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RELATIONSHIPS BETWEEN HEALTH VALUES, SPIRITUAL
WELL-BEING, AND RESILIENCE AMONG COLLEGE
STUDENTS REPORTING PERSONAL SUBSTANCE USE
AND FAMILIAL SUBSTANCE ABUSE:

AN EXPLORATORY STUDY

presented by

Debra Jo Farrell

has been accepted towards fulﬁllment
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Ph.D. degree in Counseling,
Educational PsychoIOQY. and
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RELATIONSHIPS BETWEEN HEALTH VALUES, SPIRITUAL WELL-BEING, AND
RESILIENCE AMONG COLLEGE STUDENTS REPORTING PERSONAL
SUBSTANCE USE AND FAMILIAL SUBSTANCE ABUSE:

AN EXPLORATORY STUDY

By

Debra Jo Farrell

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Counseling, Educational Psychology, and Special Education

2007

ABSTRACT
RELATIONSHIPS BETWEEN HEALTH VALUES, SPIRITUAL WELL-BEING, AND
RESILIENCE AMONG COLLEGE STUDENTS REPORTING PERSONAL
SUBSTANCE USE AND FAMILIAL SUBSTANCE ABUSE:
AN EXPLORATORY STUDY
By
Debra Jo Farrell

This exploratory study represents an initial attempt in understanding relationships
between the constructs of health values, spiritual well-being, and resilience and their
possible inﬂuences as protective factors against personal consumption of alcohol, tobacco
and other drugs (ATOD) and in promoting psychosocial adaptation to familial substance
abuse as a chronic illness and disability (CID). The constructs were tested in pairs and as
a composite variable. The research design included a 64-item web-based survey
administered to a purposive sample of 266 undergraduates attending a large Midwestern
university during Spring semester 2007. Correlational and mean differences pertaining to
students’ scores for the constructs under investigation were explored within ranges of
calculated risk for personal substance use and familial substance abuse. Risk ranges
were determined as categories of low, medium, high, and very high.

Method: The sample was drawn from eleven sections of an introductory,
undergraduate, substance abuse course. Students ranged within a variety of academic
ranks and from academic programs across the university. The overall response rate to the
survey was 87.69% which constituted a student sample of n = 233. Five scales were
utilized in the design of the survey including: modiﬁed Health-as-a-Value (mod-HAV)

scale, Resilience Scale-15 (RS-15), Spiritual Index of Well-Being (SIWB), Drug Abuse

Screening Test-10 (DAST-l 0), and Alcohol Use Disorders Identiﬁcation Test (AUDIT).

Information regarding students’ demographic characteristics, reported patterns of
personal substance use, and backgrounds of familial substance abuse was also gathered
by the survey. The concept of family was expanded to include genetic and non-genetic
family members such as: adoptive, step, cohabitating and very close friends.

Results: A variety of comparisons were made and the most prominent results
revealed: (1) a large inverse relationship exists between spiritual well-being and
resilience; a small inverse relationship exists between spiritual well-being and health
values; a small positive relationship exists between health values and resilience,

(2) individuals with low risks for personal substance use displayed a small increase in
resilience compared with high-very high risk individuals, (3) individuals with no reported
familial substance abuse displayed a large negative relationship between spiritual well-
being and resilience, (4) individuals with reported familial substance abuse also displayed
a large negative relationship between spiritual well-being and resilience, along with, a
medium positive relationship between health values and resilience, and a small negative
relationship between health values and spiritual well-being, (5) genetic factors rather than
non-genetic factors accounted for most, if not all, of the signiﬁcant results for individuals
with familial substance abuse risks, and (6) a supplemental analysis comparing
individuals at risk for both personal substance use and familial substance abuse showed
that low-medium risk groups display increased resilience and decreased spiritual well-
being compared with high-very high risk groups.

Conclusion: Despite study limitations, results appear consistent with a number of
previous research studies. Practice implications and directions for future research are

discussed for the ﬁelds of substance abuse, allied health, and rehabilitation counseling.

Copyright by
DEBRA JO FARRELL
2007

All Rights Reserved.

BOOK BLESSING

Now, as we make our way onto the path that lies before us,
may the light of Truth shine within us today, tomorrow, and always.
May we be Divinely guided to
discovering that which has been, that which is, and that which has yet to be.
Through our love,
we hold the power to change ourselves, each other, and the world.

~Debra J. Farrell

Science is nothing but developed perception, interpreted intent,
common sense rounded out and minutely articulated.

~George Santayana (1 86 3 —1 952)

DEDICATION

I lovingly dedicate this book to the memory of my dear parents, William and
Darlene, and my dear brother, Dwaine. Thank you for the everlasting love we shared as
a family and for the lifelong gifts you have instilled in me as a person. Thank you for
your bequeathal of this most cherished and sacred space that has become the portal to
enlightenment along the blessed path of my dissertation journey. If only you could be
here now to witness the Great Mystery which enshrouds this powerful passage of
metamorphosis, emergence, and transcendence. If only you could be here now to be
touched by the profound meaning of this extraordinary work which will resonate forward
to a legacy of advanced wisdom, truth, and understanding for all. I know in my heart you
would be proud.

I dedicate my efforts in writing this book to ALL MY RELATIONS. Each of you
holds a special place within my heart. Each of you has lovingly contributed in countless
and meaningful ways to the unfolding journey throughout my life and especially the

doctoral program.

vi

ACKNOWLEDGEMENTS

I humbly acknowledge the Divine intervention and guidance I received
throughout the entirety of this research project. During the writing of this document, I
was keenly aware of a spiritual presence which ﬂowed ever-through me spawning free-
flowing ideas, spontaneous insights, and invigorating winds of creativity. This invisible
presence provided me an abundance of synchronous connections to vital information and
precious resources that extended far beyond my own capacity for knowing. Truly, the
works herein serve as a written testimony to the process of surrendering to the Divine
forces within.

In acknowledgement of the Beloved forces without, I extend greatest honors to my
husband, Richard W. Farrell, for always recognizing the importance of this work, for
providing me the support I needed, and for allowing me the freedom to make my own
choices, always following the voice within.

I fondly acknowledge Dr. Nancy Crewe for recruiting me for the rehabilitation
counselor education doctoral program which has now become the launch point for my
career in academia. I greatly acknowledge Dr. Michael Leahy for serving as my major
professor, academic adviser, faculty supervisor, and chairperson of my dissertation
committee. He recognized the nature of my potential and provided me the autonomy and
freedom I needed to independently create and triumph my own challenges.

I highly acknowledge the members of my dissertation committee for all of their
contributions to the project. I especially recognize Dr. John Kosciulek for his belief in
me as a researcher and for instilling in me the conﬁdence and encouragement I needed to

believe in myself; Dr. Steven Geiger for his expertise regarding clinical assessment and

vii

treatment of individuals with substance-related disorders; Dr. Dennis Martel] for his
unique perspective and knowledge of substance use and health within a college
population and for providing me gift incentives for students which proved to be so
valuable in gathering a large response to the survey; Dr. Kimberly Maier for her expert
advice and guidance throughout the statistical analysis of data and interpretation of
results; and Dr. Timothy Tansey for his refreshing sense of humor and expert technical
advice regarding web-based surveys.

I extend special considerations to Dr. Hanoch Livneh for sharing with me his
lifelong, foundational work on psychosocial adaptation to chronic illness and disability. I
wish to thank the authors for the scales used in my survey and for granting me permission
to borrow and build upon their work. These authors include: Dr. Timothy Daaleman,

Dr. Ruth Engs, Dr. Anamara Ritt, Dr. Linda Carter Sobell, Dr. Steven Sussman, Dr. Gail
Wagnild, and Dr. Heather Young. I also wish to thank the authors of e-CHUG and
Project Match for providing expert advice to brief portions of this project. These authors
include: Dr. William Miller, Dr. Richard Moyer [I], Dr. Douglas Van Sickle, and Dr.
Scott Walters.

I give special thanks to Ms. Rebecca Allen for assisting me with initial contacts to
the e-CHUG website which guided me onto the path of building my research design. I
extend thanks to Dr. Virginia Thielsen for her guidance in uploading my survey to the
web and preparing it for launch. I also thank Ms. Karen Clark for her timely advice in
editing my survey and for her generous donation of incentive coupons to award to student

participants. Special thanks are given to the merchants of East Lansing, Michigan

viii

including Powerhouse Gym, Douglas J Salon, and Bruegger ’5 Bagels for donating their
products and services as free gift awards.

Special acknowledgement is given to Dr. Dorothy Duff Brown for her motivating
and inspiring words and practical suggestions in tackling the dissertation project. I
extend special thanks to Ms. Marilia Antunez for her approachability and kindness in
providing me expert library assistance. I give many thanks to the staff at the Social
Science, Behavioral, and Education Institutional Review Board for their patience and
dedication in getting this research project approved. I especially thank Ms. Yan Zheng for
her devoted research assistance throughout the statistical analysis and interpretation of
my survey results which was invaluable and enabled me to adhere to critical deadlines.

I extend much gratitude to members of my doctoral cohort for ﬁeld-testing my
instrument including: Ms. Roxane Minkus, Ms. Barbara Schoen, Mr. David Schroeder,
Ms. Shemya Vaughn, and Mr. Stephen Zanskas. I am very grateful to the teaching
assistants of CEP 261 for allowing me the opportunity to recruit students from their
classrooms including: Mr. Abdoulaye Diallo, Ms. Angela Maupin, Ms. Jane Nichols, Mr.
David Schroeder, Mr. Wonsun Seo, Ms. Deborah Smith-Powell, Ms. Shemya Vaughn,
Ms. Jennifer Wells, and Ms. Megan Wolvin.

Finally, I extend my utmost appreciation to the 23 3 undergraduate students of
CEP 261, enrolled during Spring Semester 2007, who participated in the study. In doing
so, I also wish to recognize the nearly 400 undergraduate students that were enrolled in
my sections of CEP 261 from Fall Semester 2003 through Fall Semester 2006. Many of
these individuals shared with me the meaning, power, and signiﬁcance of their personal

life stories which helped me to see the way.

ix

TABLE OF CONTENTS

LIST OF TABLES .................................................. xii
LIST OF FIGURES ................................................. xv
LIST OF ABBREVIATIONS ......................................... xvi
CHAPTER I INTRODUCTION ....................................... 1
Statement of the Problem ....................................... .8
Premises .............................................. 11

Assumptions ........................................... 12

Purpose of the Study ........................................... 13
Research Questions ........ - ............................. 14

Hypotheses ............................................ 17

Deﬁnition of Terms . ..................................... 18

CHAPTER 11: REVIEW OF LITERATURE ............................. 22
Campus Substance Abuse ....................................... 27
Psychosocial Adaptation to Chronic Illness and Disability ............. 3O
Coping ............................................... 36

Occupational Behavior and Temporal Adaptation ............. 39

Resilience . ............................................ 40

Risk Factors ................................................. 42
Familial .............................................. 42

Personal .............................................. 47

Protective Factors ............................................. 51
Health Values .......................................... 52

Spiritual Well-Being .................................... 56

Other Factors ................................................ 59
Primary Living Situation ................................. 60

Cumulative Grade Point Average .......................... 63

Summary .................................................... 64
CHAPTER III: METHOD ........................................... 66
Participants .................................................. 66
Protection of Human Participants .......................... 69

Research Design .............................................. 71
Instruments ............................................ 72

Procedure ............................................. 85

Survey Website ......................................... 89

Data Analysis ................................................ 90
CHAPTER IV: RESULTS ............................................ 96
Characteristics of the Sample .................................... 96

TABLE OF CONTENTS (continued)

CHAPTER IV: RESULTS (continued)

Summary of Hypotheses ........................................ 98
Supplemental Analysis for Question 3c ........................... 142
CHAPTER V: DISCUSSION ......................................... 145
Summary of Findings .......................................... 145
Limitations .................................................. 147
Implications of Results ......................................... 154
Directions for Future Research ................................... 163
Conclusion .................................................. 1 70
APPENDICES ..................................................... l 72
Appendix A. Printed Invitation — Distributed in Class ................ 173
Appendix B. Electronic Invitation — Sent via ANGEL ................ 175
Appendix C. Letter of Informed Consent — Attached to ANGEL Message 177
Appendix D. First F ollow-Up Message — Sent via ANGEL ............ 180
Appendix E. Second Follow-Up Message — Sent via ANGEL .......... 182
Appendix F. Initial Page of Survey — Posted on AFS Space ........... 184
Appendix G. Welcome Page of Survey — Posted on Website ........... 186
Appendix H. College Student Survey — Posted on Website ....... . ..... 188
REFERENCES ..................................................... 209

xi

LIST OF TABLES

 

TABLE Number Page
Table 1. Pearson Correlation for mod-HAV, SIWB, and RS-15 ................ 100
Table 2. Categorical DRUG Risk Based on Participants’ DAST-lO Scores ....... 103
Table 3. Categorical ALCOHOL Risk Based on Participants’ AUDIT Scores ..... 103
Table 4. Pearson Correlation Parametric Test for DAST-IO and AUDIT ......... 104
Table 5. Spearrnan’s rho Non-Parametric Test for DAST-lO and AUDIT ........ 105
Table 6. Frequencies for Categories of Personal SUBSTANCE USE Risk ........ 105
Table 7. Frequencies for Three Newly Combined Categories of Personal
SUBSTANCE USE Risk ....................................... 106
Table 8. Oneway ANOVA for Total Score of mod-HAV and Personal
SUBSTANCE USE Risk ....................................... 107
Table 9. Oneway ANOVA for Total Score of SIWB and Personal SUBSTANCE
USE Risk .................................................... 109
Table 10. Tukey HSD Post Hoc Test for SIWB and Personal SUBSTANCE USE
Risk Multiple Comparisons .................................... 109
Table 11. Oneway ANOVA for Total Score of RS-15 and Personal SUBSTANCE
USE Risk ................................................... 111
Table 12. Tukey HSD Post Hoc Test for RS-l 5 and Personal SUBSTANCE USE
Risk Multiple Comparisons .................................... 112
Table 13. Robust Tests of Equality of Means for Total Score of RS-15 .......... 113
Table 14. Games-Howell Multiple'Comparisons for RS-lS ................... 114
Table 15. MANOVA Tests for Personal SUBSTANCE USE Risk and mod-HAV,
SIWB, and RS-15 ............................................ 116
Table 16. Box’s M Test of Equality of Covariance Matrices .................. 117
Table 17. MANOVA Tests for Wilks’ Lambda and Eta Squared ............... 118

xii

LIST OF TABLES (continued)

 

TABLE Number Page
Table 18. Familial SUBSTANCE ABUSE Group Statistics for Scores of mod-

HAV ...................................................... 121
Table 19. Independent Samples T-test for mod-HAV and YES or NO Familial

SUBSTANCE ABUSE ........................................ 122
Table 20. Familial SUBSTANCE ABUSE Group Statistics for Scores of SIWB . . . 123
Table 21. Independent Samples T-test for SIWB and YES or NO Familial

SUBSTANCE ABUSE ........................................ 124
Table 22. Familial SUBSTANCE ABUSE Group Statistics for Scores of RS-15 . . . 125
Table 23. Independent Samples T-test for RS-15 and YES or NO Familial

SUBSTANCE ABUSE ........................................ 126
Table 24. Correlations for NO Familial SUBSTANCE ABUSE mod-HAV, SIWB,

and RS-15 .................................................. 127
Table 25. Correlations for YES Familial SUBSTANCE ABUSE and mod-HAV,

SIWB, and RS-15 ............................................ 129
Table 26. Fisher’s Transformation Correlations for YES or NO Familial

SUBSTANCE ABUSE ........................................ 131
Table 27. Z-Test for YES Familial and NO Familial SUBSTANCE ABUSE

Groups ..................................................... 131
Table 28. Total Continuous Scores GENETIC Risk for Group 2A .............. 133
Table 29. Total Continuous Scores NON-GENETIC Risk for Group 2A ......... 134
Table 30. Pearson Correlation for Group 2A GENETIC Risk and mod-HAV,

SIWB, and RS-15 ............................................ 135
Table 31. Pearson Correlation for Group 2A NON-GENETIC Risk and mod-HAV,

SIWB, and RS-15 ............................................ 136
Table 32. Oneway ANOVA for Group 2A GENETIC Risk Scores .............. 137
Table 33. Oneway ANOVA for Group 2A NON-GENETIC Risk Scores ......... 137

xiii

LIST OF TABLES (continued)

TABLE Number Page

Table 34. Oneway ANOVA for Group 2A Familial SUBSTANCE ABUSE Risk
Scores ...................................................... 138

Table 35. Crosstabulation for Personal SUBSTANCE USE Risk Categories and
Familial SUBSTANCE ABUSE Risk Categories .................... 139

Table 36. Descriptive Statistics for Low-Medium Personal SUBSTANCE USE and
High-Very High Familial SUBSTANCE ABUSE Risk Group (3)
compared with the High-Very High Personal SUBSTANCE USE and
Low-Medium Familial SUBSTANCE ABUSE Risk Group (4) ......... 140

Table 37. Independent Samples T-test for Low-Medium Personal SUBSTANCE
USE and High-Very High Familial SUBSTANCE ABUSE Risk Group (3)
compared with the High-Very High Personal SUBSTANCE USE and
Low-Medium Familial SUBSTANCE ABUSE Risk Group (4) ......... 141

Table 38. Descriptive Statistics for Low-Medium Personal SUBSTANCE USE and
Low-Medium Familial SUBSTANCE ABUSE Risk Group (5) compared
with the High-Very High Personal SUBSTANCE USE and High-Very
High Familial SUBSTANCE ABUSE Risk Group (6) ................ 142

Table 39. Independent Samples T-test for Low-Medium Personal SUBSTANCE
USE and Low-Medium Familial SUBSTANCE ABUSE Risk Group (5)
compared with the High-Very High Personal SUBSTANCE USE and
High-Very High Familial SUBSTANCE ABUSE Risk Group (6) ....... 143

xiv

LIST OF FIGURES

 

FIGURE Number Page
Figure 1. mod-HAV Means Plot for Three Categories of Personal SUBSTANCE
USE Risk .................................................. 107
Figure 2. SIWB Means Plot for Three Categories of Personal SUBSTANCE USE
Risk ...................................................... 110
Figure 3. RS-15 Means Plot for Three Categories of Personal SUBSTANCE USE
Risk ...................................................... 112

XV

ANGEL
ANOVA
ATOD
AUDIT
BAC
CASI
CDC

CID

CoA
DAST- 1 0

DSM-IV-TR

DV

GPA

IV
MANOVA
mod-HAV
NIAAA
NIDA
RS—l 5
SIWB
SPSS

Tukey’s HSD

LIST OF ABBREVIATIONS
A New Global Environment for Learning
Analysis of Variance
Alcohol, Tobacco, and Other Drugs
Alcohol Use Disorders Identiﬁcation Test
Blood Alcohol Content
Computer Assisted Self-Interview
Centers for Disease Control
Chronic Illness and Disability
Children of Alcoholics
Drug Abuse Screening Test-10

Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition,
Text Revision)

Dependent Variable

Grade Point Average

Independent Variable

Multivariate Analysis of Variance

modiﬁed Health-as-a-Value

National Institute of Alcohol Abuse and Alcoholism
National Institute on Drug Abuse

Resilience Scale-15

Spiritual Index of Well-Being

Statistical Package for the Social Sciences

Tukey’s Honestly Signiﬁcant Difference

xvi

CHAPTER I

Adversity is the ﬁrst path to truth.
~Lord Byron (1 788-1824)
Introduction

College drinking in the United States has received sensational attention within the
news media and considerable attention within the professional body of literature. Misuse
of alcohol by college students on college campuses has been cited as an important topic
of concern for over 50 years and dates back to an article submitted in 1953 by Straus and
Bacon (Presley, Meilman, & Leichliter, 2002). Although riddled by controversy
regarding the impact college drinking has to our nation’s student population and campus
way of life, excessive college drinking has been declared a major public health concern
of epidemiological proportion (Gfroerer, Greenblatt, & Wright, 1997; Senchak, Leonard,
& Greene, 1998). Due to growing concern, college alcohol consumption patterns in the
general population are monitored annually through national studies such as the National
Epidemiological Survey of Alcohol and Related Conditions, otherwise known as
NESARC (Chen, Dufour, & Yi, 2004-2005). Rates of alcohol consumption by college
students have been monitored through several other large-scale studies as well (O’Malley
& Johnston, 2002; Wechsler & Dowdall, 1998; Wechsler et al., 2002).

Within the framework of this study, and in light of these highly-charged
controversies, no conjectures or attempts were made to sort out the issues regarding the
phenomena of college drinking. A full exploration of the literature was necessary to
identify and establish the contextual environment and situational risk factors (Jackson,

Sher, & Park, 2005) of campus substance abuse that serve as a backdrop for the study.

The major focus of the study was the exploration of key factors which may contribute to
the process of psychosocial adaptation to familial substance abuse as a chronic illness and
disability (CID) and to the risks of personal substance use. The study utilized a purposive
sample of college undergraduate students within a major college campus setting.

The widespread use of alcohol, tobacco and other drugs (ATOD) among college
students in the United States has prompted researchers to probe more deeply into the
nature of campus substance use trends. Clearly, there is a desire to develop effective
strategies that incorporate sound methods for reducing the magnitude of such trends. In
2002, the National Institute on Alcohol Abuse and Alcoholism (N IAAA) launched a
large scale, federal campaign to study the scope and magnitude of college student alcohol
use patterns and to recommend strategies to address patterns of excessive drinking.
Considerable debate has taken place regarding the effects and consequences of what
some ofﬁcials contend is more accurately termed “high risk” drinking (N IAAA). The
labeling of students as “binge” drinkers by some researchers, has been overturned and a
new paradigm of thinking has emerged which focuses on the effects and consequences of
high risk drinking behaviors (N IAAA; Schuckit, 1998).

The phenomenon of drinking within campus settings has been described as a
unique pattern of behavioral norms and expectations within individual and collective
campus cultures across the United States (N IAAA, 2002). Some sources indicate that
among college undergraduates attending two and four-year institutions, excessive rates of
use are rated highest among all levels of alcohol consumption in elevating the risks to
result in alcohol-related deaths, injuries, and other health problems (Hingson, Heeren,

Zakocs, Kopstein, & Wechsler, 2002; O’Malley & Johnston, 2002; Shefﬁeld, Darkes,

Del Boca, & Goldman, 2005; Wechsler, Davenport, Dowdall, Moeykens, & Castillo,
1994; Wechsler, Kelley, Weitzman, San Giovanni, & Seibring, 2000a). Alcohol-related
problems can lead to damage to self, others, and property (Perkins, 2002). Consequential
effects regarding self include acute health conditions and adverse behavioral patterns that
can lead to legal repercussions (Dowdall & Wechsler, 2002).

In a review of data gathered from the National Highway Trafﬁc Safety
Administration (NHTSA) and Centers for Disease Control (CDC), college students
constitute 31% of the 18 to 24 year olds in the United States and 31% of the nation’s
unintentional and trafﬁc-related deaths (Hingson et al., 2002). The tendency for college
students to engage in risky use of alcohol through occasional, heavy drinking has raised
concerns by administrators at academic institutions (O’Malley & Johnston, 2002; Walters
& Baer, 2006). In addition to alcohol, use of other drugs such as cigarettes, marijuana,
cocaine, and other illegal substances has been cited with growing concern (Jones,
Oeltmann, Wilson, Brener, & Hill, 2001).

Some researchers estimate that approximately 40% of the nation’s population of
college students engages in episodes of heavy drinking (Jones et al., 2001; Walters &
Baer, 2006; Wechsler & Dowdall, 1998). Other researches assert that a pattern of heavy
drinking poses a threat to the health of college students as a community (Polonec, Major,
& Atwood, 2006). As a label, “binge drinking” has been discussed at some length with
respect to its meaning, relevance, and implications (Lederrnan, Stewart, Goodhart, &
Laitman, 2003; Schuckit; 1998; Weschler, Lee, Kuo, & Lee, 2000b; Wechsler & Kuo;
2000; Wechsler & Nelson, 2001). Within the college landscape, this term has been

widely interpreted to mean heavy consumption of alcohol within relatively short periods

of time. More speciﬁcally, binge drinking has been deﬁned as the rapid and successive
consumption of alcoholic beverages at the rate of “ﬁve or more drinks” for males and
“four or more drinks” for females on at least one occasion within a two week period
(Walters & Baer, 2006; Wechsler & Dowdall, 1998; Wechsler & Kuo, 2000; Wechsler &
Nelson, 2001). In addition to binge drinkers, problematic “frequent binge” or “consistent
heavy” users of alcohol have been identiﬁed in 19% of the college population (Walters &
Baer).

Regardless of its label, engagement in binge, heavy, or excessive drinking
practices is believed to lead to increased problematic risks for alcohol-related
consequences (Jackson et al., 2005; Perkins, 2002; Walters & Baer, 2006; Wechsler &
Dowdall, 1998; Wechsler & Nelson, 2001; Wechsler et al., 2000b; Wechsler et al., 2002).
Moreover, it is critical that researchers continue their efforts to provide categories of
distinction which describe various types of high-risk, or perhaps more accurately-termed
at-risk, drinking patterns. Efforts in monitoring alcohol abstention are important as well
and recent projections indicate the number of students who do not imbibe is estimated at
one-ﬁfth of the US. college student population (American College Health Association,
2006)

In 2006, the American College Health Association (ACHA) released a report
compiling statistics from the 2004 National College Health Assessment (ACHA-NCHA)
Health Surveys. Information from these surveys was gathered from over 4,000
participating colleges and universities across the United States. The results were based
on a college population consisting of undergraduates (86%), graduates (12%), and ﬁrst-

professional programs (2%). The ACHA-NCHA report indicated that the number of

drinks students consumed by students on the last occasion they “partied” included:
alcohol abstainers (22.0%), one to four drinks (38.3%), ﬁve to eight drinks (25.9%), and
nine or more drinks (13.8%). The ACHA—NCHA report further indicated that alcohol use
by students within the last 30 days was as follows: none used (17.9%), no use in the last
month (13.6%), 1—2 days use (18.1%), 3-5 days (18.0%), 6-9 days use (15.9%), 10-19
days use (12.7%), 20-29 days use (3.2%), and all 30 days use (.6%). Additionally, the
ACHA-NCHA report listed frequencies by which other substance such as cigarettes,
smokeless tobacco, cigars, marijuana, amphetamines, cocaine, and designer drugs
(Rohypnol, GHB, Liquid X, and Ecstasy) were used by college students. The frequency
of use varied widely depending upon the types of drugs reported.

In addition to alcohol consumption, polysubstance use has received researchers’
attention as well (McCabe, Boyd, Cranford, Morales, & Slayden, 2006; Mohler-Kuo,
Lee, & Wechsler, 2003). Polysubstance use is the combination or mixing of two or more
substances, including or excluding alcohol as a substance. The combined effects of using
more than one substance simultaneously remains largely understudied due to the
unlimited number of drug combinations and dose variations. However, experts have
voiced concern that polysubstance use magniﬁes the potential risks for users (Miller,
Zweben, DiClemente, & Rychtarik, 1995). Jones et a1. (2001) discovered that the more
frequently students engaged in binge drinking, the more likely they were to use other
substances.

A common example of polysubstance use is the concurrent use of alcohol and
marijuana. Mohler-Kuo et al. (2003) collected data from four College Alcohol Study

(CAS) surveys administered between the years of 1993 to 2001 and found that greater

than 98% of college marijuana and other illicit drug users either smoked tobacco or were
binge drinkers or used another substance. Results for the study did not indicate whether
the drugs were self-administered simultaneously or taken during non-overlapping
intervals (Mohler—Kuo et a1.). Age of initiation was further considered within the
Mohler-Kuo et al. study and researchers concluded that drug prevention programs should
commence in high school or sooner.

Shillington and Clapp (2006) compared a sample of alcohol-only users (71.2%) to
alcohol and marijuana users (28.8%). The authors concluded that college students who
used the dual substances of alcohol and marijuana were twice as likely as the alcohol
only users to perform poorly on an exam or school assignment, get in a verbal altercation,
become physically ill, experience hangovers, miss class, operate a motor vehicle while
intoxicated, or ride with an intoxicated driver.

Over the decades, young adults in general have been studied rather extensively for
related risks and exposures with substance abuse and substance use disorders. Aside
from college students, research efforts have been directed toward non-college attending
adults within the same age group. In a study performed by O’Malley and Johnston
(2002), researchers concluded that non-college-attending individuals consumed less
alcohol than their college-attending peers and greater amounts of marijuana, cocaine, and
cigarettes. O’Malley and Johnston indicated that during high school years, college-
directed students consume alcohol in lesser amounts than peers who do not plan to attend
college and both groups increase their intake post-high school graduation. Slutske (2005)

found that although college students demonstrated signiﬁcantly greater consequences

with binge or heavy drinking episodes, they did not appear at greater risk for alcohol
dependence problems compared with non-college attending individuals.

Considerable research has been devoted to the genetic factors regarding of the
incidence of chemical dependencies and the intergenerational transmission of substance
use disorders within families (Hardie, 2002; Hartman, Lessem, Hopfer, Crowley, &
Stallings, 2006; Merikangas et al., 1998; Schuckit, 1999). The bulk of this literature has
had a widespread and far-reaching impact within the ﬁeld of substance abuse spanning
several major disciplines including: psychiatry, medicine, nursing, occupational therapy,
psychology, social work, and rehabilitation counseling. Much of the literature written on
the topic of substance use and abuse by young people is divided into two distinct
developmental periods of adolescence and early or young adulthood (Schulenberg &
Maggs, 2002).

