5.6% of individuals identified as “Undecided/Undeclared”. 

Measures 

A number of measures were administered to determine if the measure in development 

displayed convergent validity with theoretically and empirically related constructs, along with 

collecting calibration information for the ESM timing and pilot testing additional measures 

created for Study 2. A full list of items administered for Study 1 can be found in Appendix E, 

with the exception of the three response check items interspersed within scales (e.g. Please select 

“Agree a little” for this statement). In cases when item-level missing data was present, the 

average of non-missing items from the scale were utilized to represent the construct as 

recommended by Newman (2014). Within scales, item ordering was randomized across 

participants. 

Daily Short Interest Measure (DSIM) Developmental Item Pool. Individuals were 

administered the full 60 drafted items of the DSIM developmental item pool, which were used to 

determine the items used for a final 30-item measure. See Appendix B for a full list of items for 

this measure. 

34 

 

 

 

Brief Public Domain RIASEC Marker Scales – Set A. Individuals were administered 

the Brief Public Domain RIASEC Marker Scale – Set A (BPDRM) (Armstrong et al., 2008). 

This scale is a 48-item measure of trait vocational interests based on Holland’s (1959; 1997) 

model of vocational interests. Individuals were presented with activities (e.g. “Direct a play”, 

“Lay brick or tile”) and asked the degree to which they would like these tasks on a 5-point Likert 

scale (“Dislike very much” to “Like very much”). Past research has found acceptable internal 

consistency for this measure (α = .79 to .94), and strong convergent validity evidence with other 

measures of vocational interests (O*NET Occupational Interest Measure: Peterson, Mumford, 

Borman, Jeanneret, & Fleishman, 1999; Strong Interest Inventory: Harmon, Hansen, Borgen, & 

Hammer, 1994). Within the current study, the scales demonstrated adequate reliability (α = .76 to 

.90). 

Trait Personality. Individuals were administered the Big Five Inventory - 2 (BFI-2; Soto 

& John, 2017). The BFI-2 is a 60-item trait personality inventory based on the Five Factor Model 

of personality (see Digman, 1990). Individuals were presented with a number of characteristics 

(e.g. “Is outgoing, sociable”, “Often feels sad”) and are asked if they believe these characteristics 

pertain to themselves on a 5-point scale (“Disagree strongly” to “Agree strongly”). This 

inventory measures all five factors, as well as three narrower facets within each factor. Research 

has shown strong convergent validity evidence for this assessment and other personality 

inventories and generally adequate internal consistency reliability at the factor level (α = .83 to 

.90) and facet level (α = .66 to .85) (Soto & John, 2017). Within the current study, scales at the 

factor level displayed adequate reliability (α = .74 to .89), 

Additional Developed Measures. Individuals in this study were also administered two 

additional measures developed for the purpose of this study: Self-assessed variance in state 

35 

 

 

 

interests (3 items) and importance of interests (3 items). Self-assessed variance in state interests 

is focal to Study 2 and importance of interests is included for exploratory purposes and not 

necessarily integral to the purpose of Study 1. Thus, these measures are discussed in more detail 

in Study 2. The purpose of including self-assessed variance in state interests and importance of 

interests measures in the Study 1 data collection was to ensure adequate reliability and 

interpretability before including in Study 2.  

Lay Theories of Interest Variability. In order to assess lay perceptions individuals may 

have regarding interest variability, respondents were prompted with the following question and 

provided an open-ended format to respond: “Do you think your preferences for different types of 

activities change day to day? If so, what do you think may drive this change? If not, why do you 

think they might not change? Please write a few sentences or more regarding your thoughts.” 

The purpose of collecting lay perceptions of interest change was to assess participant perceptions 

as to if interests change day-to-day and, if they believe they do change, what may cause this 

change with the possibility of informing data collection and/or analyses for Study 2. 

Experience Sampling Method Calibrating Questions. Individuals were asked four 

questions to inform administration times for the morning and evening ESM surveys in Study 2: 

“On a typical day, approximately what time do you wake up in the morning?  “On a typical day, 

approximately what time do you first check email?”, “On a typical day, approximately what time 

do you go to bed at night?”, “On a typical day, approximately what time do you check email for 

the last time?”. Individuals were given a drop-down menu with options in 30-minute intervals.  

Academic Major. Individuals were asked to self-select their academic major from a 

drop-down list of majors available at MSU. If their major wasn’t listed, the option was available 

to self-report their major. 

36 

 

 

Demographics. Individuals were asked to self-report their demographic information, 

including age, gender, race/ethnicity, and college standing (e.g. Freshman, Sophomore). 

 

Results.  

To determine which items to retain for the final DSIM measure for each of Holland’s 

(1959; 1997) six vocational interests, item means, standard deviations, average correlations with 

other items within each respective interest, median correlations with other items within each 

respective interest, max correlations with other items within each respective interest, correlations 

with the BPDRM interests (e.g. Armstrong et al., 2008), EFA factor loadings, and alpha if the 

item was removed from a scale using all the ten developmental items were considered, along 

with the theoretical breadth of the items to capture each respective vocational interest. An EFA 

utilizing principal axis factoring and Promax rotation with all 60 DSIM developmental items was 

conducted to determine the extent of cross loadings for each item. Principal axis factors was 

utilized as it is suggested for non-multivariate normal data (Costello & Osborne, 2005) and the 

60 items were assessed to be non-multivariate normal (p < .05)  based on the Mardia’s test, 

Henze-Zirkler’s Test, Doornik-Hansen’s test, Royston’s test, and the E-statistic from the “mvn” 

R statistical package (see Korkmax, Goksuluk, & Zarasiz, 2014). Promax rotation was utilized as 

factors are expected to be correlated and this rotation is popular for oblique rotation methods 

(Goretzko, Pham, & Bühner, 2019). 

In choosing the number of factors to retain for the EFA, multiple criteria were 

considered. Parallel analyses (utilizing the “paran” R package: Dinno, 2018) suggested 7 factors 

should be retained. However, theory (e.g. Holland, 1959; 1997) and the Kaiser criterion (Kaiser, 

1960) of retaining factors with eigenvalues greater than one suggested retaining six factors. 

Additionally, BIC relative fit indices were compared for EFAs retaining 1 to 10 factors, with 6 

37 

 

 

factors displaying the best relative fit. Weighing the evidence, 6 factors were retained. EFA 

 

results are reported in Table 1.  

Five items were retained for each of the RIASEC dimensions for the final DSIM scales. 

For Realistic, “Using tools”, “Fixing”, Building”, “Physical activity”, and “Working with 

gadgets” were retained. For Investigative, “Research”, “Science”, “Analytic thinking”, 

“Intellectual tasks”, and “Academics” were retained. For Artistic, “Creativity”, “Imagination”, 

“Art”, “Creating”, and “Creative writing” were retained. For Social, “Helping others”, “Working 

with others”, “Guiding others”, “Communicating with others”, and “Providing services” were 

retained. For Enterprising, “Persuading others”, “Selling”, “Negotiation”, “Managing others”, 

and “Managing projects” were retained. For Conventional, retained items were “Routine”, 

“Structure”, “Systematic work”, “Organizing information”, and “Repeated tasks”. Alpha was 

calculated for these final DSIM scales. Reliability was adequate for Realistic (.75), Investigative 

(.78). Artistic (.84), Social (.81), Enterprising (.77), and Conventional (.73) scales. 

See Table 2 for N, mean, standard deviation, intercorrelations, and alpha for the final 

DSIM measures, trait vocational interests, personality, the revised interest importance measure 

(see below), and self-assessed interest variance.  

Confirmatory factor analyses were conducted for each of the DSIM scales individually to 

determine model fit, with each of the five items loading on the latent RIASEC factor. When 

model modifications were undertaken to improve model fit based on modification indices, 

deviations from this model are noted. Listwise deletion was used for CFA analyses, as missing 

data for items was very small. N for analyses ranged from 340 to 341. For Realistic, model fit for 

the CFA was adequate (X2(5) = 12.92, p = .02, RMSEA = .07, CFI = .98, SRMR = .03). For 

Investigative, model fit for the CFA was generally acceptable (X2(5) = 28.95, p < .00, RMSEA = 

38 

 

 

 

.12, CFI = .95, SRMR = .04). However, modification indices suggested that items “Research” 

and “Science” should be allowed to covary, which seemed reasonable as much of science 

involves research. In a revised model allowing this modification, model fit was considerably 

improved (X2(4) = 9.50, p = .05, RMSEA = .06, CFI = .99, SRMR = .03). For Artistic, model fit 

for the CFA was excellent (X2(5) = 8.51, p = .13, RMSEA = .05, CFI = 1.00, SRMR = .02). For 

Social, model fit was generally acceptable (X2(5) = 27.30, p < .00, RMSEA = .12, CFI = .96, 

SRMR = .04). However, modification indices suggested that items “Working with others” and 

“Communicating with others” should be allowed to covary, which seemed reasonable as item 

structure was similar and working with others almost invariably involves communicating with 

others. In the revised model allowing this modification, model fit was considerably improved 

(X2(4) = 11.10, p = .03, RMSEA = .07, CFI = .99, SRMR = .03). For Enterprising, model fit for 

the CFA was generally poor (X2(5) = 66.66, p <. .00, RMSEA = .19, CFI = .87, SRMR = .07). 

However, modification indices suggested that items “Managing others” and “Managing projects” 

should be allowed to covary, which seemed reasonable given the common managerial 

component and that managing one often entails managing the other. In the revised model 

allowing this modification, model fit was acceptable (X2(4) = 13.33, p = .01, RMSEA = .08, CFI 

= .98, SRMR = .02). For Conventional, model fit for the CFA was adequate (X2(4) = 14.44, p = 

.01, RMSEA = .07, CFI = .97, SRMR = .03). CFA plots for these final models can be found in 

Figure 1 through 6. 

Convergent validity of the DSIM was assessed utilizing an established measure of trait 

RIASEC interests (i.e. BPDRM: Armstrong et al., 2008) and an established trait measure of the 

Five Factor Model of personality (BFI-2: Soto & John, 2017). Though the BPDRM is a measure 

of trait vocational interests and the DSIM is a measure of state vocational interests, a correlation 

39 

 

 

 

would still be expected between respective RIASEC dimensions. In other words, we would 

expect a relationship between corresponding individual trait interests and state interests. Across 

the RIASEC dimensions, considerable relationships were found between BPDRM trait and 

DSIM state Realistic (r = .53, p < .01), Investigative (r = .45, p < .01), Artistic (r = .63, p < .01), 

Social (r = .51, p < .01), Enterprising (r = .42, p < .01), and Conventional (r = .33, p < .01) 

interests supporting the convergent validity of the DSIM measure.  

In addition to assessing convergent validity of the DSIM with an established trait 

vocational interest measure, convergent validity was also assessed with an established trait 

personality measure. Trait personality and trait vocational interests have displayed considerable 

relationships in past research (e.g. Larson, Rottinghaus, & Borgen, 2002). Though the DSIM is a 

state vocational interest measure and past research has found a relationship between trait 

vocational interests and trait personality, we would still expect relationships between trait 

personality and state vocational interests as assessed by this measure to hold. Table 3 displays 

relationships based on meta-analytic research (i.e. Larson et al., 2002) between trait vocational 

interests and trait personality variables, relationships between trait vocational interests (i.e. 

BPDRM: Armstrong et al., 2008) and trait personality variables (BFI-2: Soto & John, 2017) 

within the present study, and relationships between state vocational interests assessed utilizing 

the DSIM and trait personality variables (BFI-2: Soto & John, 2017) within the present study. Of 

the 30 relationships between state RIASEC dimensions and Five Factor Model personality 

variables, 14 were statistically significant (p < .05). All of these 14 significant relationships 

between state vocational interests and trait personality were directionally consistent with meta-

analytic relationships found between trait personality and trait vocational interests. Within each 

of the RIASEC dimensions, the most robust meta-analytic relationship between trait vocational 

40 

 

 

 

interests and personality variables were also found and were the relationships of greatest 

magnitude for state vocational interests and trait personality relationships (e.g. artistic and 

openness). Taken as a whole, the relationships between state vocational interests and trait 

personality are generally consistent with what would be expected based on prior empirical 

research on the relationship between trait vocational interests and trait personality within the 

literature. 

 

Within the vocational interests literature, Holland’s (1959; 1997) hexagonal structure of 

six vocational interests is a widely used and accepted model of trait interests. However, it is 

unclear if this model would fit state vocational interests. To test the fit of the Holland hexagonal 

structure of interests to state vocational interests, a CFA was conducted in which each of the five 

items for each of the six interests loaded onto their respective interest, and the six latent interest 

variables were allowed to correlate. Listwise deletion was utilized with missing data, as less than 

1% of cases had missing data, resulting in a final sample size of 339 for analyses. Results 

indicated mixed evidence of fit for this model to the state vocational interests data (X2(390) = 

968.03, p < .01, RMSEA = .07, CFI = .84, SRMR = .07). A second model was fit to the data, 

allowing the modifications for the CFAs of individual RIASEC interests previously noted. 

Results indicated a small increase in model fit, but there was still mixed evidence of fit for this 

model to the state vocational interests data (X2(387) = 904.19, p < .01, RMSEA = .06, CFI = .86, 

SRMR = .07). Based on information from modification indices, four items were allowed to cross 

load. “Systematic work” was allowed to cross load on Realistic, “Routine” was allowed to cross 

load on Investigative, “Managing others” was allowed to cross load on Social, and “Managing 

projects” was allowed to cross load on Conventional. Based on these modifications, the resulting 

41 

 

 

model demonstrated adequate fit (X2(383) = 752.34, p < .01, RMSEA = .05, CFI = .90, SRMR = 

 

.06). See Figure 7 for this model plotted.  

Based upon the analyses conducted, the DSIM displays adequate reliability, adequate fit 

for CFAs modeling each individual interest, and reasonable convergent validity with trait 

vocational interests and personality. The DSIM vocational interest measure fills a research need 

within study 2 and the interests literature for a short, state vocational interest inventory. Through 

the results of study 1, reasonable confidence can be established that the DSIM is an adequate 

measure of state vocational interests.  

Other Measures Under Development. In addition to developing the DSIM within Study 

1, two additional measures were tested: Self-assessed variance in state interests (3 items) and 

importance of interests (3 items). Alpha for self-assessed variance in state interests was 

acceptable (α = .70). Alpha for importance of interests was low (α = .62). Based on the alpha if 

removed information, the first item within this scale was dropped (i.e. “Being interested in the 

activities I am involved in is important to me”). The resulting alpha for the revised scale was 

marginal (α = .66). This revised 2-item self-assessed variance in state interests scale is utilized in 

Study 2. 

Qualitative Results. Individuals were asked if they thought their preference for different 

activities changed day-to-day, and if so, what may drive this change. Respondents reported a 

variety of lay theories as to why their interests may change day-to-day. Some lay theories were 

too abstract to measure (e.g. maturity), would be difficult to quantify (e.g. schedule 

requirements), would be too invasive to measure (e.g. money), would have little variance in our 

sample (e.g. weather), or seemed unlikely (e.g. food). However, three lay theories as to why 

interests may fluctuate that seemed plausible and reasonable to collect during Study 2 were 

42 

 

 

participant mood, sleep quality, and subjective perceptions of energy. These three measures will 

be included in the Study 2 morning survey and will be included in exploratory analyses to 

 

determine if they may influence state vocational interests. 

Study 2 

 

In order to capture within-person fluctuations in vocational interests and other constructs 

of interest, experience sampling methodology (ESM) was utilized (see Beal, 2015; Gabriel et al., 

2019). Individuals were administered short surveys in the morning and evening, which were 

supplemented with pre- and post-survey information. The ESM portion of the survey lasted one 

week (i.e. seven days). The timeframe of one week for the ESM portion of this study was chosen 

to prevent participant fatigue from multiple weeks of surveys, capture natural cyclical variability 

of tasks and contexts over a natural unit of time (i.e. week), and allow for enough within-person 

data to be collected to have sufficient power for analyses. 

Pilot Study 

Prior to Study 2, a pilot study was conducted to ensure adequate functioning for 

methodology, survey distribution, response rates, and length of the ESM portion of the study. 

Methodology was identical to Study 2 methodology, but occurred three weeks prior. 51 

individuals were involved in this pilot study. As methodology was identical, pilot study 

participants were folded into Study 2 and included in all analyses. 

Sample 

For Study 2, a student sample from Michigan State University’s SONA subject pool was 

utilized. A student sample is commonly used in studies of interests (e.g. Nye et al., 2018a; Tracy 

& Sodano, 2008). For participating in this research study, individuals were compensated with 

43 

 

 

SONA research credit. Participants were compensated based on their completion rates for the 

various surveys (see Appendix F for details of the compensation plan within the Study 2 consent 

 

form).  

A total of 178 individuals participated in this research study1. All 178 individuals 

completed the pre-survey. Of a possible 1,246 responses for morning and evening surveys, 1,000 

responses were received for daily morning surveys (80.3% response rate) and 992 responses 

were received for daily evening surveys (79.6% response rate). 162 out of 178 individuals 

completed the post survey (91.0% response rate). 

Prior to analyses, responses were removed based on completion rates, attention check 

items, and response times per page. For the pre-survey, individuals were excluded if they 

completed less than 10% of the total survey, missed two or more of the five attention check items 

(e.g. Choose “Like very much” for this statement”), or responded in under two seconds per item 

for four or more of the nine pages with scales (excluding the demographic page). These criteria 

resulted in a final usable sample of 176 pre-survey datapoints (98.9% of initial cases).  

For morning surveys, individuals were excluded if they completed less than 10% of the 

survey, missed one or more of the two attention checks, or responded in under 1.5 seconds per 

item for two or more of the four pages of scales. These criteria resulted in a final usable sample 

of 953 usable morning datapoints (95.3% of initial cases).  

For evening surveys, individuals were excluded if they completed less than 10% of the 

survey, missed one or more of the two attention checks, or responded in under 1.5 seconds per 

item for three or more of the five pages of scales without display logic (i.e. all individuals 

                                                            
1 Note individuals were not included in this count if they enrolled for the study, but did not attend the first in-person 
session required for participation in the study.  

44 

 

 

responded to). These criteria resulted in a final usable sample of 869 usable evening datapoints 

 

(87.6% of initial cases).  

For the post-survey, individuals were excluded if they completed less than 10% of the 

survey, missed two or more of the four attention checks, or responded in under 2 seconds per 

item for three or more of the six pages of scales. These criteria resulted in a final usable sample 

of 136 usable post-survey datapoints (84.0% of initial cases).  

For the 178-individual sample, demographic information was available for 176 

individuals2. For these individuals, gender information was not specified for 1.7% (3) 

individuals, 18.2% (32) individuals identified as male, 79.5% (140) as female, and less than 1% 

(1) of individuals identified as non-binary. Of these 176 individuals, 15 (8.5%) individuals self-

identified as Black or African American, 19 (10.8%) individuals identified as East Asian, 13 

(7.4%) individuals identified as Hispanic or Latinx, 7 (4.0%) individuals identified as 

multiracial, 4 (2.3%) individuals identified as South Asian, 110 (62.5%) individuals identified as 

White, 6 (3.4%) individuals identified as other, and 2 (1.1%) individuals declined to specify 

race/ethnicity information. The average age of these individuals was 19.41 years old (SD = 2.93). 

For these individuals thirty-seven majors were reported, with the highest numbers self-

identifying as majoring in Psychology (43, 24.4%), Human Biology (15, 8.5%), Nursing (15, 

8.5%), and Neuroscience (13, 7.4%). 11 individuals (6.3%) identified as undecided or 

undeclared. Of these 176 individuals, 75 (42.6%) individuals reported being currently employed. 

 

                                                            
2 For individuals not passing data quality checks for the pre-survey, demographic information is not reported as 
information can not be determined as reliable. Demographic information is reported for the remaining 176 
individuals.  

45 

 

 

Measures.  

 

Within the study, individuals were administered four different surveys: A pre-survey, 

daily morning surveys, daily evening surveys, and a post-survey.  

The pre-survey was administered the week prior to the ESM portion of the study during 

an in-person session. A short 5 to 10-minute presentation was given during this in-person session 

outlining the study and providing instructions, such as incentive structure for SONA credits, 

timing of surveys, and study purpose, along with an opportunity for participants to ask questions. 

Individuals were then given the link to complete the pre-survey. Individuals must have attended 

this session to be considered for inclusion in the rest of the study. In other words, individuals 

who do not attend this initial session were not invited to complete the ESM portion of the study 

or the post-survey. 

Daily surveys were sent via email in the morning and evening to participants and 

responded to electronically. Morning surveys were distributed at 8:00am, a reminder was sent at 

11:00am to those who had not yet responded, and the survey was available to respond to until 

1:00pm the same day. Evening surveys were distributed at 6:00pm, a reminder was sent at 

9:00PM to those who had not yet responded, and the survey was available to respond to until 

midnight. 

The post-survey was sent electronically to participants the week after the ESM portion of 

the study on the following Monday at 8:00am. A reminder email was sent if individuals had not 

responded to this post-survey as of Tuesday at 12:00pm. Individuals had until Wednesday at 

midnight to complete the final survey. 

46 

 

 

Below, measures within each of these surveys are outlined. See Appendix F for the 

consent form for Study 2, completed during the pre-survey. Items for each survey are available 

in Appendices G through J, excluding attention check items. See Appendix K for the Study 2 

 

debriefing form.  

Pre-Survey. See Appendix G for a full list of items composing each scale administered 

during the pre-survey, except for attention check items.  

Survey Identifiers. In order to link surveys for individuals across days, individuals were 

asked to report their first name, last name, and MSU email address.  

Trait Vocational Interests. Trait vocational interests were measured utilizing both the 

Brief Public Domain RIASEC Marker scale (Armstrong et al. 2008) and a trait version of the 

Daily Short Interest Measures (DSIM), created in Study 1.  

 

Brief Public Domain RIASEC Marker- Set A. See previous description from Study 1. In 

Study 2, this measure demonstrated adequate reliability within the pre-survey (α = .78 to .89) 

Daily Short Interest Measure – Trait Version. This scale was created based on results 

from Study 1 and consisted of 30 items measuring Holland’s (1959; 1997) six vocational 

interests. Instead of asking individuals to indicate their daily preference individuals were asked 

to report their general preference, or trait vocational interests. More specifically individuals 

taking the pre-survey were asked to “Please indicate your preference for the following activities 

in general, not just for today. In general, to what extent would you like to engage in the 

following tasks?” This modified measure demonstrated adequate reliability (α = .70 to .85), 

except for Conventional which demonstrated poor reliability for the pre-survey (α = .54). 

47 

 

 

Trait Personality. See previous description from Study 1. These trait measures 

demonstrated adequate reliability in the pre-survey (α = .75 to .90) 

 

Curiosity. Trait curiosity was measured utilizing the Curiosity and Exploration Inventory 

– II, which is a ten-item measure of curiosity and its facets (Kashdan et al., 2009). Trait curiosity 

as measured by this inventory demonstrated adequate reliability (α = .85, Study 3) and strong 

convergent validity with related constructs (Kashdan et al., 2009) in previous research. In the 

current study, the inventory demonstrated adequate reliability (α = .88)  

Trait Grit. Grit and its subscales of consistency of interests and perseverance of effort 

was measured utilizing the twelve-item Grit Scale (Duckworth et al., 2007). Adequate internal 

consistency reliability has been found for the Grit Scale overall (α = .85), and for the consistency 

of interests (α = .84) facet (Duckworth et al., 2007) in previous research. In the pre-survey, 

adequate reliability was found for Grit (α = .79) and consistency of interests (α = .80) 

Implicit Interest Theory. An individual’s implicit theory of interests was measured 

utilizing the four-item scale from O’Keefe et al. (2018) adapted from the Theory-of-Intelligence 

scale (Dweck, 1999). An example item is, “You can be exposed to new things, but your core 

interests won’t really change.” Individuals responded to these items on a six-point scale 

(“Strongly disagree” to “Strongly agree”). This measure demonstrated adequate internal 

consistency reliability in previous research (Study 1: α = .77) (O’Keefe et al., 2018). For the pre-

survey, this measure demonstrated adequate reliability (α = .81) 

Self-Assessed Variance in State Interests. Self-assessed variance in state interests was 

assessed utilizing a measure created for the present study. Individuals were asked the degree to 

which they agree or disagree with several statements on a seven-point scale (“Strongly agree” to 

48 

 

 

“Strongly disagree”): “The activities I’m interested in change day to day”, “From day to day I 

enjoy doing different activities”, and “I generally prefer to do the same activities every day” 

(reverse coded). Within the pre-survey, this measure demonstrated marginal reliability (α = .69). 

 

Vocational Identity. Vocational identity was measured utilizing the 20-item Vocational 

Identity Measure (Gupta, Chong, & Leong, 2015). This inventory was developed to measure 

“how aware individuals are of their stable career goals, interests, and abilities” (Gupta et al., 

2015, pp. 79). This scale has demonstrated considerable convergent validity and excellent 

reliability in previous research (α = .97) (Gupta et al., 2015). Individuals were asked to indicate 

the extent to which they agreed or disagreed with statements on a 5-point scale (“Strongly 

disagree” to “Strongly agree”). For the pre-this measure demonstrated excellent reliability (α = 

.97) 

Trait Affect. Trait affect was measured utilizing the Brief Measure of Positive and 

Negative Affect Scales (PANAS) which is a twenty-item measure of both positive and negative 

affectivity (Watson, Clark, & Tellegan, 1988). Individuals were presented with a number of 

mood states (e.g. “Distressed”, “Inspired”) and asked to identify how much they have felt that 

way over a specified time interval (e.g. In general, yearly, past few days, today). For the pre-

survey, the dispositional (i.e. in general) form was used. This form has demonstrated adequate 

internal consistency reliability (positive affect: α = .88; negative affect: α = .87). This scale in 

general has also shown considerable convergent validity with other measures of depression, 

distress, and affect (Watson et al., 1988). Within the pre-survey, both positive (α = .90) and 

negative (α = .85) trait affect scales demonstrated adequate reliability. 

Importance of Interests. An individual’s self-described importance of their interests were 

measured by two items developed for this research project. These items are: “I must be interested 

49 

 

 

in something for me to do it” and “I don’t take part in activities that don’t interest me”. These 

items were rated on a seven-point scale, from “Strongly disagree” to “Strongly agree”. Within 

the pre-survey, this measure demonstrated marginal reliability (α = .66). 

 

Academic Major. See previous description from Study 1.  

Demographics. See previous description. Two additional demographic questions were 

added to those collected from Study 1 participants, asking students about their employment 

status and number of hours worked per week, if employed. 

 

Daily Survey – Morning. See Appendix H for a full list of items composing each scale 

administered during the daily morning surveys, excluding attention checks. Within scales, the 

order of items was randomized across respondents and days. 

Survey Identifiers. See previous description. 

State Vocational Interests. State vocational interests were measured utilizing the 30-item 

DSIM measure, based on the measure developed in Study 1. Individuals was asked, “People may 

prefer to engage in different activities. Please indicate how much you would prefer to do the 

following activities today. ‘Today, I prefer activities that involve’” to assessment state vocational 

interests. These scales demonstrated adequate reliability (α = .82 to .89).  

State Depletion. State depletion was measured utilizing a five-item subset of items from 

Twenge and colleagues (2004), which has been used in previous ESM research (Lanaj, Johnson, 

& Barnes, 2014). This reduced scale has demonstrated adequate reliability in previous research 

(α = .91) (Lanaj et al., 2014). Individuals were asked to indicate the extent to which they felt like 

the statements given on a 5-point scale (“Very slightly or not at all” to “Very much”). For the 

morning surveys, this measure demonstrated adequate reliability (α = .92). 

50 

 

 

 

Sleep Quality/Quantity. Sleep quality and sleep quantity were measured using a reduced 

set of modified items from the Pittsburgh Sleep Quality Index (Buysse, Reynolds, Monk, 

Berman, & Kupfer, 1989). For sleep quality, individuals were prompted with, “Last night, how 

would you rate your sleep overall?” and asked to respond on a four-point scale ranging from 

“Very bad” to “Very good”. Sleep quantity was measured by prompting individuals with, “How 

many hours of actual sleep did you get last night?” paired with a drop box menu with options 

ranging from “Less than 1 hour” to “10 or more hours” in half hour increments.  

State Affect. State affect was measured utilizing a modified version of the PANAS 

(Watson et al., 1988). This scale was reduced to 10 items, with 5 for both positive and negative 

affect, based on published factor analytic information (Watson et al., 1988) and modified to 

measure state affect. The full daily PANAS has shown adequate internal consistency reliability 

in past research (Positive affect: α = .90, Negative affect: α = .87). For the morning surveys, this 

measure demonstrated adequate reliability (Positive affect: α = .90, Negative affect: α = .79). 

Daily Survey – Evening. See Appendix I for a full list of items composing each scale 

administered during the daily evening survey, excluding attention checks. Within scales, the 

order of items was randomized across respondents and days, with the exception of situational 

interests. 

Survey Identifiers. See previous description. 

State Affect. State affect was measured utilizing the reduced version of the PANAS 

(Watson et al., 1988) utilized within the morning survey, modified to measure affect over the 

previous day. The full daily PANAS has shown adequate internal consistency reliability in past 

51 

 

 

research (Positive affect: α = .90, Negative affect: α = .87). Within the evening surveys, these 

measures demonstrated adequate reliability (Positive affect: α = .88, Negative affect: α = .82). 

 

Daily Activities. Daily activities were measured utilizing a modified version of the 30 

item DSIM, asking what activities individuals took part in during their day instead of their 

preferences. More specifically, instead of asking for daily preferences individuals were prompted 

with, “Today, I did activities involving…”.  A score for how much an individual performed 

activities within each interest was created by averaging the scores across the items corresponding 

to those DSIM scales. Individuals responded to this measure utilizing a seven-point scale, 

ranging from “Never” to “Constantly”. Reliability is not reported for these scales as they are 

observed counts (i.e. how much of an activity did you do today?) as opposed to representing a 

latent characteristic.  

State Perseverance. State perseverance was measured using a modified version of the 

perseverance of effort subscale of the Grit Scale (Duckworth et al., 2007) to measure daily 

perseverance. For instance, instead of “I have overcome setbacks to conquer an important 

challenge”, a modified item read, “Today, I overcame setbacks to conquer an important 

challenge. Five of the six original items were modified, except for “I have achieved a goal that 

took years of work” as it does not lend itself to be modified easily. For the evening surveys, this 

measure demonstrated adequate reliability (α = .89) 

Situational Interest. Situational interest in each of the RIASEC domains were measured 

utilizing items derived from Su and colleagues’ (2019) conceptualization of situational interest as 

affective reactions, cognitive appraisal of value or valence, and cognitive appraisal of 

identification. Three items were written for each vocational interest, with one item measuring 

each of these theoretical components of situational interests within the TSID model (Su et al., 

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2019), for a total of 18 items. Individuals were asked if they engaged in activities corresponding 

to a RIASEC interest today based on the DSIM items (e.g. Today, did you engage in activities 

that involved using tools, fixing, building, physical activity, or working with gadgets?) and 

asked to indicate Yes or No. Individuals were only asked for situational interests for RIASEC 

interests that they reported engaging in during that day. For example, if an individual indicated 

they did not engage in any investigative interests that day they would not be asked for their 

situational interests in that domain. 

 Individuals were asked to reflect on the final items from the DSIM pertaining to a 

specific vocational interest, then asked to rate their experience of these three components of 

situational interests within that category of activities that day. For instance, the prompt for 

enterprising interests stated, “Please reflect on the activities you did today that involved 

persuading others, selling, negotiation, managing others, or managing projects. While 

engaging in these activities, to what extent did you feel…” Individuals were then prompted with 

three questions answered on a seven-point scale ranging from “Not at all” to “Very Often”. 

These questions were, “A sense of engagement with these activities (affective reactions), “like 

these activities were valuable” (cognitive appraisal of value or valence), and “like you identified 

with these activities” (cognitive appraisal of identification). Within the evening surveys, these 

scales demonstrated adequate reliability (α = .86 to .91). 

State Engagement. State engagement was measured utilizing a modified state adaption of 

the Utrecht Work Engagement Scale (Breevaart, Bakker, Demerouti, & Hetland, 2012). Though 

the original empirical research was completed on the Dutch version of the scale (English 

translation was provided), this work demonstrated adequate internal consistency reliability (α = 

.93). Only six of the nine items were retained for the present study in order to accommodate 

53 

 

 

 

space constraints, based on factor analytic evidence in the published literature (Breevaart et al., 

2012) and equal coverage of the three facets of engagement. Items were adapted from a work 

focus to fit the context of the current research project when needed (e.g. “Today I was immersed 

in my activities”, “Today, I was enthusiastic about my activities”). Items were assessed on a 

seven-point scale ranging from “Strongly disagree: to “Strongly agree”. Within the evening 

surveys, this measure demonstrated adequate reliability (α = .93). 

 

State Motivation. State intrinsic motivation was measured utilizing an adapted version of 

the intrinsic motivation components of the Situational Motivation Scale (Guay, Vallerand, & 

Blanchard, 2000). This inventory measures various forms of motivation within Self-

Determination Theory (Deci & Ryan, 2000). However, only the four-item measure of intrinsic 

motivation was utilized in the present study. Past research with this scale has demonstrated 

adequate internal consistency reliability (Study 1: α = .95) and convergent validity with 

theoretically related constructs (Guay et al., 2000). The stem was modified to fit the design of the 

study (i.e. “How well do the statements below describe the reasons why you engaged in the 

activities you did today?”), as well as the items (e.g. “Because the activities were fun”). Within 

the evening surveys, this measure demonstrated excellent reliability (α = .95). 

Post-Survey. See Appendix J for a full list of items composing each scale administered 

during the post-survey. Within scales, the order of items was randomized across respondents. 

Survey Identifiers. See previous description. 

Trait Vocational Interests. Both the trait vocational interest inventories administered 

during the pre-survey were administered. See previous description. Within the post survey, both 

54 

 

 

the trait DSIM scales (α = .81 to .89) and BPDRM scales (α = .81 to .88) demonstrated adequate 

 

reliability. 

Trait Personality. See previous description of this scale. Trait scales demonstrated 

adequate reliability in the post-survey (α = .80 to .88). 

Week Affectivity. Positive and negative affectivity from the past week was assessed with 

the same modified 10-item version of the PANAS (Watson et al., 1988) from the morning and 

evening ESM surveys. However, the inventory asked about affect during the previous week as 

opposed to the preceding day. Within the post-survey, this measure demonstrated adequate 

reliability (Positive Affect α = .87, Negative Affect α = .79). 

Academic Satisfaction. Academic satisfaction was measured utilizing the five-item 

academic satisfaction scale from Schmitt and colleagues (2008). This scale has demonstrated 

adequate internal consistency reliability in past research (α = .81) and was administered utilizing 

a five-point scale (“Strongly disagree” to “Strongly agree”). Within the post-survey, this measure 

demonstrated adequate reliability (α = .84). 

Dropout Intentions. Intentions to dropout of Michigan State University were measured 

utilizing six items from previous research (Nye, Prasad, & Rounds, 2019) adapted from 

Drzakowski, Friede, Imus, Kim, and Shivpuri (2005). Past research has demonstrated adequate 

internal consistency reliability (α = .87) and convergent validity with theoretically related 

constructs (Nye et al., 2019). Individuals were asked to respond to items on a five-point scale 

(“Strongly disagree” to “Strongly agree”). Within the post-survey, this measure demonstrated 

adequate reliability (α = .79). 

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Results 

See Tables 4 and 5 for descriptive information, intercorrelations, and reliability for 

between person and within-person variables, respectively. Within this section and tables, T is 

utilized to denote temporality of variables. T represents time, where T+1 represents the following 

day, T+2 represents two days after T, etc.  

Variability in State Vocational Interests (H1a – H1c). Hypotheses 1a through 1c pertain 

to variability in state vocational interests, referring to different forms of variability. Hypothesis 

1a states, “State vocational interests will display day-to-day absolute variability.” referring to 

variability in observed scores (Tracey & Sodano, 2008). In order to assess absolute variability, 

standard deviations were calculated for individuals across measurements of state vocational 

interests. There were seven possible instances in which state vocational interests were reported 

(i.e. within morning surveys), however there were many cases in which individuals had missed 

one or more of these surveys and thus did not have all seven datapoints available. In calculating 

standard deviations for state vocational interests, analyses were limited to individuals in which 

four or more state vocational interest scores were available to ensure representative standard 

deviations. This restriction left 148 out of a possible 178 cases available for absolute variability 

analyses. See Table 6 for average standard deviations for all six RIASEC state interests across 

individuals, median standard deviation, the standard deviation for state interests’ standard 

deviation across individuals, and the range across the sample, as well as this information 

averaged across the six interests. Averaged across the six interests, the mean standard deviation 

for individuals was .77 on a seven-point scale day-to-day, with a median of .71. The standard 

deviation of this averaged variability across individuals was .40, with a range in the sample from 

.10 to 2.10.  

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It is difficult to determine what may be considered “enough” absolute variability in state 

vocational interests to warrant support or a lack of support for hypothesis 1a. Past research in 

state individual differences has compared variability in newly introduced state concepts to 

variability in established state constructs as evidence of absolute variability (e.g. Fleeson, 2001). 

Past research has found absolute variability of .60 to .84 for FFM personality states on a seven-

point Likert scale (Study 2: Fleeson, 2001), similar in magnitude for absolute variability for state 

vocational interests (.66 to .84 across all six state RIASEC dimensions). Based on these results 

and comparable magnitude of absolute variability in the more established state personality 

literature (Fleeson, 2001; 2017), hypothesis 1a was supported. There were no clear differences 

across the six RIASEC state interests in terms of variability. 

 

Hypothesis 1b states, “State vocational interests will display day-to-day relative 

variability.” referring to changes in ranking of individuals over time in reference to one another 

(Tracey & Sodano, 2008). This hypothesis was tested utilizing multilevel regression, accounting 

for nesting within individuals, with each next day state vocational interest (T+1) regressed on the 

same state interest the day prior (T). Level 2 N (individual) was 165 for analyses, with 704 to 

705 cases at level 1 (within-individual). See Tables 7 through 12 for results of these analyses. 

For all six state vocational interests, the interest the day prior (T) was a significant (p < .01) 

predictor of the same state interest the next day (T+1) (Realistic: β = .78, Investigative: β = .69, 

Artistic: β = .79, Social: β = .68, Enterprising: β = .68, Conventional: β = .71). Based on these 

analyses, approximately 46.2% to 62.4% of the variance in an observed state vocational interest 

can be explained by the individual’s observed state vocational interest the day prior.  

 

Similar to absolute variability, it is difficult to establish how much relative variability is 

“enough” to warrant support or a lack of support for hypothesis 1b. Comparisons can also be 

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made between relative variability found for state vocational interests and established state 

constructs. However, relative stability in the state individual differences literature frequently 

compares randomly selected timepoints to one another within individuals which may display 

more variability than consecutive timepoints (e.g. Fleeson, 2001). Alternatively, it may be useful 

to compare to trait individual differences research to gauge relative variability. Low and 

colleagues (2005) examined changes in trait vocational interests across age periods and found 

meta-analytic test-retest stability coefficients between ρ = .51 to .77. Though correcting for 

measurement artifacts likely increased these coefficients, these stability coefficients are 

comparable to those found for state vocational interests in the present study (β = .68 to .79). The 

authors conclude that trait vocational interests were, “moderately to highly stable over the life 

course.” (pp. 732), “…but stability estimates were not so high as to warrant the conclusion that 

no change occurred in adulthood.” (pp. 727). Based on the results and comparison to previous 

research as a benchmark, it may be similarly concluded that relative variability for state 

vocational interests is low, but existent. Thus, hypothesis 1b is supported, with the caveat of 

limited variability.  

 

Hypothesis 1c states, “State vocational interests will display day-to-day rank order 

variability” referring to change in the ranking of interests relative to other others within 

individuals (Allemand et al., 2013; Tracey & Sodano, 2008). To test this hypothesis, state 

vocational interests within individuals were ranked from highest to lowest each day. For 

example, if Artistic interest is the highest RIASEC interest within an individual one day then it 

would receive a rank of 1, if Conventional was second highest within the same day it would 

receive the rank of 2, etc. Analyses were conducted utilizing Spearman rank-order correlations, 

or sometimes Spearman’s Rho. Multilevel analyses were not utilized as there is no effect of 

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nesting within individuals. Within each individual for each day, state interests are ranked from 1 

to 7 and any level 2 effect of the individual on state vocational interests would have been 

removed. Multiple Spearman correlations were conducted to evaluate hypothesis 1c. Ranked 

state vocational interests from T were correlated with ranked state vocational interests from T+1, 

simultaneously assessing the rank order variability across all days in the study. 703 cases of 

matched T and T+1 state vocational interests ranked within individual within day were available 

for analyses. See Table 13 for the results of Spearman rank order correlations of ranked state 

interests (T) and next day ranked state interests (T+1). For each state interest, this correlation 

was significant (Realistic: ρ = .46, Investigative: ρ = .53, Artistic: ρ = .62, Social: ρ = .44, 

Enterprising: ρ = .46, Conventional: ρ = .48). Spearman correlations were also conducted 

individuals between ranked state vocational interests from day 1 to day 2, day 2 to day 3, etc. and 

the average Spearman correlation across these analyses were computed. See Table 14 for N and 

results of these analyses for each comparison. The averaged Spearman correlations across days 

ranged from ρ = .44 to .62, and were identical to the previous simultaneous results.  

 

Again, it is difficult to know exactly how much rank order variability is “enough” to 

warrant support or a lack of support for hypothesis 1c, but comparison with past research is 

helpful for benchmarking purposes. Roberts and DelVecchio (2000), quantitatively reviewing 

rank-order consistency of personality, found meta-analytic estimates of consistency ranging from 

.35 to .753. The authors conclude that, “…traits are quite consistent over the life course.”, but 

even at peak consistency the level is not high enough to, “…infer a complete lack of change in 

personality traits.” (pp. 20). As Spearman correlations found in the current study are comparable, 

                                                            
3 Numbers represent population meta-analytic correlations 

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it would be reasonable to conclude as well that some day-to-day consistency occurs but changes 

day-to-day occur as well. Thus, hypothesis 1c is supported, with the caveat of limited variability. 

 

 Supplementary Analyses. In addition to analyses testing hypotheses pertaining to state 

vocational interest variability, additional exploratory analyses were conducted to further explore 

this variability. ICC values were calculated to determine the amount of variance in within-person 

state vocational interests that can be attributed to individuals and the reliability of these ratings 

within-individuals. N for analyses was 178 for level 2 (individual), and ranged from 949 to 951 

for level 1 (within-individual). See Table 15 for ICC(1) and ICC(2) values for nesting of state 

vocational interests within individuals. Based on ICC(1) values, between 51% to 65% of 

variance in observed state interests can be explained by nesting within the individual. ICC(2) 

values were high (see Fleiss, 1986) ranging from .85 to .91, indicating high stability (i.e. 

reliability) in state vocational interest ratings day-to-day within individuals.  

 

The issue could be raised that any variability in vocational state interest observed scores 

could be attributable to measurement error, as opposed to true variability in state vocational 

interests. To account for this possibility, multilevel structural equation modeling with nesting of 

responses within individuals was utilized to account for measurement error and find the 

relationship between latent state interests (T) and next day latent state interests (T+1). Latent 

state vocational interest variables for T and T+1 were created utilizing the five DSIM items for 

each interest from each respective measurement occasion. Latent state vocational interests at T 

were allowed to correlate with the same latent state vocational interests at T+1, with separate 

analyses conducted for each RIASEC dimension. Residuals for each item at T were allowed to 

correlate with the residuals for that same item at T+1. Level 2 N for analyses was 165, and 703 

to 704 level 1 cases were available where state vocational interests at T were matched to state 

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vocational interests at T+1. Model fit statistics for these analyses were excellent. See Table 16 

for the results of these analyses and model fit statistics. See Figures 8 through 13 for plots of 

these models. For all state vocational interests, latent state interests at T were a significant 

predictor of latent state interests at T+1 (p < .01) (Realistic: β = .86, Investigative: β = .71, 

Artistic: β = .85, Social: β = .74, Enterprising: β = .81, Conventional: β = .79). Based on the 

results of these analyses, approximately 50.4% to 74.0% of latent state vocational interest in a 

domain can be explained by the previous day’s latent state vocational interest in that domain. 

 

Hypotheses were focused on day-to-day variability in state vocational interests; however, 

the question can be raised of how stable state vocational interests are beyond this timeframe. 

Supplemental analyses were conducted to explore relative and rank order stability of state 

vocational interests across seven days, the longest period available for testing state vocational 

interest stability in this study. State vocational interests (T) were correlated with state vocational 

interests six days later (T+6). 89 to 91 cases were available where state vocational interests at T 

could be matched to state vocational interests at T+6. See Table 17 for results of these analyses. 

For all state RIASEC vocational interests, the correlations between interests at T and T+6 were 

significant (Realistic: r = .44, Investigative: r = .37, Artistic: r = .44, Social: r = .35, 

Enterprising: r = .25, Conventional: r = .48). Spearman rank order correlations were also 

conducted for interests at T and T+6 in a manner similar to hypotheses 1b. N for these analyses 

was 89. Results for these analyses can be found in Table 18. For all state RIASEC vocational 

interests, the Spearman rank order correlations between interests at T and T+6 were significant 

(p < .05) (Realistic: ρ = .26, Investigative: ρ = .31, Artistic: ρ = .58, Social: ρ = .26, Enterprising: 

ρ = .25, Conventional: ρ = .42). Across all RIASEC state vocational interests, there was greater 

variability from T to T+6 than T to T+1. 

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Summary. Hypotheses 1a through 1c pertained to variability in state vocational interests, 

in order to determine if considerable variability exists day-to-day. Based on the results outlined, 

all six RIASEC state vocational interests display some variability day-to-day. Hypotheses 1 

through 1c were supported. State vocational interests demonstrate absolute variability, with a 

standard deviation of nearly 1 scale point a day (on a seven-point scale) and comparable to more 

established state constructs (e.g. Fleeson, 2001). State vocational interests demonstrate relative 

variability, though based on benchmarks established in previous published research this relative 

variability is low. State vocational interests also demonstrate a lack of rank order stability, with 

less rank order stability being found day-to-day than relative stability. However, again this rank 

order stability may be considered low utilizing previously published research as benchmarks. 

Based on supplemental analyses, as the timeframe increases stability tends to decrease.  

 

Between-Person Predictors of State Vocational Interest Variability (H2a – H2c & 

RQ1). Hypotheses 2a though 2c and research question 1 pertain to predictors of individual 

differences in variability of state vocational interests. Based on the results of hypotheses 1a 

through 1c and supplementary analyses, state vocational interests vary day-to-day and there are 

individual differences in the degree to which these interests vary (see standard deviation and 

range in Table 7). For these analyses, variability was operationalized as the standard deviation 

across rating occasions for state vocational interests within a RIASEC domain. Analyses were 

conducted utilizing linear regression, with standard deviations of state vocational interests 

regressed onto each individual difference predictor, the individual’s mean level of the state 

vocational interest, and squared mean level of the state vocational interest. The individual’s 

mean level of the state vocational interest and squared mean level of the state vocational interest 

was controlled for as previous analyses have demonstrated that failing to control for these 

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variables when testing associations between individual differences and density distributions can 

lead to misleading results (e.g. Jones, Brown, Serfass, & Sherman, 2017). Note that although 

correlations between potential predictors of variability and variability for each individual 

RIASEC state vocational interest are reported in Table 19, results will only be interpreted in 

regards to the averaged variability across the six RIASEC domains for individuals to avoid false 

positives from a large number of correlations. For these analyses, the averaged standard 

deviation across all six RIASEC dimensions were regressed onto the individual difference, as 

well as the averaged and squared average level of state vocational interests averaged across all 

six RIASEC dimensions. Analyses were again limited to individuals who had four or more state 

vocational interest measurement occasions as to ensure representativeness of means and standard 

deviations. N for these analyses was 146. See Table 19 for results of analyses. 

 

Hypothesis 2a states, “Trait openness to experience will be positively related to 

variability in state vocational interests, where a higher degree of openness to experience will be 

related to greater variability in state vocational interests.” The relationship between trait 

openness to experience and averaged variability in state vocational interests was positive and 

significant (β = .16, p < .01). Thus, hypothesis 2a was supported. 

 

Hypothesis 2b states, “Trait curiosity will be positively related to variability in state 

vocational interests, where a higher degree of curiosity will be related to greater variability in 

state vocational interests.” The relationship between curiosity and averaged variability in state 

vocational interests was positive and significant (β = .33, p < .01). Thus, hypothesis 2b was 

supported. 

Hypothesis 2c states, “A growth theory of interests will be positively related to variability 

in state vocational interests, where a high degree of growth theory endorsement will be related to 

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greater variability in state vocational interests.” The relationship between growth theory of 

interests and averaged variability in state vocational interests was not significant (β = .06, p = 

 

.39). Thus, hypothesis 2c was not supported. 

Research question 1 states, “Will self-reported level of variability regarding state 

vocational interests converge with actual degree of state vocational interest variability?”. The 

relationship between self-reported variability in state vocational interests and averaged 

variability in state vocational interests was not significant (β = .05, p = .49). This suggests that 

individuals are unable to accurately report the degree to which their interests vary. 

Supplemental Analyses. In addition to testing the relationships between the hypothesized 

predictors and averaged state vocational interest variability, additional potential predictors were 

tested as well. These included trait conscientiousness, trait extraversion, trait agreeableness, trait 

negative emotionality, grit, consistency of interests, and vocational identity Analyses were 

conducted between these variables and averaged state vocational interest variability in the same 

manner as for hypothesized effects. These analyses were again limited to individuals with four or 

more state vocational interest measurement occasions available and the sample size for these 

analyses was 146. Of these variables, significant relationships were found between averaged 

state variability and trait conscientiousness (β = .15, p = .04), grit (β = .19, p = .01), and 

vocational identity (β = .16, p =.03). Note these results should be interpreted with caution as they 

were non-hypothesized and due to the number of analyses conducted.   

 

Incremental validity of the four focal variables (openness to experience, curiosity, 

implicit theory of interests, and self-assessed state vocational interest variability) and the three 

exploratory variables demonstrating significant relationships with averaged state variability 

(vocational identity, grit, and conscientiousness), controlling for mean averaged state vocational 

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interests and squared mean averaged state vocational interests, was assessed utilizing a 

regression with all seven of these variables predicting average state vocational interest 

 

variability. These analyses were again limited to individuals with four or more state vocational 

interest measurement occasions available and the sample size for this analysis was 146. See 

Table 20 for the results of this analysis. Only curiosity demonstrated prediction of averaged state 

interest variability (β = .29, p < .01). 

Summary. Of the four focal variables for analyses, curiosity and openness to experience 

demonstrated a significant relationship with averaged state vocational interest variability. Of the 

variables tested in exploratory analyses, trait conscientiousness, grit, and vocational identity were 

significant predictors. However, when all these variables were included in a regression to test 

incremental prediction, only curiosity was a significant predictor. 

 

Predictive Validity of State Vocational Interest Congruence (H3a – H3e). Hypotheses 

3a through 3e pertain to the predictive validity of state vocational interest congruence in 

predicting various theoretically linked outcomes. For these hypotheses, multilevel regressions 

were utilized accounting for nesting of responses within individuals. Fit was operationalized 

utilizing polynomial regression (Edwards, 1993), which has been put forward as an alternative to 

traditional fit indices and as overcoming a variety of their shortcomings (see Edwards, 1993 for a 

discussion). Polynomial regression has demonstrated considerable promise for operationalizing 

fit in previous vocational interests research (e.g. Nye et al., 2018a).  

Separate analyses were conducted for each RIASEC dimension. Terms were added into 

the regression in three steps. The first step included individuals’ state vocational interest. The 

second step included the state vocational interest and matching daily activities for that interest 

domain. The final step was the full polynomial regression model, including the state vocational 

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interest, a squared term for the state vocational interest, the matching daily activities, a squared 

term for the daily activities, and an interaction between the state vocational interest and 

corresponding daily activities. Main effects were grand mean centered and standardized, and 

these centered and scaled variables were used to calculate the squared and interaction terms in 

 

the model. 

 

Since multilevel analyses were utilized, variance explained by the model corresponds to 

both random (i.e. person) and fixed (i.e. within-person) effects. All terms in the polynomial 

regression model representing congruence are within-person variables. In other words, 

congruence is represented in these models by the within-person components of the multilevel 

regression. Due to this, the significance of congruence in predicting outcomes must be evaluated 

based on within-person effects, separating out the variance explained by between person effects. 

To separate variance explained by between and within-person effects, the model fit for the full 

polynomial regression models with within and between person effects were compared to model 

fit for a null model with only between person effects. A significant increase in fit for a model 

predicting outcomes with both between individual and within-individual (i.e. congruence) 

relative to a null model consisting of solely between person effects would indicate significant 

effects of congruence on the outcome. To assess if the polynomial model (i.e. between and 

within-person effects) fits the data better than a null model (i.e. only between person effects), 

likelihood ratio tests were utilized4. This test compares nested models and determines if one 

model fits the data better than another based on log-likelihood fit information, differences in 

degrees of freedom, and a Chi-squared distribution. More information regarding this test can be 

                                                            
4 Analyses were conducted utilizing the restricted maximum likelihood (REML) estimation. However, to compare 
nested models analyses were rerun utilizing maximum likelihood (ML) estimation, as REML comparison with 
different fixed effects are not meaningful. However, the pattern of significance and non-significance were identical 
utilizing REML and ML estimation for all polynomial analyses for hypotheses 3a through 3e. 

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found in Bliese (2016). Variance explained by within-person effects (i.e. congruence) is 

represented by marginal R2, which represents the variance explain by fixed effects in the model 

(i.e. within-person effects) only (see Johnson, 2014; Nakagawa & Schielzeth, 2013). 

 

In addition to postulating a significant relationship between congruence and outcomes, 

hypotheses 3a through 3e hypothesizes a specific form of congruence. More specifically, these 

hypotheses suggest that when individuals’ preferences better match their activities (i.e. greater 

degree of congruence), outcomes will be higher. To test this specific form of fit, response surface 

analyses were also utilized in conjunction with polynomial regression. To test this specific form 

of congruence, there would need to be significant curvilinearity of the slope along the misfit line 

of the response surface. The fit line within response surface methodology is when variable 1 is 

perfectly matched to variable 2 (for example, within this study daily activities = 3 and state 

vocational interests = 3). In contrast, the misfit line is when variable 1 is equal to negative 

variable 2 (e.g. daily activities = 3 and state vocational interests = -3). By progressing down the 

misfit line, from daily activities exceeding state vocational interests to a perfect match at 0,0, to 

state vocational interests exceeding daily activities the absolute misfit decreases (e.g. -3 and 3 = 

6, -2 and 2 = 4, -1 and 1 = 2, 0 and 0 = 0). The response surface for the hypothesized form of 

congruence should demonstrate a saddle shape, where outcomes are maximized along the line of 

fit and where they decrease in a curvilinear fashion as it moves away from the fit line. See Figure 

14 for an example of how this response surface would be expected to look, with the exception of 

the negative affect outcome where the lowest point is expected to be along the line of fit 

increasing along the misfit line. 

