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F44 i... _.2_ n. 7 0 (0356190 LIBRARIES MICHIGAN STATE UNIVERSITY EAST LANSING, MICH 48824-1048 This is to certify that the dissertation entitled A STUDY TO DETRMINE THE INFLUENCE OF TEACHING PROBLEM SOLVING TOOLS TO EDUCATIONALLY AT-RISK HIGH SCHOOL STUDENTS presented by JULIE A. MORTON . has been accepted towards fulfillment of the requirements for the Doctoral degree in The Department of Communication (Em 3 mm. Major Professor’s Signature Max! 3.8) 9~<305— l Date MSU is an Affirmative Action/Equal Opportunity Institution PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. OCT 02 20 DATE DUE _ DATE DUE I DATE DUE 39 2/05 (:lCIHC/DateDqundd-pJS A STLDY SOLVING T04 A STUDY TO DETERMINE THE INFLUENCE OF TEACHING PROBLEM SOLVING TOOLS TO EDUCATIONALLY AT-RISK HIGH SCHOOL STUDENTS By Julie A. Morton A Dissertation Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Arts and Communication 2005 A STL'DY TC SOLVING I001 An expcn ;".‘. L ' i ‘ ' n fdJCEIIOI‘Lal-flék it» £35335 of behax :t clusroom training mo non-risk. it are .' A path mod; pmbkm soiting sh: “.3635 to occur 6: tltir in“ ‘iuence on pt .CJSU ESSUS. tax , . may For a}: V ’ ABSTRACT A STUDY TO DETERMINE THE INFLUENCE OF TEACHING PROBLEM SOLVING TOOLS TO EDUCATIONALLY AT-RISK HIGH SCHOOL STUDENTS By Julie A. Morton An experiment manipulated problem solving skills training (yes, no) and the educational-risk level of students (at-risk, non-risk) to determine their influence on measures of behavioral-outcome efficacy. Students participated in problem solving classroom training, once a week, for 7 consecutive weeks. Four schools, two at-risk and two non-risk, were used. A path model was tested based on the belief that both educational-risk level and problem solving skills training would influence behavioral efficacy. This influence was expected to occur directly through their influence on self-efficacy and indirectly through their influence on problem solving cognitive skills and cognitive skill’s subsequent influence on locus of control. Results suggest that problem solving skills training does influence behavioral efficacy. For all students the indirect impact of problem solving skills training on self- efficacy was significant, and the direct impact on problem solving cognitive skills approached significance. Clear inter-group differences are apparent in the variables of locus of control and behavioral outcome efficacy. Specifically, in the at-risk group, the direct impact on locus of control was significant and the indirect influence of behavior outcome efficacy neared significance. The small sample size may explain why the positive path from self-efficacy to behavior outcome efficacy did not reach levels of sig‘ificmcc. 1n the is found; nex crit: significant. Resuits ind: factors usociat-cd v o. ' ' I , ‘ control mil mutt I15 lead to gthli outco 356 Will not has c significance. In the non-risk group, no evidence of a positive affect on locus of control was found; nevertheless, the indirect influence of behavior outcome efficacy was significant. Results indicate that problem solving skills training may reduce some of the risk factors associated with educationally at-risk students. Specifically, increases in locus of control will impact an at-risk student in that s/he will no longer perceive that her/his life is subject to fate, luck or the actions of another individual, but rather that given acts will lead to given outcomes. Increases in self-efficacy will impact an at-risk student in that s/he will now have the perceived ability to carry out the desired act(s). Further, the positive path observed from self-efficacy to behavior outcome efficacy suggests that the at-risk students are more likely to use their problem-solving skills to solve the personnel and/or academic problems that they encounter. In an in just can't ha 6 i non-Her. daughli balancing at b it? per tl much longer to . got in the vs dV 0 r.“ l- .l. urns “at I not c would repeat 3:. Jake. Ma. motion: a“ e' to UV In v it u '0 UT On‘Eth . s ‘1 ‘ A]... b 5:“ IChA ‘ MOnw. NH a. "F... "iii d. ”141w . u in; f DEDICATION In an interview Maria Shriver once said, “You can have everything in life — you i just can’t have it all at the same time.” As a woman who dons the various hats of wife, mother, daughter, friend and professional, I heartily concur with this sentiment. Life is a balancing act, but everything does have its time. As per the needs dictated by my personal and familial life, this dissertation took much longer to complete than I ever thought it would. Without doubt, my personal life got in the way of my professional one. Yet, when I look at my family, I cannot help but think that I not only made the right decisions for the time, I also made decisions that I would repeat again today. Jake, Max, Charlie-Bug and Nick — you provide me with an endless source of emotion: awe; joy; love; exasperation; satisfaction; respect; frustration; laughter and fatigue! Interaction with you forces me to grow and stretch in ways I never imagined possible. I dedicate this work to my 4 Boyz. I hope you realize that with tenacity and interest you can make any dream come true. Ron — you are my partner in every sense of the word. You not only inspire me to be a better person, you still love me as much when I fall short of my goals. I have a better time with you than with anybody else in the world. This is true, even when we are just sitting quietly in the same room doing our own things. I dedicate this work to you for all of your on-going and continual support. Whatever I do — or wherever I go — you are my biggest cheering section. My love truly knows no bounds. Mommy and Daddy, you always told me to find my dreams and then you helped me to make them come true. . .Ron. . .School. . .Kids. . .Health. . .Career. No daughter could iv be luckier. . .l 1ch [his m to you i to. both as an indi Finally. to Shfixer is right. l time. Eventful? u l a ~ be luckier. . .I love you. . .You’re doing the right thing... I’m proud of you too. I dedicate this work to you for teaching me again and again the kind of love which I can only aspire to, both as an individual and, as a parent. Finally, to those students who are at-risk, I also dedicate this work to you. Maria Shriver is right. You can have everything in life — you just can’t have it all at the same time. Everything does have its time. Dreams can, and do, come true. [started m} I}: people hm 6 imp~1~ Special {hanks Th6 Departme aim? and 50:" Frank Boslcr. ‘ interruptions a: My committee Paul Skafsk: {0 improve the Clo Patrick Ribbon.- The Jerome D. Academy. and l their students; Beitma Knshn. 555ml and or u ACKNOWLEDGMENTS I started my first Master’s credit 13 years ago. In the intervening time, numerous people have impacted my life. You all know who you are; I am deeply indebted to you. Special thanks must go to: o The Department of Communication at MSU. The faculty and staff continually went above and beyond and their actions were noted and appreciated; 0 Frank Boster, who encouraged me not to drop out of the PhD. program despite the interruptions and frustrations caused by my frequent and reoccurring seizures; 0 My committee members for their time and assistance. I actually enjoyed my defense! 0 Paul Skalski for his comments on the earlier versions of my paper which were used to improve the clarity and quality of the ideas expressed therein; 0 Patrick Ribbons for his on-going help with my computer problems; 0 The Jerome D. Diamond Center, the Fieldstone Day School, the Merle L. Levine Academy, and Royal St. George College for their willingness to give me access to their students; 0 Belhma Krishna whose wonderful care of both my children and my house let me go to school and/or work with a free mind and a light heart; 0 Lauren Backman and Jenn Bullwagner whose involvement with our family delights all of us; 0 The extended O’Brien clan for their on-going support and interest; 0 Phil Weeks, who has been a real friend both in times of sickness and in health; vi 0 Elisa and Gil P needed them. 7 also acted as ti. o Md last. but ct completed the E of his hand in 2‘ cleaner. and b: have med ox er friend and as a . continue to lg“. exceptional bot. Elisa and Gil Palter, and Henry and Illana Morton for always being their when I needed them. They not only provided extra hands, meals, and strong shoulders, they also acted as dictionaries, sounding boards, and real friends; And last, but certainly not least, Ron Tamborini. Without Ron, I would not have completed the PhD. He let me cry in his office and laugh in his house. The extension of his hand in fi'iendship never lessoned his demands that I think clearer, write cleaner, and broaden my outlook so that I help more than “just one at-risk student.” I have tried over the years to tell Ron how appreciative I am of his actions both as a friend and as a professor. He ignored my comments then, as I am sure he will continue to ignore my comments now. However, I want the record to show that he is exceptional both as a teacher and as a man. vii ACKVOWLEDG TABLE OF C O\' .LDPENDIC ES llSI OF TABLE: LIST OF FlGL'RE Introduction ..... Chapter 1 ......... Literature Rm 2: HcRJkofE sttoricczi’ii: Current/user Restfiwm ctr: Problem .83 [.51 Problem Sui". Problem San-r1 Problem Stu". LOCHSCVICVVH P r 051.602 3-1". Locxsof(?n; St‘fjilffl‘imc: [dammit R duct-12mm! . duwtsomh ’6nnse ‘,. ~~ -,. TABLE OF CONTENTS ACKNOWLEDGMENTS ................................................................................................ VI TABLE OF CONTENTS ................................................................................................ VIII APPENDICES ................................................................................................................... X LIST OF TABLES .......................................................................................................... XIII LIST OF FIGURES ........................................................................................................ XIV Introduction ..................................................................................................................... 1 Chapter 1 ......................................................................................................................... 6 Literature Review ............................................................................................................ 6 The Risk of Educational Failure ................................................................................. 6 Historical Intervention Strategies for At-Risk Students .............................................. 8 Current Intervention Strategies for At-Risk Students ............................................... 10 Resiliency and At-Risk Students ................................................................................ 13 Problem Solving Skills Training ............................................................................... 14 Problem Solving Skills Training and Acquisition of Cognitive Skills ...................... 14 Problem Solving Skills Training and Self-Efficacy ................................................... 15 Problem Solving Cognitive Skills and Behavior Outcome Eflicacy ......................... 16 Locus of Control ....................................................................................................... 17 Problem Solving Cognitive Skills and Locus of Control .......................................... 19 Locus of Control and Behavioral Outcome Eflicacy ................................................ 20 Self-Eflicacy and Behavioral Outcome Efficacy ....................................................... 22 Education Risk Level ................................................................................................. 23 Educational Risk Factors and Problem Solving Cognitive Skills ............................. 23 Educational Risk and Self-Eflicacy ........................................................................... 24 Premise ..................................................................................................................... 25 Chapter 2 ....................................................................................................................... 27 Methods ........................................................................................................................ 27 Overview ................................................................................................................... 27 Research Participants ............................................................................................... 28 Procedure .................................................................................................................. 31 Materials................................. .................................................................................. 33 Problem Identification .............................................................................................. 34 Brainstorming ........................................................................................................... 35 Highlighting .............................................................................................................. 35 Advantages, Limitations, Unique Opportunities and Overcoming Limitations (AL UO) ...................................................................................................................... 36 Planning for Action ................................................................................................... 36 viii 315.3511: PIT/45517 565:1: [06215 ‘3 Bolton “3 (bu/'56" 4 Chapter 3 .. Results ...... Ergmsnd DE.“ riff} 7’55; off/II. Pow-Han" Chapter 4 ..... Discussion HIE [It’llcc’l 772615.654: /. 7716/1??qu Erna-Wig [it Linzztttrzmzs Dcszgn Con Sample 5; . u 0.. 0. Measures ................................................................................................................... 3 7 Problem Solving Cognitive Skill ............................................................................... 3 7 Self-Efficacy .............................................................................................................. 41 Locus of Control ....................................................................................................... 42 Behavioral Outcome Eflicacy ................................................................................... 42 Course Evaluation .................................................................................................... 44 Chapter 3 ....................................................................................................................... 45 Results ........................................................................................................................... 45 Examination of Data ................................................................................................. 45 Descriptive Analyses ................................................................................................. 45 Test of the Hypothesized Model ................................................................................ 49 Post-Hoe Analyses .................................................................................................... 52 Chapter 4 ....................................................................................................................... 64 Discussion ..................................................................................................................... 64 The Influence of Problem Solving Skills Training on At-Risk Students .................... 65 The Influence of Problem Solving Skills Training on Non-Risk Students ................. 69 The Influence of Problem Solving Skills Training on Combined Sample ................. 72 Existing Difi’erences Between Non-Risk and At-Risk Students ................................ 74 Limitations ................................................................................................................ 76 Design Constraint. .................................................................................................... 76 Sample Size ........................................................................................................... 76 Class Size. ............................................................................................................. 76 Course Interruptions .............................................................................................. 78 Cross-Sectional Design of Study. ......................................................................... 78 Training Protocol ..................................................................................................... 79 Access to Student Files ......................................................................................... 79 Homework Completion ......................................................................................... 79 Cross-functional Tool Usage. ............................................................................... 80 Training Relevance to Student Concerns .............................................................. 80 Application of Training Techniques. .................................................................... 81 Responsibility for Action. ..................................................................................... 81 Recommendations for Future Research .................................................................... 82 Recommendations for Changes in Future Interventions. ...................................... 82 Recommendations for Those Working With At-Risk Students ............................ 83 Recommendations of Future Variables for Study. ................................................ 84 Closing Thoughts ...................................................................................................... 84 Endnotes ........................................................................................................................ 87 References ..................................................................................................................... 