Within the present study, college undergraduate students are regarded as young or
emerging adults (Amett, 2000) with major emphasis given to the distinct roles and
characteristics they manifest as they transition into full adulthood (Schulenberg & Maggs,
2002; Walters & Baer, 2006). It should be noted that the term “emerging” implies a
dynamic state of change that is not clearly marked by beginning or end (Amett). In
addition, it should be emphasized that emerging adults, between the ages of 18 and 21,
are considered “minors” relative to this state’s drinking laws. In all other aspects
however, individuals who have attained the age of 18 years share the full legal privileges,
rights, and responsibilities as all other adults.

Students who are transitioning directly from high school into college are a

particularly interesting group to study regarding the short and long-term effects of

 

substance abuse (White et al., 2006). This transitional period has been linked to increases
in drinking for high school students regardless of their college or non-college attending
goal status (Senchak et al., 1998; White et al., 2006). For those who are college bound,
the act of attending college has been referred to as a “situational” risk factor (Jackson et
al., 2005). This situation is marked by rapidly changing roles as students transition out of
adolescence and high school into emerging adulthood, moving from living at home with
parents, to residing in college dormitories or independent apartments (Amett, 2000,
Schulenberg & Maggs, 2002).

Given the removal of parental controls and exposures to new social contexts
(Senchak etal., 1998), it is not surprising that ﬁrst-year college students, despite their
minority status for legal-aged drinking, have heightened risks especially if they have a
positive family history of alcohol problems and positive expectancies toward alcohol
consumption (Del Boca, Darkes, Greenbaum, & Goldman, 2004; VanVoorst & Quirk,
2003). Scant literature was located on the topic of college students raised in families
where substance abuse or dependency is a past or present condition. This may support
the claim made that many individuals raised in homes where substance abuse is present
do not achieve the opportunities to enroll in college or complete a college degree program
(Baer, 2002).

Statement of the Problem

This study represents an exploratory step in combining the three constructs of
health values, spiritual well-being, and resilience in an effort to identify primary factors
that have assisted certain college students in overcoming the risk factors of personal

substance use and familial substance abuse. The study investigated the relationships

between these constructs. The presence of person-environment interactions (Livneh,
1987; Schoon, 2006) were explored, relative to reported levels of personal substance use
within the campus environment and reported presence of familial substance abuse.

To begin, it was espoused that in combination, health values, spiritual well-being.
and resilience serve as key factors that enhance the psychosocial adaptation of individuals
with a current or past environment of familial substance abuse. The combination of
health values, spiritual well-being, and resilience were theorized to enable certain
individuals to successfully emerge from adverse environmental conditions and to excel
academically in a demanding and competitive college environment. In combination,
these factors were believed to serve in protecting individuals from the bio-psycho-social
risks posed by familial substance abuse environments and additional personal risks posed
by exposure to a substance-saturated college lifestyle.

To achieve success, the world of higher education demands a rigorous routine and
a productive lifestyle. The world of higher education also poses environmental risks that
can compromise one’s quest toward success and can lead to a situation of academic
failure. The current study offers a refreshingly, optimistic perspective on what could
otherwise be deemed a dismal situation. The current study investigated questions about
relationships between the positive factors of health values, spiritual well-being, and
resilience that may serve, not only as protective factors that buffer the effects of familial
substance abuse, but as sources of strength, empowerment, and self-efﬁcacy for some
individuals. It was theorized that this act of empowerment may serve to enable certain
individuals to thrive academically within a goal-directed environment and to refrain from

or avoid the pressures of engaging in personal substance use behaviors. This is consistent

 

 

 

with the person-environment rehabilitation perspective in assessment of individuals with
chronic illnesses or disabilities as they function within settings of treatment, home, work,
and community (Livneh, 1987)

Past literature has indicated that the degree of health values and spiritual well-
being combined with the process of resilience may serve as protective factors or have
buffering effects of strong social stressors imposed on college students by peers and other
external stressors resulting from enduring a college lifestyle (Ritt-Olson et al., 2004).
Individuals who do not succumb to these pressures have an optimal chance for not only
surviving, but thriving, against the odds. Achievement in academic excellence may be a
sign of ultimate adaptation within a competitive environment.

Although this study identiﬁed age, primary living situation, and cumulative grade
point average as important factors, the analysis and ﬁndings do not focus on the speciﬁc
relationships of these related factors with the constructs under investigation. Preliminary
data regarding these factors were collected from the sample and their possible inﬂuences
are discussed in the literature review section within Chapter II of this document. The
factors are important to identify and test in future research applications that extend
beyond the scope of this exploratory study. It is also important to emphasize that
although socioeconomic status has been determined to be a predictor regarding the
monetary affordability of substances by college students, it was not pursued as a factor
within this study.

The study was intended, as a beginning point, to reveal information about a
combination of invisible human qualities and protective processes that serve as buffers

against at-risk factors of personal substance use and familial substance abuse.

10

 

Furthermore, the study was intended to describe and enhance our understanding of the

intrinsic processes, qualities, or characteristics that promote some individuals to achieve a

state of homeostatic balance or well-being among mind, body, and spirit. It was

conceived that these critical factors may combine to increase the possibilities individuals

possess toward fulﬁlling their life goals thereby increasing their overall quality of life and

life satisfaction (Tate & Forchheimer, 2002).

The inquiries of this research study were based upon a series of eight statements

or premises that are supported empirically within the literature. These premises provide

an overview of the empirical evidence pertinent to this study. The premises are as

follows:

Premises

1.

Substance abuse is a diagnosable condition described by a set of criteria listed in
the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Text
Revision (DSM-IV-TR) (American Psychiatric Association, 2000).

As a condition, substance abuse can be diagnosed as a primary or co-existing
disorder (American Psychiatric Association, 2000).

Addiction to substances, particularly alcohol, tends to run in families (Barnes &
Welte, 1990; Hardie, 2002; Hartman et al., 2006; Merikangas et al., 1998;
Schuckit, 1999).

Substance abuse is a predictor for disability or a mediator of a disabling condition
(Livneh & Wilson, 2003).

Although maladaptive, substance abuse serves as a form of coping with stress and

trauma (Graf, 2004; Livneh & Wilson, 2003).

11

 

6. Familial substance abuse is a psychiatric illness which poses a myriad of

challenges and can evoke processes of familial resilience for coping and
adaptation (Walsh, 2002).
The population of US. college students faces elevated risks for consequences

caused from high risk drinking (N IAAA, 2002).

. The excessive use of alcohol and drugs compromise one’s health, safety, and

well-being by increasing one’s risks for adverse effects or negative consequences
(Chen et al., 2004-2005; Hanson, Venturelli, & Fleckenstein, 2006; Jackson et al.,
2005; Perkins, 2002; Walters & Baer, 2006; Wechsler & Dowdall, 1998;
Wechsler & Nelson, 2001; Wechsler et al., 2000b; Wechsler et al., 2002).

In addition to the premises noted above, this study was based upon the following

three assumptions which are suggestions of inﬂuence regarding psychosocial adaptation

to CID. Although there may be empirical evidence to support these assumptions, they

remain largely under investigation within the literature. The assumptions are as follows:

Assumptions

1.

The degree of value an individual displays regarding his or her health (physical
and mental well-being) inﬂuences his or her psychosocial adaptation to familial
substance abuse as a CID.

The degree of spiritual well-being an individual displays inﬂuences his or her
psychosocial adaptation to familial substance abuse as a CID.

The degree of resilience an individual displays in overcoming stress, adversity,
and changing conditions inﬂuences his or her psychosocial adaptation to familial

substance abuse as a CID.

12

 

Purpose of the Study

The purpose of the study was to investigate possible constructs related to the
intrinsic nature and processes of psychosocial adaptation to familial substance abuse as a
CID for college undergraduate students within a campus setting. The study considered
relationships between the internal factors of health values, spiritual well-being, and the
process of resilience. The study also considered personal substance use and familial
substance abuse as key risk factors. This study probed the mysteries behind why certain
individuals who emanate from families of substance abuse are successful in enrolling in
college and accessing the opportunities offered by an academic setting. As constructs,
health values, spiritual well-being, and resilience have been researched separately or in
pairs (Ritt-Olson et al., 2004; Sundararajan-Reddy, 2005). No instances were found
where the three constructs had been researched together using a combined design. By
exploring these three constructs along with key risk factors, it was hoped the results of
this research would shed new light into the world of elevated risks for substance use and
abuse for this uniquely challenged population.

The goals for this study were four-fold: (1) to contribute research that would add
to the growing body of literature in our understanding of the factors that promote
psychosocial adaptation to CID and the relationships that may exist between the
constructs of health values, spiritual well-being, and resilience; (2) to advance our
understanding of how these constructs, in combination, may improve the psychosocial
adaptation processes for individuals that stem from families of substance abuse;

(3) to demonstrate how the relationships between these constructs may interact with risks

for personal substance use and familial substance abuse; (4) to assist other researchers in

13

launching future investigations into combinations or clusters of constructs (Livneh, Lott,
& Antonak, 2004) which may promote psychosocial adaptation for individuals who are
affected by familial substance abuse as a CID.

The search for answers to reduce the exposures and potential risks for college
students may uncover an opportunity to begin to understand why certain individuals
perpetuate a life of substance abuse and others do not. As emphasized within the
literature, the negative dysfunction and breakdown within families and family systems
contributes to the initiation and perpetuation of substance abuse across generations. In a
more positive light, it is important to acknowledge the family for its strengths including
the aspects of resilience that are present in certain emerging adults which enable them to
overcome the inﬂuences of familial abuse of ATOD. Additionally, it is important to
acknowledge individual and familial resilience in overcoming adverse effects created by
substance abuse and dependency disorders. Although the focus of this study was not
speciﬁcally directed to families’ inﬂuence on resilience (Johnson et al., 1998) and
psychosocial adaptation of its individual members, it is indirectly implied.

The following research questions speciﬁcally address psychosocial adaptation for
college students as individuals.

Research Questions

Data were collected from a purposive sample of college students enrolled in an
introductory, undergraduate substance abuse course at a major Midwestern university
during Spring semester 2007. The following research questions were posited:

1. What relationships exist between the reported health values, spiritual well-being,

and resilience for all participants?

14

 

2.

Are there differences in reported health values, spiritual well-being, and resilience
among participants who score low, medium, high, or very high risk for personal
substance use?

The sample was subdivided into two groups: Group 1 consisted of participants

with no reported past or present familial substance abuse problems; Group 2 consisted of

participants with reported past or present familial (genetic and non-genetic relatives).

The latter group was subdivided into Group 2A, participants reporting genetic relatives

and Group 28, participants reporting non-genetic relatives. Participants who reported

both genetic and non-genetic relatives were placed in Group 2A.

3a.

3b.

Are there differences in reported health values, spiritual well-being, and resilience
between participants with no reported past or present familial substance abuse
problems (Group 1) and participants with reported past or present familial
(genetic or non—genetic) substance abuse problems (Group 2)?

Are there differences in reported health values, spiritual well-being and resilience
between participants reporting genetic relatives (Group 2A) and participants

reporting non-genetic relatives (Group 2B)?

30. Are there differences in reported health values, spiritual well-being,

and resilience between participants who score low, medium, high, or very high
risk for personal substance use and report no familial substance abuse and
participants who score low, medium, high, or very high risk for personal substance
use and who score low, medium, high, or very high for (genetic or non-genetic)
familial substance abuse. Comparison groups 3 and 4 were created to answer this

question (see Hypothesis 3c listed below).

15

 

As stated, no previous research was uncovered that measured the three constructs
of health values, spiritual well-being, and resilience in combination. The hypotheses for
the study were based upon observations of characteristics that were typical of students
enrolled in the substance abuse classrooms over a three year span.

The reader should bear in mind that Hypotheses 1 and 2 (listed below) addressed
general characteristics pertinent to the entire sample of students. Hypotheses number 3a,
3b, and 30 (listed below) divided the sample into groups based on students’ reporting of
indicators for familial substance abuse. Students noting positive familial indicators
reported either genetic or non-genetic familial substance abuse or both. Hypotheses
number 3b and 3c were intended to measure the positive and overcoming nature of the
process of resilience. Thus, the reader may be surprised by the positive formulation of
these hypotheses.

The reader should be aware that the essence of this study was intended to improve
our understanding of psychosocial adaptation to CID. In much of the literature, the
portrayal of substance abuse as a CID is negatively-toned and typically focuses on the
pathological aspects of substance use, abuse, and dependence. This study was
constructed to advance our understanding of the positive human factors that may combine
to inﬂuence psychosocial adaptation to substance abuse as a CID for college students and
their families.

When considering psychosocial adaptation within families, family resilience is
deﬁned as a key family process that mediates recovery and serves to strengthen
vulnerable family members (Walsh, 2002). Family resilience is necessary to endure and

re bound from crises and challenges that families face (Walsh). The approach to family

16

 

resilience utilizes concepts and language that serve to humanize the experience of CID
(Walsh). A few of the key factors within the process of family resilience are related with
the three constructs investigated by this study and include: (1) making meaning of
adversity, (2) sense of coherence, (3) positive outlook, (4) transcendence and spirituality,
(5) larger goals and purpose, and (6) future goals and dreams (Walsh).

Based on the research questions identiﬁed above, the hypotheses for this study are
as follows:
Hypotheses

1. Participants who demonstrate increased resilience will also demonstrate increased
health values and spiritual well-being.

2. Participants who score low to medium risk for personal substance use will
demonstrate increased health values, spiritual well-being, and resilience compared
with participants who score high to very high risk for personal substance use.

3a. Participants with reported past or present familial substance abuse (Group 2) will
demonstrate increased health values, spiritual well-being, and resilience compared
with participants with no reported past or present familial substance abuse
(Group 1).

3b. Participants reporting past or present substance abuse problems with genetic
relatives (Group 2A) will demonstrate increased reported health values, spiritual
well-being, and resilience compared with participants reporting past or present

substance abuse with non-genetic relatives (Group 2B).

17

 

3c. Participants considered low to medium risk for personal substance use and high to

very high risk for familial substance abuse (comparison group 3) will demonstrate

increased health values, spiritual well-being, and resilience compared with

participants considered high to very high risk for personal substance use and low

to medium risk for familial substance abuse (comparison group 4).
Deﬁnition of Terms

As a term, the deﬁnition of family resilience has already been discussed.
Additional terms which appear throughout this document are substance abuse, substance
use, and family. These terms and their deﬁnitions of use within this study appear below.

There is an important need for viable research efforts regarding college alcohol
use patterns to utilize deﬁnitions that are aligned with the American Psychiatric
Association’s DSM-IV—TR criteria (Clements, 1999). Anchoring research in clinical
context will assist in paving the way to identify the marked symptoms, and prevalence of,
alcohol-related disorders within the college student population (Clements). Although one
of the goals of this study was to identify personal substance use risk factors for college
students within the sample, this study sought to identify risk factors based upon reported
patterns for past or present substance abuse within families. Therefore, adoption of a
working deﬁnition of substance abuse was important to provide to students as they
responded to survey questions regarding familial substance abuse. For the purposes of
this study, the deﬁnition of substance abuse has been adopted from the DSM-IV-TR
(American Psychiatric Association, 2000). The deﬁnition appears as follows:

A maladaptive pattern of substance use leading to clinically signiﬁcant

impairment or distress, as manifested by one (or more) of the following, occurring
within a 12-month period:

18

 

 

(l) Recurrent substance use resulting in a failure to fulﬁll major role obligations
at work, school, or home (e.g. repeated absences or poor work performance
related to substance use; substance-related absences, suspensions, or
expulsions from school, neglect of children or household)

(2) Recurrent substance use in situations in which it is physically hazardous (e. g.
driving an automobile or operating a machine when impaired by substance
use)

(3) Recurrent substance-related legal problems (e. g. arrestsfor substance-related
disorderly conduct)

(4) Continued substance use despite having persistent or recurrent social or
interpersonal problems caused or exacerbated by the effects of the substance
(e. g. arguments with spouse about consequences of intoxication, physical
fights). (p. 199)

Another term deserving clariﬁcation is substance use. Personal substance use is
deﬁned as the experimental, recreational, or prescribed consumption of licit or illicit
substances which is not regarded as maladaptive or pathological (Hanson et al., 2006;
Hardie, 2002). Let it remain clear that personal substance use may become risky or
excessive with respect to the quantity, frequency, and types of substances consumed and
this enhances the likelihood of harm to an individual’s health, safety, and well-being
(Chen et al., 2004-2005; Hanson et al.).

For purposes of clarity, it is important to establish boundaries of distinction that
this study was not designed to be diagnostic or prescriptive regarding the personal use of
substances by students within the sample. However, applying deﬁnitions that have a
functional basis or outcome was important to individuals’ personal inventories of
themselves and their families as they reported information regarding their personal
substance use and familial substance abuse patterns. No assessments regarding the

presence or absence of pathology within prospective participants or their families were

pursued in relation to personal substance use risk factors. This study gathered a personal

19

 

reporting of information by participants regarding personal substance use and relied
solely on self-report measures for quantity, frequency, and types of substances used. All
data regarding substance use were analyzed in relation to calculated categories of risk.

Individuals who participated in the study stemmed from families with no reported
past or present substance abuse problems and families with reported past or present
substance abuse problems. This warranted a need to further deﬁne family within the
context of this study. The deﬁnition of family was not limited to genetic or blood
relatives only. The individual’s perception, or sense of family, included non-genetic
family members such as: (1) adoptive, (2) step, or (3) cohabitating family members as
well as (4) very close friends (Dorsey, Scherer, & Real, 1999; Mead & Heyman, 1965;
Weitzman, Nelson, & Wechsler, 2003).

In summary, this study portrays a unique view of the factors that may be related
with the process of psychosocial adaptation to the risks posed by personal substance use
and familial substance abuse as experienced by a sample of college undergraduates.
Much of the previous research pertaining to this topic has provided a negative slant
toward college users and the possible risks and consequences they face (Keeling, 2000;
Ritt-Olson et al., 2004). This study was intended to illuminate the positive, person-
environment (Livneh, 1987) interactions that are possible between adverse conditions and
the outcome of psychological health and spiritual well-being which can further lead to
favorable psychosocial adaptation (Kosciulek, 2004a) and increased adaptive functioning
(Schoon, 2006). The study explored the characteristics and processes that certain young

adults may cultivate which enable them to emerge from a background of substance abuse

20

 

and take on the added stress of fulﬁlling new roles in responding successfully to the

challenges and risks posed by academia.

21

 

CHAPTER II

The following chapter provides an indepth review of several key topics that are
integral to the present study. The broad topics included in the literature review are:
(1) campus substance abuse, (2) psychosocial adaptation to CID, (3) risk factors,
(4) protective factors, and (5) other factors. This exhaustive review of the literature
embodies the most premier and seminal research efforts for these topics and further
represents the collective work of many theorists across many disciplines.

Review of Literature

The potential impact of campus substance abuse as a major public health concern
and so-terrned “epidemic” was suggested at the beginning of this document. Substance
abuse as an overall social epidemic is implicated as well and is best described by the term
equal opportunity aﬂliction (Hanson et al., 2006). In other words, substance abuse is
known to every gender, racial, ethnic, religious, socioeconomic, or cultural subgroup,
because it has permeated virtually every segment of the American population. Put
simply, the potential for use or abuse of licit or illicit substances knows no human
boundaries and therefore no person is immune to its risks as a potential chronic illness or
disease (Hanson et al.).

The etiology of substance abuse remains largely unanswered by science although
some new evidence has come to light regarding genetic markers (Hardie, 2002; Prescott
& Kendler, 1999; Schuckit, 1999). From the evidence, it appears that approximately
50% of the risk for alcoholism is explained by a combination of genetic factors related to
a mixture of receptor, enzymatic, and other biophysiological reactions (Prescott, &

Kendler; Schuckit). In a population-based study of male twins in the United States, it

22

 

was determined that genetic factors play a signiﬁcant role in the susceptibility of males to
acquire alcoholism, alcohol abuse, or alcohol dependence disorders (Prescott & Kendler).
In a paper published by Schuckit, individuals identiﬁed as having a low sensitivity to
alcohol were found to be more susceptible to alcohol dependence in the presence of
certain personality characteristics such as enhanced boredom or tendency toward
impulsive behaviors.

The primary presentations for the destructive and pathological use of substances
appear as substance use disorders, substance abuse disorders, or substance dependence
(Hardie, 2002). The criteria for diagnosing substance abuse and dependence are clearly
established within the DSM-IV-TR (American Psychiatric Association, 2000; Hardie).
Substance abuse manifests as an acute or chronic condition or it may appear as a
coexisting disorder in combination with other primary or secondary disorders (Hardie).
Substance dependence may appear with any combination of physical, psychological, and
behavioral symptoms which vary from one individual to another (Hanson et al., 2006;
Hardie)

Substance abuse is viewed as a predictor for disability or as a mediator to a
disabling condition (Livneh & Wilson, 2003). The risk factors for use or abuse of
substances are triggered or exacerbated by calamitous events and may be further
complicated by symptoms of post-traumatic stress disorders, traumatic brain injuries, and
other mental and physical traumas (Beck & Franke, 1996; Graf, 2004). These types of
crisis-related, traumatic events precipitate major life changes which result in a period of

psychological disequilibrium for those involved (Beck & Franke).

23

 

As a coexisting disorder, substance abuse mimics a constellation of signs and
symptoms similar to a number of other disorders, making a clinical diagnosis difﬁcult at
best. A classic example of this is observed in patients diagnosed with a dual condition of
mental disorder and a co-occurring substance abuse disorder. For trained clinicians, it is
difﬁcult to discern whether the etiologies of clients’ symptoms are attributable to one
disorder or the other because many of the symptoms are characteristic to both sets of
diagnostic criteria.

The literature is full of a variety of studies that attempt to answer the etiology of
substance abuse and dependency from disease, psychosocial, or moral perspectives
(Hanson et al., 2006). Miller and Hester (2003) used an innovative approach in
constructing an eclectic model that encompasses thirteen conceptual and treatment
perspectives. This model is referred to as the “public health” model. Miller and Hester’s
public health model includes: (1) the moral, (2) temperance, (3) spiritual,

(4) dispositional, (5) disease, (6) biological, (7) characterological, (8) conditioning,

(9) social learning, and (10) cognitive approaches. Three additional conceptual
approaches important to the eclectic framework of the public health model are: (11) the
general systems model which addresses family roles and dysfunction, (12) the
sociocultural model which addresses environment and cultural norms, and (13) the
educational model which addresses lack of knowledge and motivation. Miller and Hester
envisioned the public health model to consist of three important environmental contexts
of: (1) agent, (2) host, and (3) environment. The authors suggested that the public health
model provides us an opportunity to strive toward informed eclecticism within the ﬁeld of

alcohol treatment. The authors further asserted that the public health model, and its

24

concept of “informed eclecticism”, encompasses a much larger and broader perspective
of alcohol disorders and serves to guide theory and practice by matching individuals with
treatment approaches that are best-suited to their needs.

Given the nature of the present study, and in viewing the family from a systems
perspective, it was necessary to adopt a more eclectic and holistic framework that
included viewing college students’ risk variables from a combination of genetic,
personality, familial, environmental, and transcendental factors. This type of perspective
is consistent with the bio-psycho-socio-spiritual perspective or viewing the “whole”
person. As mentioned previously, the present study represents a pursuit to understand the
person-environment factors (Livneh, 1987) of young adults who have successfully
transitioned from high school into college. The study connected key factors to the
process of psychosocial adaptation to CID using a bio-psycho-spiritual frame of
reference.

Substance abuse as a medical and clinical diagnosis was established within the
ﬁeld of psychiatry. In the world of science and medicine, substance abuse can lead to a
state of CID that can, in turn, impact one’s ability to function within their home and
vocational environments. As a social epidemic, substance abuse is viewed as a detriment
to society for its negative affects to individuals, families, and communities. Speciﬁc to
individuals, substance abuse affects the totality of individual human experiences
including not only those of mind and body, but of the emotional, social, and spiritual
dimensions as well. In some cases, substance abuse may lead to a chronic relapsing

condition of substance dependence.

25

 

Numerous studies have validated a number of key factors that serve as predictors.
or indicators, of substance abuse, particularly for a college or young adult population.
The literature has veriﬁed that a signiﬁcant degree of variation exists among individual
college substance users. Given this variability, it would be unrealistic to search for
simple answers to a complex situation through a single study. Moreover, it must be
stated that no one factor can be singled out and studied in isolation from all other factors.

The current study considered protective factors and is unique in its positive view
of these factors in combination with the adaptive process of resilience. It was theorized
that these protective factors may serve to synergize and potentiate the process of
resilience and may buffer the risks for the college students under investigation. Miller
(1998) indicated that Alcoholics Anonymous points us toward character traits, or “‘active
ingredients’, that may play a key role in recovery” (p. 986). It was conceived that the
process of resilience may serve as the main active ingredient and protective factors of
health values and spiritual well-being may serve to enhance this protective process by
strengthening the mixture. Regardless of the outcome, this combination provided a
unique lens which was well worth exploring.

It must be emphasized that this exploratory investigation considered a mere
portion of the extensive risk factors and other factors that may be evident in the college
population at large. The major goal of the study was to determine whether an interactive
relationship exists between the three, identiﬁed constructs for individuals within a
selected sample. And, this study considered the existence of such a relationship
combined with environmental risk factors posed by personal substance use within a

college setting and those posed by a positive history of familial substance abuse.

26

Campus Substance Abuse

In 1994, Wechsler et a1. published a landmark study wherein 140 colleges were
surveyed for the purpose of examining the extent of college student drinking and the
resulting health and behavioral consequences. The ﬁndings demonstrated that
approximately 44% of college students were “binge” drinkers and 19% of those binge
drinkers were considered “frequent” binge drinkers (Wechsler et al.). Weschler et a1.
maintained that binge drinkers were much more likely to experience serious health
problems and other consequences. The report ﬁndings further indicated that 47% of the
frequent binge drinkers did not regard their drinking as problematic (Wechsler et al.).

Another landmark study performed by Hingson et al. (2002) analyzed statistics
gathered by three major national survey databanks. The survey databanks included the
Substance Abuse and Mental Health Services Administration’s (SAMHSA) National
Household Survey on Drug Abuse (NHSDA), the CDC’s National College Health Risk
Behavior Survey (NCHRBS), and the Harvard School of Public Health’s College Alcohol
Survey (CAS). Findings for the study estimated that over 1,400 college students between
the ages of 18 and 24, attending two and four-year institutions, died in 1998 from
alcohol-related accidental injuries including motor vehicle crashes (Hingson et al.). In
1998, there were an estimated 500,000 alcohol-related unintentional injuries and 600,000
alcohol-related physical assaults for full-time college students attending four-year
institutions (Hingson et al.). Of the eight million college students in the United States in
1998, two million were estimated to drive under the inﬂuence of alcohol and three

million rode with someone who had been drinking (Hingson et al.).

27

 

Within the venue of campus life, alcohol has been identiﬁed as the major
psychoactive drug of choice and it is well recognized for its potentially harmful effects to
this population (O’Malley & Johnston, 2002). In addition to alcohol, college students
engage in the less frequent use of other dangerous licit and illicit psychoactive
substances. Drugs other than alcohol pose a threat to the overall health and safety of the
student population as well. Other drugs identiﬁed by the NHSDA, NCHRBS, CAS,
University of Michigan’s Monitoring the Future (MTF), and Southern Illinois
University’s CORE Institute (CORE) studies were: nicotine, marijuana, and stimulants
(O’Malley & Johnston).

In response to the nation’s growing concern, the NIAAA launched a major
initiative to alter drinking cultures at US. colleges in 2002. The NIAAA Task Force
cited statistical ﬁndings from these landmark studies. The task force urged all
universities and colleges to join forces in cooperation with an overarching national
framework established to reduce high risk drinking among students (N IAAA). Strategies
involving student motivation and cognitive-behavioral techniques, population-based
social norming campaigns, and the adoption of changes to law enforcement, public
regulation, alcohol retail, and campus policies were highlighted by the report (NIAAA).

In response to this federal initiative, university administrators, health educators,
and prevention specialists around the country began to unify their efforts by instituting
and further developing the recommendations of the NIAAA Task Force. Today, many
universities and colleges have already launched large-scale programs targeted to prevent
and/or modify alcohol behaviors amidst their student populations (Wechsler et al.,

2000a). These recent efforts by college ofﬁcials focus on improving the overall safety

28

 

and health needs of students via the introduction of social norming campaigns into the
campus environment.

Social norming campaigns incorporate the use of strategies designed to inﬂuence
students to choose behavioral patterns of healthy substance use by ﬁrst uncovering
exaggerated misperceptions students have of the drinking habits and behaviors of their
peers, ultimately redeﬁning campus drinking normative behaviors (Walters & Baer,
2006; Wechsler & Kuo, 2000). Social norming strategies also include awareness-raising
campaigns designed to correct misperceptions about alcohol and to promote the
responsible use of this substance.

The major goal of these campaigns is to promote harm reduction. Many
campaigns encourage students to exercise moderation in drinking rather than abstinence.
One example of this is seen in the “party smart” campaign which educates students about
the hazards of binge drinking but does not encourage them to necessarily stop drinking
(Polonec et al., 2006). The social norming campaigns are designed to precipitate a mass
change in students’ attitudes and perceptions about drinking (Polonec et a1.; Wechsler &
Kuo, 2000). The desired outcome of these campaigns is to strategically decrease the
number of preventable, substance-related accidents, injuries, illnesses, and deaths
incurred by college students each academic year.

Campus-based social norming campaigns are just one form of the many strategies
recommended for implementation by the task force. A more recent approach
demonstrating success in modifying student alcohol behavior is aided by the use of
computer technology. Many electronically-based alcohol education and intervention

programs have become commercially available which address the issues of high risk

29

 

drinking (Walters, Miller, & Chiauzzi, 2005). These programs provide personalized
feedback to students regarding their substance use behaviors via the Internet (Walters et
al.). The programs have been found to be very promising in demonstrating overall
success to curbing alcohol risk behaviors (Walters et al.).