In order to determine the statistical significance of the slope and curvature along the fit 

and misfit line, bootstrapping procedures outlined in Edwards (2002) were followed. Bootstrap 

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sampling was conducted utilizing the original sample 10,000 times, multilevel regressions 

accounting for nesting within individuals were conducted for each bootstrapped sample, and 

coefficients for each regression equation were saved. Using linear combinations of coefficients 

outlined in Edwards (2002), estimates of the coefficients for the slope and curvature of fit and 

misfit lines were obtained for the original sample. This was then repeated for the results of the 

bootstrapped multilevel regression to form 95% confidence intervals for these coefficients. If 

these confidence intervals contained 0, the respective slope or curvilinearity was not significant.  

 

Hypothesis 3a states, “State vocational interest congruence will be positively related to 

state positive affect, where a greater degree of state interest congruence will be related to higher 

state positive affect.” Level 2 N (individual) for these analyses was 165 cases, while there were 

741 level 1 cases (within-individual) where morning surveys and evening surveys could be 

matched. See Tables 21 through 32 for results of these analyses. See Figures 15 through 20 for 

response surface plots from the state vocational interest congruence polynomial regressions 

predicting daily positive affect. For step 1, all six RIASEC state vocational interests were 

significant positive predictors of daily positive affect (p < .05). On days in which state vocational 

interests were high, daily positive affect tended to be high as well. For step 2, all six RIASEC 

daily activity variables were significant predictors of daily positive affect (p < .05). Individuals 

tended to be higher in positive affect on days in which they did more activities, regardless of the 

RIASEC domain of those activities. However, in step 2 none of the six RIASEC state vocational 

interests remained significant predictors when controlling for daily activities. This suggests that 

desire for more activities does not predict daily positive affect incrementally over doing more 

activities, and that doing more daily activities may in fact be driving this relationship between 

state interests and daily positive affect. In step 3, the remaining terms for the polynomial 

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regression were added and the response surface was plotted. Fit for a null model containing only 

between person effects predicting daily positive affect was compared to each RIASEC 

dimension’s polynomial regression model with both between and within-person effects 

predicting daily positive affect utilizing likelihood ratio tests. In all six comparisons, the full 

polynomial regression model fit the data significantly better than the null model (L. ratio = 50.17 

to 74.52, p < .01). In other words, congruence (i.e. within-person effects) significantly predicted 

daily positive affect for all six RIASEC dimensions. Marginal R2, representing variance 

explained by within-person effects only, ranged from .03 to .11. The bootstrapping procedure 

noted above was conducted to determine significance of response plot slopes. Only one of the 

misfit lines demonstrated significant curvature (p < .05) indicative of the anticipated congruence 

form. Only state Investigative interests demonstrated significant misfit line curvilinearity (b = -

.25, p < .05). See Tables 23 and 24, and Figure 16 for these results. Though within-person effects 

representing congruence were significant predictors of daily positive affect, the forms of 

congruence were generally not consistent with the hypothesized effects. Thus, H3a was generally 

not supported.  

 

Hypothesis 3b states, “State vocational interest congruence will be negatively related to 

state negative affect, where a greater degree of state interest congruence will be related to lower 

state negative affect.” Level 2 N (individual) for these analyses was 165 cases, while there were 

741 level 1 cases (within-individual) where morning surveys and evening surveys could be 

matched. See Tables 33 through 44 for results of these analyses. See Figures 21 through 26 for 

response surface plots from the state interest congruence polynomial regressions predicting daily 

negative affect. For step 1, none of the six RIASEC state vocational interests were significant 

predictors of daily negative affect. In other words, daily state vocational interests were not 

69 

 

 

 

significantly related to negative affect within the same day. For step 2, four of the six RIASEC 

daily activity variables were significant positive predictors of daily negative affect (Realistic, 

Investigative, Enterprising, and Conventional) (p < .05). Individuals felt more daily negative 

affect on days in which they took part in more Realistic, Investigative, Enterprising, and 

Conventional activities. In step 3, the remaining terms for the polynomial regression were added 

and the response surface was plotted. Fit for a null model containing only between person effects 

predicting daily negative affect was compared to each RIASEC dimension’s polynomial 

regression model with both between and within-person effects predicting daily negative affect 

utilizing likelihood ratio tests. In five comparisons (Realistic, Investigative, Artistic, Social, and 

Enterprising), the full polynomial regression model did not fit the data significantly better than 

the null model (L. ratio = 3.99 to 10.05, p > .05). In other words, congruence (i.e. within-person 

effects) did not significantly predict daily negative affect for these five RIASEC dimensions. 

However, the polynomial regression model for Conventional fit the data significantly better than 

the null model (L. ratio = 13.42, p = .02). Congruence (i.e. within-person effects) for 

Conventional significantly predicted daily negative affect. Marginal R2, representing variance 

explained by within-person effects only was .01 for each RIASEC dimension. The bootstrapping 

procedure noted above was conducted to determine significance of response plot slopes. None of 

the misfit lines demonstrated significant curvature (p < .05) indicative of anticipated congruence 

form. Though within-person effects representing congruence for Conventional was a significant 

predictor of daily negative affect, congruence did not generally predict daily negative affect and 

the forms of congruence were generally not consistent with the hypothesized effects. Thus, H3b 

was not supported. 

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Hypothesis 3c states, “State vocational interest congruence will be positively related to 

state intrinsic motivation where a greater degree of state interest congruence will be related to 

greater state intrinsic motivation.” Level 2 N (individual) for these analyses was 165 cases, while 

there were 741 level 1 cases (within-individual) where morning surveys and evening surveys 

could be matched. See Tables 45 through 56 for results of these analyses. See Figures 27 through 

32 for response surface plots from the state interest congruence polynomial regressions 

predicting daily intrinsic motivation. For step 1, all six RIASEC state vocational interests were 

significant predictors of intrinsic motivation (p < .05). On days in which individuals had higher 

state vocational interests they also tended to have higher intrinsic motivation, regardless of 

RIASEC domain. For step 2, four of the six RIASEC daily activity variables were significant 

positive predictors of intrinsic motivation (Realistic, Artistic, Social, and Enterprising) (p < .05). 

In other words, on days in which individuals had higher intrinsic motivation individuals tended 

to take part in more Realistic, Artistic, Social, and Enterprising activities. However, Artistic and 

Enterprising state interests were no longer significant predictors of intrinsic motivation (p > .05). 

This suggests that the variance in daily intrinsic motivation captured by Artistic and Enterprising 

state vocational interests within individuals is not incremental over daily activities in those 

domains.  In step 3, the remaining terms for the polynomial regression were added and the 

response surface was plotted. Fit for a null model containing only between person effects 

predicting daily intrinsic motivation was compared to each RIASEC dimension’s polynomial 

regression model with both between and within-person effects predicting daily intrinsic 

motivation utilizing likelihood ratio tests. In all six comparisons, the full polynomial regression 

model fit the data significantly better than the null model (L. ratio = 19.49 to 64.05, p < .01). In 

other words, congruence (i.e. within-person effects) significantly predicted daily intrinsic 

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motivation for all six RIASEC dimensions. Marginal R2, representing variance explained by 

within-person effects only, ranged from .03 to .10. The bootstrapping procedure noted above was 

 

conducted to determine significance of response plot slopes. None of the misfit lines 

demonstrated significant curvature (p < .05) indicative of the anticipated congruence form. 

Though within-person effects representing congruence were significant predictors of daily 

intrinsic motivation, the forms of congruence were generally not consistent with the 

hypothesized effects. Thus, H3c was not supported. 

Hypothesis 3d states, “State vocational interest congruence will be positively related to 

state engagement, where a greater degree of state interest congruence will be related to greater 

state engagement.” Level 2 N (individual) for these analyses was 165 cases, while there were 

741 level 1 cases (within-individual) where morning surveys and evening surveys could be 

matched. See Tables 57 through 68 for results of these analyses. See Figures 33 through 38 for 

response surface plots from the state interest congruence polynomial regressions predicting daily 

engagement. For step 1, all six RIASEC state vocational interests were significant predictors of 

daily engagement (p < .05). Individuals reported greater engagement on days in which they also 

reported higher state vocational interests, regardless of domain. For step 2, all six RIASEC daily 

activity variables were significant positive predictors of daily engagement (p < .05). On days in 

with individuals reported feeling more engaged, individuals tended to take part in more activities 

regardless of RIASEC domain. However, Conventional and Artistic state interests were no 

longer significant predictors of daily engagement (p > .05) suggesting these state vocational 

interests may not predict daily engagement incrementally over daily activities. In step 3, the 

remaining terms for the polynomial regression were added and the response surface was plotted. 

Fit for a null model containing only between person effects predicting daily engagement was 

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compared to each RIASEC dimension’s polynomial regression model with both between and 

within-person effects predicting daily engagement utilizing likelihood ratio tests. In all six 

comparisons, the full polynomial regression model fit the data significantly better than the null 

model (L. ratio = 57.37 to 115.17, p < .01). In other words, congruence (i.e. within-person 

effects) significantly predicted daily engagement for all six RIASEC dimensions. Marginal R2, 

representing variance explained by within-person effects only, ranged from .08 to .17. The 

bootstrapping procedure noted above was conducted to determine significance of response plot 

slopes. Only one of the misfit lines demonstrated significant curvature (p < .05) indicative of the 

anticipated congruence form. Only state Investigative interests demonstrated significant misfit 

line curvilinearity (b = -.57, p < .05). See Tables 59 and 60 and Figure 34 for these results. 

Though the polynomial regression equations representing congruence were significant predictors 

of daily engagement, the forms of congruence were generally not consistent with the 

hypothesized effects. Thus, H3d was generally not supported.  

Hypothesis 3e states, “State vocational interest congruence will be positively related to 

state perseverance, where a greater degree of state interest congruence will be related to greater 

state perseverance.” Level 2 N (individual) for these analyses was 165 cases, while there were 

741 level 1 cases (within-individual) where morning surveys and evening surveys could be 

matched. See Tables 69 through 80 for results of these analyses. See Figures 39 through 44 for 

response surface plots from the state interest congruence polynomial regressions predicting daily 

perseverance. For step 1, all six RIASEC state vocational interests were significant predictors of 

daily perseverance (p < .05). On days in which individuals reported higher state vocational 

interests they also tended to report higher perseverance, regardless of RIASEC domain. For step 

2, all six RIASEC daily activity variables were significant positive predictors of daily 

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perseverance (p < .05). In other words, individuals tended to take part in more activities, 

regardless of RIASEC domain, on days in which they reported higher perseverance. However, 

Investigative, Artistic, Enterprising, and Conventional state interests were no longer significant 

predictors of daily perseverance (p > .05) suggesting that they might not predict daily 

perseverance incrementally over daily activities. In step 3, the remaining terms for the 

polynomial regression were added and the response surface was plotted. Fit for a null model 

containing only between person effects predicting daily perseverance was compared to each 

RIASEC dimension’s polynomial regression model with both between and within-person effects 

predicting daily perseverance utilizing likelihood ratio tests. In all six comparisons, the full 

polynomial regression model fit the data significantly better than the null model (L. ratio = 47.77 

to 129.58, p < .01). In other words, congruence (i.e. within-person effects) significantly predicted 

daily perseverance for all six RIASEC dimensions. Marginal R2, representing variance explained 

by within-person effects only, ranged from .07 to .16. The bootstrapping procedure noted above 

was conducted to determine significance of response plot slopes. None of the misfit lines 

demonstrated significant curvature (p < .05) indicative of anticipated congruence form. Though 

within-person effects representing congruence were significant predictors of daily perseverance, 

the forms of congruence were generally not consistent with the hypothesized effects.  Thus, H3e 

was not supported. 

Supplemental Analyses. In addition to if state vocational interests can predict 

theoretically relevant outcomes, it would be useful to know if state vocational interests can 

incrementally predict outcomes over trait interest. In order to test this, the same three step 

procedure outlined previously was conducted for each trait vocational interest individually 

predicting the outcomes of interest. More specifically, for step 1 multilevel regressions 

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accounting for nesting within individuals were conducted with each trait vocational interest 

predicting the outcome of interest. For step two, daily activities were entered into the equation. 

For step 3, the rest of the polynomial regression terms were added (i.e. squared trait interest, 

squared daily activities, and the interaction between trait vocational interests and daily activities). 

Predicted scores from this trait polynomial regression equations were saved, along with predicted 

scores from the state polynomial regression equations. These predicted scores were then 

simultaneously entered into a multilevel regression accounting for nesting within individuals to 

assess incremental validity of state interests over trait interests for outcomes. For these analyses, 

165 cases were available at level 2 (individual) and 741 cases with matched morning and 

evening survey data were available for level 1 (within individual). 

 

See Tables 81 through 92 for the results of the multilevel analyses for steps 1 through 3 

for trait vocational interests and the multilevel analyses assessing incremental validity of state 

vocational interest polynomial regressions over trait vocational interest polynomial regressions 

for daily positive affect. Based on the results of the incremental validity analyses, only the state 

Realistic and Investigative polynomial regression predicted values predicted daily positive affect 

over the trait predicted values (p < .05). The polynomial regression equations for trait vocational 

interests were not significant in incrementally predicting daily positive affects for any RIASEC 

domain (p > .05). 

 

See Tables 93 through 104 for the results of the multilevel analyses for steps 1 through 3 

for trait vocational interests and the multilevel analyses assessing incremental validity of state 

vocational interest polynomial regressions over trait vocational interest congruence for daily 

negative affect. Based on the results of the incremental validity analyses, only the state 

Conventional polynomial regression predicted values predicted negative affect over the trait 

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predicted values (p < .05). Only the trait Enterprising polynomial regression incrementally 

 

predicted daily negative affect (p < .05). 

 

See Tables 105 through 116 for the results of the multilevel analyses for steps 1 through 3 

for trait vocational interests and the multilevel analyses assessing incremental validity of state 

vocational interest polynomial regressions over trait vocational interest polynomial regressions 

for daily intrinsic motivation. Based on the results of the incremental validity analyses, five 

RIASEC state polynomial regression predicted values (all except for Enterprising) predicted 

intrinsic motivation over the trait predicted values (p < .05). Trait polynomial regression 

equations for all RIASEC dimensions, except for Artistic, incrementally predicted daily intrinsic 

motivation (p < .05). 

 

See Tables 117 through 128 for the results of the multilevel analyses for steps 1 through 3 

for trait vocational interests and the multilevel analyses assessing incremental validity of state 

vocational interest polynomial regressions over trait vocational interest polynomial regressions 

for daily engagement. Based on the results of the incremental validity analyses, three RIASEC 

state polynomial regression predicted values (Realistic, Investigative, and Social) predicted daily 

engagement over the trait predicted values (p < .05). Trait polynomial regression equations for 

all RIASEC dimensions, except for Realistic, incrementally predicted daily engagement (p < 

.05). 

 

See Tables 129 through 140 for the results of the multilevel analyses for steps 1 through 3 

for trait vocational interests and the multilevel analyses assessing incremental validity of state 

interest polynomial regressions over trait interest polynomial regressions for daily perseverance. 

Based on the results of the incremental validity analyses, five RIASEC state polynomial 

regression predicted values (all except Enterprising) predicted daily perseverance over the trait 

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predicted values (p < .05). The polynomial regression equations for trait vocational interests 

were not significant in incrementally predicting daily perseverance for any RIASEC domain (p > 

 

.05). 

 

In addition to incremental prediction of state vocational interest polynomial regressions 

over trait vocational interest polynomial regressions, multilevel analyses accounting for nesting 

within individuals were conducted with predicted scores for all six state vocational interest 

polynomial regressions and with all six state and trait vocational interest polynomial regressions 

to test if models for different interests predicted incrementally over others (e.g. does the 

polynomial regression for Conventional predict over the other RIASEC interests?) and over all 

trait polynomial regression models.  For these analyses, 165 cases were available at level 2 

(individual) and 741 cases with matched morning and evening survey data were available for 

level 1 (within individual). See Tables 141 through 145 for results of these analyses. 

For daily positive affect, polynomial regression predicted scores for state Investigative, 

Artistic, Social, and Conventional predicted incrementally over other RIASEC state vocational 

interest predicted scores (p < .05). When predicted trait scores were included in the model, only 

state Investigative polynomial regression predicted scores predicted over other state and trait 

vocational interest predicted scores (p < .01).  

For daily negative affect, polynomial regression predicted scores for state Conventional 

predicted incrementally over other RIASEC state vocational interest predicted scores (p < .05). 

When predicted trait scores were included in the model, no state polynomial regression predicted 

scores predicted over other state and trait vocational interest predicted scores. Only the trait 

Artistic polynomial regression equation predicted incrementally over all others in the model (p < 

.05) 

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For daily intrinsic motivation, polynomial regression predicted scores for state 

Investigative, Artistic, and Social predicted incrementally over other RIASEC state vocational 

interest predicted scores (p < .01). When predicted trait scores were included in the model, only 

state Social polynomial regression predicted scores predicted over other state and trait vocational 

 

interest predicted scores (p < .05).  

For daily engagement, polynomial regression predicted scores for state Investigative and 

Social predicted incrementally over other RIASEC state interest predicted scores (p < .01). When 

predicted trait scores were included in the model, only state Investigative, trait Investigative, and 

trait Conventional polynomial regressions predicted scores predicted over other state and trait 

interest vocational predicted scores (p < .05).  

For daily perseverance, polynomial regression predicted scores for state Investigative, 

Social, and Conventional predicted incrementally over other RIASEC state interest predicted 

scores (p < .05). When predicted trait scores were included in the model, only state Conventional 

polynomial regression and trait Artistic predicted scores predicted over other state and trait 

vocational interest predicted scores (p < .05).  

Summary. Based on the results of these analyses, the congruence between state 

vocational interests and daily activities, represented by within-person effects within the 

polynomial regression equations, were significant predictors for daily positive affect, daily 

intrinsic motivation, daily engagement, and daily perseverance for all RIASEC dimensions. For 

daily negative affect, state vocational interest congruence was generally not predictive with the 

exception of Conventional state vocational interests. Components of the polynomial regression 

equation (i.e. state interests and daily activities) also display relatively consistent relationships 

with the outcomes of interest. Between these two components of the regression equation, the 

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daily activities seem to more consistently and strongly predict outcomes. When these variables 

 

and the polynomial regression terms are used to predict over trait polynomial regression 

equations, they generally display incremental validity for daily intrinsic motivation and 

perseverance, display inconsistent incremental effects for engagement, and generally non-

significant incremental predictions over affective outcomes. Trait polynomial regression 

equations did not generally display incremental prediction over state polynomial regression 

equations for daily affective or perseverance outcomes, but displayed relatively consistent 

incremental prediction for motivational outcomes. When all state vocational interest polynomial 

regression predicted scores were included in a regression, there was inconsistent evidence for 

incremental prediction of these predicted values over others. When trait predicted values are also 

included, few state predicted values add incrementally to prediction. 

 

Situational Interests Predicting Day-to-Day Changes in State Vocational Interests 

(H4). Hypothesis 4 states, “Changes in state vocational interests are predicted by situational 

interests, in that greater or lower situational interests within a domain one day will lead to an 

increase or decrease in state interests within that domain the following day, respectively.”. In 

order to test this hypothesis, multilevel regressions were utilized accounting for nesting within 

individuals with situational interests (T) predicting state interests within that domain the 

following day (T+1), controlling for the previous day’s state interest in that domain (T). These 

analyses required linking an individual’s morning surveys to the same day’s evening survey 

responses and the next morning’s survey responses. In addition to this, situational interests were 

only assessed when individuals reported engaging in activities within that respective RIASEC 

domain that day. These restrictions severely limited the number of cases available for analyses 

for some interests. Sample size for these analyses ranged from 16 to 113 at level 2 (individual) 

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and 38 to 318 for level 1 (within-person). All variables were grand mean centered and 

standardized prior to analyses. Results of these analyses are reported in Tables 146 through 151. 

Within these analyses, situational interests for Investigative, Social, and Conventional 

significantly predicted their respective next day state interests (p < .01) (Investigative: β = .15, 

Social: β = .18, Conventional: β = .14). In other words, higher situational interests for these three 

RIASEC dimensional one day was related to higher state vocational interest in that domain the 

next. Note that of the six regressions conducted, the three with the largest sample size available 

found situational interests to be a significant predictor of next day state interests. The average 

sample size for these analyses were roughly three times the size of the sample for the non-

significant results (Significant: Average Level 1 = 284.33, Average Level 2 = 100.67, Non-

Significant: Average Level 1 = 79, Average Level 2 = 35.5), suggesting the difference in results 

across interests may be a power issue. Note that controlling for trait vocational interests when 

testing each of the respective relationships between situational interests and state vocational 

interests did not change the pattern of significant results. See Tables 146 through 151 for results 

of these analyses. Considering the possible influence of statistical power on discrepant results 

across RIASEC dimensions, tentative support was found for hypothesis 4.  

Supplementary Analyses. Supplemental analyses were conducted to determine if trait 

vocational interests moderate their respective situational interests to next day state vocational 

interest relationship. In other words, would the ability of situational interests in a domain to drive 

changes in that domain’s state vocational interests the next day depend on an individual’s trait 

standing on that interest? Previous day state vocational interests were controlled for, as in the 

previous analyses. The interaction between trait interests and state interests was created based on 

grand mean centered and standardized main effects. Sample size for these analyses ranged from 

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16 to 113 at level 2 (individual) and 38 to 318 for level 1 (within-person). Of the six possible 

cross-level moderating effects tested, two were significant. (p < .05). See Tables 146 through 151 

 

for results of these analyses. 

Trait Artistic vocational interests significantly moderated the relationship between 

Artistic situational interests and next day state Artistic vocational interests (β = .31, p < .01). See 

Figure 45 for this interaction plotted. Among individuals with high trait Artistic vocational 

interests, Artistic situational interests were associated with higher next day state Artistic 

vocational interests. However, among individuals with low trait Artistic vocational interests, 

Artistic situational interests were associated with lower next day state Artistic vocational 

interests. 

Trait Social vocational interests significantly moderated the relationship between Social 

situational interests and next day state Social vocational interests (β = -.10, p = .02). See Figure 

46 for this interaction plotted. Among individuals with high trait Social vocational interests, 

Social situational interests were associated with lower next day state Social interests. However, 

among individuals with low trait Social vocational interests, Social situational interests were 

associated with higher next day state social vocational interests.  

Hypothesis 4 was focused on the effect of situational interests predicting next day state 

vocational interests, and tentative support was found for this relationship. However, the reverse 

may be true where state vocational interests in a domain predict situational interests in that 

domain. In other words, individuals may enjoy, identity, and value activities more on days in 

which they have a higher preference for that type of work. To test this, multilevel regressions 

were conducted accounting for nesting within individuals with state vocational interests collected 

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in the morning (T) predicting situational interests in that interest domain measured in the evening 

(T), controlling for previous day’s situational interest (T-1).  

 

These analyses required linking an individual’s previous day’s evening survey and the 

focal day’s morning and evening surveys. In addition to this, situational interests were only 

assessed when individuals reported engaging in activities within that respective RIASEC domain 

that day. These restrictions severely limited the number of cases available for analyses for some 

interests. Sample size for these analyses ranged from 16 to 113 at level 2 (individual) and 38 to 

318 for level 1 (within person). All variables were grand mean centered and standardized prior to 

analyses. Results of these analyses are reported in Tables 152 through 157. Within these 

analyses, state vocational interests for Investigative, Social, and Conventional significantly 

predicted their respective same day situational interests (p < .01) (Investigative: β = .32, Social: β 

= .33, Conventional: β = .20). In other words, for Investigative, Social, and Conventional 

interests having a higher degree of state interests in that domain predicted higher situational 

interests in that domain the same day. Note that of the six regressions conducted, the three with 

the largest sample size available found state vocational interests to be a significant predictor of 

same day situational interests suggesting similar power issues as analyses for hypothesis 4. 

Controlling for trait vocational interests when testing each of the respective relationships 

between state vocational interests and same day situational interests did not change the pattern of 

significant results. See Tables 152 through 157 for results of these analyses.  

 

Analyses were also conducted to determine if trait vocational interests moderated their 

respective same day state vocational interest to situational interest relationships. In other words, 

does the effect of state vocational interests in a domain on the degree to which individuals enjoy, 

value, and identify activities within that domain the same day depend on an individual’s trait 

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standing for that interest? Similar to previous analyses, prior day situational interests were 

controlled for. The interaction between trait vocational interests and state vocational interests 

was created based on grand mean centered and standardized main effects. Sample size for these 

analyses ranged from 16 to 113 at level 2 (individual) and 38 to 318 for level 1 (within-person). 

Of the six possible cross-level moderating effects tested, two were significant (p < .05).  

Trait Investigative vocational interests significantly moderated the relationship between 

state Investigative interests and same day Investigative situational interests (β = .12, p < .01). See 

Figure 47 for this interaction plotted. Along individuals with high trait Investigative vocational 

interests, the relationship between State Investigative vocational interests and same day 

Investigative situational interests was stronger than for those with low trait Investigative 

interests. 

Trait Social vocational interests significantly moderated the relationship between state 

Social vocational interests and same day Social situational interests (β = .14, p = .03). See Figure 

48 for this interaction plotted. Along individuals with high trait Social vocational interests, the 

relationship between State Social vocational interests and same day Social situational interests 

was stronger than for those with low trait Social vocational interests. 

Summary.  Based on the results of these analyses, tentative support was found for 

situational interests predicting next day state vocational interests, with higher situational interests 

in a domain leading to higher next day state vocational interest in that domain. Though all of 

these analyses did not find significant results, analyses with larger samples found support for the 

hypotheses suggesting not supportive results may be a result of limited sample size. 

Interestingly, tentative support was found for the opposite relationship as well, with state 

vocational interests predicting same day situational interests within the same domain. Again, 

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significant results were found for analyses with larger sample sizes. Moderations tested 

 

displayed inconsistent results.  

State Vocational Interest Congruence Predicting Next Day State Vocational 

Interests (H5). Hypothesis 5 states, “The congruence between an individual’s state vocational 

interests and activities the previous day will predict an individual’s state vocational interest in the 

next, such that less of an activity than desired will elicit greater state vocational interest in that 

domain the following day and more of an activity than desired will elicit lower state vocational 

interest in that domain the following day.” This hypothesis was evaluated utilizing the three-step 

multilevel regression previously outlined for hypotheses 3a-3e and response surface analyses 

with bootstrap-based confidence intervals to determine significant response surface components.  

However, instead of the outcomes described in hypotheses 3a through 3e, these models predicted 

next day state vocational interests.  

Support for this hypothesis was evaluated based on three components. First, in step 2 

when both the state vocational interests and daily activities for this interest are included in the 

multilevel regression the coefficients for state vocational interests should be positive while the 

coefficients for daily activities should be negative. In order words, holding state vocational 

interests constant doing more of an activity should lead to less interest in that type of activity the 

next day. Holding daily activities constant, higher state vocational interests should lead to higher 

state vocational interests in that domain the next day. Second, the slope along the misfit line 

should be positive. As we move down the misfit line from daily activities exceeding the desired 

level of the activity to where the amount of daily activities is perfectly matched to desired level 

to the desired amount of the activity exceeding the amount of daily activities in that domain, the 

state vocational interests in that domain the following day should increase. Third, the slope along 

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the line of fit should be non-significant. When desired amount of an activity is perfectly matched 

with the amount of that activity done during that day, the state vocational interest in that domain 

should be the same the following day. These analyses required matching an individual’s morning 

responses to evening responses the same day to morning responses the following data, leaving 

156 cases at level 2 (individual) and 582 at level 1 (within-person) for analyses. Main effects for 

state interests and daily activities were grand mean centered and standardized prior to analyses 

and creation of squared and interaction terms.  

See Tables 158 through 169 for the results of these analyses. See Figures 49 through 54 

for response surfaces from the polynomial regressions. For step 2 of the multilevel regressions, 

all six RIASEC state vocational interests were positively related to next day state vocational 

interests holding daily activities constant (p < .01) in line with Hypothesis 5. However, contrary 

to Hypothesis 5 all six RIASEC daily activities were positively related to next day state 

vocational interests (p < .01). Also contrary to Hypothesis 5, all six fit lines for the response 

plots from the polynomial regression equations demonstrated a significant positive slope (p < 

.05). For three of the six RIASEC misfit lines (Investigative, Social, and Enterprising), the slope 

was positive and significant (p < .05) in line with Hypothesis 5.  

Summary. Based on the analyses conducted, limited support was found for Hypothesis 5. 

The slopes for state vocational interests controlling for daily activities and some misfit lines were 

in line with the hypothesis. However, the slopes for daily activity controlling for state vocational 

interests and the slopes for the fit lines were not in line with the hypothesis.  

 

Supplementary Analyses. In addition to analyses and supplemental analyses tied to 

specific hypotheses, additional supplemental analyses with the potential to further interests 

research not tied to specific hypotheses were conducted.  

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State depletion, positive state affect, negative state affect, sleep quality, and sleep 

quantity were collected during daily morning surveys along with state vocational interest to 

determine if these resource variables were significantly related to state vocational interests. Table 

170 reports bivariate relationships for these variables and each RIASEC state vocational interest. 

N for these analyses ranged from 949 to 950. State depletion demonstrated a significant negative 

relationship with all six RIASEC state vocational interests (r = -.16 to -.31, p < .01), while state 

positive affect was significantly and positively related to all six RIASEC state vocational 

interests (r = .23 to .35, p < .01). As these bivariate relationships neglect nesting within 

individuals and possible overlap in variance explained, multilevel regressions were also 

conducted with state depletion, state positive affect, state negative affect, sleep quality, and sleep 

quantity predicting each RIASEC state vocational interest as an outcome to account for this 

nesting and assess incremental validity. 173 level 2 (Individual) cases, and 948 to 950 (within 

individual) level 1 cases were available for these analyses. See Tables 171 through 176 for the 

results of these analyses. State positive affect was positively related to all six RIASEC state 

vocational interests (p < .01). However, state depletion remained a significant negative predictor 

for only three state vocational interests (Investigative, Social, and Conventional (p < .05)). These 

results suggest that on days when individuals have higher positive affect, they also tend to 

display higher state vocational interests across all RIASEC domains. Additionally, on days when 

individuals feel depleted, they tend to desire less Investigative, Social, and Conventional 

activities. 

 

In addition to testing relationships between the resource variables and state vocational 

interests, relationships between these variables and daily activities were also estimated. Table 

177 reports bivariate relationships among resource variables and daily activities. 748 cases were 

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available for these analyses. State affect measured in the morning was a significant positive 

predictor of daily activities for all six RIASEC interests (r = .15 to .31, p < .01). See Tables 178 

through 183 for the results of multilevel regressions accounting for nesting within individuals 

and assessing the incremental relationships between these variables and daily activities. For all 

six RIASEC daily activities, state positive affect was a significant positive predictor (p < .01). 

These results suggest that on days when individuals have higher positive affect, they tend to do 

more activities regardless of RIASEC domain. 

A core tenant of interests theory is that interests drive the selection of activities, in that 

individuals who have a higher interest in a domain are more likely to seek out activities and 

domains congruent with those interests (Holland, 1997). The relationship between state interests 

and daily activities was evaluated at the within-person, daily level within the current study. See 

Table 184 for the bivariate correlations between state vocational interests and daily activities. N 

for these analyses ranged from 748 to 749. Correlations between daily state interests and daily 

activities ranged from r = .56 to .68. As these analyses neglect nesting within individuals, 

multilevel analyses were conducted regressing daily activities onto the same day state vocational 

interests accounting for this nesting. 167 level 2 (individual) and 748 level 1 (within person) 

cases were available for these analyses. See Tables 185 through 190 for the results of these 

analyses. Each relationship between state RIASEC interests and their respective daily activities 

was significant (β = .43 to .66, p < .01). These results suggest that on days when an individual 

desires more of a type of activity they tend to do more of that type of activities. However, a 

caveat should be noted in regards to the causal relationship between state vocational interests and 

daily activities. Though these measures were temporally separated in a manner that would be 

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support causality, a third variable may responsible for these relationships (e.g. omitted variable: 

see Sackett, Laczo, & Lippe, 2003; Nye, Butt, Bradburn, & Prasad, 2018b). 

 

As trait vocational interest scores were available for individuals for the pre and post 

survey, change in trait vocational interests during the period of the study were able to be 

assessed. Trait interests were available at both time points for the trait DSIM and BPDRM 

measure for 133 individuals. The correlations between pre and post scores for the trait DSIM 

measure ranged from r = .34 to .58 (p <.01) and for the BPDRM r = .72 to .87 (p < .01) (see 

Table 191). The lower correlation between pre and post scores for the DSIM measure as 

compared to the BPDRM scales is expected. Given the shorter nature of the DSIM and lower 

reliability, these scores likely reflect a greater degree of measurement error than BPDRM scores.  

Analyses were also conducted to determine if change in trait interests from pre to post 

surveys could be predicted based on interests and experiences over the course of the ESM 

portion of this study. Correlations were conducted between interest change from pre to post for 

both the trait DSIM and BPDRM for each interest and average state interest in that domain over 

the course of the ESM, average daily activities in that domain over the course of the ESM, and 

the difference between the average state vocational interests in that domain over the course of the 

ESM minus the score in that interest domain from the pre-survey. Note that analyses were 

limited to cases in which 4 or more measurement occasions for the predictor of change (i.e. state 

interests, daily activities, or situational interests) were available similar to the restriction for 

analyses for standard deviation of observed scores for Hypothesis 1. This restriction led to 

sample sizes ranging from 32 to 133 for analyses. See Table 192 for results of these analyses.  

Across all state vocational interests and the DSIM and BPDRM, the difference in average 

state vocational interests and pre-survey trait vocational interest scores was a significant 

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predictor of trait interest change (DSIM: r = .57 to .72, p < .01, BPDRM: r = .22 to .37, p < .01), 

with an exception for change in trait Investigative interests for BPDRM (r = .12, p = .20). In 

other words, when state vocational interests in a domain during the course of the ESM were 

higher than the individual’s trait vocational interests in that domain when the study began, the 

individual’s trait vocational interests in that domain were likely to increase from the pre-survey 

to the post-survey. Note that 122 cases were available for this subset of analyses. 

Summary. Within the present study, state positive affect collected concurrently with state 

vocational interests in the morning was significantly related to higher state vocational interests in 

all six domains and higher daily activities in all six domains. This relationship remained when 

utilizing multilevel regression accounting for nesting and state positive affect demonstrated 

incremental validity over other variables included in this model for all six state interests and 

daily activities. State depletion collected in the morning demonstrated a significant negative 

bivariate relationship with all six state vocational interests, but utilizing multilevel regressions 

only demonstrated incremental validity over other variables included for three of six state 

interests. Results from these analyses also support the relationship between interests and 

activities at the within person level, with state vocational interests in a domain related to daily 

activities in that domain within the same day. Results also demonstrate that change in trait 

interest scores can be predicted based on state vocational interests between the times of 

measurement relative to an individual’s trait vocational interests at the time of first measurement.  

DISCUSSION 

 

Within the organizational sciences, there has been nearly universal neglect of short-term 

within-person variability of vocational interests. However, based on the results of this research 

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study this neglect may be problematic. Results of analyses suggest that absolute, relative, and 

rank-order variability exists for state vocational interests. Though compared to benchmarks in 

the published literature relative and rank order variability for state vocational interests may be 

low, they seem to exist none the less. The comment could be raised that this variability is merely 

the result of measurement error. However, this is unlikely. Based on the results outlined, 

variability in state vocational interests can be predicted day-to-day, between individuals, and 

within measurement occasions suggesting this variability is substantive.  

Based on the overall variability in state vocational interests, a distributional 

conceptualization of vocational interests based on Whole Trait Theory (Fleeson, 2001; 2017) 

may be appropriate where state vocational interests form a distribution around more stable trait 

vocational interests. More instability was typically found for rank-ordered vocational interests as 

compared to raw observed scores, which may be problematic for many of the fit indices 

commonly used in vocational interests research, as many rely on rank-ordered interests (see 

Brown & Gore, 1994; Camp & Chartrand, 1992). Supplemental analyses also suggest that state 

vocational interests display less stability as the period of time between measurements increases, 

as would be expected by transitory states. However, as discussed previous the correct 

temporality for state vocational interests as a constructs and states more generally are hard to 

determine. 

 

Results of analyses also suggest that individuals can vary substantially in terms of 

their variability of state vocational interests. That is, the degree of variability in state vocational 

interests seems to be an individual difference, mirroring results found in the personality literature 

for personality states within Whole Trait Theory (e.g. Fleeson, 2001). Openness to experience 

and curiosity were found to be significant predictors of state vocational interest variability, 

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suggesting individual who are more dispositionally open to experiences and curious see their 

interests shift more day-to-day. Both self-assessed variability in interests and the consistency of 

interests subscale of the grit construct displayed non-significant relationships with actual state 

interest variability. This suggests that individuals may not be able to accurately report the degree 

to which their own state interests vary and this variance may need to be determined objectively 

with repeated measurements.  

Within exploratory analyses trait conscientiousness, grit, and vocational identity was also 

found to predict variability in state vocational interests, where individuals with higher 

conscientiousness, grit, and those having a more defined vocational identity see greater changes 

in their state vocational interests day-to-day. Though this result for vocational identity seems 

counterintuitive, a post-hoc explanation may be that individuals who have worked to cultivate a 

stronger vocational identity are also more prone to seek out different types of experiences day-to-

day to further cultivate this identity. Greater variability in interests may be the process by which 

individuals refine and develop this identity over time. However, results of these exploratory 

analyses should be considered with caution due to the large number of relationships assessed and 

their non-hypothesized nature.  

Of all the hypothesized predictors and significant exploratory predictors, only curiosity 

demonstrated incremental validity over other predictors in relation to greater state interest 

variability. This suggests that much of the variance explained in predicting variability in state 

vocational interests by variables tested is shared, while curiosity may provide unique predictive 

power for state interest variability when these other variables are controlled.  

Much of the resurgence of vocational interests research in the organizational sciences has 

been attributed to new evidence of the predictive validity of trait vocational interests for 

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meaningful outcomes (Nye et al., 2012; 2017; Van Iddekinge et al., 2011), and for trait interest 

congruence in particular (Nye et al., 2012; 2017). At the state level, vocational interest 

congruence seems to be predictive of outcomes as well. This finding is in contrast to the limited 

existing research that has explored state vocational interest congruence and outcomes (i.e. Phan, 

2018). Within the current research project, state vocational interest congruence with daily 

activities was predictive of daily positive affect, intrinsic motivation, engagement, and 

perseverance. However, state vocational interest congruence was not generally predictive of 

negative affect.  

Though state vocational interest congruence was predictive of most outcomes examined, 

it did not generally demonstrate the anticipated congruence form. More specifically, outcomes 

were generally not maximized (or in the case of negative affect minimized) along the line of fit 

as hypothesized. However, these results mirror results found at the trait level where outcomes are 

frequently not maximized when trait vocational interests and environments are perfectly matched 

(e.g. Nye et al., 2018a; Wiegand, 2018).  For example, Wiegand (2018) found at the trait level 

that, depending at the specific RIASEC dimension, job satisfaction was highest when vocational 

interests were higher or lower than the what the environment provided. Though in the present 

research project there does not seem to be a consistent location where outcomes are maximized 

(or minimized), it did not generally seem to be along the line of fit. These results provide 

evidence at the state level, in addition to previous evidence at the trait level, that the match 

between an individual’s desired level of activities and actual activities does not need to be 

perfect in order for outcomes to be maximized.  

In addition to the predictive validity for state vocational interest congruence, components 

of this congruence generally displayed relationships with outcomes of interest. All six RIASEC 

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state vocational interests displayed significant relationships with daily positive affect, intrinsic 

motivation, engagement, and perseverance. These results suggest that within individuals, having 

higher state interests in the morning is related to higher positive affect, intrinsic motivation, 

engagement, and perseverance that same day. Higher daily activities across RIASEC domains 

also generally displayed significant positive relationships with daily positive affect, intrinsic 

motivation, engagement, and perseverance, along with higher negative affect. These results 

suggest that days in which individuals felt greater positive affect, negative affect, intrinsic 

motivation, engagement, and perseverance were also days in which they were also more likely to 

do more activities across RIASEC domains. When both state vocational interests and daily 

activities were included in a regression, daily activities were typically a stronger predictor of 

outcomes and frequently reduced the predictive validity of state vocational interests to be non-

significant. This suggests that actually doing more activities during the day as opposed to 

wanting to do activities generally better predicts daily affective, motivational, and persistence-

based outcomes. One interesting takeaway from these results may be that even if someone 

doesn’t have a high desire for activities one day, pushing themselves do them despite this may be 

related to positive outcomes such as higher daily positive affect and intrinsic motivation. 

State vocational interest polynomial regressions displayed mixed results in terms of 

incrementally predicting over trait vocational interest polynomial regressions. These state 

vocational interest polynomial regressions were generally more incremental in predicting 

motivational and persistence outcomes as compared to affective outcomes, though overall 

incremental prediction was inconsistent. This leaves the incremental contribution of considering 

state vocational interests in predicting outcomes over trait vocational interests a somewhat open 

question.  

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In addition to results suggesting state vocational interests fluctuate day-to-day, tentative 

support was found for a relationship between situational interests and changes in next day state 

vocational interests. Within RIASEC domains with the largest sample size, enjoying, identifying 

more with, and valuing activities in an interest domain was related to higher interest in that 

domain the following day. Interestingly, within domains with the largest sample size a 

relationship was found between higher state interests in a domain and higher situational interests 

in that domain the same day as well. Being more interested in a domain of activities was related 

to higher enjoyment, identification, and value assigned to activities within that domain the same 

day. Though these significant relationships were only found in three of the six interest domains 

tested, these domains had roughly three times the sample size as compared to analyses that found 

non-significant results, suggesting the lack of effects for those three domains may be a power 

issue as opposed to substantive differences.  

These two results regarding situational interests and state vocational interests suggest that 

a positive feedback loop may be occurring for situational interests and state vocational interests 

and may speak to how individuals develop career interests over time. Within RIASEC domains, 

higher enjoyment, identification with, and value assigned to activities may lead to individuals 

desiring more of that activity the following day. During the next day, higher interest in a domain 

may lead to higher enjoyment, identification with, and value assigned to those activities, leading 

to higher domain interest the next day, etc. Past research into interests has put forward a 

feedback loop-type mechanism driving trait interest development (e.g. Su, 2019), and these 

results suggest that this positive feedback loop may extend to state vocational interests as well. 

This state feedback loop may, in time, influence trait development, discussed in the next section. 

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Though situational interests were found to predict next-day state vocational interests, 

previous day state vocational interest congruence did not relate to changes in state vocational 

interest in the hypothesized manner. The match or mismatch between an individual’s preferences 

and activities in a domain did not seem to influence the next day’s preference for those activities 

in the expected way. However, components of congruence significantly predicted the outcome of 

interest. State vocational interests in a domain and greater activities in that domain were 

consistently positively and significantly related to higher interest in that domain the following 

day.  

Within supplemental analyses, the average state vocational interest in a domain during 

the daily survey component of Study 2 minus the individual’s trait standing at the beginning of 

the study was significantly related to differences in trait interests during the course of the study 

for nearly all RIASEC interests across two different trait vocational interest measures. It seems 

that if a person has a consistently higher or lower state interest in a domain than their trait 

standing, their trait standing shifts towards the direction of this difference. For instance, if a 

person has a relatively low trait interest in Enterprising activities but over the course of a week 

tends to have state Enterprising interests higher than their trait tendency, at the end of the week 

their trait standing is likely to increase based on these results. Alternatively, if a person has a 

relatively high trait interest in Enterprising activities but over the course of a week tends to have 

state Enterprising interests lower than their trait tendency, at the end of the week their trait 

standing is likely to decrease based on these results.  

This exploratory finding seems to demonstrate how state vocational interests influence 

trait vocational interests over time. However, it should be considered that instead of actually 

reflecting a change in trait vocational interests, the change in trait interest scores from pre to post 

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surveys reflect an individual’s better understanding of their own interests by responding to daily 

surveys of these interests for the past week. Individual’s may have come to better understand 

their own interests over the course of the study, and the relationship between average daily state 

vocational interests and trait interest “change” may instead be reflecting this increased self-

understanding. Due to this possibility, as well as exploratory nature of these analyses and modest 

sample size, these results should be interpreted with caution. 

While limitations of the present study and results must be considered, the relationships 

between situational interests and state vocational interests and the relationship between mean 

state vocational interests and interests change illustrate a compelling model of how trait 

vocational interests may change over time. Based on the results of analyses, situational interests 

relate to higher state interests in that domain the next day. Subsequent higher state interests in 

that domain leads to higher situational interest in that domain. Over time, this cycle is likely to 

push state vocational interests consistency higher than a person’s trait standing, which based on 

the supplementary analyses suggests that trait vocational interests will shift in this direction over 

time. Note the opposite could also be true, where low situational interests leads to decreasing 

state vocational interests and over time decreases trait vocational interests. 

In additional to these more substantive contributions, an additional contribution of the 

present study is the creation of viable measures of state vocational interests and situational 

interests. The DSIM represents the first measure of state vocational interests and has 

demonstrated adequate reliability and convergent validity within the present research study. 

Future research may utilize this measure, and the existence of a content valid and reliable 

measure of state vocational interests should hopefully spur research in this domain. Additionally, 

though situational interest measures have existed previously this research project created a viable 

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and reliable measure of situational interests based on Su’s (2019) cross-disciplinary Trait State 

Interest Dynamic theory. With a useable measure of this conceptualization of situational 

interests, research into this theory and its tenants will hopefully be enhanced.  

 

Implications for Theory 

 

In terms of implication for theory for state vocational interests, the biggest implication is 

that we need theory. There has been nearly universal neglect of state vocational interests in the 

organizational sciences, and due to this the short-term within-person variability of vocational 

interests has not been integrated in or subject to most theory in our science. The biggest 

implication for theory from this research project is that we need to evaluate how variability of 

vocational interests at the state level integrates within our existing interest theory and develop 

theory to explain state vocational interests. For instance, do state vocational interests fit well 

within Holland’s (1959; 1997) RIASEC interests model or warrant a different model for short-

term interests? Why might state vocational interest congruence be predictive of daily positive 

affect but not negative affect? This research project raises more questions for theory than 

provides answers. 

 

Within the state-trait literature, this research project also has theoretical implications. 

Based on results of this study, there is further evidence that Whole Trait Theory (Fleeson, 2001; 

2017) may be a generalizable model for conceptualizing states and traits within the individual 

differences literature beyond its origins in personality (Jayawickreme et al., 2019). For instance, 

similar to results found in personality, comparable absolute variability was found for state 

vocational interests. Distributional aspects of density distributions for state vocational interests 

seem to vary person-to-person as well, such as degree of state variability. The results of this 

research project generally provide support for the tenants of the TSID theory of trait interest 

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development, with some modifications. At a high level, TSID theory suggests interests drive 

individuals to seek out environments congruent with those interests and situational interests in 

those domains over times lead to changes in trait interests, with higher situational interests 

leading to increases in trait interests and lower situational interests leading to decreases in the 

domain (Su, 2018). Results of the current study suggest that this process might be responsible for 

trait interest change, but the meso-level construct of state vocational interests should be 

incorporated into the model. State vocational interests may function as the link through which 

highly transitory situational interests shift highly stable trait vocational interests. Instead of 

situational interests shifting trait interests as suggested within TSID, situational interests seem to 

shift state vocational interest. In turn, when state vocational interests diverge from trait 

vocational interests evidence suggests that trait interests shift in the direction of this divergence. 

State vocational interests provide a mechanism through which situational interests can shift trait 

interests, and this construct should be incorporated into existing and future theorizing for trait 

interests development.   

Implications for Practice 

 

In addition to theoretical implications outlined, this research project has a number of 

meaningful implications for practice. For instance, this study holds practical implications for job 

design. Upending conventional wisdom that preferences for different activities are stable day-to-

day warrants reconsidering how we design jobs. Since day-to-day interest seems to display a fair 

degree of fluctuation, managers may want to design jobs to accommodate these fluctuations. 

This may include adding more variety to the tasks done within jobs and/or allowing greater 

autonomy for employees to choose tasks so that they may complete tasks as their align with their 

daily interests (Cordery & Parker, 2012; Humphrey et al., 2007).  

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The study also highlights the role of positive affect as a potent mechanism for increasing 

 

state interests, which may prove useful for managers to consider. State positive affect was 

associated with greater state vocational interests across all six RIASEC dimensions, suggesting 

that increasing state positive affect may be related to increased interest in different activities 

regardless of an individual’s disposition. This may be useful if a manager has a task for an 

individual or a group that may not align with individuals’ interests as an intervention to raise 

positive affect may be effective to raise interest in this unappealing task.  

 

This study also illustrates potential levers that may be utilized on a day-to-day basis to 

shift trait interests, which may be useful in situations such as closing the gender gaps that exist in 

STEM occupations and majors (Wang & Degol, 2017) fueled in part due to differences in  

interests (Su & Rounds, 2015; Su, Rounds, & Armstrong, 2009). A reciprocal pattern was found 

between situational interests and state vocational interests, where situational interests predicted 

greater state vocational interests in that RIASEC domain and greater state vocational interests in 

a domain predicted greater situational interests in that domain. Supplementary analyses suggest 

that over time this cycle can shift trait interests, thus illustrating how both situational interests 

and state vocational interests may act as levers to close labor pool gaps, such as the STEM 

gender gap, fueled by interests. For situational interests, interventions may be designed to 

increase affective reactions to, identification with, and value assigned to a domain to facilitate 

the positive feedback loop that would lead to higher trait interests in that domain. For instance, 

interventions for females introducing STEM fields in an enjoyable way (i.e. increasing affective 

reactions), illustrating potential female role models in the STEM fields (i.e. increasing 

identification with), and showing the societal impact of STEM (i.e. increasing value assigned) 

may be effective interventions to shift situational interests and eventually trait interests. For state 

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vocational interests, interventions may be designed to increase state interests in a domain that 

would facilitate the positive feedback loop leading to higher trait interest in that domain. As 

previously noted, positive affect was related to higher state vocational interests across all six 

RIASEC dimensions and may be a useful intervention mechanism for raising state vocational 

 

interests. 

Limitations 

 

As with any research study, this research project was subject to a number of limitations 

that should be considered in regards to conclusions drawn from this study. Many of the 

limitations of the current study are a result of the deficient literature for state vocational interests, 

leading to a limited research base to consider in designing the present study. For instance, the 

length in which state vocational interests persists is unclear based on a review of the literature. 

As outlined previously, a daily timespan seems reasonable for state vocational interests but it 

may be the case that state vocational interest may be more transitory or persist longer than this. If 

this is the case, then measuring state vocational interests daily may limit the accuracy of results 

in regards to true state vocational interests variability and related results from this study.  

 

Because of the limited research into state vocational interests, much of the basis of the 

present study was rooted in vocational interests research at the trait level, with the assumption 

that homology exists between state and trait vocational interests. In the present research project, 

some evidence of homology was found such as the general parallels in predictive validity for 

congruence at the state and trait levels. A lack of parallelism elsewhere might call into question 

assumptions on which the design of this study and measures were based. For instance, Holland’s 

(1959; 1997) model of vocational interests was utilized to design the DSIM in study 1 and the 

basis for state vocational interests in study 2. However, as Holland’s (1959; 1997) is a trait 

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model of vocational interests it is unclear if this structure of interests generalizes to the state 

level. In other words, it may be that there may be more or less than six vocational interests at the 

state level or that their interrelationships don’t match onto the Holland (1959; 1997) hexagonal 

structure. If state vocational interests are not represented well by the RIASEC model, the results 

 

of this study may be questionable.  

 

The sample utilized within this research project, may also be considered a limitation. The 

current study utilized a college student sample, and although this sample is commonly used in 

vocational interest research (e.g. Nye et al., 2018a), some research has found differences in 

interests for this population. For instance, past research has found less variability in vocational 

interests at the between individual level in younger age groups (Ion, Nye, Illiescu, 2019) and past 

research suggests vocational interests at the trait level may be less stable during typical college 

years than adulthood (Low et al., 2005) possibly limiting generalizability. The samples utilized 

also demonstrated a lack of equal representation for gender, where in both study 1 and 2 the 

samples utilized were nearly 80% female. Interests have been found to differ by gender (Su et 

al., 2009) and although many aspects of interests theory have been found to generalize across 

gender at the trait level (e.g. Anderson et al., 1997), this may not generalize to the state level 

(Kozlowski & Klein, 2000). Drawing conclusions based on such a highly gender skewed sample 

may be problematic as results may fail to generalize. There may also be limited ability for 

college students to choose what activities that partake in, impacting congruence and the 

relationship between state vocational interests and daily activities, due to course requirements, 

work requirements, lack of resources, etc. However, this limitation is present in some way for all 

adults. Even employees have limited ability to choose the work in which they do day-to-day. 

Even retirees, who have no work requirements and ample schedule flexibility, may still be 

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limited in terms of choice of activities by factors such as finances, weather, location, etc. No 

population has unlimited choice in terms of the activities they participate in; however, this 

limitation should still be considered within the current research project. 

 

In addition to the source of the sample, sample size may be a limitation of the current 

study for some analyses. For some hypothesis-focused and exploratory analyses, the sample size 

available for analyses was very low. This leaves ambiguity in regard to if patterns of significant 

and non-significant results are due to actual substantive differences or differences in power. For 

instance, analyses for hypothesis 4 regarding situational interests predicting next day state 

vocational interests found support for the three RIASEC dimensions with the largest sample size 

but did not find support in the three dimensions with the lowest. It is difficult to know if these 

differences are due to true differences across dimensions or power differences due to sample size 

issues.  

One issue with determining the variability of state vocational interests is determining if 

variability found is due to actual variability in the construct across days or simply measurement 

error. The state DSIM measures are relatively short with imperfect reliability, suggesting that 

measurement error may play a role in this variability. Latent correlations between state 

vocational interests day-to-day were conducted to account for this measurement error and 

variability was still found. Additionally, as previously outlined, variability in state vocational 

interests was able to be predicted across days, individuals, and measurement occasions 

suggesting this variability is substantive as opposed to error. However, the results must still be 

interpreted in light of measurement error. 