88 ix Appendix 1. APandix 6: linemen 7; ‘ng‘elldlll S; A 5' ..1 ’ i l‘-- .3 Appendix 1: Appendix 2: Appendix 3: Appendix 4: Appendix 5: Appendix 6: Appendix 7: Appendix 8: Appendix 9: Appendix 10: Appendix 11: Appendix 12: Appendix 13: Appendix 14: Appendix 15: Appendix 16: Appendix 17: Appendix 18: Appendix 19: APPENDICES Heppner & Petersen’s (1982) Personal Problem-Solving Inventory ______ Bosscher & Smit (1998) General Self-Efficacy 12 (GSES-12) adapted from Sherer, Maddux, Mercandante, Prentice-Dunn, Jacobs & Rogers (1982) General Self Efficacy Scale ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Nowicki & Strickland (1973) Locus of Control Scale for Children Abbreviated Scale (B) for Grades 7-12 ____________________________________________________ Torrance (1966) Unusual Uses Activity (F orrn A) — Torrance Tests of Creative Thinking ________________________________________________________________________ Torrance (1966) Unusual Uses Activity (Form B) — Torrance Tests of Creative Thinking _______________________________________________________________________ Course Evaluation ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Behavioral Outcome Efficacy .................................................................. Diary Worksheet _______________________________________________________________________________________ Osborn’s (1963) Brainstorming Technique ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Identifying the Problem Technique ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Treffinger, Isaksen & Firestien’s (1982) Highlighting Technique _________ Isaksen & Treffinger’s (1985) Advantages, Limitations, Unique Opportunities Technique (ALUO) ____________________________________________________________ Planning for Action and Acceptance Technique: 5 W’s and an H ,,,,,,,,, Guidelines for Divergent and Convergent Thinking Handout ,,,,,,,,,,,,,,, Brainstorming Cheat-Sheet ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, How to Define a Problem Cheat-Sheet ____________________________________________________ Highlighting Cheat-Sheet ......................................................................... ALUO Cheat-Sheet ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo Planning for Action & Acceptance Cheat-Sheet: 5 W’s & an H ___________ _94 __99 103 107 108 109 113 117 118 120 122 124 126 128 129 130 131 132 133 Appendix 2'. " . ‘fi Appendzx -l Anaendix 22: Ascendn 23; ”.933le 39: ‘ "F‘HWJ. ‘l' _ . Ff “lurk .5 j? Appendix 20: Lesson Plan 1: Pretest: Torrance Test of Creative Thinking, Locus of Control, Problem Solving Inventory & General Self- Efficacy Scale (GSES-12) ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 135 Appendix 21: Homework 1: Problems and Barriers ______________________________________________________ 136 Appendix 22: Lesson Plan 2: Brainstorming _________________________________________________________________ 137 Appendix 23: Homework 2: Brainstorming ___________________________________________________________________ 140 Appendix 24: Lesson Plan 3: How to Define a Problem ________________________________________________ 141 Appendix 25: Homework 3: How to Define a Problem ________________________________________________ 143 Appendix 26: Lesson Plan 4: Blocks and Barriers to Problem Solving _______________________ 145 Appendix 27: Homework 4: Blocks and Barriers to Problem Solving ________________________ 148 Appendix 28: Lesson Plan 5: Highlighting ____________________________________________________________________ 148 Appendix 29: Homework 5: Highlighting ______________________________________________________________________ 150 Appendix 30: Lesson Plan 6: ALUO ______________________________________________________________________________ 151 Appendix 31: Homework 6: ALUO _______________________________________________________________________________ 153 Appendix 32: Lesson Plan 7: Planning for Action and Acceptance _____________________________ 154 Appendix 33: Homework 7: Planning for Action and Acceptance ______________________________ 157 Appendix 34: Lesson Plan 8: Complete Planning for Action if Uncompleted, Questions, Uncertainties and Concerns; Course Evaluation: Begin Post-Testing _______________________________________________________________________________________________ l 59 Appendix 35: Lesson Plan 9: Complete Post-Test: Torrance Test of Creative Thinking, Locus of Control, Problem Solving Inventory & General Self-Efficacy Scale (GSES-12) and Behavior Outcome Efficacy Survey .......................................................................................... 160 Appendix 36: 94 Ways of Saying Terrific ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 161 Appendix 37: Parental Consent F onn (Control Group — Royal St. George’ College ________________________________________________________________________________________________________ 163 xi Appendix 38: Appendix 39: Appendix 40: TU Appendix 38: Appendix 39: Appendix 40: Parental Consent Form (Control Group — Merle L. Levine Academy _____________________________________________________________________________________________________ 165 Parental Consent Form (Treatment Group - Fieldstone Day School ,,,,, 167 Parental Consent Form (Treatment Group - Jerome Diamond Center 169 oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo xii Table 1: Table 2: Table 3: Table 4: 2: F1 Table 1: Table 2: Table 3: Table 4: Table 5: Table 6: Table 7: Table 8: LIST OF TABLES Descriptive Statistics for Key Variables in Path Model ,,,,,,,,,,,,,,,,,,,,,,,,,,, Zero-Order Correlations Used to Calculate Parameter Estimates in Model ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo Pre-induction Comparisons of Non-Risk and At-Risk Students ______________ Zero-Order Correlations Used to Calculate Parameter Estimates in Figure 4 _________________________________________________________________________________________________________ Zero-Order Correlations Used to Calculate Parameter Estimates in Figure 5 _________________________________________________________________________________________________________ Multiple Correlations for At-Risk Students ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Multiple Correlations for Non-Risk Students ............................................ Zero-Order Correlations Used to Calculate Parameter Estimates in Figure 8 _________________________________________________________________________________________________________ xiii 46 47 48 55 56 58 59 63 Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: LIST OF FIGURES Path Model of Hypothesized Relationships ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 4 Path Model of Hypothesized Relationships ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Revised Model Adding Path Between Self-Efficacy and Locus of Control 50 Revised Model Using Only At-Risk Students ____________________________________________ Revised Model Using Only Non-Risk Students _________________________________________ Second Revised Model Using Only At-RiSk Students ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Second Revised Model Using Only Non-Risk Students ............................. Second Revised Model Using Combined Sample ______________________________________ xiv 59 61 63 academic educ variety of econ associated \\ Eli”. One pit: .3» ', 1 ntsdildie COIliii Introduction Educationally at-risk students are young people who have a statistically high probability of encountering failure, attrition or inadequacies with regard to their formal academic education. They constitute an ever-growing problem in society. There are a variety of economical and sociological phenomena that increase the risk factors associated with educationally at-risk students. One phenomenon associated with educationally at-risk students is their ability to negotiate conflict and resolve problems. There is reason to believe that at-risk students have less ability than others to solve acute and/or chronic problems in their life. Many educationally at-risk students may not know how to make good decisions. And if they do, they lack the communication skills to effectively discuss their challenges and or implement their solutions. These students have not developed the skills to analyze, nor to solve, problems effectively. Studies demonstrate that their problem solving attempts lack logical development, thoroughness and sufficient effort (Blum & Spangehl, 1982). Further, “their impulsive, unsystematic [problem solving] styles consistently create more problems than they solve” (McCluskey, Place, McCluskey, & Treffinger, 1998, p 3). A second phenomenon associated with educationally at-risk students is their inability to implement a proposed solution. When compared to national norms, at-risk students have higher levels of communication apprehension, and lower levels of self- perceived communication competence (Chesebro et al., 1992). Communication apprehension is a learned condition that occurs for a variety of reasons. It is associated with shyness and/or fear of speaking publicly to a group of people, or privately to strangers. Communication competence is the capacity to create messages that are understood. 31% English proticn of oral comm at their willingnes for Education S: communication execute their ide A third p3 external circumst tit-control refers t ‘3’? in these ”och; are Victims of la" , destinies and an r “Dr-3L - " “ ~ NOT] (\1 B? . fires (19‘s mam C understood, and that obtain their objective. Numerous at-risk students have limited English proficiency, reside in environments that limit opportunities fostering the growth of oral communication skills, and have experienced prior education failures affecting their willingness and/or readiness to communicate orally (Delpit, 1990; National Center for Education Statistics, 1990B). Both high communication apprehension and low communication confidence make it more challenging for at-risk students to successfully execute their ideas. A third phenomenon associated with educationally at-risk kids is their belief that external circumstance controls the success in their lives (Blum & Spangehl, 1982). Locus of control refers to the beliefs that we have about our control over life’s events. People vary in these beliefs from an external to an internal locus. Maintaining the belief that we are victims of fate, or that attaining desired goals and rewards depends upon luck, circumstance or powerful others rather than our own efforts is characteristic of an external locus of control. This type of belief is associated both with poor problem-solving skills (Houtz, Ringenback & Feldhousen, 1973), and with academic failure (Findley & Cooper, 1983). People with an internal locus of control believe that they control their own destinies and are responsible for what happens to them. They are better problem solvers (Houtz et. a1, 1973), better communicators (Daly, Kreiser & Roghaar, 1992), and have greater academic achievement (Findley & Cooper, 1983) and less communication apprehension (McCrosky, Daily & Sorenson, 1976) than those with an external locus. Although locus of control is thought to be a relatively stable trait, Nowicki and Barnes (1973) demonstrate that through experience, it is possible to transform an orientation from an external to an internal locus of control. When attempting to change a student's loc about the ca:.r die student u reinforcem er: result from th onentation. cc 1 1 I utzlizable slug. applm the I]; h (I'D in or '2: Sets such as ho he“ to plan for he most commo pend-finance (Ha. CRUX» “tn-Se the 501130 ‘ ’ “51561: A . d-Jv-e student’s locus of control, educators must realize that the attributions the student makes about the cause of her/his outcomes will determine the type of belief (internal/extemal) the student will have about locus of control. To develop a generalized belief that reinforcement is contingent on effort, individuals must perceive their own successes to result from their own actions (Rotter, 1966). Thus, in order to modify a locus of control orientation, educators must endeavor to teach students specific, comprehensible and utilizable skill sets whose objective level of success is clearly reliant upon the individual applying the skills. In order to make proper communication decisions, certain problem solving skill sets, such as how to identify a problem, how to generate ideas, how to evaluate ideas and, how to plan for action, constitute the type of objectively measurable, specific, comprehensible and utilizable skill sets necessary to change a control orientation. Since the most commonly stated reason for dropping out of school is poor academic performance (Hahn, 1987; Pallas, 1990), problem-solving skills may not only help to change the control orientation, they may also reduce the overall risk factor associated with some at-risk students by increasing their ability to negotiate and resolve some of the problems they are experiencing in their lives. The present study attempts to investigate essential processes relating problem- solving skills to the performance of at-risk students. The core feature of the model proposed is a simple direct path from problem solving skills training to the acquisition of problem solving cognitive skills to increased behavioral outcome efficacy. Simply put, it is expected that training will provide skills that will effectively change behaviors. In addition to recognizing this straightforward influence process, however, the model goes further ‘0‘ eiticac} a} 5 Figure L P' / PILJbicm SO: Skills Traini Did it: to instigate rele et‘icacs shorts . outcomes. Seco: the ‘ ‘ ‘ . netted acquis: outer ~ 5 L A 16:. Al the further to explicate the manner in which educational risk, locus of control and self- efficacy also impact behavioral outcome efficacy (See Figure 1). Figure I . Path Model of Hypothesized Relationships Problems Solvin Cognitive Skill + + / / + \A + Behavioral Outcome Problem Solving Educational Risk Locus of Control - Efficacy 7 Skills Training \ \ + Self-Efficacy The basic model begins with problem-solving skills training. Training is expected to instigate relevant processes through two paths. First, a path from training to self- efficacy shows enhanced self-efficacy necessary to initiate the desired behavioral outcomes. Second, a path from training to problem solving cognitive skills indicates the expected acquisition of the cognitive skills necessary to initiate the desired behavioral outcomes. At the same time, an indirect path from cognitive skills to behavioral outcome efficacy is proposed through locus of control. Separate from its direct path, cognitive skills are expected to cultivate internal locus of control, which then increases behavioral outcome efficacy. In addition to the influence of problem solving skills training identified in the basic model, the study takes into consideration the level of educational-risk experienced prior to training. The model represents the influence of risk with negative paths from the level of student educational risk to both self-efficacy and problem solving cognitive skills. The two paths suggest that high risk will be associated with diminished levels of self-efficacy and cognitive skills. As such, when level of educational risk is high, the ’- Wienu skills .1' 33673133 I f be more a used to da: support an. potential need and opportunity for skills training to facilitate the acquisition of cognitive skills and self-efficacy is high. In non-risk students, ceiling effects are expected to attenuate the influence of training. If the proposed model were supported, interventions based on this model would be more affordable and sustainable than those more complex programs that have been used to date. However, decisions for interventions of this nature call for empirical support, and no prior research has tested this model with adolescent at-risk students. N16- Rr‘Sk’ 07E 'RLs}. harm or loss 1 has me possiT: necessanl} ex 5: are. .\'e\ ertl paper. an acad; people who are inadequate Tom -1" ’ i..citiduals u ho cam :Us and ion Chapter 1 Literature Review The Risk of Educational Failure ‘Risk’, as defined in Webster’s Dictionary, means the possibility of suffering harm or loss (Webster, 1987). When individuals are ‘at-risk’, they are in a situation that has the possibility of some sort of negative outcome. At-risk individuals have not necessarily experienced that outcome, and are not guaranteed to experience it in the future. Nevertheless, they have a high probability of doing so. For the purpose of this paper, an academic risk definition will be used. Specifically, at-risk students are young people who are thought to be at—risk of educational failure, educational attrition, or inadequate formal education. Specifically, educationally at-risk students are comprised of individuals who perform below grade level expectation; study at modified or basic levels; earn 505 and low 605 without having mastered the foundation skills, and; are disengaged or have poor attendance. The precise number of children who drop out of schools is difficult to ascertain. Schools utilize different leave codes, and use different methods of calculating the dropouts from year to year, and hem school to school (Hahn, 1987). While exact attrition numbers may be difficult to establish, it is possible to determine with accuracy the national graduation rate. When the Urban Institute calculated national graduation rates for the class of 2001 (the most recent year for which statistics were available), only 68% of all public high school students graduated (Swanson, 2004). This means that nearly one- third of all public high school students who should have graduated failed to do. Further mahsis indicat graduation rate Certain . considered to he Trembla}. 199" and 18 (Nationa retained in the St They tend to be ; addere more free; 39W frequentl} t lwgl Al-TTSR Stu lC‘nesebro. McCr. loner leVels ofco 1‘11, " ’ Bel“ een t}- are hl§her Concern: analysis indicated that for Black, American Indian, and Hispanic students, the national graduation rate hovers at 50% (50, 51, and 53% respectively) (Swanson, 2004). Certain objective, stable, facts are obtainable with regard to those who are considered to have increased educational risk factors (J anosz, LeBlanc, Boulerice & Tremblay, 1997). The vast percent of students who drop out do so between the ages of 17 and 18 (National Center for Education Statistics, 2002). Potential dropouts tend to be retained in the same grade, have poor academic grades, and feel disengaged from school. They tend to be part of a large peer group, to be involved in more passive activities, to adhere more frequently to deviant norms, to manifest behavior problems, to be arrested more frequently by the police, and to exhibit psychological vulnerability (J anosz et. a1, 1997). At-risk students tend to have higher levels of communication apprehension (Chesebro, McCroskey, Atwater, Bahrenfuss, Cawelti, Guadino & Hodges, 1992) and lower levels of conflict negotiation skills (National Mental Health and Education Center, 2002). Between the ages of 15 and 24 male dropouts out-number female drop-outs; there are higher concentrations of at-risk students in urban centers and rural areas than in suburban areas; drop-out rates are lowest in the Northeast and highest in the South; the most commonly stated reason for dropping out is poor academic performance, and disadvantaged students are at an increased risk of dropping out (Hahn, 1987; Pallas, 1990). Several economical and sociological factors that have been highly correlated with increased educational risk. Children are more likely to perform poorly and drop out of school if they live in poverty (Stedman, Salganik, & Celebuski, 1988), have Black or Hispanic heritage (Bruno, 1988), live with single parents (Stedman et al., 1988), have mothers that have not completed high school (Natriello, McDill, & Pallas, 1990; Barro & Kostad 198‘ elebuslci. 