Another promising approach to changing the culture of high risk drinking
includes reinforcing the use of consumptive protective strategies that college students
employ when planning to drink alcohol. Strategies reported by students in the ACHA-
NCHA data included: (1) eating before consuming alcohol, (2) alternating alcohol with
non-alcoholic beverages, (3) pacing consumption of alcoholic beverages to one or fewer
per hour, (4) avoiding drinking games, (5) setting limits in advance to plan the numbers
of drinks consumed, (6) keeping track of the number of drinks consumed, (7) establishing
a designated driver, (8) or choosing not to consume alcohol at all (ACHA, 2006).

Psychosocial Adaptation to Chronic Illness and Disability

Substance abuse is known to have detrimental effects for families. Substance
abuse can prompt long lasting changes throughout family systems, affecting not one, but
all members of a single family unit (Kosciulek, 2004a). Systemic changes elicit a shared
adaptive response for families as a unit. As a part of the family circle, individual
members respond by making a series of individual adjustments within the family context.
Within the ﬁeld of rehabilitation counseling, this process is described as the process of
psychosocial adaptation to CID (Livneh, 2001).

The process of psychosocial adaptation to CID is much like the cycle of addiction
which ebbs and ﬂows in patterns of abstinence and relapse. The process of psychosocial

adaptation is made over time through a series of ongoing responses and adjustments.

30

 

These responses and adjustments continue to inﬂuence college students even as they
reside apart from family. The current study viewed psychosocial factors related with
college and family life that lend themselves to this vital process of adaptation.

Substance abuse can ﬁinction as the precipitating event to CID or as the response
to other traumatic or life-altering circumstances (Graf, 2004) which have signiﬁcant

impact on the family. Developing children, adolescents, and young adults are

particularly susceptible to the harmful effects of ATOD within the family unit. These F
n
effects may precipitate any number of responses including use, disuse, misuse, and abuse
of substances.
Given that substance abuse is a familial condition, it is potentially passed from :

 

one generation to the next through any number of genetic, psychosocial, or environmental
factors. For college students who have emerged from a familial background of substance
abuse, the blend of these factors may facilitate the process of successful psychosocial
adaptation in overcoming their family situations. This leads one to question what factors
may play a role in this successful adaptation.

Resilience can play a signiﬁcant role in a young person’s psychosocial adaptation
to stressful familial conditions caused by alcohol and drug abuse. Resilience may be the
key factor in the process of psychosocial adaptation that families develop to safeguard
their members from harmful and damaging situations. Resilience has been found to assist
in preserving the integrity of family as a systemic unit by instilling positive changes or
adjustments within the family where needed to continue to operate effectively (Walsh,

2002)

31

Resilience is not merely a personality characteristic or a trait (Schoon, 2006).
Resilience is a process in itself, an adaptive response to situations in the face of adversity
(Schoon; Walsh, 2002). The process of positive adaptation operates through three
mechanisms identiﬁed as: (1) protective, (2) compensatory, or (3) the challenge model
(Schoon). In particular, the challenge model states that the effects of protective processes
are dependent upon the degree of the exposure to risk factors (Schoon). To better
understand resilience, we must ﬁrst consider some models of psychosocial adaptation.

The foundational model of psychosocial adaptation to CID within the ﬁeld of
rehabilitation counseling was theorized and developed by Hanoch Livneh (2001). Two
essays served as major literary pieces that inﬂuenced his work in constructing this model
(H. Livneh, personal communication, December 18, 2006). The essays were found to
have particular importance in laying the groundwork for the current study.

The ﬁrst essay was written by Schlossberg (1981). Schlossberg examined and
borrowed ideas from other theorists in constructing an eclectic approach regarding the
process of human adaptation to transition. This continuum of human adaptation and
transition was presented in the context of non-linear and individualistic life growth
processes. Schlossberg theorized that adaptation is a dynamic process of transition and
reorganization that is dependent upon the act of balancing between one’s resources and
deﬁcits. In the model, Schlossberg named three major sets of factors as determinants to
one’s movements through the phases of adaptation and transition: (1) the characteristics
of the particular transition, (2) the characteristics of the transitioning individual, and

(3) the pre and post-transitioning environments.

32

 

The conceptual components to Schlossberg’s (1981) model provided signiﬁcant
support to the theoretical underpinnings of the present study. Three of the model’s
components reﬂected in the framework of this study are: (1) internal support systems of
intimate relationships, (2) family unit, and (3) network of friends to pre and post-
transitioning environments. Other typical characteristics mentioned by Schlossberg that
lend themselves to the continuum of adaptation and transition and are emphasized within
this study include: (1) psychosocial competence, and (2) health and value orientation.
Schlossberg named additional transitional factors that were also relevant to the study and
these are identiﬁed as: (1) role change, (2) degree of stress, and (3) timing of events.
Ultimately, Schlossberg illustrated possible outcomes to the process of adaptation as:

(1) a state of adaptation or (2) failure to adapt.

The second inﬂuential essay, written by Moos and Schaefer in 1984, envisioned
maladaptation as a response to a set of crises resulting from the onset of physical illness.
The authors posed three possible outcomes by individuals: (1) some learn to cope,

(2) some experience psychological consequences, and (3) some gain a deeper and more
meaningful appreciation of life (Moos & Schaefer). In their model, Moos and Schaefer
consider personal factors, illness-related factors, and physical and social environmental
factors as pre-determinants of the process in dealing with a physical crisis. The authors
asserted that the process for determining the psychosocial outcome of the crisis was
further determined by: (1) an individual’s cognitive appraisal of the illness, (2) the
necessary adaptive tasks they must perform, and (3) the coping skills they incorporate
into the situation. Moos and Schaefer envisioned the major adaptive tasks as divided into

into: (1) illness-related tasks and (2) general emotional, psychological, and relational

33

 

 

tasks. The authors further divided these tasks into major types of coping skills which are:
(l) appraisal-focused, (2) problem-focused, and (3) emotion-focused.

Coping skills are particularly relevant to the current study. In understanding
substance abuse as a maladaptive response to coping with CID, one must understand the
types of coping skills one may possess or choose in response to a physical health crisis.
The use of substances in one’s life appears to typify a state of perpetual suffering (Lam,
2002) through the mechanisms of avoidance and denial rather than working toward the
stages of acceptance, healing, and adaptation. This phenomenon is exempliﬁed by the
early work of Elisabeth Kﬁbler-Ross (1969) in her depiction of loss as a transitional
process represented by the ﬁve-stage process of grieving. Ktibler-Ross identiﬁed the ﬁve
stages as: (1) denial, (2) anger, (3) bargaining, (4) depression, and (5) acceptance.
Progression through these stages is dependent upon each individual and their personal
appraisal of and experiences with grief. Grief may cause some individuals to become
absorbed into an ongoing state or chronicity of suffering over an indeﬁnite period of
time.

Kiibler-Ross’ (1969) groundbreaking theories regarding the transitional issues of
death and dying, inspired the foundational work within the ﬁeld of rehabilitation
counseling in understanding the psychosocial reactions to CID. In 1990, Livneh and
Antonak performed empirical testing on the reactions to disability. In 1991, Antonak and
Livneh investigated hierarchical patterns of emotional reactions to disability. In 1998,
Kendall and Buys expanded the rehabilitation concept of psychosocial adjustment to
disability by incorporating the principles from recurrent models of psychosocial

adjustment to stage models of psychosocial adjustment. This integrated approach

34

 

suggested that adjustment to disability is a continuous life transition rather than a gradual,
linear process (Kendall & Buys).

In 2001, Livneh conceptualized the model of psychosocial adaptation to CID,
incorporating ideas from the models of aforementioned theorists; Schlossberg (1981) and
Moos and Schaefer (1984). Livneh envisioned three major phases in the process of
psychosocial adaptation to CID which are: (l) antecedents, (2) process, and
(3) outcomes. Livneh’s Model described a complex and interactive process between
three areas: (1) triggering events and contextual variables or antecedents, (2) experienced
reactions and responses to the onset of CID and contextual inﬂuences or process, and

(3) intrapersonal, interpersonal, and extrapersonal domains or outcomes which inﬂuence

 

quality of life. In applying Livneh’s Model to the current study, the antecedents are
represented by familial substance abuse and the degree of familial risk factors, the
process is represented by primary living situation and peer inﬂuences, and the outcome is
represented by the degree of personal substance use in combination with college and
cumulative grade point average.

More recent work on the topic of psychosocial adaptation to CID was contributed
by Bishop in 2005. Bishop compared existing measures of quality of life and adaptation.
Bishop found quality of life was positively correlated with adaptation to CID. Through
this investigation, Bishop further postulated that satisfaction and perceived control serve
as mediating factors to quality of life and adaptation. Bishop concluded that satisfaction
within the context of family means different things depending on one’s deﬁnition of

domain.

35

Coping
Indeed, human beings are complex organisms and it is difﬁcult, perhaps
impossible, to ascertain whether substance abuse is the result of: (l) a disease process
based on biological or genetic explanations, (2) a symptom of a characterological defect
resulting from psychological conditions, or (3) a moral sin based on deteriorating
sociological factors (Hanson et al., 2006; Peterson, Skinstead, & Trobliger, 2004). In
other words, modern theorists argue that substance abuse can be a matter of genetics,
moral conditions, or a volitional, maladaptive behavior that is otherwise deemed a “bad”
choice (Hanson et al.; Peterson et al.).
Medical advances regarding the human genome are demonstrating more and more

evidence that chemical addictions are genetically-based (Hardie, 2002; Schuckit, 1999).
For individuals with genetic markers, substance dependency has been shown to have

higher medical probability than for those without genetic markers (Hardie; Schuckit).

Within the present study, it is important to differentiate substance abuse from chemical
dependency. Substance abuse is characterized by excessive and maladaptive use of
substances (Hanson et al., 2006). In contrast, substance dependence is marked by
physical cravings and an uncontrollable desire or compulsion to abuse substances
(Hanson et al.). Substance abuse, therefore, can be regarded as a performance-based
behavior, maladaptive response, or poor coping strategy in reaction to chronic illness,
disability, or other stress-inducing events (Beck & Franke, 1996; Livneh, 2001; Livneh &
Wilson, 2003).

To expound on this point, coping strategies are not synonymous with the concept

0f psychosocial adaptation. Coping serves as a mediator to psychosocial adaptation

36

 

(Livneh, 2001). Coping strategies produce either favorable or less than favorable
outcomes. However, this is highly individualized, unpredictable, and remains largely
understood. In most cases, substance abuse is considered a poor or faulty adaptation
response that does not lead to a series of adjustments or to psychosocial bonadaptation
(Kosciulek, 2004a) as a favorable adaptation outcome. As a maladaptive coping strategy,
substance abuse serves both as a predictor or a mediator of disability and psychosocial

adaptation (Livneh & Wilson, 2003).

 

In the lives of many college students, “drinking to cope” with stress and problems

is an accepted, daily occurrence (Park, Armell, & Tennen, 2004; Park & Levenson,

 

2002). And, engagement in the excessive use of substances is not only accepted, it

depicts normative college behavior. And in many instances, the practice of imbibing in
ATOD is done so not only in response to stress but as a celebration to a rite of passage or
other event (Del Boca et al., 2004; Walters & Baer, 2006). For college students,
milestones of achievement such as passing a major exam or winning a sports event
deserve social celebration which may include the use of alcohol or other substances as
social lubricants (Hanson et al., 2006), or perhaps more accurately termed, social glue
(Lederman et al., 2003).

Britton (2004) analyzed key strategies college students employ to cope. These
strategies fall into three categories: (1) emotion-focused, (2) problem-focused, or
(3) avoidance coping. Interestingly, the author concluded that avoidance coping was not
related to alcohol consumption within college samples but it was related to alcohol-

related consequences (Britton). Britton suggested that college students’ reliance upon

37

substances may increase deﬁcits in adaptive coping and hence, adaptive coping may

 

increase reliance upon substances. This pattern is reminiscent of a vicious cycle.

A recent study by Park and Levenson (2002) suggested that drink to cope by
college students is employed as an avoidance-focused measure more often than emotion-
focused or problem-focused measures. The authors noted that interpersonal variables
make some students more vulnerable to drink, and yet other students who possess
protective resources, to refrain from this practice. The study by Park and Levenson
further revealed that college students who did not drink to cope demonstrated higher

coping abilities and a negative correlation with alcohol-use variables. The researchers

 

"I... "

noted that coping ability in predicting monthly quantity, frequency, and heavy episodic
drinking, was linked to higher alcohol consumption. And, Park and Levenson found that
situational drinking to cope was a strong predictor for a variety of alcohol use indicators.
With these surprising and contradictory results, the authors concluded that coping ability
has multiple interactions with alcohol consumption (increased use or not) and this is
explained by individuals who derive a sense of improved coping or increased self control.
Britton (2004) carried out a study which suggested that the tendency of college
students to use substances to cope with stress may be a determinant regarding an
individual’s alcohol consumption and related consequences. Britton’s results showed
that a much, more complex relationship exists between coping and the frequency,
quantity, and behavioral outcomes of drinking. Britton’s study further validated the
results of previous studies which established religious coping as a protective factor for

coping through the use of substances.

38

Occupational Behavior and Temporal Adaptation

Two important concepts, occupational behavior and temporal adaptation, are akin
to the processes of resilience and psychosocial adaptation. Occupational behavior is a
biopsychosocial frame of reference within the ﬁeld of occupational therapy introduced by
Reilly (1962) who theorized active human engagement to affect three important areas of
life which are: (1) work, (2) play, and (3) self-care (Reilly, 1962). Mention of these

concepts is critical in linking the mind to the body regarding human adaptation. Reilly’s

 

theories also link the relationship of health to human adaptation (Miller & Walker, 1993).

Three subsystems deﬁned within this theoretical framework are: (1) volition,

 

(2) habituation, and (3) performance as well as environmental inﬂuences (Miller &
Walker). A contributing theme to the present study exists within the components of the
volitional subsystem which are identiﬁed as a person’s: (1) values, (2) interests, and

(3) personal causation or beliefs about one’s personal capacity or efﬁcacy (Miller &
Walker). Reilly articulated that human beings have a need to produce, create, master, and
improve their environments through competence in achieving their daily occupations
(Miller & Walker).

In 1977, Kielhofner advanced the Model of Human Occupation by promoting the
concept of temporal adaptation. Kielhofner theorized that human beings learn to
temporally adapt both psychosocially and biologically through three avenues which are:
(1) awareness of one’s own agenthood, (2) placement in time, and (3) conscious planning
of action and these avenues are linked to a sense of purpose that is guided both by

hindsight and foresight. Echoing Reilly, Kielhofner described health as experienced by

39

achieving a state of balance within the vital areas of human occupation and homeostasis
as seen in the biological and psychosocial health of the human organism.
Resilience

Glicken (2006) consulted numerous sources and compiled a list of deﬁnitions
for the term resilience. The four deﬁnitions that directly pertain to the current study are
listed as follows:

Gordon (1996) “Resilience is ‘the ability to thrive, mature, and increase
competence in the face of adverse circumstances’” (p. 4).

Glick (1994) “‘Resilience’ is the ability to ‘bounce back’ from adversity, to
overcome the negative inﬂuences that often block achievement” (p. 4-5).

Henry (1999) “Resilience is ‘the capacity for successful adaptation, positive

functioning, or competence despite high risk, chronic stress, or prolonged or

severe trauma’” (p. 5).

Walsh (2003) “the concept of family resilience extends our understanding of

healthy family functioning to situations of adversity. Although some families are

shattered by crisis or chronic stresses, what is remarkable is that many others

emerge strengthened and more resourceful” (p. 5).

It is important to recognize that resilience in overcoming the effects of alcohol
and drug abuse is not synonymous with recovery (Bonanno, 2004). Resilience is a
reﬂection of one’s ability to maintain a level of homeostasis or equilibrium (Bonanno).
This principle is applicable to both individuals and families. Other theorists have
maintained that resilient individuals are characterized by positive emotion granularity
during the coping process and positive emotions have distinct beneﬁts in improving one’s
health (Tugade, F redrickson, & Barrett, 2004). Further investigation into understanding
resilience is important to health psychology in understanding the mind-body connection

because a sense of resilience triggers strong physiological responses in protecting

individuals against stressful stimuli (Tugade et al.).

40

 

The Resiliency Model, written by Richardson in 2002, is built upon the premise
that individuals seek their comfort zone through a continual process of maintaining
biopsychospiritual homeostasis or balance within mind-body-spirit. This state of balance
represents the point at which individuals adapt physically, mentally, and spiritually to a
set of circumstances whether good or bad (Richardson). Furthermore, resilience is
viewed in the context of an ecosystem broken down into four subcomponents of:

(1) individual factors, (2) family factors, (3) community factors, and (4) cultural or ethnic
identity (Waller, 2001).

Within the ecological context of family, Walsh (2002) proposed that families
faced with crisis, traumatic, or life-altering conditions undergo a process of rebalancing,
adaptation, recovery, and growth through collaboration. This is a key concept to the ﬁeld
of rehabilitation counseling and theories which speak to families’ capacities to endure the
changes precipitated by any combination of conditions within the span of physical,
mental, intellectual, emotional, and sensorial disorders. Families as social systems
possess their own forms of resilience through the resources and coping methods they
possess to overcome stress (Patterson, 2002). Family adaptation is dependent upon
several factors including: (1) risk status, (2) risk exposure, (3) strengths status,

(4) protective processes, (5) cohesiveness, (6) ﬂexibility, (7) communication, and
(8) making of meaning (Patterson).

One example of disability-speciﬁc family adaptation is seen in Kosciulek,
McCubbin, and McCubbin’s Resiliency Model of Family Stress, Adjustment, and
Adaptation (Kosciulek, 2004a). The model depicts the dynamic process involving use of

family resources in adjusting to the onset in a situation of family crisis that is precipitated

41

 

by a head-injury event sustained by one of its family members (Kosciulek). The model is
important to mention in that it acknowledges that the quality of the family adaptation
outcome is represented along a dynamic continuum of maladaption to “bonadaptation”
(Kosciulek). The model implies that family resilience is a factor that aids adaptation to
disability. Although the model was written speciﬁc to families who are impacted by
closed-head injury disability, it provides a disability perspective and a foundation to be
studied and expanded upon to serve other forms of disabilities such as substance abuse.
Risk Factors

Risk factors are represented by situations or characteristics that potentially lead to

 

substance use or abuse and may compromise one’s health, safety, and well-being. The
study addressed two primary areas of risk factors: (1) familial risk factors and
(2) personal risk factors.
Familial

Family systems theory (F ST) is consistent to the general systems perspective
whereby individual family members interact within a larger social system of family
(Miller & Hester, 2003; Walsh, 2002). Families represent complex networks of
relationships and interactions that encompass all of the intrapersonal and interpersonal
possibilities among individuals and combinations of individuals within each family unit
(Walsh). Our perception, or sense, of today's modern family was inspired by the early
work of Margaret Mead and has evolved today to include a number of relationships that
extend beyond genetic or blood-related ties (Mead & Heyman, 1965). A recent paper
written by Barker and Hunt (2004) called for the need to expand our current

conceptualizations of family within the alcohol and drug literature to focus upon more

42

accurate representations of family diversity. In keeping with the trends of contemporary
life and a college student context, the deﬁnition of family used for the present study was
expanded to include adoptive, step, or cohabitating family members and very close
friends.

Family variations cover a wide range of structures, systems of communication,
family management, and child management practices (Hanson et al., 2006). Family
structures have been impacted by today’s high rate of divorce and the blending and
reconﬁguration of families through remarriage, adoption, and shared living (Mead &
Heyman, 1965). Kirby (2006) considered whether the initiation of alcohol during
adolescence was impacted by families as they moved from single-parent to stepfamily
status. Kirby found that children from divorced single-parent households moving to
stepfamily status were more likely to show increased rates of alcohol use than children
from unwed, single-parent households.

Parenting styles and perceived parental approval of drinking impact students’
behaviors as well (Boyle & Boekeloo, 2006). In one study, Boyle and Boekeloo reported
that 69% of college students said their parents would approve of them drinking upon
occasion (Boyle & Boekeloo). The authors concluded that overt disapproval and
perceived disapproval by parents may likely decrease drinking habits for some college
students (Boyle & Boekeloo). In addition, families vary widely with respect to substance
abuse patterns and patterns of coping (Karwacki & Bradley, 1996).

It was established early in this document that alcohol dependency runs in families
(Barnes & Welte, 1990; Schuckit, 1999). However, having genetic relatives with alcohol

problems does not predict alcohol use among college students (Alterman, Searles, &

43

 

 

Hall, 1989; Baer, 2002). It has been shown that college-aged sons of alcoholics are more

 

likely than college-aged sons of non-alcoholics to develop alcoholism (Alterrnan et al.).
However, patterns of drinking among college students were found to be uncorrelated to
familial background of alcohol abuse (Engs, 1990).

A recent twins study found children of alcoholics (CoAs) exposed to paternal
alcoholism during the ﬁrst 12 years of their lives held signiﬁcantly increased risks of
developing alcohol abuse or dependency compared with children of nonalcoholic fathers
(Duncan et al., 2006). The Duncan et al. study determined that CoAs who were not

exposed to paternal alcoholism during the same time period, were no more likely to

 

develop alcohol use disorders compared with controls. The Duncan et al. study
postulated that shared family and rearing environments in alcoholic homes do not predict
alcohol-use disorders because many other non-alcoholic homes share factors of child
abuse, lower socioeconomic status, low performing schools, and poor neighborhoods.

Another study performed by Jung (1995) discovered that matched-drinking levels
between child and parents demonstrated a correlation between males and the drinking
patterns of either parent but did not ﬁnd a relationship between females and either parent.
Barnes and Welte (1990) suggested that parental drinking coupled with family structure
are important factors for male and female drinking patterns.

A percentage of students display alcohol morbidity which extends beyond the
excessive use of alcohol. College students with alcohol morbidity have been found to
have comorbidity with depression or anxiety disorders (Hinz, 1990; Kushner & Sher,
1993). College students who display a positive history of familial alcoholism belong to a

special category referred to as collegiate CoAs (Havey & Dodd, 1993; Perkins &

44

 

Berkowitz, 1991 , Wright & Heppner, 1991). Our knowledge about this group is
hampered by the fact that very little research has been done on collegiate CoAs as a
group of students that have become academically successful despite the adverse effects of
familial alcohol abuse (Perkins & Berkowitz). To elaborate, much of the writing on
collegiate CoAs focuses on isolating risks or identifying particular problem behaviors and
overlooks the strengths of resiliency as a specialized group (Keeling, 2000).

A population study reported by Dawson and Grant (1998) reported that saturation
levels in the United States for adults age 18 and over are signiﬁcant to those having
reported one or more of their relatives as ever having been alcoholic. Of the 42,862
persons surveyed by the study, greater than 50% of the sample reported a positive family
history of one or more of their relatives having ever been alcoholic (Dawson & Grant).
Of that portion, 25% reported alcoholism in less than 10% of relatives, 20% in 10.0 to
24.9% of relatives, and 10% in 25% or more of their relatives. The Dawson and Grant
ﬁndings indicated that around 50% reported a history in male relatives and 20% in female
relatives. The same study demonstrated that familial transmission of alcoholism, with the
chance of exhibiting alcohol dependence alone or with comorbid depression, increased
proportionately with the reporting of ﬁrst and second degree relatives with alcoholism.
Finally, Dawson and Grant observed that the most common pattern of drinking among
relatives was the exhibition of greater alcoholism by maternal female and paternal male
relatives that was associated with greater dependence and depression compared with
maternal male and paternal female relatives.

Perkins and Berkowitz (1991) noted that a further problem for collegiate CoAs is

that few research efforts have been devoted to the transmission of alcohol problems

45

across multigenerations. Perkins and Berkowitz determined that in many ways college
students with alcoholic parents or alcoholic grandparents were more likely to be problem
drinkers than other students. The researchers concluded that students with combined
parent and grandparent alcoholism were particularly affected.

A non-college speciﬁc study by Bierut et al. (1998) considered familial
transmission of substance dependency. Bierut et al. established that dependence for
alcohol, marijuana, and cocaine and habitual smoking is transmitted through families and
each type of dependence is attributable to independent causative factors. Two other
studies on non-college speciﬁc young adults performed by Chassin, Flora, and King
(2004) and Zhou, King, and Chassin (2006) looked at relationships between family
harmony and personality to familial transmission of alcoholism. One study concluded
that families transmitting higher risks for alcohol and drug use was partially due to
increased impulsivity, lowered emotionality, and lowered agreeableness (Chassin et al.).
The other study concluded that harmonious family environments may serve as a
protective factor against substance dependence (Zhou et al.).

College students who emanate from a dysfunctional family background have been
shown to have increased substance use (Hall, 1997). A study performed by Fischer et al.
(2000) compared collegiate CoAs with adult children from dysfunctional families in
search of stress factors. The researchers discovered that dysfunction in the family of
origin was a much stronger predictor of stress among college students than parental
alcoholism (Fischer et al.). A study by Havey and Dodd (1993) considered the severity
of problem drinking among collegiate CoAs and concluded that there was no relationship

between CoA status and the probability for, or severity of, problematic drinking among

46

CoAs. Social nonconformity, or a tendency toward antisocial behavior, appeared to be
the variable that was most consistently related with problem drinking (Havey & Dodd).
Personal
With regard to personal risk factors, the National Institute on Drug Abuse (N IDA)
and the Ofﬁce of Juvenile Justice and Delinquency Prevention funded a study to review
the literature pertaining to risk and protective factors for ATOD from the period of
adolescence to early adulthood (Hawkins, Catalano, & Miller, 1992). Although its focus
was primarily on aspects of prevention, the authors provided an impressive compilation
of literature on these topics and analyzed the studies for their signiﬁcance and literary
contributions. Hawkins et al. concluded that most research efforts focused on subsets of
risks and were inconclusive in determining any of the following three things: (1) which
risk factors are related to the etiologies of substance abuse, (2) which ones are considered
the most critical, and (3) which ones can be modiﬁed to produce a more favorable
outcome. Hawkins et al. identiﬁed 17 risk factors from the literature. These risk factors
are divided into social factors and individual factors. The factors are listed as follows:
Social and Cultural Contextual Risk Factors
(1) Laws and norms favorable toward behavior
(2) Availability
(3) Extreme economic deprivation
(4) Neighborhood disorganization
Individual and Interpersonal Risk Factors
(5) Physiological factors

(6) Family alcohol and drug behavior and attitudes

47

(7) Poor and inconsistent family management practices

(8) Family conﬂict

(9) Low bonding to family

(10) Early and persistent problem behaviors

(11) Academic failure

(12) Low degree of commitment to school

(13) Peer rejection in elementary grades

(14) Association with drug-using peers

(15) Alienation and rebelliousness

(16) Attitudes favorable to drug use

(17) Early onset of drug use

The compilation of literature by Hawkins et al. (1992), clearly established that
interpersonal risk factors and reasons for variations among adult substance users have
received considerable attention in the literature. The need to focus increased research
efforts on environmental and social contexts including family design, family cohesion,
style of parenting, gender-matching, bi-directional use patterns, child and adolescent
development, self-esteem, and impulsivity as moderating and mediating factors of
substance abuse has been strongly recommended by numerous authors (Bijttebier,
Goethals, & Ansoms, 2006; Chassin & Handley, 2006; F romme, 2006; Patock-Peckham
& Morgan-Lopez, 2006; van der Vorst, Engels, Meeus, Dekovic', & Vermulst 2006a; van
der Vorst, Engels, Meeus, Dekovic', & Vermulst 2006b).
According to Hanson et al. (2006) engagement in the use of substances varies

according to one’s mindset and setting in which the activity takes place. A good example

48

of this was shared by Harford, Wechsler, and Seibring (2002) in a study that considered
select settings and the proportion of heavy drinking within each setting. Harford et al.
reported the proportion of heavy drinking by setting to include: fraternity parties (49%),
off-campus parties (46%), dormitory parties (40%), and off-campus bars (3 7%). The
ﬁndings suggested that increased interventions to reshape social drinking per
environments should be implemented (Harford et al.).

One such intervention is teaching students how to calculate their personal rate for
metabolizing and eliminating alcohol safely from their bodies. This is accomplished by
teaching students how to estimate their blood alcohol content (BAC) levels for the
number of drinks they consume over a speciﬁed period of time. To calculate BAC,
students must be taught how to account for relative differences such as: (1) gender,

(2) body weight, (3) drinking rate, and (4) tolerance (Walters & Baer, 2006). This type of
intervention can help reshape the drinking culture by informing students how to consume
alcohol within safe levels appropriate to their characteristics as individuals.

Gender is the best predictor for alcohol consumption among the college
population and females have been shown to drink in lower quantities and with less
frequency than their male counterparts (Chen et al., 2004-2005; Jackson et al., 2005;
Nolen-Hoeksema, 2004; O’Malley & Johnston, 2002; Weschler, Dowdall, Davenport, &
Rimm, 1995). This difference between females and males is based upon several factors
including women: (1) display a greater sensitivity to alcohol, (2) suffer from more
alcohol-related illnesses, (3) show greater cognitive and motor difﬁculties with ingestion,

and (4) are potentially impacted by reproductive health consequences, sexual or physical

49

assault, and risky sexual behavior (Nolen-Hoeksema). Males are more likely to engage
in the use of alcohol due to their display of: (1) personality traits of aggressiveness,

(2) low impulse control, (3) sensation-seeking behavior, and (4) positive expectancies of
alcohol use, all accompanied by (5) the social sanctions awarded for drinking (N olen-
Hoeksema).