102 

 

 

Future Research 

 

 

As research into state vocational interests in the organizational sciences is so sparse, 

ample opportunities are available for meaningful future research in this domain. For instance, 

within the current study state vocational interests were measured daily for a period of one week. 

These timeframes were based on reasonable assumptions, however future research may adjust 

these timeframes. It may be the case that state vocational interest vary with higher frequency, as 

in hour-to-hour or even moment-to-moment, which would have been variability neglected in the 

present study. Although the present study utilized the period of one week to measure daily state 

vocational interests, it would be interesting to measure state vocational interests over a longer 

period as well. Future research could determine if state vocational interests are cyclical in nature 

or display trends over days of the week. For instance, do individuals prefer conventional interest 

activities towards the end of the week where mental resources may be more limited relative to 

earlier in the week? Do individuals prefer more social activities during the weekends? 

Future research could be conducted to determine the rate at which state vocational 

interests change and possible moderators of this change. Within the present study, a pattern 

emerged where the stability of state vocational interests decreased as the timeframe between 

measurements increased from T to T+1 to T+6. Future research could chart this decay in 

stability, to determine the stability over days to weeks to months and if the decay in stability is 

linear or curvilinear in nature. Within the current research project, individuals were found to 

differ in regards to the stability of their interests and predictors of this variability were found. In 

testing the decay of stability across timeframes, it may be that individual differences may also 

moderate the slope of this decay with some individuals exhibiting slower change in state 

vocational interests than others. 

103 

 

 

 

As noted previously, much of the present study relied on the assumption of homology 

 

between state vocational interests and trait vocational interests. Future research could test if 

parallelism can be determined between state and trait vocational interests. For instance, 

Holland’s (1959: 1997) model of trait vocational interests was used as the basis for state 

vocational interests within this study. Holland’s RIASEC model may represent trait vocational 

interests well, but may not do the same for state vocational interests.  

 

Interestingly, in the present research study state vocational interest congruence was 

predictive of daily positive affect but generally not daily negative affect. At the trait level, 

vocational interest congruence has demonstrated only a modest, though significant, relationship 

with affective outcomes such as job satisfaction (Hoff et al., 2018) despite theoretical (e.g. 

Holland, 1959: 1997) and intuitive rationale for a stronger relationship. These results from the 

present study may hold insights for this modest relationship. If homology across state and trait 

levels holds in these relationships between congruence and positive and negative affect, trait 

congruence may predict positive affective outcomes well but not negative affective outcomes. 

For complex affective outcomes, such as job satisfaction, that likely blend positive and negative 

affective feelings, this lack of relationship between congruence and negative affective outcomes 

may explain modest relationships found for complex affective outcomes. Future research may 

evaluate the homology between state and trait vocational interest congruence and relationships 

with positive and negative affect, as well as evaluate this possible explanation for modest 

relationships found between trait vocational interest congruence and some affective outcomes. 

104 

 

 

Conclusion 

 

After a long period of dormancy within organizational sciences, research into vocational 

interests is again thriving with renewed promise for academics and practitioners alike. Interest 

researchers are continuously looking to expand the theory of vocational interests to further 

understand the construct, and state vocational interests represent a valuable extension for theory 

and practice. Results suggest that variability day-to-day exists for state vocational interest, state 

vocational interests can be useful in predicting valuable outcomes, and this variability can 

influence trait vocational interest development. Though the organizational sciences have 

neglected state variability in vocational interests, the results of this research project suggest we 

should reevaluate our current assumptions and theories for vocational interests with an eye 

towards acknowledging this variability.  

 

 

105 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDICES 

 

106 

 

 

Table 1: 

APPENDIX A – Tables and Figures 

Item level Information for DSIM Developmental Item Pool. 

 

Median 
Cor w/ 
Others 

Max Cor 

w/ 

Others 

Which 
Max 

Item 

Item Text 

“Hands-on” tasks 
Being outdoors 
Using tools 
Fixing 
Building 
Physical activity 
Technical activities 

Straight forward problems 
Practical tasks 
Research 

Science 
Exploration 
Analytic thinking 
Abstract ideas 
Intellectual tasks 
Individually focused tasks 
Academics 
New ideas 

DSIM_1_R 
0.31  R4 
DSIM_2_R 
0.32  R6 
0.57  R5 
DSIM_3_R 
0.54  R5 
DSIM_4_R 
0.57  R3 
DSIM_5_R 
0.32  R2 
DSIM_6_R 
DSIM_7_R 
0.44  R8 
0.49  R3 
DSIM_8_R  Working with gadgets 
DSIM_9_R 
0.34  R10 
DSIM_10_R 
0.35  R7 
0.50  I13 
DSIM_11_I 
DSIM_12_I  Mathematics 
0.34  I13 
0.50  I11 
DSIM_13_I 
DSIM_14_I 
0.38  I20 
0.53  I17 
DSIM_15_I 
DSIM_16_I 
0.50  I20 
0.53  I15 
DSIM_17_I 
DSIM_18_I 
0.41  I15 
0.49  I17 
DSIM_19_I 
DSIM_20_I 
0.50  I16 
Note: Items retained are in bold. DSIM = Daily Short Interest Measure. BPDRMS = Brief Public Domain RIASEC Maker Scales (Armstrong et al., 2008). For DSIM item 
labels, letters represent the first letter of the RIASEC dimension the item was developed to measure (e.g. DSIM 49 E = Enterprising item).For Cor w/ BPDRMS columns, the 
letter indicates the correlation of the item with the BPDRMS interests starting with that letter (e.g. Cor w/ BPDRMS I = correlation with trait Investigative interests measures 
using BPDRMS. Mean, Median, and Max Cor/ others columns represent the mean, median, or max correlation the item had with other items in the DSIM development item 
pool. Which max refers to which other item the item in question had the strongest relationship with. 

SD  Median 
6 
1.44 
2.04 
5 
4 
1.73 
4 
1.73 
1.72 
4 
5 
1.78 
4 
1.53 
1.74 
4 
5 
1.38 
5 
1.4 
4 
1.84 
1.86 
3 
5 
2.01 
6 
1.36 
1.63 
5 
5 
1.66 
5 
1.53 
5 
1.41 
1.62 
5 
6 
1.28 

N  Mean 
5.33 
341 
341 
4.48 
3.81 
341 
4.01 
341 
341 
3.72 
4.81 
341 
3.74 
341 
341 
3.92 
5.21 
341 
5.00 
341 
4.06 
341 
341 
3.14 
4.14 
341 
5.41 
341 
341 
4.54 
4.71 
341 
4.97 
341 
5.1 
340 
341 
4.46 
5.37 
341 

Mean Cor 
w/ Others 
0.25 
0.16 
0.32 
0.32 
0.34 
0.20 
0.31 
0.28 
0.13 
0.21 
0.35 
0.21 
0.28 
0.19 
0.37 
0.25 
0.36 
0.26 
0.31 
0.30 

0.25 
0.19 
0.25 
0.31 
0.29 
0.24 
0.35 
0.20 
0.13 
0.21 
0.30 
0.23 
0.31 
0.17 
0.37 
0.24 
0.36 
0.21 
0.35 
0.28 

107 

 

 

Median 
Cor w/ 
Others 

Max Cor 

w/ 

Others 

Which 
Max 

DSIM_21_A  Creativity 
DSIM_22_A  Artistic expression 
DSIM_23_A 
DSIM_24_A 
DSIM_25_A 
DSIM_26_A  Art 
DSIM_27_A  Creating 
DSIM_28_A  Creative writing 
DSIM_29_A  Music 
DSIM_30_A  Design 
DSIM_31_S 
DSIM_32_S  Helping others 
DSIM_33_S 
DSIM_34_S  Working with others 
DSIM_35_S  Guiding others 
DSIM_36_S 
DSIM_37_S  Volunteering 
DSIM_38_S  Communicating with others 
DSIM_39_S 
Providing services to others 
DSIM_40_S 
Close relationships 
Note: Items retained are in bold. DSIM = Daily Short Interest Measure. BPDRMS = Brief Public Domain RIASEC Maker Scales (Armstrong et al., 2008). 
For DSIM item labels, letters represent the first letter of the RIASEC dimension the item was developed to measure (e.g. DSIM 49 E = Enterprising item).For 
Cor w/ BPDRMS columns, the letter indicates the correlation of the item with the BPDRMS interests starting with that letter (e.g. Cor w/ BPDRMS I = 
correlation with trait Investigative interests measures using BPDRMS. Mean, Median, and Max Cor/ others columns represent the mean, median, or max 
correlation the item had with other items in the DSIM development item pool. Which max refers to which other item the item in question had the strongest 
relationship with. 

0.62  A27 
0.76  A26 
0.52  A25 
0.38  A22 
0.59  A27 
0.76  A22 
0.63  A22 
0.49  A26 
0.46  A22 
0.53  A26 
0.41  S35 
0.67  S33 
0.67  S32 
0.51  S38 
0.58  S32 
0.64  S38 
0.60  S39 
0.64  S36 
0.60  S37 
0.40  S36 

SD  Median 
1.47 
6 
5 
1.8 
5 
1.45 
4 
1.69 
1.42 
6 
5 
1.86 
6 
1.47 
1.91 
4 
6 
1.67 
5 
1.61 
1.68 
5 
6 
1.16 
6 
1.27 
6 
1.45 
1.42 
5 
6 
1.38 
5 
1.55 
1.21 
6 
6 
1.4 
1.17 
6 

N  Mean 
341 
5.33 
4.57 
341 
4.81 
341 
3.8 
340 
341 
5.36 
4.6 
341 
5.25 
341 
341 
3.92 
5.38 
341 
4.84 
341 
341 
4.39 
5.85 
340 
5.74 
341 
5.36 
341 
341 
5.11 
5.65 
341 
5.16 
341 
341 
5.61 
5.33 
341 
341 
5.87 

Mean Cor 
w/ Others 
0.47 
0.52 
0.35 
0.27 
0.48 
0.51 
0.49 
0.37 
0.31 
0.41 
0.29 
0.48 
0.42 
0.41 
0.42 
0.39 
0.36 
0.40 
0.44 
0.30 

0.51 
0.49 
0.35 
0.27 
0.52 
0.52 
0.48 
0.41 
0.30 
0.47 
0.32 
0.47 
0.41 
0.42 
0.41 
0.40 
0.37 
0.43 
0.45 
0.31 

 

Table 1 (cont’d) 

Item 

Item Text 

Innovation 
Little structure 
Imagination 

Teaching 

Caring for others 

Socializing 

 

108 

 

 

Median 
Cor w/ 
Others 

Max Cor 

w/ 

Others 

Which 
Max 

DSIM_41_E 
DSIM_42_E  Persuading others 
DSIM_43_E 
DSIM_44_E 
DSIM_45_E  Negotiation 
DSIM_46_E  Achievement 
DSIM_47_E 
DSIM_48_E  Managing others 
DSIM_49_E  Making decisions 
DSIM_50_E  Managing projects 
DSIM_51_C  Routine 
DSIM_52_C  Organization 
DSIM_53_C 
DSIM_54_C 
DSIM_55_C  Detail focused tasks 
DSIM_56_C  Clear rules 
DSIM_57_C  Business 
DSIM_58_C  Analyzing data 
DSIM_59_C  Organizing information 
DSIM_60_C  Repeated tasks 
Note: Items retained are in bold. DSIM = Daily Short Interest Measure. BPDRMS = Brief Public Domain RIASEC Maker Scales (Armstrong et al., 2008). 
For DSIM item labels, letters represent the first letter of the RIASEC dimension the item was developed to measure (e.g. DSIM 49 E = Enterprising item).For 
Cor w/ BPDRMS columns, the letter indicates the correlation of the item with the BPDRMS interests starting with that letter (e.g. Cor w/ BPDRMS I = 
correlation with trait Investigative interests measures using BPDRMS. Mean, Median, and Max Cor/ others columns represent the mean, median, or max 
correlation the item had with other items in the DSIM development item pool. Which max refers to which other item the item in question had the strongest 
relationship with. 

0.58  E48 
0.59  E45 
0.41  E42 
0.40  E50 
0.59  E42 
0.36  E41 
0.32  E45 
0.58  E50 
0.42  E41 
0.58  E48 
0.46  C56 
0.51  C59 
0.50  C56 
0.47  C58 
0.38  C58 
0.50  C53 
0.33  C54 
0.47  C54 
0.51  C52 
0.43  C51 

SD  Median 
1.52 
5 
5 
1.71 
3 
1.79 
5 
1.56 
1.68 
4 
6 
1.14 
4 
1.74 
1.64 
5 
5 
1.53 
5 
1.66 
1.47 
5 
6 
1.42 
5 
1.49 
4 
1.53 
1.46 
5 
5 
1.45 
4 
1.8 
1.79 
4 
5 
1.52 
1.62 
4 

N  Mean 
341 
5.03 
4.32 
341 
3.52 
341 
4.82 
341 
341 
4.06 
5.78 
341 
4.01 
341 
340 
4.39 
4.9 
341 
4.41 
341 
341 
5.15 
5.43 
341 
4.94 
341 
4.18 
341 
341 
4.83 
5.14 
340 
3.67 
341 
341 
3.78 
5 
341 
341 
3.91 

Mean Cor 
w/ Others 
0.38 
0.33 
0.24 
0.25 
0.37 
0.22 
0.24 
0.38 
0.32 
0.38 
0.30 
0.30 
0.36 
0.32 
0.29 
0.33 
0.19 
0.27 
0.34 
0.30 

0.40 
0.30 
0.26 
0.24 
0.39 
0.29 
0.24 
0.39 
0.31 
0.36 
0.40 
0.29 
0.38 
0.31 
0.29 
0.32 
0.20 
0.26 
0.32 
0.28 

 

Table 1 (cont’d) 

Item 

Item Text 

Leadership 

Selling 
Strategic thinking 

Risk 

Structure 
Systematic work 

 

109 

 

 

Table 1 (cont’d) 

Item 

Item Text 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

R 

I 

A 

S 

E 

C 

 

DSIM_1_R 
DSIM_2_R 
DSIM_3_R 
DSIM_4_R 
DSIM_5_R 
DSIM_6_R 
DSIM_7_R 
DSIM_8_R 
DSIM_9_R 
DSIM_10_R 
DSIM_11_I 
DSIM_12_I 
DSIM_13_I 
DSIM_14_I 
DSIM_15_I 
DSIM_16_I 
DSIM_17_I 
DSIM_18_I 
DSIM_19_I 
DSIM_20_I 

“Hands-on” tasks 
Being outdoors 
Using tools 
Fixing 
Building 
Physical activity 
Technical activities 
Working with gadgets 
Straight forward problems 
Practical tasks 
Research 
Mathematics 
Science 
Exploration 
Analytic thinking 
Abstract ideas 
Intellectual tasks 
Individually focused tasks 
Academics 
New ideas 

0.04 
0.04 
0.30 
0.34 
0.37 
0.11 
0.39 
0.34 
0.13 
0.19 
0.16 
0.37 
0.11 
-0.02 
0.22 
0.07 
0.14 
0.12 
0.21 
0.09 
Note: Items retained are in bold. DSIM = Daily Short Interest Measure. BPDRMS = Brief Public Domain RIASEC Maker Scales (Armstrong et al., 2008). 
For DSIM item labels, letters represent the first letter of the RIASEC dimension the item was developed to measure (e.g. DSIM 49 E = Enterprising 
item).For Cor w/ BPDRMS columns, the letter indicates the correlation of the item with the BPDRMS interests starting with that letter (e.g. Cor w/ 
BPDRMS I = correlation with trait Investigative interests measures using BPDRMS. Mean, Median, and Max Cor/ others columns represent the mean, 
median, or max correlation the item had with other items in the DSIM development item pool. Which max refers to which other item the item in question 
had the strongest relationship with. 

0.15 
0.17 
0.46 
0.41 
0.50 
0.17 
0.31 
0.34 
-0.03 
0.03 
0.11 
0.20 
0.13 
0.06 
0.08 
0.11 
-0.03 
-0.04 
-0.05 
0.08 

0.23 
0.27 
0.20 
0.23 
0.20 
0.14 
0.20 
0.16 
0.07 
0.13 
0.43 
0.26 
0.60 
0.21 
0.20 
0.04 
0.15 
0.09 
0.20 
0.15 

0.14 
0.14 
0.23 
0.20 
0.19 
-0.07 
0.19 
0.19 
-0.04 
0.02 
0.11 
0.00 
0.07 
0.23 
0.14 
0.36 
0.11 
0.04 
-0.07 
0.26 

0.22 
0.18 
-0.03 
0.16 
0.04 
0.15 
-0.01 
-0.04 
0.23 
0.18 
0.05 
-0.01 
0.00 
0.21 
-0.04 
0.16 
0.19 
0.05 
0.07 
0.21 

0.06 
0.11 
0.27 
0.28 
0.34 
0.11 
0.19 
0.21 
0.13 
0.12 
-0.06 
0.13 
-0.08 
0.10 
0.04 
0.17 
-0.01 
0.02 
0.01 
0.13 

 

110 

 

 

 

Table 1 (cont’d) 

Item 

Item Text 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

R 

I 

A 

S 

E 

C 

Creativity 
DSIM_21_A 
Artistic expression 
DSIM_22_A 
Innovation 
DSIM_23_A 
Little structure 
DSIM_24_A 
Imagination 
DSIM_25_A 
Art 
DSIM_26_A 
Creating 
DSIM_27_A 
Creative writing 
DSIM_28_A 
Music 
DSIM_29_A 
Design 
DSIM_30_A 
Teaching 
DSIM_31_S 
Helping others 
DSIM_32_S 
Caring for others 
DSIM_33_S 
Working with others 
DSIM_34_S 
Guiding others 
DSIM_35_S 
Socializing 
DSIM_36_S 
DSIM_37_S 
Volunteering 
DSIM_38_S  Communicating with others 
Providing services to others 
DSIM_39_S 
DSIM_40_S 
Close relationships 

0.09 
0.04 
0.20 
0.15 
0.13 
0.11 
0.12 
0.06 
-0.05 
0.13 
0.07 
-0.14 
-0.20 
-0.07 
-0.03 
-0.11 
-0.05 
-0.15 
-0.10 
-0.12 

0.07 
0.15 
0.19 
0.09 
0.02 
0.08 
0.12 
0.05 
0.03 
0.03 
0.14 
0.06 
0.03 
-0.03 
0.08 
0.00 
0.18 
-0.04 
0.09 
-0.01 

0.44 
0.57 
0.34 
0.28 
0.49 
0.58 
0.47 
0.50 
0.49 
0.38 
0.14 
0.05 
0.06 
0.03 
0.09 
0.12 
0.11 
0.05 
0.00 
0.12 

0.16 
0.15 
0.14 
0.01 
0.20 
0.16 
0.17 
0.15 
0.18 
0.09 
0.40 
0.47 
0.45 
0.35 
0.39 
0.23 
0.47 
0.28 
0.43 
0.28 

0.13 
0.19 
0.26 
0.16 
0.27 
0.23 
0.21 
0.13 
0.16 
0.26 
0.12 
0.06 
-0.02 
0.12 
0.18 
0.11 
0.08 
0.09 
0.08 
0.03 

-0.03 
0.02 
0.20 
0.12 
0.08 
0.01 
0.03 
0.03 
0.02 
0.09 
0.08 
-0.13 
-0.15 
-0.04 
0.01 
-0.08 
-0.02 
-0.09 
-0.07 
-0.12 

Note: Items retained are in bold. DSIM = Daily Short Interest Measure. BPDRMS = Brief Public Domain RIASEC Maker Scales (Armstrong et al., 2008). 
For DSIM item labels, letters represent the first letter of the RIASEC dimension the item was developed to measure (e.g. DSIM 49 E = Enterprising 
item).For Cor w/ BPDRMS columns, the letter indicates the correlation of the item with the BPDRMS interests starting with that letter (e.g. Cor w/ 
BPDRMS I = correlation with trait Investigative interests measures using BPDRMS. Mean, Median, and Max Cor/ others columns represent the mean, 
median, or max correlation the item had with other items in the DSIM development item pool. Which max refers to which other item the item in question 
had the strongest relationship with. 

 

111 

 

 

Table 1 (cont’d) 

Item 

Item Text 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

Cor w/ 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

BPDRMS 

R 

I 

A 

S 

E 

C 

 

DSIM_41_E 
DSIM_42_E 
DSIM_43_E 
DSIM_44_E 
DSIM_45_E 
DSIM_46_E 
DSIM_47_E 
DSIM_48_E 
DSIM_49_E 
DSIM_50_E 
DSIM_51_C 
DSIM_52_C 
DSIM_53_C 
DSIM_54_C 
DSIM_55_C 
DSIM_56_C 
DSIM_57_C 
DSIM_58_C 
DSIM_59_C 
DSIM_60_C 

Leadership 
Persuading others 
Selling 
Strategic thinking 
Negotiation 
Achievement 
Risk 
Managing others 
Making decisions 
Managing projects 
Routine 
Organization 
Structure 
Systematic work 
Detail focused tasks 
Clear rules 
Business 
Analyzing data 
Organizing information 
Repeated tasks 

0.04 
0.11 
0.35 
0.24 
0.21 
0.10 
0.14 
0.15 
0.02 
0.24 
0.14 
0.01 
0.21 
0.36 
0.18 
0.13 
0.45 
0.38 
0.17 
0.26 
Note: Items retained are in bold. DSIM = Daily Short Interest Measure. BPDRMS = Brief Public Domain RIASEC Maker Scales (Armstrong et al., 2008). 
For DSIM item labels, letters represent the first letter of the RIASEC dimension the item was developed to measure (e.g. DSIM 49 E = Enterprising 
item).For Cor w/ BPDRMS columns, the letter indicates the correlation of the item with the BPDRMS interests starting with that letter (e.g. Cor w/ 
BPDRMS I = correlation with trait Investigative interests measures using BPDRMS. Mean, Median, and Max Cor/ others columns represent the mean, 
median, or max correlation the item had with other items in the DSIM development item pool. Which max refers to which other item the item in question 
had the strongest relationship with. 

0.04 
-0.04 
-0.05 
0.15 
-0.02 
0.15 
0.13 
0.02 
0.00 
0.10 
0.02 
0.02 
0.10 
0.15 
0.09 
0.10 
-0.05 
0.22 
0.09 
-0.01 

0.00 
0.07 
0.27 
0.13 
0.16 
-0.10 
0.21 
0.05 
0.01 
0.17 
-0.07 
-0.13 
0.00 
0.24 
0.04 
-0.07 
0.30 
0.19 
-0.07 
0.08 

0.05 
0.10 
0.13 
0.18 
0.09 
-0.01 
0.10 
0.06 
-0.01 
0.11 
-0.19 
-0.11 
0.00 
0.04 
-0.02 
-0.06 
0.07 
0.06 
-0.04 
-0.08 

0.29 
0.10 
0.02 
0.05 
0.06 
0.30 
0.03 
0.22 
0.13 
0.14 
0.29 
0.32 
0.22 
0.09 
0.08 
0.27 
-0.01 
-0.05 
0.25 
0.18 

0.22 
0.22 
0.50 
0.07 
0.23 
0.06 
0.18 
0.25 
0.06 
0.30 
0.04 
0.01 
0.10 
0.16 
0.05 
0.11 
0.50 
0.10 
0.10 
0.14 

 

112 

 

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1 
0.33 
0.26 
-0.21 
-0.01 
-0.14 
0.25 

2 
0.04 
0.02 
0.11 
0.04 
0.08 
-0.24 

3 
-0.02 
0.03 
0 
0.04 
-0.09 
-0.17 

4 
-0.07 
-0.11 
0.09 
0.1 
0.1 
0.06 

5 

0.09 
-0.2 
0.07 
0.1 
0.12 
0.02 

6 
0.35 
0.39 
0.66 
0.57 
0.71 
0.46 

-0.19 

0.08 

0.21 

0.13 

0.23 

0.38 

-0.17 

0.1 

0.12 

0.17 

0.04 

0.43 

0.1 
0.02 
0.02 
-0.25 
0.11 
0.27 
-0.09 
0.05 
0.17 

0.02 
0.1 
-0.06 
-0.13 
-0.2 
0.27 
0.09 
0.54 
0.05 

-0.07 
0.16 
0.81 
0.42 
0.68 
0.09 
0.8 
0.26 
0.71 

-0.05 
-0.01 
-0.11 
0.01 
-0.26 
-0.1 
0.11 
0.12 
0.07 

0.5 
0.4 
-0.16 
0.08 
-0.23 
-0.18 
-0.06 
-0.19 
-0.01 

DSIM_9_R 

DSIM_8_R 

DSIM_1_R 
DSIM_2_R 
DSIM_3_R 
DSIM_4_R 
DSIM_5_R 
DSIM_6_R 
DSIM_7_R 

“Hands-on” tasks 
Being outdoors 
Using tools 
Fixing 
Building 
Physical activity 
Technical 
activities 
Working with 
gadgets 
Straight forward 
problems 
Practical tasks 
DSIM_10_R 
Research 
DSIM_11_I 
Mathematics 
DSIM_12_I 
Science 
DSIM_13_I 
DSIM_14_I 
Exploration 
DSIM_15_I  Analytic thinking 
Abstract ideas 
DSIM_16_I 
Intellectual tasks 
DSIM_17_I 
Individually 
DSIM_18_I 
focused tasks 
Academics 
New ideas 

 

Alpha if 
Dropped 

0.75 
0.78 
0.73 
0.73 
0.73 
0.76 

0.74 

0.74 

0.78 
0.76 
0.76 
0.8 
0.78 
0.8 
0.76 
0.79 
0.76 

0.79 
0.77 
0.78 

Ten 
Item 
Alpha 
0.77 

 
 
 
 
 

 

 

 
 
0.8 

 
 
 
 
 
 

 
 
 

0.1 
0.08 
0 
0.23 
0.22 
0.21 
-0.16 
-0.01 
-0.22 

-0.01 
-0.18 
0.04 

 

Table 1 (cont’d) 

Item 

Item Text 

EFA 

EFA 

EFA 

EFA 

EFA 

EFA 

0.18 
0.19 
-0.07 
Note: Items retained are in bold. EFA factor loadings in bold were the highest for each item on the six factors.   

DSIM_19_I 
DSIM_20_I 

0.06 
-0.06 
0.36 

-0.06 
0.03 
-0.01 

0.03 
0 
0.19 

0.39 
0.63 
0.31 

 

113 

 

 

Table 1 (cont’d) 

Item 

Item Text 

EFA 

EFA 

EFA 

EFA 

EFA 

EFA 

Loading 

Loading 

Loading 

Loading 

Loading 

Loading 

Creativity 
DSIM_21_A 
DSIM_22_A  Artistic expression 
DSIM_23_A 
Innovation 
Little structure 
DSIM_24_A 
DSIM_25_A 
Imagination 
Art 
DSIM_26_A 
DSIM_27_A 
Creating 
DSIM_28_A  Creative writing 
Music 
DSIM_29_A 
Design 
DSIM_30_A 
DSIM_31_S 
Teaching 
Helping others 
DSIM_32_S 
Caring for others 
DSIM_33_S 
Working with 
DSIM_34_S 
others 
Guiding others 
DSIM_35_S 
Socializing 
DSIM_36_S 
Volunteering 
DSIM_37_S 
DSIM_38_S  Communicating 
with others 
Providing 
services to others 
Close 
relationships 

DSIM_39_S 

DSIM_40_S 

1 
0.07 
-0.05 
0.08 
-0.12 
0.08 
-0.07 
0.09 
0 
0.04 
-0.01 
0.34 
0.81 
0.71 

0.61 
0.6 
0.59 
0.54 

2 
0.72 
0.9 
0.36 
0.41 
0.67 
0.91 
0.74 
0.58 
0.55 
0.61 
-0.01 
-0.04 
-0.01 

-0.01 
-0.04 
0.1 
0 

3 
0.03 
0.01 
0.12 
-0.08 
-0.07 
-0.04 
-0.03 
0.18 
-0.21 
-0.09 
0.13 
-0.03 
0.01 

-0.06 
-0.07 
-0.13 
0.08 

4 
-0.04 
-0.24 
0.11 
0.07 
0.07 
-0.19 
0.02 
0.07 
-0.12 
0.13 
0.05 
-0.05 
-0.16 

0.18 
0.17 
0.29 
-0.2 

5 
-0.01 
0.07 
-0.07 
-0.19 
-0.07 
0.11 
-0.01 
-0.03 
0.11 
0.1 
0.16 
0 
0.11 

0.08 
0.12 
-0.05 
0.1 

6 
-0.01 
0 
0.29 
0.16 
0.1 
-0.01 
0.04 
-0.26 
-0.01 
0.08 
0.28 
-0.03 
-0.13 

-0.04 
0.09 
-0.1 
0.18 

0.63 

0.07 

-0.06 

0.22 

0.01 

-0.24 

0.62 

0.04 

0.05 

-0.06 

0.05 

0.09 

0.43 

0.22 

0.08 
Note: Items retained are in bold. EFA factor loadings in bold were the highest for each item on the six factors.   

-0.14 

0.05 

-0.06 

 

114 

 

 

Alpha if 
Dropped 

0.86 
0.85 
0.87 
0.88 
0.86 
0.85 
0.86 
0.87 
0.88 
0.86 
0.86 
0.84 
0.84 

0.84 
0.84 
0.85 
0.85 

0.85 

0.84 

0.86 

Ten 
Item 
Alpha 
0.88 

 
 
 
 
 
 
 
 
 
0.86 

 
 

 
 
 
 

 

 

 

 

Table 1 (cont’d) 

Item 

Item Text 

EFA 

EFA 

EFA 

EFA 

EFA 

EFA 

Loading 

Loading 

Loading 

Loading 

Loading 

Loading 

 

Alpha if 
Dropped 

0.79 

0.8 
0.82 
0.81 
0.79 
0.82 
0.82 
0.79 
0.8 

0.79 
0.79 
0.79 
0.78 
0.78 

0.79 
0.78 
0.81 
0.79 

0.78 
0.79 

Ten 
Item 
Alpha 
0.82 

 
 
 
 
 
 
 
 

 
0.8 

 
 
 

 
 
 
 

 
 

6 
0.01 

-0.1 
0.26 
0 
0.03 
0.08 
0.3 
0.08 
-0.06 

0.16 
0.01 
-0.1 
0.1 
0.18 

0 
0 
0.22 
0.08 

-0.16 
0.02 

DSIM_41_E 
DSIM_42_E 

Leadership 
Persuading 
others 
DSIM_43_E 
Selling 
DSIM_44_E  Strategic thinking 
Negotiation 
DSIM_45_E 
Achievement 
DSIM_46_E 
DSIM_47_E 
Risk 
DSIM_48_E  Managing others 
DSIM_49_E  Making decisions 
DSIM_50_E 
Managing 
projects 
Routine 
Organization 
Structure 
Systematic work 
Detail focused 
tasks 
Clear rules 
Business 
Analyzing data 
Organizing 
information 
Repeated tasks 

DSIM_51_C 
DSIM_52_C 
DSIM_53_C 
DSIM_54_C 
DSIM_55_C 

DSIM_56_C 
DSIM_57_C 
DSIM_58_C 
DSIM_59_C 

1 
0.48 

0.19 
-0.09 
-0.02 
0.1 
0.43 
0.06 
0.36 
0.35 

0.16 
0.12 
0.34 
0.09 
-0.21 

0.05 
0.18 
-0.13 
-0.18 

2 
-0.07 

-0.03 
0.04 
0.15 
-0.1 
-0.02 
-0.02 
-0.12 
-0.06 

-0.05 
-0.03 
-0.01 
0 
0.03 

-0.02 
-0.06 
-0.02 
-0.06 

3 
-0.01 

-0.01 
-0.21 
0.67 
0.17 
0.09 
-0.02 
-0.03 
0.23 

0.17 
-0.15 
0.06 
0.12 
0.29 

0.51 
0.02 
0 
0.63 

4 
0.41 

0.72 
0.55 
0.03 
0.71 
-0.09 
0.31 
0.51 
0.26 

0.38 
-0.17 
-0.04 
-0.03 
0.12 

0 
-0.1 
0.62 
0.12 

5 

0.01 

-0.15 
0.1 
0 
-0.19 
0.25 
-0.13 
0.04 
-0.02 

0.1 
0.79 
0.43 
0.54 
0.36 

0.16 
0.59 
0.05 
0.05 

0.44 
DSIM_60_C 
0.56 
Note: Items retained are in bold. EFA factor loadings in bold were the highest for each item on the six factors.   

0.17 
-0.03 

-0.01 
-0.01 

0.29 
-0.04 

0.02 
0.12 

 

115 

 

 

 

Table 2: 

Mean, Standard Deviation, Intercorrelations, and Alpha for Variables in Study 1. 

 
M 
1.  State Realistic 
4.05 
2.  State Investigative 
4.43 
3.  State Artistic 
4.89 
4.  State Social 
5.45 
5.  State Enterprising 
4.14 
6.  State Conventional  4.64 
7.  Trait Realistic 
1.86 
8.  Trait Investigative 
3.08 
9.  Trait Artistic 
2.72 
10. Trait Social 
3.55 
11. Trait Enterprising 
2.76 
12. Trait Conventional  2.25 
13. Trait Extraversion 
3.37 
14. Trait 
3.79 

SD 
1.23 
1.27 
1.28 
1.01 
1.23 
1.06 
.76 
.94 
.95 
.71 
.84 
.83 
.72 
.55 

1 
(.75) 
.30*** 
.22*** 
.21*** 
.46*** 
.33*** 
.53*** 
.26*** 
.21*** 
.08 
.34*** 
.41*** 
.07 
.00 

2 
 
(.78) 
.22*** 
.28*** 
.32*** 
.43*** 
.07 
.45*** 
.10 
.07 
-.03 
.22*** 
.09 
-.02 

3 
 
 
(.84) 
.27*** 
.25*** 
.04 
.13* 
.08 
.63*** 
.21*** 
.25*** 
.03 
.12* 
.05 

4 
 
 
 
(.81) 
.45*** 
.35*** 
-.13* 
.04 
.06 
.51*** 
.14*** 
-.08 
.44*** 
.35*** 

5 
 
 
 
 
(.77) 
.35*** 
.20*** 
.00 
.14* 
.15*** 
.42*** 
.29*** 
.37*** 
-.09 

6 
 
 
 
 
 
(.73) 
.06 
.10 
-.08 
.30*** 
.16*** 
.33*** 
.08 
.05 

7 
 
 
 
 
 
 
(.90) 
.24*** 
.31*** 
-.04 
.45*** 
.61*** 
-.12* 
-.14*** 

8 
 
 
 
 
 
 
 
(.87) 
.19*** 
.19*** 
.07 
.16*** 
-.10 
.07 

9 
 
 
 
 
 
 
 
 
(.86) 
.18*** 
.40*** 
.17*** 
.01 
-.04 

Agreeableness 

15. Trait 

3.60 

.65 

-.02 

.16*** 

-.11* 

.29*** 

.05 

.32*** 

-.19*** 

.00 

-.22*** 

Conscientiousness 
16. Trait Neuroticism 
3.02 
17. Trait Openness 
3.69 
18. Interest Importance  4.66 
19. Self-Assessed 
4.48 

Interest Variance 

.81 
.68 
1.37 
1.11 

-.21*** 
.06 
-.02 
.11* 

-.06 
.20*** 
-.02 
.09 

-.01 
.67*** 
-.08 
.26*** 

-.14* 
.17*** 
.01 
.05 

-.19*** 
.07 
-.02 
.09 

-.06 
-.06 
.05 
-.15*** 

-.13* 
-.03 
-.05 
-.01 

-.02 
.10 
-.08 
.06 

.08 
.55 
-.04 
.25*** 

Note: State vocational interests represent final DSIM scales. Interest importance represents revised two item scale. Numbers on 
diagonals represent alphas. N = 341. *p < .05 *** p < .01 

 

 

116 

 

 

 

Table 2 (cont’d) 

 
   10. Trait Social 

11. Trait Enterprising 
12. Trait Conventional 
13. Trait Extraversion 
14. Trait Agreeableness 
15. Trait Conscientiousness 
16. Trait Neuroticism 
17. Trait Openness 
18. Interest Importance 
19. Self-Assessed Interest 

Variance 

10 
(.76) 
.21*** 
.02 
.22*** 
.34*** 
.15*** 
.00 
.14* 
.04 
.03 

11 
 
(.84) 
.58*** 
.13* 
.01 
-.08 
-.17*** 
.05 
.00 
.05 

12 
 
 
(.89) 
-.12* 
-.09 
.01 
-.10 
-.05 
-.03 
-.13* 

13 
 
 
 
(.86) 
17*** 
.20*** 
-.35*** 
.13* 
-.11* 
.21*** 

14 
 
 
 
 
(.74) 
.40*** 
-.23*** 
.12* 
-.10 
-.02 

15 
 
 
 
 
 
(.84) 
-.21*** 
-.01 
-.03 
-.17*** 

16 
 
 
 
 
 
 
(.89) 
-.02 
.16*** 
-.11* 

17 
 
 
 
 
 
 
 
(.85) 
-.15*** 
.31*** 

18 
 
 
 
 
 
 
 
 
(.66) 
-.01 

19 
 
 
 
 
 
 
 
 
 
(.70) 

Note: State vocational interests represent final DSIM scales. Interest importance represents revised two item scale. Numbers on diagonals represent alphas. N = 
341. *p < .05 *** p < .01 

 

 

 

 

 

 

 

 

 

 

117 

 

 

Table 3: 

Relationships Between Vocational Interests and Personality. 

 

Realistic 

Neuroticism 
Extraversion 

Openness 

Agreeableness 

Conscientiousness 

 
Investigative 

Neuroticism 
Extraversion 

Openness 

Agreeableness 

Conscientiousness 

 
Artistic 

Neuroticism 
Extraversion 

Openness 

Agreeableness 

Conscientiousness 

 
 

Trait Vocational Interest – 
Trait Personality 
Relationship  
(Meta-Analytic Estimate) 
 
-.09 
.04 
.08 
.01 
.07 
 
 
-.10 
.03 
.28 
.03 
.09 
 
 
.02 
.11 
.48 
.01 
-.05 
 
 

 

State Vocational Interest – 
Trait Personality 
Relationship  
(Current Study) 
 
-.21 
.07 
.06 
.00 
-.02 
 
 
-.06 
.09 
.20 
-.02 
.16 
 
 
-.01 
.12 
.67 
.05 
-.11 
 
 

Trait Vocational Interest – 
Trait Personality Relationship  
(Current Study) 

 
-.13 
-.12 
-.03 
-.14 
-.19 
 
 
-.02 
-.10 
.10 
.07 
.00 
 
 
.08 
.01 
.55 
-.04 
-.22 
 
 

Note: Meta-analytic estimates of relationships represent mean effect size estimates from Larson, Rottinghaus, & Borgen, 2002. 
Relationships in bold are statistically significant (p < .05). 

 

118 

 

 

 

Table 3 (cont’d) 

 

Social 

Neuroticism 
Extraversion 

Openness 

Agreeableness 

Conscientiousness 

 
Enterprising 

Neuroticism 
Extraversion 

Openness 

Agreeableness 

Conscientiousness 

 
Conventional 

Neuroticism 
Extraversion 

Openness 

Agreeableness 

Conscientiousness 

 

Trait Vocational Interest – 
Trait Personality 
Relationship  
(Meta-Analytic Estimate) 
 
-.12 
.31 
.19 
.19 
.13 
 
 
-.19 
.41 
.09 
-.07 
.22 
 
 
-.10 
.08 
-.11 
.03 
.25 
 

 

State Vocational Interest – 
Trait Personality 
Relationship  
(Current Study) 
 
-.14 
.44 
.17 
.35 
.29 
 
 
-.19 
.37 
.07 
-.09 
.05 
 
 
-.06 
.08 
-.06 
.05 
.32 
 

Trait Vocational Interest – 
Trait Personality Relationship  
(Current Study) 

 
.00 
.22 
.14 
.34 
.15 
 
 
-.17 
.13 
.05 
.01 
-.08 
 
 
-.10 
-.12 
-.05 
-.09 
.01 
 

Note: Meta-analytic estimates of relationships represent mean effect size estimates from Larson, Rottinghaus, & Borgen, 2002. 
Relationships in bold are statistically significant (p < .05).

119 

 

 

Table 4: 

Descriptives and Intercorrelations for Between-Person Variables in Study 2. 

 

1.  Trait Realistic 
(DSIM) (Pre)  

N 
176 

M 
4.36 

SD 
1.04 

1. 
(.75) 

2. 
 

3. 
 

2.  Trait Investigative 

176 

4.91 

.92 

.21*** 

(.70) 

 

(DSIM) (Pre) 
3.  Trait Artistic 
(DSIM) (Pre) 

4.  Trait Social 

(DSIM) (Pre) 

176 

5.00 

1.15 

.07 

.02 

(.85) 

176 

5.60 

.90 

.02 

.16* 

.19* 

(.83) 

4. 
 

 

 

5. 
 

 

 

 

 

6. 
 

7. 
 

8. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5.  Trait Enterprising 

176 

4.33 

1.08 

.32*** 

.10 

.13 

.37*** 

(.77) 

(DSIM) (Pre) 

6.  Trait Conventional 

176 

5.08 

.75 

.26*** 

.28*** 

-.01 

.15 

.23*** 

(.54) 

(DSIM) (Pre) 
7.  Trait Realistic 

(BPDRM) (Pre) 

176 

2.00 

.65 

.54*** 

.14 

-.07 

-.11 

.18* 

.07 

(.87) 

8.  Trait Investigative 

176 

3.31 

.91 

.16* 

.59*** 

.08 

.02 

-.14 

.02 

.18* 

(.89) 

(BPDRM) (Pre) 

9.  Trait Artistic 

176 

2.80 

.88 

.13 

.07 

.49*** 

.03 

.13 

-.13 

.27*** 

.16* 

(BPDRM) (Pre) 

10. Trait Social 

176 

3.75 

.64 

-.07 

.10 

.11 

.46*** 

.09 

.08 

-.04 

.20*** 

(BPDRM) (Pre) 

11. Trait Enterprising 

176 

2.95 

.71 

.22*** 

-.15* 

.09 

.12 

.49*** 

.05 

.32*** 

-.16* 

(BPDRM) (Pre) 

12. Trait Conventional 

176 

2.51 

.79 

.38*** 

.25*** 

-.18* 

-.18* 

.20*** 

.30*** 

.60*** 

.12 

(BPDRM) (Pre) 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 
 

 

120 

 

 

 

Table 4 (cont’d) 

 

13. Extraversion (Pre) 
14. Agreeableness 

(Pre) 

N 
176 
176 

M 
3.42 
3.81 

SD 
.74 
.55 

1. 
.09 
-.11 

2. 
-.01 
-.05 

3. 
.08 
.15* 

4. 
.49*** 
.36*** 

5. 
.40*** 
-.18* 

6. 
.00 
-.06 

7. 
.03 
-.08 

8. 
-.11 
-.01 

15. Conscientiousness 

176 

3.80 

.64 

-.01 

.06 

.02 

.21*** 

-.02 

.33*** 

-.07 

-.05 

(Pre) 

16. Negative 

Emotionality (Pre) 
17. Open-mindedness 

(Pre) 

18. Curiosity (Pre) 
19. Grit (Pre) 
20. Consistency of 
Interests (Pre) 

176 

2.96 

.82 

176 

3.72 

.64 

-
.21*** 
.03 

-.01 

.11 

-.11 

-.12 

.04 

-.14 

.12 

.20*** 

.68*** 

.07 

-.08 

.01 

-.04 

.16* 

176 
176 
176 

3.24 
3.45 
3.03 

.82 
.56 
.77 

.27*** 
.03 
-.01 

.31*** 
.01 
-.12 

.26* 
.01 
-.05 

.39*** 
.22*** 
.07 

.36*** 
.00 
-.06 

.09 
.20*** 
.11 

.07 
-.03 
.03 

.16* 
-.13 
-.19* 

21. Implicit Interests 

176 

3.49 

1.02 

-.08 

-.03 

-.07 

-.03 

-.01 

-.12 

-.09 

-.02 

Theory (Pre) 
22. Self-Assessed 

Interest Variance 
(Pre) 

23. Vocational 

Identity (Pre) 
24. Trait Positive 

Emotions (Pre) 

176 

4.02 

1.13 

.17* 

.13 

.02 

.17* 

.13 

-.15 

.03 

.11 

176 

3.62 

.88 

-.11 

.25*** 

-.13 

.25*** 

-.08 

.09 

-.17* 

.17* 

176 

3.57 

.72 

.05 

.06 

.13 

.43*** 

.23*** 

.12 

-.02 

.03 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 
 

 

 

 

 

121 

 

 

 

Table 4 (cont’d) 

 

25. Trait Negative 
Emotions (Pre) 

26. Interest 

Importance (Pre) 

27. Trait Realistic 
(DSIM) (Post) 

N 
176 

M 
2.15 

SD 
.69 

1. 
.01 

2. 
-.01 

3. 
-.06 

4. 
-.17* 

5. 
-.05 

6. 
.02 

176 

4.60 

1.24 

-.04 

.11 

-.02 

-.02 

.04 

.05 

7. 
.02 

.10 

8. 
.09 

-.05 

136 

3.33 

1.17 

.49*** 

.19* 

.01 

-.02 

.10 

.06 

.44*** 

.28*** 

28. Trait Investigative 

136 

4.56 

1.18 

.08 

.57*** 

.04 

.02 

-.11 

.08 

.03 

.54*** 

(DSIM) (Post) 

29. Trait Artistic 

(DSIM) (Post) 

30. Trait Social 

(DSIM) (Post) 

136 

4.00 

1.42 

.18 

.02 

.58*** 

.00 

-.04 

-.09 

.14 

.18* 

136 

5.07 

1.19 

-.07 

.10 

.22* 

.53*** 

.14 

.01 

-.09 

.09 

31. Trait Enterprising 

136 

3.22 

1.14 

.19* 

.04 

.14 

.16 

.46*** 

.00 

.17 

-.02 

(DSIM) (Post) 

32. Trait Conventional 

136 

4.51 

1.15 

.03 

.22*** 

-.04 

-.03 

-.11 

.34*** 

-.04 

.18* 

(DSIM) (Post) 
33. Trait Realistic 

(BPDRM) (Post) 

136 

1.86 

.66 

.42*** 

.04 

-.04 

-.12 

.12 

-.08 

.74*** 

.20* 

34. Trait Investigative 

136 

3.13 

.88 

.27*** 

.56*** 

.06 

-.02 

-.15 

-.03 

.20* 

.87*** 

(BPDRM) (Post) 

35. Trait Artistic 

136 

2.61 

.86 

.15 

.06 

.43*** 

.08 

.17 

-.09 

.29*** 

.16 

(BPDRM) (Post) 

36. Trait Social 

136 

3.55 

.72 

.01 

.08 

.30*** 

.47*** 

.05 

.09 

.02 

.10 

(BPDRM) (Post) 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 
 

 

 

122 

 

 

Table 4 (cont’d) 

 

37. Trait Enterprising 
(BPDRM) (Post) 

N 
136 

M 
2.59 

SD 
.77 

1. 
.08 

38. Trait Conventional 

136 

2.59 

.74 

.18* 

(BPDRM) (Post) 

2. 
-
.20*** 
.12 

39. Extraversion 

136 

3.27 

.75 

.09 

-.06 

(Post) 

3. 
.16 

4. 
.09 

5. 
.42*** 

6. 
-.10 

7. 
.28*** 

8. 
-.21* 

-
.26*** 
.12 

-.22*** 

.08 

.14 

.49*** 

.02 

.44*** 

.40*** 

.06 

.05 

-.12 

40. Agreeableness 

136 

3.73 

.59 

-.03 

-.03 

-.01 

.36*** 

-.14 

.03 

-.11 

-.12 

(Post) 

41. Conscientiousness 

136 

3.76 

.66 

-.07 

.06 

.04 

.18* 

-.10 

.33*** 

-.16 

-.13 

 

(Post) 

42. Negative 

Emotionality 
(Post) 

136 

3.01 

.75 

-.17 

-.07 

.06 

-.14 

-.13 

.00 

-.10 

.11 

43. Open-mindedness 

136 

3.58 

.64 

.13 

.13 

.62*** 

.06 

-.07 

.07 

-.02 

.19* 

136 

2.96 

.84 

.04 

.01 

.12 

.22* 

.21* 

.18* 

-.05 

-.08 

136 

1.83 

.69 

-
.23*** 
-.08 

-.10 

-.02 

-.07 

-.05 

.02 

-.11 

.07 

.07 

-.01 

.21* 

.00 

-.11 

-.18* 

.04 

(Post) 

44. Positive Emotions 
(Past Week) (Post) 
45. Positive Emotions 
(Past Week) (Post) 

46. Academic 

136 

5.60 

.85 

Satisfaction (Post) 
47. Dropout Intentions 

(Post) 

136 

2.17 

.99 

-.07 

-.11 

-.06 

-.18 

.06 

.08 

.15 

-.10 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 
 

 

 

 

123 

 

 

Table 4 (cont’d) 

 
         9. Trait Artistic 

9. 
(.84) 

10. 
 

11. 
 

12. 
 

13. 
 

14. 
 

15. 
 

16. 
 

17. 
 

18. 
 

19. 
 

 

(BPDRM) (Pre) 

10. Trait Social 

.15 

(.78) 

 

(BPDRM) (Pre) 

11. Trait Enterprising 

.26*** 

.13 

(.79) 

 

 

(BPDRM) (Pre) 

12. Trait Conventional 

.11 

-.10 

.36*** 

(84) 

 

 

 

 

 

 

(BPDRM) (Pre) 

13. Extraversion (Pre) 
14. Agreeableness 

.02 
.03 

.19*** 
.17* 

.26*** 
.02 

-.12 
-.17* 

(.87) 
.24*** 

 
(.75) 

 

 

 

 
 

(Pre) 

15. Conscientiousness 

-.12 

.15* 

-.05 

.06 

.12 

.32*** 

(.85) 

 

 

 

 
 

 

 

 

 

 
 

 

 

 

 

 

 
 

 

 

 

 

 

 

 
 

 

 

 

(Pre) 

16. Negative 

Emotionality (Pre) 
17. Open-mindedness 

(Pre) 

18. Curiosity (Pre) 
19. Grit (Pre) 
20. Consistency of 
Interests (Pre) 

.05 

.10 

-.16* 

-.04 

-.44*** 

-.16* 

-.13 

(.90) 

.54*** 

.03 

-.06 

-.15 

.13 

.23*** 

.07 

.05 

(.82) 

.21*** 
-.08 
-.04 

.16* 
.11 
.08 

.16* 
-.07 
-.05 

-.01 
.00 
.02 

.45*** 
.33*** 
.20*** 

.13 
.26*** 
.17* 

.00 
.64*** 
.45*** 

-.27*** 
-.38*** 
-.29*** 

.35*** 
.04 
-.08 

(.88) 
.13 
-.06 

 
(.79) 
.85*** 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 
 

 

 

 

 

124 

 

 

9. 
.04 

10. 
-.06 

11. 
-.06 

12. 
-.08 

13. 
-.15 

14. 
.15 

15. 
-.07 

16. 
.16* 

17. 
.05 

18. 
-.04 

19. 
-.25*** 

.09 

.07 

.16* 

-.14 

.14 

.10 

-.14 

.02 

.11 

.24*** 

-.17* 

-.19* 

.14 

.03 

.20*** 

-
.23*** 
.16* 

-.13 

.16* 

.10 

.15* 

-.19* 

-.03 

.22*** 

.34*** 

-.06 

.61*** 

.32*** 

.30*** 

-.42*** 

.16* 

.55*** 

.40*** 

-.01 

.06 

-.03 

.01 

-.35** 

.02 

.00 

-.09 

.06 

.00 

-
.23*** 
-.01 

-
.22*** 
-.01 

.53*** 

-.06 

-.10 

-.44*** 

.04 

-.03 

-.06 

.00 

.14 

-.10 

.08 

.34*
** 
.10 

.11 

-.11 

-.09 

-.20* 

.03 

.19* 

-.01 

-.03 

-.02 

-.03 

.06 

..10 

.23*** 

-.11 

 

Table 4 (cont’d) 

 

21. Implicit Interests 

Theory (Pre) 
22. Self-Assessed 

Interest Variance 
(Pre) 

23. Vocational 

Identity (Pre) 
24. Trait Positive 

Emotions (Pre) 
25. Trait Negative 
Emotions (Pre) 

26. Interest 

Importance (Pre) 

27. Trait Realistic 
(DSIM) (Post) 

28. Trait Investigative 

.08 

.08 

-.12 

(DSIM) (Post) 

29. Trait Artistic 

(DSIM) (Post) 

30. Trait Social 

(DSIM) (Post) 

.51*** 

.01 

.09 

.02 

.05 

.06 

-.06 

.04 

.53*** 

.12 

-.09 

.09 

.39*** 

.12 

-.17* 

.30*** 

.38*** 

.15 

-.15 

.05 

.15 

.12 

31. Trait Enterprising 

.26*** 

.02 

.35*** 

.16 

.35*** 

.03 

-.09 

-.17 

-.02 

.22* 

.01 

(DSIM) (Post) 

32. Trait Conventional 

-.04 

.07 

-.15 

.16 

-.05 

.04 

.10 

.11 

-.02 

-.07 

-.04 

(DSIM) (Post) 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 
 

 

 

125 

 

 

Table 4 (cont’d) 

 

33. Trait Investigative 

(BPDRM) (Post) 

34. Trait Artistic 

(BPDRM) (Post) 

35. Trait Social 

(BPDRM) (Post) 
36. Trait Enterprising 
(BPDRM) (Post) 

 

9. 
.14 

10. 
.08 

11. 
-.20* 

12. 
.11 

13. 
-.14 

14. 
-.07 

15. 
-.12 

16. 
.14 

17. 
.15 

18. 
.23*** 

19. 
-.21* 

.80*** 

.13 

.35*** 

.14 

.13 

-.03 

-.19* 

-.05 

.45*** 

.26*** 

-.16 

.22*** 

.72*** 

.17* 

-.08 

.17* 

.29*** 

.21* 

.03 

.17* 

.19* 

.18* 

.34*** 

.00 

.74*** 

.17* 

.32*** 

.03 

-.14 

-.18* 

-.03 

.14 

-.01 

37. Trait Conventional 

-.01 

-.21* 

.19 

(BPDRM) (Post) 

38. Extraversion (Post) 
.07 
39. Agreeableness (Post)  -.06 
40. Conscientiousness 
-.15 

.19* 
.18* 
.15 

.26*** 
.03 
-.17 

.73*
** 
-.14 
-.10 
.00 

-.09 

-.16 

-.03 

-.01 

-.22* 

-.13 

-.06 

.91*** 
.25*** 
.10 

.24*** 
.76*** 
.36*** 

.15 
.38*** 
.85*** 

-.49*** 
-.22* 
-.10 

.11 
.02 
.04 

.34*** 
.08 
-.06 

.36*** 
.28*** 
.62*** 

(Post) 

41. Negative 

Emotionality (Post) 

.05 

.10 

-.07 

-.04 

-.41*** 

-.08 

-.12 

.83*** 

.11 

42. Open-mindedness 

.51*** 

.03 

-.05 

-.11 

.11 

.20* 

.14 

.02 

.83*** 

-
.32*** 
.30*** 

-
.36*** 
.04 

(Post) 

43. Positive Emotions 
(Past Week) (Post) 
44. Positive Emotions 
(Past Week) (Post) 

45. Academic 

Satisfaction (Post) 
46. Dropout Intentions 

(Post) 

-.04 

.08 

.11 

-.04 

.29*** 

.11 

.23*** 

-.21* 

.07 

.16 

.24*** 

.04 

.09 

-.01 

-.11 

-.16 

-.12 

-.14 

.38* 

.05 

-.10 

-.10 

.07 

.02 

-.14 

.17 

.08 

.12 

-.13 

-.01 

.17 

-
.31*** 
.13 

.02 

-.06 

.05 

.13 

-.18* 

-.21* 

-.16 

.25*** 

-.08 

.18* 

-.16 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 
 

 

126 

 

 

Table 4 (cont’d) 

 

20. Consistency of 
Interests (Pre) 

20. 
(.80) 

21. 
 