19 more than thrt Interstate Corr Association. a; explanation to: and sociologic: often interact u abilin' to perforr Regard}: out of the educat Eighschool drop. can less money t Den rmmelll 01‘ Ed Pete‘- , l‘ he pdbllC a5; I993) Females u :KU'R and a‘rn s mO‘rL. y . .. )lib .Jmflm Kostad, 1987), have limited English proficiency (Natriello et al., 1990; Salganik & Celebuski, 1987), have a sibling who has dropped out of school, or are home alone for more than three hours a day (The National Center for Education Statistics, 1989; Western Interstate Commission for Higher Education, Teachers Insurance and Annuity Association, and The College Board, 1988). Despite the fact that there is no consensus explanation for these phenomena, there does seem to be agreement that these economic and sociological factors are external forces beyond the control of the at-risk student which often interact with one another (F rymier & Robertson, 1990; Jones & Watson, 1990). Unquestionably, these factors act individually and collectively as barriers to the student’s ability to perform in school. Regardless of the impetus for leave-taking, the consequence of a student dropping out of the educational system prior to completing high school can be severe. For instance, high-school dropouts are more likely to be unemployed than high school graduates and to earn less money than high school graduates when they eventually do secure work (US Department of Education [USDE], 1999). High school dropouts are also more likely to receive public assistance than high school graduates who do not go on to college (U SDE, 1998). Females who drop out of high school are morel likely to have children at younger ages and are more likely to be single parents than high school graduates (McMillen & Kaufmann, 1996). Finally, at eighty-two percent of the population, dropouts make up a disproportionate percentage of the nation’s prison inmates (Harlow, 1996). Historical Intervention Strategies for At-Risk Students Intervention programs focusing upon minimizing risk factors associated with at- risk students have existed since the 1930s. The various theoretical and practical orientations of Each epoch st: In the 1930‘s a the cognitii e si courses tKulzlt. education vs ere materials were _; dewgopmental a aomexxorlr. refe" '4 ‘ 7 truer lilll'llilllE tF' tT~ . . .iornpson. Gran attention “as Iar-I' lnthe I93 Monnance u 35 ' [Brucknen 1995) orientations of these programs can generally be categorized by different time periods. Each epoch strived for improvement through fashionable communication interventions: In the 1930’s and 40’s, intervention programs focused upon remedial skills by improving the cognitive skills of at-risk students through non-credit reading and learning skills courses (Kulik, Kulik & Schwalb, 1983). From the 50’s to the 70’s, programs in special education were developed. High school graduation requirements were raised and special materials were provided for students (Frymier & Roberson, 1990). On-going developmental and remedial courses were taught by teams composed of faculty members and counselors. Services, such as directive guidance sessions, counseling, and advisement, were added (Kulik, Kulik & Schwalb, 1983). Schools communicated directly with parents, spent extra time on reading, writing and arithmetic, assigned extra homework, referred students to psychologists or social workers, and emphasized higher order thinking (Frymier & Roberson, 1990), as well as study and listening skills (Thompson, Grandgenett & Grandgenett, 1999). Nevertheless, the focus of all of this attention was largely on factors external to the student. In the 1980’s school systems first accepted the fact that a student’s classroom performance was highly correlated with family and other environmental factors (Bruckner, 1995). Students with behavioral problems, such as those associated with communication, conflict resolution, and attention span deficits, were identified as constituting a large contingency of the at-risk students (Thompson, Grandgenett & Grandgenett, 1999). In response to these admissions, intervention programs began to address and manipulate internal and external factors affecting the at-risk student. At this time psyfilOlOglc’E cogsime "a" and locus oft this nets app? and inIIUeDCC continued thi 5 provide stride: nould be "3?? Iazen ention pr academic skills external factors Current lfile’rl’e’.‘ ln intern. eound junior hi 1: 1999). Despite bx best of educator rr “ith at-n'sk studs. psychological variables were also added to intervention programs. Research into cognitive variables, task motivation, cultural aspiration, task performance, self-esteem and locus of control was undertaken. Blum and Spangehl (1982), two ardent advocates of this new approach, proposed that psychological and behavioral variables must be assessed and influenced before the at-risk student’s academic deficits can be affected. They continued this train of thought with the rational that a focus on internal factors would provide students with self-knowledge, coping skills, and problem-solving skills which would be “applicable and transferable to everyday educational and personal challenges.” Intervention programs would thus provide students with the communication and academic skills, as well as the internal resources needed to overcome the challenging external factors with which they have to deal. Current Intervention Strategies for At-Risk Students In international comparatives, students in the US. begin to falter academically around junior high school (Office of Education & Research Improvements [OERI], 1999). Despite both the changing theoretical orientation of intervention programs, and the best of educator intentions, many academic institutions continue to deal inappropriately with at-risk students, especially those who demonstrate basic academic deficiencies. The educational gaps of such students may manifest inadequacies with regard to basic academic skills such as reading, writing, math, and study skills (Jones & Watson, 1990). They are also more likely to perform poorly on standardized tests of intelligence and achievement that are educational predictors of talent and success (Jones & Watson, 1990). 10 (Dnec shHsutoigT system. Altho haiereachedt Anah: gnanmemt despite the fact children Ild\ e a likely to drop 0: Another fining academ :5 more likely than mainstream, Tm. for them (Hal ' I illld The abox One course of action for students who demonstrate a paucity of basic academic skills is to ignore the problem and to simply continue to process the students through the system. Although undesirable, it is estimated that since 1983 over 10 million Americans have reached the 12th grade without learning to read at a basic level (OERI, 1999). An alternative academic solution is to hold children back and make them repeat a grade in the hope of strengthening their core knowledge base. This approach continues despite the fact that studies have established that when a school holds students back these children have a lower opinions of self, have fewer friends, and are up to four times more likely to drop out than those who have been promoted (Hahn, 1987). Another academic solution is to place the students in alternative streams with varying academic challenges. In the case when at-risk children are not held back, they are more likely than other students to be placed in academic programs apart from the mainstream. The separate tracking of students can have devastating unintended outcomes for them (Hallinan, 1987; Gamoran & Berends, 1987). The above information notwithstanding, it is important to note that academic institutions do not always deal inappropriately with at-risk students, nor are all at-risk students educationally challenged, or experiencing academic failure. In fact, Torrance (1969) found that at-risk children perform well on the figural tests of creative thinking ability; exhibit high non-verbal fluency and originality; display high creative productivity in small groups; are adept in visual art activities; are highly creative in movement, dance and other physical activities; are highly motivated by games, music, sports, humor and concrete objects; and have language rich in mental imagery. 11 In Or both in and ‘ based i rem This must be Hottet er. U3: practitioners t approach. In fa based on this n' tees to ma\im:. progra rs achrei might hate reg particularly user‘; fail » e. ures. nhen m In order to help an at-risk child cope with the internal and external challenges, both in and out of the educational environment, Torrance (1969) suggests that a school- based intervention program must break the cycle of failure experienced by these children. This must be done without holding students back or placing them into academic streams. However, unlike the suggestions put forth by Torrance, all too often scholars and practitioners create a list of edicts, or “thou shall nots,” without suggesting a viable “thou shall” alternative. One method currently used with great success in classrooms is the “five senses” approach. In fact, entire school systems, such as the Heschel and the Waldorf Schools, are based on this method wherein educators identify which sense each child most effectively uses to maximize learning, and teach specifically to that sense. Alternatively, other programs achieve educational success by focusing on various types of success a student might have (e.g., artistic, athletic, interpersonal. . .). This “whole person” approach is particularly useful for at-risk individuals who are often use to thinking of themselves as failures, when in fact they are only experiencing academic failure. I believe that the most effective intervention practice may be to teach problem- solving skills to at-risk students in order to modify their belief that they control their own destiny, or what can be called their “locus of control.” In this sense, the “problem” in “problem-solving skills,” can be viewed as parameters (i.e. conditions, objects, or information) that interfere with the problem solver’s wishes to move from a current state t0 a desired future state. The “problem” is that the at-risk student lacks the correct answer and/or the appropriate chain of action to solve the problem (Mayer, 1983). 12 Teachin tiecrs: (l) Suid the ability. to cc use the knots lee begin to effectiy Wege an this expectation. that “problem so appraisal of their problems. and rh qr}. . ‘ cc....ol onentatrc r. . In: p.ob..m soly ers .' Teaching at-risk students to become better problem solvers should have two net effects: (1) students should become cognizant that they not only have the skills, but also the ability, to control (at least parts of) the world around them; and (2) students should use the knowledge of both their new skill base, and their modified locus of control, to begin to effectively deal with their myriad life problems. Wege and Moller’s (1995) work with at-risk students provides some support for this expectation. In their study of twenty-nine undergraduate students, they hypothesized that “problem solving training would enhance a group of ineffective problem solvers’ appraisal of their problem-solving skills, the quality of their solutions to specific problems, and their self-efficacy expectations while contributing towards an internal control orientation (p.508).” The primary difference between the thirteen ineffective problem solvers and the sixteen effective problem solvers was that the ineffective students appraised their problem-solving skills more negatively, had lower self-efficacy levels and an external control orientation. Not only was the hypothesis supported, but the results were maintained also at a two-month follow-up. The present study attempts to identify causal processes associated with these observations that lead to different behavioral outcome efficacy. Resiliency and At-Risk Students Although many students may be defined as at-risk, not all become subject to the pitfalls associated with elevated risk levels. Some at-risk students show great resiliency, the process of, capacity for, or outcome of successful adaptation despite challenging or threatening circumstances (Masten, Best & Gannenzy, 1990). Rotter (1987, 1990) identified several types of protective factors which reduced a child’s exposure to risk. 13 Among ”7'05" mastery of in‘ included in :2: Prohi’c’m Soil. When a gap in inforn and comm uni c closing the gap uncertainties. e. perplexity; and thaltsen. 1994, I skills training it Problem sols ins betray-10m] outco P 713,1) :6: "1 Sci/tin; Among those highlighted are beliefs about self-efficacy, agency (locus of control), and mastery of important life skills such as problem solving. The following variables were included in the present study for this reason. Problem Solving Skills Training When we have a problem we must orient ourselves towards sensing a problem or a gap in information, forming ideas or hypotheses, testing and modifying the hypotheses, and communicating the results (Torrance, 1988). Thus, problem solving is “the process of closing the gap between what is and what is desired; answering questions, clearing up uncertainties, explaining that which was not understood or known, or removing perplexity; and inclusive of perceiving, thinking (cognition), feeling and behaving (Isaksen, 1994, p. 7). The model offered here begins by positing that problem solving skills training1 will have direct positive effects on both self-efficacy and the acquisition of problem solving cognitive skills. The simple path then ensues from cognitive skills to behavioral outcome efficacy. Problem Solving Skills Training and Acquisition of Cognitive Skills Research shows that training designed to enhance problem-solving techniques can effectively cultivate the cognitive skills necessary to successfully engage in problem solving behavior. Specifically, Guilford (1967) shows that problem solving skills training can increase problem solving cognitive skills associated with idea fluency (the ability to produce large numbers of ideas, options, or possibilities), idea flexibility (the ability to produce a variety of kinds of ideas, options or possibilities, or the ability to use a variety of strategies to produce ideas, options or possibilities), and idea originality (the ability to produce novel, or unique ideas, options or possibilities). Skill training in problem solving 14 has had other tendency to "3 the style 0ft"? training this be confidence in c‘ identification. 1 Petersen. 1933. P r 0.5116”! 50:“ 4' -’ Seltief‘z'; em out desiret factor detennxn; upended. and h EXpefiCnCES (Bar ‘19 - -' .ehaxtoral outeo liiCC fi‘i: - at) Brown :dgn9 has had other important influence as well. Training has been shown to increase the tendency to “approach” in terms of one’s approach/ avoidance problem solving style (i.e., the style of evaluating problems, either approaching or avoiding them). In addition, training has been shown to increase problem-solving confidence (an individual’s confidence in engaging in a wide variety of problem solving activities including problem identification, solution finding and implementation) in the general population (Heppner & Petersen, 1982). Problem Solving Skills Training and Self-Eflicacy Self-efficacy is a learned behavioral trait associated with the perceived ability to carry out desired action (Bandura, 1977). Self-efficacy has been shown to be an important factor determining whether adaptive behaviors will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences (Bandura, 1982). As such, self-efficacy is an important factor in determining behavioral outcome efficacy in many different circumstances. Bandura (1982) suggests that both the level and strength of self-efficacy can be modified through modeling, observation, and reinforcement. In particular, Bandura (1977) identified “problem-based learning” as a method for raising the level of self- efficacy. Brown (1999) defined problem-based learning as a form that “engages the student in investigating a problem situation for which there is no right or wrong answer. The situation raises concepts and principles relevant to the subject matter that reflect real- life issues of the students’ world. Problem-based learning requires observation, investigation, solution building and resolution by students who “own the problem” and who must formulate their own solutions. The ill-structured problems offer students 15 opponunizie perceive as I this t‘onn of. solving skills sliils that the help students abilit} needed nerds. proble: Frag-”'15”! SOll‘.’. V r l. The C... €iilC3C}' is one training. that is. erieetii'e behax‘i orientation of lo Ethll an mCTC' d ider if} “ behax rear to u h‘CIher : problem 50 ll'm: opportunities to test their skills and confront the internal and external barriers2 they may perceive as limiting their successful achievement of a goal or objective (p.3).” In essence, this form of problem-based learning is precisely what students learn in effective problem solving skills training. As a result, in addition to providing the problem-solving cognitive skills that they need to handle tasks successfully, problem-solving skills training should help students set realistic goals and pursue them with the recognition that they have the ability needed to reach those goals if they apply reasonable effort (Brophy, 1998). In other words, problem-solving skills training should increase self-efficacy. Problem Solving Cognitive Skills and Behavior Outcome Eflicacy The direct path from problem solving cognitive skills to behavior outcome efficacy is one of the core features of the model. It completes the expected outcome of training, that is, a change in effective behaviors. Simply put, people cannot produce effective behavioral outcomes without the necessary skills, regardless of the level and orientation of locus of control. Thus, students with problem-solving skills are expected to exhibit an increased ability to resolve personal and/or academic problems — what we identify as behavioral outcome efficacy. In this case, behavior outcome efficacy might refer to whether students utilize one or more of the acquired problem solving tools for problem solving. Several studies demonstrate the expected relationship between problem-solving skills and behavior efficacy outcomes in children, in individuals who have dropped out of the educational forum, and in at-risk university students. Tellado (1984) found that when sixty-six students (grades 7 - 9) participated in a problem solving skills training program the students in the treatment group demonstrated better problem-solving skills than did 16 those in the CC behavioral et‘f‘ eighn-eight ta focusing on lit mentored is orl hill-time enzpfe the skills traini: some ofthe at-r students t lS-ZS sections foeusin 503W problems. imPA’OVé‘d self-cc Wk With other; financed outloo' those in the control group. McCluskey, Baker, O’Hagan, and Treffinger (1998) found behavioral effects on several outcome measures at two points in time. In their study, eighty-eight talented, at-risk high-school dropouts participated in Lost Prizes, a study focusing on life-skills training (including problem solving), multiple growth plans, and mentored work studies programs. In the first year, sixty-five percent of the individuals responded by completing high school, entering post-secondary programs, or obtaining full-time employment. At a one year follow-up the researchers found that the influence of the skills training and in many cases the skills themselves were still being benefiting some of the at-risk youth. Avarello (1993) found that when twenty-two at-risk university students (18-25) participated in an introductory fifteen-week creativity course with sections focusing on problem-solving skills, students experienced an increased ability to solve problems. They also experienced improved communication and listening skills, improved self-confidence, enhanced ability to be more open-minded, increased ability to work with others, greater confidence in their ideas, improved ‘can-do’ attitude, and enhanced outlook on their lives. Students reported that the knowledge and skills gained as a result of this course were transferable and that they employed them in other college courses, their family life, their place of work, and their personal lives. Locus of Control Beyond the obvious importance of acquiring problem-solving skills, one of the most critical features of the proposed model is locus of control. Locus of control of reinforcement (referred to henceforth as locus of control) deals with an individual’s beliefs about the nature of the world, and specifically with regard to whether or not individuals maintain an expectation that reinforcement is internally or externally l7 controlled. Tm we can control can effect the e As a CO ObSCTVallOl’lS 0 success experie Rotter ( 1906; d b) the suhieet a his action. then. as under the con complexity of th “3."