Individuals, in and of themselves, are regarded as complex agents. In considering
an individual’s engagement in the use of substances, multiple risk factors need to be
considered which are important to acknowledging the unique and distinct characteristics
of people. These factors include cultural orientation and psychological characteristics
identiﬁed as: (1) stress and coping resources, (2) locus of control, (3) levels of
extroversion, (4) impulsivity, (5) depression, (6) anxiety, and (7) perceptions regarding
the normative use and trends of alcohol and drug use among college students (Baer,
2002)

Recent attention in preventing and moderating the alcohol use habits of college
students has focused on dose-response as a more individualized and accurate method of
assessing the risks and consequences for individuals within this population (Gruenewald,
Johnson, Light, Lipton, & Saltz, 2003). The dose-response method entails looking at the
amounts of alcohol ingested by individuals on each drinking occasion combined with the
individualized risk and exposure factors for these individuals (Gruenewald et al.).
Gruenewald et al. asserted that using the dose—response method in calculating risks for
speciﬁc problem behaviors would avoid the inaccuracies posed by skewed data resulting

from a standardized scale of binge drinking for all students. Gruenewald et al. contended

50

that alcohol-related problems that result from moderate drinking are overlooked within
the current methodology.
Protective Factors

Protective factors are characteristics or processes that guard against the use or
abuse of substances and function to preserve one’s health, safety, and well-being.
Protective factors stem from within the family as a unit, amidst an atmosphere of
increased parental monitoring (Hardie, 2002; White et al., 2006). Protective factors also
arise from within individuals. Individuals who show increased intrinsic spirituality
display increased health status and increased protective factors which guard against
substance use (Larson & Larson, 2003; Williams, Reed, Nelson, & Brose, 2002; White et
al.). Furthermore, it has been demonstrated that the absence of sensation-seeking
qualities within individuals serves as a protective factor against substance use (White et
al.).

Jessor, Costa, Krueger, and Turbin (2006) suggested the use of a psychosocial and
behavioral risk-protection model as a means to explain individual variations in heavy,
episodic drinking amongst college students. J essor et al. suggested that the higher the
risk and the less the protective factors, the higher the incidence of heavy episodic
drinking. The same authors suggested that protective factors moderate the potential for
heavy, episodic use of alcohol among college students.

It is important to differentiate between protective strategies and protective factors.
Protective strategies employed by college students to reduce drinking have been shown to
be more effective amongst females than males (Benton et al., 2004). Benton et al.

advocated that the most obvious and effective protective strategy that students can use is

51

to drink less. The authors claimed that the best predictor for males to experience alcohol-
related consequences is by the number of drinks they consume. However, it is important
to add the merit of consumptive protective strategies (ACHA, 2006), mentioned earlier in
this chapter, to the growing list of protective strategies.

In contrast, protective factors serve to buffer the risks and effects of substance
abuse. In 1992, the Hawkins et al. grant-funded research project addressed protective
factors. In the report, Hawkins et al. referred to an article written by Rutter in 1995 that
described protective factors as having “interactive processes” or the capacity to stimulate
multiple interactions or synergistic effects that reduce risk and enhance mediating effects
of substance abuse. Hawkins et al. cited a study by Brook, Brook, Gordon, Whiteman,
and Cohen in 1990 which described two protective mechanisms that reduce the risk
factors for adolescent substance users: (1) risk/protective and (2) protective/protective.
The “risk/protective” mechanism was deﬁned as the presence of protective factors which
moderate exposure to risk factors for substance abuse. The “protective/protective"
mechanism was deﬁned as one protective factor serves to strengthen or potentiate the
effect of another protective factor.

Health Values

As mentioned above, occupational therapists have linked health to human
adaptation (Miller & Walker, 1993). And, the provision of holistic services is an
important philosophy currently highlighted within both the rehabilitation counseling and
occupational therapy professions. The aim to provide services which address the needs
of the “whole” person is a key goal to serving individuals with disabilities. For this

reason, it is important to integrate holistic health approach into research that is conducted.

52

To compliment this holistic perspective, the current study was constructed to
measure the comprehensive subjective well-being of individuals. Two existing
instruments were carefully selected and combined to represent a holistic view of the total
human experience. The goal of the ﬁrst—selected instrument was intended to measure
health values by quantifying the degree of physical and mental health or well-being
displayed by individuals within the sample. The goal of the second—selected instrument
was intended to measure and quantify the degree of spiritual health or well-being
displayed by individuals within the sample.

The present study echoes the work of Schoon (2006) in gauging the
interconnectedness of individuals by their multiple domains or spheres of inﬂuence. To
apply the concept, this study considered three major spheres: ( 1) the college student,

(2) the college student’s family, and (3) the college community. The present study was
intended to portray health as a state of wellness within and among an individual’s own
spherical experience with self and the essence of his or her mind-body-spirit connection.
Holistic health is the reﬂection of balance or well-being within an individual’s physical,
mental, and spiritual dimensions (Adams, Bezner, Drabbs, Zambarano, & Steinhardt,
2000; Williams et al., 2002).

In 1977 Engel expanded the concept of dualism within medicine by introducing
the Biopsychosocial Model which shifted our views of the human dimensions of mind
and body to include interactions in a dynamic, and changing environment (Borrell-
Carrio', Suchman, & Epstein, 2004). Today, this model has evolved our thoughts of
causality into an even more holistic view of the human experience that extends beyond

linear processes involving multiple factors to a more useful and appropriate model of

53

complexity science (Borrell-Carrio' et al.). Further innovations in integrative medicine
(Weil, 2000) and quantum healing (Chopra, 1989) have allowed us to expand our
thinking to include interactions with invisible forces of healing and transforming energies
(Trieschmann, 2001). We are challenged to view the process of healing from a more
comprehensive and holistic view of human beings as collective sources of mind-body-
spirit energy (Chopra; Weil) that interact within the natural world and our encompassing
social spheres. This paradigmatic shift is observed by our recent inclusion of spiritual
(Riley et al.,1998) and cultural factors (Hasnain, Sotnki, & Ghiloni, 2003; Swartz-
Kulstad & Martin Jr., 1999) into the medical treatment and rehabilitative plans of patients
and clients who are in a state of healing and recovery from CID and for those who are
receiving palliative care (Lam, 2002). This mind-body-spirit approach to healing and
medicine has evolved into what is known as the biopsychospiritual approach.

As an added dimension, “spirituality is a signiﬁcant part of a holistic approach to
health (Sherman & Fischer, 2002, p. 27). According to Wright (2005), “. . .spirituality is
a part of health, not peripheral but core and central to it. . .Spirituality and health are
bonded to each other, inseparable companions”. . .(p. 15). Our health is a desire to be
whole and we wish this not only for ourselves but for others around us (Wright). And,
since “spirit” has been identiﬁed as the core component to holism and health, it is
appropriate that we should refer to this connection by placing spirit ﬁrst in the phrase
which interconnects all human dimensions by referring to it as the spirit-mind-body
connection.

As a construct within a college population, well-being is considered a protective

factor against drug use during times of stress (Hall, 1997; Lanier, Nicholson, & Duncan,

54

2001). Lanier et al. revealed that health behaviors of engaging in the use of drugs and
alcohol recreationally were associated to general well-being. Lanier et al. found that
students who drank three or four drinks per week scored lower on general well being
scales than students who drank only two drinks per week. Conversely, Lanier et al.
discovered that students who drank between eight and ﬁfteen drinks per week increased
in general well being. The authors were unable to explain these differences.

Although the quest to study health protective behaviors has been around for some
time, theories about health behaviors are in a period of infancy (Harris & Guten, 1979;
Noar & Zimmerman, 2005). It remains questionable as to how much is truly understood
about the theory of health (Harris & Guten; Noar & Zimmerman). Little hard-based
evidence has been produced to conﬁrm or deny current theories regarding the inner
processes which lead to a state of health and wellness. It has long been recognized that
responses to health vary among individuals in relation to their personally held values and
beliefs (Harris & Guten).

In studying health protective behaviors, Weiss, Larsen, and Baker (1996) showed
that among college students, gender, health value, and effort to change health behaviors
were the most direct and powerful predictors of health protection behaviors. Parental and
peer inﬂuences maintained a more indirect inﬂuence on students’ health values and effort
to change (Weiss et al.). However, a model referred to as the windows of vulnerability
appears to possess signiﬁcant value in explaining the inﬂuences that modify health
beliefs and health behaviors instilled in college students by their parents (Lau, Quadrel, &
Hartman, 1990). This model suggested that grown children exiting the family home to

attend college represents one of three vulnerable periods during the lifespan when

55

 

parental inﬂuences are challenged and overridden by exposure to differing social values
and behavioral models regarding health (Lau et al.).
Spiritual Well-Being

Spirituality is a latent construct which bears a longstanding and intertwining
relationship with religion (MacKinnon, 2004; Piedmont, 2004; Wright, 2005). Recently,
theorists have been working to operationally deﬁne spirituality apart from religion, to
provide it its own distinction, and to develop its own assessments (Miller & Thoresen,
2003; Thoresen & Harris, 2002; Thoresen, 1999). Alongside, there have been efforts to
demonstrate that spirituality and spiritual well-being lead to improved health and
increased resilience (MacKinnon; Thoresen & Harris). The present study stands apart
from many previous ones in that it focused upon spiritual well-being rather than an
interpretation of the differences between religion and spirituality.

Spiritual well-being is contextualized within the philosophical notion of health
and holistic health (Riley et al., 1998). Spirituality improves one’s lifestyle, promotes
social integration, increases one’s self-esteem and self-efﬁcacy, promotes coping
resources, instills positive emotions, and reinforces health beliefs (Ellison & Levin,
1998). George, Larson, Koenig, and McCullough (2000) listed three mechanisms in
which religion (spirituality) appears to be related to health: (1) health behaviors are
effected through prohibition of alcohol, drugs, and engagement in other risk behaviors,
(2) there is increased social support through promotion of close social bonds outside the
family, and (3) meaning is found through suffering.

The latter mechanism is referred to as the coherence hypothesis which states that

religion (spirituality) “beneﬁts health by providing a sense of coherence and meaning so

56

 

that people understand their role in the universe, the purpose of life, and develop the
courage to endure suffering. . .One of the central components of this hypothesis is that
people suffer mightily, yet minimize the risk of that suffering for health and well-being if
they ﬁnd meaning in that suffering” (George et al., p. 111). The effects explained by the
coherence hypothesis, sense of meaning and purpose in one’s life, contribute to enhanced
resilience (Glicken, 2006).

Through coping, spirituality (religion) serves to buffer against alcohol and drug
use (Britton, 2004; Larson & Larson, 2003). Spirituality holds the potential to:

(1) enhance pain management, (2) improve outcomes of medical procedures, (3) protect
against depression and suicide, and (4) reduce the risk of substance abuse (Larson &
Larson). Spiritual practices such as meditation have been shown to stimulate
physiological responses that protect against disease and facilitate healing (Powell,
Shahabi, & Thoresen, 2003; Seeman, Dubin, & Seeman, 2003). Gall et al. (2005) offered
a framework for spiritual coping. Gall et al.’s model integrated spirituality and coping
with health. The outcome of spiritual coping is in the making of meaning which
ultimately results in a balance of emotional, social, physical, and spiritual well-being
(Gall et al.).

Spirituality has a long-standing place of recognition within the ﬁeld of substance
abuse, particularly with regard to recovery (Britton, 2004; MacKinnon, 2004; Miller,
1998, Piedmont, 2004; Sherman & Fischer, 2002). In the 19703, the founders of
Alcoholics Anonymous adopted the view of alcoholism as a “disease of the spirit”
acknowledging that overcoming the grips of this powerful addiction requires the

assistance and intervention of one’s Higher Power (Alcoholics Anonymous World

57

 

Services Inc., 2001; Britton; Glicken, 2006; Peterson et al., 2004). And, the deﬁnition of
Higher Power is left to the individual to ascribe his or her personal meaning to
(Alcoholics Anonymous World Services Inc.).

Although spirituality is readily accepted as a signiﬁcant factor in the treatment
and recovery aspects of substance abuse and dependency, there have been few studies to
provide evidence as to how spirituality serves as a transforming agent. One study,
performed by Sherman and Fischer (2002) found that the longer the period of abstinence
from chemicals, the greater individuals increased in spirituality. Sherman and Fischer
reached the conclusion that addiction leads to isolation and maintained sobriety leads one
back to a social community thereby, enhancing one’s ability to cope and adapt to a
lifestyle of recovery.

Spirituality has been linked to decreased substance abuse and dependency (Miller,
1998). Sparse research has been devoted to the association between spirituality and
addiction and we have much to learn about this relationship (MacKinnon, 2004; Miller).
Likewise, little evidence has been gathered to demonstrate spirituality as a causative
factor to recovery (Miller). However, it has been observed that the presence of
spirituality is linked to a lowered risk for ATOD (Galen & Rogers, 2004; MacKinnon;
Miller).

For the college population speciﬁcally, Stewart (2001) concluded that spirituality
had a moderate buffering effect on the use of certain substances but did not appear to
buffer the use of cocaine, LSD, or Ecstasy. Stewart noted that the inﬂuence of
spirituality appears to taper off as students move through their college careers and gain

independence. Galen and Rogers (2004) concluded that presence of religiosity-

58

 

spirituality may not only reduce consumption of alcohol for college students but may
have an indirect and negative inﬂuence on their expectations and motivations to drink.

Spiritual well-being has also been tested as a construct within research that
focuses on quality of life and life satisfaction (Riley et al., 1998). In particular, well-
being is noted to be related to psychosocial adjustment of persons recovering from CID
and study efforts have been devoted to creating models and measures for
multidimensional well-being (Bishop, 2005; Landis, 1995; Ng, Yau, Chan, Chan, & Ho,
2005; Riley et al.; Ross, 1995).

In looking at the spiritual and psychological dimensions within a college
population, life purpose, optimism, and sense of coherence were found to contribute to a
state of wellness (Adams et al., 2000). One study performed by Hall (1997)
demonstrated that college students who did not come from a family of substance abuse
displayed higher self-perceived well-being than college students who did. In a similar
study performed on high school students, Sundararajan-Reddy (2005) found that meaning
or purpose of life contributed to resilience in overcoming stressful situations. The same
author found that sense of coherence predicted resilience better than spiritual well-being
but suggested that certain spiritual beliefs may contribute more strongly to resilience for
some individuals.

Other Factors

Campus environments are complex networks which are in a constant state of ﬂux
due to a continual incoming and outpouring of college students each academic year. This
complexity leads to an increased difﬁculty in understanding the vast array of contextual

interactions between individuals and their environments (Presley et al., 2002). Campus

59

cultures vary from one institution to another depending upon the size, geographic
location, reputation, academic programming, and other key factors. However, most
studies that have gathered statistics have taken a cross section of the population of
students from various institutions and have not focused upon inter-campus differences
(Jackson et al., 2005).

Senchak et al. (1998), considered the social context of drinking among college
students in large and small groups of mixed sex or same sex individuals, romantic
relationship, and solitary drinking. The results of this study showed that males are more
likely to drink in small, same-sex groups and females are more likely to drink in small,
mixed-sex groups. Both males and females were equally likely to drink in large, mixed
groups (Senchak, et al.).

Primary Living Situation

Primary living situation has been shown to be a predictor for alcohol and drug
consumption amongst college students (Gfroerer et al., 1997; Harford & Muthen, 2001;
Page & O’Hearty, 2006; Presley et al., 2002). It has been suggested that more research
be focused on examining the relationships between college students and institutions
(Presley et al.). With regard to living situation, students residing in fraternity or sorority
housing present higher indexes of drinking compared to dormitory living (Harford &
Muthen; Presley et al.). However, students with a prior history of problem behaviors
rated more highly on drinking measures aside from high-risk exposures posed by
residence (Harford & Muthen). College students living in independent housing, whether
dormitory or apartments, demonstrated higher consumption levels of alcohol compared to

those residing at home (Harford & Muthen).

60

 

Another study utilizing the NHSDA databank, compared college students with
same age non-college students (including high school dropouts) for usage levels of
marijuana, cocaine, cigarettes, and alcohol (Gfroerer et al., 1997). The Gfroerer et al.
study considered the inﬂuences of living at home with parents or away from home for
both groups. A large amount of variation was noted, however marijuana and cocaine use
overall was three times greater for this group (1 7 to 22 years of age) than for other age
groups and cigarette and alcohol use was highest among college students living away
from home. Gfroerer et al. suggested that higher marijuana use by college students is
explained by increased availability of this substance.

A study by Page and O’Hegarty (2006) speciﬁcally compared type of student
residences as a factor in alcohol consumption. The residence categories for Page and
O’Hegarty’s study were fraternities, sororities, residence halls, or apartment complexes.
The authors drew clear indications that risks for heavy drinking and related problems
were highest among students living in fraternity or sorority housing. The authors
reported that members in fraternities said they spent an average of 10.90 hours per week,
consuming an average of 12.03 drinks while engaged in the act of “partying”.

A signiﬁcant risk factor for college students is the student’s peer cohort or social
network (Dorsey et al., 1999). For young adults, peer and social inﬂuences are very real
and close friendships are critically important (Schlossberg, 1981). The single best
predictor of alcohol use for college students is found with individuals who engage in
cohesive social networks through memberships in fraternities or sororities and for those

who reside in Greek housing units (Dorsey et al.; Presley et al., 2002). Powerfully

61

 

 

inﬂuential social networks are characterized by adherence to the values, norms, and
expectations that have been held by an organization over time (Dorsey et al.).

Peer inﬂuence has also been shown to have mediating effects of alcohol use and
sensation-seeking behaviors by college students (Yanovitzky, 2006). Sensation-seeking
individuals not only derive stimulation from personal use and abuse of alcohol,
individuals with this personality trait tend to motivate high sensation-seeking users to
associate more frequently with peers who use alcohol and to promote more exaggerated
misperceptions of drinking (Yanovitzky).

To add to this effect, risky and wildly spontaneous behaviors resulting from
unrestrained consumption of ATOD are oftentimes reinforced and perpetuated from one
generation of students to the next (Dorsey, et al., 1999). As mentioned previously, this
reinforces the need to alter the misperceptions college students have regarding the
exaggeration of drinking behaviors on campuses as an avenue to modifying the overall
behaviors of student populations (Presley et al., 2002; Yanovitzky, 2006). In addition,
advancements in understanding the inﬂuences of drinking motivations and outcome
expectancies are needed (Baer, 2006; Presley et al.).

For now, these risks and dangers are deemed potentially serious and more
research is needed to investigate how the interaction of alcohol and sensation-seeking
inﬂuences may inﬂate the occurrences of sexual assault, unprotected sex, property
damage, or other spontaneous acts of violence (Dorsey, et al., 1999; Perkins, 2002;
Yankovitzky, 2006). Research is further warranted to determine the level of tolerance
surrounding communities have for increased drinking coupled with alcohol-related

consequences (Presley et al, 2002).

62

Cumulative Grade Point Average

Alcohol-related behaviors such as missing a class, performing poorly on a test,
and turning in late work are possible scenarios caused by high risk drinking. However,
studies conﬂict with regard to the effect of these behaviors on cumulative grade point
averages of college students (Hinz, 1990). In a study by Paschall and Freisthler (2003),
the authors concluded that heavy alcohol use and related problems did not have a
signiﬁcant impact on college academic performance (Paschall & Freisthler). In another
study (Hinz), collegiate CoAs did not differ from their non-CoA collegiate counterparts

in academic skills.

 

These mixed ﬁndings may be indicative of psychological resilience within the
collegiate CoA sample or it may be more accurately interpreted as psychological distance
created by overachieving measures that CoAs use to avoid failure (Hinz, 1990).
Nevertheless, individuals facing high risks must choose their pathways to resilience
among choices which seem to be limited or constrained (Schoon, 2006).

Individuals with a family history of alcoholism are noted to have more academic
problems at every educational level (Jackson et al., 2005). Academic problems and
interfering alcohol issues compromise these individuals’ goals for pursuing higher
education making them less likely to go on to college (Jackson et al.). College students
from families of substance abuse are aimed at becoming high academic achievers by the
theory that excelling in studies is a method of increasing one’s self-esteem within a
challenging environment (Hinz, 1990).

From a developmental perspective, young adults are focused upon fulﬁlling the

roles and responsibilities of educational and occupational attainment (Wood, Sher, &

63

McGowan, 2000). A study by Wood et al. demonstrated a small signiﬁcance between the
presence of alcohol abuse and educational attainment. Wood et al. reported that past
research has determined that academic aptitude and intelligence factors are not predictors
for educational attainment.

The level of meaning and competence college students assigned to pursuance of
daily life goals has been shown to be related to risky drinking behavior (Palfai & Weafer,
2006; Karwacki & Bradley, 1996). Palfai and Weafer showed that students with
decreased meaning in life goals engaged in hinge and frequent heavy drinking more often
and reported higher alcohol-related consequences. F urtherrnore, Palfai and Weafer found
that students who identiﬁed more achievement-related goals consistent to academic
achievement were less likely to engage in problematic substance use behavior. Finally,
Palfai and Weafer asserted that goal constructs may prove useful in understanding
individuals’ behaviors of self-regulation in order to motivate developmental changes
delivered through ATOD intervention strategies.

Summary

In summary, the literature clearly addresses numerous topics relevant to this
study. However, no research studies were located that address inquiries regarding the
inﬂuences that protective factors of health values and spiritual well-being may have on
the process of resilience and the ultimate psychosocial adaptation of college students and
their families who are affected by substance abuse as a CID. The results of this study
suggest important conclusions that are critical to our understanding of familial substance

abuse as a CID.

64

 

 

The information derived from this review of the literature spans the collective
research efforts put forth by several disciplines. This expansive search of the literature
was necessary in gathering the comprehensive evidence needed to support the quest for
answers regarding key factors that promote successful psychosocial adaptation and
holistic well-being of college students as they overcome the odds and gain access to

academic and vocational opportunities afforded them by a college education.

65

 

CHAPTER III

Method

The present study involved collection of data via a web-based survey using a
sample of college undergraduate students. The study examined relationships between the
protective factors of health values, spiritual well-being, and resilience within the sample.
In addition to these protective factors, the study considered risk factors for personal
substance use and familial substance abuse. Relationships were examined between the
degrees of risk posed by personal substance use within a college environment and the
presence of protective factors of health values and spiritual well-being to the process of
resilience. Relationships were examined between the degrees of risk posed to individuals
by adverse circumstances involving past or present familial substance abuse and the
presence of protective factors of health values and spiritual well-being to the process of
resilience.

Differences in degrees of health values, spiritual well-being, and resilience were
also examined among participants reporting negative indicators for a familial background
of substance abuse and participants reporting positive indicators for a familial
background of substance abuse. Among those reporting positive indicators for familial
substance abuse, differences were examined among participants reporting a familial
background of primarily genetic (blood) relatives and participants reporting primarily
non-genetic (non-blood) relatives.

Participants
A purposive sample of undergraduate students was selected for this study. The

sample was drawn from a group of students enrolled in an introductory, substance abuse

66

 

course at a major Midwestern university during Spring semester 2007. All students
enrolled within the eleven section offerings for the course were invited to participate in
the study. A total sample size of 266 was possible based upon student enrollment for the
course following the Registrar’s period for drops and adds. The general capacity for the
course was 25 students per section or a total course enrollment of 275 students.

Based upon power calculations, complexity of the constructs being investigated,
and quality of the instruments utilized, it was estimated that approximately 275
participants were needed to demonstrate differences in comparing groups. Tabachnick
and F idell (2001) stipulated that “A higher cases-to—IV ratio is needed when the DV is
skewed, a small effect size is anticipated, or substantial measurement error is expected
from less than reliable variables” (p. 117).

The majority of students within the course ranged from 18 to 24 years of age and
held undergraduate status within the university at the time of invitation. Students were
advised that they must meet minimum eligibility requirements of 18 years of age to
participate in the study. Later in the study, as data were gathered, it was determined that
all participants within the sample met the minimum age requirements.

Regarding the sample, it was conceivable that some students may be non—
traditional and some may be married with grown children. It was preconceived that most
students would range from ﬁrst-year to ﬁfth-year undergraduate status and some may
hold graduate status within the university. It was projected that the prospective student
sample would likely represent a variety of cultures and may include a few international

students.

67

The students were expected to stem from a variety of academic programs within
the university, covering a broad range of ﬁelds of study. This was typical for past student
enrollment in the introductory, substance abuse course. Data, in relation to academic
programming, were not gathered. It was also preconceived that a majority of students
would be taking the course as an academic elective or as a part of a cognate rather than as
a program requirement. Only a few programs in the past have required students to enroll
in CEP 261. These programs have included nursing, family and child ecology, and health
studies programs.

Students enrolled in CEP 261 each semester are exposed to substance abuse
content which meets typical standards for a 200-level introductory course. Eleven
sections were offered each fall and spring semester. Students enrolled in all sections
utilized the same textbook and were exposed to uniform course objectives and major
course topics.

At the time of this study, nine doctoral-level teaching assistants were assigned to
teach the eleven sections of the course. Teaching assistants were responsible to
independently create course syllabi and course schedules for their respective section/s.
Each teaching assistant was assigned to teach either one or two sections of the course per
semester. The teaching assistants performed their teaching roles under the supervision of
a faculty professor who was deemed the primary investigator to this research project.

Over the past three years, the researcher for this study performed the functions
and duties of a teaching assistant duties for eleven, independent, sections offerings for
this course. This ﬁrst-hand experience provided a unique opportunity to observe that

students who enrolled in the course presented with positive indicators for past or present

68

exposures to familial substance abuse. In particular, two frequently recurring themes
were noted: (1) students expressed a desire to gain greater understanding into the effects
of substance abuse and, (2) students expressed a desire to learn acceptable methods to
assist their family members and friends who were affected by the negative effects of
substance abuse.

Given these observations, signiﬁcant curiosity was sparked to launch an
investigative pursuit into the relationship that past or present issues of familial substance
abuse may have to the degree of health values, spiritual well-being, and process of
resilience which have been empirically shown to serve as protective and adaptive factors
against personal substance use. It was espoused that the degree and impact of
psychological resilience, in conjunction with increased degrees of health values and
spiritual well-being, would serve as key factors to improved psychosocial adaptation of
these young adults in overcoming their social and environmental risk factors.

Protection of Human Participants

The design of the web-based survey gathered students’ reporting of sensitive data
regarding their personal substance use habits and indicators pertaining to the absence or
presence of familial substance abuse patterns. Such information was regarded as highly
sensitive. To reduce the potential risks for students in reporting sensitive data, the study
was designed to maintain anonymity of all participants. Students interested in
completing the survey were directed to connect to an Internet website to enter their
individual responses. Internet settings for the survey did not track the Internet Provider
(IP) addresses of participants. This procedure supported the anonymity of the study by

ensuring the inability of identifying participants and no attempts were made to do so.

69

Once student responses were gathered, all data were encoded within the statistical
software to further assure the anonymity of all participants.

All communications with prospective participants were initiated for one of two
purposes: (1) recruitment of participants and (2) reply to student inquiries. Recruitment
was carried out during face-to-face classroom visits and through electronic messages sent
via the University’s course management 2006 version 6.3 software, “A New Global
Environment for Learning” (ANGEL) system. Invitational and reminder messages were
routed to collective groups of students, rather than individuals, utilizing the course
professor’s ANGEL mail account.

Electronic storage of data included the use of a free-standing personal computer
(PC), a personal laptop computer, and a ﬂash drive. The main workstation for data
analysis was maintained at the researcher’s home. During data analysis, data ﬁles were
transported via laptop or ﬂash drive to campus locations. Although risks were minimal,
precautions were taken to reduce computer theft by maintaining computers and ﬁles in
locked vehicles or within locked cabinets on locked premises.

The computers used for the study were password protected and were not
connected to a shared network of computers. Neither computer included multi-user
functions. Internet access for these computers was maintained through a Data Systems
Line (DSL), high-speed Internet connection. Computers used for the study, were
protected by recent ﬁrewall and spyware programs to reduce the risk of Internet intrusion.
The web-based survey was established using Zoomerang zPro, professional web-based

survey software, marketed and sold by Market Tools, Inc. (2000).

70

Use of the Internet for data collection posed minimal risks to participants.
Although remote, these risks included the possibility of “lurking” along the Internet by
unauthorized persons who might detect survey responses at the exact moment of
transmission along the World Wide Web. The probability for this occurrence was
determined to be minimal and the magnitude of harm to participants was also determined
to be minimal. The most serious threats posed by Internet lurking pertained to the
potential harm students might incur as they reported illicit or illegal use of substances.
This would include the reporting of underage drinking for students not having attained
this state’s legal drinking age of 21 and for those who may have engaged in the improper
or non-prescribed use of legal or illegal substances. As mentioned above, there were no
attempts to collect the IP addresses for participants and no effort was made to identify
students based on the responses received.

Research Design

The study was designed using quantitative research methods and involved self-
administration of a web-based survey using a sample of college students. It is should be
clearly stated that the design did not incorporate a random sampling of college students.
Therefore, the results from this study should not be broadly generalized to the larger
population of college students. Sample selection was based upon classroom experiences
in teaching the introductory substance abuse course and the hunch that students within
the selected sample would display the unique characteristics the survey was designed to
measure. It was strongly perceived that the sample of students selected for study would

exhibit positive indicators for backgrounds of familial substance abuse. The overall

71

study was exploratory in design and was intended to provide descriptive data regarding
this sample of interest.
Instruments
A series of self-reporting measures were selected to design and build the content

for the web-based survey. Careful consideration was given to the sequencing of survey
items to provide clear directions in soliciting the most accurate information from
participants as they took the survey. The survey included three well-established attitude
scales, two screening measures for alcohol and drug use, and three constructed areas of
self-reported information including: demographic, personal drug use, and familial
substance abuse. The combined questionnaire consisted of a total of 64 items. The
survey items were grouped according to the following sections and within the order of
items as they appear below:

0 Brief Demographic Questionnaire - 6 items

0 modiﬁed HAV — Health-As-A-Value — 4 items

0 SIWB — Spirituality Index of Well-Being — 12 items

0 RS-15 — Resilience Scale — 15 items

0 Personal Drug Use Information — 2 items

0 DAST-IO — Drug Abuse Screening Test — 10 items

0 AUDIT — Alcohol Use Disorders Identiﬁcation Test — 10 items

0 Family Substance Abuse Information — 5 items

The Brief Demographic Questionnaire consists of six demographic questions that

served to describe the characteristics of individuals within the sample (see Appendix H).