22. 
 

23. 
 

24. 
 

25. 
 

26. 
 

27. 
 

28. 
 

29. 
 

30. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

21. Implicit Interests 

-.33*** 

(.81) 

 

Theory (Pre) 
22. Self-Assessed 

Interest Variance 
(Pre) 

-.26*** 

.42*** 

(.69) 

 

 

23. Vocational Identity 

.23*** 

-.17* 

-.14 

(.97) 

 

 

 

(Pre) 

24. Trait Positive 

Emotions (Pre) 
25. Trait Negative 
Emotions (Pre) 

.20*** 

-.39*** 

-
.21*** 
.07 

.01 

.26*** 

(.90) 

.06 

-.18* 

-.14 

(.85) 

 

 

 

 

26. Interest Importance 

-.01 

-.03 

-.07 

.00 

-.03 

-.03 

(.66) 

(Pre) 

27. Trait Realistic 
(DSIM) (Post) 

-.06 

-.08 

.06 

-.01 

.05 

-.01 

.05 

(.83) 

28. Trait Investigative 

-.20* 

-.06 

.05 

.31*** 

.05 

.14 

.10 

.36*** 

(.82) 

(DSIM) (Post) 

29. Trait Artistic 

(DSIM) (Post) 

-.12 

-.04 

.02 

-.17* 

.01 

.02 

.00 

.46*** 

.31*** 

(.89) 

30. Trait Social (DSIM) 

.00 

.04 

.03 

.26*** 

.33*** 

-.04 

.04 

.20* 

.36*** 

.29*** 

(.89) 

(Post) 

31. Trait Enterprising 

-.02 

-.01 

.02 

-.07 

.17 

-.04 

-.04 

.48*** 

.26*** 

.40*** 

.41*** 

(DSIM) (Post) 

32. Trait Conventional 

-.05 

-.12 

-.22* 

.14 

-.01 

.13 

.17 

.36*** 

.52*** 

.38*** 

.33*** 

(DSIM) (Post) 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables were 
collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 

 

127 

 

20. 
-.08 

21. 
-.14 

22. 
.02 

23. 
-.18* 

24. 
-.05 

25. 
.10 

26. 
.03 

27. 
.61*** 

28. 
.14 

29. 
.25*** 

30. 
-.05 

 

-.26*** 

.03 

.15 

.13 

-.07 

.15 

-.08 

.35*** 

.55**
* 
.18* 

.24*** 

.10 

.60*** 

.19* 

 

Table 4 (cont’d) 

 

33. Trait Realistic (BPDRM) 

(Post) 

34. Trait Investigative 

(BPDRM) (Post) 

35. Trait Artistic (BPDRM) 

-.12 

-.04 

.05 

-.11 

.08 

.07 

.06 

.32*** 

(Post) 

36. Trait Social (BPDRM) 

(Post) 

37. Trait Enterprising 
(BPDRM) (Post) 

38. Trait Conventional 

(BPDRM) (Post) 

.15 

.07 

-.03 

.07 

.15 

.29*** 

.10 

.01 

.11 

.15 

.27*** 

.56*** 

-.12 

.11 

-.34*** 

.13 

-.12 

-.02 

.25*** 

-.10 

.24*** 

.15 

-.02 

-.07 

-.08 

-.24*** 

-.13 

.07 

.06 

.42*** 

.10 

.02 

-.12 

39. Extraversion (Post) 
40. Agreeableness (Post) 
41. Conscientiousness (Post) 
42. Negative Emotionality 

.28*** 
.21* 
.51*** 
-.30*** 

-.11 
.17* 
.01 
.18* 

.10 
.06 
-.13 
.01 

.14 
.14 
.11 
-.18* 

.56*** 
.24*** 
.25*** 
-.36*** 

-.40*** 
-.31*** 
-.21* 
.59*** 

.05 
.07 
.06 
-.05 

.13 
-.08 
-.08 
-.17 

-.02 
-.02 
.00 
.04 

.10 
.03 
-.02 
.09 

.35*** 
.37*** 
.18* 
-.08 

(Post) 

43. Open-mindedness (Post) 
44. Positive Emotions (Past 

-.05 
.21* 

.04 
-.22* 

.10 
-.06 

.00 
.11 

.17* 
.37*** 

.00 
-.12 

.01 
.06 

.16 
.27*** 

.15 
.19* 

.66*** 
.25*** 

.15 
.33*** 

Week) (Post) 

45. Positive Emotions (Past 

-.27*** 

.10 

-.01 

-.08 

-.17 

.48*** 

-.01 

-.11 

.02 

-.04 

.02 

Week) (Post) 

46. Academic Satisfaction 

.04 

-.01 

.00 

.25*** 

.27*** 

-.07 

.04 

-.03 

.14 

-.02 

.20* 

(Post) 

47. Dropout Intentions (Post) 

-.05 

.03 

-.13 

-.13 

-.27*** 

.18* 

-.13 

.03 

-.11 

-.12 

-.15 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables were 
collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 

 

 

128 

 

 

Table 4 (cont’d) 

 

20. Consistency of 
Interests (Pre) 

20. 
(.80) 

21. 
 

22. 
 

23. 
 

24. 
 

25. 
 

26. 
 

27. 
 

28. 
 

29. 
 

30. 
 

 

21. Implicit Interests 

-.33*** 

(.81) 

 

Theory (Pre) 
22. Self-Assessed 

Interest Variance 
(Pre) 

-.26*** 

.42*** 

(.69) 

 

 

23. Vocational Identity 

.23*** 

-.17* 

-.14 

(.97) 

 

 

 

(Pre) 

24. Trait Positive 

Emotions (Pre) 
25. Trait Negative 
Emotions (Pre) 

.20*** 

-.39*** 

-
.21*** 
.07 

.01 

.26*** 

(.90) 

.06 

-.18* 

-.14 

(.85) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

26. Interest Importance 

-.01 

-.03 

-.07 

.00 

-.03 

-.03 

(.66) 

(Pre) 

27. Trait Realistic 
(DSIM) (Post) 

-.06 

-.08 

.06 

-.01 

.05 

-.01 

.05 

(.83) 

28. Trait Investigative 

-.20* 

-.06 

.05 

.31*** 

.05 

.14 

.10 

.36*** 

(.82) 

(DSIM) (Post) 

29. Trait Artistic 

(DSIM) (Post) 

-.12 

-.04 

.02 

-.17* 

.01 

.02 

.00 

.46*** 

.31*** 

(.89) 

30. Trait Social (DSIM) 

.00 

.04 

.03 

.26*** 

.33*** 

-.04 

.04 

.20* 

.36*** 

.29*** 

(.89) 

(Post) 

31. Trait Enterprising 

-.02 

-.01 

.02 

-.07 

.17 

-.04 

-.04 

.48*** 

.26*** 

.40*** 

.41*** 

(DSIM) (Post) 

32. Trait Conventional 

-.05 

-.12 

-.22* 

.14 

-.01 

.13 

.17 

.36*** 

.52*** 

.38*** 

.33*** 

(DSIM) (Post) 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables were 
collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 

 

129 

 

 

Table 4 (cont’d) 

 

33. Trait Realistic 

(BPDRM) (Post) 

20. 
-.08 

21. 
-.14 

22. 
.02 

23. 
-.18* 

24. 
-.05 

25. 
.10 

26. 
.03 

27. 
.61*** 

28. 
.14 

29. 
.25*** 

30. 
-.05 

34. Trait Investigative 

-.26*** 

.03 

.15 

.13 

-.07 

.15 

-.08 

.35*** 

.55*** 

.24*** 

.10 

 

(BPDRM) (Post) 

35. Trait Artistic 

(BPDRM) (Post) 

36. Trait Social 

(BPDRM) (Post) 
37. Trait Enterprising 
(BPDRM) (Post) 

.15 

.07 

-.12 

.11 

-.12 

-.04 

.05 

-.11 

.08 

.07 

.06 

.32*** 

.18* 

.60*** 

.19* 

-.03 

.07 

.15 

.29*** 

.10 

.01 

.11 

.15 

.27*** 

.56*** 

38. Trait Conventional 

-.02 

-.07 

-.08 

(BPDRM) (Post) 

39. Extraversion (Post) 
40. Agreeableness 

.28*** 
.21* 

-.11 
.17* 

.10 
.06 

(Post) 

-
.34*** 
-
.24*** 
.14 
.14 

.13 

-.12 

-.02 

.25*** 

-.10 

.24*** 

.15 

-.13 

.07 

.06 

.42*** 

.10 

.02 

-.12 

.56*** 
.24*** 

-.40*** 
-.31*** 

.05 
.07 

.13 
-.08 

-.02 
-.02 

.10 
.03 

.35*** 
.37*** 

41. Conscientiousness 

.51*** 

.01 

-.13 

.11 

.25*** 

-.21* 

.06 

-.08 

.00 

-.02 

.18* 

(Post) 

42. Negative 

Emotionality (Post) 

-.30*** 

.18* 

.01 

-.18* 

43. Open-mindedness 

-.05 

.04 

.10 

.00 

-
.36*** 
.17* 

.59*** 

-.05 

-.17 

.04 

.09 

-.08 

.00 

.01 

.16 

.15 

.66*** 

.15 

(Post) 

44. Positive Emotions 
(Past Week) (Post) 
45. Positive Emotions 
(Past Week) (Post) 

.21* 

-.22* 

-.06 

.11 

.37*** 

-.12 

.06 

.27*** 

.19* 

.25*** 

.33*** 

-.27*** 

.10 

-.01 

-.08 

-.17 

.48*** 

-.01 

-.11 

.02 

-.04 

.02 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables were 
collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 

 

 

130 

 

 

Table 4 (cont’d) 

 

46. Academic 

Satisfaction (Post) 
47. Dropout Intentions 

(Post) 

20. 
.04 

21. 
-.01 

22. 
.00 

23. 
.25*** 

24. 
.27*** 

25. 
-.07 

26. 
.04 

27. 
-.03 

28. 
.14 

29. 
-.02 

30. 
.20* 

-.05 

.03 

-.13 

-.13 

-
.27*** 

.18* 

-.13 

.03 

-.11 

-.12 

-.15 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables were 
collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 

 

 

 

 

 

 

 

 

 

131 

 

 

 

Table 4 (cont’d) 

 

31. Trait Enterprising 

(DSIM) (Post) 

31. 
(.81) 

32. 
 

33. 
 

34. 
 

35. 
 

36. 
 

37. 
 

38. 
 

39. 
 

40. 
 

41. 
 

32. Trait Conventional 

.34*** 

(.83) 

 

(DSIM) (Post) 
33. Trait Realistic 

(BPDRM) (Post) 

.31*** 

.10 

(.88) 

 

 

34. Trait Investigative 

.01 

.20* 

.30*** 

(.87) 

(BPDRM) (Post) 

 

 

 

35. Trait Artistic 

.36*** 

.10 

.41*** 

.22*** 

(.84) 

(BPDRM) (Post) 

 

 

 

 

36. Trait Social 

.18* 

.20* 

.02 

.14 

.30*** 

(.81) 

 

 

 

 

 

(BPDRM) (Post) 
37. Trait Enterprising 
(BPDRM) (Post) 

.53*** 

-.04 

.38*** 

-.17* 

.46*** 

.11 

(.84) 

 

 

 

 

 

 

38. Trait Conventional 

.25*** 

.25*** 

.64*** 

.08 

.12 

-.09 

.38*** 

(.87) 

(BPDRM) (Post) 

 

 

 

 

 

 

 

39. Extraversion 

.37*** 

-.01 

.03 

-.14 

.14 

.23*** 

.32*** 

-.10 

(.87) 

(Post) 

 

 

 

 

 

 

 

 

40. Agreeableness 

.01 

.07 

-.27*** 

-.18* 

-.07 

.27*** 

.00 

-.13 

.24*** 

(.80) 

(Post) 

 

 

 

 

 

 

 

 

 

41. Conscientiousness 

-.04 

.10 

-.29*** 

-.19* 

-.19* 

.26*** 

-.10 

-.04 

.18* 

.46*** 

(.87) 

(Post) 

42. Negative 

Emotionality 
(Post) 

-.13 

.17* 

-.11 

.14 

.01 

.09 

-.20* 

-.06 

-.48*** 

-.19* 

-.15 

Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 

 

 

132 

 

 

Table 4 (cont’d) 

 

43. Open-mindedness 

(Post) 

44. Positive Emotions 
(Past Week) (Post) 
45. Positive Emotions 
(Past Week) (Post) 

31. 
.16 

32. 
.10 

33. 
-.02 

34. 
.17* 

35. 
.51*** 

36. 
.22*** 

37. 
.02 

38. 
-.17* 

39. 
.17 

40. 
.11 

.33*** 

.30*** 

.02 

-.05 

.03 

.24*** 

.20* 

.08 

.40*** 

.14 

-.03 

.14 

-.07 

.04 

.06 

.06 

-.13 

-.09 

-.25*** 

-.16 

 

41. 
.14 

.33*
** 
-.16 

46. Academic 

.05 

.00 

-.08 

.01 

-.02 

.17* 

.04 

-.07 

.23*** 

.18* 

.16 

Satisfaction (Post) 
47. Dropout Intentions 

(Post) 

-.02 

.02 

-
.22*
** 
Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily surveys, where post variables 
were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for variables. * p < .05 ***p < .01. 

-.30*** 

-.26*** 

-.17* 

.04 

.19* 

.00 

.03 

.14 

 

 

 

 

 

 

 

 

 

 

 

133 

 

 

Table 4 (cont’d) 

 

42. Negative Emotionality (Post) 
43. Open-mindedness (Post) 
44. Positive Emotions (Past Week) 

(Post) 

42. 
(.88) 
.07 
-.21* 

43. 
 
(.83) 
-.19* 

44. 
 
 
(.87) 

45. 
 
 
 

.46 
 
 
 

45. Positive Emotions (Past Week) 

.51*** 

-.05 

-.23*** 

(.79) 

 

(Post) 

47. 
 
 
 

 

 

46. Academic Satisfaction (Post) 
47. Dropout Intentions (Post) 

 
(.79) 
Note: Pre and post refer to the temporality of when variables were collected. Pre variables were collected before the daily 
surveys, where post variables were collected after the daily surveys. Reliability (alphas) are reported on the diagonal for 
variables. * p < .05 ***p < .01. 

(.84) 
-.55*** 

.25*** 
-.24*** 

-.20* 
.24*** 

-.19* 
.27*** 

.10 
-.19* 

 

 

 

 

 

 

 

 

 

 

 

 

134 

 

 

Table 5: 

Descriptives and Intercorrelations for Within-Person Variables in Study 2. 

 

1.  Sleep Quality 

N  M 
951  2.77 

SD 
.74 

1. 
 

2. 
 

(M)   
2.  Sleep 

Quantity (M) 

952  14.12 

2.95 

.58*** 

 

3. 
 

 

3.  State 

951  3.21 

1.21 

.06 

.01 

(.82) 

Realistic (M) 

4. 
 

 

 

4.  State 

950  4.16 

1.40 

.06 

.00 

.50*** 

(.86) 

5. 
 

 

 

 

Investigative 
(M) 

5.  State Artistic 

950  3.71 

1.47 

.06 

-.03 

.50*** 

.29*** 

(.89) 

(M) 

 

6. 
 

7. 
 

8. 
 

9. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6.  State Social 

949  4.58 

1.34 

.16*** 

.04 

.47*** 

.46*** 

.41*** 

(.88) 

950  3.00 

1.20 

.07* 

.02 

.61*** 

.42*** 

.52*** 

.56*** 

(.83) 

950  4.27 

1.35 

.06 

.05 

.53*** 

.63*** 

.33*** 

.53*** 

.50*** 

(.86) 

(M) 
7.  State 

Enterprising 
(M) 
8.  State 

Conventional 
(M) 

9.  Depletion 

952  2.18 

1.00 

(M) 

10. State Positive 
Emotion (M) 

953  2.42 

.94 

-
.40*** 
.29*** 

-
.21*** 
.05 

-
.19*** 
.27*** 

-
.23*** 
.26*** 

-.17*** 

.29*** 

-
.31*** 
.35*** 

-.16*** 

-.17*** 

(.92) 

.28*** 

.23*** 

-.40*** 

Note: M and E refers to temporality of when variables were collected. M refers to morning and E refers to evening. Reliability (alphas) are reported on the 
diagonal for variables. * p < .05 ***p < .01. 

 

 

 

135 

 

 

Table 5 (cont’d) 

 

11. State 

N  M 
953  1.51 

SD 
.64 

 

1. 
-
.19*** 

2. 
-
.09*** 

3. 
-.04 

4. 
-.03 

5. 
-.03 

6. 
-.08* 

7. 
.04 

8. 
.04 

9. 
.45*** 

Negative 
Emotion (M) 

12. Positive 
Emotion 
(Daily) (E) 

13. Negative 
Emotion 
(Daily) (E) 

14. Realistic 

Daily 
Activities (E) 
15. Investigative 

Daily 
Activities (E) 
16. Artistic Daily 
Activities (E) 
17. Social Daily 
Activities (E) 

869  2.65 

.96 

.15*** 

-.02 

.23*** 

.20*** 

.22*** 

.28*** 

.19*** 

.13*** 

-.25*** 

869  1.64 

.73 

-
.14*** 

-.02 

-.08* 

-.03 

-.06 

-
.10*** 

-.07 

.05 

.32*** 

867  2.30 

1.11 

-.03 

-.07 

.61*** 

.36*** 

.31*** 

.34*** 

.43*** 

.36*** 

-.06 

867  3.88 

1.51 

.05 

-.01 

.31*** 

.62*** 

.27*** 

.33*** 

.26*** 

.45*** 

-.05 

867  2.45 

1.35 

.04 

-.05 

.41*** 

.31*** 

.57*** 

.34*** 

.42*** 

.27*** 

-.09* 

867  3.78 

1.49 

.02 

-.04 

.32*** 

.32*** 

.27*** 

.63*** 

.39*** 

.38*** 

-.11*** 

18. Enterprising 

867  2.15 

1.14 

.01 

-.04 

.38*** 

.33*** 

.30*** 

.37*** 

.56*** 

.37*** 

.00 

Daily 
Activities (E) 
19. Conventional 

Daily 
Activities (E) 

867  3.63 

1.54 

.03 

.01 

.38*** 

.50*** 

.27*** 

.38*** 

.40*** 

.68*** 

-.01 

Note: M and E refers to temporality of when variables were collected. M refers to morning and E refers to evening. Reliability (alphas) are reported on the 
diagonal for variables. * p < .05 ***p < .01. 

 

 

136 

 

 

N  M 
869  3.30 

SD 
.93 

1. 
.11*** 

2. 
.04 

3. 
.21*** 

4. 
.27*** 

5. 
.15*** 

6. 
.30*** 

7. 
.21*** 

8. 
.17*** 

9. 
-.26*** 

344  4.98 

1.26 

.12* 

-.01 

.25*** 

.06 

.06 

.13* 

.08 

.06 

-.18*** 

626  5.01 

1.29 

.16*** 

.01 

.28*** 

.53*** 

.19*** 

.37*** 

.26*** 

.33*** 

-.25*** 

 

Table 5 (cont’d) 

 

20. Perseverance 

(Daily) E 
21. Realistic 

Situational 
Interests (E) 
22. Investigative 

Situational 
Interests (E) 

23. Artistic 

217  5.07 

1.29 

.13 

.10 

.10 

.13 

.51*** 

.10 

.12 

.08 

-.17*** 

Situational 
Interests (E) 

24. Social 

Situational 
Interests (E) 
25. Enterprising 

Situational 
Interests (E) 

583  5.38 

1.21 

.17*** 

.00 

.19*** 

.24*** 

.24*** 

.46*** 

.18*** 

.22*** 

-.21*** 

148  4.67 

1.28 

.16 

.04 

.21* 

.13 

.22* 

.14 

.14 

.12 

-.23*** 

26. Conventional 

541  4.81 

1.42 

.21*** 

.06 

.23*** 

.29*** 

.07 

.28*** 

.19*** 

.43*** 

-.21*** 

Situational 
Interests (E) 
27. Engagement 

(Daily) (E) 

28. Intrinsic 

Motivation 
(Daily) (E) 

869  4.32 

1.37 

.25*** 

.06 

.32*** 

.25*** 

.26*** 

.37*** 

.29*** 

.25*** 

-.40*** 

868  4.44 

1.51 

.24*** 

.07* 

.27*** 

.23** 

.17*** 

.32*** 

.21*** 

.23*** 

-.34*** 

Note: M and E refers to temporality of when variables were collected. M refers to morning and E refers to evening. Reliability (alphas) are reported on the 
diagonal for variables. * p < .05 ***p < .01. 

 

 

137 

 

 

Table 5 (cont’d) 

 

10. State Positive 
Emotion (M) 

10. 
(.90) 

11. 
 

12. 
 

13. 
 

14. 
 

15. 
 

16. 
 

17. 
 

18. 
 

19. 
 

20. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

11. State Negative 

.01 

(.79) 

 

Emotion (M) 

12. Positive 

.64*** 

-.06 

(.88) 

 

 

Emotion (Daily) 
(E) 

13. Negative 

-.06 

.62*** 

-.09 

(.82) 

Emotion (Daily) 
(E) 

14. Realistic Daily 

.27*** 

.00 

.33*** 

.02 

 

 

 

 

.20*** 

.05 

.27*** 

.07* 

.44*** 

Activities (E) 
15. Investigative 

Daily Activities 
(E) 

16. Artistic Daily 
Activities (E) 
17. Social Daily 
Activities (E) 

18. Enterprising 

Daily Activities 
(E) 

.31*** 

.01 

.38*** 

.00 

.61*** 

.44*** 

.25*** 

-.01 

.35*** 

-.04 

.47*** 

.42*** 

.49*** 

.25*** 

.06 

.33*** 

.08* 

.63*** 

.38*** 

.62*** 

.62*** 

19. Conventional 

.15*** 

.06 

.21*** 

.10*** 

.53*** 

.70*** 

.45*** 

.53*** 

.51*** 

Daily Activities 
(E) 

Note: M and E refers to temporality of when variables were collected. M refers to morning and E refers to evening. Reliability (alphas) are reported on the 
diagonal for variables. * p < .05 ***p < .01. 

 

 

138 

 

12. 
.56*** 

13. 
-.21*** 

14. 
.30*** 

15. 
.40*** 

16. 
.30*** 

17. 
.39*** 

18. 
.29*** 

19. 
.32*** 

20. 
(.89) 

 

.33*** 

-.20*** 

.25*** 

.12* 

.13* 

.24*** 

.10 

.16*** 

.33*** 

-.10* 

.32*** 

.54*** 

.28*** 

.42*** 

.30*** 

.38*** 

10. 
.39*** 

.29*** 

11. 
-
.21*** 
-.13* 

.32*** 

-
.13*** 

.23*** 

-.10 

.22*** 

-.15* 

.09 

.22*** 

.46*** 

.12 

.10 

.08 

.29*** 

-
.13*** 

.33*** 

-.19***  22*** 

.24*** 

.28*** 

.48*** 

.25*** 

.23*** 

.20* 

-.13 

.30*** 

-.29*** 

.20* 

.16* 

.25*** 

.27*** 

.32*** 

.24*** 

.35*** 

-.04 

.37*** 

-.11*** 

.25*** 

.32*** 

.20*** 

.31*** 

.27*** 

.40*** 

 

Table 5 (cont’d) 

 

10. Perseverance 

(Daily) E 
11. Realistic 

Situational 
Interests (E) 
12. Investigative 

Situational 
Interests (E) 

13. Artistic 

Situational 
Interests (E) 

14. Social 

Situational 
Interests (E) 
15. Enterprising 

Situational 
Interests (E) 

16. Conventional 

Situational 
Interests (E) 
17. Engagement 

(Daily) (E) 

.46**
* 

.48**
* 

.20**
* 

.36**
* 

.44**
* 

.40**
* 

.63**
* 
.42**
* 

.50*** 

-
.23*** 
-
.22*** 

.65*** 

-.31*** 

.34*** 

.24*** 

.37*** 

.41*** 

.31*** 

.26*** 

.51*** 

-.31*** 

.28*** 

.13*** 

.30*** 

.33*** 

.22*** 

.15*** 

18. Intrinsic 

.37*** 

Motivation 
(Daily) (E) 

Note: M and E refers to temporality of when variables were collected. M refers to morning and E refers to evening. Reliability (alphas) are reported on the 
diagonal for variables. * p < .05 ***p < .01. 

 

 

139 

 

 

Table 5 (cont’d) 

 

21. Realistic Situational Interests (E) 
22. Investigative Situational Interests 

(E) 

21. 
(.85) 
.35*** 

22. 
 
(.88) 

23. 
 
 

24. 
 
 

25. 
 
 

23. Artistic Situational Interests (E) 
24. Social Situational Interests (E) 
25. Enterprising Situational Interests 

.22* 
.34*** 
.43*** 

.35*** 
.46*** 
.39*** 

(.88) 
.38*** 
.41*** 

 
(.90) 
.44*** 

 
 
(.86) 

(E) 

26. 
 
 

 
 
 

26. Conventional Situational Interests 

.50*** 

.49*** 

.10 

.45*** 

.46*** 

(.91) 

(E) 

 

27. 
 
 

28. 
 
 

 
 
 

 

 
 
 

 

27. Engagement (Daily) (E) 
28. Intrinsic Motivation (Daily) (E) 

.43*** 
.34*** 

.51*** 
.44*** 

.32*** 
.23*** 

.45*** 
.42*** 

.44*** 
.28*** 

.52*** 
.44*** 

(.93) 
.76*** 

 
(.95) 

Note: M and E refers to temporality of when variables were collected. M refers to morning and E refers to evening. Reliability (alphas) are reported on 
the diagonal for variables. * p < .05 ***p < .01. 

 

 

 

140 

 

 

 

Table 6: 

Descriptive for State Interest Standard Deviations. 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 
 
Average 

Mean 
.66 
.84 
.76 
.77 
.73 
.83 
 
.77 

Median 
.60 
.76 
.64 
.65 
.71 
.75 
 
.69 

SD 
.31 
.45 
.43 
.47 
.35 
.40 
 
.40 

Min 
.10 
.11 
.00 
.17 
.08 
.12 
 
.10 

Max 
1.57 
2.50 
2.21 
2.28 
1.94 
2.10 
 
2.10 

Note: State interests were on a seven-point scale. Based on individuals who had four or more daily interest scores. Average represents the average across six 
interests. N = 148  

 

 

 

141 

 

 

Table 7: 

Multilevel Regression Results of State Realistic Interest at T+1 on State Realistic Interest at T. 

Variable 

Intercept 
State Realistic 

Unstandardized 
Coefficient 
.56*** 
.79*** 

Standard Error  T-Value 

.08 
.02 

6.76 
32.67 

Standardized 
Coefficient 
 
.78*** 

Note: Conditional R2 = .60. Marginal R2 = .60. Level 1 N = 705, Level 2 N = 165. * p < .05  ***p < .01. 

 

 

 

 

142 

 

 

Table 8: 

Multilevel Regression Results of State Investigative Interest at T+1 on State Investigative Interest at T. 

Variable 

Intercept 
State 
Investigative 

Unstandardized 
Coefficient 
1.07*** 
.71*** 

Standard Error  T-Value 

.12 
.03 

8.55 
25.46 

Standardized 
Coefficient 
 
.69*** 

Note: Conditional R2 = .48. Marginal R2 = .48. Level 1 N = 704, Level 2 N = 165. * p < .05  ***p < .01. 

 

 

 

 

143 

 

 

Table 9: 

Multilevel Regression Results of State Artistic Interest at T+1 on State Artistic Interest at T. 

Variable 

Intercept 
State Artistic 

Unstandardized 
Coefficient 
.62*** 
.79*** 

Standard Error  T-Value 

.09 
.02 

6.70 
34.32 

Standardized 
Coefficient 
 
.79*** 

Note: Conditional R2 = .63. Marginal R2 = .64. Level 1 N = 705, Level 2 N = 165. * p < .05  ***p < .01. 

 

 

 

 

144 

 

 

Table 10: 

Multilevel Regression Results of State Social Interest at T+1 on State Social Interest at T. 

Variable 

Intercept 
State Social 

Unstandardized 
Coefficient 
1.23*** 
.70*** 

Standard Error  T-Value 

.14 
.03 

8.89 
24.35 

Standardized 
Coefficient 
 
.68*** 

Note: Conditional R2 = .46. Marginal R2 = .46. Level 1 N = 704, Level 2 N = 165. * p < .05  ***p < .01. 

 

 

 

 

145 

 

 

Table 11: 

Multilevel Regression Results of State Enterprising Interest at T+1 on State Enterprising Interest at T. 

Variable 

Intercept 
State 
Enterprising 

Unstandardized 
Coefficient 
.84*** 
.67*** 

Standard Error  T-Value 

.09 
.03 

9.49 
24.65 

Standardized 
Coefficient 
 
.68*** 

Note: Conditional R2 = .49. Marginal R2 = .51. Level 1 N = 705, Level 2 N = 165. * p < .05  ***p < .01. 

 

 

 

 

146 

 

 

Table 12: 

Multilevel Regression Results of State Conventional Interest at T+1 on State Conventional Interest at T. 

Variable 

Intercept 
State 
Conventional 

Unstandardized 
Coefficient 
.93*** 
.74*** 

Standard Error  T-Value 

.13 
.03 

7.32 
26.71 

Standardized 
Coefficient 
 
.71*** 

Note: Conditional R2 = .50. Marginal R2 = .50. Level 1 N = 705, Level 2 N = 165. * p < .05  ***p < .01. 

 

 

 

 

147 

 

 

Table 13: 

Spearman Correlations Between Within-Individual Ranked Interests from T and T+1. 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 

Note: N = 703, * p < .05  ***p < .01. 

Spearman ρ 
.46*** 
.53*** 
.62*** 
.44*** 
.46*** 
.48*** 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

148 

 

 

Table 14: 

Spearman Correlations Between Days and Averaged Spearman Correlations. 

 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 

.44*** 
.55*** 
.66*** 
.45*** 
.48*** 
.52*** 

.45*** 
.60*** 
.67*** 
.54*** 
.52*** 
.47*** 

Day 1 to Day 2  Day 2 to Day 3  Day 3 to Day 4  Day 4 to Day 5  Day 5 to Day 6  Day 6 to Day 7 
.41*** 
.58*** 
.62*** 
.47*** 
.52*** 
.53*** 

.42*** 
.43*** 
.57*** 
.40*** 
.35*** 
.50*** 

.48*** 
.49*** 
.58*** 
.38*** 
.38*** 
.39*** 

54*** 
.54*** 
.63*** 
.40*** 
.52*** 
.49*** 

Note: N = 92-134, * p < .05  ***p < .01. 

 

 

 

 

 

149 

 

 

Table 15: 

Interclass Correlation Coefficient (ICC) Values for State Interests Nested Within Individuals. 

 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 

ICC(1) 
.62 
.51 
.65 
.53 
.53 
.52 

Level 1 N = 949-951, Level 2 N = 178 

 

 

 

 

ICC(2) 
.90 
.85 
.91 
.86 
.86 
.86 

150 

 

 

Table 16: 

Multilevel Structural Equation Model Results for Latent Correlations Between State Interests. 

 

Interest 

X2 

CFI 

 

 

 

 

 

Latent 
Correlation 
.86*** 
.71*** 
.85*** 
.74*** 
.81*** 
.79*** 

Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 

.98 
.98 
1.00 
.97 
.98 
.99 
Level 1 N = 703-704, Level 2 N = 165. * p < .05  ***p < .01. 

77.86*** 
76.49*** 
23.70 
118.18*** 
83.41*** 
44.39* 

TLI 

.98 
.97 
1.00 
.96 
.96 
.99 

RMSEA  SRMR 

.06 
.06 
.00 
.08 
.06 
.03 

.03 
.05 
.01 
.03 
.03 
.02 

151 

 

 

 

Table 17: 

Correlation Between State Interests at T1 and State Interests at T7. 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 

N = 89-91. * p < .05  ***p < .01. 

 

 

r 
.44*** 
.37*** 
.44*** 
.35*** 
.25* 
.48*** 

152 

 

 

 

Table 18: 

Spearman Correlations Between Within-Individual Ranked Interests from T1 and T7. 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 

Note: N = 89, * p < .05  ***p < .01. 

 

 

 

Spearman ρ 
.26* 
.31*** 
.58*** 
.26* 
.25* 
.42*** 

153 

 

 

 

 

Table 19: 

Beta Weights for Relationships Between Individual Differences and Variability of State Vocational Interests. 

 

Realistic 
SD 
.12 
.15 
.08 
.09 
-.17* 

Investigative 
SD 
.06 
.05 
-.05 
-.04 
.03 

Artistic 
SD 
.29*** 
.11 
.09 
.10 
-.05 

Social 
SD 
-.03 
.13 
.05 
.09 
.02 

.11 

Variable 

Enterprising 
SD 
.06 
.15 
.17* 
-.01 
-.03 

Conventional 
SD 
.09 
.15 
-.04 
.19* 
.09 

Mean 
SD 
.16* 
.15* 
.12 
.11 
-.09 

.23*** 
-.04 

Open-mindedness 
Conscientiousness 
Extraversion 
Agreeableness 
Negative 
Emotionality 
Grit 
Self-Assessed 
Interest Variance 
Consistency of 
Interests 
Curiosity 
Implicit Interest 
Theory 
Vocational 
Identity 
Note: Numbers represent beta weights from linear regressions controlling for mean and squared mean levels of the state 
vocational interests. Mean SD is the averaged standard deviation across six state interests. SDs included for individuals with 
4+ state interests reported. N = 146. * p < .05  ***p < .01 

.23*** 
.07 

.27*** 
-.04 

.21*** 

.07 

.15 
.01 

.10 

.09 
.01 

.01 

.15 
.10 

.04 

.18* 

.33*** 

.06 
-.05 

-.07 

.15* 
.13 

.17* 

.19* 
.05 

.07 

.16* 
.05 

.15 
.09 

.13 
.07 

.18* 
-.01 

.09 

.09 

.16* 

.33*** 
.06 

 

154 

 

 

Table 20: 

Regression Results of Hypothesized Individual Differences Predicting Average Standard Deviation of State Interests.  

 

Variable 

Unstandardized 
Coefficient 
.36 
-.07 

-.02 

.03 
-.01 

.10*** 
.03 

Intercept 
Mean Averaged 
State Interests 
Mean Averaged 
State Interests2 
Open-mindedness 
Self-Assessed 
Interest Variance 
Curiosity 
Implicit Interests 
Theory 
Vocational Identity 
Grit 
Conscientiousness 

.02 
.05 
.04 
N = 146. *p < .05, ***p < .01. 

 

 

Standardized 
Coefficient 
 
-.19 

-.36 

.07 
-.02 

.29*** 
.12 

.06 
.09 
.09 

Standard Error  T-Value 

.41 
.18 

.02 

.03 
.02 

.03 
.02 

.02 
.05 
.05 

.88 
-.37 

-.73 

.90 
-.27 

3.60 
1.46 

.74 
.91 
.92 

155 

 

 

Stand. 
Coeff. 
 
.07 
.24*** 

Polynomial Regression 
Unstand. 
Coeff. 
2.66 
.07 
.23*** 

Stand. 
Error 
.07 
.04 
.05 

T-
Value 
40.35 
1.63 
4.61 

-.02 
-.01 

.05 

 
.09 
.52 

.04 
.03 

-.68 
-.36 

-.03 
.02 

.05 

.97 

.06 

 
 
 

 
 
 

 
 
 

 

Table 21: 

Results for Polynomial Regression and Related Models for State Realistic Interest Predicting Daily Positive Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 
2.66*** 
.16*** 
 

Stand. 
Error 
.06 
.04 
 

T-
Value 
45.60 
4.33 
 

Stand. 
Coeff. 
 
.17*** 
 

 
 

 

 
 

 

Intercept 
State Realistic 
Realistic Daily 
Activities 
State Realistic2 
Realistic Daily 
Activities2  
Realistic State 
X Realistic 
Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N = 741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.03 
.51 

 
 
 

 
 

 

 
 

 

 
 
 

 

 

 

 

Stand. 
Coeff. 
 
.06 
.25*** 

 
 

 

 
 
 

Person & Environment 
Unstand. 
Coeff. 
2.65*** 
.06 
.24*** 

Stand. 
Error 
.06 
.04 
.04 

T-
Value 
47.71 
1.49 
5.99 

 
 

 

 
.09 
.52 

 
 

 

 
 
 

 
 

 

 
 
 

156 

 

 

 

 

 

 

 

 

 

 

 

Table 22: 

Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence Predicting Daily Positive Affect. 

Polynomial Regression Terms 

Realistic Fit Line (X=Y) 
Realistic Fit Line Curvilinearity 
Realistic Misfit Line (X=-Y) 
Realistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N = 165. * p < .05. 

 
 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.29* 
.01 
-.16 
.03 

.19 
-.06 
-.33 
-.28 

.39 
.07 
.01 
.12 

 

157 

 

 

Table 23: 

Results for Polynomial Regression and Related Models for State Investigative Interest Predicting Daily Positive Affect. 

Person 
Unstand. 
Coeff. 
2.66*** 
.16*** 

Stand. 
Error 
.06 
.04 

T-
Value 
45.40 
4.59 

Person & Environment 
Unstand. 
Coeff. 
2.66*** 
.08 

Stand. 
Error 
.06 
.04 

T-
Value 
46.39 
1.88 

Stand. 
Coeff. 
 
.08 

Polynomial Regression 
Unstand. 
Coeff. 
2.65*** 
.09* 

Stand. 
Error 
.07 
.05 

T-
Value 
38.15 
1.97 

 

Stand. 
Coeff. 
 
.09* 

.13*** 

.04 

3.50 

.14*** 

.14*** 

.04 

3.57 

.15*** 

 

 

 

 
.04 
.51 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

-.02 

.04 

-.39 

-.02 

-.08* 

.04 

-2.13 

-.09* 

.16*** 

.05 

3.45 

.18*** 

 
.06 
.52 

 
 
 

 
 
 

 
 
 

 

Variable 

 

 

 

 

 

 

 

 

Intercept 
State 
Investigative 
Investigative 
Daily Activities 
State 
Investigative2 
Investigative 
Daily 
Activities2  
State 
Investigative X 
Investigative 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.03 
.52 

 
 
 

 
 
 

 

 

 

 

Stand. 
Coeff. 
 
.17*** 

 

 

 

 

 
 
 

 

 

 

 

158 

 

 

Table 24: 

Polynomial Regression Fit Line Coefficients for State Investigative Interest Congruence Predicting Daily Positive Affect. 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.23* 
.07 
-.05 

.15 
-.01 
-.20 

.30 
.14 
.11 

Polynomial Regression Terms 

Investigative Fit Line (X=Y) 
Investigative Fit Line Curvilinearity 
Investigative Misfit Line  
(X=-Y) 
Investigative Misfit Line Curvilinearity 
-.25* 
Level 1 N =741 , Level 2 N =165. * p < .05, *** p < .01. 

 

 

 

 

-.50 

-.02 

159 

 

 
Table 25: 

Results for Polynomial Regression and Related Models for State Artistic Interest Predicting Daily Positive Affect. 

Stand. 
Error 
.06 
.04 
 

T-
Value 
45.31 
3.92 
 

Stand. 
Coeff. 
 
.16*** 
 

Person & Environment 
Unstand. 
Coeff. 
2.65*** 
.01 
.30*** 

Stand. 
Error 
.05 
.04 
.04 

T-
Value 
48.70 
.25 
7.60 

Stand. 
Coeff. 
 
.01 
.32*** 

Polynomial Regression 
Unstand. 
Coeff. 
2.67*** 
.01 
.29*** 

Stand. 
Error 
.07 
.04 
.05 

T-
Value 
39.65 
.19 
6.09 

 

Stand. 
Coeff. 
 
.01 
.30*** 

 
 

 

 
.11 
.52 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

-.05 
.02 

.01 

 
.11 
.52 

.04 
.03 

-1.30 
.52 

-.05 
.02 

.04 

.24 

.01 

 
 
 

 
 
 

 
 
 

 

Variable 

Person 
Unstand. 
Coeff. 
2.66*** 
.15*** 
 

 
 

 

Intercept 
State Artistic 
Artistic Daily 
Activities 
State Artistic2 
Artistic Daily 
Activities2  
State Artistic X 
Artistic Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741., Level 2 N =165. * p < .05, *** p < .01. 
 

 
.03 
.51 

 
 
 

 
 

 

 
 

 

 
 

 

 
 
 

 

 

 

 

160 

 

 

Table 26: 

Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence Predicting Daily Positive Affect. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 

Artistic Fit Line (X=Y) 
Artistic Fit Line Curvilinearity 
Artistic Misfit Line (X=-Y) 
Artistic Misfit Line Curvilinearity 
Level 1 N = 741, Level 2 N = 165. * p < .05, *** p < .01. 

.29* 
-.02 
-.28* 
-.04 

 

 

 

.20 
-.10 
-.46 
-.26 

.38 
.05 
-.08 
.26 

 

 

161 

 

 
Table 27: 

Results for Polynomial Regression and Related Models for State Social Interest Predicting Daily Positive Affect. 

Stand. 
Error 
.06 
.04 
 

T-
Value 
46.46 
5.63 
 

Stand. 
Coeff. 
 
.21*** 
 

Person & Environment 
Unstand. 
Coeff. 
2.67 
.07 
.25*** 

Stand. 
Error 
.06 
.04 
.04 

T-
Value 
47.29 
1.66 
6.68 

Stand. 
Coeff. 
 
.07 
.26*** 

Polynomial Regression 
Unstand. 
Coeff. 
2.68*** 
.06 
.25*** 

Stand. 
Error 
.07 
.05 
.04 

T-
Value 
39.56 
1.23 
6.65 

 

Stand. 
Coeff. 
 
.06 
.27*** 

 
 

 

 
.10 
.55 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

.00 
.00 

.03 
.04 

-.12 
-.03 

-.01 
.00 

-.01 

.05 

-.24 

-.01 

 
.10 
.55 

 
 
 

 
 
 

 
 
 

 

Variable 

Person 
Unstand. 
Coeff. 
2.67*** 
.20*** 
 

 
 

 

 
 

 

Intercept 
State Social 
Social Daily 
Activities 
State Social2 
Social Daily 
Activities2  
State Social X 
Daily Social 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.04 
.51 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

 

 

 

 

 
 

 

162 

 

 

Table 28: 

Polynomial Regression Fit Line Coefficients for State Social Interest Congruence Predicting Daily Positive Affect. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
Social Fit Line (X=Y) 
Social Fit Line Curvilinearity 
Social Misfit Line (X=-Y) 
Social Misfit Line Curvilinearity 
Level 1 N = 741, Level 2 N =165. * p < .05, *** p < .01. 

.31* 
-.02 
-.20* 
.01 

.22 
-.09 
-.35 
-.23 

.39 
.06 
-.04 
.24 

 

 

 

 

 

 

 

 

 

 

163 

 

 

Table 29: 

Results for Polynomial Regression and Related Models for State Enterprising Interest Predicting Daily Positive Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 
2.66*** 
.14*** 

Stand. 
Error 
.06 
.04 

T-
Value 
45.21 
3.92 

Stand. 
Coeff. 
 
.15*** 

Person & Environment 
Unstand. 
Coeff. 
2.65 
.03 

Stand. 
Error 
.06 
.04 

T-
Value 
47.02 
.69 

Stand. 
Coeff. 
 
.03 

Polynomial Regression 
Unstand. 
Coeff. 
2.69 
.04 

Stand. 
Error 
.07 
.04 

T-
Value 
40.83 
1.05 

 

Stand. 
Coeff. 
 
.04 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Enterprising  
Enterprising 
Daily 
Activities 
State 
Enterprising2 
Enterprising 
Daily 
Activities2  
State 
Enterprising X 
Enterprising 
Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.51 

 
 
 

 
 
 

 

 

 

 

 

.26*** 

.04 

6.62 

.27 

.25*** 

.05 

5.27 

.27*** 

 

 

 

 
 
 

 

 

 

 
 
 

-.06 

-.01 

.03 

-1.89 

-.08 

.03 

-.38 

-.02 

.05 

.04 

1.25 

.07 

 
.09 
.53 

 
 
 

 
 
 

 
 
 

 

 

 

 
.08 
.53 

 

 

 

 
 
 

164 

 

 

Table 30: 

Polynomial Regression Fit Line Coefficients for State Enterprising Interest Congruence Predicting Daily Positive Affect. 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.30* 
-.02 
-.21* 

.20 
-.09 
-.37 

.39 
.03 
-.06 

Polynomial Regression Terms 

Enterprising Fit Line (X=Y) 
Enterprising Fit Line Curvilinearity 
Enterprising Misfit Line  
(X=-Y) 
Enterprising Misfit Line Curvilinearity 
-.13 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

 

 

-.29 

.07 

165 

 

 

Table 31: 

Results for Polynomial Regression and Related Models for State Conventional Interest Predicting Daily Positive Affect. 

Stand. 
Error 
.06 
.04 

T-
Value 
44.58 
3.30 

Stand. 
Coeff. 
 
.12*** 

Person & Environment 
Unstand. 
Coeff. 
2.66*** 
.01 

Stand. 
Error 
.06 
.04 

T-
Value 
45.07 
.30 

Stand. 
Coeff. 
 
.01 

Polynomial Regression 
Unstand. 
Coeff. 
2.67 
.00 

Stand. 
Error 
.07 
.05 

T-
Value 
37.87 
-.06 

 

Stand. 
Coeff. 
 
.00 

 

Variable 

Person 
Unstand. 
Coeff. 
2.67*** 
.12*** 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Conventional 
Conventional 
Daily 
Activities 
State 
Conventional2 
 Conventional 
Daily 
Activities2  
State 
Conventional 
X 
Conventional 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.01 
.52 

 
 
 

 
 
 

 
 
 

 

 

 

.16*** 

.04 

4.02 

.17*** 

.17** 

.04 

4.02 

.17*** 

 

 

 

 
 
 

 

 

 

 
 
 

-.01 

.04 

-.17 

-.01 

.03 

.04 

.64 

.03 

-.04 

.06 

-.75 

-.05 

 
.03 
.53 

 
 
 

 
 
 

 
 
 

 

 

 

 
.03 
.53 

 

 

 

 
 
 

166 

 

 

Table 32: 

Polynomial Regression Fit Line Coefficients for State Conventional Interest Congruence Predicting Daily Positive Affect. 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.16* 
-.02 
-.17 

.07 
-.09 
-.32 

.25 
.05 
.01 

Polynomial Regression Terms 

Conventional Fit Line (X=Y) 
Conventional Fit Line Curvilinearity 
Conventional Misfit Line  
(X=-Y) 
Conventional Misfit Line Curvilinearity 
.06 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

 

 

 

-.33 

.39 

167 

 

 

Table 33: 

Results for Polynomial Regression and Related Models for State Realistic Interest Predicting Daily Negative Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 
1.64*** 
-.01 
 

Stand. 
Error 
.04 
.03 
 

T-
Value 
38.24 
-.44 
 

Stand. 
Coeff. 
 
-.02 
 

Person & Environment 
Unstand. 
Coeff. 
1.64*** 
-.05 
.07* 

Stand. 
Error 
.04 
.03 
.03 

T-
Value 
37.98 
-1.40 
2.32 

Stand. 
Coeff. 
 
-.07 
.10* 

Polynomial Regression 
Unstand. 
Coeff. 
1.65*** 
-.05 
.09* 

Stand. 
Error 
.05 
.03 
.04 

T-
Value 
31.67 
-1.44 
2.38 

 

Stand. 
Coeff. 
 
-.07 
.13* 

 

 

 

 

 

 

 

Intercept 
State Realistic 
Realistic 
Daily 
Activities 
State 
Realistic2 
Realistic 
Daily 
Activities2  
Realistic State 
X Realistic 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.43 

 
 
 

 
 
 

 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 
 
 

 

 

 

 
 
 

.01 

.03 

.42 

.02 

-.03 

.03 

-.97 

-.06 

.01 

.04 

.29 

.02 

 
.01 
.44 

 
 
 

 
 
 

 
 
 

 

 

 

 
.01 
.44 

 

 

 

 
 
 

168 

 

 

Table 34: 

Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence Predicting Daily Negative Affect. 

Polynomial Regression Terms 

Realistic Fit Line (X=Y) 
Realistic Fit Line Curvilinearity 
Realistic Misfit Line (X=-Y) 
Realistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 
 
 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.05 
.00 
-.14* 
-.02 

-.02 
-.05 
-.27 
-.16 

.14 
.05 
-.02 
.11 

 

 

 

 

 

 

 
 

169 

 

 

Table 35: 

Results for Polynomial Regression and Related Models for State Investigative Interest Predicting Daily Negative Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 
1.64*** 
.00 

Stand. 
Error 
.04 
.03 

T-
Value 
38.12 
.00 

Stand. 
Coeff. 
 
.00 

Person & Environment 
Unstand. 
Coeff. 
1.64*** 
-.05 

Stand. 
Error 
.04 
.03 

T-
Value 
38.40 
-1.51 

Stand. 
Coeff. 
 
-.07 

Polynomial Regression 
Unstand. 
Coeff. 
1.64*** 
-.05 

Stand. 
Error 
.05 
.04 

T-
Value 
30.52 
-1.47 

 

Stand. 
Coeff. 
 
-.08 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Investigative 
Investigative 
Daily 
Activities 
State 
Investigative2 
Investigative 
Daily 
Activities2  
State 
Investigative X 
Investigative 
Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.43 

 
 
 

 
 
 

 

 

 

 

 

.08*** 

.03 

2.67 

.11*** 

.08*** 

.03 

2.61 

.12*** 

 

 

 

 
 
 

 

 

 

 
 
 

-.01 

.03 

-.19 

-.01 

.00 

.03 

.08 

.00 

.01 

.04 

.15 

.01 

 
.01 
.43 

 
 
 

 
 
 

 
 
 

 

 

 

 
.01 
.43 

 

 

 

 
 
 

170 

 

 

Table 36: 

Polynomial Regression Fit Line Coefficients for State Investigative Interest Congruence Predicting Daily Negative Affect. 

 

 

Polynomial Regression Terms 

Investigative Fit Line (X=Y) 
Investigative Fit Line Curvilinearity 
Investigative Misfit Line  
(X=-Y) 
Investigative Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.03 
.00 
-.14 

-.02 
-.06 
-.25 

.09 
.06 
-.01 

-.01 

-.23 

.20 

171 

 

 

Stand. 
Coeff. 
 
-.02 
.04 

Polynomial Regression 
Unstand. 
Coeff. 
1.63 
-.01 
.03 

Stand. 
Error 
.05 
.04 
.04 

T-
Value 
30.10 
-.33 
.80 

.03 
-.03 

.03 

 
.01 
.43 

.03 
.03 

1.06 
1.18 

.05 
-.06 

.04 

.74 

.04 

 
 
 

 
 
 

 
 
 

 

Table 37: 

Results for Polynomial Regression and Related Models for State Artistic Interest Predicting Daily Negative Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 
1.64*** 
-.01 
 

Stand. 
Error 
.04 
.03 
 

T-
Value 
38.17 
-.30 
 

Stand. 
Coeff. 
 
-.01 
 

 
 

 

 
 

 

 
 

 

Intercept 
State Artistic 
Artistic Daily 
Activities 
State Artistic2 
Artistic Daily 
Activities2  
State Artistic 
X Artistic 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.43 

 
 
 

 
 

 

 
 
 

 
 
 

 

 

 

 

Stand. 
Coeff. 
 
-.02 
.02 

 
 

 

 
 
 

Person & Environment 
Unstand. 
Coeff. 
1.64*** 
-.02 
.01 

Stand. 
Error 
.04 
.03 
.03 

T-
Value 
38.11 
-.47 
.44 

 
 

 

 
.00 
.43 

 
 

 

 
 
 

 
 

 

 
 
 

172 

 

 

Table 38: 

Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence Predicting Daily Negative Affect. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 

Artistic Fit Line (X=Y) 
Artistic Fit Line Curvilinearity 
Artistic Misfit Line (X=-Y) 
Artistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

.02 
.03 
-.04 
-.03 

-.05 
-.03 
-.15 
-.23 

.10 
.09 
.09 
.12 

 

 

 

 

 

 

 

173 

 

 

Stand. 
Coeff. 
 
-.02 
.01 

Polynomial Regression 
Unstand. 
Coeff. 
1.65 
-.02 
.01 

Stand. 
Error 
.05 
.04 
.03 

T-
Value 
31.15 
-.42 
.18 

.04 
-.02 

-.04 

 
.01 
.43 

.03 
.03 

1.45 
-.67 

.08 
-.03 

.04 

-.91 

-.06 

 
 
 

 
 
 

 
 
 

 
Table 39: 

Results for Polynomial Regression and Related Models for State Social Interest Predicting Daily Negative Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 
1.64*** 
-.03 
 

Stand. 
Error 
.04 
.03 
 

T-
Value 
38.44 
-1.08 
 

Stand. 
Coeff. 
 
-.04 
 

 
 

 

 
 

 

 
 

 

Intercept 
State Social 
Social Daily 
Activities 
State Social2 
Social Daily 
Activities2  
State Social 
X Daily 
Social 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
00 
.43 

 
 
 

 
 
 

 
 

 

 
 
 

 

 

 

 

 
 

 

Stand. 
Coeff. 
 