- We have la? stem is coming snaraeren‘sties. u Lows or“ controlled. This control orientation affects not only the degree to which we believe that we can control our environment in important life situations, but also the notion that we can effect the environment through our own behavior (Rotter, 1966). As a construct, locus of control is particularly pertinent to at-risk students. Observations on this population regularly include statements indicating beliefs that success experienced in life is a result of external circumstance (Blum & Spangehl, 1982). Rotter (1966) described locus of control as follows: “When a reinforcement is perceived by the subject as following some action of his own but not being entirely contingent upon his action, then, in our culture, it is typically perceived as the result of luck, chance, fate, as under the control of powerful others, or as unpredictable because of the great complexity of the forces surrounding him. When an individual interprets the event in this way, we have labeled this as a belief in external control. If the person perceives that the event is contingent upon his own behavior, or his own relatively permanent characteristics, we have termed this a belief in internal control” (p. 1). Locus of control has been widely explored as a psychological variable, and scales used in its study have been subject to several criticisms.3 Despite most of these criticisms however, its extensive use in research has been considered appropriate for many purposes. As discussed below, it can be seen as especially appropriate for investigating the link between problem solving cognitive skills and behavioral outcome efficacy among the type of at-risk students of concern in the present study. The criticism particularly relevant to the focus of the present study deals with the claim that internal locus of control items assess both the belief about the likelihood of a behavior leading to a specific outcome (locus of control), and the belief about one’s 18 ability to su: manifest ch31 simply identi to have a higf’ ot’aetiyities it "people is ith a as loilouing fr necessarily {cg beheier. Stir-iii. . .. I .1 nemselyes to a D ,~ u ' . when: Salim; ~hi'aetenstic. st Nowicki and Ba II I g' “ Cum“ atter 1h: 7‘ 1'. A! 11.3145 Hear COTll‘ ltOTIl promem q M We . ii a; that be. ability to successfully perform a behavior (self-efficacy). Since the assertion in the present study is that both internal locus of control and self-efficacy work in conjunction to manifest change, both must be included in the study. The fundamental concern is to simply identify and parse out the different effects. Undoubtedly, it is possible for a person to have a high internal locus of control and yet not engage in the practical manifestation of activities which one would naturally assume are related to this locus. Specifically, “pe0ple with an internal locus of control are people who see both good and bad outcomes as following from, or contingent on, their own actions. [Nevertheless] they don’t necessarily feel that they have the competence to act in effective ways” (Carver & Scheier, 2000, p. 373). When individuals perceive a lack of competence within themselves to act in an effective manner, they are displaying a low self-efficacy. Problem Solving Cognitive Skills and Locus of Control Although locus of control is thought to be a relatively enduring dispositional characteristic, studies indicate that it is modifiable through experience. For instance, Nowicki and Barnes (1973) found increased internal locus in male students (grade seven to nine) afier they participated in a one week structured camp situation where counselors made clear connections between the camper’s behavior and resultant rewards. The path from problem solving cognitive skills to locus of control in the present model suggests one way that locus of control can be modified. Implicit in this prediction is recognition that in order to acquire cognitive skills through training, we must first receive repeated opportunities to learn, use, and rehearse the skill. Thus, in developing these skills, the problem solver experiences success with them. The path from cognitive skills to locus of control is based on recognition of this 19 combined it] Rotter l l 960 expression of. experience of cognitive skill luck or chance training and as internal attitud; Eyidene afldotnizo (19.5 combined with reasoning implicit in Rotter’s work on locus of control. According to Rotter (1966), if individuals perceive that their own skill determines success, not luck or chance, they are more likely to expect future success and to generalize expectancies of success from one task to another similar task. Repeated task success facilitates the expression of internal attitudes, while failure fosters externality. In this case, the repeated experience of successful use promoted during the acquisition of problem solving cognitive skills is expected to produce perceptions that outcomes result fi'om skill, since luck or chance does not happen repeatedly. The repetition of objective task success during training and associated attributions of internal control should facilitate the expression of internal attitudes and the development of internal locus of control. Evidence for this expected relationship can be found in work by Omizo, Cubberly and Omizo (1985), where a significant shift in locus of control was observed in sixty learning disabled children (ages 8-11) who were taught rational-emotive therapy (problem solving skills training and development of rational coping strategies session) during a twelve week program. In a similar study, Wege and Moeller (1985) observed heightened internal orientation by previously identified poor problem solvers. The results were maintained at a two-month follow-up. Locus of Control and Behavioral Outcome Eflicacy As the model suggests, a result of problem solving cognitive skills’ influence on control orientation is an expected increase in at-risk students’ ability to resolve personal and/or academic problems. In other words, elevated internal locus of control will enhance behavioral outcome efficacy. 20 A Sll engaging in their attitude ability to sol five hundred Hobson. Mel attitudinal the othersehool f. for the clear re later found by recently demo: flirty-six SlUClCT 3W5 Ofeontrol sittations. LO'CLI Inaproblcm sol mg?" Pftsisten Di» -.,- ”AOL I95”). A study by Schur (1999) demonstrated that the likelihood for people to start engaging in behavioral patterns that help in problem solving is significantly related to their attitude (i.e., control orientation). The relevance of attitude with regard to a student’s ability to solve personal and/or academic problems is underscored by a study of almost five hundred thousand (non-risk) youth across the United States (Colemen, Campbell, Hobson, McPartland, Mood, Weinfeld, & York, 1966). These researchers found that attitudinal factors had a stronger relationship to achievement than the additive value of all other school factors observed. The Colemen et a1. (1966) study provided early cogency for the clear relationship between internal locus of control and educational achievement later found by Nowicki and Strickland (1973). Support for this relationship was more recently demonstrated by DeMello and Irnms (1999). In their study of one hundred and forty-six students ages 14 t018 years, significant correlations were found between internal locus of control and productive problem solving coping styles vis-a-vis academic situations. Locus of control has also been found to have a significant affect on persistence in a problem solving behavior, with those high on internal locus of control maintaining a longer persistence rate than those high on external locus of control (Haines, McGrath, & Pirot, 1980). The expectation that those high on internal locus of control will initiate, and maintain, problem solving behavior is based more often on their inherent belief that they can control their own destiny. Believing they have this control, they should see their own efforts to govern outcomes as functional, and thus are expected to make more attempts to control their environments and/or their behaviors in important life situations (Seeman, 1963; Gore & Rotter, 1963; Strickland, 1965; Phares, 1965). As such, according to Rotter 21 (1906) lhe.‘~ 3" useful informs one exlml‘le 0 condition. plfiC concerned \\ Ill other hand. \\ h etlect their env them anyvv ay. 3 the expectation relevant to prob 563115;??ch on As the n ace. . - was) Is an ex ~34 - akuhemlc probl, be navioral 0Ut~ \i (1966) they are more likely to be alert to those aspects of the environment that provide useful information for their future behavior. The deployment of problem solving tools is one example of this. In general, internals take more steps to improve their environmental condition, place greater value on skill or achievement reinforcements and are more concerned with their ability, and particularly with their failures (Rotter, 1966). On the other hand, while internals are usually open to useful information that can positively effect their environment, extemals hold the belief that nothing they do will really affect them anyway, and therefore they will likely see no need to change. All this works toward the expectation that internal locus of control will increase behavioral outcome efficacy relevant to problem solving. Self-Eflicacy and Behavioral Outcome Eflicacy As the model indicates, an outcome of problem solving skills training on self- efficacy is an expected increase in at-risk students’ ability to resolve personal and/or academic problems. In other words, positive increases in self-efficacy will enhance behavioral outcome efficacy. Self-efficacy represents the perceived ability to carry out desired actions. Paj ares (2005) tells us that the manner in which students evaluate both the effect and their understanding of their actions strongly influences their efficacy beliefs: Successful interpreted outcomes raise self-efficacy and failed interpreted outcomes lower it. Investigators have established that self-efficacy beliefs influence performance achievement by influencing effort, persistence, and perseverance (Bandura & Schunk, 1981; Bouffard-Bouchard, 1990). Thus, students with high self-efficacy may set higher goals than people with low self-efficacy. They may be more likely to persevere when 22 confronted W1 and self-doub' who had Milli found that stut ofthe ones the We she et icacy should little or no heir. they feel comp: have no beliefi Education R35; confronted with early failure and less likely to submit to paralyzing thoughts of inability and self-doubt. For instance, in a study of students with low, middle, and high math skills, who had within each ability level, either high or low math self-efficacy, Collins (1982) found that students with high self-efficacy completed more problems and reworked more of the ones they missed than did students with low self-efficacy. We should expect that as levels of self-efficacy increase behavior outcome efficacy should become stronger. If student have low self-efficacy, they should display little or no behavioral outcome efficacy because people tend to engage in tasks in which they feel competent and confident, and avoid those tasks in which they do not. If students have no belief in their skills, they will be unlikely to use them. Education Risk Level Educational risk factors are important considerations in any attempt to identify processes that affect behavioral outcome efficacy. In the present model, its influence is identified through paths leading to self-efficacy and the acquisition of cognitive skills. Educational Risk Factors and Problem Solving Cognitive Skills The model shows a negative path from educational risk level to problem solving cognitive skills, indicating that increased risk will inhibit the acquisition of skills. While non-risk children may both learn problem-solving skills in the general course of their lives and consciously or unconsciously apply these skills, many at-risk children must overcome the barrier of never having acquired these cognitive skills. In fact, a significant body of research supports the claim that the problem solving attempts of at-risk students lack logical development, thoroughness and sufficient effort (Blum & Spangehl, 1982). Their problem solving may also be impulsive, unsystematic and may create more 23 problems that“. students are in solving and C r In addi cogitive sltzll: enters that pr children are in} impede our see emotional barn time. money. 5'. he? Come from ls . problems than they solve (McCluskey, Place, McCluskey & Treffinger, 1998). These students are inadequately prepared to function in a society that demands skills in problem solving and critical thinking. In addition to the potential barrier of never having acquired problem-solving cognitive skills, the at-risk child also faces other deeply entrenched personal or societal barriers that prevent their development and use. According to Davis (1992) at-risk children are inhibited from using their problem-solving skills by perceptual barriers that impede our seeing new possibilities, cultural barriers leading to outcome conformity, emotional barriers preventing us fi'om using all our skills, and resource barriers limiting time, money, supplies, people and information. Moreover, one can posit that combinations of economic, sociological and educational system failures make at-risk kids more likely to suffer from ingrained barriers that work against an individual’s ability to effectively solve problems. For instance, we know that children who live in single parent households are much more likely to live below the poverty line and to experience resource barriers, and that children whose primary household language is non-English often come from more traditional families and are more likely to experience cultural barriers. Educational Risk and Self-Efiicacy In addition to its path fiom educational risk to skills, the model’s negative path from risk level to self-efficacy signifies that increased risk is associated with a reduction in one’s perceived ability to carry out actions. Cubeta, Travers and Sheckley (1999) demonstrate empirically that at-risk students often experience low levels of self-efficacy, something that may be intensified by the history of failure they often experience. 24 Consequentl. to compete“: determining ‘ expended. an experiences. 1 above. appear cases individ; problem-solv i Premise The rel problem solv ll". B-lvt’l. OHdga not consider 11,, al'i'lSlt ltids. uh the?” Operate la is, ‘ CO-Tlducted 4...”. 6L“d‘10n3ll\a' Consequently, even when at-risk students acquire problem-solving skills, they feel unable to competently employ them. Bandura (1982) suggests that high self-efficacy is a factor determining whether coping behaviors will be initiated, how much effort will be expended, and how long effort will be sustained in the face of obstacles and aversive experiences. Deeply entrenched barriers to problem solving, such as those mentioned above, appear even more daunting for those with a low sense of self-efficacy. In such cases, individuals are more likely to give up quicker, expend less energy, or fail to initiate problem-solving activities. Premise The relationship between problem solving cognitive skills and the deployment of problem solving behavior is indicated in several studies (e.g., Tellado, 1984; McCluskey, Baker, O’Hagan, & Treffinger; 1998, Avarello, 1993). However, these investigations do not consider factors affecting this relationship. This is not uncommon when dealing with at-risk kids, where not only do most educational and institutional systems interacting with them operate largely on assumption and tradition, but much of the existing research has been conducted on an ad hoc basis. Nevertheless, given their unique association with educationally at-risk students, self-efficacy and locus of control seem to be particularly important factors in explicating the relationship between acquiring problem solving cognitive skills and resulting behavior-outcome efficacy. This study provides a first attempt to identify this process. It suggests that the relationship between problem solving cognitive skills and behavior outcome efficacy is mediated both by the perceived ability to carry out a desired action (self-efficacy), and the belief that we control the events in our lives (locus of control). 25 The St educational-n ehavioral on training vvill it educational-n: The model cor outcome etiica shiuid directly solving cognit: The first path i increase behav WET-Hive skills should increase ..j.pothesized rc The study tests a model in which both problem solving skills training and educational-risk level act as antecedents to psychological processes that influence behavioral outcome efficacy. The model begins by indicating that problem solving skills training will increase both self-efficacy and problem solving cognitive skills. By contrast, educational-risk level is expected to have a negative influence on each of these variables. The model continues from this point along the two separate paths leading to behavioral outcome efficacy. The single path from self-efficacy shows that heightened self-efficacy should directly increase behavioral outcome efficacy. Two paths continuing from problem solving cognitive skills show both direct and indirect influences on behavioral outcomes. The first path indicates that heightened problem solving cognitive skills should directly increase behavioral outcome efficacy. The second path indicates that problem solving cognitive skills should heighten locus of control, and this heightened locus of control should increase behavioral outcome efficacy. (See Figure 1 for the hill model of the hypothesized relationships). 26 9,1183" AD 6" educational” measllres Ofb classroom In: schools. I“ O C used. All or 1 problem 501‘ m tfin‘ A i a. t 1‘ ‘ ' 7 ii 5 mntné CCG solving cogniti solving fluency neitbility (the L onginality (the Chapter 2 Methods Overview An experiment manipulated problem solving skills training (yes, no) and the educational-risk level of students (at-risk, non-risk) to determine their influence on measures of behavioral-outcome efficacy. Students participated in problem solving classroom training, once a week, for 7 consecutive weeks, exclusive of testing. Four schools, two composed of at-risk and the others composed of non-risk students, were used. All participants were pre-tested on three scales prior to the commencement of the problem solving skills training, and post-tested on the same three scales one week after the training ceased. The scales measured locus of control, self-efficacy, and problem solving cognitive skills. Problem solving cognitive skills was composed of: problem solving fluency (the ability to produce large numbers of ideas); problem solving flexibility (the ability to produce and/or use, a variety of kinds of ideas); problem solving originality (the ability to produce novel or unique ideas); approach-avoidance (one’s style of evaluating problems, either approaching or avoiding them), and; problem solving confidence (an individual’s confidence in engaging in a wide variety of problem solving activities including problem identification, solution finding and implementation). One week after the problem solving skills training was concluded all the participants were post-tested on survey evaluating the course and the instructor. Two weeks after the problem solving skills training was concluded all the participants were post-tested on a survey examining the behavioral outcome efficacy (a student’s ability to 27 solve perSOUA skills treath Prob}: variables in tl‘ training is as 5 subjects receii indicated that and scored as ' For some cont; Research Part: One hit: in the study. Si control group. ( reduced to 35;, '1‘ . the participant solve personal and/or academic problems). Throughout the course of the problem solving skills treatment, all the participants were required to keep a diary of tool usage. Problem solving skills training and educational-risk level acted as endogenous variables in the hypothesized model. For purposes of analysis, problem solving skills training was scored as either “0,” indicating no skills training, or “1,” indicating that subjects received training. Thus, positive relationships with endogenous variables indicated that the training had a positive effect. Educational-risk level was dichotomized and scored as “0” (indicating non-risk level) or “1” (indicating at-risk level). For some comparisons, separate path models were run for each level of educational risk. Research Participants One hundred and five students, from four different schools, initially participated in the study. Sixty-nine students participated in the treatment and thirty-five acted as a control group. Of the treatment group, 30 (later reduced to 24) were at-risk and 39 (later reduced to 28) were non-risk. Of the control group, 22 were at-risk and 14 were non-risk. The participants were classified by their educational institutions as having either a statistically normal, or a statistically high probability of encountering failure, attrition or inadequacies with regard to their formal academic education. The latter group is called at- risk or high-risk, students. The former group, which should follow a normal distribution of risk, will be referred to as non-risk. The Fieldstone Day School was selected because it had a population of 39 students in the Upper School, all of whom participated in the treatment. They were considered, as a group, to have an ordinary level of risk for encountering failure, attrition, or inadequacies with regard to their formal academic education. For the purpose of the 28 Paper. Ordinaf Fieldstone DJ from 13 to 15 Royal Sca'OOl who “ Royal St. Gee? These students 144 students. r to consent for 7 parents indica'. Royal St. Geor The Jet because it had ; educational sys “ho had those, are their effort educational sv: The Dlt'.J 1“;an e .tuelll PYOL'.’ Wi'llcr The lot .llil" ‘ I‘d & can» cl ”learnt": m ”“3! Proh‘ paper, ordinary levels of risk are defined as “non-risk” students. The students at the Fieldstone Day School largely came from a middle class background. They ranged in age from 13 to 15 years. Royal St. George’s College was selected because it had 14 students in the Upper School who were willing to participate as a control group for students. The students at Royal St. George College were considered to have ordinary levels of educational risk. These students ranged in age from 11 tol6 years. Although the entire Upper School has 144 students, participation was on a volunteer basis. Both the student and his parents had to consent for the student to stay after school to complete the questionnaire. Although the parents indicated a high level of interest, the student interest was low. The students at Royal St. George’s College come from a middle class background. It is an all-boy school. The Jerome D. Diamond Adolescent Center (the Diamond Center) was selected because it had population of 30 at—risk students, who are accessible and currently in the educational system. All of these students participated in the treatment. These participants, who had chosen to stay within the system, should theoretically be more trusting of adults and their efforts to help than would the youth be who have chosen to leave the educational system. The Diamond Center is a children’s mental health center that provides a day treatment program for at-risk youth who have failed to thrive in a traditional academic center. The focus of the treatment services is the ultimate re-integration of the students into their community schools. The individuals who attend this school exhibit a wide range of learning disabilities, emotional, behavioral, psychological, family, community, and social problems. Some of the challenges experienced by the students are: high levels 29 ofanviety. hi or ideations; 2 these difficult problems. Th: They may hav exchanges Ollc treatment is be practiced. STUL cross-section c The M- ‘Aould act as a _— of anxiety, bi-polar, obsessive-compulsive, eating or conduct disorders; suicide attempts or ideations; attention deficit disorder or attention deficit hyperactivity disorder. Some of these difficulties may be expressed as school failure, alienation, or psychological problems. The students may live with their biological families or with foster families. They may have experienced physical or emotional abuse. Each child in the school exchanges one academic for one therapeutic course per semester. Milieu therapy, where treatment is built into the entire program experienced by students in the school, is practiced. Students, who may learn in combined grade level classes, come from a broad cross-section of the economic spectrum. They range in age from 12 to 16 years. The Merle L. Levine Academy Inc. was selected because it had 22 students who would act as a control group for educationally at-risk students. These students ranged in age from 12 - 16. Although the entire Upper School has 36 students, participation was on a volunteer basis. Both the student and her/his parents had to consent for the student to complete the questionnaire during class time. Some of the parents felt that their children were under enough stress without asking them to take an additional test. These parents choose not offer the Opportunity to their children. The students at the Merle Levine Academy Inc. come from a middle class background. It is a co-educational school. Although the school specializes in students with attention deficit disorder (ADD/ADHD), like the Diamond Center, it has a wide range of students with comparable behavioral and learning difficulties. Also like the Diamond Center, in the Merle Levine Academy both teachers and child-youth care workers interact with the students. Students completed the questionnaires in class time. 30 The d‘ Fieldstone D " these students The ec health issues. eticit disord; Although an L disabilities dc behavioral ant: anxiety. to obs ideations. The in .~ 3331mm arot. The data fiom 6 students in the Jerome Diamond Center, and 11 students in the Fieldstone Day School was omitted from data analysis of the treatment groups because these students had missed four or more classes. The educationally at-risk students have either learning disabilities and/or mental health issues. Learning disabilities (LD) range across the board from dyslexia to attention deficit disorder or attention deficit hyperactivity disorder to Asperger syndrome. Although an LD student may learn specific tools for dealing with her/his particular problem, s/he will continue to be an LD person for the entirety of her/his life. Learning disabilities do not go away. A student with mental health issues may suffers fiom behavioral and/or emotional problems. Mental health issues range from high levels of anxiety, to obsessive-compulsive, eating or conduct disorders, to suicide attempts or ideations. The instructor for the problem solving cognitive skills training given to the treatment group was not informed as to which students had what problems prior to the commencement of treatment. The schools had recommended that it was better for an instructor to spend several weeks learning what students can and cannot do prior to reading her/his chart. In this manner, a student is not negatively pi geon-holed by an instructor who may excessively change her/his behavior and/or attitude towards a student on the basis of the student’s history. It was only after treatment was concluded that the instructor was informed on the status of each student. Procedure The quasi experiment used a fully crossed factorial design. The population of concern was the at-risk students. The 2 X 2 design examined the influence of problem 31 soiling skillS behavioral-m SlUdLfi' one STLminu’. the treatment problem-sols. existing class. problem. to n‘ aid to create .. utilizing the p Student-gene: The re- solving and tb Wise and als he questionnu mime 0f the 5, l0 an.“ 0Tllle (1 TTSk TC\QI of IT ‘ = The fit. solving skills training (yes, no) and the student-risk level (at-risk, non-risk) on behavioral-outcome efficacy. Students in each respective school participated in a problem-solving course where one 50-minute class was allocated each week for seven consecutive weeks to introduce the treatment. Classroom training occurred whereby each week the researcher taught the problem-solving course to three existing classes at the Diamond Center and to three existing classes at the Fieldstone Day School. Students learned tools to identify a problem, to make a problem statement, to generate and evaluate possible idea solutions, and to create a plan of action. The lesson plans were designed to generate success when utilizing the problem solving tools for specific problems and for problems that are student-generated and relevant to their personal lives. The researcher explained that each school was participating in a study on problem solving and that each student must complete the measurement tests at the start of the course and also at the end of the course. Students were told that the instructor was using the questionnaires as a guide for how much improvement the students made over the course of the skills training. They were informed that there was no right or wrong answer to any of the questions and that they were not being graded on the course. The educational risk level of the students was determined a priori by the educational institutions. The fluency, flexibility and originality aspects of problem-solving cognitive skill were measured by a creativity questionnaire with one single open-ended question. The open-ended question was different, but comparable, from the pre to the posttest. The approach-avoidance and problem solving confidence aspects of problem-solving 32 Cognlll‘e 5k“! Lo‘control am All st; ofthe Pmble'r data regardin: virtually non; anCCdOlfll C“ is at home l at lL‘ .lfntertuls ‘Prol‘ identification. opportunities. class vvas but? him one class self-generated continuallv t cognitive skill were measured through self-report surveys along with self-efficacy, locus of control and behavioral outcome efficacy. All students were required to maintain a diary of tool usage throughout the course of the problem solving skills treatment. The diary was used to corroborate the self-report data regarding behavioral outcome efficacy. The diary of tool usage was dropped as virtually none of the students completed this voluntary aspect of the course. Although anecdotal evidence provided by the students indicated that many did, indeed, use the tools at home (at least on occasion), this tool could not be used to substantiate this fact. Materials ‘Problem solving skills training’ focused upon five generic tools: (1) problem identification, (2) brainstorming, (3) highlighting, (4) advantages, limitations, unique opportunities, and overcoming limitations (ALUO); and (5) planning for action. Each class was built on the material taught in previous classes. Problems were carried over from one class to the next. The instructor provided some problems; other problems were self-generated by students for themselves. The information from an earlier class was continually reviewed and incorporated into the subsequent classes. The lesson plans for training students in the five generic tools is described below. Each of the five skills-training methods was derived from the lesson plans published in Big Tools for Little Thinkers by Keller-Mathers and Puccio (2000), and Avarello’s (1993) exploratory study. Big Tools for Little Thinkers is a book with lesson plans specifically designed to teach primary students to generate and evaluate ideas more effectively. The Avarello study examined how a course on creativity would influence 22 at-risk students (ages 18-25) who took part in the introductory 15-week creativity course 33 at State Univ: wlv‘ problem st press. person.‘ Problem [den Patric They were as} the problem L; action \sord; L H0 Disnership they Were tolc either select ,3 some aSpeCI o reflected the p he action and mixed and mu. at State University of New York at Buffalo. Since this study is only interested in the problem solving aspects of the Avarello course, the other sections on process, product, press, person, creative style etc., were not included. Problem Identification Participants practiced identifying a problem by creating a problem statement. They were asked to think of a problem they wanted to solve. They were required to write the problem using: an invitational stem; clearly stated ownership of the problem; an action word; and an objective that they wanted to accomplish. When problem solvers had no ownership or control of the problem, or if they were not in a position to make changes, they were told that the problem was not theirs to solve. Problem solvers were asked to either select a new problem or to reframe the problem statement so that it represented some aspect over which the participant had control. If the problem statement accurately reflected the problem, identifying the problem was complete. If not, alternative words for the action and the objective, which were to be written beneath the original word, was mixed and matched by the participant until a problem statement was arrived at which most closely reflected the question that the problem solver wanted answered. The new problem statement may have differed from the old one with regard to specific words, or with regard to the entire idea. (For an in-depth discussion of how to identify a problem see Appendix 9; for an example of the how to identify the problem cheat sheet see Appendix 16; for an example of how to identify the problem lesson plan see Appendix 22; and for an example of the identifying the problem homework assignment see Appendix 23). 34 BratnsIOrm" ' panic idea solullon cortecmebd timed. brains time. the P'm problem 513k“ uitbboldingj idea. and; pi. Participants c brainstormintt for an exampl brainstorming homett‘ork 355 Hsgbt’ig/trmg Panic: Participants c. Week using h; Dir .. '~' . 33713313, R. Brainstorming Participants worked together on a timed brainstorming session to propose possible idea solutions for a problem identified by the instructor. After completion, they collectively debriefed the brainstorming experience. Participants engaged in a second, timed, brainstorming session for a different problem identified by the instructor. This time, the participants were required to use the following brainstorming tools: a clear problem statement or goal to direct the session; a problem dependent on idea generation; withholding judgment; coming up with lots of ideas; coming up with wild and crazy ideas, and; piggybacking off another’s idea by copying and changing it a little bit. Participants collectively debriefed the difference between the first and the second brainstorming sessions. (For an in-depth discussion of brainstorming see Appendix 10; for an example of the brainstorming cheat sheet see Appendix 15; for an example of the brainstorming lesson plan see Appendix 24; and for an example of the brainstorming homework assignment see Appendix 25). Highlighting Participants collectively identified criteria they used for making decisions. Participants categorized ideas generated in the brainstorming session from the previous week using highlighting tools: hits (marking the ideas which seem particularly promising), relates (grouping the hits into clusters of similar ideas), and hotspots (re- labeling the clusters). (For an in-depth discussion of highlighting see Appendix 11; for an example of the highlighting cheat sheet see Appendix 17; for an example of the brainstorming lesson plan see Appendix 28; and for an example of the brainstorming homework assignment see Appendix 29). 35 Adt'antagCS- Pam. l'pon compl. engaged in a instructor. T?‘ adt'antages. 5 concerns. u e qualities oft? used hits to i; Pilit‘ipants c limitations. A APl‘enclnt l8. Olthe ALL‘Q ESlgnmem SC P T .amzzng for _ Advantages, Limitations, Unique Opportunities and Overcoming Limitations (ALUO) Participants engaged in a timed evaluation of an idea identified by the instructor. Upon completion, participants collectively debriefed their evaluation. Participants engaged in a second evaluation session for a different problem identified by the instructor. They were required to use the following evaluative tools: identify the advantages, strong points, plusses or strengths of an idea; identify the limitations, concerns, weak points or challenges of the idea; identifying unique, novel, or original qualities of the idea; identify the limitation(s) most important to overcome. Participants used hits to identify the limitation(s) most important to overcome. Once identified, participants collectively brainstormed ideas on ways to strengthen and overcome the limitations. ALUO was collectively debriefed. (For an in-depth discussion of ALUO see Appendix 18; for an example of the ALUO cheat sheet see Appendix 18; for an example of the ALUO lesson plan see Appendix 30; and for an example of the ALUO homework assignment see Appendix 31). Planning for Action Participants collectively engaged in planning for action and acceptance of student generated problems. Participants transformed an idea into concrete action steps by identifying who would assist and/or resist the idea, what would assist and/or resist the idea, where would idea assistance and/or resistance occur, when would idea assistance and/or resistance occur, and why would idea assistance and/or resistance occur. Participants brainstormed the answers to these questions. Participants identified start and stop dates for each of the short- intermediate- and long-term steps that they distinguished as necessary to put the plan into action. Participants were required to identify who would 36 carry out the action, where it would be carried out, why it would be carried out, how it would be carried out, and when the participant would know that the step has been successful. (For an in-depth discussion of planning for action see Appendix 13; for an example of the planning for action cheat sheet see Appendix 19; for an example of the planning for action lesson plan see Appendix 32; and for an example of the planning for action homework assignment see Appendix 33). After each class the students were given a cheat sheet, as well as their homework exercises which were kept in the diary that records tool usage (see Appendix 8). Homework exercises focused upon problems that were student self-generated. Although there was opportunity to work with different problems, if participants desired, they could take a single problem and work with it from problem identification to plan of action. Measures Four self-report questionnaires, and one open-ended questionnaire, were used to obtain measures of problem-solving cognitive skill, self-efficacy, locus of control, and behavioral outcome efficacy. These measures were used in analyses testing the hypothesized path model. A fifih self-report measure was used to evaluate the course and the instructor. Problem Solving Cognitive Skill A composite score was used in this study to measure problem solving cognitive skills. The development of the composite score began with the consideration of several indicators. To begin with, the fluency, flexibility and originality aspects of problem- solving cognitive skill were measured by the Unusual Uses Activity (Form A, Cardboard Boxes and Form B, Tin Cans) in the Torrance Test of Creative Thinking (1966) (See 37 Appendix 4 and 5). The instrument was a timed measure (5 minutes) that focused on the participant’s immediate thoughts and impressions regarding a specific open-ended question. Participants were requested to list as many interesting and unusual uses as they could think of for cardboard boxes or tin cans. Each idea was placed in a coding category. If a subject’s response was not present a priori in the coding categories the researcher consulted with a second individual, who was familiar with the coding manual, prior to making selections. Each coder determined her coding category for the disputed item independently, and answers were compared. When discrepancies arose, a final coding category was not selected until both coders agreed upon placement. Fluency was determined by calculating the total number of different unusual uses produced for the tin cans and cardboard boxes. Fantastic or impossible uses beyond all possible reality were not counted (i.e.: inanimate matter could not be made animate). Each relevant use awarded the subject one point. Only relevant responses that received a fluency credit were scored for flexibility and originality. Higher scores indicated greater levels of fluency. Flexibility was determined through the breadth of different unusual uses produced for the tin cans and cardboard boxes. The codebook indicated twenty-eight a priori coding categories, wherein each coding category had a title and specific examples of the kind of unusual uses that are subsumed within it. One point was given for each category used. No credit was given if a category was repeated. Higher scores indicated greater levels of flexibility. 