Speciﬁc items included participants’: (1) sex, (2) age, (3) academic rank, (4) race,

72

(5) cumulative grade point average, and (6) primary living situation. Categories for
academic rank and race were derived from the ACHA-NCHA surveys (ACHA, 2006).
Categories for the cumulative grade point average were derived from Engs’ (1975)
Student Alcohol Questionnaire (SAQ). Categories for primary living situation were
derived from Weitzman et al. (2003).

The modified HA V— Health-As-A-Value consists of four items or statements that
measure the degree to which individuals value their health (see Appendix H). Using a
4-point Likert-scale ranging from 1 = disagree through 4 = strongly agree, the statements
provided students a rating of the degree to which they placed value on his or her physical
and mental well-being (Ritt-Olson et al., 2004).

The original Health-As-a-Value (HAV) scale was developed in 1986 by Lau,
Hartman, and Ware Jr. (Lau, et al.; Ritt-Olson et al., 2004). The modiﬁed scale is similar
to the original scale in that each scale is comprised of four statements which are rated on
a 4-item Likert scale (Lau et al.; Ritt-Olson et al.). The original scale was designed to
measure health as a value within diverse populations (Lau et al.). The original scale was
developed using a sample of 326 undergraduate students that were fulﬁlling a university
course requirement, 31% of whom were non-White and 60% female (Lau et al.). The
group showed a coefﬁcient alpha reliability of .67 and a test-retest reliability of .78 (Lau
et al.). The original scale also showed a test-retest reliability of .86 and a test correlation
of .62 in consistency of measure between tests (Lau et al.).

In 2004, Ritt-Olson et al. modiﬁed the original HAV statements using the same
4-point Likert scale. The authors reworded the statements to be more consistent to a high

school population (Ritt-Olson et al.). The modiﬁed version of their scale was used to

73

investigate health-as-a-value and spirituality as protective factors in guarding adolescents,
varying in levels of risk, from engaging in the use of alcohol and marijuana (Ritt-Olson et
al.). The new wording of the scale resulted in a Cronbach’s alpha of .70 for both the high
risk and low risk samples of adolescents within their study (Ritt-Olson et al.).

The SIWB -— Spirituality Index of Well-Being consists of twelve statements
designed to measure the effect of spirituality on subjective well-being (see Appendix H).
The scale is divided into two subscales: (1) six items measure self-efﬁcacy, and (2) six
items measure life scheme. The statements are rated on a 5—point Likert-scale using the
following ratings: (1) strongly disagree, (2) disagree, (3) neither agree nor disagree,

(4) agree, and (5) strongly agree. The SIWB was recently developed within the
Department of Family Medicine at University of North Carolina Chapel Hill by
Daaleman and Frey (2004). It was designed to contribute health-related quality-of-life
research to the body of literature by measuring the effect of spirituality on individuals’
subjective well-being (Daaleman & Frey).

The SIWB has very sound psychometric properties. It has a comparative ﬁt index
of .98 and a Tucker-Lewis index of .97 (Daaleman & Frey, 2004). Reliability for the
self-efﬁcacy subscale is (1 =86, life scheme a = .89, and the total scale is a = .91
(Daaleman & Frey). The test-retest reliability for the subscales within a two-week period
was r =.77 for self-efﬁcacy, r = .86 for life scheme, and r = .79 for total test-retest
reliability (Daalemann & Frey). The SIWB is signiﬁcantly correlated to other well-being
and spiritual instruments with a convergent validity measure of -.42 to the lung
Depression, .62 to the General Well-Being Scale, and .64 to the Spiritual Well-Being

Scale (Daalemann & Frey).

74

The RS—I5 — Resilience Scale is a shortened version of the original Resilience
Scale (RS) and consists of ﬁfteen items measured on a 7-point Likert Scale
(see Appendix H). Ratings for the scale range from (1) strongly disagree through
(7) strongly agree.

The original RS was developed and pre-tested by Wagnild and Young in 1988
(Wagnild & Young, 1993). The scale is a 25-item instrument designed to measure
resilience as a personal quality that enhances individual adaptation (Wagnild & Young).
The scale is based on ﬁve resilience themes which are: (1) equanimity, (2) perseverance,
(3) self-reliance, (4) meaningfulness, and (5) existential aloneness (Neill & Dias, 2001;
Wagnild & Young). In a study performed by Ahern, Kiehl, Sole, and Byers (2006), six
instruments measuring resilience were reviewed. The authors concluded that of the
instruments reviewed, the RS was the most credible and appropriate resilience scale for
testing adolescent populations (Ahem et al.).

The RS has a reliability coefﬁcient alpha of .91 and item-to-item correlations
ranging form .37 to .75 with the majority between .50 and .70 (Ahem et al., 2006;
Wagnild & Young, 1993). Using principal components analysis the RS factor scores
were correlated at .99 (Wagnild & Young). The RS has concurrent validity with other
well-established and valid measures of constructs linked to resilience and resilience
outcomes and are cited here as: depression (r = .3 7), life satisfaction (r = .30), morale -
(r = .28), and health (r = .26) (Ahem et al.; Wagnild & Young). The RS has been used in
numerous studies of various ethnicities and cultures and with many diagnostic groups and

special populations bearing equally-high reliability coefﬁcients (Ahem et al.).

75

Neill and Dias (2001) developed the RS-15, a shortened version of the original RS
instrument. In its development, the authors were aided by the use of the principal axis
factoring method. The purpose of the RS-15 was to provide researchers with a brief, yet
reliable instrument that was relatively easy to administer making it more attractive in
serving youth populations (Neill & Dias). The shortened scale has been shown to be
highly reliable at a = .91 (Neill & Dias).

The Personal Drug Use Information section consists of two questions intended to
identify types of drugs used by participants in the study (see Appendix H). Participants
were asked to identify drugs they have used over their lifespan and drugs they have used
during the past twelve months. These questions include alcohol as one type of drug.

The questions identify drugs according to types of drugs that have similar
properties. The lay-person names of the drugs appear as examples for each category (see
Appendix H). Because the present study was not considered diagnostic, the primary
purpose of these questions was to monitor types of substances or drugs that participants
used other than alcohol. Categories to measure drug use were expanded upon using
information compiled from the NCHA survey (ACHA, 2006) and studies performed by
Rimsza and Moses (2005) and O’Malley and Johnston (2002).

Findings for the study were based upon correlations of reported personal
substance use and reported past or present familial substance abuse. Therefore, no
assumptions were made that participants were abusing ATOD. The primary interest was
eliciting participants’ reported past or present use of ATOD including quantity,

frequency, and type.

76

The DAST-10 — Drug Abuse Screening Test consists of a ten-item, modiﬁed
version of the DAST (see Appendix H). The DAST was developed in 1982 by Harvey A.
Skinner for the Addiction Research Foundation (ARF) of Canada (Skinner, 1982). The
initial internal consistency-reliability factor for the original version of the DAST was
shown to be .92 (Skinner).

A recent study by McCabe et al. (2006) cited previous studies demonstrating that
the original, 20-item version of the DAST and the modiﬁed, lO-item version of the
DAST have similar psychometric properties. The DAST-20 showed a Cronbach’s alpha
of greater than .85 and test-retest reliability of greater than .70, and the DAST-10 showed
a Cronbach’s alpha of .86 and test-retest intraclass correlation coefﬁcient of .71 (McCabe
et al.).

The McCabe et al., (2006) study incorporated the DAST-10 into a web-based
survey using a large sample of 4,580 undergraduate students with a mean age of 19.9
years. Their ﬁndings demonstrated the DAST-10 is a valid and reliable measure among
college students, ﬁnding that one in every ten students reported three or more DAST
items in the previous 12 months (McCabe et al.). These ﬁndings suggested that academic
institutions may need to employ screening methods to detect use of substances other than
alcohol among students. The McCabe et al. results indicated that males are signiﬁcantly
more likely than females to report four of the ﬁve most frequently reported DAST items.
McCabe et al. stated these ﬁndings were signiﬁcant because previous studies had failed
to show gender correlation using the DAST.

According to NIDA (1982), the degree of problems measured by the DAST-10

are to be scored according to the following scale: none reported (0 points), low level (1 -2

77

points), moderate level (3-5 points), substantial level (6-8 points), and severe level (9-10
points). Results for the McCabe et al. (2006) study showed a .68 alpha coefﬁcient and
authors determined the most sensitive and speciﬁc cut-off score for screening substance
abuse problems was set at three (see original scale listed below). This information is not
particularly meaningful to the current study because determining substance abuse
problems within students’ personal levels of drug use was not a goal of the study. Rather,
the study herein focused on placing college students in risk categories for personal
substance use. The assignments of categories were based in part on their DAST-10
scores.

For the purposes of the current study, the original scoring levels for the DAST-10
were converted into the following risk categories (S. T. Walters, personal

communication, February 14, 2007).

 

 

DAST-10 Original Score Ri_sk Level Categories
0-2 = low level = Low

3-5 = moderate level = Medium

6-8 = substantial level = High

9-10 = severe level = Very High

The A UDIT — Alcohol Use Disorders Identiﬁcation Test used in this study
includes a full scale, three factor model of the instrument consisting of ten items (see
Appendix H). This full scale instrtunent was developed in 1982 by an international group
of investigators commissioned to a task force by the World Health Organization (WHO)

with the established goal of developing a psychometrically-sound measure that could

78

identify alcohol use disorders across cultures (Reinert & Allen, 2002; World Health
Organization, 2001).

The AUDIT is divided into three conceptual domains or factors including:
alcohol intake, alcohol dependence, and adverse consequences (Reinert & Allen, 2002;
Kokotailo et al., 2004). A number of abbreviated versions exist for this test and were
created using one or two of the three factors measured (Reinert & Allen). Although the
full version of the AUDIT is structured upon a three-factor model, some tests have shown
that a two-factor model is more efﬁcient and has a better ﬁt (Reinert & Allen). Reinert
and Allen identiﬁed two other studies that suggested the AUDIT is saturated by a single
factor.

Over its years of use, the AUDIT has been relied upon extensively within
multicultural populations as a research and clinical tool for screening individuals for
hazardous or harmful drinking patterns (Reinert & Allen, 2002; World Health
Organization, 2001). It should be noted that the AUDIT is a screening tool for alcohol
use disorders and it is not intended for use with individuals who have become alcohol
dependent (Reinert & Allen; World Health Organization).

Across diverse populations and settings, the AUDIT has demonstrated an internal
consistency with a Cronbach’s alpha of greater than .80 (Reinert & Allen, 2002).
Diverse studies have indicated a test-retest reliability of the full scale AUDIT at .92 over
a two week interval, .81 over six weeks, and a Pearson correlation of .64 for a study
performed on a patient population selected for an alcohol treatment intervention over an

identical two week period (Reinert & Allen).

79

A recent study performed by Kokotailo et al. (2004) tested the psychometric
properties of the AUDIT as a screening test for high-risk drinking within the college
student population. The authors designed their study to include a 28-day cycle to detect
high risk, current problem, or lifetime problem drinkers. Kokotailo et al. modiﬁed test
item number three to include the commonly accepted deﬁnition (Chen et al., 2004-2005;
Kokotailo et al.; Weschler & Austin, 1998; Wechsler & Nelson, 2001; Wechsler et al.,
1995) of binge or high risk drinking as “five or more drinks” for males and “ our or more
drinks” for females.

For the purposes of this study, slight alterations were made to enhance the
reporting of days of alcohol use and to incorporate the currently accepted deﬁnition of
high risk drinking by gender (see Appendix H). The deﬁnition of “a drink” was
expanded to a 12-ounce can or bottle of beer, a 4-ounce glass of wine, a 12-ounce bottle
or can of wine cooler, a shot of liquor (1 1/2 ounce of 80-proof spirits), either straight or in
a single-shot, mixed drink (Weitzman et al., 2003; Walters & Baer, 2006).

The minimum score for the AUDIT is zero and the maximum score is 40. A
score of eight or more indicates a strong likelihood of hazardous or harmful drinking. A
score of 13 or more for females and 15 or more for males indicates a likelihood of
alcohol dependence. Items 7 through 14 are scored 0, 1, 2, 3, or 4 respectively and items
15 and 16 are scored 0, 2, or 4 respectively.

In 2002, Reinert and Allen conducted a review of the research involving use of
the AUDIT. Citing three studies performed on college students, Reinert and Allen
indicated that the AUDIT failed to provide validity index ratings for the 8-point cut-off

value in identifying individuals with a potential alcohol problems. However, the same

80

authors claimed that with a lowered cut-off value ofsix points, the AUDIT increased to a
sensitivity of .80 and speciﬁcity of .78.

Within the current study, the goal of the AUDIT to identify individuals with
alcohol use disorders was not applicable to the sample. Instead, the scale was used to
place individuals in categories of risk for personal substance use. To elucidate, the
AUDIT was not used to evaluate harmful or hazaradous drinking within the selected
sample. Therefore, the cut-off value stated here is not pertinent to the current study for
two reasons: (1) the study was anonymous and high scorers were not identiﬁed, and
(2) raw scores for the AUDIT were assigned and combined ’with DAST-10 scores to
' create estimated categories of risk for personal substance use.

As a precautionary measure, students who participated in the study were advised
in a letter of informed consent to contact the University’s counseling center and to speak
with a trained counselor in the event they should have any personal concerns about the
research study and their personal drinking habits. The precautionary statement was
included in the letter of informed consent for two reasons: (1) to uphold policies for
protection of human subjects, and (2) to route students to appropriate services to address
any concerns or discomfort they experienced in answering the questions posed by this
instrument or other instruments within the survey.

The following screening categories for calculation of personal risk for alcohol are
borrowed from Walters and Baer (2006).

Index for Personal Drinkng Severity

Low (0-7)*

Medium (8-15)

81

High (16-25)

Very High (26 +)

*A score of 6 or more indicates elevated drinking.

The Family Substance Abuse Information section consists of ﬁve questions
regarding participants’ background of familial substance abuse (see Appendix H).
Within the survey, participants were provided a working deﬁnition of substance abuse as
borrowed from the DSM-IV-TR (see Appendix H). Based on their understanding of this
deﬁnition, participants were asked to report positive indicators for family members with
past or present substance abuse problems. The study was not designed to inquire about
length of substances abused, current versus past abuse trends, or severity of substances
abused. Participants were asked to provide numbers of family members involved based
upon speciﬁc relationship categories and the four criteria provided by the DSM-IV-TR
deﬁnition. Once participants were asked to identify whether their family members had a
past or present problem with substance abuse, those who responded “YES” continued to
answer the ﬁve remaining questions of the survey. Those who responded “NO”
concluded the survey through a skip logic sequence which led to completion and an
automatic exit out of the study. Features were built into the electronic survey to facilitate
this transition of questioning without the awareness of participants. The features of the
survey prevented students from going back and forth between pages. Questions for the
survey were carefully formatted on web pages to include instructions and related groups
of questions so they could be answered prior to advancing each screen.

The remaining ﬁve questions were applied to participants answering “YES” to

familial substance abuse. The questions were targeted to identify the numbers of genetic

82

relatives and non-genetic relatives with a past or present substance abuse problem and to
the types of drugs abused by each category of relatives. Non-genetic relatives included:
adoptive, step, or cohabitating family members, and “very” close friends.

The following screening categories for calculating familial risk for alcohol abuse
were borrowed from Walters and Baer (2006) and Miller et al. (1995).

Index for Familial Risk*

 

Low (0-1)

Medium (2-3)

High (4-6)

Very High (7 +)

*Scores are based upon participants’ identiﬁcation of affected family members.

The Familial Substance Abuse questions were devised by borrowing from the
designs of the Family Tree Questionnaire (Mann, Sobell, Sobell, & Pavan, 1985) and
Project MATCH (Miller et al., 1995). Familial risk identiﬁers were borrowed from
Walters and Baer (2006) and the “Electronic Check-Up to Go” (2006) or e-CHUG.
e-CHUG is an online intervention and feedback tool developed by a group of experts at
San Diego University to monitor alcohol use by college students for over 300 universities
and college campuses in the United States, Canada, and Australia (Electronic Check-Up
to Go). At its conception, familial risk categories for the e-CHUG were agreed upon by
the panel of experts and represent assigned values along a continuum of risk. The greater
the number of genetic relatives with substance use disorders, the greater the risk for the
individual (W. R. Miller, personal communication, November 5, 2006). The genetic risk

scale developed by Miller et al. (1995) appears as follows:

83

Relationship Points Value Added for Each Positive Indicator

Father 2 points
Mother 2 points
Each Brother 2 points
Each Sister 2 points
Each Grandparent 1 point
Each Uncle or Aunt 1 point
Each First Cousin 1 point

Generally, this scale has been used in the past to measure “genetic” relatives only.
However, this scale was expanded upon to include groups of “non-genetic” (non-blood)
family members. Because genetic indicators were shown to be low predictors using a
college population, the deﬁnition of family was expanded to include “adoptive, step, or

cohabitating” family and “very close friends”. The non-genetic risk scale is as follows:

 

 

Relationshim Points Value Added for Each Positive Indicator
Spouse or Partner 2 points

Very Close Friends 2 points

Mother (Adoptive, Step, or Cohabitating) 2 point

Father (Adoptive, Step, or Cohabitating) 2 point

Brothers (Adoptive, Step, or Cohabitating) 1 point

Sisters (Adoptive, Step, or Cohabitating) 1 point

Grandparents (Adoptive, Step, or Cohabitating) 1 point

Aunts or Uncles (Adoptive, Step, or Cohabitating) I point

84

Procedure

The 64-item survey was ﬁrst drafted as a paper-pencil survey and required an
estimated 15 minutes to complete. Next, the 64-item survey was carefully uploaded into
an electronic format using zPro Zoomerang (Market Tools, 2000) software establishing
an electronic test version of the survey appropriate for additional ﬁeld-testing. Once
approval for the study was granted through the University’s institutional review board, a
panel of students from the doctoral cohort was arranged to ﬁeld-test the instrument. For
the majority of ﬁeld-testers, the web-based version required approximately 20 minutes to
complete. In addition to members of the doctoral cohort, the instrument was reviewed by
a small panel of faculty and staff members from the university. All feedback was
incorporated to improve the format and sequencing of questions, and to clarify
instructions within the ﬁnal product.

Appropriate steps were taken to announce the study to CEP 261 teaching
assistants and to implement the study by initiating student recruitment activities. An
electronic message was sent to the nine teaching assistants designated to teach the eleven
section offerings of the introductory substance abuse course to notify them that the study
was approved. The message included information requesting permission to visit each
classroom to discuss the purpose of the research project with students and to invite them
to participate in the study. Classroom appointments were handled individually with
teaching assistants and dates were established for prospective visits. All teaching
assistants agreed to grant permission for access to their classrooms and to students.

Once the dates for the classroom visits were established, the survey was opened

for web-based data collection. Each of the eleven classrooms was visited in an effort to

85

provide a brief discussion of the research and to answer any questions the students may
have. Initial classroom visits occurred during the seventh week of the semester. During
each visit, a printed invitation bearing the website address for the study was distributed
(see Appendix A) and provided a verbal outline to students regarding participation
instructions. Students were encouraged to access the survey website at a time that was
convenient within their busy schedules and outside of their normal class times. It was
explained that access to the website survey could be accomplished from any computer
equipped with Internet access. A computer with Internet access could include the
student’s personal computer or any university computer permitting student use. Students
were advised to seek privacy while taking the survey and forewarned that the survey
contained questions that were “sensitive” in nature. Students were also advised that their
participation in the study was completely voluntary and they could choose not to
participate in the study or they could elect to exit the survey at any time once it was
initiated.

Immediately following each classroom visit, an electronic invitation message via
the course management mail system (see Appendix B) to collective groups of students
enrolled within course sections. The letter of informed consent (see Appendix C) was
attached to the transmittal of all initial, electronic messages.

The letter of informed consent assured prospective participants that participation
in the survey was strictly voluntary (see Appendix C). The letter of consent assured
prospective participants in writing that, should they decide to participate in the web-based
survey, they would be allowed to exit the survey at any time with no impact to their ﬁnal

course grade. Furthermore, the written invitation contained language that clearly

86

instructed students to read the letter of informed consent prior to taking the web-based
survey. The written invitation explained to students that participation in the survey
would conﬁrm their individual consent and voluntary agreement to participate.

The survey remained open for a total period of six weeks. Spring break week
occurred two weeks following the survey launch. Responses came in rapidly during the
ﬁrst week of data collection which was the week prior to mid-semester exam week.
Responses continued to ﬂow during mid-semester exam week and slowed during the
middle portion of Spring break week.

During Spring break week, a follow-up message (see Appendix D) was
transmitted via the course management mail system notifying students that a brief visit
would be made following the week after Spring break to award coupons to early
respondents. The message indicated to students that those having already participated in
the study would receive enhanced coupon awards (see Appendix D). This announcement
generated a spike of survey responses. Over a two-week period, all eleven classrooms
were visited to distribute the initial round of coupons. The visits occurred during the
tenth and eleventh weeks of the semester and the fourth and ﬁfth weeks of data
collection. To receive their awards, students were required to print out the last page of
the survey (see Appendix H), and were instructed to bring the print out to class in
exchange for their free gifts. Early visitation of classrooms proved to be a valuable
endeavor and again generated another spike of activity in gathering survey responses.

At the end of semester week eleven, students were notiﬁed for a second and ﬁnal
time via the course mail system (see Appendix E) to announce that the survey would be

closing in ten days. The survey was closed on the date promised which marked a full six-

87

week period from survey launch. Arrangements were made with teaching assistants
appointed to the substance abuse course to visit their respective classrooms one ﬁnal time
for the purpose of distributing the second and ﬁnal round of coupons. The ﬁnal round of
coupons was distributed during the thirteenth week of the semester to all students who
had taken the survey and who had not previously received their coupon awards. Awards
were distributed as students handed in the printed copy of the ﬁnal page of the survey.
Once the deadline had been reached to close the survey, a message was posted on the
website alerting everyone that the survey had reached a closed status.

Following the ofﬁcial close of the survey, teaching assistants were advised that
coupons would be forwarded to them for any students who had not previously collected
their participation awards. Approximately 90% of teaching assistants offered a small
token award of extra credit points to their students who had taken the survey. This may
have generated some interest from students and may have contributed to the success of
the overall survey response. However, it should be emphasized that this was not a
requirement of teaching assistants and awarding of extra credit points was not monitored
by the study due to the need to maintain anonymity of participants. Therefore, data
pertaining to differences were not kept and group response rates could not be compared.

Students agreeing to participate in the study responded by taking a web-based
survey. Participants were asked to log onto the website by clicking onto an address that
had been created through a university-allocated AFS space (see Appendix F). During the
log-on process, survey participants were automatically connected to the Internet address
where the survey had been posted at the web-based survey company (see Appendix G).

Once students arrived at the website, the name of the survey appeared on the welcome

88

page (see Appendix G) along with reminder instructions to read the letter of informed
consent.

The combined survey consisted of a total of 64 items (see Appendix H). The
items include three instruments measuring attitudes, two screening tools measuring
alcohol and drug use, and three areas of constructed questions pertaining to demographic,
personal drug use, and familial substance abuse information.

Survey Website

The survey was administered using computer-assisted self-interviews or CASIs
via the Internet (Link & Mokdad, 2005). Although there have been mixed ﬁndings in the
past regarding computer-assisted surveys, recent studies regarding mode effects suggest
that use of the World Wide Web is a very practical tool for epidemiological studies. The
CASI mode affords researchers the ease of surveying a large population with universal
access to the World Wide Web, gathering a more representative sample more quickly and
with less cost than other modes (McCabe, Boyd, Couper, Crawford, & D’Arcy, 2002).
Another recent study showed that CASIs elicit a higher reporting of heavy drinking
among college students compared with other modes of data collection such as telephone
interviews or US. Mail surveys (Link & Mokdad). Therefore, the CASI mode is
considered ideal when collecting responses regarding sensitive data such as the use of
ATOD or other less acceptable or stigmatized behaviors (Link & Mokdad; McCabe et
al.).

Zoomerang zPro, a software subscription for professional web-based surveys
(Market Tools, 2000), was selected for the study. Web-based surveys using electronic

data collection had been used successfully within the CEP 261 course on two occasions

89

during the previous academic year. The web-based survey reduced the likelihood of
errors that might occur during manual transcription of the vital data onto a statistical
analysis software program. In addition, participants could complete the survey at their
convenience and within a setting that was private and relaxing. The ﬂexibility of the web
survey format offered participants an opportunity to reduce any pressures or anxieties
they may have felt in responding to a highly personal and sensitive line of questioning.
Other advantages created by the use of computer technology were the reduced need for
written materials which eliminated material costs, reduced storage and handling of data,
and increased anonymity of responses. Consistent with any survey, a major disadvantage
was participants may have distorted their answers by exaggerating or minimizing their
use of substances. Also, there was no way to ensure that individuals invited to participate
in the study were indeed ones who truly responded to the survey questions. And, there
was no way to gauge what physical, mental, or emotional states participants were
experiencing as they completed the survey.
Data Analysis

The population of interest for this research was college undergraduate students
enrolled in an introductory substance abuse course at a major Midwestern university.
The study was designed to collect data from a college student sample using a series of
self-reported measures within a web-based survey design. The survey included questions
related to demographic characteristics of the participants and questions regarding
personal substance use and familial substance abuse.

The sample for this research included students enrolled in eleven section offerings

of CEP 261 Substance Abuse. CEP 261 is an undergraduate level course series that was

90

offered during Spring semester 2007. The anticipated enrollment for this course was 275
undergraduate students or 25 students per section. Following the Registrar’s period for
drops and adds, the population of students was checked and determined to be equivalent
to N = 266 valid student enrollments. All students enrolled in the course during Spring
semester 2007 were invited to participate in the study.

The survey was administered via a single data collection interval spanning a
period of six weeks. Data collection occurred via a web-based survey. Mid-semester
exam week and Spring break week occurred within the period for data collection. The
generous allowance of a six week data collection period enhanced student’s comfort level
in answering the survey and generated a maximum number of responses for the sample.
The added convenience of round-the—clock access offered ﬂexibility and convenience to
prospective participants in managing their schedules and in responding. Moreover, the
convenience in response time assisted students in gaining access to the survey during
times of decreased academic stress and load factors. And ﬁnally, these factors facilitated
increased responses overall with the added use of incentives.

In a study performed by Noel and Cohen (1997), the authors noted that despite
negative effects of increased anxiety, anger, and depression the week prior to exams,
students reported less alcohol consumption compared with typical weeks during the
semester. Noel and Cohen concluded that students chose to reduce their discomfort and
anxiety by choosing to study rather than imbibe in substances. Although the same
authors reached these conclusions about alcohol, the authors did not consider the possible

use and inﬂuences of other substances, such as stimulants (Adderall, Ritalin, caffeine,

91

etc.) that students may have elected to take to enhance their academic performance during
the mid-semester.

Recruitment of subjects was performed by visiting each of the eleven course
sections to announce the invitation and invite students to participate in the survey. All
students who volunteered to take the survey were compensated with a free gift in
appreciation of their participation. Gifts had no cash value and were donated by local
merchants. Participants were granted a selection between one of three gift choices. The
choices were as follows:

0 A free chair or hand massage (or make-up session) from Douglas J Salon

- A free 7-day VIP membership to Powerhouse Gym

0 Coupons for free and discounted items from Bruegger’s Bagels

To provide evidence of their participation, students were directed both verbally
and in writing to print out the last page of the survey. They were instructed to bring the
page to class to receive their awards. For the purpose of distributing giﬁ coupons,
students were visited in their respective classrooms at two intervals during the period of
data collection. The ﬁrst visit occurred during the mid-point of the data collection period
and the second visit occurred immediately following the ofﬁcial closure of the web-based
survey.

Upon closure of the survey, the data were downloaded from Zoomerang zPro
(Market Tools, 2000) software onto the 2006 version of SPSS (Statistical Package for the
Social Sciences) software for data analysis. The study utilized both categorical variables
and continuous variables. The three dependent variables of health values, spiritual well-

being, and resilience were represented by continuous data. The two variables of personal

92

substance use risks and familial substance abuse risks were represented by categorical
data and were arranged into risk categories of low, medium, high, and very high.

At the time of proposal for the study, plans for data analysis were tentative. It
was anticipated that methods for data analysis would be adjusted based upon the sample
size and number of responses collected from the sample. It was assumed that the sample
would be large enough to place participants within targeted cells for level of risk for each
of the two categorical, independent variables of personal substance use and familial
substance abuse. It was also understood that cell count frequencies were impossible to
predict in advance of data collection.

The proposed plan for data analysis included an examination of frequencies for
each of the two risk variables and a display of crosstabs which would illustrate a table of
16 cells. It was planned that if some cells had low counts, risk categories would be
combined to address the research hypotheses. Also, it was planned that if there was
insufﬁcient representation within the cells, those cells would be combined with adjacent
cells. The proposed data analysis for each of the hypotheses is described below.