-.05 
.01 

 
 

 

 
 
 

Person & Environment 
Unstand. 
Coeff. 
1.64*** 
-.04 
.01 

Stand. 
Error 
.04 
.03 
.03 

T-
Value 
38.42 
-1.09 
.33 

 
 

 

 
.00 
.43 

 
 

 

 
 
 

 
 

 

 
 
 

174 

 

 

 

Table 40: 

Polynomial Regression Fit Line Coefficients for State Social Interest Congruence Predicting Daily Negative Affect. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
Social Fit Line (X=Y) 
Social Fit Line Curvilinearity 
Social Misfit Line (X=-Y) 
Social Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

-.01 
-.02 
-.02 
.06 

-.06 
-.08 
-.14 
-.11 

.07 
.04 
.11 
.24 

 

 

 

 

 

 

 

175 

 

 

Table 41: 

Results for Polynomial Regression and Related Models for State Enterprising Interest Predicting Daily Negative Affect. 

Stand. 
Error 
.04 
.03 

T-
Value 
38.03 
.21 

Stand. 
Coeff. 
 
.01 

Person & Environment 
Unstand. 
Coeff. 
1.64*** 
-.03 

Stand. 
Error 
.04 
.03 

T-
Value 
38.25 
-1.07 

Stand. 
Coeff. 
 
-.05 

Polynomial Regression 
Unstand. 
Coeff. 
1.65*** 
-.04 

Stand. 
Error 
.05 
.03 

T-
Value 
32.34 
-1.35 

 

Stand. 
Coeff. 
 
-.06 

 

Variable 

Person 
Unstand. 
Coeff. 
1.64*** 
.01 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
Enterprising 
Social 
Enterprising 
Daily 
Activities 
State 
Enterprising2 
Enterprising 
Daily 
Activities2  
State 
Enterprising X 
Enterprising 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.43 

 
 
 

 
 
 

 
 
 

 

 

 

.09*** 

.03 

2.75 

.12 

.12*** 

.04 

2.97 

.16*** 

 

 

 

 
 
 

 

 

 

 
 
 

.02 

.03 

.57 

.03 

-.04 

.03 

-1.37 

-.08 

.02 

.03 

.45 

.03 

 
.01 
.43 

 
 
 

 
 
 

 
 
 

 

 

 

 
.01 
.44 

 

 

 

 
 
 

176 

 

 

Table 42: 

Polynomial Regression Fit Line Coefficients for State Enterprising Interest Congruence Predicting Daily Negative Affect. 

 

Polynomial Regression Terms 

Enterprising Fit Line (X=Y) 
Enterprising Fit Line Curvilinearity 
Enterprising Misfit Line  
(X=-Y) 
Enterprising Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 
 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.07* 
.00 
-.16* 

.01 
-.06 
-.26 

.16 
.06 
-.03 

-.04 

-.18 

.10 

177 

 

 

Table 43: 

Variable 

Results for Polynomial Regression and Related Models for State Conventional Interest Predicting Daily Negative Affect. 
Polynomial Regression 
 
Unstand. 
Coeff. 
1.68*** 
-.03 

Person & Environment 
Unstand. 
Coeff. 
1.64*** 
-.04 

Person 
Unstand. 
Coeff. 
1.64*** 
.02 

Stand. 
Error 
.05 
.04 

Stand. 
Coeff. 
 
-.06 

Stand. 
Error 
.04 
.04 

Stand. 
Coeff. 
 
.02 

Stand. 
Error 
.04 
.03 

T-
Value 
38.10 
-1.25 

T-
Value 
38.13 
.54 

T-
Value 
31.80 
-.81 

 

 

 

Stand. 
Coeff. 
 
-.04 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Conventional 
Conventional 
Daily 
Activities 
State 
Conventional2 
 Conventional 
Daily 
Activities2  
State 
Conventional 
X 
Conventional 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.43 

 
 
 

 
 
 

 
 
 

 

 

 

 

 

 

 

.09*** 

.03 

2.81 

.13*** 

.08* 

.03 

2.45 

.11 

 

 

 

 
 
 

 

 

 

 
 
 

.02 

.03 

.59 

.03 

-.06 

.04 

-1.81 

-.09 

.01 

.05 

.27 

.02 

 
.01 
.44 

 
 
 

 
 
 

 
 
 

 

 

 

 
.01 
.44 

 

 

 

 
 
 

178 

 

 

Table 44: 

Polynomial Regression Fit Line Coefficients for State Conventional Interest Congruence Predicting Daily Negative Affect. 

 

Polynomial Regression Terms 

Conventional Fit Line (X=Y) 
Conventional Fit Line Curvilinearity 
Conventional Misfit Line  
(X=-Y) 
Conventional Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.05 
-.03 
-.11 

-.01 
-.09 
-.24 

.11 
.03 
.01 

-.06 

-.27 

.18 

179 

 

 

Table 45: 

Results for Polynomial Regression and Related Models for State Realistic Interest Predicting Daily Intrinsic Motivation. 

Stand. 
Error 
.08 
.06 

T-
Value 
53.24 
5.77 

Stand. 
Coeff. 
 
.24*** 

Person & Environment 
Unstand. 
Coeff. 
4.41*** 
.24*** 

Stand. 
Error 
.08 
.07 

T-
Value 
54.43 
3.55 

Stand. 
Coeff. 
 
.16*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.51*** 
.25*** 

Stand. 
Error 
.10 
.07 

T-
Value 
45.41 
3.56 

 

Stand. 
Coeff. 
 
.17*** 

 

Variable 

Person 
Unstand. 
Coeff. 
4.42*** 
.35*** 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Realistic 
Realistic 
Daily 
Activities 
State 
Realistic2 
Realistic 
Daily 
Activities2  
Realistic 
State X 
Realistic 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.06 
.41 

 
 
 

 
 
 

 
 
 

 

 

 

 

.23*** 

.07 

3.54 

.16*** 

.26*** 

.08 

3.17 

.17*** 

 

 

 

 
 
 

 

 

 

 
 
 

-.08 

-.03 

.06 

-1.36 

-.07 

.05 

-.55 

-.03 

.03 

.08 

.40 

.03 

 
.09 
.41 

 
 
 

 
 
 

 
 
 

 

 

 

 
.08 
.41 

 

 

 

 
 
 

180 

 

 

Table 46: 

Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence Predicting Daily Intrinsic Motivation. 

Polynomial Regression Terms 

Realistic Fit Line (X=Y) 
Realistic Fit Line Curvilinearity 
Realistic Misfit Line (X=-Y) 
Realistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.51* 
-.08 
-.01 
-.15 

.31 
-.18 
-.29 
-.45 

.65 
.03 
.24 
.26 

 

 

 

 

 

 

 

 

181 

 

 

Table 47: 

Results for Polynomial Regression and Related Models for State Investigative Interest Predicting Daily Intrinsic Motivation. 

Person 
Unstand. 
Coeff. 
4.43*** 
.23*** 

Stand. 
Error 
.09 
.06 

T-
Value 
52.12 
3.89 

Person & Environment 
Unstand. 
Coeff. 
4.43*** 
.25*** 

Stand. 
Error 
.09 
.07 

T-
Value 
52.08 
3.55 

Stand. 
Coeff. 
 
.17*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.41*** 
.22*** 

Stand. 
Error 
.11 
.08 

T-
Value 
40.96 
2.88 

 

Stand. 
Coeff. 
 
.15*** 

-.04 

.06 

-.62 

-.03 

.01 

.07 

.16 

.01 

 

 

 

 
.02 
.40 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

-.11 

-.01 

.07 

-1.64 

-.09 

.06 

-.23 

-.01 

.23*** 

.08 

2.84 

.16*** 

 
.04 
.40 

 
 
 

 
 
 

 
 
 

 

Variable 

 

 

 

 

 

 

 

 

Intercept 
State 
Investigative 
Investigative 
Daily Activities 
State 
Investigative2 
Investigative 
Daily 
Activities2  
State 
Investigative X 
Investigative 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.40 

 
 
 

 
 
 

 

 

 

 

Stand. 
Coeff. 
 
.15*** 

 

 

 

 

 
 
 

 

 

 

 

182 

 

 

Table 48: 

Polynomial Regression Fit Line Coefficients for State Investigative Interest Congruence Predicting Daily Intrinsic Motivation. 

 

Polynomial Regression Terms 

Investigative Fit Line (X=Y) 
Investigative Fit Line Curvilinearity 
Investigative Misfit Line  
(X=-Y) 
Investigative Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 
 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.23* 
.10 
.21 

.06 
-.03 
-.09 

.34 
.24 
.45 

-.35 

-.75 

.06 

183 

 

 
Table 49: 

Results for Polynomial Regression and Related Models for State Artistic Interest Predicting Daily Intrinsic Motivation. 

Person 
Unstand. 
Coeff. 
4.43*** 
.22*** 
 

Stand. 
Error 
.09 
.06 
 

T-
Value 
50.59 
3.36 
 

Person & Environment 
Unstand. 
Coeff. 
4.42*** 
.03 
.37*** 

Stand. 
Error 
.08 
.07 
.07 

T-
Value 
53.09 
.43 
5.61 

Stand. 
Coeff. 
 
.02 
.25*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.53*** 
.02 
.37*** 

Stand. 
Error 
.11 
.07 
.08 

T-
Value 
42.56 
.27 
4.70 

 

Stand. 
Coeff. 
 
.01 
.25 

 
 

 

 
.07 
.42 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

-.14* 
-.02 

.06 
.06 

-2.26 
-.34 

-.10 
-.02 

.09 

.07 

1.14 

.06 

 
.08 
.42 

 
 
 

 
 
 

 
 
 

 

Variable 

 
 

 

Intercept 
State Artistic 
Artistic Daily 
Activities 
State Artistic2 
Artistic Daily 
Activities2  
State Artistic X 
Artistic Daily 
Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.42 

 
 
 

 
 

 

 
 

 

 
 
 

Stand. 
Coeff. 
 
.14*** 
 

 
 

 

 
 
 

 

 

 

184 

 

 

Table 50: 

Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence Predicting Daily Intrinsic Motivation. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 

Artistic Fit Line (X=Y) 
Artistic Fit Line Curvilinearity 
Artistic Misfit Line (X=-Y) 
Artistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

.39* 
-.08 
-.35* 
-.25 

.24 
-22 
-.62 
-.62 

.54 
.06 
-.08 
.04 

 

 

 

 

 

 

185 

 

 
Table 51: 

Results for Polynomial Regression and Related Models for State Social Interest Predicting Daily Intrinsic Motivation. 

Stand. 
Error 
.08 
.06 
 

T-
Value 
54.92 
6.91 
 

Stand. 
Coeff. 
 
.27*** 
 

Person & Environment 
Unstand. 
Coeff. 
4.44*** 
.26*** 
.26*** 

Stand. 
Error 
.08 
.07 
.06 

T-
Value 
56.05 
3.85 
3.99 

Stand. 
Coeff. 
 
.17*** 
.17*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.49*** 
.20* 
.27*** 

Stand. 
Error 
.10 
.08 
.06 

T-
Value 
44.48 
2.55 
4.19 

 

Stand. 
Coeff. 
 
.13* 
.18*** 

 
 

 

 
.10 
.41 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

-.03 
.04 

-.09 

 
.10 
.41 

.06 
.07 

-.49 
.61 

-.03 
.03 

.09 

-1.07 

-.07 

 
 
 

 
 
 

 
 
 

 

Variable 

Person 
Unstand. 
Coeff. 
4.44*** 
.40*** 
 

 
 

 

Intercept 
State Social 
Social Daily 
Activities 
State Social2 
Social Daily 
Activities2  
State Social X 
Daily Social 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.07 
.40 

 
 
 

 
 

 

 
 

 

 
 

 

 
 
 

 

 

 

 

 

 

186 

 

 

 

Table 52: 

Polynomial Regression Fit Line Coefficients for State Social Interest Congruence Predicting Daily Intrinsic Motivation. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
Social Fit Line (X=Y) 
Social Fit Line Curvilinearity 
Social Misfit Line (X=-Y) 
Social Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

.47* 
-.08 
-.07 
.10 

.30 
-.21 
-.34 
-.27 

.60 
.06 
.18 
.49 

 

 

 

 

 

 

 

 

 

 

187 

 

 

Table 53: 

Results for Polynomial Regression and Related Models for State Enterprising Interest Predicting Daily Intrinsic Motivation. 

Person 
Unstand. 
Coeff. 
4.43*** 
.20*** 

Stand. 
Error 
.09 
.06 

T-
Value 
51.45 
3.39 

Person & Environment 
Unstand. 
Coeff. 
4.42*** 
.10 

Stand. 
Error 
.09 
.07 

T-
Value 
51.96 
1.54 

Stand. 
Coeff. 
 
.07 

Polynomial Regression 
Unstand. 
Coeff. 
4.52*** 
.13 

Stand. 
Error 
.10 
.07 

T-
Value 
44.26 
1.93 

 

Stand. 
Coeff. 
 
.09 

.21*** 

.07 

3.24 

.14*** 

.23*** 

.08 

2.80 

.16*** 

 

 

 

 
.04 
.41 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

-.10 

-.01 

.06 

-1.76 

-.08 

.05 

-.21 

-.01 

.02 

.07 

.26 

.02 

 
.04 
.40 

 
 
 

 
 
 

 
 
 

 

Variable 

 

 

 

 

 

 

 

 

 

Intercept 
Enterprising 
Social 
Enterprising 
Daily Activities 
State 
Enterprising2 
Enterprising 
Daily 
Activities2  
State 
Enterprising X 
Enterprising 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.40 

 
 
 

 
 
 

 

 

 

Stand. 
Coeff. 
 
.13*** 

 

 

 

 

 
 
 

 

 

 

 

188 

 

 

Table 54: 

Polynomial Regression Fit Line Coefficients for State Enterprising Interest Congruence Predicting Daily Intrinsic Motivation. 

 

Polynomial Regression Terms 

Enterprising Fit Line (X=Y) 
Enterprising Fit Line Curvilinearity 
Enterprising Misfit Line  
(X=-Y) 
Enterprising Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 
 

 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.36* 
-.09 
-.10 

.14 
-.22 
-.38 

.51 
.04 
.14 

-.13 

-.39 

.18 

189 

 

 

Table 55: 

Results for Polynomial Regression and Related Models for State Conventional Interest Predicting Daily Intrinsic Motivation. 

Person 
Unstand. 
Coeff. 
4.43*** 
.26*** 

Stand. 
Error 
.08 
.06 

T-
Value 
52.50 
4.39 

Person & Environment 
Unstand. 
Coeff. 
4.43*** 
.26*** 

Stand. 
Error 
.08 
.07 

T-
Value 
52.46 
3.58 

Stand. 
Coeff. 
 
.18*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.43*** 
.29*** 

Stand. 
Error 
.11 
.08 

T-
Value 
41.37 
3.48 

 

Stand. 
Coeff. 
 
.19*** 

-.01 

.07 

-.16 

-.01 

-.03 

.07 

-.37 

-.02 

 

 

 

 
.03 
.40 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

.06 

.00 

.06 

.93 

.06 

.07 

-.07 

.00 

-.07 

.10 

-.73 

-.05 

 
.03 
.40 

 
 
 

 
 
 

 
 
 

 

Variable 

 

 

 

 

 

 

 

 

Intercept 
State 
Conventional 
Conventional 
Daily Activities 
State 
Conventional2 
 Conventional 
Daily 
Activities2  
State 
Conventional X 
Conventional 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.03 
.40 

 
 
 

 
 
 

 

 

 

 

Stand. 
Coeff. 
 
.17*** 

 

 

 

 

 
 
 

 

 

 

190 

 

 

Table 56: 

Polynomial Regression Fit Line Coefficients for State Conventional Interest Congruence Predicting Daily Intrinsic Motivation. 

 

Polynomial Regression Terms 

Conventional Fit Line (X=Y) 
Conventional Fit Line Curvilinearity 
Conventional Misfit Line  
(X=-Y) 
Conventional Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.26* 
-.02 
.32* 

.07 
-.16 
.01 

.39 
.12 
.57 

.13 

-.31 

.62 

191 

 

 

Table 57: 

Results for Polynomial Regression and Related Models for State Realistic Interest Predicting Daily Engagement. 

Person 
Unstand. 
Coeff. 
4.32*** 
.39*** 
 

Stand. 
Error 
.07 
.05 
 

T-
Value 
61.22 
7.11 
 

Person & Environment 
Unstand. 
Coeff. 
4.31*** 
.20*** 
.37*** 

Stand. 
Error 
.07 
.06 
.06 

T-
Value 
62.98 
3.38 
6.33 

Stand. 
Coeff. 
 
.15*** 
.27*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.37*** 
.19*** 
.43*** 

Stand. 
Error 
.09 
.06 
.07 

T-
Value 
51.13 
3.16 
5.92 

 

Stand. 
Coeff. 
 
.14*** 
.32*** 

 
 

 

 
.15 
.42 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

.00 
-.04 

-.02 

 
.15 
.42 

.05 
.05 

-.02 
-.86 

.00 
-.05 

.07 

-.35 

-.02 

 
 
 

 
 
 

 
 
 

 

Variable 

 
 

 

Intercept 
State Realistic 
Realistic Daily 
Activities 
State Realistic2 
Realistic Daily 
Activities2  
Realistic State 
X Realistic 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.08 
.38 

 
 
 

 
 

 

 
 

 

 
 
 

Stand. 
Coeff. 
 
.29*** 
 

 
 

 

 
 
 

 

 

 

 

192 

 

 

Table 58: 

Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence Predicting Daily Engagement. 

Polynomial Regression Terms 

Realistic Fit Line (X=Y) 
Realistic Fit Line Curvilinearity 
Realistic Misfit Line (X=-Y) 
Realistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.62* 
-.07 
-.24* 
-.02 

.47 
-.16 
-.50 
-.29 

.78 
.03 
.00 
.30 

 

 

 

 

 

 

 

 

193 

 

 

Table 59: 

Results for Polynomial Regression and Related Models for State Investigative Interest Predicting Daily Engagement. 

Person 
Unstand. 
Coeff. 
4.33*** 
.29*** 

Stand. 
Error 
.07 
.05 

T-
Value 
59.36 
5.58 

Person & Environment 
Unstand. 
Coeff. 
4.33*** 
.16*** 

Stand. 
Error 
.07 
.06 

T-
Value 
59.70 
2.59 

Stand. 
Coeff. 
 
.12*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.37*** 
.13 

Stand. 
Error 
.09 
.07 

T-
Value 
46.88 
1.89 

 

Stand. 
Coeff. 
 
.10 

.20*** 

.06 

3.49 

.15*** 

.24*** 

.06 

4.06 

.18*** 

 

 

 

 
.06 
.38 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

-.13* 

.06 

-2.29 

-.12* 

-.11 

.06 

-1.93 

-.09 

.33*** 

.07 

4.61 

.26*** 

 
.09 
.40 

 
 
 

 
 
 

 
 
 

 

Variable 

 

 

 

 

 

 

 

 

Intercept 
State 
Investigative 
Investigative 
Daily Activities 
State 
Investigative2 
Investigative 
Daily 
Activities2  
State 
Investigative X 
Investigative 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.05 
.37 

 
 
 

 
 
 

 

 

 

 

Stand. 
Coeff. 
 
.22*** 

 

 

 

 

 
 
 

 

 

 

 

194 

 

 

Table 60: 

Polynomial Regression Fit Line Coefficients for State Investigative Interest Congruence Predicting Daily Engagement. 

 

 

Polynomial Regression Terms 

Investigative Fit Line (X=Y) 
Investigative Fit Line Curvilinearity 
Investigative Misfit Line  
(X=-Y) 
Investigative Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.37* 
.09 
-.11 

.23 
-.01 
-.37 

.48 
.22 
.10 

-.57* 

-.93 

-.19 

195 

 

 

Stand. 
Coeff. 
 
.04 
.31*** 

Person & Environment 
Unstand. 
Coeff. 
4.32*** 
.06 
.44*** 

Stand. 
Error 
.07 
.06 
.06 

T-
Value 
63.33 
.98 
7.38 

Stand. 
Coeff. 
 
.05 
.32*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.38*** 
.06 
.42*** 

Stand. 
Error 
.09 
.06 
.07 

T-
Value 
48.59 
.93 
5.95 

 
 

 

 
.13 
.40 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

-.11 
-.01 

.08 

 
.13 
.40 

.06 
.05 

-1.91 
-.11 

-.09 
-.01 

.07 

1.26 

.06 

 
 
 

 
 
 

 
 
 

 
Table 61: 

Results for Polynomial Regression and Related Models for State Artistic Interest Predicting Daily Engagement. 

Person 
Unstand. 
Coeff. 
4.33*** 
.28*** 
 

Stand. 
Error 
.07 
.06 
 

T-
Value 
59.13 
4.91 
 

 

Variable 

 
 

 

Intercept 
State Artistic 
Artistic Daily 
Activities 
State Artistic2 
Artistic Daily 
Activities2  
State Artistic X 
Artistic Daily 
Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.04 
.37 

 
 
 

 
 

 

 
 

 

 
 
 

Stand. 
Coeff. 
 
.21*** 
 

 
 

 

 
 
 

 

 

 

196 

 

 

 

 

Table 62: 

Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence Predicting Daily Engagement. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 

Artistic Fit Line (X=Y) 
Artistic Fit Line Curvilinearity 
Artistic Misfit Line (X=-Y) 
Artistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

.48* 
-.03 
-.36* 
-.20 

.34 
-.16 
-.64 
-.47 

.60 
.09 
-.11 
.14 

 

 

 

 

197 

 

 
Table 63: 

Results for Polynomial Regression and Related Models for State Social Interest Predicting Daily Engagement. 

Stand. 
Error 
.07 
.05 
 

T-
Value 
64.19 
8.40 
 

Stand. 
Coeff. 
 
.32*** 
 

Person & Environment 
Unstand. 
Coeff. 
4.34*** 
.20*** 
.40*** 

Stand. 
Error 
.07 
.06 
.06 

T-
Value 
65.09 
3.47 
7.00 

Stand. 
Coeff. 
 
.15*** 
.30*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.32*** 
.16* 
.41*** 

Stand. 
Error 
.09 
.07 
.06 

T-
Value 
49.95 
2.30 
7.16 

 

Stand. 
Coeff. 
 
.12* 
.31*** 

 
 

 

 
.17 
.43 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

-.03 
.09 

-.06 

 
.17 
.43 

.05 
.06 

-.67 
1.49 

-.04 
.07 

.08 

-.81 

-.05 

 
 
 

 
 
 

 
 
 

 

Variable 

Person 
Unstand. 
Coeff. 
4.34*** 
.43*** 
 

 
 

 

Intercept 
State Social 
Social Daily 
Activities 
State Social2 
Social Daily 
Activities2  
State Social X 
Daily Social 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.10 
.37 

 
 
 

 
 

 

 
 

 

 
 

 

 
 
 

 

 

 

 

 
 

 

198 

 

 

Table 64: 

Polynomial Regression Fit Line Coefficients for State Social Interest Congruence Predicting Daily Engagement. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
Social Fit Line (X=Y) 
Social Fit Line Curvilinearity 
Social Misfit Line (X=-Y) 
Social Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

.57* 
-.01 
-.25* 
.11 

.43 
-.12 
-.50 
-.23 

.68 
.11 
-.05 
.44 

 

 

 

 

 

 

 

 

 

 

199 

 

 

Table 65: 

Results for Polynomial Regression and Related Models for State Enterprising Interest Predicting Daily Engagement. 

Person 
Unstand. 
Coeff. 
4.32*** 
.31*** 

Stand. 
Error 
.07 
.05 

T-
Value 
60.45 
5.94 

Person & Environment 
Unstand. 
Coeff. 
4.31*** 
.13* 

Stand. 
Error 
.07 
.06 

T-
Value 
61.19 
2.31 

Stand. 
Coeff. 
 
.10* 

Polynomial Regression 
Unstand. 
Coeff. 
4.38*** 
.17*** 

Stand. 
Error 
.09 
.06 

T-
Value 
50.85 
2.84 

 

Stand. 
Coeff. 
 
.13*** 

.37*** 

.06 

6.37 

.27*** 

.34*** 

.07 

4.72 

.26*** 

 

 

 

 
.12 
.41 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

-.11* 

.05 

-2.14 

-.10* 

.03 

.01 

 
.13 
.41 

.05 

.60 

.04 

.06 

.21 

.01 

 
 
 

 
 
 

 
 
 

 

Variable 

 

 

 

 

 

 

Intercept 
Enterprising 
Social 
Enterprising 
Daily Activities 
State 
Enterprising2 
Enterprising 
Daily Activities2  
State 
Enterprising X 
Enterprising 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.05 
.36 

 
 
 

 
 
 

 

 

 

 

 

 

Stand. 
Coeff. 
 
.23*** 

 

 

 

 

 
 
 

 

 

 

 

200 

 

 

Table 66: 

Polynomial Regression Fit Line Coefficients for State Enterprising Interest Congruence Predicting Daily Engagement. 

 

Polynomial Regression Terms 

Enterprising Fit Line (X=Y) 
Enterprising Fit Line Curvilinearity 
Enterprising Misfit Line  
(X=-Y) 
Enterprising Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.51* 
-.07 
-.18 

.34 
-.16 
-.43 

.65 
.04 
.01 

-.09 

-.31 

.19 

201 

 

 

Stand. 
Coeff. 
 
.10 

Person & Environment 
Unstand. 
Coeff. 
4.33*** 
.10 

Stand. 
Error 
.07 
.07 

T-
Value 
59.14 
1.57 

Stand. 
Coeff. 
 
.08 

Polynomial Regression 
Unstand. 
Coeff. 
4.29*** 
.13 

Stand. 
Error 
.09 
.07 

T-
Value 
46.05 
1.75 

.31*** 

.06 

4.98 

.23*** 

.30*** 

.06 

4.77 

.22*** 

 

 

 

 
.08 
.41 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

.02 

.00 

.03 

 
.08 
.41 

.06 

.42 

.02 

.07 

-.07 

.00 

.09 

.30 

.02 

 
 
 

 
 
 

 
 
 

 

Table 67: 

Results for Polynomial Regression and Related Models for State Conventional Interest Predicting Daily Engagement. 

Person 
Unstand. 
Coeff. 
4.33*** 
.30*** 

Stand. 
Error 
.07 
.05 

T-
Value 
59.39 
5.76 

 

Variable 

 

 

 

 

Intercept 
State 
Conventional 
Conventional 
Daily Activities 
State 
Conventional2 
 Conventional 
Daily Activities2  
State 
Conventional X 
Conventional 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.05 
.38 

 
 
 

 
 
 

 

 

 

 

 

 

 

 

Stand. 
Coeff. 
 
.23*** 

 

 

 

 

 
 
 

 

 

 

202 

 

 

Table 68: 

Polynomial Regression Fit Line Coefficients for State Conventional Interest Congruence Predicting Daily Engagement. 

 

Polynomial Regression Terms 

Conventional Fit Line (X=Y) 
Conventional Fit Line Curvilinearity 
Conventional Misfit Line  
(X=-Y) 
Conventional Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.43* 
.05 
-.17 

.28 
-.06 
-.45 

.55 
.16 
.07 

-.01 

-.52 

.52 

203 

 

 

Table 69: 

Results for Polynomial Regression and Related Models for State Realistic Interest Predicting Daily Perseverance. 

Person 
Unstand. 
Coeff. 
3.30*** 
.21*** 
 

Stand. 
Error 
.05 
.04 
 

T-
Value 
64.18 
5.37 
 

Person & Environment 
Unstand. 
Coeff. 
3.29*** 
.09* 
.25*** 

Stand. 
Error 
.05 
.04 
.04 

T-
Value 
65.03 
2.06 
6.00 

Stand. 
Coeff. 
 
.10* 
.27*** 

Polynomial Regression 
Unstand. 
Coeff. 
3.34 
.08 
.29*** 

Stand. 
Error 
.06 
.04 
.05 

T-
Value 
53.22 
1.94 
5.76 

 

Stand. 
Coeff. 
 
.09 
.32*** 

 
 

 

 
.11 
.43 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

.00 
-.05 

.02 

 
.11 
.44 

.04 
.03 

-.06 
-1.57 

.00 
-.10 

.05 

.42 

.03 

 
 
 

 
 
 

 
 
 

 

Variable 

 
 

 

Intercept 
State Realistic 
Realistic Daily 
Activities 
State Realistic2 
Realistic Daily 
Activities2  
Realistic State 
X Realistic 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.05 
.39 

 
 
 

 
 

 

 
 

 

 
 
 

Stand. 
Coeff. 
 
.22*** 
 

 
 

 

 
 
 

 

 

 

 

204 

 

 

Table 70: 

Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence Predicting Daily Perseverance. 

Polynomial Regression Terms 

Realistic Fit Line (X=Y) 
Realistic Fit Line Curvilinearity 
Realistic Misfit Line (X=-Y) 
Realistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 
 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.38* 
-.03 
-.21* 
-.08 

.29 
-.12 
-.40 
-.27 

.50 
.02 
-.05 
.11 

 

 

 

 

 

 

 

 

205 

 

 

Stand. 
Coeff. 
 
.11* 
.33*** 

Person & Environment 
Unstand. 
Coeff. 
3.30*** 
.08 
.31*** 

Stand. 
Error 
.05 
.04 
.04 

T-
Value 
68.62 
1.85 
8.33 

Stand. 
Coeff. 
 
.08 
.34 

Polynomial Regression 
Unstand. 
Coeff. 
3.27*** 
.10* 
.31*** 

Stand. 
Error 
.06 
.04 
.04 

T-
Value 
52.57 
2.25 
7.77 

 
 

 

 
.15 
.46 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

.04 
-.03 

.05 

 
.16 
.46 

.04 
.04 

1.03 
-.91 

.05 
-.04 

.05 

.98 

.05 

 
 
 

 
 
 

 
 
 

 

Table 71: 

Results for Polynomial Regression and Related Models for State Investigative Interest Predicting Daily Perseverance. 

Person 
Unstand. 
Coeff. 
3.30*** 
.27*** 
 

Stand. 
Error 
.05 
.04 
 

T-
Value 
65.44 
7.72 
 

 

Variable 

 
 

 

Intercept 
State Investigative 
Investigative Daily 
Activities 
State Investigative2 
Investigative Daily 
Activities2  
State Investigative 
X Investigative 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.09 
.41 

 
 
 

 
 

 

 
 

 

 
 
 

Stand. 
Coeff. 
 
.30*** 
 

 
 

 

 
 
 

 

 

 

 

206 

 

 

Table 72: 

Polynomial Regression Fit Line Coefficients for State Investigative Interest Congruence Predicting Daily Perseverance. 

 

 

Polynomial Regression Terms 

Investigative Fit Line (X=Y) 
Investigative Fit Line Curvilinearity 
Investigative Misfit Line  
(X=-Y) 
Investigative Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.41* 
.05 
-.20* 

.32 
-.03 
-.36 

.48 
.13 
-.05 

-.04 

-.28 

.18 

207 

 

 
Table 73: 

Results for Polynomial Regression and Related Models for State Artistic Interest Predicting Daily Perseverance. 

 

Stand. 
Coeff. 
 
-.02 
.28*** 

Person & Environment 
Unstand. 
Coeff. 
3.30*** 

Stand. 
Error 
.05 
.04 
.04 

T-
Value 
65.77 
-.27 
6.17 

Stand. 
Coeff. 
 
-.01 
.28 

Polynomial Regression 
Unstand. 
Coeff. 
3.33*** 
-.02 
.25*** 

Stand. 
Error 
.07 
.05 
.05 

T-
Value 
50.68 
-.45 
5.10 

 
 

 

 
.07 
.39 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

-.04 
.01 

-.02 

 
.07 
.40 

.04 
.04 

-.94 
.39 

-.04 
.02 

.05 

-.40 

-.02 

 
 
 

 
 
 

 
 
 

 

Variable 

Person 
Unstand. 
Coeff. 
3.31*** 
.12*** 
 

Stand. 
Error 
.05 
.04 
 

T-
Value 
63.25 
2.95 
 

Stand. 
Coeff. 
 
.13***  -.01 
 

.26*** 

 
 

 

Intercept 
State Artistic 
Artistic Daily 
Activities 
State Artistic2 
Artistic Daily 
Activities2  
State Artistic X 
Artistic Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.37 

 
 
 

 
 

 

 
 

 

 
 

 

 
 
 

 

 

 

208 

 

 

Table 74: 

Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence Predicting Daily Perseverance. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 

Artistic Fit Line (X=Y) 
Artistic Fit Line Curvilinearity 
Artistic Misfit Line (X=-Y) 
Artistic Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

.23* 
-.04 
-.27* 
.00 

.14 
-.14 
-.44 
-.22 

.33 
.03 
-.11 
.18 

 

 

 

 

 

 

 

209 

 

 
Table 75: 

Results for Polynomial Regression and Related Models for State Social Interest Predicting Daily Perseverance. 

Stand. 
Error 
.05 
.04 
 

T-
Value 
68.09 
6.81 
 

Stand. 
Coeff. 
 
.26*** 
 

Person & Environment 
Unstand. 
Coeff. 
3.31*** 
.08* 
.29*** 

Stand. 
Error 
.05 
.04 
.04 

T-
Value 
68.29 
2.02 
7.27 

Stand. 
Coeff. 
 
.09* 
.31*** 

Polynomial Regression 
Unstand. 
Coeff. 
3.28*** 
.10* 
.28*** 

Stand. 
Error 
.06 
.05 
.04 

T-
Value 
52.94 
2.18 
7.14 

 

Stand. 
Coeff. 
 
.11* 
.31*** 

 
 

 

 
.14 
.44 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

.07 
.03 

.04 
.04 

1.88 
.71 

.10 
.03 

-.11* 

.05 

-2.08 

-.13* 

 
.14 
.44 

 
 
 

 
 
 

 
 
 

 

Variable 

Person 
Unstand. 
Coeff. 
3.31*** 
.24*** 
 

 
 

 

Intercept 
State Social 
Social Daily 
Activities 
State Social2 
Social Daily 
Activities2  
State Social X 
Daily Social 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.07 
.37 

 
 
 

 
 

 

 
 

 

 
 

 

 
 
 

 

 

 

 

 
 

 

210 

 

 

Table 76: 

Polynomial Regression Fit Line Coefficients for State Social Interest Congruence Predicting Daily Perseverance. 

Polynomial Regression Terms  Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
Social Fit Line (X=Y) 
Social Fit Line Curvilinearity 
Social Misfit Line (X=-Y) 
Social Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

.39* 
-.01 
-.18 
.21 

.29 
-.10 
-.37 
-.08 

.46 
.07 
-.03 
.45 

 

 

 

 

 

 

 

 

 

 

 

211 

 

 

Table 77: 

Results for Polynomial Regression and Related Models for State Enterprising Interest Predicting Daily Perseverance. 

 

Variable 

Person 
Unstand. 
Coeff. 
3.30*** 
.17*** 

Stand. 
Error 
.05 
.04 

T-
Value 
64.31 
4.55 

Stand. 
Coeff. 
 
.18*** 

Person & Environment 
Unstand. 
Coeff. 
3.29*** 
.06 

Stand. 
Error 
.05 
.04 

T-
Value 
65.50 
1.37 

Stand. 
Coeff. 
 
.06 

Polynomial Regression 
Unstand. 
Coeff. 
3.33*** 
.07 

Stand. 
Error 
.06 
.04 

T-
Value 
53.83 
1.69 

 

Stand. 
Coeff. 
 
.08 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Enterprising  
Enterprising 
Daily 
Activities 
State 
Enterprising2 
Enterprising 
Daily 
Activities2  
State 
Enterprising X 
Enterprising 
Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.03 
.37 

 
 
 

 
 
 

 

 

 

 

 

.24*** 

.04 

5.81 

.26*** 

.23*** 

.05 

4.52 

.25*** 

 

 

 

 
 
 

 

 

 

 
 
 

-.05 

.04 

-1.54 

-.07 

.00 

.03 

.11 

.01 

.02 

.05 

.40 

.02 

 
.09 
.41 

 
 
 

 
 
 

 
 
 

 

 

 

 
.08 
.41 

 

 

 

 
 
 

212 

 

 

Table 78: 

Polynomial Regression Fit Line Coefficients for State Enterprising Interest Congruence Predicting Daily Perseverance. 

 

Polynomial Regression Terms 

Enterprising Fit Line (X=Y) 
Enterprising Fit Line Curvilinearity 
Enterprising Misfit Line  
(X=-Y) 
Enterprising Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 
 

 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.30* 
-.03 
-.16* 

.20 
-.11 
.33 

.40 
.03 
-.01 

-.07 

-.28 

.13 

213 

 

 

Table 79: 

Results for Polynomial Regression and Related Models for State Conventional Interest Predicting Daily Perseverance. 

Person 
Unstand. 
Coeff. 
3.31*** 
.18*** 

Stand. 
Error 
.05 
.04 

T-
Value 
63.33 
5.00 

Stand. 
Coeff. 
 
.20***  -.05 

Person & Environment 
Unstand. 
Coeff. 
3.30*** 

Stand. 
Error 
.05 
.04 

T-
Value 
64.46 
-1.12 

Stand. 
Coeff. 
 
-.05 

Polynomial Regression 
Unstand. 
Coeff. 
3.27*** 
-.02 

Stand. 
Error 
.06 
.05 

T-
Value 
50.89 
-.40 

 

Stand. 
Coeff. 
 
-.02 

 

Variable 

 

 

 

 

 

 

 

Intercept 
State 
Conventional 
Conventional 
Daily Activities 
State 
Conventional2 
 Conventional 
Daily 
Activities2  
State 
Conventional X 
Conventional 
Daily Activities 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.04 
.39 

 
 
 

 
 
 

 

 

 

 

 

 

 

 

214 

 

 

 

 

 

 
 
 

.36*** 

.04 

8.76 

.39*** 

.35*** 

.04 

8.37 

.38*** 

 

 

 

 
.12 
.47 

 

 

 

 
 
 

 

 

 

 
 
 

 

 

 

 
 
 

.03 

.00 

.04 

.91 

.05 

.04 

.00 

.00 

.00 

.06 

.02 

.00 

 
.12 
.47 

 
 
 

 
 
 

 
 
 

 

Table 80: 

Polynomial Regression Fit Line Coefficients for State Conventional Interest Congruence Predicting Daily Perseverance. 

 

Polynomial Regression Terms 

Conventional Fit Line (X=Y) 
Conventional Fit Line Curvilinearity 
Conventional Misfit Line  
(X=-Y) 
Conventional Misfit Line Curvilinearity 
Level 1 N =741, Level 2 N =165. * p < .05. 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.33* 
.04 
-.37* 

.24 
-.04 
-.55 

.42 
.11 
-.20 

.03 

-.29 

.32 

215 

 

 

Table 81: 

Results for Polynomial Regression and Related Models for Trait Realistic Interest Predicting Daily Positive Affect. 

Person & Environment 
Unstand. 
Coeff. 

Stand. 
Error 

Polynomial Regression 
Unstand. 
Coeff. 

Stand. 
Error 

T-
Value 

 

Stand. 
Coeff. 

Stand
. 
Coeff. 
 
 
.28**
* 
 

T-
Valu
e 
 
47.69 
7.29 

 

 

 
2.54 
.28*** 

 
.07 
.05 

 
35.66 
6.23 

 
 
.30*** 

.00 

.03 

.10 

.00 

 

-.04 

.04 

-1.11 

-.05 

 
-.46 
 
 
 
 

 
-.03 
 
 
 
 

 
.01 
.12*** 
 
.10 
.51 

 
.06 
.04 
 
 
 

 
.10 
2.84 
 
 
 

 
.01 
.18*** 
 
 
 

 

Variable 

Person 
Unstand
. Coeff. 

Stand. 
Error 

T-
Value 

Stand. 
Coeff. 

 
2.67*** 
 

 
.06 
 

 
43.97 
 

 

 

 

 

 

 

 
 
 

 

 

Level 1 

Intercept 
Realistic Daily 
Activities 
Realistic Daily 
Activities2  
Trait Realistic 
X Realistic 
Daily 
Activities 

Level 2 

 
.06 
Trait Realistic 
Trait Realistic2 
 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N = 741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.86 
 
 
 
 

 
.06 
 
 
 
 

 
.05 
 
 
.00 
.52 

 

 

 

 

 
2.65*** 
.26*** 

 
.06 
.04 

 

 

 
-.03 
 
 
.07 
.51 

 

 

 
.06 
 
 
 
 

216 

 

 

 

Table 82: 

Regression Results for Polynomial Trait and State Congruence for Realistic Interest Predicting Daily Positive Affect. 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-.48*** 
.85* 
.34 

Stand. 
Error 
.09 
.36 
.36 

Intercept 
State Realistic Congruence 
Trait Realistic Congruence 
Marginal R2=.62, Conditional R2=.62. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

T-
Value 
-5.23 
2.33 
.92 

Stand. 
Coeff. 
 
.56* 
.22 

 

 
 
 
 
 
 

 

 

217 

 

 

Table 83: 

Results for Polynomial Regression and Related Models for Trait Investigative Interests Predicting Daily Positive Affect. 

 

Stand. 
Coeff. 
 
 
.17*** 

Person & Environment 
Unstand. 
Coeff. 
 
2.66*** 
.17*** 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
46.16 
5.41 

Stand. 
Coeff. 
 
 
.18*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
2.67*** 
.16*** 

Stand. 
Error 
 
.08 
.03 

T-
Value 
 
35.29 
4.80 

 

 

 
.00 

 

 
.03 
.51 

 

 

 

 

 

 

.00 

.03 

.00 

-.03 

.05 

.03 

1.56 

.06 

 
.06 

 
.04 

 
.00 

 

 
 
 

 

 
 
 

 

 
 
 

 
-.01 

-.02 

 
.03 
.52 

 
.06 

 
-.12 

 
-.01 

.04 

-.55 

-.03 

 
 
 

 
 
 

 
 
 

Person 
Unstand. 
Coeff. 
 
2.67*** 
 

Stand. 
Error 
 
.06 
 

T-
Value 
 
43.97 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Investigative 
Daily 
Activities 
Investigative 
Daily 
Activities2  
Trait 
Investigative X 
Investigative 
Daily 
Activities 

Level 2 

 
.06 

 
.89 

 
.06 

 
.05 

 

Trait 
Investigative 
Trait 
Investigative2 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.52 

 
 
 

 

 
 
 

 

 

 

 

 

218 

 

 

 

Table 84: 

Regression Results for Polynomial Trait and State Congruence for Investigative Interests Predicting Daily Positive Affect. 

 

 

Variable 

T-
Value 
-5.45 
3.91 
1.58 

Stand. 
Coeff. 
 
.56*** 
.23 

State & Trait Congruence 
Unstand. 
Coeff. 
-.50*** 
.85*** 
.35 

Stand. 
Error 
.09 
.22 
.22 

Intercept 
State Investigative Congruence 
Trait Investigative Congruence 
Marginal R2=62. Conditional R2=.62. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

219 

 

 

Table 85: 

Results for Polynomial Regression and Related Models for Trait Artistic Interest Predicting Daily Positive Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 

Stand. 
Error 

T-
Value 

Stand. 
Coeff. 

Person & Environment 
Unstand. 
Coeff. 

Stand. 
Error 

T-
Value 

Stand. 
Coeff. 

Polynomial Regression 
Unstand. 
Coeff. 

Stand. 
Error 

T-
Value 

 
2.65*** 
.31*** 

 
.05 
.04 

 
48.64 
8.60 

 
 
.32*** 

 
2.60*** 
.30*** 

 
.07 
.04 

 
35.31 
7.11 

 

Sta
nd. 
Coe
ff. 
 
 
.32*
** 
.01 

.03 

.20 

.04 

.29 

.01 

 
.06 
.04 
 
 
 

 
.17 
1.00 
 
 
 

 
.01 
.07 
 
 
 

Level 1 

Intercept 
Artistic Daily 
Activities 
Artistic Daily 
Activities2  
Trait Artistic X 
Artistic Daily 
Activities 

 
2.67*** 
 

 
.06 
 

 
44.06 
 

 

 

 

 

 

 

Level 2 
Trait Artistic 
Trait Artistic2 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.96 
 
 
 
 

 
.06 
 
 
 
 

 
.06 
 
 
.00 
.52 

 
 
 

 

 

 
.06 
 
 
 
 

 

 

 

 

 
-.02 
 
 
.10 
.52 

 

 

 
.05 
 
 
 
 

 

 

 
-.39 
 
 
 
 

 

 

 
-.02 
 
 
 
 

.01 

.01 

 
.01 
.04 
 
.11 
.52 

220 

 

 

 

Table 86: 

Regression Results for Polynomial Trait and State Congruence for Artistic Interest Predicting Daily Positive Affect. 

 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-.46 
.87 
.30 

Stand. 
Error 
.09 
.59 
.59 

Intercept 
State Artistic Congruence 
Trait Artistic Congruence 
Marginal R2=.63, Conditional R2=.63. Level 1 N =741., Level 2 N =165. * p < .05, *** p < .01. 

-5.05 
1.46 
.51 

T-Value 

Stand. 
Coeff. 
 
.59 
.21 

 

 

 

221 

 

 

Table 87: 

Results for Polynomial Regression and Related Models for Trait Social Interest Predicting Daily Positive Affect. 

Person 
Unstand. 
Coeff. 
 
2.68*** 
 

Stand. 
Error 
 
.06 
 

T-
Value 
 
44.53 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
2.67 
.28*** 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
46.94 
8.40 

Polynomial Regression 
Unstand. 
Coeff. 
 
2.65 
.27*** 

Stand. 
Error 
 
.07 
.03 

T-
Value 
 
36.37 
8.08 

 

Stand. 
Coeff. 
 
 
.28*** 

Stand. 
Coeff. 
 
 
.29*** 

 

 

 
.04 
 
 
 
 

-.02 

.03 

-.74 

-.02 

.03 

 
.07 
.03 
 
.10 
.55 

.03 

.83 

.03 

 
.07 
.04 
 
 
 

 
1.02 
.93 
 
 
 

 
.07 
.06 
 
 
 

 

Variable 

Level 1 

Intercept 
Social Daily 
Activities 
Social Daily 
Activities2  
Trait Social X 
Daily Activities 

 

 

 

 

 

 

Level 2 
Trait Social 
Trait Social2 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.12* 
 
 
.02 
.52 

 
.06 
 
 
 
 

 
2.03 
 
 
 
 

 

 

 

 

 

 
.13* 
 
 
 
 

 

 

 
.04 
 
 
.09 
.55 

 

 

 
.06 
 
 
 
 

 

 

 
.63 
 
 
 
 

222 

 

 

Table 88: 

Regression Results for Polynomial Trait and State Congruence for Social Interest Predicting Daily Positive Affect. 

 

 

Variable 

T-
Value 
-5.08 
1.09 
1.19 

Stand. 
Coeff. 
 
.38 
.42 

State & Trait Congruence 
Unstand. 
Coeff. 
-.45 
.56 
.61 

Stand. 
Error 
.09 
.51 
.51 

Intercept 
State Social Congruence 
Trait Social Congruence 
Marginal R2=.64, Conditional R2=.64. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

 
 

223 

 

 

Table 89: 

Results for Polynomial Regression and Related Models for Trait Enterprising Interest Predicting Daily Positive Affect. 

 

Stand. 
Coeff. 
 
 
.28*** 

Person & Environment 
Unstand. 
Coeff. 
 
2.64*** 
.26*** 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
47.01 
7.53 

Stand. 
Coeff. 
 
 
.28*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
2.62*** 
.27*** 

Stand. 
Error 
 
.07 
.04 

T-
Value 
 
36.17 
6.08 

 

 

 
.07 

 

 
.09 
.53 

 

 

 

 

 

 

-.01 

.03 

-.22 

-.01 

.00 

.04 

-.06 

.00 

 
.06 

 
1.32 

 
.08 

 

 
 
 

 

 
 
 

 

 
 
 

 
.08 

.03 

 
.09 
.54 

 
.06 

 
1.37 

 
.08 

.04 

.83 

.05 

 
 
 

 
 
 

 
 
 

Person 
Unstand. 
Coeff. 
 
2.66*** 
 

Stand. 
Error 
 
.06 
 

T-
Value 
 
44.35 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Enterprising 
Daily Activities 
Enterprising 
Daily 
Activities2  
Trait 
Enterprising X 
Enterprising 
Daily Activities 

Level 2 

 
.12* 

 

Trait 
Enterprising 
Trait 
Enterprising2 
 
Marginal R2 
Conditional R2 
Level 1 N =741., Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.52 

 
 
 

 

 
 
 

 
.06 

 
2.10 

 

 
.13* 

 

 
 
 

 

 

 

224 

 

 

 

Table 90: 

Regression Results for Polynomial Trait and State Congruence for Enterprising Interest Predicting Daily Positive Affect. 

 

 

Variable 

T-
Value 
-5.20 
1.78 
.92 

Stand. 
Coeff. 
 
.52 
.27 

State & Trait Congruence 
Unstand. 
Coeff. 
-.47 
.78 
.40 

Stand. 
Error 
.09 
.44 
.44 

Intercept 
Enterprising State Congruence 
Enterprising Trait Congruence 
Marginal R2=.63, Conditional R2=.63. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

225 

 

 

Table 91: 

Results for Polynomial Regression and Related Models for Trait Conventional Interest Predicting Daily Positive Affect. 

Person 
Unstand. 
Coeff. 
 
2.67*** 
 

Stand. 
Error 
 
.06 
 

T-
Value 
 
43.95 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
2.66*** 
.17*** 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
44.91 
5.20 

Stand. 
Coeff. 
 
 
.18*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
2.67*** 
.17*** 

Stand. 
Error 
 
.08 
.03 

T-
Value 
 
33.66 
4.95 

 

Stand. 
Coeff. 
 
 
.18*** 

 

Variable 

Level 1 

Intercept 
Conventional 
Daily Activities 
Conventional 
Daily 
Activities2  
Trait 
Conventional X 
Conventional 
Daily Activities 

Level 2 

 

 

 

 

 

 

 

 

 
.02 

 

 
 
 

 

 

 
-.01 

 

 
.03 
.53 

 

 

 

 

 

 

-.01 

.03 

-.24 

-.01 

.01 

.03 

.20 

.01 

 
.06 

 
-.19 

 
-.01 

 

 
 
 

 

 
 
 

 

 
 
 

 
-.01 

-.01 

 
.03 
.53 

 
.06 

 
-.21 

 
-.01 

.05 

-.15 

-.01 

 
 
 

 
 
 

 
 
 

 
.06 

 
.39 

 

 
.02 

 

Trait 
Conventional 
Trait 
Conventional2 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.52 

 
 
 

 

 
 
 

 

 

226 

 

 

Table 92: 

Regression Results for Polynomial Trait and State Congruence for Conventional Interest Predicting Daily Positive Affect. 

 

 

Variable 

T-
Value 
-5.48 
1.20 
.53 

Stand. 
Coeff, 
 
.55 
.24 

State & Trait Congruence 
Unstand. 
Coeff. 
-.51 
.83 
.37 

Stand. 
Error 
.09 
.69 
.69 

Intercept 
State Conventional Congruence 
Trait Conventional Congruence 
Marginal R2=.62, Conditional R2=.62. Level 1 N =741. Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

 

227 

 

 

Stand
. 
Coeff. 
 
 
.13* 

Person & Environment 
Unstand
. Coeff. 

Stand
. 
Error 
 
.04 
.03 

T-
Valu
e 
 
38.38 
2.43 

Stand. 
Coeff. 

 
 
.10* 

Polynomial Regression 
Unstand
. Coeff. 

Stand
. 
Error 
 
.06 
.04 

T-
Valu
e 
 
28.77 
2.51 

 
1.61*** 
.09* 

 

 

 
.04 
 
 
 
 

 

 

 

 

-.02 

.02 

-.95 

-.05 

.01 

.03 

.22 

.01 

 
-2.93 
 
 
 
 

 
-.18*** 
 
 
 
 

 
-.11* 
.05 
 
.04 
.45 

 
.05 
.03 
 
 
 

 
-2.40 
1.60 
 
 
 

 
-.15* 
.10 
 
 
 

 

Table 93: 

Results for Polynomial Regression and Related Models for Trait Realistic Interest Predicting Daily Negative Affect. 

Person 
Unstand
. Coeff. 

 
1.64*** 
 

Stand
. 
Error 
 
.04 
 

T-
Valu
e 
 
38.84 
 

Stand
. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 
1.64 
.07* 

 

 

 

Variable 

Level 1 

Intercept 
Realistic Daily 
Activities 
Realistic Daily 
Activities2  
Trait Realistic X 
Realistic Daily 
Activities 

Level 2 

 
-.11* 
 
 
.02 
.43 

Trait Realistic 
Trait Realistic2 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.04 
 
 
 
 

 
-2.52 
 
 
 
 

 
-.15* 
 
 
 
 

 
-.13*** 
 
 
.03 
.45 

 

 

 

 

 

228 

 

 

 

Table 94: 

Regression Results for Polynomial Trait and State Congruence for Realistic Interest Predicting Daily Negative Affect. 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-.45*** 
.64 
.64 

Stand. 
Error 
.07 
.42 
.42 

Intercept 
State Realistic Congruence 
Trait Realistic Congruence 
Marginal R2=.55, Conditional R2=.55. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

T-
Value 
-6.29 
1.52 
1.52 

Stand. 
Coeff. 
 
.37 
.37 

 

 
 
 
 
 
 
 
 
 
 

 

 

 

 
 

229 

 

 

Table 95: 

Results for Polynomial Regression and Related Models for Trait Investigative Interests Predicting Daily Negative Affect. 

Person 
Unstand. 
Coeff. 
 
1.64*** 
 

Stand. 
Error 
 
.04 
 

T-
Value 
 
38.19 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
1.64 
.06* 

Stand. 
Error 
 
.04 
.03 

T-
Value 
 
38.25 
2.42 

Stand. 
Coeff. 
 
 
.09* 

Polynomial Regression 
Unstand. 
Coeff. 
 
1.61*** 
.07* 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
28.52 
2.52 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.02 

.02 

.61 

.02 

-.03 

.03 

-1.08 

-.04 

 

Stand. 
Coeff. 
 
 
.10* 

 

Variable 

Level 1 

Intercept 
Investigative 
Daily Activities 
Investigative 
Daily Activities2  
Trait 
Investigative X 
Investigative 
Daily Activities 

Level 2 

 
.04 

 
-1.05 

 
-.05 

 

Trait 
Investigative 
Trait 
Investigative2 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.43 

 
 
 

 

 
 
 

 

 
-.06 

 
-.06 

 
.04 

 
-1.46 

 
-.09 

 
-.06 

 
.04 

 
-1.33 

 
-.08 

 

 
 
 

 

 
.01 
.44 

 

 
 
 

 

 
 
 

 

 
 
 

.02 

 
.01 
.44 

.03 

.77 

.05 

 
 
 

 
 
 

 
 
 

 

 

 

230 

 

 

 

Table 96: 

Regression Results for Polynomial Trait and State Congruence for Investigative Interests Predicting Daily Negative Affect. 