38 Originality was determined through the novelty, or uniqueness of the unusual uses for tin cans and cardboard boxes. The codebook indicated 26 and 29 respective a priori “zero-originality” coding categories for the tin cans and cardboard boxes. If a subject’s response was specifically highlighted on the “zero-originality” list, s/he received a score of zero for that idea. All other responses were given scores of one. Higher scores indicated greater levels of originality. Previous tests of internal consistency for the Torrance Test of Creative Thinking (1966) showed fluency, alpha = .75, flexibility, alpha = .60, and originality, alpha = .64 (Yamamoto, 1962). Estimates of test-retest reliability were fluency, r = .75, flexibility, r = .74, and originality, r = .66. (Mackler , 1962). The approach-avoidance and problem solving confidence aspects of problem- solving cognitive skill were measured using the Personal Problem-Solving Inventory (PSI) by Heppner and Petersen (1982) (See Appendix 1). The PS1 used a 6-point, Likert- type format ranging from strongly agree to strongly disagree. It consisted of 32 self-report items that examined personal problem solving behavior and attitudes. The total score was comprised of three distinct subscales: Confidence, Style, and Control. For the purpose of this study only the first two scales were used, as the third scale was irrelevant to the focus of the study. Thus, only 26 of the 32 items on the scale were used. The Confidence subscale (questions 1 to 10) consisted of items that assessed students’ confidence in their problem solving ability: the Style subscale (questions 11 — 26) consisted of items that referred to one’s style of approaching problems (either avoidance or direct action). A total score was derived by summing the individual item scores to get a general index of problem solving appraisal. The total ranges from 26 to 39 156. High scores indicated behaviors (e. g., generating multiple problem solutions), and attitudes (e. g., confronting rather than avoiding a problem), which were typically associated with successful problem solving. Research across a number of populations and cultures has found the PSI to have acceptable internal consistency (e.g., Heppner, 1988; Heppner 2002). When summed across a variety of studies, the PSI total obtained average alpha coefficients in the high .805, and problem- solving confidence and approach-avoidance style obtained average alpha coefficients in the low to mid .808 (Heppner, Witty, Dixon, 2004). Additional tests showed that these indicators were stable over a two week period (total PSI scores r = .80) (Heppner, 1988). The PSI scores have been significantly correlated with observational ratings of problem solving behavioral competence. It has not been correlated with social undesirability factors (N ezu, 1986). The three primary scores (flexibility, fluidity, originality) were summed to create a higher level variable called Problem Solving Cognitive Skills. These sums comprised the composite scores used in this study. This decision was made both to make the model more parsimonious, and because the three variables are believed to be types or indicators of problem solving cognitive skills. The alpha reliability of the higher level variable called Problem Solving Cognitive Skills was .89. For non-risk kids M =15.13, SD = 7.55. For at-risk kids M= 14.80, SD = 7.51. Two PSI subscales were NOT used because of measurement problems, specifically, poor reliability. Tests of internal consistency and parallelism were performed, and those measures did not pass. The decision to drop the scales from subsequent analysis was made for two reasons. First, the low reliability of the scale meant 40 I‘l- that it would not be an accurate indication of the item that it was intended to measure. Second, the fact the even without the PSI scales, problem solving cognitive skills was able to be accurately measured by the new composite variable, comprised of the summed fluency, flexibility, and originality scores. Self-Efi‘icacy The self-efficacy measure in this study was based on the Bosscher and Smit (1998) General Self-Efficacy Scale (GSES-12) (See Appendix 2), which was adapted from the General Self-Efficacy Scale created by Sherer, Maddux, Mercandante, Prentice- Dunn, Jacobs and Rogers (1982). It employed a 5-point, Likert-type format ranging from strongly agree to strongly disagree. Using confirmatory factor analysis, Bosscher and Smit (1998) produced a 12-item scale with three distinct factor loadings (initiative, effort and persistence) that measured the belief of generalized personal mastery expectations. The initiative subscale consisted of questions 1 to 3 of the questionnaire. The effort subscale consisted of questions 4 to 8 of the questionnaire. The persistence subscale consisted of questions 9 t012 of the questionnaire. Questions 1 to 3 and 9 t012 were reverse coded. Higher scores indicated a greater belief that one can successfully perform the behavior in question (Maddux, Sherer & Rogers, 1982). Bosscher and Smit reported estimates of internal consistency of alpha = .69 for the GSES-12; alpha = .64 for initiative; alpha = .63 for effort; and alpha = .64 for persistence. Test-retest scores were stable over a two-week period. For the present study, participant scores on the Bosscher and Smit’s (1998) General Self-Efficacy Scale (GSES-12) were reduced to a 7-item scale. Included were the first three initiative items and the first four perseverance items. Alpha reliability = .77. 41 The third subscale, effort, was dropped due to measurement problems. With the effort items included, reliability dropped below .60, which was designated as the minimum acceptable level. The reduced scale showed non-risk kids M = 4.36, SD = 1.43, and at- risk kids M: 4.84, SD = 1.28 Locus of Control The locus of control was measured using the Nowicki—Strickland (1973) Scale for Locus of Control for Children, abbreviated version B". (See Appendix 3). This scale was a 21-item yes/no format test that measures a person’s belief or expectations about how reinforcement is controlled. With the exception of questions 4 and 13 (which were reverse coded), a yes response indicated an external locus of control orientation. Higher scores were associated with an external orientation. Nowicki-Strickland reported estimates of internal consistency of alpha = .63 (grades 3 to 5); alpha = .68 (grades 6 to 8); alpha = .74 (grades 9 toll); and alpha = .81 (grade 12). Test-retest reliabilities were .63 for the third grade, .66 for the seventh grade, and .7l for the tenth grade. In the present study, all but three of the 21 items were used. The dropped items were 4, 13, and 20 (See Appendix 3). The final 18-item index Alpha reliability = .76. For non-risk kids M =1.69, SD = .19. For at-risk kids M: 1.59, SD = .19. Behavioral Outcome Efficacy Treatment subjects were post-tested on a 15 item self-report measure (See Appendix 7) examining the behavioral outcome efficacy of tool usage. Most of these questions were taken from the Avarello (1993) study. Questions 1 to 3 focused on idea generation; questions 4 to 6 focused on idea evaluation; questions 7 to 11 focused on planning for action; questions 12 to 15 focused on efficacy of tool usage. The question 42 formats include a 5-point, Likert-type format ranging from strongly agree to strongly disagree, rankings, and closed-ended responses. Students were specifically informed that the behavioral outcome efficacy questionnaire would ask them questions about whether or not they had used any of the tools outside of the class exercises. Students were also asked the circumstance surrounding their tool use or non-use. In order to minimize response bias, the researcher stressed that since students were not being graded on the course, nothing bad could happen to them by being completely truthful. In fact, it was explained that in some ways the students were grading the course. It was spelled out that honesty was requested because the best way that the researcher could adapt the course to help other kids in the future was by using the comments and responses these students made as her foundation. Students were further told that the instructor’s feelings would not be hurt if students did not use the tools, or if they did not use them very ofien. Since Avarello conducted a qualitative study, there was no quantitative analysis to determine the quality of the instrument. However, throughout the course of the problem solving skills treatment, participants were required to keep a diary of tool usage (see Appendix 8) that was used as a secondary measure validating the behavioral outcome efficacy data. The diary required students to record the date, the tools, the situation in which the tools were used, and whether or not the problem was satisfactorily resolved. The diary also required students to record if they thought about using the tool but didn’t, and why the tool was not employed. Additionally, at the beginning of each class, participants were also routinely asked who used the tools to solve a problem. Participants 43 were invited to discuss what occurred. Everyone was reminded to attempt to use the tools and to keep maintaining their diary. For the present study, the first 11 items from this scale were summed to form the behavioral outcome efficacy measure. Alpha reliability = .95. Non-risk kids M =3.32, SD = .80; At-risk kids M = 2.96, SD = 1.98. Items 12-15 were dropped due to measurement problems. Course Evaluation The last questionnaire completed by each participant was a 22-item survey (see Appendix 6) evaluating the course and the instructor. It was modified from the standard course evaluation from Michigan State University. The modification eliminated questions relating to student background since the researcher was cognizant of the background information. The modification included open-ended questions about what the student liked about the course, what the student would change about the course, and what part of the course, if any, most helped the student and why. Chapter 3 Results Analyses began by examining the data for issues concerning validity and reliability. Following this, descriptive analyses were performed. Finally, path analyses were conducted to test the hypothesized model. Examination of Data Two steps were taken to determine the validity of the data. The first step dealt with missing values. When a student had a missing value(s) from a questionnaire (from a single question up unto a page), the average score from all of the classes (either high or non-risk) was substituted for the missing value(s). If more than one page of a completed questionnaire was missing, the questionnaire was dropped from the study. For all instances where an entire questionnaire was missing, due either to absence on the day of testing or by the subject overlooking a booklet, the data from other questionnaires for this subject were broken down by questionnaire subscales for use in analyses testing relevant hypotheses. In this manner the researcher was able to utilize the vast majority of the information that was provided by each participant. The second step was to create histograms for each variable to check for outliers. There were never more than moderate outliers and no data points were dropped for this reason. Descriptive Analyses Prior to conducting path analysis, descriptive statistics for all key variables were run in the model. Descriptive statistics are shown in Table 1. Table 2 demonstrates the correlations among these variables. 45 Table 1 Descriptive Statistics for Key Variables in Path Model Risk Level Problem Solving Self-Efficacy Locus of Behavioral Cognitive Skill Control Outcome Efficacy Non-Risk N Valid 42 42 42 28 Missing 0 0 0 14 Mean 16.12 3.86 1.69 3.32 Std. Deviation 7.24 .78 .21 .80 Minimum .00 2.0 1.0 1.0 Maximum 30.33 5 2 4.45 At-Risk N Valid 46 46 43 24 Missing 0 0 0 22 Mean 15.80 3.59 1.60 2.96 Std. Deviation 6.70 .70 .20 1.18 Minimum 3.33 1.86 1.17 1.00 Maximum 38.0 5 1.94 5.0 46 Table 2 Zero-Order Correlations Used to Calculate Parameter Estimates in Model 1 2 3 4 5 6 1. Problem Solving Skills 1.00 Training 2. Risk Level -.15 1.00 3. Problem Solving .25* -.02 .89 Cognitive Skills 4. Self-Efficacy .08 -.18 .17 .77 5. Locus of Control .20 -.26* .11 .55* .76 6. Behavioral Outcome 3‘. -.18 .17 .27 .22 .95 Efficacy Note. Skills training was coded such that l = received skills training and 0 = did not receive skills training. Risk level was coded such that 1 = at-risk and 0 = non-risk. Standardized item alpha appears in the diagonal. *indicated p < .05, two tailed. '. Cannot be computed because at least one of the variables is constant. The results from Tables 1 and 2 were inspected both for abnormalities, and to establish whether or not the variables in the model were able to demonstrating the predicted relationships. If a variable manifested problems, its role in the model would have been reassessed. The variables appeared to have means, standard deviations and bivariate relationships that fall in the expected ranges and directions. Independent samples t-tests were then run to compare non- and at-risk students on pre-induction measures of locus of control, self-efficacy and problem-solving cognitive skills. Results shown in Table 3 indicate that at-risk students score significantly lower on 47 pre-test measures of self-efficacy and locus of control than do non-risk students. There was no difference in problem solving cognitive skills prior to the manipulation. Table 3 Pre-induction Comparisons of Non-Risk and At-Risk Students Risk Level N M SD SE ’ P 2-tailed Problem Solving Non-Risk 42 11.5285 4.56924 .70505 -0.592 0.555 Cognitive Skills At-Risk 46 12.2274 6.27390 .92504 Self-Efficacy Non-Risk 42 3.8469 .66513 .10263 2.599 0.011* At-Risk 46 3 .4658 .70658 .10418 Locus of Control Non-Risk 42 1.6842 .16844 .02599 2.817 0.006* At-Risk 46 1.5782 .18322 .02701 48 -r1 Test of the Hypothesized Model Path analysis was performed on the hypothesized model using the least-squares method. This involves estimating the sizes of the model parameters and testing the overall model fit. Parameter size was estimated by regressing each endogenous variable onto its causal antecedent, and model fit was tested by comparing estimated parameter sizes to the reproduced correlations. For the present study, a model was considered consistent with the data if (1) it passed the test of overall model fit, indicated by a non-significant chi-square goodness of fit result, (2) it had substantial path coefficients, and (3) it had differences between parameter estimates and reproduced correlations (errors) no greater than what would be expected through sampling error. A model was considered consistent with the data if it satisfied all three of these criteria. Specifically, in order to pass the test of overall model fit, a non-significant chi-square test would have to be observed. Second, a substantial path coefficient is one that passes a test of statistical significance at p < .05. Third, for parameter error to be acceptable, all error terms must satisfy a conservative criterion for z-differences set at p <.10. The PATH program was used to determine if each model met these rigid criteria. It should be noted that the correlations reported in the tables below were corrected for attenuation due to measurement error during procedures used for model testing. The model hypothesized that problem solving skills training would positively affect problem solving cognitive skills and self-efficacy, depending on risk level. Skill, in turn, would affect locus of control, which would then affect behavioral outcome efficacy in conjunction with skills and self-efficacy. As the objective of the research was to 49 examine the roles of problem solving skills training and levels of educational risk on behavior outcome efficacy, all models were inspected for evidence of substantial continuous paths from the former to the latter. If this type of continuous path was absent from the model, the model was considered unable to demonstrate support for the logic underlying the study. The correlations used to test the model are shown in Table l. The results of the path model are shown in figure 2. Figure 2. Path Model of Hypothesized Relationships Problem Solving Cognitive Skill Problem Solving Educational Locus of ¢Behavioral Outcome Skills Training Risk Control .0 Efficacy .12 Self-Efficacy * Significant at p < .05, two-tailed. Examination of these results reveals several significant things. First, the predicted model did not meet the three criteria established to determine if the model was consistent with the data. Second, the observed model does not show the type of substantial continuous paths from problem solving and educational risk to behavior outcome efficacy that was necessary to support the logic underlying this study. 50 Notably, observations were not consistent with evidence of a good model fit. Results of analysis showed that although most paths appeared to be in the predicted direction, not all were large in magnitude. First, the only significant path was the one between problem solving skills training and problem solving cognitive skill, .27 P (.05 S ,o _<_ .49) = .95. Second, examining predicted and obtained correlations for the unconstrained bivariate relationships shows one error was substantial for the association between self-efficacy and locus of control (difference = .71 , z = 3.60, p = .01). Another error between locus of control and risk level was close to being substantial (difference = - 30, z = -l .70, p = .09). Third, and most notable, the chi—square global test of goodness of fit was significant, 12(6, N = 88) = 18.81, p < .01. The combined results forced a decision to reject this model. As a result, alternative models were searched for. Although tests on hypothesized model did not satisfy the criteria needed to conclude an overall good model fit, patterns consistent with the model’s logic were observed. In addition, inspection of error terms suggests that a better fitting model could be produced by small changes that would remain consistent with the original underlying logic. While realizing that using path analysis to test non-hypothesized models has considerable limitations, since only minor changes were suggested the decision was made to conduct post-hoe analyses on a revised model. Holbert and Stephenson (2002) argue that analysis on respecified models usually produce difficult to replicate findings. As such, any interpretation based on this type of post-hoe analysis should be viewed with skepticism. Moreover, the results of these analyses should be used to guide future research. The results of post-hoc analyses conducted here are reported with these caveats in mind. 51 Post-Hoe Analyses Revisions in the post-hoe model were undertaken with two problems in mind: First, the substantial residual errors found for the predicted and obtained correlations between self-efficacy and locus of control, and between locus of control and risk level. Second, the weak paths observed for some links in the model. The large residual errors suggested that existing relationships between the paired variables were not represented in the model. Due to the large correlation between self-efficacy and locus of control, the first alternative model posited a positive relationship between the two variables, with the expectation being that more internal locus of control would lead to greater self-efficacy. This was the only change from the first model. The correlations used in this analysis are the same as those displayed in Table 1. The results of this analysis are shown in Figure 3. Figure 3. Revised Model Adding Path Between Self-Efficacy and Locus of Control Problem Solving Cognitive Skill W06 1 .01 \13 Problem Solving Educational Locus of A, Behavioral Outcome Skills Training Risk Control .07 Efficacy .12 -.22 .72 .24 Self-Efficacy * Significant at p < .05, two-tailed. Although the new model still failed to meet the necessary criteria necessary for model fit, considerable improvement was observed from the previous model. Notably, no 52 differences between predicted and obtained correlations in unconstrained bivariate relationships were significantly different than what would be expected by chance, and the chi-square test of global fit was non-significant, 12(5, N = 88) = 3.32, p = .651. However, concern over the small path coefficients led to further revision. Specifically, the paths from problem solving cognitive skill to locus of control, educational risk to problem solving cognitive skill, and locus of control to behavior outcome efficacy were deemed to small to be acceptable. The key difference in this revision was the designation of educational risk level as a moderating variable. This decision was made both because of the theoretical importance of educational risk level in this study and because of the considerable residual error observed between the predicted and obtained correlations for locus of control and risk level (difference = -.15, z = -.88, p = .38). The inclusion of educational risk level as a moderator was accomplished by testing separate models for both non-risk and at-risk kids. In essence, this provided an opportunity to compare how well the model applies to these two different groups. The results of these analyses are shown in Figures 4 and 5. Tables 3 and 4 show the correlations used to test these models. Both models are similar to the one in Figure 3. The only change is the addition of a path between skills training and locus of control. This path was added to account for expected increases in locus of control among at-risk kids with training. 