Hypothesis 1 Proposed Analysis: The independent variable is resilience. The
dependent variables are health values and spiritual well-being. All three variables are
represented as continuous data. A Pearson product moment correlation coefﬁcient test
was planned to calculate the relationships between resilience and health values. A
Pearson correlation test was planned to calculate the relationships between resilience and
Spiritual well-being. Values for resilience were to be measured in relation to increased

values on health values and values for resilience would be measured in relation to

93

increased values on spiritual well-being. If signiﬁcant correlations were found, the effect
sizes would be calculated to determine the strength or magnitude of the relationships.

Hypothesis 2 Proposed Analysis: The dependent variables are health values,
spiritual well-being, and resilience and are represented as continuous data. The
independent variable is personal substance use which was ﬁrst represented by continuous
data and then converted to categorical data. The methods for data analysis were planned
to include a calculation of means for each value for the three constructs of health values,
spiritual well-being, and resilience. The proposed methods were also planned to include
a calculation of means for the three dependent variables followed by a t-test to compare
the means of the two groups (low-medium personal substance use risk group and high—
very high personal substance use risk group).

Hypothesis 3a Proposed Analysis: The dependent variables are health values,
spiritual well-being, and resilience and are represented as continuous data. The
independent variables are positive (YES) indicators for the presence of familial substance
abuse or negative (NO) indicators for the presence of familial substance abuse. The
independent variables are represented as categorical data. The proposed methods for
analysis were planned to include a calculation of means for the three dependent variables
followed by t-tests to compare the means of the groups (positive for familial substance
abuse group or negative for familial substance abuse group).

Hypothesis 3b Proposed Analysis: The dependent variables are health values,
spiritual well-being, and resilience and are represented as continuous data. The
independent variables are risk factors for genetic familial substance abuse and risk factors

for non-genetic familial substance abuse. Genetic family and non-genetic family risk

94

variables are represented as categorical data. The proposed method for data analysis was
planned to include a t-test to compare the two groups for the three constructs of health
values, spiritual well-being, and resilience.

Hypothesis 3c Proposed Analysis: A t-test was planned to compare the two
groups regarding the constructs of health values, spiritual well-being, and resilience.

In summary, the upcoming chapter contains the actual analyses that were carried
out for each of the hypothesis. It also contains the results derived from data gathered

from survey respondents.

95

CHAPTER IV

Results

This chapter contains detailed information regarding the proposed and actual
analyses in relation to each of the hypotheses as stated in Chapter I of this document.
The upcoming and ﬁnal chapter (Chapter V) includes an interpretation of results and
conclusions reached for each hypothesis.

Characteristics of the Sample

The sample included 233 participants out of a total possible N = 266. The level of
participation was received at a survey response rate of 87.69%. As data were collected,
the inﬂux of student responses was monitored using the Zoomerang website (Market
Tools, 2000). It was observed that responses were submitted by students at a fairly
steady rate throughout these weeks, although some spikes of activity and some dormant
periods were noted. It was felt the web-based format provided students the opportunity
to take the survey at a time that was convenient within their busy schedules. It was
possible that the offering of incentives drew students to participate despite the pressures
of school and likely time constraints. It is also plausible that the provision of incentives
provided them the impetus to participate where they may not have done so otherwise.

In describing the overall sample of 233 participants, frequencies for the
demographic variables of sex, age, academic rank, race, grade point average, and living
situation were examined. The total sample included approximately one-third male
participants (32.2%) and approximately two-thirds female participants (67.8%). Students
were predominantly White at 76.0% of the sample. Other races included Black (13.7%),

Hispanic (6.0%), Asian (3.0%), Eastern Indian (.4%), and Mixed race (.9%).

96

The average age of participants was 20.42 years. Most participants’ ages (95.7%)
fell within an expected range of 18 to 22 years old. Ages within the sample were
distributed fairly evenly among this range including: 18 years (14.3%), 19 years
(19.0%), 20 years (18.2%), 21 years (26.0%), 22 years, (18.2%). Additional ages
reported by participants were: 23 years (1.7%), 24 years (1.3%), 29 years (.9%), and
40 years (.4%). Two respondents had missing values (one value was not reported and
one value was unrecognizable) and therefore could not be included in the analysis.

Students ranked predominantly in their fourth year of undergraduate studies,
comprising 27.7% of the sample. Other ranks included ﬁrst-year undergraduates
(24.2%), second-year undergraduates (24.2%), third-year undergraduates (18.6%), and
ﬁfth-year undergraduates (5.2%) of the valid sample. Once again, two respondents had
missing values (one value was not reported and one value was unrecognizable) and
therefore could not be included in the analysis.

Original categories of cumulative grade point average (GPA) fell along a
continuous scale of measurement and were measured to one-tenth of a point on a typical
grading scale of “4.0” maximum to “1.5 or below” minimtun (see Appendix H). During
the analytic procedures, new categories of GPA were created from the original survey
categories. The new categories included GPA ranges which were determined based on
ﬁve-tenths or one-half of a point, using the same 4-point scale. The most frequently
reported categorical range for cumulative GPA was 3.5-3.1 at 38% of participants within
the sample. Other GPA categories included: 4.0-3.6 (13.7%), 3.0-2.5 (29.2%), 2.5-2.1
(11.2%), 2.0-1.6 (6.0%), and 1.5 or below (1.3%). The mean, cumulative GPA for the

overall sample was 2.65.

97

Student responses gathered by the survey regarding primary living situation were
condensed during the data analysis. The nine original categories within the survey
included: (1) single-sex residence hall, (2) co-ed residence hall, (3) Greek housing
(fraternity or sorority), (4) non-Greek shared living, (5) off—campus with roommate/s,

(6) off—campus with parent/s, (7) off-campus alone, (8) substance-free residence hall, and
(9) other (see Appendix H). The nine original categories were condensed into three new
categories: (1) residence hall, (2) shared housing, and (3) off-campus living. Using the
new categories, the ﬁndings show that most participants within the sample lived off-
campus at 57.5%, a moderate percentage of students lived in residence halls (36.9%), and
a small percentage of students reported living in shared housing units such as fraternities
or sororities (5.6%).

The proposed data analysis mentioned in Chapter III, was unable to be carried out
exactly as planned and required some modiﬁcations. This was mainly due to insufﬁcient
numbers of students within groups of interest. Although the overall sample size was
sufﬁcient to answer broad questions, some data were insufﬁcient to answer speciﬁc
questions which addressed differences among groups of students within the sample. In
particular, this impacted the students’ reporting of genetic and non-genetic familial
substance abuse. As a result, the analysis for Question 3b was altered to ﬁt the data.
Question 3c was investigated as planned and included a supplemental analysis.

Summary of Hypotheses

This section includes a restatement of the hypotheses from Chapter I, followed by

a summary of the analyses, interpretation of the ﬁndings, and conclusion for each. As a

word of caution, the overall analyses and conclusions to these ﬁndings were not designed

98

to imply causation among variables. Rather, the results described within this study were
intended to provide associative meanings which were indicative of correlations and
comparisons among the variables.

Hypothesis 1: Participants who demonstrate increased resilience will also
demonstrate increased health values and spiritual well-being.

Analysis: A Pearson correlation test was planned for Hypothesis 1. The number
of responses gathered by the survey was more than adequate to conduct the planned
analysis for this hypothesis. To begin, the three dependent variables of health values,
spiritual well-being, and resilience were respectively represented as the modiﬁed Health-
as-a-Value (mod-HAV), Spiritual Index of Well-Being (SIWB), and Resilience Scale-15
(RS-15) instruments. Each of the three scales was represented as continuous data.

Among the responses received by participants regarding these instruments,
missing data were noted for the SIWB and RS-15 scales. The missing data were noted
for only two participants. It is uncertain if these participants elected to skip these
questions or missed the items altogether. Prior to scoring, all missing data were treated
as zero for these two scales. The missing responses added no positive or negative value
and had no impact on the total scores for these scales. Total scores were based only upon
data received. There were no missing data for the mod—HAV scale. Prior to scoring the
mod-HAV, the single, negatively-worded item (Health isn’t one of my big concerns) was
reverse-coded.

Next, the total scores were calculated for each of the three scales and three new

variables were created to represent these scores. A Pearson correlation was calculated

99

using the total score variables for each of the three scales (see Table 1). The total scores

were represented as continuous data.

TABLE 1

Pearson Correlation for mod-HAV, SIWB, and RS-15

 

Total Score of Total Score of Total Score of

 

SIWB RS- 1 5 mod-HAV

Total Score of SIWB Pearson Correlation l -.727(**) -.159(*)

Sig. (2-tailed) .000 .015

N 233 233 233
Total Score of RS-15 Pearson Correlation -.727(**) I .259(**)

Sig. (2-tailed) .000 .000

N 233 233 233
Total Score of mod-HAV Pearson Correlation -.159(*) .259(**) l

Sig. (2-tailed) .015 .000

N 233 233 233

 

** Correlation is signiﬁcant at the 0.01 level (2-tailed).
* Correlation is signiﬁcant at the 0.05 level (2-tailed).

Interpretation: The ﬁndings in Table 1 demonstrate correlation values. A
positive Pearson correlation value (.259) is observed for the mod-HAV and the RS-15 at
the p < 0.01 signiﬁcance level (2-tailed test). A negative Pearson correlation value
(-.727) is observed between the SIWB and the RS-15 at the p < 0.01 signiﬁcance level
(2-tailed test). A negative Pearson correlation value (-.159) is observed between the
mod-HAV and the SIWB at the p < 0.05 signiﬁcance level (2-tailed test).

Effect size is a measurement of the strength of the relationship between two
variables. A Pearson correlation test is one method for measuring the size of the effect.
Within the ﬁeld of social and behavioral sciences, Cohen (1988) suggested the following
general deﬁnitions to determine effect size: r = .10 to .29 or -10. to -.29 = small, r = .30

to .49 or -.30 to -.49 = medium, r = .50 to 1.0 or -.50 to -1 .0 = large. Using Cohen’s

100

deﬁnitions, a small positive effect was observed between mod-HAV and RS—15, a large
negative effect was observed between SIWB and RS-15, and a small negative effect was
observed between mod-HAV and SIWB (see Table 1).

Conclusion: Hypothesis 1 cannot be supported. For the overall sample of 233
participants, health values demonstrated a small, positive relationship with resilience.
Therefore, as health values increased, resilience mildly increased among participants.
Spiritual well-being demonstrated a large, negative relationship with resilience within the
sample. As spiritual well-being increased, resilience largely decreased among
participants. Spiritual well-being demonstrated a small, negative relationship with health
values within the sample. As spiritual well-being increased, health values mildly
decreased among participants.

The results for Hypothesis 1 indicated that relationships among the three
variables were signiﬁcant. However, the ﬁndings did not indicate which variable
impacted the other variable. Within the general sample and among the cluster of
dependent variables, participants displayed both positive and negative associations
depending upon paired combinations of variables and this inﬂuenced the direction of the
relationships among the pairs as either increased or decreased.

Hypothesis 2: Participants who score low to medium risk for personal substance
use will demonstrate increased health values, spiritual well-being, and resilience
compared with participants who score high to very high risk for personal substance use.

Analysis: A calculation of means for each of the dependent variables (health

values, spiritual well-being, and resilience), was planned for Hypothesis 2. Following the

101

calculation of means, a t-test was planned to compare the two groups of low-medium and
high-very-high risk for personal substance use.

The actual analysis for Hypothesis 2 was altered slightly from the proposed plan.
The alteration included a one-way, between-groups analysis of variance (ANOVA) for
each of the dependent variables (health values, spiritual well-being, and resilience),
followed by an illustration of differences using means plots. Then, due to insufﬁcient
cell sizes among groups, personal substance use risk became represented as three,
categorical comparative groups of low, medium, and high-very high personal substance
use risk. Post hoc analyses included Tukey’s Honestly Signiﬁcant Difference (HSD)
tests. The total sample of 233 participants was used for the Hypothesis 2 analysis.
Details analyses appear below.

Drug and Alcohol Risk: First, the scores for the Drug Abuse Screening Test-10
(DAST-10) and the Alcohol Use Disorders Identiﬁcation Test (AUDIT) were calculated
in compliance with standard scoring methods for each instrument. The calculations
included a summation of the weighted scores for each of the item responses. Test score
totals were stored as continuous variables. By their score totals, participants were
assigned into categories of low, medium, high, or very high risk based upon the
interpretive score categories included in the scoring procedures for the DAST-10
(S. T. Walters, personal communication, February 14, 2007). The categories for the
AUDIT were based upon those of Walters and Baer (2006). The risk categories for drug
use became named as the categorical variable of DRUG Risk. The risk categories for
alcohol use became the categorical variable named ALCOHOL Risk. Tables 2 and 3

display frequency charts for these two categorical variables.

102

TABLE 2

Categorical DRUG Risk Based on Participants’ DAST-10 Scores

 

 

 

Frequency Percent Valid Percent Cumulative Percent
Valid Low 114 48.9 48.9 48.9
Medium 83 35.6 35.6 84.5
High 32 13.7 13.7 98.3
Very High 4 1.7 1.7 100.0
Total 233 100.0 100.0

 

TABLE 3

Categorical ALCOHOL Risk Based on Participants’ AUDIT Scores

 

 

 

Frequency Percent Valid Percent Cumulative Percent
Valid Low 79 33.9 33.9 33.9
Medium 92 39.5 39.5 73.4
High 53 22.7 22.7 96.1
Very High 9 3.9 3.9 100.0
Total 233 100.0 100.0

 

Personal Substance Use Risk: The individual scores of the DAST-10 and AUDIT
were transformed into 2 scores and the two 2 scores were added together to determine the
combined, continuous scores for personal substance use risk. Then, the continuous
scores were categorized into four groups of low, medium, high, and very high risk. This
was accomplished by inspecting the distribution of scores for the continuous variable and
determining the cut-off points that would be used to divide the sample into four groups.
In creating the four personal substance use risk categories, the decision rule included
adding the frequency percentages for both DRUG Risk and ALCOHOL Risk and

computing an average of the two. There were some notable ﬂaws to this method which

103

were of some concern. For example, if an individual scored high risk for alcohol and low
risk for drugs, it was possible they could be placed in a medium risk category for
personal substance use. To double check the reliability in the applying this method,
correlations were performed for the DAST-10 and the AUDIT scores to determine
whether the parametric and non-parametric correlations demonstrated a positive
relationship (see Tables 4 and 5).

Table 4 displays the correlation between the total score of the DAST-10 and the
total score of the AUDIT. The results display a correlation between two continuous
variables. Table 5 displays the Spearrnan’s rho non-parametric correlation between
DRUG Risk and ALCOHOL Risk. The results display a correlation between two
categorical variables. The parametric and non-parametric correlations were performed to
ensure that the two variables demonstrated a positive relationship which would indicate
consistency in converting the continuous scores to categorical scores. The results
determined that the Pearson correlation value (.517 parametric) and the Spearrnan’s rho
correlation value (.487 non-parametric) were each signiﬁcant at a level of p < 0.01

(2-tailed test).

TABLE 4

Pearson Correlation Parametric Test for DAST-10 and AUDIT

 

Total Score of DAST Total Score of AUDIT

 

Total Score of DAST Pearson Correlation l .517(**)
Sig. (2-tailed) .000
N 233 233
Total Score of AUDIT Pearson Correlation .517("*) I
Sig. (2-tailed) .000
N 233 233

 

*"‘ Correlation is signiﬁcant at the 0.01 level (2-tailed).

104

TABLE 5

Spearrnan’s rho Non-Parametric Test for DAST-10 and AUDIT

 

DRUG RISK
(Categorical)

ALCOHOL RISK
(Categorical)

 

DRUG
RISK
(Categorical)

Spearman's rho

ALCOHOL RISK

(Categorical)

Correlation l .000
Coefﬁcient

Sig. (2—tailed)

N 233
Correlation .487(**)
Coefﬁcient

Sig. (2-tailed) .000
N 233

.487(**)

.000
233
1.000

233

 

** Correlation is signiﬁcant at the 0.01 level (2-tailed).

Next, group sizes within categories were inspected. Due to the low number of

cases observed within the very high risk group (see Table 6), this category was combined

with the adjacent cell representing the high risk group. This resulted in a total of three

personal substance use risk categories of low, medium, and high to very high for the

personal substance use risk variable. The high to very high group became represented as

a new total of 49 cases (see Table 7).

TABLE 6

Frequencies for Categories of Personal SUBSTANCE USE Risk

 

 

 

Frequency Percent Valid Percent Cumulative Percent
Valid Low 96 41.2 41.2 41.2
Medium 88 37.8 37.8 79.0
High 42 18.0 18.0 97.0
Very High 7 3.0 3.0 100.0
Total 233 100.0 100.0

 

105

TABLE 7

Frequencies for Three Newly Combined Categories of Personal SUBSTANCE USE Risk

 

 

 

Frequency Percent Valid Percent Cumulative Percent
Valid Low 96 41.2 41.2 41.2
Medium 88 37.8 37.8 79.0
High and Very High 49 21.0 21.0 100.0
Total 233 100.0 100.0

 

The three newly—created categories of personal substance use risk were named by
a new variable, PERSRISK3. Next, a series of tests was performed for each of the
univariate variables of mod-HAV, SIWB, and RS-15 in combination with PERSRISK3.
Results for these tests appear below.

Health Values: A series of tests was performed between each of the dependent
variables in relation to personal substance use risk. The ﬁrst dependent variable to be
tested in combination with PERSRISK3 was health values (mod-HAV). The ﬁrst test for
mod-HAV was Levene’s test of homogeneity of variances. The second test for
mod-HAV was ANOVA (see Table 8). The means for the mod-HAV group are
displayed in the means plot (see Figure 1).

The null hypothesis for ANOVA states that the means of mod-HAV are equal
among groups. The purpose of the ANOVA was to reveal differences among the three
groups. The upcoming test results show the effect of personal substance use risk on the
dependent variable mod-HAV.

Levene’s test was performed and the data were found to meet the assumption of
homogeneity for ANOVA. Thus, the null hypothesis was not rejected which indicates

there were no differences in the population variance among groups.

106

TABLE 8

Oneway ANOVA for Total Score of mod-HAV and Personal SUBSTANCE USE Risk

 

 

 

Sum of Squares df Mean Square F Sig.
Between Groups 2.922 2 1.461 .634 .531
Within Groups 530.040 230 2.305
Total 532.961 232

 

FIGURE 1

mod-HAV Means Plot for Three Categories of Personal SUBSTANCE USE Risk

 

 

 

 

 

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e—
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<
i?
3
E 12-
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o
2
o
8
1.3 11.94
0
l-
M—
o
c
s 1.
E1 .
I I I
LOW MEDIUM HIGH AND VERY

HIGH
NEW CATEGORIES OF PERSRISK3

107

Interpretation: Based upon the sample of n = 233, the ANOVA shows that the
total score of mod-HAV was not statistically different among the means for three
personal substance use risk groups. The null hypothesis states that the means of all
groups are equal. As a result, the null hypothesis was not rejected and the results show
the means of the mod-HAV are equal among groups.

Spiritual Well-Being: For the dependent variable spiritual well-being, a series of
four tests was performed. The ﬁrst test to be calculated for SIWB was Levene’s test of
homogeneity of variances. The second test for SIWB was ANOVA (see Table 9). The
third test for SIWB was Tukey’s HSD (see Table 10). The means for the SIWB group
are displayed in the means plot (see Figure 2). The fourth test for SIWB was a
calculation of omega squared.

The null hypothesis for ANOVA states that the means of SIWB are equal among
groups. The purpose of the ANOVA was to reveal differences among the three groups.
The Tukey post hoc test compares means for each combination of pairs of means. The
omega squared calculates the effect size for the differences among means. The upcoming
tests show the effect of personal substance use risk on the dependent variable SIWB.

Levene’s test was performed and the data were found to meet the assumption of
homogeneity for ANOVA. Thus, the null hypothesis was not rejected which indicated

there were no differences in variance among groups.

108

TABLE 9

Oneway ANOVA for Total Score of SIWB and Personal SUBSTANCE USE Risk

 

 

 

 

 

 

Sum of Squares df Mean Square F Sig.
Between Groups 814.687 2 407.344 7.201 .001
Within Groups 13010.918 230 56.569
Total 13825.605 232
TABLE 10
Tukey HSD Post Hoc Test for SIWB and Personal SUBSTANCE USE Risk Multiple
Comparisons
(1) NEW (.1) NEW Mean Std. Error Sig. 95% Conﬁdence Interval
CATEGORIES CATEGORIES OF Difference
OF PERSRISK3 (l-J)
PERSRISK3
Lower Upper
Bound Bound
Low Medium -2.205 1.1 10 .1 18 -4.82 .41
High and Very -4.964(*) 1.321 .001 -8.08 -l .85
High
Medium Low 2.205 1.1 10 .1 18 -.41 4.82
High and Very -2.759 1.341 .101 -5.92 .40
High
High and Very Low 4.964(*) 1.321 .001 1.85 8.08
High
Medium 2.759 1.341 .101 —.40 5.92

 

* The mean difference is signiﬁcant at the .05 level.

109

FIGURE 2

SIWB Means Plot for Three Categories of Personal SUBSTANCE USE Risk

 

274

8
1

N
u.
1

Mean of Total Score of SIWB
N N
w A
h 1

f

N
l

 

 

 

j I I
LOW MEDIUM HIGH AND VERY
HIGH

NEW CATEGORIES OF PERSRISK3

Interpretation: Based on the sample of n = 233, the ANOVA shows the effect of
personal substance use risk was signiﬁcant to spiritual well-being (p = .001) (see Table
9). The Tukey’s HSD post hoc test of multiple comparisons revealed more speciﬁc mean
differences for spiritual well-being between the low risk group and the hi gh-very high
risk group (£4964) at a signiﬁcance level of p < 0.05 (see Table 10). It appeared that the
true differences exist between these two groups and differences for the medium group are
not signiﬁcant. The means plot demonstrates that the high-very high group has higher
spiritual well-being than the low group (see Figure 2).

An omega squared calculation was performed for SIWB and indicated a result of

.05053. According to Cohen’s (1988) scale this demonstrates a large effect. The test

110

does not indicate the direction of difference but is used to indicate that a difference exists
between two groups. Therefore, the high-very high personal substance use risk group
displayed a larger effect on spiritual well-being than the low personal substance use risk
group.

Resilience: The ﬁnal dependent variable to be tested was resilience and a series
of four tests was performed for RS-15 in combination with PERSRISK3. The ﬁrst test
for RS-l 5 was Levene’s test of homogeneity of variances. The second test for RS-15 was
ANOVA (see Table 11). The third test for RS-15 was Tukey’s HSD (see Table 12). The
means for the RS-15 group are displayed in the means plot (see Figure 3). The fourth test
for RS-15 was a calculation of omega squared. Because data failed to meet the
assumption of homogeneity, three additional tests were performed (see Tables 13 & 14).

The null hypothesis for ANOVA states that the means of RS-15 are equal among
groups. The purpose of the ANOVA was to reveal differences for one of the combination
pairs of group means. The Tukey’s HSD post hoc test compares means for each
combination of pairs of means. The omega squared calculates the effect size for
differences among means. The upcoming tests show the effect of personal substance use

risk on the dependent variable RS-15.

TABLE 1 1

Oneway ANOVA for Total Score of RS-15 and Personal SUBSTANCE USE Risk

 

 

 

Sum of Squares df Mean Square F Sig.
Between Groups 1316.307 2 658.153 5.410 .005
Within Groups 27978.457 230 121.645
Total 29294.764 232

 

111

 

TABLE 12

Tukey HSD Post Hoc Test for RS-15 and Personal SUBSTANCE USE Risk Multiple
Comparisons

 

 

(1) NEW (J) NEW Mean Std. Error Sig. 95% Conﬁdence Interval
CATEGORIES CATEGORIES Difference
OF PERSRISK3 OF PERSRISK3 Q-J)
Lower Upper
Bound Bound
Low Medium 3.214 1.628 .121 -.63 7.05
High and Very 6.202(*) 1.936 .004 1.63 10.77
High
Medium Low -3.214 1.628 .121 -7.05 .63
High and Very 2.988 1.966 .283 -l.65 7.63
High
High and Very Low -6.202(*) 1.936 .004 -10.77 -1.63
High
Medium -2.988 1.966 .283 -7.63 1.65

 

* The mean difference is signiﬁcant at the .05 level.

FIGURE 3

RS-15 Means Plot for Three Categories of Personal SUBSTANCE USE Risk

 

 

 

 

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112

Levene’s test was performed and the data failed to meet the assumption of
homogeneity for ANOVA. Therefore, the null hypothesis was rejected which indicated
there were differences in variance among the groups. Due to violation of the
homogeneity of variance assumption, three additional nonparametric tests were
conducted. The three tests consisted of the Welch robust test of equality of means, the
Brown-Forsythe robust test of equality of means, and the Games-Howell post hoc test.

The ﬁrst nonparametric measure was a Welch robust test of equality of means and
was found to be signiﬁcant at p = .008 (see Table 13). Because there was a signiﬁcant
effect of the personal substance use risk on the resilience, this indicated a need to inspect
both the Brown-Forsythe and Welch versions of the F -ratio. The second nonparrnetric
measure was a Brown-Forsythe robust test of equality of means and it too was found to
be signiﬁcant at p = .008 (see Table 13).

The third nonparametric test was a Games-Howell post-hoc test. The Games-
Howell showed that the mean difference (i6.202) between low group and high and very

high group is statistically signiﬁcant at the p < 0.05 level (see Table 14).

TABLE 13

Robust Tests of Equality of Means for Total Score of RS-15

 

 

Statistic(a) dfl df2 Sig.
Welch 5.068 2 1 18.744 .008
Brown-Forsythe 4.987 2 154.859 .008

 

a Asymptotically F distributed.

113

TABLE 14

Games-Howell Multiple Comparisons for RS-15

 

 

(1) NEW (J) NEW Mean Std. Error Sig. 95% Conﬁdence Interval
CATEGORIES CATEGORIES Difference
OF PERSRISK3 OF PERSRISK3 (l-J)
Lower Upper
Bound Bound
LOW MEDIUM 3.214 1.560 .101 -.47 6.90
HIGH AND 6.202(*) 2.090 .01 1 1.21 11.20
VERY HIGH
MEDIUM LOW -3.214 1.560 .101 -6.90 .47
HIGH AND 2.988 2.175 .359 -2.20 8.17
VERY HIGH
HIGH AND LOW -6.202(*) 2.090 .01 1 -1 1.20 -1.21
VERY HIGH
MEDIUM -2.988 2.175 .359 -8.17 2.20

 

* The mean difference is signiﬁcant at the .05 level.

Interpretation: Based on the sample of n = 233, the ANOVA shows the effect of
personal substance use risk was signiﬁcant to resilience (p value = .005) (see Table 11).
However, the Tukey HSD post hoc test of multiple comparisons revealed more speciﬁc
differences exist for resilience between the low risk group and the high-very high risk
group Q) value .004) at a signiﬁcance level of p < 0.05 (see Table 12). It appears that
signiﬁcant differences exist solely between these two groups and differences for the
medium group were not signiﬁcant. The means plot demonstrates that the high-very high
personal substance use risk group has lower resilience than the low personal substance
use risk group (see Figure 3).

An omega squared calculation was performed for RS-15 and indicated a result of
.03647. According to Cohen’s (1988) scale this demonstrates a small effect. The test

does not indicate the direction of difference but is used to indicate that a difference exists

114

between two groups. Post hoc analyses of Welch robust test, Brown-Forsythe robust test,
and Games-Howell conﬁrmed there was a difference between the groups. Therefore, the
high-very high personal substance use risk group demonstrated a smaller degree of
resilience than the low personal substance use risk group.

Composite Analysis of Health Values, Spiritual Well-Being, and Resilience: As a
ﬁnal step to Hypothesis 2, a multivariate analysis of variance (MANOVA) between the
three levels of personal substance use risk and the total scores for the mod-HAV, SIWB,
and RS-15 was performed (see Table 15). This was done because ﬁndings revealed a
correlation among the three dependent variables. The MANOVA was used to investigate
the effect of personal substance use risk on the three variables of mod-HAV, SIWB, and
RS-15 as a composite variable and to view their combined correlation with the
independent variable.

MANOVA was used to test the means of the constructs (mod-HAV, SIWB, and
RS-15) among varying levels of risk (low, medium, high-very high) for the categorical
independent variable (personal substance use risk). ANOVA tested only differences in
means between the three constructs and the three risk levels of the independent variable
of personal substance use. MANOVA tested the differences between a central vector of

the three constructs combined with the levels of risk for personal substance use.

115

TABLE 15

MANOVA Tests for Personal SUBSTANCE USE Risk and mod-HAV, SIWB, and
RS-15

 

 

Effect Value F Hypothesis Error Sig.
df Df

Intercept Pillai's Trace .995 16801.361(a) 3.000 228.000 .000

Wilks' Lambda .005 16801 .36l(a) 3 .000 228.000 .000

Hotelling's Trace 221.071 l6801.36l(a) 3.000 228.000 .000
Roy's Largest 221.071 l6801.36l(a) 3.000 228.000 .000

Root

PERSRISK3__CAT Pillai‘s Trace .065 2.545 6.000 458.000 .020
Wilks' Lambda .936 2.571(a) 6.000 456.000 .019
Hotelling's Trace .069 2.595 6.000 454.000 .018
Roy's Largest .066 5.019(b) 3.000 229.000 .002
Root

 

a Exact statistic
b The statistic is an upper bound on F that yields a lower bound on the signiﬁcance level.
c Design: Intercept+PERSRISK3_CAT

Interpretation: Based on Table 15, the MANOVA demonstrates the effect of
personal substance use risk on the three variables simultaneously. The MANOVA test
demonstrated signiﬁcance for all of the tests including: Pillai’s Trace (p = .020), Wilks’
Lambda (p = .019), Hotelling’s Trace (p = .018), and Roy’s Largest Root (p = .002). All
results demonstrate a signiﬁcance of p < 0.05. The results show that the effect for
personal substance use risk is signiﬁcant for its effect on each of the independent
variables (mod-HAV, SIWB, RS-15) and for the interactions between them.