 

 

Variable 

T-
Value 
-6.35 
1.35 
1.09 

Stand. 
Coeff. 
 
.41 
.33 

State & Trait Congruence 
Unstand. 
Coeff. 
-.45*** 
.71 
.57 

Stand. 
Error 
.07 
.53 
.52 

Intercept 
State Investigative Congruence 
Trait Investigative Congruence 
Marginal R2=.55, Conditional R2=.55. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

231 

 

 

Table 97: 

Results for Polynomial Regression and Related Models for Trait Artistic Interest Predicting Daily Negative Affect. 

 

Variable 

Person 
Unstand. 
Coeff. 

Stand. 
Error 

T-
Value 

Stand. 
Coeff. 

Person & Environment 
Unstand. 
Coeff. 

Stand. 
Error 

T-
Value 

Stand. 
Coeff. 

Polynomial Regression 
Unstand. 
Coeff. 

Stand. 
Error 

T-
Value 

 

Stan
d. 
Coef
f. 
 
 
.02 

 
37.95 
.18 

 
 
.01 

 
1.67*** 
.01 

 
.06 
.03 

 
28.41 
.40 

 

 

 
.39 
 
 
 
 

 

 

 
.02 
 
 
 
 

-.01 

.03 

-.47 

-.02 

.00 

.03 

.04 

.00 

 
.00 
-.02 
 
.00 
.44 

 
.05 
.03 
 
 
 

 
.06 
-.58 
 
 
 

 
.00 
-.04 
 
 
 

Level 1 

Intercept 
Artistic Daily 
Activities 
Artistic Daily 
Activities2  
Trait Artistic X 
Artistic Daily 
Activities 

 
1.64*** 
 

 
.04 
 

 
38.03 
 

 

 

 

 

 

 

Level 2 
Trait Artistic 
Trait Artistic2 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.43 
 
 
 
 

 
.04 
 
 
 
 

 
.02 
 
 
.00 
.43 

 

 

 
 
 

 

 

 
.03 
 
 
 
 

 
1.64 
.01 

 

 

 
.02 
 
 
.00 
.44 

 
.04 
.03 

 

 

 
.04 
 
 
 
 

232 

 

 

Table 98: 

Regression Results for Polynomial Trait and State Congruence for Artistic Interest Predicting Daily Negative Affect. 

 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-.46*** 
.72 
.56 

Stand. 
Error 
.07 
.50 
.50 

Intercept 
State Artistic Congruence 
Trait Artistic Congruence 
Marginal R2=.55, Conditional R2=.33. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

-6.41 
1.44 
1.13 

T-Value 

Stand. 
Coeff. 
 
.41 
.33 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

 

233 

 

 

Stand. 
Coeff. 
 
 
-.01 

Polynomial Regression 
Unstand. 
Coeff. 
 
1.66*** 
-.01 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
29.37 
-.31 

-.03 

.02 

-1.33 

-.05 

.01 

.03 

.29 

.01 

 
-.01 
.02 
 
.00 
.44 

 
.05 
.03 
 
 
 

 
-.20 
.57 
 
 
 

 
-.01 
.04 
 
 
 

 

Table 99: 

Results for Polynomial Regression and Related Models for Trait Social Interest Predicting Daily Negative Affect. 

Person 
Unstand. 
Coeff. 
 
1.64*** 
 

Stand. 
Error 
 
.04 
 

T-
Value 
 
38.04 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Social Daily 
Activities 
Social Daily 
Activities2  
Trait Social X 
Daily 
Activities 

 
-.03 
 
 
.00 
.43 

Level 2 
Trait Social 
Trait Social2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.04 
 
 
 
 

 
-.61 
 
 
 
 

 
-.04 
 
 
 
 

 

 

 

Stand. 
Coeff. 
 
 
-.01 

 

 

 
-.04 
 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
1.64*** 
.00 

Stand. 
Error 
 
.04 
.03 

T-
Value 
 
38.04 
-.14 

 

 

 
-.03 
 
 
.00 
.43 

 

 

 
.04 
 
 
 
 

 

 

 
-.57 
 
 
 
 

234 

 

 

Table 100: 

Regression Results for Polynomial Trait and State Congruence for Social Interest Predicting Daily Negative Affect. 

 

 

Variable 

T-
Value 
-6.39 
.93 
1.78 

Stand. 
Coeff. 
 
.25 
.49 

State & Trait Congruence 
Unstand. 
Coeff. 
-.46*** 
.44 
.84 

Stand. 
Error 
.07 
.47 
.47 

Intercept 
State Social Congruence 
Trait Social Congruence 
Marginal R2=.55, Conditional R2=.55. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

 
 

235 

 

 

Table 101: 

Results for Polynomial Regression and Related Models for Trait Enterprising Interest Predicting Daily Negative Affect. 

Person 
Unstand. 
Coeff. 
 
1.64*** 
 

Stand. 
Error 
 
.04 
 

T-
Value 
 
38.11 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
1.64*** 
.07*** 

Stand. 
Error 
 
.04 
.03 

T-
Value 
 
37.94 
2.64 

Stand. 
Coeff. 
 
 
.10*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
1.66*** 
.10*** 

Stand. 
Error 
 
.06 
.04 

T-
Value 
 
29.63 
2.69 

 

Stand. 
Coeff. 
 
 
.14*** 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-.02 

.02 

-1.06 

-.05 

.02 

.03 

.53 

.02 

 

Variable 

Level 1 

Intercept 
Enterprising 
Daily 
Activities 
Enterprising 
Daily 
Activities2  
Trait 
Enterprising X 
Enterprising 
Daily 
Activities 

Level 2 

 
-.03 

 
-.64 

 
-.04 

 

 
.04 

 

 

Trait 
Enterprising 
Trait 
Enterprising2 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.43 

 
 
 

 

 
 
 

 
-.04 

 

 
.01 
.44 

 
.04 

 
-.97 

 
-.06 

 
-.04 

 
.04 

 
-.96 

 
-.06 

 

 
 
 

 

 
 
 

 

 
 
 

.00 

 
.01 
.45 

.03 

-.10 

-.01 

 
 
 

 
 
 

 
 
 

 

 

 

236 

 

 

 

Table 102: 

Regression Results for Polynomial Trait and State Congruence for Enterprising Interest Predicting Daily Negative Affect. 

 

 

Variable 

T-
Value 
-6.19 
-.07 
2.32 

Stand. 
Coeff. 
 
-.02 
.76* 

State & Trait Congruence 
Unstand. 
Coeff. 
-.44*** 
-.04 
1.31* 

Stand. 
Error 
.07 
.57 
.57 

Intercept 
Enterprising State Congruence 
Enterprising Trait Congruence 
Marginal R2=.55, Conditional R2=.55. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

237 

 

 

Table 103: 

Results for Polynomial Regression and Related Models for Trait Conventional Interest Predicting Daily Negative Affect. 

Person 
Unstand. 
Coeff. 
 
1.64*** 
 

Stand. 
Error 
 
.04 
 

T-
Value 
 
38.12 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
1.64*** 
.07*** 

Stand. 
Error 
 
.04 
.03 

T-
Value 
 
38.16 
2.76 

Polynomial Regression 
Unstand. 
Coeff. 
 
1.66*** 
.07* 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
28.69 
2.46 

Stand. 
Coeff. 
 
 
.10*** 

 

Stand. 
Coeff. 
 
 
.09* 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-.04 

.02 

-1.69 

-.06 

-.01 

.03 

-.34 

-.01 

 

Variable 

Level 1 

Intercept 
Conventional 
Daily 
Activities 
Conventional 
Daily 
Activities2  
Trait 
Conventional 
X 
Conventional 
Daily 
Activities 

Level 2 

 
-.05 

 
-1.26 

 
-.08 

 
.04 

 

 

Trait 
Conventional 
Trait 
Conventional2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

 
.01 
.43 

 
 
 

 

 
 
 

 

 
 
 

 
-.07 

 

 
.01 
.44 

 
.04 

 
-1.61 

 
-.10 

 
-.06 

 
.04 

 
-1.36 

 
-.08 

 

 
 
 

 

 
 
 

.02 

 
.02 
.44 

.03 

.61 

.04 

 
 
 

 
 
 

 
 
 

 

 
 
 

238 

 

 

 
Table 104: 

Regression Results for Polynomial Trait and State Congruence for Conventional Interest Predicting Daily Negative Affect. 

 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-.44 

Stand. 
Error 
.07 
.47 
.47 

Intercept 
State Conventional Congruence  1.04* 
Trait Conventional Congruence 
Marginal R2=.55, Conditional R2=.55. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

.24 

T-
Value 
-6.29 
2.22 
.50 

Stand. 
Coeff, 
 
.61* 
.14 

 

 

 

 

 

 

 

 

239 

 

 

Table 105: 

Results for Polynomial Regression and Related Models for Trait Realistic Interest Predicting Daily Intrinsic Motivation. 

 

 

Person 

Variable  Unstand. 

Stand. 
Error 
 
.09 
 

T-
Value 
 
50.08 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
4.42*** 
.32*** 

Stand. 
Error 
 
.08 
.06 

T-
Value 
 
53.53 
5.36 

Stand. 
Coeff. 
 
 
.22*** 

Polynomial Regression 
T-
Unstand. 
Coeff. 
Value 
 
 
40.88 
4.45*** 
.38*** 
5.02 

Stand. 
Error 
 
.11 
.08 

Stand. 
Coeff. 
 
 
.26*** 

Coeff. 
 
4.43*** 
 

Level 1 
Intercept 
Realistic 
Daily 
Activities 
Realistic 
Daily 
Activities2  
Trait 
Realistic X 
Realistic 
Daily 
Activities 
Level 2 

 

 

 

 

 

 

 

 

 
.09 

 
2.43 

 
.14* 

 
.22* 

 

Trait 
Realistic 
Trait 
Realistic2 
 
Marginal 
R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 

.42 

 

 
 

 

 
 

 

 

 
 

 

 

 

 

 

 
.12 

 

 
.06 

.41 

 

 

 

 

 

 

-.03 

.05 

.63 

-.03 

-.07 

.07 

-1.06 

-.05 

 
.11 

.02 

 
.07 

.41 

 
.09 

 
1.24 

 
.07 

.06 

.30 

.02 

 
 

 

 
 

 

 
 

 

 
.08 

 
1.40 

 
.08 

 

 
 

 

 

 
 

 

 

 
 

 

240 

 

 

 

 

 

 
 
 
 
 
 

 

Table 106: 

Regression Results for Polynomial Trait and State Congruence for Realistic Interest Predicting Daily Intrinsic Motivation. 

 

Variable 

T-
Value 
-6.23 
2.55 
2.38 

Stand. 
Coeff. 
 
.38* 
.35* 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.27*** 
.66* 
.62* 

Stand. 
Error 
.20 
.26 
.26 

Intercept 
State Realistic Congruence 
Trait Realistic Congruence 
Marginal R2=.52, Conditional R2=.52. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
 
 
 

 

 

 

 
 

241 

 

 

Table 107: 

Results for Polynomial Regression and Related Models for Trait Investigative Interests Predicting Daily Intrinsic Motivation. 

 

 

Variable 

Level 1 

Intercept 
Investigative 
Daily 
Activities 
Investigative 
Daily 
Activities2  
Trait 
Investigative 
X 
Investigative 
Daily 
Activities 
Level 2 

Person 
Unstand. 
Coeff. 
 
4.43*** 
 

Stand. 
Error 
 
.09 
 

T-
Value 
 
50.04 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 
.09 

 
2.12 

 
.13* 

Person & Environment 
Unstand. 
Coeff. 
 
4.43*** 
.07 

Stand. 
Error 
 
.09 
.05 

T-
Value 
 
50.46 
1.25 

Stand. 
Coeff. 
 
 
.05 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.43*** 
.08 

Stand. 
Error 
 
.12 
.06 

T-
Value 
 
38.05 
1.42 

Stand. 
Coeff. 
 
 
.06 

 

 

 
.17 

 

 
.02 
.42 

 

 

 

 

 

 

.06 

.05 

1.18 

.04 

.09 

.06 

1.59 

.06 

 
.09 

 
1.88 

 
.11 

 
.12 

 
.09 

 
1.36 

 
.08 

 

 
 
 

 

 
 
 

 

 
 
 

-.08 

.06 

-1.35 

-.08 

 
.03 
.42 

 
 
 

 
 
 

 
 
 

 
.19* 

 

Trait 
Investigative 
Trait 
Investigative2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.42 

 
 
 

 

 
 
 

 

 
 
 

 

 

242 

 

 

 

Table 108: 

Regression Results for Polynomial Trait and State Congruence for Investigative Interests Predicting Daily Intrinsic Motivation. 

 

 

Variable 

T-
Value 
-6.76 
2.28 
3.61 

Stand. 
Coeff. 
 
.28* 
.45*** 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.39 
.51* 
.80*** 

Stand. 
Error 
.21 
.22 
.22 

Intercept 
State Investigative Congruence 
Trait Investigative Congruence 
Marginal R2=.53, Conditional R2=.53. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 
 

 

 

 

243 

 

 

Table 109: 

Results for Polynomial Regression and Related Models for Trait Artistic Interest Predicting Daily Intrinsic Motivation. 

 

Person 

Variable  Unstand. 

Stand. 
Error 
 
.09 
 

T-
Value 
 
49.30 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
4.42*** 
.41*** 

Stand. 
Error 
 
.08 
.06 

T-
Value 
 
53.49 
6.81 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.40*** 
.43*** 

Stand. 
Error 
 
.11 
.07 

T-
Value 
 
38.66 
5.97 

Stand. 
Coeff. 
 
 
.27*** 

Coeff. 
 
4.44*** 
 

 

Stand. 
Coeff. 
 
 
.29*** 

Level 1 
Intercept 
Artistic 
Daily 
Activities 
Artistic 
Daily 
Activities2  
Trait 
Artistic X 
Artistic 
Daily 
Activities 
Level 2 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-.03 

.05 

-.53 

-.02 

.01 

.07 

.09 

.00 

 
.09 

 
-.34 

 
-.02 

 
-.03 

 

 

 
 

Trait 
Artistic 
Trait 
Artistic2 
 
Marginal 
R2 
Condition
al R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 

.42 

 

 
 

 

 

 
 

 

 

 

 

 
-.14 

 

 
.07 

.42 

 
.08 

 
-1.62 

 
-.09 

 
-.10 

 
.09 

 
-1.11 

 
-.07 

 

 
 

 

 

 
 

 

.04 

 
.07 

.42 

.06 

.68 

.04 

 
 

 

 
 

 

 
 

 

 

 
 

 

244 

 

 

 

 

Table 110: 

Regression Results for Polynomial Trait and State Congruence for Artistic Interest Predicting Daily Intrinsic Motivation. 

 

 

Variable 

T-Value 

Stand. 
Coeff. 
 
.44* 
.29 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.21*** 
.77* 
.50 

Stand. 
Error 
.20 
.37 
.37 

Intercept 
State Artistic Congruence 
Trait Artistic Congruence 
Marginal R2=.53, Conditional R2=.53. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

-6.12 
2.07 
1.35 

 

 

 

 

 

 

 

 

 

 

 
 

 

245 

 

 

Stand. 
Coeff. 
 
 
.24*** 

Person & Environment 
Unstand. 
Coeff. 
 
4.44*** 
.37*** 

Stand. 
Error 
 
.08 
.06 

T-
Value 
 
54.25 
6.53 

Stand. 
Coeff. 
 
 
.25*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.43*** 
.36*** 

Stand. 
Error 
 
.11 
.06 

T-
Value 
 
41.01 
6.28 

 

 

 
.11 
 
 
.08 
.42 

 

 

 
.08 
 
 
 
 

 

 

 
1.32 
 
 
 
 

 

 

 
.07 
 
 
 
 

-.04 

.05 

-.87 

-.03 

-.02 

.06 

-.40 

-.01 

 
.17 
.06 
 
.08 
.42 

 
.09 
.05 
 
 
 

 
1.74 
1.17 
 
 
 

 
.11 
.07 
 
 
 

 

Table 111: 

Results for Polynomial Regression and Related Models for Trait Social Interest Predicting Daily Intrinsic Motivation. 

Person 
Unstand. 
Coeff. 
 
4.44*** 
 

Stand. 
Error 
 
.09 
 

T-
Value 
 
50.08 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Social Daily 
Activities 
Social Daily 
Activities2  
Trait Social X 
Daily 
Activities 

 
.23* 
 
 
.02 
.43 

Level 2 
Trait Social 
Trait Social2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.09 
 
 
 
 

 
2.55 
 
 
 
 

 
.15* 
 
 
 
 

 

 

 

246 

 

 

Table 112: 

Regression Results for Polynomial Trait and State Congruence for Social Interest Predicting Daily Intrinsic Motivation. 

 

 

Variable 

T-
Value 
-6.10 
2.53 
3.05 

Stand. 
Coeff. 
 
.33* 
.40*** 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.21*** 
.57* 
.70*** 

Stand. 
Error 
.20 
.23 
.23 

Intercept 
State Social Congruence 
Trait Social Congruence 
Marginal R2=.53, Conditional R2=.53. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

 
 

247 

 

 

Table 113: 

Results for Polynomial Regression and Related Models for Trait Enterprising Interest Predicting Daily Intrinsic Motivation. 

 

 

Variable 

Level 1 

Intercept 
Enterprising 
Daily 
Activities 
Enterprising 
Daily 
Activities2  
Trait 
Enterprising 
X 
Enterprising 
Daily 
Activities 

Level 2 

Person 
Unstand. 
Coeff. 
 
4.43*** 
 

Stand. 
Error 
 
.09 
 

T-
Value 
 
49.99 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 
.20* 

 
2.33 

 
.14* 

Person & Environment 
Unstand. 
Coeff. 
 
4.42*** 
.25*** 

Stand. 
Error 
 
.09 
.06 

T-
Value 
 
51.63 
4.17 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.47*** 
.28*** 

Stand. 
Error 
 
.11 
.08 

T-
Value 
 
40.02 
3.76 

Stand. 
Coeff. 
 
 
.16*** 

Stand. 
Coeff. 
 
 
.19*** 

 

 

 
.16 

 

 
.04 
.42 

 

 

 

 

 

 

-.03 

.04 

-.59 

-.03 

-.04 

.06 

-.70 

-.03 

 
.08 

 
1.85 

 
.10 

 
.16 

 
.09 

 
1.87 

 
.11 

 

 
 
 

 

 
 
 

 

 
 
 

-.01 

.06 

-.25 

-.01 

 
.05 
.42 

 
 
 

 
 
 

 
 
 

 
.09 

 

 

Trait 
Enterprising 
Trait 
Enterprising2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.42 

 
 
 

 

 
 
 

 

 
 
 

 

248 

 

 

 

Table 114: 

Regression Results for Polynomial Trait and State Congruence for Enterprising Interest Predicting Daily Intrinsic Motivation. 

 

 

Variable 

T-
Value 
-6.39 
.78 
2.86 

Stand. 
Coeff. 
 
.16 
.57*** 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.31*** 
.28 
1.02*** 

Stand. 
Error 
.21 
.36 
.36 

Intercept 
Enterprising State Congruence 
Enterprising Trait Congruence 
Marginal R2=.53, Conditional R2=.53. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

249 

 

 

 

Table 115: 

 

Results for Polynomial Regression and Related Models for Trait Conventional Interest Predicting Daily Intrinsic Motivation. 

 

Person 
Unstand. 
Coeff. 
 
4.45*** 
 

Stand. 
Error 
 
.09 
 

T-
Value 
 
50.66 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
4.44*** 
.12* 

Stand. 
Error 
 
.09 
.06 

T-
Value 
 
51.41 
2.07 

Stand. 
Coeff. 
 
 
.08* 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.62*** 
.11 

Stand. 
Error 
 
.12 
.06 

T-
Value 
 
39.66 
1.88 

Stand. 
Coeff. 
 
 
.07 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-.05 

.05 

-1.03 

-.04 

.04 

.05 

.76 

.03 

 

Variable 

Level 1 

Intercept 
Conventional 
Daily 
Activities 
Conventional 
Daily 
Activities2  
Trait 
Conventional 
X 
Conventional 
Daily 
Activities 

Level 2 

 
.26*** 

 

 
2.97 

 
.18*** 

 
.09 

 

Trait 
Conventional 
Trait 
Conventional2 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.03 
.42 

 
 
 

 

 
 
 

 

 
.24*** 

 
.09 

 
2.71 

 
.16*** 

 
.20* 

 
.09 

 
2.24 

 
.13* 

 

 
 
 

 

 
 
 

-.14* 

.07 

-2.10 

-.13* 

 
.05 
.42 

 
 
 

 
 
 

 
 
 

 

 
.04 
.42 

 

 
 
 

250 

 

 

 

Table 116: 

Regression Results for Polynomial Trait and State Congruence for Conventional Interest Predicting Daily Intrinsic Motivation. 

 

 

Variable 

T-
Value 
-6.71 
2.92 
2.56 

Stand. 
Coeff, 
 
.39*** 
.34* 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.39*** 
.70*** 
.61* 

Stand. 
Error 
.21 
.24 
.24 

Intercept 
State Conventional Congruence 
Trait Conventional Congruence 
Marginal R2=.53, Conditional R2=.53. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

251 

 

 

Table 117: 

Results for Polynomial Regression and Related Models for Trait Realistic Interest Predicting Daily Engagement. 

 

Person 

Variable  Unstand. 

Stand. 
Error 
 
.08 
 

T-
Value 
 
56.68 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
4.31*** 
.45*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
62.19 
8.55 

Stand. 
Coeff. 
 
 
.34*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.33*** 
.53*** 

Stand. 
Error 
 
.09 
.07 

T-
Value 
 
47.13 
7.96 

Coeff. 
 
4.33*** 
 

 

Stand. 
Coeff. 
 
 
.40*** 

 

 

 
.07 

 

 
.13 

.41 

 

 

 

 

 

 

-.06 

.04 

-1.55 

-.08 

-.03 

.06 

-.47 

-.02 

 
.08 

.06 

 
.13 

.41 

 
.07 

 
1.11 

 
.06 

.05 

1.06 

.06 

 
 

 

 
 

 

 
 

 

 
.07 

 
.98 

 
.05 

 

 
 

 

 

 
 

 

 

 
 

 

252 

 

Level 1 
Intercept 
Realistic 
Daily 
Activities 
Realistic 
Daily 
Activities2  
Trait 
Realistic X 
Realistic 
Daily 
Activities 
Level 2 

 

 

 

 

 

 

 

 

 
.20*** 

 
.08 

 
2.66 

 
.15*** 

 

Trait 
Realistic 
Trait 
Realistic2 
 
Marginal 
R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 

.38 

 

 
 

 
 

 

 
 

 

 

 

 

 

 

 

Table 118: 

Regression Results for Polynomial Trait and State Congruence for Realistic Interest Predicting Daily Engagement. 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.11 
.84*** 
.42 

Stand. 
Error 
.20 
.31 
.31 

Intercept 
State Realistic Congruence 
Trait Realistic Congruence 
Marginal R2=.52, Conditional R2=.52. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

T-
Value 
-5.66 
2.69 
1.33 

Stand. 
Coeff. 
 
.48*** 
.24 

 

 
 
 
 
 
 
 
 
 
 

 

 

 

 
 

253 

 

 

Table 119: 

Results for Polynomial Regression and Related Models for Trait Investigative Interests Predicting Daily Engagement. 

 

Stand. 
Coeff. 
 
 
.22*** 

Person & Environment 
Unstand. 
Coeff. 
 
4.33*** 
.28*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
58.50 
5.77 

Stand. 
Coeff. 
 
 
.21*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.35*** 
.29*** 

Stand. 
Error 
 
.10 
.05 

T-
Value 
 
43.96 
5.68 

 

 

 
.06 

 

 
.05 
.39 

 

 

 

 

 

 

.05 

.05 

1.00 

.04 

.04 

.05 

.80 

.03 

 
.08 

 
.76 

 
.04 

 
.02 

 
.08 

 
.26 

 
.02 

 

 
 
 

 

 
 
 

 

 
 
 

-.08 

.05 

-1.54 

-.09 

 
.06 
.39 

 
 
 

 
 
 

 
 
 

Person 
Unstand. 
Coeff. 
 
4.33*** 
 

Stand. 
Error 
 
.08 
 

T-
Value 
 
56.16 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Investigative 
Daily 
Activities 
Investigative 
Daily 
Activities2  
Trait 
Investigative 
X 
Investigative 
Daily 
Activities 

Level 2 

 
.08 

 
1.81 

 
.10 

 
.14 

 

 

Trait 
Investigative 
Trait 
Investigative2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.01 
.38 

 
 
 

 

 
 
 

 

 
 
 

 

254 

 

 

 

 

Table 120: 

 

Regression Results for Polynomial Trait and State Congruence for Investigative Interests Predicting Daily Engagement. 
 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.31*** 
.82*** 
.49* 

Stand. 
Error 
.20 
.18 
.19 

Intercept 
State Investigative Congruence 
Trait Investigative Congruence 
Marginal R2=.52, Conditional R2=.52. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

T-
Value 
-6.43 
4.45 
2.58 

Stand. 
Coeff. 
 
.46*** 
.27* 

 

 
 

 

 

 

255 

 

 

Table 121: 

Results for Polynomial Regression and Related Models for Trait Artistic Interest Predicting Daily Engagement. 

 

Stand. 
Coeff. 
 
 
.35*** 

Person 
Unstand
. Coeff. 
 
4.33*** 
 

Stand. 
Error 
 
.08 
 

T-
Value 
 
55.75 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Artistic 
Daily 
Activities 
Artistic 
Daily 
Activities2  
Trait Artistic 
X Artistic 
Daily 
Activities 
Level 2 

 
.07 
 

 
.08 
 

 
 
 

Trait Artistic 
Trait 
Artistic2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
00 
.38 

 
 
 

 
.05 
 

 
.85 
 

 
 
 

 

 

Person & Environment 
Unstand. 
Coeff. 
 
4.32*** 
.47*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
63.14 
8.96 

Stand. 
Coeff. 
 
 
.35*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.24*** 
.47*** 

Stand. 
Error 
 
.09 
.06 

T-
Value 
 
44.94 
7.52 

 

 

 

 

-.03 

.05 

-.56 

-.02 

.11 

.06 

1.88 

.08 

 
-.79 
 

 
-.04 
 

 
 
 

 
 
 

 
.02 
.07 

 
.14 
.40 

 
.08 
.05 

 
 
 

 
.29 
1.40 

 
.02 
.08 

 
 
 

 
 
 

 

 

 
-.05 
 

 
.12 
.39 

 

 

 
.07 
 

 
 
 

256 

 

 

 

Table 122: 

Regression Results for Polynomial Trait and State Congruence for Artistic Interest Predicting Daily Engagement. 

 

 

Variable 

Stand. 
Coeff. 
 
Intercept 
.31 
State Artistic Congruence 
Trait Artistic Congruence 
.41* 
Marginal R2=.14, Conditional R2=.40. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.13*** 
.55 
.71* 

Stand. 
Error 
.20 
.33 
.32 

T-Value 

-5.66 
1.68 
2.21 

 

 

 

 

 

 

 

 

 

 

 

 

257 

 

 

Stand. 
Coeff. 
 
 
.36*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.24*** 
.48*** 

Stand. 
Error 
 
.09 
.05 

T-
Value 
 
46.39 
9.69 

.02 

.04 

.56 

.02 

-.02 

.05 

-.46 

-.02 

 
.12 
.08 
 
.16 
.44 

 
.08 
.04 
 
 
 

 
1.56 
1.82 
 
 
 

 
.09 
.11 
 
 
 

 

Table 123: 

Results for Polynomial Regression and Related Models for Trait Social Interest Predicting Daily Engagement. 

Person 
Unstand. 
Coeff. 
 
4.34*** 
 

Stand. 
Error 
 
.08 
 

T-
Value 
 
56.73 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Social Daily 
Activities 
Social Daily 
Activities2  
Trait Social X 
Daily 
Activities 

 
.21*** 
 
 
.02 
.38 

Level 2 
Trait Social 
Trait Social2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
2.72 
 
 
 
 

 
.08 
 
 
 
 

 
.16*** 
 
 
 
 

 

 

 

Stand. 
Coeff. 
 
 
.37 

 

 

 
.04 
 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
4.34*** 
.49*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
62.99 
9.96 

 

 

 
.06 
 
 
.14 
.43 

 

 

 
.07 
 
 
 
 

 

 

 
.82 
 
 
 
 

258 

 

 

Table 124: 

Regression Results for Polynomial Trait and State Congruence for Social Interest Predicting Daily Engagement. 

 

 

Variable 

T-
Value 
-5.53 
2.38 
2.78 

Stand. 
Coeff. 
 
.34* 
.40*** 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.04*** 
.57* 
.67*** 

Stand. 
Error 
.19 
.24 
.24 

Intercept 
State Social Congruence 
Trait Social Congruence 
Marginal R2=.54, Conditional R2=.54. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

 
 

259 

 

 

Table 125: 

Results for Polynomial Regression and Related Models for Trait Enterprising Interest Predicting Daily Engagement. 

 

Stand. 
Coeff. 
 
 
.31*** 

Person & Environment 
Unstand. 
Coeff. 
 
4.31*** 
.42*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
60.11 
8.18 

Stand. 
Coeff. 
 
 
.32*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.29*** 
.42*** 

Stand. 
Error 
 
.09 
.07 

T-
Value 
 
45.34 
6.37 

 

 

 
.09 

 

 
.11 
.42 

 

 

 

 

 

 

.02 

.04 

.39 

.02 

-.09 

.05 

-1.61 

-.07 

 
.07 

 
1.24 

 
.07 

 

 
 
 

 

 
 
 

 

 
 
 

 
.09 

02 

 
.11 
.43 

 
.07 

 
1.31 

 
.07 

.05 

.32 

.02 

 
 
 

 
 
 

 
 
 

Person 
Unstand. 
Coeff. 
 
4.33*** 
 

Stand. 
Error 
 
.08 
 

T-
Value 
 
56.26 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Enterprising 
Daily 
Activities 
Enterprising 
Daily 
Activities2  
Trait 
Enterprising X 
Enterprising 
Daily 
Activities 

Level 2 

 
.17* 

 

 
2.22 

 
.13* 

 
.08 

 

Trait 
Enterprising 
Trait 
Enterprising2 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.38 

 
 
 

 

 
 
 

 

 

 

 

260 

 

 

 

Table 126: 

Regression Results for Polynomial Trait and State Congruence for Enterprising Interest Predicting Daily Engagement. 

 

 

Variable 

T-
Value 
-5.93 
1.13 
3.63 

Stand. 
Coeff. 
 
.17 
.55*** 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.16*** 
.30 
.97*** 

Stand. 
Error 
.20 
.27 
.27 

Intercept 
Enterprising State Congruence 
Enterprising Trait Congruence 
Marginal R2=, Conditional R2=. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

261 

 

 

 

Table 127: 

Results for Polynomial Regression and Related Models for Trait Conventional Interest Predicting Daily Engagement. 

 

Variable 

Level 1 

Intercept 
Conventional 
Daily 
Activities 
Conventional 
Daily 
Activities2  
Trait 
Conventional 
X 
Conventional 
Daily 
Activities 

Level 2 

Person 
Unstand. 
Coeff. 
 
4.34*** 
 

Stand. 
Error 
 
.08 
 

T-
Value 
 
56.43 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 
.17* 

 
2.25 

 
.13* 

 
.08 

 

 

Trait 
Conventional 
Trait 
Conventional2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.02 
.38 

 
 
 

 

 
 
 

 

 
 
 

 

Stand. 
Coeff. 
 
 
.26*** 

Person & Environment 
Unstand. 
Coeff. 
 
4.33*** 
.35*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
58.51 
7.21 

Stand. 
Coeff. 
 
 
.26*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
4.37*** 
.34*** 

Stand. 
Error 
 
.10 
.05 

T-
Value 
 
43.17 
6.81 

 

 

 
.10 

 

 
.08 
.42 

 

 

 

 

 

 

.00 

.05 

-.05 

.00 

.08 

.05 

1.71 

.06 

 
.08 

 
1.30 

 
.07 

 
.08 

 
.08 

 
1.02 

 
.06 

 

 
 
 

 

 
 
 

-.06 

.06 

-1.01 

-.06 

 
.08 
.42 

 
 
 

 
 
 

 
 
 

 

 
 
 

262 

 

 

 

 

Table 128: 

Regression Results for Polynomial Trait and State Congruence for Conventional Interest Predicting Daily Engagement. 

 

 

Variable 

T-
Value 
-6.14 
1.02 
2.29 

Stand. 
Coeff, 
 
.22 
.50* 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.22*** 
.40 
.88* 

Stand. 
Error 
.20 
.39 
.39 

Intercept 
State Conventional Congruence 
Trait Conventional Congruence 
Marginal R2=.52, Conditional R2=.52. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

 

263 

 

 

Table 129 

Results for Polynomial Regression and Related Models for Trait Realistic Interest Predicting Daily Perseverance. 

 

Person 

Variable  Unstand. 

Stand. 
Error 
 
.05 
 

T-
Value 
 
61.98 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
T-
Value 
Coeff. 
 
 
65.35 
3.30*** 
.29*** 
7.90 

Stand. 
Error 
 
.05 
.04 

Stand. 
Coeff. 
 
 
.32*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
3.29*** 
.35*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
49.05 
7.53 

Coeff. 
 
3.31*** 
 

 

Stand. 
Coeff. 
 
 
38*** 

 

 

 
-.06 

 

 
.09 
.42 

 

 

 

 

 

 

-.06* 

.03 

-2.25 

-.12* 

.03 

.04 

.74 

.04 

 
.05 

 
-1.06 

 
-.06 

 
-.03 

 
.05 

 
-.48 

 
-.03 

 

 
 
 

 

 
 
 

.07 

 
.10 
.43 

.04 

1.65 

.10 

 
 
 

 
 
 

 
 
 

 

 
 
 

264 

 

Level 1 
Intercept 
Realistic 
Daily 
Activities 
Realistic 
Daily 
Activities2  
Trait 
Realistic X 
Realistic 
Daily 
Activities 
Level 2 

 

 

 

 

 

 

 

 

 

 
.03 

 

 
.05 

 
.03 

 
.59 

 

Trait 
Realistic 
Trait 
Realistic2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.37 

 
 
 

 

 
 
 

 

 
 
 

 

 
 
 
 
 
 
 
 
 
 

 

 

 

 

Table 130: 

Regression Results for Polynomial Trait and State Congruence for Realistic Interest Predicting Daily Perseverance. 

 

Variable 

T-
Value 
-6.18 
2.19 
1.07 

Stand. 
Coeff. 
 
.49* 
.24 

State & Trait Congruence 
Unstand. 
Coeff. 
-.93*** 
.86* 
.42 

Stand. 
Error 
.15 
.39 
.39 

Intercept 
State Realistic Congruence 
Trait Realistic Congruence 
Marginal R2=.53, Conditional R2=.53. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 
 

265 

 

 

Table 131: 

Results for Polynomial Regression and Related Models for Trait Investigative Interests Predicting Daily Perseverance. 

 

Stand. 
Coeff. 
 
 
.37*** 

Person & Environment 
Unstand. 
Coeff. 
 
3.31*** 
.35*** 

Stand. 
Error 
 
.05 
.03 

T-
Value 
 
68.46 
11.19 

Stand. 
Coeff. 
 
 
.38*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
3.28*** 
.34*** 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
50.52 
10.16 

 

 

 
.01 

 

 
.15 
.45 

 

 

 

 

 

 

.00 

.03 

-.16 

-.01 

.03 

.03 

.91 

.04 

 
.05 

 
.11 

 
.01 

 

 
 
 

 

 
 
 

 

 
 
 

 
.01 

.02 

 
.15 
.45 

 
.05 

 
.23 

 
.01 

.03 

.68 

.04 

 
 
 

 
 
 

 
 
 

Person 
Unstand. 
Coeff. 
 
3.31*** 
 

Stand. 
Error 
 
.05 
 

T-
Value 
 
62.74 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Investigative 
Daily 
Activities 
Investigative 
Daily 
Activities2  
Trait 
Investigative 
X 
Investigative 
Daily 
Activities 

Level 2 

 
.05 

 
2.10 

 
.12* 

 
.11* 

 

 

Trait 
Investigative 
Trait 
Investigative2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.01 
.37 

 
 
 

 

 
 
 

 

 
 
 

 

266 

 

 

 

Table 132: 

Regression Results for Polynomial Trait and State Congruence for Investigative Interests Predicting Daily Perseverance. 

 

 

Variable 

T-
Value 
-5.44 
2.41* 
1.12 

Stand. 
Coeff. 
 
.51* 
.24 

State & Trait Congruence 
Unstand. 
Coeff. 
-.74*** 
.83* 
.39 

Stand. 
Error 
.14 
.35 
.35 

Intercept 
State Investigative Congruence 
Trait Investigative Congruence 
Marginal R2=.55, Conditional R2=.55. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 
 

 

 

 

267 

 

 

Table 133: 

Results for Polynomial Regression and Related Models for Trait Artistic Interest Predicting Daily Perseverance. 

 

Person 
Variable  Unstand
. Coeff. 
 
3.31*** 
 

Stand. 
Error 
 
.05 
 

T-
Value 
 
62.09 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand
. Coeff. 
 
3.30*** 
.26*** 

Stand. 
Error 
 
.05 
.04 

T-
Value 
 
65.67 
6.85 

Polynomial Regression 
Unstand
. Coeff. 
 
3.23 
.26*** 

Stand. 
Error 
 
.07 
.04 

T-
Value 
 
46.87 
5.86 

Stand. 
Coeff. 
 
 
.28*** 

 

Stand. 
Coeff. 
 
 
.28*** 

 

 

 
-.03 

 

 
.07 
.39 

 

 

 

 

 

 

-.01 

.03 

-.18 

-.01 

.00 

.04 

.09 

.00 

 
.05 

 
-.53 

 
-.03 

 

 
 
 

 

 
 
 

 

 
 
 

 
.03 

.08 

 
.08 
.40 

 
.06 

 
.47 

 
.03 

.04 

1.96 

.13 

 
 
 

 
 
 

 
 
 

Level 1 
Intercept 
Artistic 
Daily 
Activities 
Artistic 
Daily 
Activities2  
Trait 
Artistic X 
Artistic 
Daily 
Activities 
Level 2 

 

 

 

 

 

 

 

 

 
.04 

 

 
.05 

 

 
.74 

 
.04 

Trait 
Artistic 
Trait 
Artistic2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

 
.00 
.37 

 
 
 

 

 
 
 

 

 
 
 

 

 

 

268 

 

 

 

Table 134: 

Regression Results for Polynomial Trait and State Congruence for Artistic Interest Predicting Daily Perseverance. 

 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-1.00*** 
1.23* 
.07 

Stand. 
Error 
.16 
.48 
.48 

Intercept 
State Artistic Congruence 
Trait Artistic Congruence 
Marginal R2=.51, Conditional R2=.51. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

-6.34 
2.56 
.15 

T-Value 

Stand. 
Coeff. 
 
.67* 
.04 

 

 

 

 

 

 

 

 

 

 

 

 

269 

 

 

Table 135: 

Results for Polynomial Regression and Related Models for Trait Social Interest Predicting Daily Perseverance. 

 

Stand. 
Coeff. 
 
 
.34*** 

Person & Environment 
Unstand. 
Coeff. 
 
3.31*** 
.32*** 

Stand. 
Error 
 
.05 
.03 

T-
Value 
 
67.75 
9.28 

Stand. 
Coeff. 
 
 
.34*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
3.31*** 
.32*** 

Stand. 
Error 
 
.06 
.03 

T-
Value 
 
51.02 
9.06 

 

 

 
.08 
 
 
.14 
.44 

 

 

 
.05 
 
 
 
 

 

 

 
1.56 
 
 
 
 

 

 

 
.08 
 
 
 
 

-.02 

-.04 

 
.11 
.03 
 
.15 
.45 

.03 

-.68 

.02 

.04 

-1.03 

-.04 

 
.06 
.03 
 
 
 

 
1.92 
1.10 
 
 
 

 
.12 
.07 
 
 
 

 
.19*** 
 
 
 
 

Person 
Unstand. 
Coeff. 
 
3.31*** 
 

Stand. 
Error 
 
.05 
 

T-
Value 
 
64.13 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Social Daily 
Activities 
Social Daily 
Activities2  
Trait Social X 
Daily 
Activities 

 
.17*** 
 
 
.03 
.37 

Level 2 
Trait Social 
Trait Social2 
 
Marginal R2 
Conditional 
R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
3.37 
 
 
 
 

 
.05 
 
 
 
 

 

 

 

270 

 

 

Table 136: 

Regression Results for Polynomial Trait and State Congruence for Social Interest Predicting Daily Perseverance. 

 

 

Variable 

T-
Value 
-5.79 
2.17 
1.89 

Stand. 
Coeff. 
 
.39* 
.34 

State & Trait Congruence 
Unstand. 
Coeff. 
-.83*** 
.67* 
.58 

Stand. 
Error 
.14 
.31 
.31 

Intercept 
State Social Congruence 
Trait Social Congruence 
Marginal R2=.54, Conditional R2=.54. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

 

 
 

271 

 

 

Table 137: 

Results for Polynomial Regression and Related Models for Trait Enterprising Interest Predicting Daily Perseverance. 

Person 
Unstand. 
Coeff. 
 
3.31*** 
 

Stand. 
Error 
 
.05 
 

T-
Value 
 
61.97 
 

Stand. 
Coeff. 
 
 
 

Person & Environment 
Unstand. 
Coeff. 
 
3.29*** 
.26*** 

Stand. 
Error 
 
.05 
.04 

T-
Value 
 
65.21 
7.29 

Stand. 
Coeff. 
 
 
.29*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
3.25*** 
.27*** 

Stand. 
Error 
 
.07 
.05 

T-
Value 
 
48.94 
5.92 

 

Stand. 
Coeff. 
 
 
.30*** 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-.01 

.03 

-.38 

-.02 

.00 

.04 

.06 

.00 

 

Variable 

Level 1 

Intercept 
Enterprising 
Daily 
Activities 
Enterprising 
Daily 
Activities2  
Trait 
Enterprising X 
Enterprising 
Daily 
Activities 

Level 2 

 
.03 

 
.51 

 
.03 

 

 
.05 

 

 

Trait 
Enterprising 
Trait 
Enterprising2 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.00 
.37 

 
 
 

 

 
 
 

 
-.02 

 

 
.08 
.41 

 
.05 

 
-.44 

 
-.02 

 
-.02 

 
.05 

 
-.32 

 
-.02 

 

 
 
 

 

 
 
 

 

 
 
 

.05 

 
.09 
.41 

.03 

1.46 

.08 

 
 
 

 
 
 

 
 
 

 

 

 

272 

 

 

 

Table 138: 

Regression Results for Polynomial Trait and State Congruence for Enterprising Interest Predicting Daily Perseverance. 

 

 

Variable 

T-
Value 
-6.31 
1.94 
1.26 

Stand. 
Coeff. 
 
.44 
.28 

State & Trait Congruence 
Unstand. 
Coeff. 
-.98*** 
.79 
.51 

Stand. 
Error 
.16 
.40 
.40 

Intercept 
Enterprising State Congruence 
Enterprising Trait Congruence 
Marginal R2=.52, Conditional R2=.52. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 

 

273 

 

 

 

Table 139: 

Results for Polynomial Regression and Related Models for Trait Conventional Interest Predicting Daily Perseverance. 

Person 
Unstand. 
Coeff. 
 
3.31*** 
 

Stand. 
Error 
 
.05 
 

T-
Value 
 
62.48 
 

Stand. 
Coeff. 
 
 
 

 

 

 

 

 

 

 

 

 

Variable 

Level 1 

Intercept 
Conventional 
Daily 
Activities 
Conventional 
Daily 
Activities2  
Trait 
Conventional 
X 
Conventional 
Daily 
Activities 

Level 2 

 
.05 

 
1.61 

 
.09 

 
.09 

 

Trait 
Conventional 
Trait 
Conventional2 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

 
.01 
.37 

 
 
 

 

 
 
 

 

 

 

 

 

Stand. 
Coeff. 
 
 
.35*** 

Person & Environment 
Unstand. 
Coeff. 
 
3.30*** 
.33*** 

Stand. 
Error 
 
.05 
.03 

T-
Value 
 
64.21 
10.01 

Stand. 
Coeff. 
 
 
.36*** 

Polynomial Regression 
Unstand. 
Coeff. 
 
3.26*** 
.33*** 

Stand. 
Error 
 
.07 
.03 

T-
Value 
 
46.61 
9.60 

 

 

 
.02 

 

 
.13 
.47 

 

 

 

 

 

 

.01 

.03 

.28 

.01 

.03 

.03 

.83 

.03 

 
.02 

.03 

 
.13 
.47 

 
.05 

 
.40 

 
.02 

.04 

.63 

.04 

 
 
 

 
 
 

 
 
 

 
.05 

 
.30 

 
.02 

 

 
 
 

 

 
 
 

 

 
 
 

274 

 

 

 

Table 140: 

Regression Results for Polynomial Trait and State Congruence for Conventional Interest Predicting Daily Perseverance. 

 

 

Variable 

State & Trait Congruence 
Unstand. 
Coeff. 
-.80*** 

Stand. 
Error 
.14 
.51 
.51 

Intercept 
State Conventional Congruence  1.01* 
Trait Conventional Congruence 
Marginal R2=.55, Conditional R2=.55. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 
 

.23 

T-
Value 
-5.83 
1.98 
.44 

Stand. 
Coeff, 
 
.61* 
.14 

 

 

 

 

275 

 

 

Table 141: 

Results for Polynomial Regression for Full RIASEC Model Congruence Predicting Daily Positive Affect. 

 

 
Variable 

Intercept 
State Realistic Congruence 
State Investigative Congruence 
State Artistic Congruence 
State Social Congruence 
State Enterprising Congruence 
State Conventional Congruence 
Trait Realistic Congruence 
Trait Investigative Congruence 
Trait Artistic Congruence 
Trait Social Congruence 
Trait Enterprising Congruence 
Trait Conventional Congruence 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

Full RIASEC State Congruence 
Unstand. 
Coeff. 
-.54*** 
-.03 
.47*** 
.37*** 
.65*** 
.18 
-.43* 
 
 
 
 
 
 
 
.65 
.65 

Stand. 
Error 
.09 
.17 
.15 
.14 
.15 
.17 
.21 
 
 
 
 
 
 
 
 
 

T-
Value 
-6.05 
-.15 
3.02 
2.66 
4.43 
1.04 
-2.05 
 
 
 
 
 
 
 
 
 

Stand. 
Coeff. 
 
-.02 
.31*** 
.25*** 
.44*** 
.12 
-.28* 
 
 
 
 
 
 
 
 
 

Full RIASEC State & Trait Congruence 
Unstand. 
Coeff. 
-.54*** 
-.12 
.82*** 
.38 
-.03 
.32 
.97 
.15 
-.49 
-.01 
.72 
-.19 
-1.32 
 
.65 
.65 

Stand. 
Coeff. 
 
-.08 
.54**** 
.26 
-.02 
.21 
.64 
.10 
-.32 
.00 
.49 
-.13 
-.87 
 
 
 

Stand. 
Error 
.09 
.38 
.22 
.60 
.54 
.45 
.72 
.36 
.30 
.59 
.54 
.45 
.75 
 
 
 

T-
Value 
-5.99 
-.31 
3.71 
.64 
-.06 
.71 
1.36 
.43 
-1.66 
-.01 
1.33 
-.42 
-1.76 
 
 
 

 

 

 

276 

 

 

Table 142: 

Results for Polynomial Regression for Full RIASEC Model Congruence Predicting Daily Negative Affect. 

 

 
Variable 

Intercept 
State Realistic Congruence 
State Investigative Congruence 
State Artistic Congruence 
State Social Congruence 
State Enterprising Congruence 
State Conventional Congruence 
Trait Realistic Congruence 
Trait Investigative Congruence 
Trait Artistic Congruence 
Trait Social Congruence 
Trait Enterprising Congruence 
Trait Conventional Congruence 
 
Marginal R2 
Conditional R2 
Note: † Beta weights can exceed 1.00 in cases where extensive multicollinearity exists. Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

Full RIASEC State & Trait Congruence 
Unstand. 
Coeff. 
-.45 
.34 
-.08 
.46 
.22 
-.35 
.79 
.21 
.13 
-2.12* 
.85 
1.08 
-.26 
 
.56 
.56 

Stand. 
Coeff. 
 
.20 
-.05 
.27 
.13 
-.20 
.46 
.12 
.08 
-1.22†* 
.49 
.63 
-.15 
 
 
 

Stand. 
Error 
.07 
.53 
.59 
.53 
.53 
.68 
.56 
.49 
.62 
.92 
.81 
.81 
.66 
 
 
 

T-
Value 
-6.18 
.64 
-.14 
.87 
.42 
-.51 
1.42 
.42 
.21 
-2.30 
1.06 
1.34 
-.40 
 
 
 

Full RIASEC State Congruence 
Unstand. 
Coeff. 
-.46*** 
.37 
-.07 
-.14 
.15 
.27 
.70* 
 
 
 
 
 
 
 
.55 
.55 

Stand. 
Error 
.07 
.41 
.39 
.42 
.36 
.33 
.30 
 
 
 
 
 
 
 
 
 

T-
Value 
-6.49 
.91 
-.17 
-.33 
.42 
.82 
2.29 
 
 
 
 
 
 
 
 
 

Stand. 
Coeff. 
 
.22 
-.04 
-.08 
.09 
.16 
.41* 
 
 
 
 
 
 
 
 
 

 

 

 

277 

 

 

Table 143: 

Results for Polynomial Regression for Full RIASEC Model Congruence Predicting Daily Intrinsic Motivation. 

 

 
Variable 

Intercept 
State Realistic Congruence 
State Investigative Congruence 
State Artistic Congruence 
State Social Congruence 
State Enterprising Congruence 
State Conventional Congruence 
Trait Realistic Congruence 
Trait Investigative Congruence 
Trait Artistic Congruence 
Trait Social Congruence 
Trait Enterprising Congruence 
Trait Conventional Congruence 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

Full RIASEC State Congruence 
Unstand. 
Coeff. 
-1.42*** 
.09 
.46* 
.62*** 
.62*** 
-.46 
-.01 
 
 
 
 
 
 
 
.55 
.55 

Stand. 
Error 
.20 
.20 
.19 
.17 
.16 
.26 
.24 
 
 
 
 
 
 
 
 
 

T-
Value 
-7.01 
.44 
2.44 
3.57 
3.87 
-1.76 
-.04 
 
 
 
 
 
 
 
 
 

Stand. 
Coeff. 
 
.05 
.25* 
.36*** 
.36*** 
-.26 
-.01 
 
 
 
 
 
 
 
 
 

Full RIASEC State & Trait Congruence 
Unstand. 
Coeff. 
-1.41*** 
.08 
.33 
.47 
.55* 
-.77 
.07 
.05 
.24 
.12 
.14 
.33 
-.28 
 
.55 
.55 

Stand. 
Coeff. 
 
.04 
.19 
.27 
.32* 
-.43 
.04 
.03 
.13 
.07 
.08 
.18 
-.16 
 
 
 

Stand. 
Error 
.20 
.30 
.24 
.39 
.26 
.42 
.28 
.32 
.33 
.39 
.30 
.44 
.33 
 
 
 

T-
Value 
-6.93 
.26 
1.39 
1.21 
2.11 
-1.85 
.26 
.14 
.72 
.31 
.46 
.75 
-.85 
 
 
 

 

 

 

278 

 

 

Table 144: 

Results for Polynomial Regression for Full RIASEC Model Congruence Predicting Daily Engagement. 

 

 
Variable 

Intercept 
State Realistic Congruence 
State Investigative Congruence 
State Artistic Congruence 
State Social Congruence 
State Enterprising Congruence 
State Conventional Congruence 
Trait Realistic Congruence 
Trait Investigative Congruence 
Trait Artistic Congruence 
Trait Social Congruence 
Trait Enterprising Congruence 
Trait Conventional Congruence 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

Full RIASEC State Congruence 
Unstand. 
Coeff. 
-1.49*** 
.21 
.40*** 
.19 
.55*** 
.01 
-.02 
 
 
 
 
 
 
 
.56 
.56 

Stand. 
Error 
.20 
.15 
.14 
.14 
.12 
.16 
.17 
 
 
 
 
 
 
 
 
 

T-
Value 
-7.61 
1.40 
2.85 
1.42 
4.54 
.06 
-.14 
 
 
 
 
 
 
 
 
 

Stand. 
Coeff. 
 
.12 
.23*** 
.11 
.33*** 
.01 
-.01 
 
 
 
 
 
 
 
 
 

Full RIASEC State & Trait Congruence 
Unstand. 
Coeff. 
-1.41*** 
.19 
.72*** 
-.16 
.49 
-.42 
-.78 
.03 
-.66*** 
.35 
.08 
.54 
.97* 
 
.57 
.57 

Stand. 
Coeff. 
 
.11 
.41*** 
-.09 
.29 
-.24 
-.44 
.02 
-.36*** 
.20 
.05 
.31 
.55* 
 
 
 

Stand. 
Error 
.20 
.34 
.18 
.33 
.27 
.30 
.41 
.33 
.25 
.31 
.28 
.30 
.39 
 
 
 

T-
Value 
-7.14 
.55 
3.93 
-.50 
1.82 
-1.41 
-1.90 
.09 
-2.71 
1.13 
.28 
1.80 
2.51 
 
 
 

 

 

 

 

279 

 

 

Table 145: 

Results for Polynomial Regression for Full RIASEC Model Congruence Predicting Daily Perseverance. 

 

 
Variable 

Intercept 
State Realistic Congruence 
State Investigative Congruence 
State Artistic Congruence 
State Social Congruence 
State Enterprising Congruence 
State Conventional Congruence 
Trait Realistic Congruence 
Trait Investigative Congruence 
Trait Artistic Congruence 
Trait Social Congruence 
Trait Enterprising Congruence 
Trait Conventional Congruence 
 
Marginal R2 
Conditional R2 
Level 1 N =741, Level 2 N =165. * p < .05, *** p < .01. 