53 Figure 4. Revised Model Using Only At-Risk Students Problem Solving Cognitive Skill Problem Solving .53* _ Locus of .12 _ Behavioral Outcome Skills Training 7 Control fl Efficacy \ 1.72,“ /2 Self-Efficacy * Significant at p < .05, two-tailed. Figure 5. Revised Model Using Only Non-Risk Students Problems Solving Cognitive Skill Problem Solving -.24 - Locus of .16 _ Behavioral Outcome Skills Training a Control ' Efficacy -.01 174* .21 Self-Efficacy * Significant at p < .05, two-tailed. 54 Table 4 Zero-Order Correlations Used to Calculate Parameter Estimates in Figure 4 1 2 3 4 5 1. Problem solving skills training 1.00 2. Problem solving cognitive .24 .89 skills 3. Self efficacy .26 .22 . 77 4. Locus of control .49* .23 .50* . 76 5. Behavioral outcome efficacy —- .10 .26 .23 .95 Note. Skills training was coded such that l = received skills training and 0 = did not receive skills training. Standardized item alpha appears in the diagonal. * indicates p < .05, two-tailed. 55 Table 5 Zero-Order Correlations Used to Calculate Parameter Estimates in Figure 5 1 2 3 4 5 1. Problem solving skills training 1.00 2. Problem solving cognitive .26 .89 skills 3. Self efficacy -.20 .11 .77 4. Locus of control -.15 -.02 .57* . 76 5. Behavioral outcome efficacy 3- .26 .31 .26 .95 Note. Skills training was coded such that l = received skills training and 0 = did not receive skills training. Standardized item alpha appears in the diagonal. * indicates p < .05, two-tailed. ‘. Cannot be computed because at least one of the variables is constant. Inspection of the two models is informative. Given the small N resulting from splitting the sample, it is not surprising that the model was unable to satisfy all three criteria needed to consider the model consistent with the data. In particular, it is unlikely that all path coefficients would be found substantial at p < .05, and this was the case observed in both models. Yet despite the weak power behind these tests, several relationships still emerged as significant (p < .05), and several others approached significance (p < .10). Locus of control had a significant influence on self-efficacy in both instances (at-risk path coefficient = .72, P (.28 < p <1 .00) = .95; non-risk path coefficients = .74, P (.44 < p < 1.00) = .95). Among at-risk kids, problem solving skills 56 training had a significant, positive influence on their locus of control (path coefficient = .52, P (.24 < .80) = .95). This finding is in stark contrast to the one observed for non-risk kids, where skills-training seems to have had a negative (though non-significant) effect on their locus of control (path coefficient = -.24, P (-.60 < p < .12) = .95). When we examine the other two criteria used to evaluate model fit, we find that neither model had errors greater than what would be expected through sampling, and that both models passed the test of overall model fit determined by the observation of a non-significant chi-square. The chi-square for at-risk kids was x2(2, N = 46) = 3.07, p = .215, and the chi-square for non-risk kids was x2(2, n = 42) = 5.65, p = .059. As such, although tests failed to produce the type of evidence needed to conclude that the data provide a good fit for the hypothesized models overall, the observed outcomes provide some evidence consistent with the model. Further evidence for these models being consistent with the data can be observed by examining additional global tests. The first test, a multiple correlation analysis, tells the strength of the relationship between each exogenous variable and the combination of the variables leading to it. Among both the high and non-risk kids, behavioral outcome efficacy (the main dependent variable in the study) was positively associated with the combination of problem solving cognitive skills, locus of control, and self-efficacy. In the case of at-risk kids, all of the exogenous variables except for problem solving cognitive skills were significantly associated with the variables preceding them (see Tables 5 and 6). As such, although all path coefficients were not significant at p < .05, the combined effect of these variables does seem substantial in most cases. 57 Table 6 Multiple Correlations for At-Risk Students R Problem solving skills training -- 1. Problem solving cognitive .24 skills 2. Self-efficacy .58* 3. Locus of control .36* 4. Behavioral outcome efficacy .32* * Significant at p < .05. Note. This table shows the results of four multiple correlation tests, in which numbered each row (other than row 1) indicates the results of R for the variable listed in that row with all of the antecedent variables listed in the rows above it. Row 1 is the simple correlation (r) between problem solving cognitive skills and problem solving skills training. 58 Table 7 Multiple Correlations for Non-Risk Students R Problem solving skills training -- 1. Problem solving cognitive .28 skills 2. Self—efficacy .23 3. Locus of control .25 4. Behavioral outcome efficacy .45* * Significant at p < .05. Note. This table shows the results of four multiple correlation tests, in which each numbered row (other than row 1) indicates the results of multiple R for the variable listed in that row with all of the antecedent variables listed in the rows above it. Row 1 is the simple correlation (r) between problem solving cognitive skills and problem solving skills training. Given indications that the variables envisioned as determinants of behavioral outcome efficacy operate in the proximal order hypothesized, I am hesitant to dismiss the hypothesized model as completely uninformative. Since the observations suggest a path between skills training and behavioral outcome efficacy mediated by locus of control and self-efficacy, a final model with fewer paths was tested. Notably, the indications of this model are stronger with the at-risk kids for whom this work has special implications. As such a final model was tested with the removal of several weak paths. The goal of the more parsimonious model was to observe the strength of associations and fit a model for at-risk kids that still represented the fundamental logic hypothesized without the residual error introduced by including weak paths in the initial model. The path between problem 59 solving and problem solving cognitive skill was retained due to the robustness of this relationship. Once again, this model was tested for evidence of substantial continuous paths from problem solving skills training to behavioral outcome efficacy. The final model for at-risk kids and its associated path coefficients is show in Figure 6. Figure 6. Second Revised Model Using Only At—Risk Students, Corrected for Attenuation Problem Solving Cognitive Skill V Problem Solving .56* Locus of Behavioral Outcome Skills Training > Control Efficacy 174* .21 Self-Efficacy "‘ Significant at p < .05, two-tailed. This model received stronger support. Two paths (between problem solving skills training and locus of control, and locus of control and self-efficacy) were significant. The path from problem solving cognitive skills to locus of control was .56, P (.28 < p < .84) = .95, and the path from locus of control to self-efficacy was .65, P (.35 < p < .95) = .95). The other two paths approach significance. The path from problem solving cognitive skills training to problem solving cognitive skill was .24, P (-.08 < p < .56) = .95, and the path from self-efficacy to behavioral outcome efficacy was .30, P (-.04 < p < .64) =.95. The chi-square test of overall fit was highly non-significant, x2 (6, N = 46) = 1.32, p = .970, and no individual predicted and obtained correlations differ significantly. 60 For comparison purposes, the same parsimonious model was tested with non—risk students. The model and its associated path coefficients are shown in Figure 7. The results of analyses on this model also show considerable support. Two paths (between locus of control and self-efficacy, and self-efficacy and behavioral outcome efficacy) were significant. The path from locus of control to self-efficacy was .73, P (.45 < p < 1.00) = .95, and the path from self-efficacy to behavioral outcome efficacy was .36, P (.02 < p < .70) = .95. The other two (between problem solving skills training and problem solving skills training and problem solving cognitive skills) approached significance. Specifically, the path from problem solving skills training to locus of control was -.23, P (-.57 < p < .11) = .95, and the path from problem solving skills training to problem solving cognitive skill was .28, P (-.02 < p < .58) = .95. The chi-square test of overall fit was non-significant: x2(6, N = 42) = 2.13, p = .907. Once again, no significant residual errors were observed between predicted and obtained correlations were observed. Figure 7. Second Revised Model Using Only Non-Risk Students, Corrected for Attenuation Problems Solving Cognitive Skill .28 Problem Solving -.23 L Locus of Behavioral Outcome Skills Training , T Control Efficacy l 73* 36* Self-Efficacy "‘ Significant at p < .05, two-tailed 61 In order to inspect the strength of the model for all students, a final test on the parsimonious model was conducted with the entire sample of students. Figure 8 shows the model and its associated path coefficients. Table 7 shows the correlations used to test the model. The results of tests on this model were the strongest yet. The chi-square test of overall fit was non-significant: 12 (6, N = 88) = 2.35, p = .885, and no significant residual errors were observed between predicted and obtained correlations. As for the path coefficients, all were significant but one, with the final path nearing significance at p < .10. Problem solving skills training had a significant positive effect on problem solving cognitive skills, path coefficient = .26, P (.04 < p < .48) = .95. Locus of control continued to have a significant positive effect on self-efficacy, path coefficient = .72, P (.52 < p < .92) = .95. Self-efficacy had a significant positive effect on behavioral outcome efficacy, path coefficient = .32, P (.02 5 p S .62) = .95. Finally, the path from problem solving skills training to locus of control approached significance, path coefficients = .23, P (-.01

J.°‘S"PW.N." What are the advantages of ALUO? What are the disadvantages of ALUO? What is interesting about ALUO? How can you use ALUO immediately? How can you use ALUO in your life? How does ALUO relate to our initial list of problems? How does ALUO relate to our initial list of barriers? WWPP-PS’TP 152 Appendix 31 Homework 6: ALUO Last week we used highlighting on: (1) the types of problems that members of the class experience, and (2) the types of barriers that that keep class members from effectively dealing with their problems. Look at your newly renamed groups. Select one idea from the types of problems that members of the class experience Q one idea from the types of barriers that keep class members from effectively dealing with their problems and use ALUO on these ideas. Begin by: 1. Reviewing the guidelines for convergent thinking a. Be positive -Pick ideas by saying the ones we like, not complaining or crossing out ones we dislike. b. Look at new item_s -Don’t forget about an idea that might be a bit weird. -Think about whether it might be good, also. c. Use your head -Plan out the tools you’ll use and the choices that are best for you. d. Look where you’re going -Know what your goal is and keep your eyes on the target. 2. Generate all of the advantages of the idea. (You must have at least four). 3. Generate the limitations of the idea. (You must have at least four). Phrase the limitations “How to...?” 4. Generate the unique qualities of the idea. Ask yourself, “What is new about it? What are the star qualities?” Try to finish the sentence “It might...,” “We might...,” or “It is...” (You must have at least four). 5. Select at least one key limitation (use hits). 6. Generate ideas to overcome the key limitation (3). (Try to think up 2-4). 7. IF YOU WANT, generate ideas to overcome a second key limitation. Corrrpare ideas to see which idea should be carried forward. 153 Appendix 32 Lesson Plan 7: Planning for Action and Acceptance Goal: To have students develop a plan of action. Objectives: Students will be able to... l. 2. Experience and practice tools and techniques learned in the last 4 weeks. Experience the balance of divergent and convergent thinking. Instructional activities: 1. Announcements a. Review highlights of last class b. Any questions c. Review goals for today d. Did anybody use any of the problem solving techniques for anything since I last saw you? What Why? Did anybody think of using it for something and didn’t? What? Why? 2. Salesperson and 5W’s and an H a. Introduction to concept: (i) Have any of you ever had a part time job? (ii) Have any of you ever been a sales person? Where? (iii) Would about the rest of you, have any of you ever bought anything? (iv) The reason that I’m asking is because today we’re going to be discussing planning for action, and a when you’re trying to do this you’re taking on a role very much like a salesperson. b. Role of salesperson (i) For instance, what are some of the things a salesperson would do? -use highlighting to cluster ideas c. What are we going to sale? - let’s make it relevant to all of you ...... -baby-sitting skills? -tutoring skills? -right to go to a concert that your parents don’t want you to go to? -increased permanent curfew? d. How to sell a... idea generation and idea evaluation salesperson Acceptance idea generation -looks at all the possible things -generate assisters & resisters that will ASSIST/RESIST in of the solution(s) and over- selling product come resisters idea evaluation -prepares a plan to sell the -develop a step-by-step plan products based on the for implementing the assisters & resisters solution 154 c. How to sell a resisters & assisters (I) What is an assister? Resister? SOURCES OF SOURCES OF ASSISTANCE RESISTANCE . WHO Helpful people? Critics or opponents. Who might have something to lose if your idea works or something to gain if it fails? Who might be threatened or uncomfortable in dealing with the idea? WHAT Helpful resources, things, objects or activities What important things or resources you need for successful action? rrright be missing, unavailable when you need them, lost or overlooked? WHERE The best places to implement the plan The worst possible place to implement the plan? WHEN The best times or situations to carry out your ideas? The worst possible time to carry out your ideas? WHY The best, most important, or most persuasive The least persuasive justification for reasons or justifications for your idea? your idea. Why might people turn away from the idea? * Key Words: What about... What if... What else... What other f. How to sell an implementation plan (i) List the first steps that must be taken in order to put your plan into action. (ii) Be sure to include something you can accomplish within the next 24 hours because momentum is important. (iii) When making plans, consider whether you want to focus on immediate, short- or long- terrn plans... or all three. (iv) A plan of action may require more than three steps. Feel free to add specific details until you have reached the level of specificity that you think will be most beneficial to you. (v) Make sure that you include a criterion determining how will you know that you have been successful with regard to each of your steps. ACTION: H2... Who Start Finish Where Measure of success Why How 3. Planning for action debrief: What are the advantages of planning for action? (when would you use it?) What are the disadvantages of planning for action? (why would you not use it?... ALUO) What is interesting about planning for action? How might you come up with a contingency plan? In what way did looking at the assisters and resisters help us make a plan? 9999‘!” 155 How can you use planning for action immediately? How can you use planning for action in your life? How does planning for action relate to our initial list of problems? i. How does planning for action relate to our initial list of barriers? 156 Appendix 33 Homework 7: Planning for Action and Acceptance Last week we used ALUO on: ( l) the types of problems that members of the class experience, and (2) the types of barriers that that keep class members from effectively dealing with their problems. Plan for action on the idea that you decided to carry forward on EITHER (l) the types of problerm that members of the class experience _(_)_R (2) the types of barriers that keep class members from effectively dealing with their problems. Begin by: 1. Reviewing the guidelines for convergent thinking a. Be positive -Pick ideas by saying the ones we like, not complaining or crossing out ones we dislike. b. Look at new item -Don’t forget about an idea that might be a bit weird. -Think about whether it might be good, also. c. Use your head -Plan out the tools you’ll use and the choices that are best for you. (I. Look where you’re going -Know what your goal is and keep your eyes on the target. 2. Identify as many of the assisters and resisters as you can. The key words to think about when you are doing this are: WHAT ABOUT... WHAT IF... WHAT ELSE...WHAT OTHER... SOURCES OF SOURCES OF ASSISTANCE RESISTANCE . WHO Helpful people? Critics or opponents. Who might threatened or WHAT WHERE WHEN WHY Helpful resources, things, objects or activities you need for successful action? The best places to implement the plan The best times or situations to carry out your ideas? The best, most important, or most persuasive reasons or justifications for your idea? have something to lose if your idea works or something to gain if it fails? Who might be uncomfortable in dealing with the idea? What important things or resources might be missing, unavailable when you need them, lost or overlooked? The worst possible place to implement the plan? The worst possible time to carry out your ideas? The least persuasive justification for your idea. Why might people turn away from the idea? "' Key Words: What about... What if... What else... What other 157 List the steps necessary to put your plan into action. List the first steps that must be taken in order to put your plan into action. Be sure to include something you can accomplish within the next 24 hours because momentum is important. When making plans, consider whether you want to focus on immediate, short- or long-term plans... or all three. A plan of action may require more than three steps. Feel free to add specific details until you have reached the level of specificity that you think will be most beneficial to you. Make sure that you include a criterion determining how you will know that you have been successful with regard to each of your steps. 