MANOVA assumes that for each group (each cell in the factor design matrix) the
covariance matrix is similar. Box's M tests this assumption. In order to conclude there is
insufﬁcient evidence that the covariance matrices differ, M should be non-signiﬁcant.
Table 16 shows that M is signiﬁcant and this indicates the assumption has been violated.
This further indicates that the group levels of personal substance use risk differ with

respect to their covariance matrices.

116

TABLE 16

Box’s M Test of Equality of Covariance Matrices

 

Box's M 13.020
F 1.062
dfl 12
df2 123524.775
Sig. .388

 

Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across
groups.
a Design: Intercept+PERSRISK3_CAT

Wilk’s Lambda was used as an approximation because it is a widely-reported
robust approximation (see Table 17). The size of the effect is gauged by considering the
Eta Squared column (see Table 17). It provides an estimate of the proportion of variation
in the dependent variable accounted for by the effect (IV or interaction between IVs). So,
personal substance use risk accounts for approximately 33% of the change in RS-15,
mod-HAV, and SIWB.

Power is a measure of the probability of not making a type 11 error or a false
acceptance of the null hypothesis. Cohen (1988) suggested that power should be at least
.80 (indicating that there is a 20% chance of making a type 11 error). The observed power

is above .80 (see Table 17).

117

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Conclusion: Hypothesis 2 cannot be supported.

The univariate tests demonstrated differences among means for the low, medium,
and high-very high personal substance use risk with regard to spiritual well-being, and
resilience. The low risk group demonstrated decreased spiritual well-being and increased
resilience as compared with the high-very high risk group. Likewise, the high-very high
risk group demonstrated increased spiritual well-being and decreased resilience than the
low risk group. The medium risk group displayed no signiﬁcant differences.

The multivariate tests demonstrated signiﬁcant correlations between personal
substance use risk and the three dependent variables of health values, spiritual well-being,
and resilience as a composite. In summary, it appears that although the composite
variable of health values, spiritual well-being, and resilience were signiﬁcantly correlated
to personal substance use risk, differences exist between the individual variables of
spiritual well-being and resilience between the low and the high-very high personal
substance use risk groups. Conclusions from the multivariate test alone did not capture
the differences between these groups and univariate tests provided the more conclusive
evidence for this hypothesis.

A brief review of the proposed plan of analysis for Hypotheses 3a, 3b, and 3c is
presented below:

The proposed plan for analysis included subdividing the sample into two groups
prior to answering addressing Hypotheses 3a, 3b, and 3c. Group 1 was planned to consist
of participants displaying negative indicators for reported past or present familial
substance abuse problems and Group 2 was planned to consist of participants displaying

positive indicators for reported past or present familial (genetic and non-genetic

119

relatives). Then, the latter group (Group 2) was planned to be subdivided into Group 2A,
participants reporting genetic relatives and Group 2B, participants reporting non-genetic

relatives. Participants who reported both genetic and non-genetic relatives were planned
to be placed in Group 2A, participants reporting genetic relatives.

During the analysis of data, it was discovered that Group 2A contained 128 cases
and Group 2B contained only three cases. This impacted the proposed plan for analysis
for Hypothesis 3b, making it impossible to carry out the analysis as planned. Therefore,
the analysis for Hypothesis 3b was altered to reﬂect further exploration of Group 2A.
And, the analysis for Hypothesis 3c was carried out, but because cell sizes were unequal,
variance was calculated to determine whether the analysis would be valid.

Hypothesis 3a: Participants with reported past or present familial (genetic or non-
genetic) substance abuse (Group 2) will demonstrate increased health values, spiritual
well-being, and resilience compared with participants with no reported past or present
familial substance abuse (Group 1).

Analysis: Four cases were omitted from this portion of the data analysis because
four participants did not answer the survey question regarding familial substance abuse.
Thus, the analysis for this question was based on a reduced valid sample size of 229
cases. The proposed methods for analysis included a calculation of means for the three
dependent variables followed by independent samples t-tests to compare the means of the
groups (positive for familial substance abuse group and negative for familial substance
abuse group).

The actual analysis for this question compared the two groups in four ways: (1) a

t-test compared means of mod-HAV, (2) a t-test compared means of SIWB, (3) a t-test

120

compared means of RS-15, and (4) a correlation was performed among mod-HAV,
SIWB, and RS-15. A Levene’s test was also performed to ensure that the assumption for
equal variance was met for all three dependent variables of mod-HAV, SIWB, and
RS-15.

The descriptive statistics regarding indicators for familial substance abuse and
mod-HAV appear in Table 18. The Levene’s value for mod-HAV (.666) was not
signiﬁcant because it is greater than .05 and implies that the variance was the same
(see Table 19). The t-test value (-.550) was not signiﬁcant and subsequently, the null
hypothesis could not be rejected. Ultimately, this indicates the means for the groups were

the same in the population (see Table 19).

TABLE 18

Familial SUBSTANCE ABUSE Group Statistics for Scores of mod-HAV

 

 

Family SA? N Mean Std. Deviation Std. Error Mean
Total Score of Yes 131 11.97 1.569 .137
mod-HAV

No 98 12.08 1.469 .148

 

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The descriptive statistics regarding indicators for familial substance abuse and

SIWB appear in Table 20. The Levene’s value for SIWB (.243) was not signiﬁcant

(see Table 21). However, the t-test value (1.989) was signiﬁcant and the null hypothesis

was subsequently rejected. Essentially, this indicates the means of the two groups were

different in the population (see Table 21).

TABLE 20

Familial SUBSTANCE ABUSE Group Statistics for Scores of SIWB

 

 

Family SA? N Mean Std. Deviation Std. Error Mean
Total Score of Yes 131 24.11 7.920 .692
SIWB

No 98 22.08 7.212 .729

 

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The descriptive statistics regarding indicators for familial substance abuse and
RS-15 appear in Table 22. The Levene’s value for RS-15 (1.125) was not signiﬁcant
because it is greater than .05 and implies that the variance was the same (see Table 23).
The t-test value (-1 .250) was not signiﬁcant and subsequently, the null hypothesis could
not be rejected. Once again, this indicates the means for the two groups were the same in

the population (see Table 23).

TABLE 22

Familial SUBSTANCE ABUSE Group Statistics for Scores of RS-15

 

 

Family SA? N Mean Std. Deviation Std. Error Mean
Total Score of Yes 131 83.41 1 1.707 1.023
RS-15 .

No 98 85.29 10.533 1.064

 

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126

As noted by the results for the three t-tests (see Tables 19, 21, & 23) comparing
the two groups of NO (Group 1) and YES (Group 2) for familial substance abuse, a
difference was noted in spiritual well-being. A subsequent test was performed to
investigate this difference.

Group 1 was represented as negative indicators (response of NO) for presence of
familial substance abuse. This was reported by 98 cases (42.8%) of the valid case total
(n = 229). A Pearson correlation was performed for Group 1 for the dependent variables

of mod-HAV, SIWB, and RS-15 (see Table 24).

TABLE 24

Correlations for NO Familial SUBSTANCE ABUSE and mod-HAV, SIWB, and RS—l 5

 

 

Total Score of Total Score of Total Score of
RS-15 SIWB mod-HAV

Total Score of RS- 1 5 Pearson 1 -.754(**) .188

Correlation

Sig. (2-tailed) .000 .064

N 98 98 98
Total Score of SIWB Pearson -.754(**) 1 -.089

Correlation

Sig. (2-tailed) .000 .383

N 98 98 98
Total Score of mod- Pearson .188 -.089 1
HAV Correlation

Sig. (2-tailed) .064 .383

N 98 98 98

 

** Correlation is signiﬁcant at the 0.01 level (2-tailed).
Family SA? = NO

127

Interpretation: Group 1 demonstrated a negative correlation value (-.754)
between SIWB and RS-15 at p < 0.01 signiﬁcance level (2-tailed test) (see Table 24). As
stated previously effect size is a measure of the strength of the relationship between two
variables. Using the aforementioned scale for measuring size of effect size, the effect
size between these two variables was large.

Conclusion: Hypothesis 3a cannot be supported. As spiritual well-being
increased, resilience largely decreased for participants reporting NO familial substance
abuse. The opposite is true as well. As spiritual well-being decreased, resilience largely
increased for participants reporting NO familial substance abuse. No correlations were
observed between negative indicators for familial substance abuse and health values for
Group 1. No correlations were observed between negative indicators for familial

substance abuse and resilience for Group 1.

128

Group 2 was represented as positive indicators (response of YES) for presence of
familial substance abuse (genetic, non-genetic, or both). This was reported by 131 cases
(57.2%) of the valid case total (n = 229). This represented a majority of the sample. A
Pearson correlation was performed for Group 2 for the dependent variables of mod-HAV,

SIWB, and RS-15 (see Table 25).

TABLE 25

Correlations for YES Familial SUBSTANCE ABUSE and mod-HAV, SIWB, and RS-15

 

Total Score of Total Score of Total Score of

 

RS-15 SIWB mod-HAV

Total Score of RS-15 Pearson 1 -.701(**) .304(**)

Correlation

Sig. (2-tailed) .000 .000

N 131 131 131
Total Score of SIWB Pearson -.701(**) 1 -.199(*)

Correlation

Sig. (2-tailed) .000 .023

N 131 131 131
Total Score of mod- Pearson .304(**) -.199(*) l
HAV Correlation

Sig. (2-tailed) .000 .023

N 131 131 131

 

** Correlation is signiﬁcant at the 0.01 level (2-tailed).
" Correlation is signiﬁcant at the 0.05 level (2-tailed).
Family SA? = YES

129

Interpretation: Group 2 shows a positive correlation value (.304) between mod-
HAV and RS-15 at signiﬁcance level of p < 0.01 (2-tailed test) (see Table 25). Group 2
shows a negative correlation value (-.701) between SIWB and RS-15 at signiﬁcance level
of p < 0.01 (2-tai1ed test) and a negative correlation value (-.199) between mod-HAV and
SIWB at signiﬁcance level p < 0.05 (2-tailed test) (see Table 25).

Using Cohen’s (1988) scale, the effect size between mod-HAV and RS—l 5 is
medium at .304. The effect size between SIWB and RS-15 is large at -.701. The effect
size between mod-HAV and SIWB is small at -.199.

To be certain if there actually was a difference between Group 1 and Group 2,
further analyses were performed which included a two-step process. The two steps
included a Fisher’s transformation (see Table 26) followed by a 2 test comparison
(see Table 27). First, the Fisher’s transformation tested the correlations between Group 1
and Group 2. This was a formal hypothesis test between variables and the null
hypothesis indicates equal correlations between Group 1 (N O-family) and Group 2 (YES-
family). This was accomplished with a 2 test comparing Group 1 (NO-family) to Group
2 (YES-family) regarding their correlations to the mod-HAV, SIWB, and RS-15 to
determine if the differences in correlation were statistically signiﬁcant.

Because a Fisher’s transformation was performed, it was not necessary to
calculate the conﬁdence intervals for each of the correlations. The Fisher’s
transformation test is not often used so it could not be readily calculated using SPSS

software. It was necessary to create the syntax to perform this test using SPSS.

130

TABLE 26

F isher’s Transformation Correlations for YES or NO Familial SUBSTANCE ABUSE

 

 

Family Total Score of Total Score of Total Score of
SA? mod-HAV SIWB RS-15
YES Total Score of Fisher's 1 -.55 .31392
mod-HAV Transformation
N 131 131 131
Total Score of Fisher's -.55 1 -.869
SIWB Transformation
N 131 131 131
Total Score of Fisher's .31392 -.869 1
RS- 1 5 Transformation
N 131 131 131
NO Total Score of F isher‘s 1 -.09 .190
mod-HAV Transformation
N 98 98 98
Total Score of Fisher's -.09 1 -.982
SIWB Transformation
N 98 98 98
Total Score of Fisher's .190 -.982 1
RS-15 Transformation
N 98 98 98

 

Table 27 shows the comparison of correlation between negative (Group 1)

familial substance abuse and positive family substance abuse (Group 2) to address the

question if they were differences between the two groups.

TABLE 27

Z-Test for YES Familial and NO Familial SUBSTANCE ABUSE Groups

 

Z-TEST

 

Correlation of RS-lS and mod-HAV

Correlation of SIWB and mod-HAV

Correlation of RS-l 5 and SIWB

.913

-3.422

.834

Sig. (2-tailed)

.360
.000

.404

Difference

.122

-.463

-.113

 

131

Interpretation: The differences for the correlations of SIWB and mod-HAV were
signiﬁcant at a correlation value of z > .000 (see Table 27). It indicates that the NO
group and the YES group are different with respect to their correlations between SIWB
and mod-HAV. The differences of RS-15 and mod-HAV correlation and the RS-15 and
SIWB correlation were not signiﬁcant between the two groups.

Conclusion: Hypothesis 3a still cannot be supported. Participants reporting
positive indicators for familial substance abuse demonstrated a signiﬁcant difference in
their correlations between the two variables of spiritual well-being and health values as
compared to participants reporting no indicators for familial substance abuse.

Hypothesis 3b: Participants reporting past or present substance abuse problems
with genetic relatives (Group 2A) will demonstrate increased health values, spiritual
well-being, and resilience compared with participants reporting past or present substance
abuse with non-genetic relatives (Group 2B).

Analysis: Group 2A indicated genetic risk (including participants who reported
both genetic family only risk and combined genetic and non-genetic family risk) and 128
cases were reported. Group 2B indicated non-genetic risk (including participants who
reported non-genetic family only risk) and only three cases were reported (see Table 28).

Due to a small representation (3 cases) for Group 2B (non-genetic risk sample),
the proposed analyses could not be performed as planned. It was not possible to compare
Group 2A (genetic risk) and Group 2B (non-genetic risk). Instead of looking at
comparisons between genetic and non-genetic risk differences, the data were used to look
exclusively at the dependent variables within the genetic risk group in relation to their

familial substance abuse risk categories and personal substance use risk factors.

132

To accomplish this, continuous scores were calculated for participants reporting
presence of genetic familial substance abuse (see Table 28). This was done in
accordance with the scale published by Miller et al. (1995). Non-genetic indicators,
reported by the 128 participants, were calculated according to the scale developed for the
non-genetic family members (see Table 29). Both scales appear in Chapter III of this
document. Continuous scores for the genetic and non-genetic indicators, reported by the
128 participants, were calculated separately and then combined to determine a familial
substance abuse risk continuous score. The family substance abuse risk scores were then

placed into categories of low, medium, high, and very high.

TABLE 28

Total Continuous Scores GENETIC Risk for GROUP 2A

 

Frequency Percent Valid Percent Cumulative Percent

 

 

Valid 1 11 8.6 8.6 8.6
2 18 14.1 14.1 22.7
3 16 12.5 12.5 35.2
4 20 15.6 15.6 50.8
5 12 9.4 9.4 60.2
6 15 11.7 11.7 71.9
7 9 7.0 7.0 78.9
8 5 3.9 3.9 82.8
9 6 4.7 4.7 87.5
10 3 2.3 2.3 89.8
11 2 1.6 1.6 91.4
12 2 1.6 1.6 93.0
13 2 1.6 1.6 94.5
14 2 1.6 1.6 96.1
16 1 .8 .8 96.9
17 2 1.6 1.6 98.4
20 1 .8 .8 99.2
21 1 .8 .8 100.0
Total 128 100.0 100.0

 

a GENETIC VS NON-GENETIC = GENETIC Risk

133

TABLE 29

Total Continuous Scores Non-Genetic Risk for GROUP 2A

 

Frequency Percent Valid Percent Cumulative Percent

 

 

Valid 0 48 37.5 37.5 37.5
1 2 1.6 1.6 39.1
2 18 14.1 14.1 53.1
3 5 3.9 3.9 57.0
4 13 10.2 10.2 67.2
5 3 2.3 2.3 69.5
6 6.3 6.3 75.8
7 2 1.6 1.6 77.3
8 10 7.8 7.8 85.2
9 l .8 .8 85.9
10 4 3.1 3.1 89.1
11 2 1.6 1.6 90.6
12 3 2.3 2.3 93.0
13 2 1.6 1.6 94.5
15 1 .8 8 95.3
19 1 .8 8 96.1
20 1 .8 .8 96.9
22 1 .8 .8 97.7
24 1 .8 8 98.4
42 l .8 .8 99.2
81 1 .8 .8 100.0
Total 128 100.0 100.0

 

a GENETIC VS NON-GENETIC = NON-GENETIC Risk

134

Next, a Pearson correlation was performed to determine the relationships between
genetic risk factors on the three dependent variables of mod-HAV, SIWB, and RS-15

(see Table 30).

TABLE 30

Pearson Correlation for GROUP 2A GENETIC Risk and mod-HAV, SIWB, and RS-15

 

 

Total Score of Total Score of Total Score of
RS-15 SIWB mod-HAV

Total Score of Pearson 1 -.704(**) .31 l(**)
RS- 1 5 Correlation

Sig. (2-tailed) .000 .000

N 128 128 128
Total Score of Pearson -.704(**) 1 -.188(*)
SIWB Correlation

Sig. (2-tailed) .000 .033

N 128 128 128
Total Score of Pearson .31 1(**) -.188(*) 1
mod-HAV Correlation

Sig. (2-tailed) .000 .033

N 128 128 128

 

** Correlation is signiﬁcant at the 0.01 level (2-tailed).
"‘ Correlation is signiﬁcant at the 0.05 level (2-tailed).
a GENETIC VS NONGENETIC = GENETIC

Interpretation: For Group 2A, a large, signiﬁcant negative correlation value
(-.704) was observed between SIWB and RS-15 at p < 0.01 (see Table 30). A small,
signiﬁcant negative correlation value (-.188) was noted between SIWB and mod-HAV at
p < 0.05 (see Table 30). A medium, signiﬁcant positive correlation value (.311) was
noted between RS-15 and mod—HAV at p < 0.01 (see Table 30).

Conclusion: Due to insufﬁcient data, Hypothesis 3b was unable to be tested. The
analysis for this hypothesis was modiﬁed to provide ﬁndings for Group 2A only. The

ﬁndings showed that participants reporting genetic indicators for familial substance abuse

135

demonstrated a large, inverse relationship between spiritual well-being and resilience, a
small, inverse relationship between spiritual well-being and health values, and a
moderate, positive relationship between resilience and health values.

Next, a Pearson correlation was performed to determine the relationships between
non-genetic risk factors on the three dependent variables of mod-HAV, SIWB, and RS-15

(see Table 31).

TABLE 31

Pearson Correlation for GROUP 2A NON-GENETIC Risk and mod-HAV, SIWB, and
RS-15

 

Total Score of Total Score of Total Score of

 

RS-l 5 SIWB mod-HAV

Total Score of Pearson Correlation 1 -.400 .181
RS-15

Sig. (2-tai1ed) .738 .884

N 3 3 3
Total Score of SIWB Pearson Correlation -.400 1 -.974

Sig. (2-tailed) .738 .146

N 3 3 3
Total Score of Pearson Correlation .181 -.974 1
mod-HAV

Sig. (2-tailed) .884 .146

N 3 3 3

 

b GENETIC VS NONGENETIC = NONGENETIC

Interpretation: No signiﬁcant correlations were observed for the non-genetic
familial substance abuse risk scores.

In preparation for the investigation of Hypothesis 3c, ANOVA tests were
performed for each of the following three sets of scores for GROUP 2A: (1) genetic
scores only (see Table 32), non-genetic scores only (see Table 33), and combined familial

substance abuse risk scores (see Table 34).

136

TABLE 32

Oneway ANOVA for GROUP 2A GENETIC Risk Scores

 

 

 

 

 

Sum of (If Mean Square F Sig.
Squares
Total Score of Between Groups 382.894 3 127.631 2.086 .105
SIWB
Within Groups 7771.610 127 61.194
Total 8154.504 130
Total Score of Between Groups 1433.201 3 477.734 3.703 .014
RS-15
Within Groups 16382.539 127 128.996
Total 17815.740 130
Total Score of Between Groups 9.978 3 3.326 1.363 .257
mod-HAV
Within Groups 309.900 127 2.440
Total 319.878 130
TABLE 33
Oneway ANOVA for GROUP 2A NON-GENETIC Risk Scores
Sum of df Mean Square F Sig.
Squares
Total Score of Between Groups 607.1 15 3 202.372 3.405 .020
SIWB
Within Groups 7547.389 127 59.428
Total 8154.504 130
Total Score of Between Groups 1568.609 3 522.870 4.087 .008
RS-15
Within Groups 16247.131 127 127.930
Total 17815.740 130
Total Score of Between Groups 14.850 3 4.950 2.061 .109
mod-HAV
Within Groups 305.028 127 2.402
Total 319.878 130

 

137

TABLE 34

Oneway ANOVA for GROUP 2A Familial SUBSTANCE ABUSE Risk Scores

 

 

Sum of df Mean Square F Sig.
Squares
Total Score of Between Groups 645.573 3 215.191 3.640 .015
SIWB
Within Groups 7508.931 127 59.125
Total 8154.504 130
Total Score of Between Groups 1584.033 3 528.01 1 4.131 .008
RS-15
Within Groups 16231.708 127 127.809
Total 17815.740 130
Total Score of Between Groups 2.345 3 .782 .313 .816
mod-HAV
Within Groups 317.533 127 2.500
Total 319.878 130

 

Next, a frequency test was performed to determine the sizes of the cells in relation
to the two independent variables of personal substance use risk and familial substance
abuse risk (see Table 35). Personal substance use risk was represented as the three
categories of low, medium, and high to very high. Familial substance abuse risk was
represented as four categories of low, medium, high, and very high. The categories were
then crosstabulated for a 12 cell display. Some cell sizes were too small to compare
groups individually among the 12 cells. Some cells were combined in preparation to

answer Hypothesis 30.

138

TABLE 35

Crosstabulation for Personal SUBSTANCE USE Risk Categories and Familial
SUBSTANCE ABUSE Risk Categories

 

 

 

Family Risk (categorical) Total
Low Medium High Very High

PERSRISK_ LOW 61 12 15 4 92
CAT_3

MEDIUM 47 9 17 15 88

HIGH AND 23 3 10 13 49

VERY HIGH
Total 13 1 24 42 32 229

 

Hypothesis 3c: Participants considered low to medium risk for personal substance

 

use and high to very high risk for familial substance abuse (genetic or non-genetic) will
demonstrate increased reported health values, spiritual well-being, and resilience
compared with participants considered high to very high risk for personal substance use
and low to medium risk for familial substance abuse (genetic or non-genetic).

Analysis: Students who displayed low to medium risk for personal substance use
and high to very high risk for familial substance abuse were placed in comparison group
3. Students who displayed high to very high risk for personal substance use and low to
medium risk for familial substance abuse were placed in comparison group 4.
Comparison group 3 contained 51 cases and comparison group 4 contained 26 cases
(see Table 36). An independent samples t-test was performed to compare the two groups

(see Table 37).

139

TABLE 36

Descriptive Statistics for Low-Medium Personal SUBSTANCE USE and High-Very
High Familial SUBSTANCE ABUSE Risk Group (3) compared with the High-Very
High Personal SUBSTANCE USE and Low-Medium Familial SUBSTANCE ABUSE
Risk Group (4)

 

 

Q3c__CAT1 N Mean Std. Deviation Std. Error Mean
Total Score of 3 51 24.84 7.953 1.114
SIWB
26 24.04 7.006 1.374
Total Score of 3 51 81.67 10.457 1.464
RS-15
26 83.35 11.503 2.256
Total Score of 3 51 12.04 1.549 .217
mod-HAV
4 26 12.08 1.623 .318

 

140

 

 

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141

Interpretation: No signiﬁcant differences were noted between the two groups.
Conclusion: Hypothesis 3c cannot be supported.
Supplemental Analysis for Question 3c

Two additional comparison groups (5 & 6) were created and compared as a
supplemental inquiry to Question 3c. Students who displayed low to medium risk for
personal substance use and low to medium risk for familial substance abuse were placed
in comparison group 5. Students who displayed high to very high risk for personal
substance use and high to very high risk for familial substance abuse were placed in
comparison group 6. Comparison group 5 contained 129 cases and comparison group 6
contained 23 cases (see Table 38). An independent samples t-test was performed to

compare the two groups (see Table 39).

TABLE 38

Descriptive Statistics for Low-Medium Personal SUBSTANCE USE and Low-Medium
Familial SUBSTANCE ABUSE Risk Group (5) compared with the High-Very High
Personal SUBSTANCE USE and High-Very High Familial SUBSTANCE ABUSE Risk
Group (6)

 

 

Q3c_CAT2 N Mean Std. Deviation Std. Error Mean
Total Score of mod- 5 129 12.09 1.471 .129
HAV

6 23 11.48 1.648 .344
Total Score of 5 129 21.47 7.031 .619
SIWB

6 23 28.70 8.193 1.708
Total Score of RS- 5 129 86.56 10.391 .915
15

6 23 77.70 13.723 2.861

 

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Interpretation: The t—test value (2.950) was signiﬁcant for RS-l 5 at p = .007
(2-tailed test) (see Table 39). The t-test value (-3.975) was signiﬁcant for SIWB at
p > .000 (2-tailed test) (see Table 39). The null hypothesis can be rejected which says
they are all the same and is essentially interpreted to mean the groups were different for
these two variables.

Conclusion: Participants with low to medium risk for personal substance use and
familial substance abuse demonstrate increased resilience (86.56) compared with
participants considered high to very high risk for personal substance use and familial
substance abuse (77.70) (see Table 38). Participants with low to medium risk for
personal substance use and familial substance abuse demonstrated decreased spiritual
well-being (21 .47) compared with participants considered high to very high risk for
personal substance use and familial substance abuse (28.70) (see Table 38).

In summary, the data analysis for the study demonstrated a number and variety of
signiﬁcant ﬁndings. Although none of the research hypotheses were supported by the
ﬁndings, research ﬁndings provided sufﬁcient data to analyze in providing answers to
each of the research questions. The implications of these results are discussed in
Chapter V of this document, along with limitations for the study, directions for future

research, and ﬁnal concluding remarks.

144

CHAPTER V

Discussion

Chapter V is the ﬁnal chapter of this document and includes a summary of
ﬁndings from Chapter IV. Due to the variety of questions posed and the extent of
analyses performed, there were a number of ﬁndings, many of which were signiﬁcant.
The summary appears in the paragraphs below and relates back to initial research
questions as they appear in Chapter I of this document. The meanings and implications
of these ﬁndings are derived from this summary and appear in the Implications of Results
section later in this chapter. Additionally, this chapter contains sections regarding study
limitations, directions for future research, and a general conclusion to the overall results.

Summary of Findings

Question 1: Within the general sample of college undergraduate students, the
individual constructs of health values, spiritual well-being, and resilience exhibit
signiﬁcant inﬂuences both positively and negatively. In particular, spiritual well-being
has a large, negative relationship with resilience and a small, negative relationship with
health values. Health values have a small, positive relationship with resilience.

Question 2: Regarding the effect of personal substance use and its levels of risk
on the major constructs, initial tests show that the high-very high risk group displays a
large increase in spiritual well-being and a small decrease in resilience in contrast with
the low risk group. The medium risk group was not found to be signiﬁcantly different.
Subsequent testing for this question, involving more sophisticated analyses, demonstrated
that a signiﬁcant relationship exists between the levels of personal substance use risk and

the major constructs of health values, spiritual well-being, and resilience as a composite

145

variable. However, this finding was dismissed and initial tests were found to provide the
more conclusive evidence.

Question 3a: Findings reveal that differences exist between the constructs of
spiritual well-being and health values for students answering NO (Group 1) and students
answering YES to past or present familial substance abuse (Group 2). Additional tests
revealed that for students reporting NO familial substance abuse, as resilience increases,
spiritual well-being largely decreases. For students responding YES to familial substance
abuse (genetic, non-genetic, or both), the same large effect was observed showing that as
resilience increases, spiritual well-being decreases. Also, for students responding YES to
past or present familial substance abuse, a medium effect was observed showing that as
resilience increases, health values increase. Finally, for this same group of YES students,
a small effect was observed showing that as health values increase, spiritual well-being
decreases.

Question 3b: Although data were insufﬁcient to answer the original research
question, they were used to obtain results for the group of 128 students who reported
genetic risks for familial substance abuse (Group 2A). Many students in this group
reported non-genetic familial substance abuse risk factors in addition to genetic familial
substance abuse risk factors. Data from genetic and non-genetic risk factors were studied
separately to determine how these factors interact with the major constructs. For this
group, the genetic risk factor of familial substance abuse displays a large, negative
correlation between spiritual well-being and resilience. Also, genetic risk displays a
small, negative correlation between spiritual well-being and health values and a medium,

positive correlation between health values and resilience. For this same group of

146

students, non-genetic risk factors bear no signiﬁcant relationships with the major
constructs.

Question 3c: When comparing individuals exhibiting low-medium personal
substance use risk and high-very high familial substance abuse risk (comparison group 3)
with individuals exhibiting high-very high personal substance use risk and low-medium
familial substance abuse risk (comparison group 4), the results reveal no signiﬁcant
ﬁndings relative to the major constructs of this study.

Supplemental Analysis for Question 3c: In a supplemental analysis comparing
individuals exhibiting low-medium risks for both personal substance use and familial
substance abuse (comparison group 5) with individuals exhibiting high-very high risks
for both personal substance use and familial substance abuse (comparison group 6), the
former group demonstrates a signiﬁcant increase in resilience and a signiﬁcant decrease
in spiritual well-being compared with the latter group.