Full RIASEC State Congruence 
Unstand. 
Coeff. 
-1.04*** 
.18 
.52*** 
-.05 
.47*** 
-.13 
.32* 
 
 
 
 
 
 
 
.58 
.58 

Stand. 
Error 
.15 
.16 
.12 
.15 
.12 
.17 
.14 
 
 
 
 
 
 
 
 
 

T-
Value 
-7.10 
1.12 
4.50 
-.35 
3.89 
-.72 
2.35 
 
 
 
 
 
 
 
 
 

Stand. 
Coeff. 
 
.10 
.32* 
-.03 
.28*** 
-.07 
.19* 
 
 
 
 
 
 
 
 
 

Full RIASEC State & Trait Congruence 
Unstand. 
Coeff. 
-1.04*** 
-.06 
.67 
.80 
.30 
.03 
1.13* 
.28 
-.10 
-.92* 
.22 
-.17 
-.87 
 
.59 
.59 

Stand. 
Error 
.15 
.41 
.34 
.45 
.30 
.42 
.52 
.39 
.35 
.45 
.31 
.40 
.55 
 
 
 

Stand. 
Coeff. 
 
-.04 
.41 
.44 
.18 
.02 
-68* 
.16 
-.06 
-.50* 
.13 
-.10 
-.52 
 
 
 

T-
Value 
-7.06 
-.15 
1.96 
1.76 
1.00 
.08 
2.15 
.73 
-.27 
-2.04 
.70 
-.41 
-1.59 
 
 
 

 

 

 

280 

 

 

Table 146: 

Results for Prior Situational Realistic Interest (T) and Related Models Predicting Next Day Realistic Interest (T+1). 

 

 

Variable 

Level 1 

State Realistic (T) 
Realistic Situational 
Interest (T) 
Realistic Situational 
Interest (T) X Trait 
Realistic 

Prior State and Situational 
Stand. 
Coeff. 
 
.86*** 
-.05 

Stand. 
Error 
 
.05 
.05 

T-Value  Stand. 
Coeff. 
 
.83*** 
-.06 

 
16.90 
-1.01 

State, Situational, and Trait 

Stand. 
Error 
 
.06 
.05 

T-Value 

 
14.81 
-1.12 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.83*** 
.06 
.05 
-.06 

T-Value 

 
14.71 
-1.18 

 

 

 

 

 

 

-.03 

.06 

-.58 

 
Level 2 
 
Trait Realistic 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =128, Level 2 N =56. * p < .05, *** p < .01. 
 

 
 
 
.71 
.71 

 
 
 
 
 

 
.06 
 
.71 
.71 

 
.05 
 
 
 

 
1.14 
 
 
 

 
.05 
 
.71 
.71 

 
.06 
 
 
 

 
.76 
 
 
 

 

 

 

 

 

 

 

281 

 

 

 

 

Table 147: 

Results for Prior Situational Investigative Interest (T) and Related Models Predicting Next Day Investigative Interest (T+1). 

 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.65*** 

Stand. 
Error 
 
.04 

T-Value  Stand. 
Coeff. 
 
.57*** 

 
14.94 

Stand. 
Error 
 
.05 

T-Value 

 
11.66 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.57*** 
.05 

T-Value 

 
11.67 

.15*** 

.04 

3.36 

.13*** 

.04 

3.09 

.14*** 

.04 

3.21 

 

 

 

 

 

 

.04 

.04 

1.10 

 

Variable 

Level 1 

State Investigative 
(T) 
Investigative 
Situational Interest 
(T) 
Investigative 
Situational Interest 
(T) X Trait 
Investigative 

Level 2 

 
.14*** 
 
.55 
.56 

 
.05 
 
 
 

 
3.05 
 
 
 

 
.14 
 
.55 
.56 

 
.05 
 
 
 

 
3.12 
 
 
 

 
 
 
.54 
.54 

Trait Investigative 
 
Marginal R2 
Conditional R2 
Level 1 N =318, Level 2 N =113. * p < .05, *** p < .01. 
 

 
 
 
 
 

 
 
 
 
 

 

 

 

 

282 

 

 

T-Value 

 
4.13 
1.37 

3.44 

 
.76 
 
 
 

 

Table 148: 

Results for Prior Situational Artistic Interest (T) and Related Models Predicting Next Day Artistic Interest (T+1). 

 

Variable 

Level 1 

State Artistic (T) 
Artistic Situational 
Interest (T) 
Artistic Situational 
Interest (T) X Trait 
Artistic 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.60*** 
.10 

Stand. 
Error 
 
.11 
.11 

T-Value  Stand. 
Coeff. 
 
.51*** 
.11 

 
5.60 
.92 

Stand. 
Error 
 
.15 
.11 

T-Value 

 
3.45 
.99 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.58*** 
.14 
.10 
.14 

 

 

 

 

 

 

31*** 

.09 

Level 2 

 
 
Trait Artistic 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =71, Level 2 N =34. * p < .05, *** p < .01. 
 

 
 
 
.42 
.42 

 
 
 
 
 

 

 

 

 

 

 

 

 
.11 
 
.43 
.43 

 
.13 
 
 
 

 
.83 
 
 
 

 
.10 
 
.51 
.52 

 
.13 
 
 
 

283 

 

 

Table 149: 

Results for Prior Situational Social Interest (T) and Related Models Predicting Next Day Social Interest (T+1). 

 

 

Variable 

Level 1 

State Social (T) 
Social Situational 
Interest (T) 
Social (T) X 
Situational Interest 
Trait Social 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.53*** 
.18*** 

Stand. 
Error 
 
.05 
.05 

T-Value  Stand. 
Coeff. 
 
.34*** 
.16*** 

 
10.38 
3.50 

Stand. 
Error 
 
.06 
.05 

T-Value 

 
6.03 
3.20 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.36*** 
.06 
.05 
.16 

T-Value 

 
6.50 
3.20 

 

 

 

 

 

 

-.10* 

.05 

-2.25 

Level 2 

 
 
Trait Social 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =284, Level 2 N =97. * p < .05, *** p < .01. 
 

 
 
 
.39 
.39 

 
 
 
 
 

 
.30*** 
 
.40 
.49 

 
.06 
 
 
 

 
5.24 
 
 
 

 
.28*** 
 
.42 
.48 

 
.05 
 
 
 

 
5.15 
 
 
 

 

 

 

 

 

 

 

284 

 

 

Table 150: 

Results for Prior Situational Enterprising Interest (T) and Related Models Predicting Next Day Enterprising Interest (T+1). 

 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.69*** 

Stand. 
Error 
 
.12 

T-Value  Stand. 
Coeff. 
 
.60*** 

 
5.59 

Stand. 
Error 
 
.14 

T-Value 

 
4.38 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.59*** 
.14 

T-Value 

 
4.28 

.10 

.12 

.85 

.08 

.12 

.61 

.11 

.13 

.85 

 

 

 

 

 

 

.14 

.12 

1.17 

 

Variable 

Level 1 

State Enterprising 
(T) 
Enterprising 
Situational Interest 
(T) 
Enterprising 
Situational Interest 
(T) X Trait 
Enterprising 

Level 2 

 
 
Trait Enterprising 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =38, Level 2 N =16. * p < .05, *** p < .01. 

 
 
 
.52 
.52 

 
 
 
 
 

 
.18 
 
.54 
.54 

 
.14 
 
 
 

 
1.34 
 
 
 

 
.17 
 
.55 
.55 

 
.14 
 
 
 

 
1.22 
 
 
 

 

 

 

 

 

 

285 

 

 

Table 151: 

Results for Prior Situational Conventional Interest (T) and Related Models Predicting Next Day Conventional Interest (T+1). 

 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.57*** 

Stand. 
Error 
 
.05 

T-Value  Stand. 
Coeff. 
 
.53*** 

 
10.59 

Stand. 
Error 
 
.05 

T-Value 

 
9.67 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.52*** 
.06 

.14*** 

.05 

2.66 

.13* 

.06 

2.25 

.14* 

.06 

T-Value 

 
9.43 

2.27 

 

 

 

 

 

 

.02 

.06 

.38 

 
.08 
 
.39 
.42 

 
.06 
 
 
 

 
1.46 
 
 
 

 
.09 
 
.39 
.43 

 
.06 
 
 
 

 
1.56 
 
 
 

 

Variable 

Level 1 

State Conventional 
(T) 
Conventional 
Situational Interest 
(T) 
Conventional 
Situational Interest 
(T) X Trait 
Conventional 
Level 2 

 
 
Trait Conventional 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =251, Level 2 N =92. * p < .05, *** p < .01. 
 

 
 
 
.40 
.40 

 
 
 
 
 

 

 

 

 

 

286 

 

 

Table 152: 

Results for State Realistic Interest and Related Models Predicting Realistic Situational Interest. 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.10 
.58*** 

Stand. 
Error 
 
.07 
.07 

 
1.31 
7.99 

T-Value  Stand. 
Coeff. 
 
.09 
.58*** 

Stand. 
Error 
 
.08 
.07 

T-Value  Stand. 
Coeff. 
 
.09 
.58*** 

Trait Moderation 
Stand. 
Error 
 
.08 
.07 

 
1.07 
7.82 

 

T-Value 

 
1.07 
7.76 

 

 
 
 
 
 

 

 

 

.00 

.07 

.02 

 
.02 
 
.36 
.36 

 
.08 
 
 
 

 
.29 
 
 
 

 
.02 
 
.36 
.36 

 
.09 
 
 
 

 
1.07 
 
 
 

 

Variable 

Level 1 

State Realistic (T) 
Realistic Situational 
Interest (T-1) 
Realistic Situational 
Interest (T-1) X Trait 
Realistic 

Level 2 

 

 

Trait Realistic 
 
Marginal R2 
Conditional R2 
Level 1 N =128, Level 2 N =56. * p < .05, *** p < .01. 

 
 
 
.37 
.37 

 
 
 
 
 

 

 

 

 

 

 

 

 

287 

 

 

Table 153: 

Results for State Investigative Interest and Related Models Predicting Investigative Situational Interest. 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.34*** 
.44*** 

Stand. 
Error 
 
.05 
.05 

 
7.26 
9.39 

T-Value  Stand. 
Coeff. 
 
.32*** 
.42*** 

Stand. 
Error 
 
.05 
.05 

T-Value  Stand. 
Coeff. 
 
.34*** 
.43*** 

Trait Moderation 
Stand. 
Error 
 
.05 
.05 

 
6.24 
8.93 

 

T-Value 

 
6.60 
9.05 

 

 
 
 
 
 

 

 

 

.12*** 

.04 

2.76 

 
.05 
 
.45 
.47 

 
.05 
 
 
 

 
.98 
 
 
 

 
.08 
 
.47 
.48 

 
.05 
 
 
 

 
1.63 
 
 
 

 

Variable 

Level 1 

State Investigative (T) 
Investigative 
Situational Interest (T-
1) 
Investigative 
Situational Interest (T-
1) X Trait Investigative 

Level 2 

 

 

 
 
 
.46 
.47 

Trait Investigative 
 
Marginal R2 
Conditional R2 
Level 1 N =318, Level 2 N =113. * p < .05, *** p < .01. 
 

 
 
 
 
 

 

 

 

 

 

 

 

288 

 

 

Table 154: 

Results for State Artistic Interest and Related Models Predicting Artistic Situational Interest. 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.15 
.46*** 

Stand. 
Error 
 
.11 
.11 

T-Value  Stand. 
Coeff. 
 
.11 
.44*** 

 
1.41 
4.30 

Stand. 
Error 
 
.12 
.11 

T-Value 

 
.91 
3.99 

 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.11 
.12 
.11 
.44*** 

T-Value 

 
.89 
3.87 

 

 

 

-.04 

.11 

-.34 

 
.10 
 
.30 
.43 

 
.13 
 
 
 

 
.80 
 
 
 

 
.09 
 
.30 
.43 

 
.13 
 
 
 

 
.70 
 
 
 

 

Variable 

Level 1 

 

 

 

State Artistic (T) 
Artistic 
Situational 
Interest (T-1) 
Artistic 
Situational 
Interest (T-1) X 
Trait Artistic 
 
Level 2 
 
Trait Artistic 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N =71, Level 2 N =34. * p < .05, *** p < .01. 
 

 
 
 
.31 
.41 

 
 
 
 
 

 

 

 

 

 

 

289 

 

 

Table 155: 

Results for State Social Interest and Related Models Predicting Social Situational Interest. 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.35*** 
-.01 

Stand. 
Error 
 
.06 
.05 

T-Value  Stand. 
Coeff. 
 
.33*** 
-.02 

 
5.90 
-.24 

Stand. 
Error 
 
.06 
.05 

T-Value 

 
5.24 
-.36 

 

Trait Moderation 
Stand. 
Stand. 
Error 
Coeff. 
 
 
.36*** 
.06 
.05 
-.01 

T-Value 

 
5.61 
-.27 

 

 

 

.14* 

.06 

2.17 

 
.05 
 
.12 
.57 

 
.08 
 
 
 

 
.61 
 
 
 

 
.09 
 
.14 
.57 

 
.08 
 
 
 

 
1.02 
 
 
 

 

Variable 

Level 1 

 

 

 

State Social (T) 
Social 
Situational 
Interest (T-1) 
Social 
Situational 
Interest (T-1) X 
Trait Social 
Level 2 
Trait Social 
 
Marginal R2 
Conditional R2 
Level 1 N =284, Level 2 N =97. * p < .05, *** p < .01. 
 

 
 
 
.12 
.56 

 
 
 
 
 

 
 
 
 
 

 

 

 

 

 

 

290 

 

 

Table 156: 

Results for State Enterprising Interest and Related Models Predicting Enterprising Situational Interest. 

State, Situational, and Trait 

Prior State and Situational 
Stand. 
Coeff. 
 
.13 
.51*** 

Stand. 
Error 
 
.15 
.15 

 
.87 
3.47 

T-Value  Stand. 
Coeff. 
 
.13 
.50*** 

Stand. 
Error 
 
.17 
.15 

T-Value  Stand. 
Coeff. 
 
.13 
.44*** 

Trait Moderation 
Stand. 
Error 
 
.17 
.17 

 
.75 
3.23 

 

T-Value 

 
.75 
2.62 

 

 
 
 
 
 

 

 

 

.15 

.16 

.93 

 
-.01 
 
.30 
.33 

 
.17 
 
 
 

 
-.04 
 
 
 

 
.07 
 
.30 
.34 

 
.19 
 
 
 

 
.36 
 
 
 

 

Variable 

Level 1 

State Enterprising (T) 
Enterprising 
Situational Interest (T-
1) 
Enterprising 
Situational Interest (T-
1) X Trait Enterprising 

Level 2 

 

 

Trait Enterprising 
 
Marginal R2 
Conditional R2 
Level 1 N =38, Level 2 N =16. * p < .05, *** p < .01. 

 
 
 
.31 
.33 

 
 
 
 
 

 

 

 

 

 

 

 

291 

 

 

Table 157: 

Results for State Conventional Interest and Related Models Predicting Conventional Situational Interest. 

Prior State and Situational 
Stand. 
Coeff. 
 
.20*** 
.62*** 

Stand. 
Error 
 
.05 
.05 

T-
Value 
 
4.35 
13.20 

State, Situational, and Trait 
Stand. 
Coeff. 
 
.20*** 
.60*** 

Stand. 
Error 
 
.05 
.05 

 
4.16 
12.25 

T-Value  Stand. 
Coeff. 
 
.20*** 
.60*** 

Trait Moderation 
Stand. 
Error 
 
.05 
.05 

 

T-
Value 
 
4.14 
12.23 

 

 

 
 
 
 
 

 

 
 
 
 
 

.07 

.05 

1.46 

.01 

.04 

.18 

 
 
 
.52 
.52 

 
 
 
 
 

 
 
 
 
 

 
.07 
 
.52 
.52 

 
.05 
 
 
 

 
1.47 
 
 
 

 

Variable 

Level 1 

State Conventional (T) 
Conventional Situational 
Interest (T-1) 
Conventional Situational 
Interest (T-1) X Trait 
Conventional 

Level 2 

Trait Conventional 
 
Marginal R2 
Conditional R2 
Level 1 N =251, Level 2 N =92. * p < .05, *** p < .01. 
 

 
 
 
.52 
.52 

 

 

 

 

292 

 

 

Table 158: 

Results for Polynomial Regression and Related Models for State Realistic Interest (T) Predicting Next Day Realistic Interest (T+1). 

 

 

Variable 

Person 
Unstand. 
Coeff. 
3.06*** 
 
 

Stand. 
Error 
.03 
 
 

T-
Value 
95.55 
 
 

Stand. 
Coeff. 
.77***  3.06*** 
 
 

.70*** 
.30*** 

Person & Environment 
Unstand. 
Coeff. 

Stand. 
Error 
.03 
.04 
.04 

T-
Value 
91.96 
17.87 
7.64 

Stand. 
Coeff. 
 
.59*** 
.25*** 

Polynomial Regression 
Unstand. 
Coeff. 
3.07*** 
.70*** 
.30*** 

Stand. 
Error 
.05 
.04 
.05 

T-
Value 
66.66 
17.36 
6.14 

Stand. 
Coeff. 
 
.58*** 
.25*** 

 

 

 

 

 

 

 

 

 

 

Intercept 
State Realistic 
Realistic 
Daily 
Activities 
State 
Realistic2 
Realistic 
Daily 
Activities2  
State Realistic 
X Realistic 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N = 582, Level 2 N =156. * p < .05, *** p < .01. 
 

 
.61 
.61 

 
 
 

 
 
 

 
 
 

 

 

 

 

 

 

 

 

 

 
 
 

 

 

 

 
 
 

-.01 

.04 

-.30 

-.01 

.01 

.03 

.22 

.01 

-.01 

.05 

-.27 

-.01 

 
.62 
.65 

 
 
 

 
 
 

 
 
 

 

 

 

 
.62 
.65 

 

 

 

 
 
 

293 

 

 

Table 159: 

Polynomial Regression Fit Line Coefficients for State Realistic Interest Congruence Predicting Next Day Realistic Interest 
(T+1). 

Polynomial Regression Terms 

Realistic Fit Line (X=Y) 
Realistic Fit Line Curvilinearity 
Realistic Misfit Line (X=-Y) 
Realistic Misfit Line Curvilinearity 
Level 1 N =582, Level 2 N = 156. * p < .05. 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
1.00* 
-.02 
.40 
.01 

.64 
-.08 
-.07 
-.42 

.91 
.06 
.36 
.14 

 

 

 

 

 

 

 

 

294 

 

 

Table 160: 

Results for Polynomial Regression and Related Models for State Investigative Interest (T) Predicting Next Day Investigative Interest 
(T+1). 

 

 

Variable 

Person 
Unstand. 
Coeff. 
4.08*** 
.96*** 

Stand. 
Error 
.04 
.04 

T-
Value 
98.12 
23.12 

Stand. 
Coeff. 
 
.69*** 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Investigative 
Investigative 
Daily 
Activities 
State 
Investigative2 
Investigative 
Daily 
Activities2  
State 
Investigative X 
Investigative 
Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N = 582, Level 2 N =156. * p < .05, *** p < .01. 

 
.48 
.48 

 
 
 

 
 
 

 

 

 

 

 

 

 

 

 

Person & Environment 
Unstand. 
Coeff. 
4.08*** 
.86*** 

Stand. 
Error 
.04 
.05 

T-
Value 
98.78 
16.30 

Stand. 
Coeff. 
 
.62*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.05*** 
.88*** 

Stand. 
Error 
.06 
.06 

T-
Value 
62.04 
15.01 

Stand. 
Coeff. 
 
.64*** 

.16*** 

.05 

2.98 

.11 

.15*** 

.06 

2.62 

.11*** 

 

 

 

 
 
 

 

 

 

 
 
 

.04 

.05 

.78 

.03 

-.03 

.05 

-.63 

-.03 

.03 

.07 

.41 

.02 

 
.49 
.49 

 
 
 

 
 
 

 
 
 

 

 

 

 
.49 
.49 

 

 

 

 
 
 

295 

 

 

Table 161: 

Polynomial Regression Fit Line Coefficients for State Investigative Interest Congruence Predicting Next Day Investigative 
Interest (T+1). 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
1.03* 
.03 
.74* 
-.02 

.57 
-.03 
.08 
-.17 

.91 
.19 
.64 
.46 

Polynomial Regression Terms 

Investigative Fit Line (X=Y) 
Investigative Fit Line Curvilinearity 
Investigative Misfit Line (X=-Y) 
Investigative Misfit Line Curvilinearity 
Level 1 N =582, Level 2 N =156 . * p < .05. 

 

 

 

 

296 

 

 

Table 162: 

Results for Polynomial Regression and Related Models for State Artistic Interest (T) Predicting Next Day Artistic Interest (T+1). 

 

 

Variable 

Person 
Unstand. 
Coeff. 
3.54*** 
1.20*** 
 

Stand. 
Error 
.04 
.04 
 

T-
Value 
99.04 
33.48 
 

 
 

 

Intercept 
State Artistic 
Artistic Daily 
Activities 
State Artistic2 
Artistic Daily 
Activities2  
State Artistic X 
Artistic Daily 
Activities 
 
 
Marginal R2 
 
Conditional R2 
 
Level 1 N = 582, Level 2 N =156. * p < .05, *** p < .01. 
 

 
.66 
.66 

 
 
 

 
 
 

 
 

 

 
 

 

 
 

 

Person & Environment 
Unstand. 
Stand. 
Coeff. 
Coeff. 
 
3.54*** 
.81***  1.05*** 
 

Stand. 
Error 
.04 
.04 
.04 

T-
Value 
95.89 
24.19 
4.76 

.21*** 

Polynomial Regression 
Unstand. 
Stand. 
Coeff. 
Coeff. 
 
3.56*** 
.71***  1.06*** 
.14*** 

Stand. 
Error 
.06 
.05 
.05 

T-
Value 
62.25 
23.38 
4.04 

.21*** 

Stand. 
Coeff. 
 
.72*** 
.14 

 
 

 

 
.66 
.67 

 
 

 

 
 
 

 
 

 

 
 
 

 
 

 

 
 
 

-.02 
.01 

-.03 

 
.66 
.67 

.04 
.04 

-.39 
.28 

-.01 
.01 

.05 

-.50 

-.02 

 
 
 

 
 
 

 
 
 

 

 

 

297 

 

 

Table 163: 

Polynomial Regression Fit Line Coefficients for State Artistic Interest Congruence Predicting Next Day Artistic Interest 
(T+1). 

Polynomial Regression Terms 

Artistic Fit Line (X=Y) 
Artistic Fit Line Curvilinearity 
Artistic Misfit Line (X=-Y) 
Artistic Misfit Line Curvilinearity 
Level 1 N =582, Level 2 N = 156. * p < .05. 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
1.26* 
-.03 
.85 
.02 

1.09 
.10 
.58 
.19 

.71 
-.11 
-.03 
-.34 

 

 

 

 

 

 

 

 

298 

 

 

Table 164: 

Results for Polynomial Regression and Related Models for State Social Interest (T) Predicting Next Day Social Interest (T+1). 

 

Person 
Unstand. 
Coeff. 
4.50*** 
.89*** 
 

Stand. 
Error 
.04 
.04 
 

T-
Value 
101.14 
20.92 
 

Stand. 
Coeff. 
 
.65*** 
 

 

Variable 

 
 

 

Intercept 
State Social 
Social Daily 
Activities 
State Social2 
Social Daily 
Activities2  
State Social X 
Social Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N = 582, Level 2 N =156. * p < .05, *** p < .01. 
 

 
.45 
.47 

 
 
 

 
 

 

 
 

 

 
 
 

 
 

 

 
 
 

 

 

 

Stand. 
Coeff. 
 
.53*** 
.20*** 

 
 

 

 
 
 

Polynomial Regression 
Unstand. 
Coeff. 
4.50*** 
.75*** 
.27*** 

Stand. 
Error 
.07 
.06 
.05 

T-
Value 
69.24 
11.98 
5.06 

Stand. 
Coeff. 
 
.55*** 
.19 

.04 
.01 

.05 
.05 

.95 
.18 

.05 
.01 

-.09 

.07 

-1.35 

-.07 

 
.48 
.50 

 
 
 

 
 
 

 
 
 

Person & Environment 
Unstand. 
Coeff. 
4.50*** 
.73*** 
.27*** 

Stand. 
Error 
.04 
.05 
.05 

T-
Value 
102.62 
14.08 
5.17 

 
 

 

 
.47 
.50 

 
 

 

 
 
 

 
 

 

 
 
 

299 

 

 

Table 165: 

Polynomial Regression Fit Line Coefficients for State Social Interest Congruence Predicting Next Day Social Interest (T+1). 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
1.02* 
-.04 
.48* 
.15 

.55 
-.11 
.01 
-.29 

.92 
.11 
.47 
.38 

Polynomial Regression Terms 

Social Fit Line (X=Y) 
Social Fit Line Curvilinearity 
Social Misfit Line (X=-Y) 
Social Misfit Line Curvilinearity 
Level 1 N =582, Level 2 N = 156. * p < .05. 

 

 

 

 

 

 

 

 

 

 

 

300 

 

 

Table 166: 

Results for Polynomial Regression and Related Models for State Enterprising Interest (T) Predicting Next Day Enterprising 
Interest (T+1). 

 

Person 
Unstand. 
Coeff. 
2.79*** 
.79*** 

 

Stand. 
Error 
.03 
.03 

T-
Value 
80.02 
23.48 

Stand. 
Coeff. 
 
.70*** 

 

Variable 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Enterprising 
Enterprising 
Daily 
Activities 
State 
Enterprising2 
Enterprising 
Daily 
Activities2  
State 
Enterprising 
X 
Enterprising 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N = 582, Level 2 N =156. * p < .05, *** p < .01. 
 

 
.51 
.53 

 
 
 

 
 
 

 
 
 

 

 

Person & Environment 
Unstand. 
Coeff. 
2.78*** 
.69*** 

Stand. 
Error 
.03 
.04 

T-
Value 
86.15 
18.49 

Stand. 
Coeff. 
 
.61*** 

Polynomial Regression 
Unstand. 
Coeff. 
2.79*** 
.70*** 

Stand. 
Error 
.05 
.04 

T-
Value 
59.66 
18.70 

Stand. 
Coeff. 
 
.62*** 

.23*** 

.04 

6.04 

.20*** 

.22*** 

.05 

4.64 

.19*** 

 

 

 

 
 
 

 

 

 

 
 
 

-.01 

.04 

-.15 

-.01 

.02 

.03 

.74 

.03 

-.05 

.04 

-1.06 

-.05 

 
.56 
.56 

 
 
 

 
 
 

 
 
 

 

 

 

 
.55 
.56 

 

 

 

 
 
 

301 

 

 

 

Table 167: 

Polynomial Regression Fit Line Coefficients for State Enterprising Interest Congruence Predicting Next Day Enterprising 
Interest (T+1). 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
.92* 
-.03 
.48* 
.06 

.58 
-.06 
.05 
-.18 

.82 
.09 
.43 
.32 

Polynomial Regression Terms 

Enterprising Fit Line (X=Y) 
Enterprising Fit Line Curvilinearity 
Enterprising Misfit Line (X=-Y) 
Enterprising Misfit Line Curvilinearity 
Level 1 N =582, Level 2 N = 156. * p < .05. 

 

 

 

 

302 

 

 

Table 168: 

Results for Polynomial Regression and Related Models for State Conventional Interest (T) Predicting Next Day Conventional 
Interest (T+1). 

 

 

Variable 

Person 
Unstand. 
Coeff. 
4.19*** 
.99*** 

Stand. 
Error 
.04 
.04 

T-
Value 
103.87 
24.56 

Stand. 
Coeff. 
 
.71*** 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 
State 
Conventional 
Conventional 
Daily 
Activities 
State 
Conventional2 
Conventional 
Daily 
Activities2  
State 
Conventional 
X 
Conventional 
Daily 
Activities 
 
Marginal R2 
Conditional 
R2 
Level 1 N = 582, Level 2 N =156. * p < .05, *** p < .01. 
 

 
.51 
.51 

 
 
 

 
 
 

 
 
 

 

Person & Environment 
Unstand. 
Coeff. 
4.19*** 
.77*** 

Stand. 
Error 
.04 
.05 

T-
Value 
106.99 
14.57 

Stand. 
Coeff. 
 
.56*** 

Polynomial Regression 
Unstand. 
Coeff. 
4.15*** 
.85** 

Stand. 
Error 
.06 
.06 

T-
Value 
68.85 
13.81 

Stand. 
Coeff. 
 
.61*** 

.32*** 

.05 

6.03 

.23*** 

.28*** 

.05 

5.16 

.20*** 

 

 

 

 
 
 

 

 

 

 
 
 

.10 

.05 

2.14 

.11 

-.05 

.06 

-.82 

-.04 

-.03 

.08 

-.40 

-.02 

 
.54 
.54 

 
 
 

 
 
 

 
 
 

 

 

 

 
.54 
.54 

 

 

 

 
 
 

303 

 

 

Table 169: 

Polynomial Regression Fit Line Coefficients for State Conventional Interest Congruence Predicting Next Day Conventional 
Interest (T+1). 

 

Polynomial Regression Terms 

Conventional Fit Line (X=Y) 
Conventional Fit Line Curvilinearity 
Conventional Misfit Line (X=-Y) 
Conventional Misfit Line Curvilinearity 
Level 1 N =582, Level 2 N =156 . * p < .05. 

 

 

 

Unstand. Coefficient  Lower 95% CI  Upper 95% CI 
1.14* 
.03 
.57 
.09 

1.01 
.14 
.39 
.57 

.72 
-.05 
-.11 
-.33 

304 

 

 

Table 170: 

Bivariate Within-Person Relationships Between State Interests and Resources. 

 

Variable  State 

Realistic 
-.19*** 
.27*** 

State 
Investigative 
-.23*** 
.26*** 

State 
Artistic 
-.17*** 
.29*** 

State 
Social 
-.31*** 
.35*** 

State 
Enterprising 
-.16*** 
.28*** 

-.03 

-.04 

Depletion 
Positive 
Affect 
Negative 
Affect 
Sleep 
Quality 
Sleep 
Quantity 
Note: Variables were collected concurrently in the morning. N = 949-950. * p < .05 ***p < .01 
 

.16*** 

.07* 

-.08* 

.04 

.06 

.01 

.06 

.00 

-.03 

.04 

.02 

-.03 

.06 

State 
Conventional 
-.17*** 
.23*** 

.04 

.06 

.05 

 

 

 

305 

 

 

Table 171: 

 

Multilevel Regression Results for Relationships Between State Realistic Interests and 
Resources. 
Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
2.78*** 
-.04 
.20*** 

.24 
.04 
.04 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 950, Level 2 N = 173. * p < .05 *** p < .01 
 

.08 
-.02 
 
.04 
.63 

.05 
.01 
 
 
 

.05 

.06 

11.45 
-1.08 
4.74 

.81 

1.46 
-1.23 
 
 
 

Stand. 
Coefficient 
 
-.04 
.16*** 

.03 

.05 
-.04 
 
 
 

 

 

 

 

 

 

 

 

306 

 

 

Table 172: 

Multilevel Regression Results for Relationships Between State Investigative Interests and 
Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
4.24*** 
-.17*** 
.23*** 

.05 

.30 
.05 
.05 

.07 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 949, Level 2 N = 173. * p < .05 *** p < .01 
 

.00 
-.02 
 
.06 
.51 

.07 
.02 
 
 
 

 

 

 

 

 

Stand. 
Coefficient 
 
-.12*** 
.16*** 

.02 

.00 
-.05 
 
 
 

14.05 
-3.37 
4.34 

.73 

-.06 
-1.45 
 
 
 

307 

 

 

Table 173: 

Multilevel Regression Results for Relationships Between State Artistic Interests and Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
2.93*** 
.08 
.34*** 

-.06 

.29 
.05 
.05 

.07 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 949, Level 2 N = 173. * p < .05 *** p < .01 
 

.09 
-.02 
 
.05 
.66 

.06 
.01 
 
 
 

 

 

 

 

 

 

Stand. 
Coefficient 
 
.05 
.22*** 

-.03 

.04 
-.05 
 
 
 

10.25 
1.66 
6.66 

-.88 

1.40 
-1.53 
 
 
 

308 

 

 

Table 174: 

Multilevel Regression Results for Relationships Between State Social Interests and Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
4.24*** 
-.15*** 
.30*** 

.02 

.28 
.05 
.05 

.07 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 948, Level 2 N = 173. * p < .05 *** p < .01 
 

.09 
-.02 
 
.09 
.53 

.06 
.02 
 
 
 

 

 

 

 

 

 

 

Stand. 
Coefficient 
 
-.11*** 
.21*** 

.01 

.05 
-.05 
 
 
 

15.00 
-3.13 
5.91 

.30 

1.46 
-1.59 
 
 
 

309 

 

 

Table 175: 

Multilevel Regression Results for Relationships Between State Enterprising Interests and 
Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
2.33*** 
-.05 
.26*** 

.16* 

.26 
.04 
.05 

.06 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 949, Level 2 N = 173. * p < .05 *** p < .01 
 

.08 
-.02 
 
.06 
.55 

.06 
.01 
 
 
 

 

 

 

Stand. 
Coefficient 
 
-.04 
.20*** 

.09* 

.05 
-.05 
 
 
 

9.05 
-1.06 
5.56 

2.57 

1.49 
-1.45 
 
 
 

310 

 

 

Table 176: 

Multilevel Regression Results for Relationships Between State Conventional Interests and 
Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
4.01*** 
-.10* 
.23*** 

.07 

.29 
.05 
.05 

.07 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 949, Level 2 N = 173. * p < .05 *** p < .01 
 

-.02 
-.01 
 
.04 
.52 

.06 
.02 
 
 
 

 

 

 

 

Stand. 
Coefficient 
 
-.08* 
.16*** 

.04 

-.01 
-.02 
 
 
 

13.73 
-2.09 
4.46 

1.04 

-.32 
-.65 
 
 
 

311 

 

 

Table 177: 

Bivariate Relationships Between Daily Activities and Resources. 

 

Variable  Realistic 
Activities 
-.06 
.27*** 

Investigative 
Activities 
-.05 
.20*** 

Activities 
Artistic 
-.09 
.31*** 

Social 
Activities 
-.11*** 
.25*** 

Enterprising 
Activities 
.00 
.25*** 

Conventional 
Activities 
-.01 
.15*** 

.00 

Depletion 
Positive 
Affect 
Negative 
Affect 
Sleep 
Quality 
Sleep 
Quantity 
Note: Resource variables were collected in the morning while activities variables were collected in the evening. N 

-.01 

-.03 

.06 

.03 

.06 

.01 

.01 

.04 

.05 

.05 

-.07 

-.05 

-.04 

-.04 

-.01 

.01 

.02 

= 748. * p < .05 ***p < .01 

 

 

 

 

 

 

 

 

 

312 

 

 

Table 178: 

Multilevel Regression Results for Relationships Between Realistic Daily Activities and 
Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
1.76*** 
.05 
.20*** 

.09 

.26 
.04 
.05 

.06 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 749, Level 2 N = 167. * p < .05 *** p < .01 
 

-.03 
00 
 
.03 
.60 

.06 
.01 
 
 
 

 

 

 

Stand. 
Coefficient 
 
.05 
.17*** 

.05 

-.02 
-.01 
 
 
 

6.71 
1.17 
4.34 

1.41 

-.54 
-.21 
 
 
 

313 

 

 

Table 179: 

Multilevel Regression Results for Relationships Between Investigative Daily Activities and 
Resources. 

 

Variable 

Stand. Error 

T-Value 

Stand. 
Coefficient 
 
.02 
.16*** 

.07 

.03 
-.03 
 
 
 

Unstand. 
Coefficient 
3.03*** 
.03 
.25*** 

.16 

.41 
.07 
.07 

.10 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 749, Level 2 N = 167. * p < .05 *** p < .01 
 

.06 
-.01 
 
.03 
.32 

.10 
.02 
 
 
 

7.46 
.48 
3.58 

1.64 

.64 
-.59 
 
 
 

 

 

 

 

 

 

 

 

314 

 

 

Table 180: 

 

Multilevel Regression Results for Relationships Between Artistic Daily Activities and 
Resources. 
Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
1.85*** 
.02 
.19*** 

.32 
.05 
.06 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 749, Level 2 N = 167. * p < .05 *** p < .01 
 

.10 
-.01 
 
.02 
.59 

.07 
.02 
 
 
 

.07 

.08 

 

 

 

 

 

 

Stand. 
Coefficient 
 
.01 
.13*** 

.04 

.06 
-.03 
 
 
 

5.82 
.31 
3.33 

.97 

1.47 
-.81 
 
 
 

315 

 

 

Table 181: 

Multilevel Regression Results for Relationships Between Social Daily Activities and Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
3.21*** 
.04 
.26*** 

.03 

.38 
.07 
.07 

.09 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 749, Level 2 N = 167. * p < .05 *** p < .01 
 

.00 
-.01 
 
.03 
.46 

.09 
.02 
 
 
 

 

 

 

 

 

Stand. 
Coefficient 
 
.03 
.17*** 

.01 

.00 
-.02 
 
 
 

8.43 
.56 
3.91 

.36 

.00 
-.58 
 
 
 

316 

 

 

Table 182: 

Multilevel Regression Results for Relationships Between Enterprising Daily Activities and 
Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
1.44*** 
.06 
.20*** 

.09 

.27 
.05 
.05 

.07 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 749, Level 2 N = 167. * p < .05 *** p < .01 
 

.06 
.01 
 
 
 

.02 
.00 
 
.03 
.57 

 

 

 

 

 

Stand. 
Coefficient 
 
.06 
.17*** 

.05 

.01 
-.01 
 
 
 

5.31 
1.34 
4.08 

1.40 

.35 
-.21 
 
 
 

317 

 

 

Table 183: 

Multilevel Regression Results for Relationships Between Conventional Daily Activities and 
Resources. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
2.82*** 
.08 
.25*** 

.07 

.41 
.07 
.07 

.10 

Intercept 
Depletion 
Positive Affect 
(State) 
Negative Affect 
(State) 
Sleep Quality 
Sleep Quantity 
 
Marginal R2 
Conditional R2 
Level 1 N = 749, Level 2 N = 167. * p < .05 *** p < .01 
 

.09 
.02 
 
 
 

.02 
.00 
 
.02 
.41 

 

 

 

 

Stand. 
Coefficient 
 
.05 
.16*** 

.03 

.01 
-.01 
 
 
 

6.93 
1.09 
3.52 

.74 

.18 
-.18 
 
 
 

318 

 

 

 

 

 

Table 184: 

Within-Person Correlation Between State Interests and Daily Activities. 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 
N = 748-749 *p <.05 *** p < .01 

 

r 
.61*** 
.62*** 
.57*** 
.63*** 
.56*** 
.68*** 

319 

 

 

Table 185: 

Multilevel Regression Results for Relationships Between Realistic Daily Activities and State 
Realistic Interest. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
.96*** 
.43*** 
 
.25 
.56 

Intercept 
State Realistic 
 
Marginal R2 
Conditional R2 
Level 1 N = 748, Level 2 N = 167. * p < .05 *** p < .01 
 

.11 
.03 
 
 
 

 

 

Stand. 
Coefficient 
 
.47*** 
 
 
 

8.43 
13.80 
 
 
 

320 

 

Unstand. 
Coefficient 
1.10*** 
.68*** 

Intercept 
State 
Investigative 
 
Marginal R2 
Conditional R2 
Level 1 N = 748, Level 2 N = 167. * p < .05 *** p < .01 
 

 
.39 
.53 

.15 
.03 

 
 
 

 

 

 

 

 

 

 

 

Table 186: 

Multilevel Regression Results for Relationships Between Investigative Daily Activities and State 
Investigative Interest. 

 

Variable 

Stand. Error 

T-Value 

Stand. 
Coefficient 
 
.63*** 

 
 
 

7.11 
19.90 

 
 
 

321 

 

 

Table 187: 

Multilevel Regression Results for Relationships Between Artistic Daily Activities and State 
Artistic Interest. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
.88*** 
.44*** 
 
.24 
.62 

Intercept 
State Artistic 
 
Marginal R2 
Conditional R2 
Level 1 N = 748, Level 2 N = 167. * p < .05 *** p < .01 
 

.14 
.03 
 
 
 

 

 

Stand. 
Coefficient 
 
.48*** 
 
 
 

6.38 
13.83 
 
 
 

322 

 

 

Table 188: 

Multilevel Regression Results for Relationships Between Social Daily Activities and State 
Social Interest. 

 

Variable 

Stand. Error 

T-Value 

Unstand. 
Coefficient 
.96*** 
.62*** 
 
.34 
.52 

Intercept 
State Social 
 
Marginal R2 
Conditional R2 
Level 1 N = 748, Level 2 N = 167. * p < .05 *** p < .01 
 

.17 
.03 
 
 
 

 

 

Stand. 
Coefficient 
 
.56*** 
 
 
 

5.61 
17.69 
 
 
 

323 

 

Unstand. 
Coefficient 
.97*** 
.41*** 

Intercept 
State 
Enterprising 
 
Marginal R2 
Conditional R2 
Level 1 N = 748, Level 2 N = 167. * p < .05 *** p < .01 
 

 
.20 
.58 

.11 
.03 

 
 
 

 

 

 

 

 

 

 

 

Table 189: 

Multilevel Regression Results for Relationships Between Enterprising Daily Activities and State 
Enterprising Interest. 

 

Variable 

Stand. Error 

T-Value 

Stand. 
Coefficient 
 
.43*** 

 
 
 

8.92 
13.46 

 
 
 

324 

 

Unstand. 
Coefficient 
.52*** 
.74*** 

Intercept 
State 
Conventional 
 
Marginal R2 
Conditional R2 
Level 1 N = 748, Level 2 N = 167. * p < .05 *** p < .01 
 

 
.44 
.58 

.16 
.03 

 
 
 

 

 

 

 

Table 190: 

Multilevel Regression Results for Relationships Between Conventional Daily Activities and 
State Conventional Interest. 

 

Variable 

Stand. Error 

T-Value 

Stand. 
Coefficient 
 
.66*** 

 
 
 

3.35 
21.79 

 
 
 

325 

 

 

Table 191: 

Correlation Between Pre and Post Trait Interests for DSIM and BPDRM. 

Interest 
Realistic 
Investigative 
Artistic 
Social 
Enterprising 
Conventional 
N= 133. * p < .05 *** p < .01 

DSIM 
.49*** 
.57*** 
.58*** 
.53*** 
.46*** 
.34*** 

BPDRM 
.74*** 
.87*** 
.80*** 
.72*** 
.74*** 
.73*** 

 

 

 

 

 

 

 

 

 

 

 

 

 

326 

 

 

 

 

Table 192: 

Correlations of Trait Interest Change from Pre to Post Survey. 

 

Realistic 

.06 

.25**
* 

Artistic 

Social 

.33**
* 

.37*** 

.09 

.32*** 

.17 

Investigative 

Enterprising 

 
Variable  DSIM  BPDRM  DSIM  BPDRM  DSIM  BPDRM  DSIM  BPDRM  DSIM  BPDRM  DSIM  BPDRM 
Mean 
State 
Interest 
Mean 
Daily 
Activities 
Dif. in 
Average 
from Pre 
Note: Only cases in which 4 or more state interests or daily activities in each respective interest domain were retained for analyses. N = 32 - 133. 
* p < .05, *** p < .01 

Conventional 

.72*** 

.24*** 

.24* 

.25**

.16 

.32**
* 

.68**
* 

.28**
* 

.27**
* 

.61**
* 

.28**
* 

.33**
* 

.57**
* 

.46**
* 

.46**
* 

.70**
* 

.11 

.05 

.04 

.12 

.12 

.13 

* 

.72**
* 

.26*** 

.22*** 

.24*** 

.24*** 

.32*** 

.37*** 

 

327 

 

 

Figure 1: 

Plot of CFA for State Realistic Interest. 

 

Note: Paths represent standardized coefficients. R.D. represents latent state Realistic interest. 

 

328 

 

 

Figure 2: 

Plot of CFA for State Investigative Interest. 

 

Note: Paths represent standardized coefficients. I.D. represents latent state Investigative interest. 

 

329 

 

 

Figure 3: 

Plot of CFA for State Artistic Interest. 

 

Note: Paths represent standardized coefficients. A.D. represents latent state Artistic interest. 

 

330 

 

 

Figure 4: 

Plot of CFA for State Social Interest. 

 

Note: Paths represent standardized coefficients. S.D. represents latent state Social interest. 

 

331 

 

 

Figure 5: 

Plot of CFA for State Enterprising Interest. 

 

Note: Paths represent standardized coefficients. E.D. represents latent state Enterprising interest. 

 

332 

 

 

Figure 6: 

Plot of CFA for State Conventional Interest. 

 

Note: Paths represent standardized coefficients. C.D. represents latent state Conventional interest. 

 

 

333 

 

 

Figure 7: 

Plot of CFA for State Interests Replicating Holland Structure.

 

Note: Paths represent standardized coefficients 

 

334 

 

 

Figure 8: 

Plot of Structural Equation Model for Latent Correlation Between Realistic State Interests (T) and Next Day Realistic State Interest 
(T+1). 

 

 

335 

 

 

Note: Paths represent standardized coefficients 

 

Figure 9: 

Plot of Structural Equation Model for Latent Correlation Between Investigative State Interests (T) and Next Day Investigative State 
Interest (T+1). 

 

Note: Paths represent standardized coefficients 

336 

 

 

 

Figure 10: 

Plot of Structural Equation Model for Latent Correlation Between Artistic State Interests (T) and Next Day Artistic State Interest 
(T+1). 

 

Note: Paths represent standardized coefficients 

 

337 

 

 

Figure 11: 

Plot of Structural Equation Model for Latent Correlation Between Social State Interests (T) and Next Day Social State Interest (T+1). 

 

Note: Paths represent standardized coefficients 

 

338 

 

 

Figure 12: 

Plot of Structural Equation Model for Latent Correlation Between Enterprising State Interests (T) and Next Day Enterprising State 
Interest (T+1). 

 

Note: Paths represent standardized coefficients 

 

339 

 

 

 

Figure 13: 

Plot of Structural Equation Model for Latent Correlation Between Conventional State Interests (T) and Next Day Conventional State 
Interest (T+1). 

 

Note: Paths represent standardized coefficients 

340 

 

 

 

Figure 14: 

Example of Response Surface curvature expected for Hypotheses 3a through 3e. 

 

7

6

5

4

3

2

1

0

Z

 

1.8

-0.6

Y

-3

3
-

8

.

1
-

6

.

0
-

.

6
0

X

3

.

8
1

Note: The response plot curvature for negative affect is expected to be rotated 180 degrees, with the lowest point along the fit line. 

341 

 

 

Figure 15: 

Response Plot of Congruence Between State Realistic Interests and Daily Realistic Activities Predicting Daily Positive Affect. 

 

Positive Affect

4

3.5

3

2.5

2

1.5

1

0.5

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Realistic

 

1.8

-0.6

Realistic 
Activities

 

 

342 

 

 

Figure 16: 

Response Plot of Congruence Between State Investigative Interests and Daily Investigative Activities Predicting Daily Positive Affect. 

 

Positive Affect

4

3.5

3

2.5

2

1.5

1

0.5

0

3

.

8
1

.

6
0

State 

Investigative

343 

 

 

1.8

-0.6

Invest.

Activities

-3

3
-

8

.

1
-

6

.

0
-

 

 

 

Figure 17: 

Response Plot of Congruence Between State Artistic Interests and Daily Artistic Activities Predicting Daily Positive Affect. 

 

Positive
Affect

4

3.5

3

2.5

2

1.5

1

0.5

0

1.8

-0.6

Artistic 
Activities

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Artistic

 

 

 

344 

 

 

Figure 18: 

Response Plot of Congruence Between State Social Interests and Daily Social Activities Predicting Daily Positive Affect. 

 

Positive Affect

3.5

3

2.5

2

1.5

1

0.5

1.8

-0.6

Social 
Activities

-3

3
-

8

.

1
-

6

.

0
-

.

8
1

.

6
0

0

3

State 
Social

 

 

 

345 

 

 

Figure 19: 

Response Plot of Congruence Between State Enterprising Interests and Daily Enterprising Activities Predicting Daily Positive Affect. 

 

Positive
Affect

4

3.5

3

2.5

2

1.5

1

0.5

0

1.8

-0.6

Enterprising 

Activities

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Enterprising

 

 

 

346 

 

 

Figure 20: 

Response Plot of Congruence Between State Conventional Interests and Daily Conventional Activities Predicting Daily Positive 
Affect. 

Positive Affect

 

4

3.5

3

2.5

2

1.5

1

0.5

0

1.8

-0.6

Convent.
Activities

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Conventional

 

 

 

347 

 

 

Figure 21: 

Response Plot of Congruence Between State Realistic Interests and Daily Realistic Activities Predicting Daily Negative Affect. 

 

Z

2

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

 

3

.

8
1

348 

 

1.8

-0.6
Y

-3

3
-

8

.

1
-

6

.

0
-

.

6
0

X

 

 

 

Figure 22: 

Response Plot of Congruence Between State Investigative Interests and Daily Investigative Activities Predicting Daily Negative Affect. 

 

Z

2

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

1.8

-0.6

Y

-3

3
-

8

.

1
-

6

.

0
-

.

6
0

X

3

.

8
1

 

 

 

349 

 

 

Figure 23: 

Response Plot of Congruence Between State Artistic Interests and Daily Artistic Activities Predicting Daily Negative Affect. 

 

2

1.8

1.6

1.4

1.2

Z

1

0.8

0.6

0.4

0.2

0

 

1.8

-0.6
Y

-3

3
-

8

.

1
-

6

.

0
-

.

6
0

X

 

 

3

.

8
1

350 

 

 

Figure 24: 

Response Plot of Congruence Between State Social Interests and Daily Social Activities Predicting Daily Negative Affect. 

 

2.5

2

1.5

Z

1

0.5

0

3

.

8
1

351 

 

 

1.8

-0.6

Y

-3

3
-

8

.

1
-

6

.

0
-

.

6
0

X

 

 

 

Figure 25: 

Response Plot of Congruence Between State Enterprising Interests and Daily Enterprising Activities Predicting Daily Negative Affect. 

 

Z

2

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

1.8

-0.6

Y

-3

3
-

8

.

1
-

6

.

0
-

.

6
0

X

3

.

8
1

 

 

 

352 

 

 

Figure 26: 

Response Plot of Congruence Between State Conventional Interests and Daily Conventional Activities Predicting Daily Negative 
Affect. 

 

Z

2

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

1.8

-0.6

Y

-3

3
-

8

.

1
-

6

.

0
-

.

6
0

X

3

.

8
1

 

 

 

353 

 

 

Figure 27: 

Response Plot of Congruence Between State Realistic Interests and Daily Realistic Activities Predicting Daily Intrinsic Motivation. 

 

Intrinsic 
Motivation

6

5

4

3

2

1

0

1.8

-0.6

Realistic 
Activities

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Realistic

 

 

 

354 

 

 

Figure 28: 

Response Plot of Congruence Between State Investigative Interests and Daily Investigative Activities Predicting Daily Intrinsic 
Motivation. 

 

Intrinsic 
Motivation

7

6

5

4

3

2

1

0

3

.

8
1

.

6
0

State 

Investigative

355 

 

 

1.8

-0.6

Invest. 
Activities

-3

3
-

8

.
1
-

6

.
0
-

 

Figure 29: 

Response Plot of Congruence Between State Artistic Interests and Daily Artistic Activities Predicting Daily Intrinsic Motivation. 

 

Intrinsic 
Motivation

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Artistic

1.8

-0.6

Artistic 
Activities

 

 

 

356 

 

 

Figure 30: 

Response Plot of Congruence Between State Social Interests and Daily Social Activities Predicting Daily Intrinsic Motivation. 

 

Intrinsic 
Motivation

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Social

1.8

-0.6

Social 
Activities

 

 

 

357 

 

 

Figure 31: 

Response Plot of Congruence Between State Enterprising Interests and Daily Enterprising Activities Predicting Daily Intrinsic 
Motivation. 

 

Intrinsic 
Motivation

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Enterprising

1.8

-0.6
Enter. Activities

 

 

 

358 

 

 

Figure 32: 

Response Plot of Congruence Between State Conventional Interests and Daily Conventional Activities Predicting Daily Intrinsic 
Motivation. 

 

Intrinsic 
Motivation

7

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Conventional

 

359 

 

1.8

-0.6

Convent. 
Activities

 

Figure 33: 

Response Plot of Congruence Between State Realistic Interests and Daily Realistic Activities Predicting Daily Engagement. 

 

Engage.

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Realistic

1.8

-0.6

Realistic 
Activities

 

 

 

360 

 

 

Figure 34: 

Response Plot of Congruence Between State Investigative Interests and Daily Investigative Activities Predicting Daily Engagement. 

 

Engage.

7

6

5

4

3

2

1

0

-1

-2

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Investigative

1.8

-0.6

Invest. 
Activities

 

 

 

361 

 

 

Figure 35: 

Response Plot of Congruence Between State Artistic Interests and Daily Artistic Activities Predicting Daily Engagement. 

 

Engage.

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Artistic

1.8

-0.6

Artistic 
Activities

 

 

 

362 

 

 

Figure 36: 

Response Plot of Congruence Between State Social Interests and Daily Social Activities Predicting Daily Engagement. 

 

Engage.

7

6

5

4

3

2

1

0

1.8

-0.6

Social 
Activities

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Social

 

 

 

363 

 

 

Figure 37: 

Response Plot of Congruence Between State Enterprising Interests and Daily Enterprising Activities Predicting Daily Engagement. 

 

Engage.

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Enterprising

1.8

-0.6

Enter. 
Activities

 

 

 

364 

 

 

Figure 38: 

Response Plot of Congruence Between State Conventional Interests and Daily Conventional Activities Predicting Daily Engagement. 

 

Engage.

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Conventional

1.8

-0.6

Convent. 
Activities

 

 

 

365 

 

 

 

Figure 39: 

Response Plot of Congruence Between State Realistic Interests and Daily Realistic Activities Predicting Daily Perseverance. 

 

Persever.

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

3

.

8
1

.

6
0

State 
Realistic

366 

 

 

1.8

-0.6

Realistic 
Activities

-3

3
-

8

.
1
-

6

.
0
-

 

Figure 40: 

Response Plot of Congruence Between State Investigative Interests and Daily Investigative Activities Predicting Daily Perseverance. 

 

Persever.

5

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Investigative

1.8

-0.6

Invest. 
Activities

 

 

 

367 

 

 

Figure 41: 

Response Plot of Congruence Between State Artistic Interests and Daily Artistic Activities Predicting Daily Perseverance. 

 

Persever.

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Artistic

1.8

-0.6

Artistic 
Activities

 

 

 

368 

 

 

Figure 42: 

Response Plot of Congruence Between State Social Interests and Daily Social Activities Predicting Daily Perseverance. 