158 Appendix 34 Lesson Plan 8: Complete Planning for Action if Uncompleted, Questions, Uncertainties and Concerns; Course Evaluation Survey; Begin Post-Test Instructional activities: 1. Announcements a. Any questions about what we did over the course of the last 8 weeks? . If time, review key steps of tools c. If time, conduct ALUO on tools use 2. Post-Test -Eight weeks ago we did a class using a creative thinking tool, and I told you that you would have a chance to do it a second time. Today is that time. One of the reasons why we do this is because we would like to find out how much you improve between now and later in your ability to think up new ideas, use your imagination, and solve problems. It is the same kind of thing that your doctor does. If she wanted to find out how much you grow in weight or height during a particular period, she would weigh you or measure your height now and again at the end of that period. This is what we want to do regarding your ability to think up ideas. We are going to take a second measurement today and then compare them with the work that you did the first day we met each other. So, put on your best thinking caps and let’s get to work. -There is a new questionnaire that you have not seen before. One questionnaire asks you 6 questions about the course. It also leaves you room to let me know if there is anything that you really liked, or didn’t like, or just thought that I should know about. 159 Appendix 35 Lesson Plan 9: Complete Post Test: Torrance Test of Creative Thinking, Locus of Control, Problem Solving Inventory, General Self-Efficacy Scale (GSES-12), Behavior Outcome Efficacy Survey 1 . Announcements a. Note on post-test surveys -Today we are going to continue to redo the questionnaires that we did eight weeks ago. Just like we talked about last week, I am only asking you to do this so that I can know how effective the course was and how much change, if any, occurred. -There is a new questionnaire that you have not seen before. It asks you a bunch of questions about whether or not you have used any of the things that you have been taught over the last couple of weeks. If you did use any of the tools, than I am interested in the circumstance under which you used them If you did not use any of the tools, I have some questions about that too. -My feelings will not be hurt if you did not use the tools, or if you did not use them very often. Please be honest, because that it the best way that I can help other kids in the firture. You are not being graded by me so nothing bad can happen to you by being completely honest. 2. Testing (complete) a. Unusual Uses Activity Test b. Personal Problem Solving Inventory c. Locus of Control (1. Outcome Efficacy Survey 3. Follow-up a. Any questions about what we did in the last 9 weeks? 160 Appendix 36 94 Ways of Saying Terrific (Adapted from Roger Firestien’s 101 Ways of Saying Very good for Adults (1992) and Roger Firestien’s 101 Ways of Saying Very Good for Children (1992) 9°N9‘5‘PP’P?‘ wwwWWWWWNNNNNNNNNNH—‘F‘H—‘F‘H—‘fl—‘c \lOM-fiWNr-‘OOOOQGMhWNr-‘OCOONQUI-BWN—‘O' You’re right! Good Work! Well done. It’s a pleasure to work with you. Now you have it. You did a lot of work today. Fine job! That’s right! You must have been practicing it. . Super! . Nice going. . That’s coming along nicely. . That’s great! . You did it that time! . Fantastic! . Terrific! . Good for you. . Excellent! . That’s better. . Good job (name). . Good going. . That’s really nice. . WOW! . You’re a great example. . Keep up the good work. . Outstanding! . What talent! . Good thinking. . Fantastic. . Exactly right! . You make it look so easy. . YES! . You had a good day. . Way to go. . Perfect. . OKAY! . You’ve really tried hard. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 161 You/re learning fast. You certainly did that well today. I’m glad your approach is working. Keep it up! I’m proud of you. That’s the way! You’re learning a lot. That‘s better than ever. Quite nice. You’ve figured it out. Perfect! Fine! Thank you for helping others. Excellent example. I like the way you’re working together. You really out-did yourself. Your imagination is soaring. I like the way you worked that out. Your best yet. I really like it. Do it again for me. I knew you could do it. What a good listener. You did it without reminders. You are really improving. You’re learning a lot. That’s better than ever. You certainly did well today. Now that’s what I call a fine job. I couldn’t do it better myself. Congratulations. You don’t miss a thing. Thank you. Clever idea. I couldn’t have done it better myself. Impressive. Very resourcefirl. 38. 39. 40. 41. 42. 43. . You’re really good at that. 45. 46. 47. Thanks for finishing what you started. This is worth repeating. I like the way you thought it through. Very imaginative. You’re always willing to try. Good observation. What an improvement. You did it all by yourself. You rise to the challenge. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 162 You’re learning fast. What a great idea. That’s good thinking. You’re going in the right direction. You should be proud of this. That is a good observation. I like the way you’re working together. What an improvement. Can I share this with others? Well done. Appendix 37 Parental Consent Form (Control Group - Royal St. George’s College) Dear Parent: I am a researcher who is studying the effects of creative problem solving skills training on a student’s belief that he has the ability to control his environment in important life situations. A person’s belief about the degree to which he can control the events in his life is associated both with problem-solving skills and with academic success (especially at the high school level). The information derived from this study will be therefore be useful to schools in designing better programs to help students solve the academic, personal, interpersonal, and professional problems which they may encounter in the course of their lives. The Royal St. George’s College has given permission for this study to be carried out in your son’s school. If sufficient interest is expressed in the opportunity to obtain problem-solving skills, RSGC may offer such training next year to interested students. This study will follow the ethical standards for research set forth by the Ontario Institute for Studies in Education and Michigan State University. Your son will be asked to complete two sets of questionnaires (each taking approximately 1 hour), on Thursday, April 5 and Thursday, May 24, from 3-4 PM. The questionnaires will include questions about: idea generation (e.g.: I generally go with the first good idea that comes to my mind); idea evaluation (e.g.: I have a systematic method for comparing alternatives and making decisions); planning for action (e.g.: I trust my ability to solve new and difficult problems); belief in one’s ability to control the environment (e.g.: Most of the time, do you feel that you can change what might happen tomorrow by what you do today?); and, belief in one’s ability to successfully accomplish a task (e.g.: When I make plans, I am certain I can make them work). Participation in this study will not affect your son’s attendance in class or his evaluation by the school. Your child may withdraw from the research at any time. Although the information will be shared with the primary educators involved in your child’s academic education, the data will otherwise only be published or reported in the aggregate. Subject confidentiality will be protected to the maximum extent allowable by law. All information collected will be strictly confidential. After the data have been collected, the students will not be identified individually. Please indicate on the attached form whether you permit your son to take part in this study, and return it to Nancy Steinhauer by March 30th. Your cooperation will be very much appreciated. Contact me at (416.446.1799) if you have further questions. Additionally, if participants have any questions regarding their role and rights as a subject of research, they may contact David Wright, Ph.D. Chair, University Committee on Research Involving Human Subjects, Michigan State University. His telephone number is 517.355.2180. Thank you very much. Julie A. Morton 163 I would be interested in enrolling my son if workshops were to be offered in problem solving at RSGC. Y / N (please circle) I agree to allow to take part in this study. (Son’s name) I do not want to take part in this study. (Son’s name) Parent’s signature I, , agree to take part in this study. (Student’s name) I, , do not agree to take part in this study. (Student’s name) Date Please rettun this form to Nancy Steinhauer at Royal St. George’s College as soon as possible. THANK YOU 164 Appendix 38 Parental Consent Form (Control Group — Merle L. Levine Academy) Dear Parent: I am a researcher who is studying the effects of creative problem solving skills training on a student’s belief that he has the ability to control his environment in important life situations. A person’s belief about the degree to which s/he can control the events in her/Iris life is associated both with problem-solving skills and with academic success (especially at the high school level). The information derived from this study will be therefore be useful to schools in designing better programs to help students solve the academic, personal, interpersonal, and professional problems which they may encounter in the course of their lives. The Merle L. Levine Academy has given permission for this study to be carried out in your daughter/son’s school. If sufficient interest is expressed in the opportunity to obtain problem-solving skills, Merle Levine Academy may offer such training next year to interested students. This study will follow the ethical standards for research set forth by the Ontario Institute for Studies in Education and Michigan State University. Your daughter/son will be asked to complete two sets of questionnaires (each taking approximately 1 hour). The questionnaires will include questions about: idea generation (e.g.: I generally go with the first good idea that comes to my mind); idea evaluation (e.g.: I have a systematic method for comparing alternatives and making decisions); planning for action (e.g.: I trust my ability to solve new and difficult problems); belief in one’s ability to control the environment (e.g.: Most of the time, do you feel that you can change what might happen tomorrow by what you do today?); and, belief in one’s ability to successfully accomplish a task (e.g.: When I make plans, l am certain I can make them work). Participation in this study will not affect your daughter/son’s attendance in class or her/his evaluation by the school. Your child may withdraw from the research at any time. Although the information will be shared with the primary educators involved in your child’s academic education, the data will otherwise only be published or reported in the aggregate. Subject confidentiality will be protected to the maximum extent allowable by law. All information collected will be strictly confidential. After the data have been collected, the students will not be identified individually. Please indicate on the attached form whether you permit your daughter/son to take part in this study, and return it to Merle Levine by March 30th. Your cooperation will be very much appreciated. If you have any further questions please call the Merle Levine Academy with your queries, and they will in turn contact me. Alternatively, if participants have any questions regarding their role and rights as a subject of research, they may contact David Wright, Ph.D. Chair, University Committee on Research Involving Human Subjects, Michigan State University. His telephone number is 1.517.355.2180 (long distance charges will occur). Thank you very much. Julie A. Morton 165 I would be interested in enrolling my son if workshops were to be offered in problem solving at the Merle Levine Academy. Y / N (please circle) I agree to allow to take part in this study. (Daughter/Son’s name) I do not want to take part in this study. (Daughter/Son’s name) Parent’s signature I, , agree to take part in this study. (Student’s name) I, , do not agree to take part in this study. (Student’s name) Date Please return this form to Merle Levine at the Merle Levine Academy as soon as possible. THANK YOU 166 Appendix 39 Parental Consent Form (Treatment Group — F ieldstone Day School) Dear Parent: I am a researcher who is studying the effects of creative problem solving skills training on a student’s belief that s/he has the ability to control her/his environment in important life situations. A person’s belief about the degree to which s/he can control the events in her/Iris life is associated both with problem-solving skills and with academic success (especially at the high school level). The information derived from this study will be therefore be usefirl to schools in designing better programs to help students solve the academic, personal, interpersonal, and professional problems which they may encounter in the course of their lives. The Fieldstone Day School has given permission for this study to be carried out in your son/daughter’s school. This study will follow the ethical standards for research set forth by the Ontario Institute for Studies in Education and Michigan State University. Your daughter/son has been selected to take part in this study. Your child will be asked to complete two sets of questionnaires (each taking approximately 1 hour), in March and May 2001. The questionnaires will include questions about: idea generation (e.g.: I generally go with the first good idea that comes to my mind); idea evaluation (e.g.: I have a systematic method for comparing alternatives and making decisions); planning for action (e.g.: I trust my ability to solve new and difficult problems); belief in one’s ability to control the environment (e. g.: Most of the time, do you feel that you can change what might happen tomorrow by what you do today?); and, belief in one’s ability to successfully accomplish a task (e.g.: When I make plans, I am certain I can make them work). Between these two questionnaires, your child will participate in a seven week problem solving skills training course. S/he will be taught how to select and define a problem, how to brainstorm potential solutions, how to evaluate potential solutions, and how to create a plan of action. Participation in this study will not affect your daughter/son’s attendance in class or her/his evaluation by the school. Your child may withdraw from the research at any time. Although the information will be shared with the primary educators involved in your child’s academic education, the data will otherwise only be published or reported in the aggregate. Subject confidentiality will be protected to the maximum extent allowable by law. Please indicate on the attached form whether you permit your daughter/son to take part in this study. Your cooperation will be very much appreciated. Contact me at (416.446.1799) if you have further questions. Additionally, if participants have any questions regarding their role and rights as a subject of research, they may contact David Wright, Ph.D. Chair, University Committee on Research Involving Human Subjects, Michigan State University. His telephone number is 517.355.2180. Thank you very much. Julie A. Morton 167 I agree to allow to take part in this study. (Daughter/Son’s name) I do not want to take part in this study. (Daughter/Son’s name) Parent’s signature Date I agree to take part in this study. I, , do not agree to take part in this study. (Student’s name) (Student’s name) Student’s signature Date Please return this form to the Fieldstone Day School as soon as possible. 168 Appendix 40 Parental Consent Form (Treatment Group - Jerome Diamond Center) Dear Parent: I am a researcher who is studying the effects of creative problem solving skills training on a student’s belief that s/he has the ability to control her/Iris environment in irrrportant life situations. A person’s belief about the degree to which s/he can control the events in her/Iris life is associated both with problem-solving skills and with academic success (especially at the high school level). The information derived from this study will be therefore be useful to schools in designing better programs to help students solve the academic, personal, interpersonal, and professional problems which they may encounter in the course of their lives. The Jerome Diamond Center has given permission for this study to be carried out in your son/daughter’s school. This study will follow the ethical standards for research set forth by the Jerome Diamond Center, the Ontario Institute for Studies in Education and, Michigan State University. Participation in this study will not affect your daughter/son’s attendance in class or her/his evaluation by the school. Although the information will be shared with the primary educators involved in your child’s academic education, the data will otherwise only be published or reported in the aggregate. Subject confidentiality will be protected to the maximum extent allowable by law. After the data have been collected, the students will not be identified individually in any report of that data. Your child will be asked to complete two sets of questionnaires (each taking approximately 1 hour), in March and May 2001. The questionnaires will include questions about the generation, evaluation, planning and belief about one’s ability to acconrplish problem solving. Between these two questionnaires, your child will participate in a seven week problem solving skills training course. Please sign the attached form agreeing to your child’s participation in this study, the results of which will be used for research purposes. Your cooperation is very much appreciated. If you have any further questions please call the Diamond Center with your queries, and they will in turn contact me. Alternatively, if participants have any questions regarding their role and rights as a subject of research, they may contact David Wright, Ph.D. Chair, University Committee on Research Involving Human Subjects, Michigan State University. His telephone number is 1.517.355.2180 (long distance charges will occur). Thank-you Julie A. Morton I agree to ’s participation in this study. (Daughter/Son’s name) I do no agree to ’s participation in this study. (Daughter/Son’s name) Parent’s signature Date Please return this form to JDD as soon as possible. THANK YOU 169 Dear Parent: Upon receiving written consent, your child has completed the first of two sets of questionnaires. The questionnaires included questions about: idea generation (e.g.: I generally go with the first good idea that comes to my mind); idea evaluation (e.g.: I have a systematic method for comparing alternatives and making decisions); planning for action (e.g.: I trust my ability to solve new and difficult problems); belief in one's ability to control the environment (e.g.: Most of the time, do you feel that you can change what might happen tomorrow by what you do today?);and, belief in one's ability to successfully accomplish a task (e.g.: When I make plans, I am certain I can make them work). Some parents have asked if they can have access to the individual results of their child; some students have also asked for the individual results. However, the consent form stipulated that subject confidentiality will be protected to the maximum extent allowable by the law and that the individual score information will only be shared with the primary educators involved in a student's academic education. In order to share individual student information with anybody other than a primary educator, a second consent form must be completed and returned to your school. If you have any further questions please call me with your queries (Julie A. Morton, 416.446.1799). Alternatively, if participants have any questions regarding their role and rights as a subject of research, they may contact David Wright, Ph.D. Chair ,University Committee on Research Involving Human Subjects, Michigan State University. His telephone number is 1.517.355.2180 (long distance charges will occur). Thank you very much. Julie A. Morton 170 I request that I might have access to the results of my individual test scores. (please print student's name) ‘ student's signature date I request that I might have access to the results of my child's individual test scores (please print student's name) student's signature date parent's signature date 171 r"illlgltjljtjitrip