 

Persever.

6

5

4

3

2

1

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 
Social

1.8

-0.6

Social 
Acticities

 

 

 

369 

 

 

Figure 43: 

Response Plot of Congruence Between State Enterprising Interests and Daily Enterprising Activities Predicting Daily Perseverance. 

 

Persever.

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Enterprising

1.8

-0.6

Enter. Activities

 

 

 

370 

 

 

Figure 44: 

Response Plot of Congruence Between State Conventional Interests and Daily Conventional Activities Predicting Daily Perseverance. 

 

Persever.

5

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

3

.

8
1

.

6
0

State 

Conventional

 

371 

 

1.8

-0.6

Convent. 
Activities

-3

3
-

8

.

1
-

6

.

0
-

 

Figure 45: 

Plot of Moderation of Artistic Situational interests and Next Day State Artistic Interests Relationship by Trait Artistic Interests. 

 

2

1.5

1

0.5

0

-0.5

-1

-1.5

t
s
e
r
e
t
n
I
 
c
i
t
s
i
t
r
A
 
e
t
a
t
S
 
y
a
D

 
t
x
e
N

Low Artistic Situational

High Artistic Situational

Low Trait
Artistic
High Trait
Artistic

 

 

 

 

372 

 

 

Figure 46: 

Plot of Moderation of Social Situational interests and Next Day State Social Interests Relationship by Trait Social Interests. 

 

1

0.5

0

-0.5

-1

-1.5

t
s
e
r
e
t
n
I
 
l
a
i
c
o
S
 
e
t
a
t
S
 
y
a
D

 
t
x
e
N

 

 

Low Social Situational

High Social Situational

Low Trait
Social
High Trait
Social

 

373 

 

 

Figure 47: 

Plot of Moderation of State Investigative interests and Same Day Investigative Situational Interests Relationship by Trait Investigative 
Interests. 

 

1.5

1

0.5

0

-0.5

-1

-1.5

t
s
e
r
e
t
n
I
 
l
a
n
o
i
t
a
u
t
i
S
 
e
v
i
t
a
g
i
t
s
e
v
n
I

Low State Investigative

High State Investigative

Low Trait
Investigative
High Trait
Investigative

 

374 

 

 

Figure 48: 

Plot of Moderation of State Social interests and Same Day Social Situational Interests Relationship by Trait Social Interests. 

 

2

1.5

1

0.5

0

-0.5

-1

-1.5

t
s
e
r
e
t
n
I
 
l
a
n
o
i
t
a
u
t
i

S

 
l
a
i
c
o
S

 

 

Low Trait
Social
High Trait
Social

 

Low Social Situational

High Social Situational

375 

 

 

 

Figure 49: 

Response Plot of Congruence Between State Realistic Interests (T) and Daily Realistic Activities (T) Predicting Next Day State 
Realistic Interests (T+1). 

State Realistic 

(T+1)

 

6

5

4

3

2

1

0

-1

1.8

-0.6

Realistic 

Activities (T)

-3

3
-

8

.
1
-

3

6

.

0
-

.

8
1

.

6
0

State 

Realistic (T)

 

376 

 

 

Figure 50: 

Response Plot of Congruence Between State Investigative Interests (T) and Daily Investigative Activities (T) Predicting Next Day 
State Investigative Interests (T+1). 

State Invest. 

(T+1)

 

8

7

6

5

4

3

2

1

0

1.8

-0.6

Invest. 

Activities (T)

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Investigative (T)

 

 

 

377 

 

 

Figure 51: 

Response Plot of Congruence Between State Artistic Interests (T) and Daily Artistic Activities (T) Predicting Next Day State Artistic 
Interests (T+1). 

State Artistic 

(T+1)

 

8

7

6

5

4

3

2

1

0

-1

1.8

-0.6

Artistic 

Activities (T)

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Artistic (T)

 

 

 

378 

 

 

Figure 52: 

Response Plot of Congruence Between State Social Interests (T) and Daily Social Activities (T) Predicting Next Day State Social 
Interests (T+1). 

 

State 
Social 
(T+1)

8

7

6

5

4

3

2

1

0

1.8

-0.6

Social 

Activities (T)

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Social (T)

 

 

 

379 

 

 

Figure 53: 

Response Plot of Congruence Between State Enterprising Interests (T) and Daily Enterprising Activities (T) Predicting Next Day State 
Enterprising Interests (T+1). 

 

State 

Enterprising 

(T+1)

6

5

4

3

2

1

0

-1

-3

3
-

8

.

1
-

6

.

0
-

3

.

8
1

.

6
0

State 

Enterprising (T)

1.8

-0.6

Enter. 

Activities (T)

 

 

 

380 

 

 

Figure 54: 

Response Plot of Congruence Between State Conventional Interests (T) and Daily Conventional Activities (T) Predicting Next Day 
State Conventional Interests (T+1). 

 

State 

Convent. 

(T+1)

8

7

6

5

4

3

2

1

0

3

8
6 1
0

.

.

State 

Conventional (T)

 

381 

 

1.8

-0.6

Convent. 
Activities (T)

-3

3
-

8

.

1
-

6

.

0
-

 

APPENDIX B – DSIM Item Pool 

 

Developmental Item Pool for the Daily Short Interest Measure (DSIM) 

People may prefer to engage in different activities. Please indicate how much you would prefer 
to do the following activities today. 
 
"Today, I would prefer to do activities that involve…" 

Realistic 

1. “Hands-on” tasks 

2. Being outdoors 

3. Using tools 

4. Fixing 

5. Building 

6. Physical activity 

7. Technical activities 

8. Working with gadgets 

9. Straight forward problems 

10. Practical tasks 

Investigative 

11. Research 

12. Mathematics 

13. Science 

14. Exploration 

15. Analytic thinking 

16. Abstract ideas 

17. Intellectual tasks 

18. Individually focused tasks 

19. Academics 

20. New ideas 

Artistic 

21. Creativity 

382 

 

 

 

22. Artistic expression 

23. Innovation 

24. Little structure 

25. Imagination 

26. Art 

27. Creating 

28. Creative writing 

29. Music 

30. Design 

Social 

31. Teaching 

32. Helping others 

33. Caring for others 

34. Working with others 

35. Guiding others 

36. Socializing 

37. Volunteering 

38. Communicating with others 

39. Providing services to others 

40. Close relationships 

Enterprising 

41. Leadership 

42. Persuading others 

43. Selling 

44. Strategic thinking 

45. Negotiation 

46. Achievement 

47. Risk 

48. Managing others 

49. Making decisions 

383 

 

 

 

50. Managing projects 

Conventional 

51. Routine 

52. Organization 

53. Structure 

54. Systematic work 

55. Detail focused tasks 

56. Clear rules 

57. Business 

58. Analyzing data 

59. Organizing information 

60. Repeated tasks 

384 

 

 

 

APPENDIX C - Study 1 Consent Form 

Research Participant Information and Consent Form 

You are being asked to take part in a research study. Researchers are required to provide research 
participants with a consent form to inform them of the research study, convey that participation 
is voluntary, explain the risks and benefits of participation in the study, and empower 
participants to make an informed decision to participate or not in the study. 
 
Study Title: Measuring Interests and Interest Variability 
This study has been approved by Michigan State University’s Institutional Review Board 
(STUDY00003564) 
 
1.  PURPOSE OF RESEARCH 
The purpose of this survey is to develop a measure of interests, and compare this measure to 
related psychological constructs and a previously established measure from the psychological 
literature. A secondary aim of the present study is to pilot test additional measures and collect 
calibration data for a future study.  
 2. WHAT YOU WILL DO 
If you choose to participate in this study, you will be asked a series of questions regarding the 
type of activities you prefer, your behavioral tendencies, thoughts on interests in general, and 
thoughts on your own interests, along with basic demographic information. Participation in this 
study is expected to take approximately 25 minutes. 
3. POTENTIAL BENEFITS  
You may not benefit personally from being in this study. However, we hope that this research 
will assist in the development of a measure of interests that can be used in future research, as 
well as help advance our understanding of interests broadly. 
 4. POTENTIAL RISKS 
There are no foreseeable risks to participating in the present study. 
 5.  PRIVACY AND CONFIDENTIALITY 
Information provided in this research study will be treated as confidential. Only members of the 
research team will have access to your full survey responses and every effort will be made to 
keep your information safe. Any survey responses shared outside of the research team will be 
deidentified, in that any information tying responses to you will be removed before being shared. 
 6. YOUR RIGHTS TO PARTICIPATE, SAY NO, OR WITHDRAW 
You have the right to say no to participating in this research. You have the right to stop at any 
time after you have started with no consequences to you. You will not lose any benefits that you 
would normally receive for completing the survey if you decide to stop before finishing. 
7.  COSTS AND COMPENSATION FOR BEING IN THE STUDY  
Participants who consent to take part in this survey will be awarded SONA credits through 
https://msu-psychology.sona-systems.com. In the SONA system, 1 hour of research participation 
is worth 1 SONA credit and this credit is pro-rated in 15-minute increments. It is up to individual 
course instructors to determine how many points this converts to in their classes (this should be 
specified in the syllabus for each course).   

385 

 

 

 

The duration of this online survey is approximately 25 minutes. Hence, participants who 
complete this survey will receive .50 SONA credits.  
Please do not complete this survey if you did not register for it on SONA. Some studies have 
prerequisites. If you did not see this study advertised in your SONA account (e.g., if a friend 
forwarded you the link), you should not complete this study. In order to receive credit for 
participation you MUST be registered for this study. 
Participation in this online survey is voluntary. You may withdraw at any time without penalty. 
This means that no SONA credits will be deducted from your account, nor will withdrawal have 
any effect on your relationship with any of your instructors. However, to receive credit for 
participation you must click through the entire survey.  
 8.  CONTACT INFORMATION 
If you have concerns or questions about this study or to report an injury, please contact Jacob 
Bradburn, Department of Psychology, Michigan State University, East Lansing, MI 48824, e-
mail: bradbu17@msu.edu.  

OR 

Ann Marie Ryan, Ph.D., Department of Psychology, Michigan State University, East Lansing, 
MI 48824, phone: (517) 353-8855, e-mail: ryanan@msu.edu 

If you have questions or concerns about your role and rights as a research participant, would like 
to obtain information or offer input, or would like to register a complaint about this study, you 
may contact, anonymously if you wish, the Michigan State University’s Human Research 
Protection Program at 517-355-2180, Fax 517-432-4503, or e-mail irb@msu.edu or regular mail 
at 4000 Collins Rd, Suite 136, Lansing, MI 48910. 

  

 

 

 

 

 

 

386 

 

 

 

APPENDIX D – Study 1 Debriefing Form 

Debriefing Form 

Thank you for participating in our study. This form is designed to provide you with information 
about the purpose and importance of this study. 
  
The purpose of this study was to develop a measure that can accurately gauge an individual’s 
interests day-to-day. By interests, we are referring to an individual’s preferences for different 
types of work or activities. If we are interested in studying constructs such as interests, 
personality, and attitudes that cannot be readily observed, we must develop measures that are 
able to assess these characteristics. Creating measures of psychological constructs that are 
accurate and reliable is critical for psychological research.  
  
To learn more about interests, please see these articles: 
 
Rounds, J., & Su, R. (2014). The Nature and Power of Interests. Current Directions in 
Psychological Science, 23(2), 98-103. DOI: 10.1177/0963721414522812 
 
Holland, J. L. (1959). A Theory of Vocational Choice. Journal of Counseling Psychology, 6(1), 
35-45. DOI: 10.1037/h0040767 
  
Armstrong, P. I., Allison, W., & Rounds, J. (2008). Development and initial validation of brief 
public domain RIASEC marker scales. Journal of Vocational Behavior, 73(2), 287-299. DOI: 
10.1016/j.jvb.2008.06.003 
  
If you are interested in measuring your own interests and what occupations might fit these 
interests, consider taking the U.S. Department of Labor’s Interest Profiler assessment. 
  
Given the nature of the questions within this survey, we anticipate that there are and will be no 
risks involved for any of our participants. However, if you did recall an event that negatively 
impacted you, please contact the appropriate number below:  

  

MSU Human Research Protection Program (517-355-2180) 

Office of Institutional Equity (517-353-3922) 

National suicide hotline (1-800-273-8255) 

Emergency number (911) 

  
If you have questions or concerns regarding this study, please do not hesitate to contact the 
investigators. Additionally, if you would like more information about the study or have further 
questions about it, please feel free to contact: 
  

387 

 

 

Jacob Bradburn, Department of Psychology, Michigan State University, East Lansing, MI 48824, 
e-mail: bradbu17@msu.edu.  

 

Ann Marie Ryan, Ph.D., Department of Psychology, Michigan State University, East Lansing, 
MI 48824, phone: (517) 353-8855, e-mail: ryanan@msu.edu 

OR 

 

388 

 

 

 

Daily Short Interest Measure Developmental Item Pool 

APPENDIX E – Study 1 Measures 

People may prefer to engage in different activities. Please indicate how much you would prefer 
to do the following activities today. 
 
"Today, I would prefer to do activities that involve…" 

 [“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither disagree or agree”, 
“Somewhat agree”, “Agree”, and “Strongly agree”] 

1. “Hands-on” tasks 

2. Being outdoors 

3. Using tools 

4. Fixing 

5. Building 

6. Physical activity 

7. Technical activities 

8. Working with gadgets 

9. Straight forward problems 

10. Practical tasks 

11. Research 

12. Mathematics 

13. Science 

14. Exploration 

15. Analytic thinking 

16. Abstract ideas 

17. Intellectual tasks 

18. Individually focused tasks 

19. Academics 

20. New ideas 

21. Creativity 

22. Artistic expression 

23. Innovation 

389 

 

 

 

24. Little structure 

25. Imagination 

26. Art 

27. Creating 

28. Creative writing 

29. Music 

30. Design 

31. Teaching 

32. Helping others 

33. Caring for others 

34. Working with others 

35. Guiding others 

36. Socializing 

37. Volunteering 

38. Communicating with others 

39. Providing services to others 

40. Close relationships 

41. Leadership 

42. Persuading others 

43. Selling 

44. Strategic thinking 

45. Negotiation 

46. Achievement 

47. Risk 

48. Managing others 

49. Making decisions 

50. Managing projects 

51. Routine 

52. Organization 

53. Structure 

390 

 

 

 

54. Systematic work 

55. Detail focused tasks 

56. Clear rules 

57. Business 

58. Analyzing data 

59. Organizing information 

60. Repeated tasks 

 

Brief Public Domain RIASEC Markers Scales (Armstrong et al., 2008) 

Please indicate your preference for the following activities in general, not just for today.  
 
In general, how much would you like to engage in the following tasks? 

 [“Dislike very much”, “Dislike”, “Neither like nor dislike”, “Like”, “Like very much”] 

1. Test the quality of parts before shipment 
2. Lay brick or tile 
3. Work on an offshore oil-drilling rig 
4. Assemble electronic parts 
5. Operate a grinding machine in a factory 
6. Fix a broken faucet 
7. Assemble products in a factory 
8. Install flooring in houses 
9. Study the structure of the human body 
10. Study animal behavior 
11. Do research on plants or animals 
12. Develop a new medical treatment or procedure 
13. Conduct biological research 
14. Study whales and other types of marine life 
15. Work in a biology lab 
16. Make a map of the bottom of an ocean 
17. Conduct a musical choir 
18. Direct a play 
19. Design artwork for magazines 
20. Write a song 
21. Write books or plays 
22. Play a musical instrument 
23. Perform stunts for a movie or television show 
24. Design sets for plays 
25. Give career guidance to people 
26. Do volunteer work at a non-profit organization 
27. Help people who have problems with drugs or alcohol 

391 

 

 

 

28. Teach an individual an exercise routine 
29. Help people with family-related problems 
30. Supervise the activities of children at a camp 
31. Teach children how to read 
32. Help elderly people with their daily activities 
33. Sell restaurant franchises to individuals 
34. Sell merchandise at a department store 
35. Manage the operations of a hotel 
36. Operate a beauty salon or barber shop 
37. Manage a department within a large company 
38. Manage a clothing store 
39. Sell houses 
40. Run a toy store 
41. Generate the monthly payroll checks for an office 
42. Inventory supplies using a hand-held computer 
43. Use a computer program to generate customer bills 
44. Maintain employee records 
45. Compute and record statistical and other numerical data 
46. Operate a calculator 
47. Handle customers' bank transactions 
48. Keep shipping and receiving records 

 

Big Five Inventory-2 (Soto & John, 2017a) 

Here are a number of characteristics that may or may not apply to you. For example, do you 
agree that you are someone who likes to spend time with others? Please indicate the extent to 
which you agree or disagree that each statement applies to you.  

 [“Disagree strongly”, “Disagree a little”, “Neutral; no opinion”, “Agree a little”, “Agree 
Strongly”] 

1. Is outgoing, sociable. 

2. Is compassionate, has a soft heart. 

3. Tends to be disorganized. 

4. Is relaxed, handles stress well. 

5. Has few artistic interests. 

6. Has an assertive personality. 

7. Is respectful, treats others with respect. 

8. Tends to be lazy. 

9. Stays optimistic after experiencing a setback. 

10. Is curious about many different things. 

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11. Rarely feels excited or eager. 

12. Tends to find fault with others. 

13. Is dependable, steady. 

14. Is moody, has up and down mood swings. 

15. Is inventive, finds clever ways to do things. 

16. Tends to be quiet. 

17. Feels little sympathy for others. 

18. Is systematic, likes to keep things in order. 

19. Can be tense. 

20. Is fascinated by art, music, or literature. 

21. Is dominant, acts as a leader. 

22. Starts arguments with others. 

23. Has difficulty getting started on tasks. 

24. Feels secure, comfortable with self. 

25. Avoids intellectual, philosophical discussions. 

26. Is less active than other people. 

27. Has a forgiving nature. 

28. Can be somewhat careless. 

29. Is emotionally stable, not easily upset. 

30. Has little creativity. 

31. Is sometimes shy, introverted. 

32. Is helpful and unselfish with others. 

33. Keeps things neat and tidy. 

34. Worries a lot. 

35. Values art and beauty. 

36. Finds it hard to influence people. 

37. Is sometimes rude to others. 

38. Is efficient, gets things done. 

39. Often feels sad. 

40. Is complex, a deep thinker. 

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41. Is full of energy. 

42. Is suspicious of others’ intentions. 

43. Is reliable, can always be counted on. 

44. Keeps their emotions under control. 

45. Has difficulty imagining things. 

46. Is talkative. 

47. Can be cold and uncaring. 

48. Leaves a mess, doesn’t clean up. 

49. Rarely feels anxious or afraid. 

50. Thinks poetry and plays are boring. 

51. Prefers to have others take charge. 

52. Is polite, courteous to others. 

53. Is persistent, works until the task is finished. 

54. Tends to feel depressed, blue. 

55. Has little interest in abstract ideas. 

56. Shows a lot of enthusiasm. 

57. Assumes the best about people. 

58. Sometimes behaves irresponsibly. 

59. Is temperamental, gets emotional easily. 

60. Is original, comes up with new ideas. 

 

Self-Assessed Variance in State Interests 

To what extent do you agree with the following statements: 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither agree or disagree”, 
“Somewhat agree”, “Agree”, “Strongly agree”] 

1.  The activities I’m interested in change day to day. 
2.  From day to day I enjoy doing different activities. 
3.  I generally prefer to do the same activities every day. (Reverse coded) 

 

Importance of Interests 

To what extent do you agree with the following statements: 

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[“Strongly disagree”, “disagree”, “Somewhat disagree”, “Neither agree or disagree”, “Somewhat 
agree”, “Agree”, “Strongly agree”] 

 

1.  Being interested in the activities I am involved in is important to me. 
2.  I must be interested in something for me to do it. 
3.  I don’t take part in activities that don’t interest me. 

 

Experience Sampling Method Calibrating Questions 

On a typical day, approximately what time do you wake up in the morning?  [Drop down menu 
from 4am to 12pm, in half hour increments] 

On a typical day, approximately what time do you first check email?  [Drop down menu from 
5am to 1pm, in half hour increments] 

On a typical day, approximately what time do you go to bed at night?  [Drop down menu from 
9pm to 3am, in half hour increments] 

On a typical day, approximately what time do you check email for the last time? ? [Drop down 
menu from 4pm to 1am, in half hour increments] 

 

Lay Theories of Interest Change 

Do you think your preferences for different types of activities change day to day? If so, what do 
you think may drive this change? If not, why do you think they might not change? Please write a 
few sentences or more regarding your thoughts. 

 [Open response box for text answer] 

 

Demographics 

What is your age? [Numeric response] 

What is your gender identity? (Male/Female/”I identify as…” (text box response) /Prefer not to 
specify) 

What is your race/ethnicity? (Black or African American/Hispanic or Latinx/Other/White/Prefer 
not to specify/East Asian/ South Asian) (Can check multiple) 

What is your academic major? [Drop down list with “Other” as an option and display logic for 
when other is selected to enable text entry] 

What is your current college standing? (Freshman/Sophomore/Junior/Senior/Other). 

 

 

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APPENDIX F – Study 2 Consent Form 

Research Participant Information and Consent Form 

            You are being asked to take part in a research study. Researchers are required to provide 
research participants with a consent form to inform them of the research study, convey that 
participation is voluntary, explain the risks and benefits of participation in the study, and 
empower participants to make an informed decision to participate or not in the study. 
Study Title: Measuring Interests and Interest Variability 
This study has been approved by Michigan State University’s Institutional Review Board 
(STUDY00003564) 
1.  PURPOSE OF RESEARCH                                                               
The purpose of this study is to better understand individual interests, interests day-to-day, the 
influence of different characteristics on the range of interests experienced, effects of interest-
activity match on outcomes, and what may influence changes in interests day-to-day. 
 2. WHAT YOU WILL DO                                                                         
If you choose to participate in this study, you will be administered a variety of surveys. You will 
take a pre-survey reflecting your personality characteristics, interests, attitudes, and behavioral 
tendencies. This survey and instructions on the larger study this survey is related to is expected 
to take approximately 40 minutes.  
You will then be asked to respond to daily morning and evening surveys over a period of seven 
days, for a total of 14 daily surveys. These surveys will ask you about topics such as your day-to-
day experiences, preferences, and tendencies. Each of these surveys are expected to take 
approximately 10 minutes. 
After this week of daily surveys, you will be asked to respond to a post-survey reflecting 
personality characteristics, interests, attitudes, and thoughts on interests. This survey is expected 
to take approximately 25 minutes. 
3. POTENTIAL BENEFITS          
You may not benefit personally from being in this study. However, we hope that this research 
will assist in the advancement of our understanding of interests. 
 4. POTENTIAL RISKS                       
There are no foreseeable risks to participating in the present study. 
 5.  PRIVACY AND CONFIDENTIALITY                                                 
Information provided in this research study will be treated as confidential. Only members of the 
research team will have access to your full survey responses and every effort will be made to 
keep your information safe. Any survey responses shared outside of the research team will be 
deidentified, in that any information tying responses to you will be removed before being shared. 
 6. YOUR RIGHTS TO PARTICIPATE, SAY NO, OR WITHDRAW  
You have the right to say no to participating in this research. This includes the right to say no to 
completing any surveys administered or any items asked. You have the right to stop at any time 
after you have started each survey with no consequences to you. You will not lose any benefits 
that you would normally receive for completing the survey if you decide to stop before finishing. 
However, to receive SONA credit for a survey you must click all the way through. 
7.  COSTS AND COMPENSATION FOR BEING IN THE STUDY        

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Participants who consent to take part in this survey will be awarded SONA credits through 
https://msu-psychology.sona-systems.com. In the SONA system, 1 hour of research participation 
is worth 1 SONA credit and this credit is pro-rated in 15-minute increments. It is up to individual 
course instructors to determine how many points this converts to in their classes (this should be 
specified in the syllabus for each course).   
 
Total compensation in the form of SONA credit will depend on the degree to which you 
participate in the research study. A maximum total of 5.0 hours of SONA credit hours may be 
obtained for responding to all surveys administered. For taking part in the pre-survey and 
training, you will receive 1.0 hour of SONA credit. For each of the morning and evening surveys 
you take part in, you will receive 0.25 hours of SONA credit. A total of 14 morning and evening 
surveys will be administered, for a possible total of 3.5 hours of SONA credit for this portion of 
the study. For taking part in the post-survey, you will receive 0.5 hours of SONA credit.  

Please do not complete this survey if you did not register for it on SONA. Some studies have 
prerequisites. If you did not see this study advertised in your SONA account (e.g., if a friend 
forwarded you the link), you should not complete this study. In order to receive credit for 
participation you MUST be registered for this study. 
Participation in this study is voluntary. You may withdraw at any time without penalty. This 
means that no SONA credits will be deducted from your account, nor will withdrawal have any 
effect on your relationship with any of your instructors. However, to receive credit for 
participation for each online survey you must click through the entire survey.  
 
 8.  CONTACT INFORMATION                                                       
If you have concerns or questions about this study or to report an injury, please contact Jacob 
Bradburn, Department of Psychology, Michigan State University, East Lansing, MI 48824, e-
mail: bradbu17@msu.edu  

OR 

Ann Marie Ryan, Ph.D., Department of Psychology, Michigan State University, East Lansing, 
MI 48824, phone: (517) 353-8855, e-mail: ryanan@msu.edu 

 If you have questions or concerns about your role and rights as a research participant, would like 
to obtain information or offer input, or would like to register a complaint about this study, you 
may contact, anonymously if you wish, the Michigan State University’s Human Research 
Protection Program at 517-355-2180, Fax 517-432-4503, or e-mail irb@msu.edu or regular mail 
at 4000 Collins Rd, Suite 136, Lansing, MI 48910. 

 

 

 

 

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Survey Identifiers 

APPENDIX G – Study 2 – Pre-Survey Measure 

First Name [Open text box for responses] 

Last Name [Open text box for responses] 

MSU Email Address [Open text box for responses] 

Please reenter your MSU Email Address [Open text box for responses] 
Note that in order to be considered for the additional components of this research study, 
you must provide an accurate email address. 

 

Daily Short Interest Measure  

People may prefer to engage in different activities. Please indicate how much you would 

prefer to do the following activities in general. 

“In general, I prefer activities that involve…” 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither disagree or agree”, 
“Somewhat agree”, “Agree”, and “Strongly agree”] 

1. Using tools 

2. Fixing 

3. Building 

4. Physical activity 

5. Working with gadgets 

6. Research 

7. Science 

8. Analytic thinking 

9. Intellectual tasks 

10. Academics 

11. Creativity 

12. Imagination 

13. Art 

14. Creating 

15. Creative writing 

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16. Helping others 

17. Working with others 

18. Guiding others 

19. Communicating with others 

20. Providing services to others 

21. Persuading others 

22. Selling 

23. Negotiation 

24. Managing others 

25. Managing projects 

26. Routine 

27. Structure 

28. Systematic work 

29. Organizing information 

30. Repeated tasks 

 

Brief Public Domain RIASEC Markers Scales (Armstrong et al., 2008) 

Please indicate your preference for the following activity in general, not just for today. 

In general, to what extent would you like to engage in the following tasks? 

[“Dislike very much”, “Dislike”, “Neither like nor dislike”, “Like”, “Like very much”] 

1. Test the quality of parts before shipment 
2. Lay brick or tile 
3. Work on an offshore oil-drilling rig 
4. Assemble electronic parts 
5. Operate a grinding machine in a factory 
6. Fix a broken faucet 
7. Assemble products in a factory 
8. Install flooring in houses 
9. Study the structure of the human body 
10. Study animal behavior 
11. Do research on plants or animals 
12. Develop a new medical treatment or procedure 

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13. Conduct biological research 
14. Study whales and other types of marine life 
15. Work in a biology lab 
16. Make a map of the bottom of an ocean 
17. Conduct a musical choir 
18. Direct a play 
19. Design artwork for magazines 
20. Write a song 
21. Write books or plays 
22. Play a musical instrument 
23. Perform stunts for a movie or television show 
24. Design sets for plays 
25. Give career guidance to people 
26. Do volunteer work at a non-profit organization 
27. Help people who have problems with drugs or alcohol 
28. Teach an individual an exercise routine 
29. Help people with family-related problems 
30. Supervise the activities of children at a camp 
31. Teach children how to read 
32. Help elderly people with their daily activities 
33. Sell restaurant franchises to individuals 
34. Sell merchandise at a department store 
35. Manage the operations of a hotel 
36. Operate a beauty salon or barber shop 
37. Manage a department within a large company 
38. Manage a clothing store 
39. Sell houses 
40. Run a toy store 
41. Generate the monthly payroll checks for an office 
42. Inventory supplies using a hand-held computer 
43. Use a computer program to generate customer bills 
44. Maintain employee records 
45. Compute and record statistical and other numerical data 
46. Operate a calculator 
47. Handle customers' bank transactions 
48. Keep shipping and receiving records 

 

Big Five Inventory-2 (Soto & John, 2017) 

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Here are a number of characteristics that may or may not apply to you. For example, do you 
agree that you are someone who likes to spend time with others? Please indicate the extent to 
which you agree or disagree that each statement applied to you.  

[“Disagree strongly”, “Disagree a little”, “Neutral; no opinion”, “Agree a little”, “Agree 
Strongly”] 

 

1. Is outgoing, sociable. 

2. Is compassionate, has a soft heart. 

3. Tends to be disorganized. 

4. Is relaxed, handles stress well. 

5. Has few artistic interests. 

6. Has an assertive personality. 

7. Is respectful, treats others with respect. 

8. Tends to be lazy. 

9. Stays optimistic after experiencing a setback. 

10. Is curious about many different things. 

11. Rarely feels excited or eager. 

12. Tends to find fault with others. 

13. Is dependable, steady. 

14. Is moody, has up and down mood swings. 

15. Is inventive, finds clever ways to do things. 

16. Tends to be quiet. 

17. Feels little sympathy for others. 

18. Is systematic, likes to keep things in order. 

19. Can be tense. 

20. Is fascinated by art, music, or literature. 

21. Is dominant, acts as a leader. 

22. Starts arguments with others. 

23. Has difficulty getting started on tasks. 

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24. Feels secure, comfortable with self. 

25. Avoids intellectual, philosophical discussions. 

 

26. Is less active than other people. 

27. Has a forgiving nature. 

28. Can be somewhat careless. 

29. Is emotionally stable, not easily upset. 

30. Has little creativity. 

31. Is sometimes shy, introverted. 

32. Is helpful and unselfish with others. 

33. Keeps things neat and tidy. 

34. Worries a lot. 

35. Values art and beauty. 

36. Finds it hard to influence people. 

37. Is sometimes rude to others. 

38. Is efficient, gets things done. 

39. Often feels sad. 

40. Is complex, a deep thinker. 

41. Is full of energy. 

42. Is suspicious of others’ intentions. 

43. Is reliable, can always be counted on. 

44. Keeps their emotions under control. 

45. Has difficulty imagining things. 

46. Is talkative. 

47. Can be cold and uncaring. 

48. Leaves a mess, doesn’t clean up. 

49. Rarely feels anxious or afraid. 

50. Thinks poetry and plays are boring. 

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51. Prefers to have others take charge. 

52. Is polite, courteous to others. 

53. Is persistent, works until the task is finished. 

54. Tends to feel depressed, blue. 

55. Has little interest in abstract ideas. 

56. Shows a lot of enthusiasm. 

57. Assumes the best about people. 

58. Sometimes behaves irresponsibly. 

59. Is temperamental, gets emotional easily. 

60. Is original, comes up with new ideas. 

 

Curiosity and Exploration Inventory – II (Kashdan et al., 2009) 

Rate the statements below for how accurately they reflect the way you generally feel and behave. 

[“Very Slightly or Not At All”, “A Little”, “Moderately”, “Quite a Bit”, “Extremely”] 

1.  I actively seek as much information as I can in new situations. 
2.  I am the type of person who really enjoys the uncertainty of everyday life. 
3.  I am at my best when doing something that is complex or challenging. 
4.  Everywhere I go, I am out looking for new things or experiences. 
5.  I view challenging situations as an opportunity to grow and learn. 
6.  I like to do things that are a little frightening 
7.  I am always looking for experiences that challenge how I think about myself and the 

world. 

8.  I prefer jobs that are excitingly unpredictable. 
9.  I frequently seek out opportunities to challenge myself and grow as a person. 
10. I am the kind of person who embraces unfamiliar people, events, and places. 

 

The Grit Scale (Duckworth et al., 2007) 

How much do the following statements describe you?: 

[“Not like me at all”, “Not much like me”, “Somewhat like me”, “Mostly like me”, Very much 
like me”] 

1.  I often set a goal but later choose to pursue a different one. 

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2.  New ideas and new projects sometimes distract me from previous ones. 
3.  I become interested in new pursuits every few months. 
4.  My interest change from year to year. 
5.  I have been obsessed with a certain idea or project for a short time but later lost interest. 
6.  I have difficulty maintaining my focus on projects that take more than a few months to 

complete. 

7.  I have achieved a goal that took years of work. 
8.  I have overcome setbacks to conquer an important challenge. 
9.  I finish whatever I begin. 
10. Setbacks don’t discourage me. 
11. I am a hard worker. 
12. I am diligent. 

 

 

Implicit Theory of Interests (O’Keefe et al., 2018) 

How much do you agree or disagree with the following statements? 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Somewhat agree”, “Agree”, “Strongly 
agree”] 

1.  To be honest, your core interests will remain your core interests. They won’t really 

change. 

2.  No matter how central your interests are to you, they can change substantially. 
3.  You can be exposed to new things, but your core interests won’t really change. 
4.  Even if you have very strong interests, they can change dramatically.  

 

Self-Assessed Variance in State Interests 

To what extent do you agree with the following statements?: 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither agree or disagree”, 
“Somewhat agree”, “Agree”, “Strongly agree”] 

1.  The activities I’m interested in change day to day. 
2.  From day to day I enjoy doing different activities. 
3.  I generally prefer to do the same activities every day. (Reverse coded) 

 

Vocational Identity Measure (Gupta et al., 2015) 

The following statements describe thoughts and feelings about one’s career. Please indicate how 
much you agree or disagree with each statement. 

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 [“Strongly disagree”, “Disagree”, “Neither agree nor disagree”, “Agree”, “Strongly agree”] 

*Specific items not included per request of inventory developers* 

 

 

The Positive and Negative Affect Scales (Watson et al., 1987) 

This scale consists of a number of words that describe different feelings and emotions. 

Read each item and mark the appropriate answer in the space next to that word. Indicate to what 
extent you generally feel this way, that is, how you feel on average. 

[“Very slightly or not at all’, “A little”, “Moderately”, “Quite a bit”, “Extremely”] 

1.  Interested 
2.  Distressed 
3.  Excited 
4.  Upset 
5.  Strong 
6.  Guilty 
7.  Scared 
8.  Hostile 
9.  Enthusiastic 
10. Proud 
11. Irritable 
12. Alert 
13. Ashamed 
14. Inspired 
15. Nervous 
16. Determined 
17. Attentive 
18. Jittery 
19. Active 
20. Afraid 

 

Importance of Interests 

To what extent do you agree with the following statements?: 

[“Strongly disagree”, “disagree”, “Somewhat disagree”, “Neither agree or disagree”, “Somewhat 
agree”, “Agree”, “Strongly agree”] 

1.  I must be interested in something for me to do it. 
2.  I don’t take part in activities that don’t interest me. 

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Academic Major 

What is your academic major? [Drop down list with “Other” as an option and display logic for 
when other is selected to enable text entry] 

 

Demographics 

What is your age? [Numeric response] 

What is your gender identity? (Male/Female/”I identify as…” [open text box response] /Prefer 
not to specify) 

What is your race/ethnicity? (Asian/Black or African American/Hispanic or 
Latinx/Other/White/Prefer not to specify/East Asian/South Asian) (Can select multiple options) 

What is your current college standing? (Freshman/Sophomore/Junior/Senior/Other). 

Are you currently employed? 

{if Yes} On average, how many hours a week do you work at your job? 

 

 

 

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APPENDIX H – Study 2 – ESM Morning Survey Measures 

 

Survey Identifiers 

First Name [Open text box for responses] 

Last Name [Open text box for responses] 

MSU Email Address [Open text box for responses] 

 

Daily Short Interest Measure  

People may prefer to engage in different activities. Please indicate how much you would 

prefer to do the following activities today. 

“Today, I prefer activities that involve…” 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither disagree or agree”, 
“Somewhat agree”, “Agree”, and “Strongly agree”] 

1. Using tools 

2. Fixing 

3. Building 

4. Physical activity 

5. Working with gadgets 

6. Research 

7. Science 

8. Analytic thinking 

9. Intellectual tasks 

10. Academics 

11. Creativity 

12. Imagination 

13. Art 

14. Creating 

15. Creative writing 

16. Helping others 

17. Working with others 

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18. Guiding others 

19. Communicating with others 

20. Providing services to others 

21. Persuading others 

22. Selling 

23. Negotiation 

24. Managing others 

25. Managing projects 

26. Routine 

27. Structure 

28. Systematic work 

29. Organizing information 

30. Repeated tasks 

 

Depletion (Lanaj et al., 2014) 

Indicate to what extent you feel this way currently 

[“Very slightly or not at all’, “A little”, “Moderately”, “Quite a lot”, “Very much”] 

1.  I feel drained. 
2.  My mind feels unfocused. 
3.  My mental energy is running low. 
4.  I feel like my willpower is gone. 
5.  It would take a lot of effort for me to concentrate on something.  

 

Sleep Quality (Buysse et al., 1989) 

1.  Last night, how would you rate your sleep quality overall?  

[“Very bad”, “Fairly bad”, “Fairly good”, “Very good”] 

 

Sleep Quality (Buysse et al., 1989) 

1.  How many hours of actual sleep did you get last night? 

[Drop down box with “Less than 1 hour”, 1 hour to 9.5 hours in half hour intervals, “10 
or more hours”] 

 

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Modified Positive and Negative Affect Scales (Watson et al., 1987) 

This scale consists of a number of words that describe different feelings and emotions. 

Read each item and mark the appropriate answer in the space next to that word. Indicate to what 
extent you have felt this way today. 

[“Very slightly or not at all’, “A little”, “Moderately”, “Quite a bit”, “Extremely”] 

 

1.  Interested 
2.  Distressed 
3.  Excited 
4.  Upset 
5.  Scared 
6.  Enthusiastic 
7.  Inspired 
8.  Determined 
9.  Jittery 
10. Afraid 

 

 

 

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APPENDIX I – Study 2 – ESM Evening Survey Measures 

Survey Identifiers 

 

First Name [Open text box for responses] 

Last Name [Open text box for responses] 

MSU Email Address [Open text box for responses] 

 

Modified Positive and Negative Affect Scales (Watson et al., 1987) 

This scale consists of a number of words that describe different feelings and emotions. 

Read each item and mark the appropriate answer in the space next to that word. Indicate to what 
extent you have felt this way today. 

[“Very slightly or not at all’, “A little”, “Moderately”, “Quite a bit”, “Extremely”] 

11. Interested 
12. Distressed 
13. Excited 
14. Upset 
15. Scared 
16. Enthusiastic 
17. Inspired 
18. Determined 
19. Jittery 
20. Afraid 

 

Daily Activities 

Today, I did activities involving… 

[“Never”, “Rarely”, “Rather infrequently”, “Occasionally”, “Sometimes”, “Often”, 

“Constantly”] 

1. Using tools 

2. Fixing 

3. Building 

4. Physical activity 

5. Working with gadgets 

6. Research 

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7. Science 

8. Analytic thinking 

9. Intellectual tasks 

10. Academics 

11. Creativity 

12. Imagination 

13. Art 

14. Creating 

15. Creative writing 

16. Helping others 

17. Working with others 

18. Guiding others 

19. Communicating with others 

20. Providing services to others 

21. Persuading others 

22. Selling 

23. Negotiation 

24. Managing others 

25. Managing projects 

26. Routine 

27. Structure 

28. Systematic work 

29. Organizing information 

30. Repeated tasks 

 

State Perseverance (Modified Perseverance subscale of the Grit Scale; Duckworth et al., 
2007) 

How much do the following statements describe you? 

[“Not like me at all”, “Not much like me”, “Somewhat like me”, “Mostly like me”, Very much 
like me”] 

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1.  Today, I overcame setbacks to conquer an important challenge. 
2.  Today, I finished whatever I began. 
3.  Today, setbacks didn’t discourage me. 
4.  Today, I was a hard worker. 
5.  Today, I was diligent. 

 

Situational Interests 

Today, did you engage in activities that involved using tools, fixing, building, physical 

activity, or working with gadgets? 

Yes/No 

**Next question displayed if answer to previous is “Yes”** 

Please reflect on the activities you did today that involved using tools, fixing, building, 
physical activity, or working with gadgets. While engaging in these activities, to what extent 
did you feel… 

[“Not at all”, “Rarely”, “Not often”, “Occasionally”, “Sometimes”, “Often”, “Very 

Often”] 

1.  A sense of engagement with these activities 
2.  Like these activities were valuable. 
3.  Like you identified with these activities 

 

Today, did you engage in activities that involved research, science, analytic thinking, 

intellectual tasks, or academic? 

Yes/No 

**Next question displayed if answer to previous is “Yes”** 

Please reflect on the activities you did today that involved research, science, analytic 

thinking, intellectual tasks, or academic. While engaging in these activities, to what extent did 
you feel… 

[“Not at all”, “Rarely”, “Not often”, “Occasionally”, “Sometimes”, “Often”, “Very 

Often”] 

 

1.  A sense of engagement with these activities 
2.  Like these activities were valuable. 
3.  Like you identified with these activities 

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Today, did you engage in activities that involved creativity, imagination, art, creating, 

and creative writing? 

Yes/No 

**Next question displayed if answer to previous is “Yes”** 

Please reflect on the activities you did today that involved creativity, imagination, art, 

creating, and creative writing. While engaging in these activities, to what extent did you feel… 

[“Not at all”, “Rarely”, “Not often”, “Occasionally”, “Sometimes”, “Often”, “Very 

Often”] 

1.  A sense of engagement with these activities 
2.  Like these activities were valuable. 
3.  Like you identified with these activities 

 

Today, did you engage in activities that involved helping others, working with others, 

guiding others, communicating with others, or providing services to others? 

Yes/No 

**Next question displayed if answer to previous is “Yes”** 

Please reflect on the activities you did today that involved helping others, working with 

others, guiding others, communicating with others, or providing services to others. While 
engaging in these activities, to what extent did you feel… 

[“Not at all”, “Rarely”, “Not often”, “Occasionally”, “Sometimes”, “Often”, “Very 

Often”] 

 

1.  A sense of engagement with these activities 
2.  Like these activities were valuable. 
3.  Like you identified with these activities 

Today, did you engage in activities that involved persuading others, selling, 

negotiation, managing others, or managing projects? 

Yes/No 

**Next question displayed if answer to previous is “Yes”** 

 

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Please reflect on the activities you did today that involved persuading others, selling, 
negotiation, managing others, or managing projects. While engaging in these activities, to 
what extent did you feel… 

[“Not at all”, “Rarely”, “Not often”, “Occasionally”, “Sometimes”, “Often”, “Very 

 

Often”] 

 

1.  A sense of engagement with these activities 
2.  Like these activities were valuable. 
3.  Like you identified with these activities 

Today, did you engage in activities that involved routine, structure, systematic work, 

organizing information, or repeated tasks? 

Yes/No 

**Next question displayed if answer to previous is “Yes”** 

Please reflect on the activities you did today that involved routine, structure, systematic 

work, organizing information, or repeated tasks. While engaging in these activities, to what 
extent did you feel… 

[“Not at all”, “Rarely”, “Not often”, “Occasionally”, “Sometimes”, “Often”, “Very 

Often”] 

 

1.  A sense of engagement with these activities 
2.  Like these activities were valuable. 
3.  Like you identified with these activities 

State Engagement (Modified Ultrech Work Engagement Scale; Breevart et al., 2012) 

How much do you agree or disagree with the following statements? 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither agree or disagree”, 
“Somewhat agree”, “Agree”, “Strongly agree”] 

1.  Today, I felt bursting with energy. 
2.  Today, I felt strong and vigorous at my activities. 
3.  Today, I was enthusiastic about my activities. 
4.  Today, my activities inspired me. 
5.  Today, I felt happy when I was working intensely at my activities. 
6.  Today, I was immersed in my activities. 

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Intrinsic Motivation (Modified Situational Motivation Scale – Intrinsic Motivation 
subscale; Guay et al., 2000)   

How well do the statements below describe the reasons why you engaged in the activities you 
did today? 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither agree or disagree”, 
“Somewhat agree”, “Agree”, “Strongly agree”] 

 

1.  Because I thought the activities were interesting. 
2.  Because I thought the activities were pleasant. 
3.  Because the activities were fun. 
4.  Because I felt good while doing the activities. 

 

 

 

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Survey Identifiers 

APPENDIX J – Study 2 – Post-Survey Measures 

 

First Name [Open text box for responses] 

Last Name [Open text box for responses] 

MSU Email Address [Open text box for responses] 

 

Daily Short Interest Measure  

People may prefer to engage in different activities. Please indicate how much you would 

prefer to do the following activities in general. 

“In general, I prefer activities that involve…” 

[“Strongly disagree”, “Disagree”, “Somewhat disagree”, “Neither disagree or agree”, 
“Somewhat agree”, “Agree”, and “Strongly agree”] 

1. Using tools 

2. Fixing 

3. Building 

4. Physical activity 

5. Working with gadgets 

6. Research 

7. Science 

8. Analytic thinking 

9. Intellectual tasks 

10. Academics 

11. Creativity 

12. Imagination 

13. Art 

14. Creating 

15. Creative writing 

16. Helping others 

17. Working with others 

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18. Guiding others 

19. Communicating with others 

20. Providing services to others 

21. Persuading others 

22. Selling 

23. Negotiation 

24. Managing others 

25. Managing projects 

26. Routine 

27. Structure 

28. Systematic work 

29. Organizing information 

30. Repeated tasks 

 

Brief Public Domain RIASEC Markers Scales (Armstrong et al., 2008) 

Please indicate your preference for the following activities in general, not just for today. 

In general, to what extent would you like to engage in the following tasks? 

[“Dislike very much”, “Dislike”, “Neither like nor dislike”, “Like”, “Like very much”] 

1. Test the quality of parts before shipment 
2. Lay brick or tile 
3. Work on an offshore oil-drilling rig 
4. Assemble electronic parts 
5. Operate a grinding machine in a factory 
6. Fix a broken faucet 
7. Assemble products in a factory 
8. Install flooring in houses 
9. Study the structure of the human body 
10. Study animal behavior 
11. Do research on plants or animals 
12. Develop a new medical treatment or procedure 
13. Conduct biological research 
14. Study whales and other types of marine life 
15. Work in a biology lab 

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16. Make a map of the bottom of an ocean 
17. Conduct a musical choir 
18. Direct a play 
19. Design artwork for magazines 
20. Write a song 
21. Write books or plays 
22. Play a musical instrument 
23. Perform stunts for a movie or television show 
24. Design sets for plays 
25. Give career guidance to people 
26. Do volunteer work at a non-profit organization 
27. Help people who have problems with drugs or alcohol 
28. Teach an individual an exercise routine 
29. Help people with family-related problems 
30. Supervise the activities of children at a camp 
31. Teach children how to read 
32. Help elderly people with their daily activities 
33. Sell restaurant franchises to individuals 
34. Sell merchandise at a department store 
35. Manage the operations of a hotel 
36. Operate a beauty salon or barber shop 
37. Manage a department within a large company 
38. Manage a clothing store 
39. Sell houses 
40. Run a toy store 
41. Generate the monthly payroll checks for an office 
42. Inventory supplies using a hand-held computer 
43. Use a computer program to generate customer bills 
44. Maintain employee records 
45. Compute and record statistical and other numerical data 
46. Operate a calculator 
47. Handle customers' bank transactions 
48. Keep shipping and receiving records 

 

Big Five Inventory-2 (Soto & John, 2017) 

Here are a number of characteristics that may or may not apply to you. For example, do you 
agree that you are someone who likes to spend time with others? Please indicate the extent to 
which you agree or disagree that each statement applies to you.  

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[“Disagree strongly”, “Disagree a little”, “Neutral; no opinion”, “Agree a little”, “Agree 
Strongly”] 

 

1. Is outgoing, sociable. 

2. Is compassionate, has a soft heart. 

3. Tends to be disorganized. 

4. Is relaxed, handles stress well. 

5. Has few artistic interests. 

6. Has an assertive personality. 

7. Is respectful, treats others with respect. 

8. Tends to be lazy. 

9. Stays optimistic after experiencing a setback. 

10. Is curious about many different things. 

11. Rarely feels excited or eager. 

12. Tends to find fault with others. 

13. Is dependable, steady. 

14. Is moody, has up and down mood swings. 

15. Is inventive, finds clever ways to do things. 

16. Tends to be quiet. 

17. Feels little sympathy for others. 

18. Is systematic, likes to keep things in order. 

19. Can be tense. 

20. Is fascinated by art, music, or literature. 

21. Is dominant, acts as a leader. 

22. Starts arguments with others. 

23. Has difficulty getting started on tasks. 

24. Feels secure, comfortable with self. 

25. Avoids intellectual, philosophical discussions. 

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26. Is less active than other people. 

27. Has a forgiving nature. 

28. Can be somewhat careless. 

29. Is emotionally stable, not easily upset. 

30. Has little creativity. 

31. Is sometimes shy, introverted. 

32. Is helpful and unselfish with others. 

33. Keeps things neat and tidy. 

34. Worries a lot. 

35. Values art and beauty. 

36. Finds it hard to influence people. 

37. Is sometimes rude to others. 

38. Is efficient, gets things done. 

39. Often feels sad. 

40. Is complex, a deep thinker. 

41. Is full of energy. 

42. Is suspicious of others’ intentions. 

43. Is reliable, can always be counted on. 

44. Keeps their emotions under control. 

45. Has difficulty imagining things. 

46. Is talkative. 

47. Can be cold and uncaring. 

48. Leaves a mess, doesn’t clean up. 

49. Rarely feels anxious or afraid. 

50. Thinks poetry and plays are boring. 

51. Prefers to have others take charge. 

52. Is polite, courteous to others. 

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53. Is persistent, works until the task is finished. 

54. Tends to feel depressed, blue. 

55. Has little interest in abstract ideas. 

56. Shows a lot of enthusiasm. 

57. Assumes the best about people. 

58. Sometimes behaves irresponsibly. 

59. Is temperamental, gets emotional easily. 

60. Is original, comes up with new ideas. 

 

Modified Positive and Negative Affect Scales (Watson et al., 1987) 

This scale consists of a number of words that describe different feelings and emotions. 

Read each item and mark the appropriate answer in the space next to that word. Indicate to what 
extent you felt this way last week (Monday, DATE through Sunday, DATE). 

[“Very slightly or not at all’, “A little”, “Moderately”, “Quite a bit”, “Extremely”] 

1.  Interested 
2.  Distressed 
3.  Excited 
4.  Upset 
5.  Scared 
6.  Enthusiastic 
7.  Inspired 
8.  Determined 
9.  Jittery 
10. Afraid 

 

Academic Satisfaction (Schmitt et al., 2008) 

Please indicate how much you agree or disagree with the following statements: 

1.  All in all, I am satisfied with my education I can get in this school. 
2.  I’m satisfied with the intelligence of my teachers here. 
3.  I’m satisfied with the extent to which my education will be useful in getting future 

employment. 

4.  I’m happy with the amount I learn in my classes. 

421 

 

5.  I’m satisfied with the extent to which attending this school will have a positive effect on 

my future career. 

 

 

 

Dropout Intentions (Nye, Prasad, & Rounds, 2019) 

Please indicate how much you agree or disagree with the following statements: 

1  Overall, I am happy with my decision to attend MSU. 
2  MSU was the right choice for me. 
3 
4 
5 
6 

I am considering transferring to another school. 
I am considering other job options instead of continuing in school. 
I am gathering lots of information about other schools I could transfer to. 
I am gathering lots of information about job options as opposed to continuing in 
school. 

 

Lay Theories of Interest Change 

Do you think your preferences for different types of activities change day to day? If so, what do 
you think may drive this change. If not, why do you think they might not change? Please write a 
few sentences or more regarding your thoughts. 

 [Open response box for text answer] 

 

 

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APPENDIX K – Study 2 Debriefing Form 

Debriefing Form 

Thank you for participating in our study. This form is designed to provide you with information 
about the purpose and importance of this study. 
  
The purpose of this study was to explore if individuals' interests change from day-to-day. By 
interests, we are referring to an individual’s preferences for different types of work or 
activities. Most research considers interests to be relatively stable, however we were interested in 
exploring how interests may change over the course of a week. 
 
In addition to determining if interests change day-to-day, the purpose of this study was also to 
explore what other individual characteristics may relate to this variability, if the match between 
individuals' daily interests and activities relates to outcomes, and what may cause interests to 
change, if they do, day-to-day.  
 
A summary of the different interest types, as well as example jobs that fit well with these 
interests, can he found here. To learn more about interests, please see these articles: 
 
Rounds, J., & Su, R. (2014). The Nature and Power of Interests. Current Directions in 
Psychological Science, 23(2), 98-103. DOI: 10.1177/0963721414522812 
 
Holland, J. L. (1959). A Theory of Vocational Choice. Journal of Counseling Psychology, 6(1), 
35-45. DOI: 10.1037/h0040767 
  
Armstrong, P. I., Allison, W., & Rounds, J. (2008). Development and initial validation of brief 
public domain RIASEC marker scales. Journal of Vocational Behavior, 73(2), 287-299. DOI: 
10.1016/j.jvb.2008.06.003 

If you are interested in measuring your own interests and what occupations might fit these 
interests, consider taking the U.S. Department of Labor’s Interest Profiler assessment. 
  
Given the nature of the questions within this survey, we anticipate that there are and will be no 
risks involved for any of our participants. However, if you did recall an event that negatively 
impacted you, please contact the appropriate number below: 

  

MSU Human Research Protection Program (517-355-2180) 

Office of Institutional Equity (517-353-3922) 

National suicide hotline (1-800-273-8255) 

Emergency number (911) 

If you have questions or concerns regarding this study, please do not hesitate to contact the 
investigators. Additionally, if you would like more information about the study or have further 
questions about it, please feel free to contact: 

423 

 

 

  
Jacob Bradburn, Department of Psychology, Michigan State University, East Lansing, MI 48824, 
e-mail: bradbu17@msu.edu.  

 

OR 

Ann Marie Ryan, Ph.D., Department of Psychology, Michigan State University, East Lansing, 
MI 48824, phone: (517) 353-8855, e-mail: ryanan@msu.edu 

 

 

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