Limitations

There were a number of limitations for the study which are recorded in the
paragraphs below. As a word of caution, this should by no means be considered an
exhaustive list of limitations. Other important limitations may well exist that have not
been mentioned here.

Sampling Bias: Due to selection of a purposive sample, sampling bias is an
inherent characteristic to all results obtained from this study. Although the overall
response to the survey was considered highly successful for the design and size of sample
chosen (n = 233), no inferences can be made regarding college students who attend large

Midwestern universities within the United States. It should be emphasized that the

147

sample of students invited to participate in the study were enrolled in a block of sections
offered within an introductory, substance abuse course. Although the students within the
study represented academic programs throughout the university, this fact did not replace
random sampling methods necessary to making population inferences. The sample did,
however, display the desired characteristic of familial substance abuse as was reﬂected
by 57.2% of the sample. The reported density of familial substance abuse for this study
appears consistent with national saturation levels of familial alcoholism reported by
Dawson and Grant in 1998 (slightly greater than 50%). Because alcohol and drug
reporting within families was not separated by the study, the slightly elevated density of
substance abuse likely reﬂects the added reporting of drug abuse conditions other than
alcoholism. The elevated density may have been further reﬂected by the fact that
students participating in the survey were exposed to educational concepts which assisted
them to more readily deﬁne and identify substance abuse problems.

Sample Limitations: Although the sample included a distribution of students from
within a variety of academic ranks and across a variety of academic disciplines,
representation within the sample is named as a possible limitation to the study. The
ﬁndings show that the sample represents mostly female and mostly White students.
Despite the implications of gender in alcohol dependence studies, the differences between
males and females were not emphasized in the design of this initial investigation.

The study did not consider the inﬂuence of CEP 261 course content on students’
responses given their exposure to substance abuse concepts delivered within their
respective classrooms during the ﬁrst six weeks of the semester. It was assumed that a

large majority of students were taking the course on the topic of substance abuse for the

148

ﬁrst time. However, it remains unknown as to how the content may have inﬂuenced their
responses. Additionally, many students may have had previous exposures to Drug Abuse
Resistance Education (DARE) through their years of secondary education, but this was
unknown at the time of the study and its relative impact was not considered within the
ﬁndings.

Another limitation imposed upon the sample pertained to the age of participants
and the measurement of the constructs of spiritual well-being and resilience. It is
possible that young adults within the sample had difﬁculty responding to the existential
themes and meaning of life questions presented by the Spiritual Index of Well-Being
(Daaleman & Frey, 2004) and the Resilience Scale-15 (Neil & Dias, 2001).

Researcher Bias: Another area of limitation was possible researcher bias. For
three years prior to conception of the study, teaching observations made within the
context of the introductory substance abuse course and elevated considerable curiosity.
The purposeful classroom environment made it an inviting selection and unique
opportunity in which to collect data from undergraduate students. At the time of data
collection, the researcher for this study was serving as the appointed coordinator for the
same block of CEP 261 substance abuse course sections. Although the researcher had no
direct involvement with CEP 261 students, no authority over the CEP 261 teaching
assistants, and no jurisdiction over the sections, it still should not be ruled out as a
potential bias.

Biases may have been present in the relationships that developed during the
orientation and training activities that were provided to the teaching assistants during the

academic year in which the study occurred. It is uncertain as to how teaching assistants

149

for the course may have directly or indirectly inﬂuenced their students in participating in
the survey. And, it is worth mentioning again that most of the teaching assistants elected
to award their students with extra credit points for taking the survey. Although, the
award of extra credit participation points was not mandated through the study, it is
unclear as to how this may have affected students’ responses.

Psychological and Personality Limitations: Psychological and personality
characteristics were not accounted for within the research design. lndividuals’ perceived
locus of control, inﬂuences of loss or losses, and other mood considerations, such as
positive emotion granularity (Tugade et al., 2004) were not queried by the study. No
considerations were given to individuals’ emotional stage responses or adjustments to
familial substance abuse as a CID (Antonak & Livneh, 1991; Kendall & Buys, 1998;
Livneh & Antonak, 1990). And, the research design did not consider optimism, life
purpose, or sense of coherence among participants (Adams et al., 2000). The study did
not isolate possible factors related to substance abuse or chemical dependency for
participants. When computing personal substance use risk categories, the study did not
distinguish between types of substances used by college students, including students’
possible engagement in the use of polysubstances.

Community and Environmental Limitations: There were limitations with regard
to environmental factors posed by living situation and the surrounding residential
community. These factors were not investigated by the study. Additional stress load
factors such as school pressures posed within the university setting and timing of the
study were not factored into the design. The design of the study did not consider size,

geographical location, or characteristics of the university or the student body. And, the

150

study did not investigate history of drinking for the selected campus setting or the
availability of alcohol within local businesses holding licenses for liquor retail or illicit
and/or illegal drug availability within the college and surrounding residential district.

Family Limitations: A further limitation to the study pertains to variations of
family structures, attitudes or roles within families, or the types and severity of familial
substance abuse within the sample. In gathering reported information about families
from participants, the study did not distinguish substance abuse from possible substance
dependence for family members. With respect to psychosocial adaptation for individuals,
the study did not consider patterns of family resilience. The presence of polysubstance
abuse, compounding disabilities, or mood disorders was not borne out by the study. In
addition, the study did not investigate family members affected by substance abuse in
connection to levels of resilience within participants.

Cultural Limitations: Racial or ethnic considerations were not considered in the
study and no inquiries were made as to differences in cultural perspectives regarding
health values, spiritual well-being, or substance abuse as a CID. In considering cultural
differences, it may have been important to examine differences in the perspectives of
male versus female students.

Methodological Limitations: Limitations regarding the use of surveys as a tool
for data collection should be emphasized as an inherent methodological limitation for this
study. Some students may have provided exaggerated, misleading, or untruthful
responses in the survey. Responses may have also been tainted by misperceptions
surrounding the use and abuse of substances and these types of responses would most

certainly have affected the overall results. The nature of web-based surveys sets up the

151

potential atmosphere for participants to be less than honest when entering their responses.
Nevertheless, dishonesty in responses is difﬁcult, if not impossible, to detect.

Existing limitations overshadow our current methodologies for gauging the risks
for college students’ substance use and familial substance abuse. Within the present
study, a personal substance use risk variable was created by combining risks for use of
both drugs and alcohol. Because there appear to be no universally-accepted standards in
calculating the risks of substance use or abuse for at-risk populations, it was necessary to
begin to forge new territory in assessing personal substance use risk as a variable.
Although ﬂaws are apparent in the methods that were used to combine these risk factors,
it should be considered as an initial attempt to be improved upon through future
investigations. And, because there are no universally-accepted standards for assessing
ATOD risks based upon future projections for and a past history of negative
consequences per individual, this study provides one example of weighing such risks.
The methods used herein represent an attempt at using both objective and subjective
methods in creating combined personal substance use risk categories.

Too, we cannot discount the interplay of psychosocial risk variables in assessing
how risk may be counteracted by internal protective factors. The dose-response method
for calculating risk and exposures to alcohol (Gruenewald et al., 2003) may be an initial
step toward establishing a clearer, scientiﬁc method by which to assess physiological and
metabolic risk variables for individuals. However, the dose-response method lacks
applicability to substances other than alcohol. Therefore, precision regarding the risks of
ATOD use and abuse remains outside the bounds of our current technology. The present

study did not attempt to factor in individuals’ BAC as a component in the calculation of

152

personal substance use risk factors. The study did not consider previous experiences
participants may have had with ATOD as contributing risk factors. Related to personal
substance use risk, the study did not look at alcohol or other drug-related consequences
previously incurred by participants.

Another signiﬁcant limitation that deserves mention is in creating a scale that
measures risk factors for non-genetic familial members. Although the study did not fully
test this hypothesis due to insufﬁcient group sizes, it became apparent during the process
of data analysis that the rating scales for genetic and non-genetic may not be comparable
based upon the sizes of the score ranges. For example if a student indicated they had ten
friends who were affected by substance abuse and received a score of 20, how would that
compare to a student who indicated their genetic father was affected and received a score
of two? Would this place the ﬁrst student at a higher risk for familial substance abuse
than the second student?

Instrument Limitations: The modiﬁed Health-as-a-Value scale was the only
health values scale located by an exhaustive search of the literature. Fortunately, it was
deemed suitable to the college sample under investigation. Psychometrically speaking, it
was perhaps the weakest instrument used for this study because the scale consisted of a
mere four items which were rated on a 4-p0int Likert scale. The psychometric properties
demonstrated minimal reliability standards showing a Chronbach’s alpha of .70. It
should be acknowledged that health values scales are difﬁcult to construct and there is no
universally-accepted deﬁnition for the concept of health. As a construct, one can derive
various meanings to health which are dependent upon the populations and contextual

environments under consideration. In pursuing advanced research, it is important to

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consider that health values vary greatly among particular groups of people and scales
must be carefully constructed to ensure validity and reliability to the populations under
study.

Other Limitations: Cumulative GPA and primary living situation were named as
key factors in relation to the theories posed by this study for overcoming familial
substance abuse as a CID through protective factors and the processes of resilience and
psychosocial adaptation. Descriptive data were collected on these factors and included in
the demographic characteristics for the sample. Due to the exploratory nature and
conﬁnes of this study, no analyses were performed for these variables. It is
recommended that future studies be designed to investigate the relationships of these
variables with the major constructs.

Implications of Results

Although inferences from this exploratory study cannot be made to the population
of college students at large, and in light of the signiﬁcant number of limitations posed by
the study, results do appear consistent with a number of previous ﬁndings from
aforementioned studies (see Chapter II). The implications listed below are arranged by
the following topics: (1) relationships between constructs, (2) personal substance use
risk, (3) familial substance abuse risk, (4) genetic versus non-genetic risk, and
(5) implications to practice.

Relationships Between Constructs: In the ﬁnal analysis, the three constructs of
health values, spiritual well-being, and resilience demonstrate signiﬁcant relationships
both positively and negatively within the sample. In the overall sample, a large inverse

relationship exists between spiritual well-being and resilience. This suggests that

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psychological dimensions of perceived intrinsic and extrinsic control within individuals
inﬂuence the key factors of psychosocial adaptation. To elaborate, the ﬁndings suggest
that increased spiritual well-being is related to perceived extrinsic control and trust in
one’s environment that all is well. Contrastingly, increased resilience is related to
perceived intrinsic control and trust in oneself to adapt. This supposition is supported by
ﬁndings from Bishop (2005) who declared that perceived control serves as a mediating
factor to quality of life and adaptation.

These conclusions concur with past studies which suggest that spirituality or
spiritual/religious coping serve as a protective factor against the risks of personal
substance use (Britton, 2004, Galen & Rogers, 2004; Larson & Larson, 2003;
MacKinnon, 2004; Miller 1998). It further validates that the decreased risk of alcohol
and drugs through the practice of controlled usage or abstinence, leads to increases in
spirituality and spiritual well-being (Sherman & Fischer, 2002). This may imply the
reverse conclusion that increases in spirituality and spiritual well-being, may lead to
decreased personal substance use or increased abstinence among individuals.

Personal Substance Use Risk: Regarding the risk factor of personal substance use
and its various levels, the ﬁndings are consistent to the Lanier et al. (2000) study which
discovered that students who drank three or four drinks per week scored lower on general
well-being than students who drank only two drinks per week and students who drank
between eight and ﬁfteen drinks per week increased in general well-being. This variation
points to Park and Levenson’s (2002) observation that perceived increase of self-control
through consumption of alcohol may increase “situational coping” which leads to an

improved sense of well-being. Situational risk coping may suggest a temporary state of

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relief or a possible false sense of security and enhanced well-being all induced by the
effects of the substance/s under use. It foreshadows the possibility that some individuals
in the high-very high risk group use ATOD as an “avoidance coping” strategy (Britton,
2004) which would lead one to further consider implications of emotional numbing or
denial. Although personal substance use as a risk factor demonstrated a signiﬁcant
correlation to the overall composite variable of health values, spiritual well-being, and
resilience, there remains insufﬁcient evidence to deﬁnitively interpret these ﬁndings
(Lanier et al., 2000). With the additional results contributed by this study, the evidence is
mounting and interpretation may be conﬁrmed through future investigations.

Familial Substance Abuse Risk: With regard to familial substance abuse as a risk
factor, the ﬁndings suggest that students who display NO evidence of familial substance
abuse possess a greater sense of spiritual well-being (external control) by placing their
faith and trust in a Higher Power. And, in the presence of changing conditions, these
individuals are inﬂuenced by their environments. Along with this notion, it was
suggested previously that sense of coherence inﬂuences spiritual coping and a state of
well-being (Adams et al., 2000; George et al. 2000).

In contrast, students from families of substance abuse employ a greater sense of
resilience (internal control) by empowering themselves to adapt in the face of adverse
and changing conditions. The latter indicates that resilient individuals empower
themselves to make responsible choices, relying upon intrinsic abilities and survival
instincts to attain a sense of competence and mastery over their personal life’s paths.
These results are supported by the Adams et al. (2000) study which identiﬁed the need,

through continued research efforts, to increase our understanding of life purpose,

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optimism, and sense of coherence in relationship to resilience. These ﬁndings seem to
point to the conclusion that individuals’ underlying beliefs and perceptions about their
personal causation (Miller & Walker, 1993) and competencies, including sense of
purpose and coherence in life, are important to adaptation and survival. This provides
evidence to suggest there are differences in how resilient individuals engage their
personal sense of intrinsic power, including their internal abilities to inﬂuence their
environments. The inverse relationship that spiritual well—being has to resilience and
health values may speak to the acceptance by more spiritually-attuned individuals to
allow extrinsic and external forces to guide and direct the events that occur along their
life paths and for more resilient individuals to make their own choices in providing
direction for self. It foreshadows the possibility that individuals who display both
spiritual well-being and resilience are even more likely to be successful in adapting to
adverse conditions.

Another important ﬁnding suggests that in the presence of familial substance
abuse as a risk factor, health values serve as a “protective/protective” factor (Hawkins et
al., 1992). In other words, health values serve to potentiate or strengthen resilience
(Ritt-Olson et al., 2004). Results indicate the relationship between health values and
resilience increased from “mild” within the general sample of students to “moderate”
within the group of students reporting familial substance abuse. This says that an
increased relationship of health values exists for individuals demonstrating resilience in
overcoming the risks of familial substance abuse.

In analyzing the value contributed by the supplemental ﬁndings, there is evidence

to show that the higher the risk factors for individuals displaying both personal substance

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use and familial substance abuse, the higher their level of resilience and the lower their
level of spiritual well-being. Jessor et al.’s (2006) claim that the higher the risk factors
and the less the protector factors the higher the incidence of drinking, may further imply
that resilience is indeed a protective process against both risk factors of personal
substance use and familial substance abuse.

Genetic versus Non-genetic Risk: The ﬁndings suggest that non-genetic risk
factors for familial substance abuse displayed no signiﬁcant relationships with the major
constructs under investigation. Students’ genetic risk factors of familial substance abuse
displayed a large inverse relationship between spiritual well-being and resilience, a small
inverse relationship between spiritual well-being and health values, and a medium
positive correlation between health values and resilience. Within the present study, these .
results are the same as those revealed for the presence of familial substance abuse.
Hence, genetic familial substance abuse risk factors accounted for most, if not all, of the
ﬁndings between the constructs under investigation and the process of psychosocial
adaptation compared with non-genetic familial substance abuse risk factors. These
ﬁndings are in agreement with previous authors who argued that intergenerational risks
for substance use disorders are genetically-based (Hardie, 2002; Hartman et al., 2006;
Merikangas et al., 1998; Schuckit, 1999).

To summarize, the salient conclusions for this research support the espousal that
health values, spiritual well-being, and resilience serve to reduce the risks for personal
substance use and familial substance abuse within the sample. However, the combination
of relationships between these identiﬁed protective factors and risk factors appear to have

some complexities which warrant further study. Advanced investigations into these

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relationships may prove to reveal valuable information relevant to the future clinical
practices of allied health and counseling.

Implications to Practice: First, this study has the potential to launch a program of
research speciﬁc to the ﬁeld of rehabilitation counseling that seeks to continue this
investigation into the key human factors and processes that play a role in the successful
psychosocial adaptation to CID. Psychosocial adaptation is an important and central
concept within the ﬁeld of rehabilitation counseling. As our knowledge and
understanding increases in this area, we may be able to shape our conceptual models and
hone our rehabilitative efforts that empower consumers impacted by CID to learn new
ways of overcoming adverse circumstances created by psychosocial barriers.
Empowering consumers, facilitating psychosocial adaptation to CID, and improving
vocational opportunities and outcomes are basic elements within the rehabilitation
counseling process and the working alliance between counselor and client (Lustig,
Strauser, Rice, & Rucker, 2002).

Second, additional opportunities created by this study may lead to increased
awareness and understanding of the factors and processes that are essential to
psychosocial adaptation of individuals and their families who are affected by stressful
and traumatic conditions of CID such as substance abuse. In particular, this may improve
rehabilitation services for transitioning high school youth who are impacted by personal
or familial substance abuse. Many times, youth who stem from families of substance are
overlooked in gaining access to rehabilitation services. Understandably, transitioning
youth who are enabled by resilience, health values, and favorable bonadaptation

(Kosciulek, 2004a) are overlooked as well. Further study is needed to increase our

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understanding of the processes of resilience, success, and empowerment (Kosciulek
2003; Kosciulek, 2005).

Third, this study may aid our understanding of informed consumer choice
(Kosciulek, 2004b; Kosciulek, 2007) and the role that psychosocial processes play in
promoting the internal drive to effectively seek, acquire, and utilize precious resources.
Because there appears to be a parallel between the concepts of Miller and Hester’s (2003)
informed eclecticism within the world of alcohol and drug treatment and Kociulek’s
model of informed consumer choice within rehabilitation, opportunities for advanced
model-building are possible. Quite likely, these concepts are also linked to Bishop’s
(2005) suggestion that perceived control in making choices that inﬂuence one’s life leads
to enhanced quality of life and adaptation. Therefore, we may wish to examine how
individuals with disabilities achieve successful adaptation by measuring their intangible
qualities of locus of control, sense of coherence, and process of resilience combined with
the availability of and access to precious tangible resources.

Fourth, we may wish to understand if the manner or ease in accessing tangible
resources bears any signiﬁcant relationships with the internal drive to seek and adapt. It
would be of interest to know if acquisition of resources is impacted by the quality of
instruction clients receive to successfully apply and integrate use of adaptive devices into
their settings of work and home. With future studies in this area, it is important to
consider technical complexities of the devices, aided by individuals’ capacities to apply
and use the device, giving full consideration to the accessibility or barriers posed within
the physical surroundings and contextual environments. This is critically important to

both the ﬁelds of rehabilitation counseling and occupational therapy because many

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devices dispensed to individuals with disabilities and clients or patients deemed
medically at-risk may be inappropriately used or underutilized. Inappropriate use of
adaptive equipment leads to elevated risks for injury and underutilization of adaptive
equipment leads to wasted resources. It is essential that proper training and support be
implemented to teach clients to use all prescribed equipment correctly and safely
ensuring short and long-term beneﬁts of use. Results of such studies may educate us to
be more stringent in our prescriptions, recognizing that empowerment of consumers’
intrinsic abilities may contribute to efﬁcient use of resources, thereby reducing costs.
This may ease ﬁnancial barriers that are imposed by the ever-shrinking insurance
reimbursement dollars and limited access to grant-funded opportunities.

Fifth, further implications to rehabilitation counseling include infusion of health
values into disability paradigms by focusing the ﬁeld on the positive aspects of “health”
and its resulting beneﬁts. In turn, this focus can be used to educate personnel in the
medical community in counteracting the perpetuation of negative labeling and stigmas
resulting from misconceptions surrounding the concepts and conditions of “disability”
and “disease”. There is clearly a strong need to minimize the negative inﬂuences of
Reductionism within allopathic medicine and conventional medical paradigms.

Sixth, the focus of health in disability has the potential to spill over to campus
substance use programs. The views of health and holistic health can serve as a focus to
the development and reﬁnement of campus-based prevention, intervention, and education
programs. Knowledge derived from further research will help to increase the efﬁcacy of
existing strategies we currently employ to promote campus-wide cultural changes toward

health, safety, and well-being within our college populations and settings. Student

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populations may be empowered in “choosing” to curb their use of substances and in
selecting healthier habits. In doing so, students can be directed in the steps of self-
management in overcoming the stresses of the academic community and in relieving the
anxieties of social tension. Empowerment in teaching students healthy ways to reward
self through non-chemical means (e. g. meditation) is strongly encouraged. Campus-wide
prevention campaigns designed around the implementation of complementary strategies
of auricular acupuncture (Moner, 1996; Otto, 2003; Reuben et al., 2005), shamanic
drumming (Winkelman, 2003), and mindfulness (Leigh, Bowen, & Marlatt, 2005) will
open doors by teaching students natural and safe methods to disengage normal stress
responses and to achieve a state of biopsychospiritual homeostasis (Richardson, 2002).
These strategies would be strengthened when accompanied by training in basic health
concepts. Each of these modalities can also easily be blended into health educational
seminars designed to modify the behaviors of college students, particularly those who
have attained a status of academic probation due to legal repercussions with ATOD.

On the topic of campus substance abuse, more work is needed to understand the
population of collegiate CoAs (Perkins & Berkowitz, 1991) and their aspects of resilience
(Keeling, 2000) in overcoming their backgrounds of familial substance abuse and in
navigating their way through obstacles and adversity in achieving academic success.
Indeed, the at-risk needs of this special population can be isolated from the general
student population and studied intensively. It is possible these individuals possess the
overcoming nature that is so-believed to be characteristic of the process of resilience.

Seventh, this study will serve to inspire us with new insights into the ongoing

nature of transition that occurs throughout the lives of human beings. Through future

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studies on family adaptation, it may be possible to probe more deeply into our
understanding of how individuals with disabilities, as a part of family systems, promote
positive changes. As we gain new insights about family resilience as a process, we will
strive to disentangle the intricacies of the human factors that combine to promote familial
adaptation and contribute to overall changes within the family units of those who are
affected by substance abuse as a CID.

Eighth, this study can assist rehabilitation counselors in articulating and
advocating for the advanced understanding of the needs of disability consumers within
the public, private for-proﬁt, and private non-proﬁt sectors. There is a vast need to
articulate and expand the concept of disability within the world of research so that other
disciplines will gain an improved understanding of the needs of individuals with
disabilities. Advancement of the psychosocial theories to adaptation will support
professional distinction for the ﬁeld of rehabilitation counseling and will seek to further
the professional identity of rehabilitation counseling professionals.

Finally, results from this study can be used to provide a forum in contemplating
advancement of the framework of psychosocial adaptation to CID (Livneh, 2001) by
linking it to the systemic ecological model of rehabilitation counseling (Hershenson,
1998). By doing so, we will link human processes of adaptation to contextual
environments. The potential outcome is in improving the overall service delivery within
the ﬁeld of rehabilitation.

Directions for Future Research
This study represents a ﬁrst attempt in exploring the constructs of health values,

spiritual well-being, and resilience and their possible links to each other and to personal

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substance use risks within a college population and familial substance abuse risks as a
CID. Future research is urged to advance our understanding of relationships that exist for
these major constructs.

First, this study can inspire the work of researchers to pursue answers related to
intrinsic factors that combine to promote resilience and assist us as human beings to
overcome the challenges and adversities imposed on us by the inﬂuences of CID within
families. Initially, it is recommended that future studies focus on gathering large,
randomly-selected samples of college students to conduct repeated investigations into
combinations of psychosocial factors that promote psychosocial adaptation. Future
studies should also focus on college student differences of gender and primary living
situation as integral risk factors to personal substance use. The design of these studies is
recommended to investigate academic achievement as a possible outcome of resilience
for college students from families of substance abuse. As our studies increase in these
areas, it may be possible to build upon the conceptual framework to include the inﬂuence
of these factors.

Second, this study needs to be expanded upon to include research designs using
combinations or clusters of constructs including ones in addition to the three constructs
investigated here. It is advisable to include sense of coherence and locus of control as
possible composite variables that promote psychosocial adaptation. With enhanced
understanding into the positive factors that enable us as human beings to survive and
thrive against the odds, rehabilitation counselors can develop and reﬁne counseling

techniques and strategies used to assist disability consumers to acquire, engage in, or

164

strengthen the psychosocial skills that are necessary to overcome seemingly
insurmountable odds.

Third, research investigations are needed to focus on the development of more
deﬁnitive scales which measure spirituality and spiritual well-being. And, as inquiries
into the essence of our spiritual nature as human beings continue, instruments used to
measure this phenomenon can be created or honed. In time, we may become successful
in our struggles to deﬁne and understand spirituality separately and distinctly from
religion. As we do so, we may wish to use spirituality as a construct to test future
samples.

Fourth, we must continue our crusade to understand and deﬁne the meaning of
health to include the concepts, principles, and practices of holistic health and wellness.
As we explore this realm, it may shed new insights into deﬁning and testing viable health
constructs including the concept of health among disability and special populations. On
this same note, it is necessary to continue our development of health values scales. The
availability of added scales may lead us to discover a set of universal health values that
stretch across populations and cultures. When this happens, we will need to develop and
test health values scales that are unique to populations as we investigate them. On a
related note, advanced research is needed to more fully comprehend health protective
behaviors in relation to decreasing the risks of personal substance use and familial
substance abuse and in promoting the processes of psychosocial adaptation to CID.

Fifth, research is needed to aid our understanding of the construct of resilience as
a process rather than a characteristic (Schoon, 2006; Walsh, 2002). We are urged to

understand how the process of resilience functions within the larger parameter of

165

psychosocial adaptation to CID. The process of psychosocial adaptation may include the
subprocess of resilience in relation to adapting to speciﬁc and contextualized
environments. It is conceivable that adaptation truly is not a linear process to a single
event (Antonak & Livneh, 1991). Rather, the process of adaptation inﬂuences a series of
adaptations and readaptations which extend to other peoples, other contexts, and other
environments, all within of our spheres of inﬂuence. Within the ﬁeld of rehabilitation
counseling, these spheres of inﬂuence are labeled as four major subsystems:

(1) consumer, (2) functional, (3) provider, and (4) contextual environments (Hershenson,
1998)

Sixth, as we begin to consider differences within these various spheres of
inﬂuence and contextual environments, it will become important to build studies that
investigate rehabilitation outcomes of individuals with disabilities by looking at
psychosocial factors that contribute to successful psychosocial adaptation and to the
supports and goal-directed structures provided within various contexts. The efforts of
rehabilitation counselors to produce studies on efﬁcacy and evidence-based outcomes
must begin by assessing the methods used to match individuals with disabilities to types
of agencies and services provided within those agencies.

Disability management specialists are urged to accurately assess and consider the
situational risk factors individuals with disabilities present and how those risk factors
affect development of effective vocational rehabilitation plans in facilitating achievable
goals and successful outcomes for disability consumers. The analysis of consumer-based

needs, including situational risk, adaptive, and protective factors should be made a

166

routine part of the vocational rehabilitation continuum within all service delivery
systems.

Seventh, substance abuse pervades the lives of individuals with disabilities on
every conceivable level. Substance abuse can compound pre-existing and co-existing
disabilities or manifest as a new disability. Therefore, it is crucial that advancements
occur in understanding psychosocial adaptation to substance abuse as a CID. The
expansion and development of ﬁeld concepts and models outside of substance abuse
disability will need to take place over time. This can happen by understanding the
inﬂuences of key factors of psychosocial adaptation within other types of disability
groups, age groups, contexts, and settings. As we repeat our inquiries regarding
psychosocial adaptation with regard to disability variations, it is possible that patterns of
behavior will emerge that will lead us to greater and greater conclusions about this vital
process. Repeated research inquiries will assist in reﬁning research methodologies.
Disability, like substance abuse, is an equal opportunity afﬂiction. As we expand upon
our research of speciﬁc disability groups, perhaps we will begin to promote a more
universal, transcultural, and transdisciplinary concept of CID.

Eighth, with regard to personal substance use and familial substance abuse, it is
critical that we gauge more accurate methods for assessing and calculating levels of
substance use risk, based upon not only genetic factors, but on non-genetic familial and
psychosocial factors as well. The discussion of alcohol oftentimes dominates our
discussions of the risks of substance abuse. The risks for drugs other than alcohol need to

be investigated as well. In our effort to understand the risks of substance abuse, we may

167

wish to consider how the variables of drug and alcohol risk impact each other separately
and in combination.

Livneh and Wilson (2003) identiﬁed substance abuse as both a predictor and a
mediator for disabling conditions. Thus, it is important that we begin to understand the
potential of substances to exacerbate or mask a condition. There is a strong need to
develop improved scales for measuring the risks for non-genetic familial and
environmental inﬂuences that are based upon reliable and replicable scientiﬁc methods.
Along with the established risks that genetics have, the psychosocial risks of non-genetic
and environmental inﬂuences must be explored.

Ninth, aside from concentrating solely on legal recreational substances
(e. g. alcohol, tobacco, and caffeine) and illegal recreational substances (e. g. marijuana,
cocaine, heroin), we must begin to investigate the impact of legal instrumental
(e. g. prescription and over-the-counter) medications and their compounding factors
(Hanson et al., 2006) especially with regard to our disability populations. For example,
the legal instrumental drugs include pain medications that can be used to alter perceptions
of chronic pain conditions making life more tolerable. However, chronic pain in itself is
a disabling condition. To complicate matters, prescription medications also produce
unpleasant side effects that can cause some individuals to use substances to self-medicate
and self-regulate undesirable symptoms. This potential for polysubstance use further
aggravates the impact of medically-disabling conditions including exacerbation of
individuals’ symptomologies. Exacerbation of symptoms directly impacts cognitive,
emotional, and physical abilities individuals rely upon to successfully engage in

functional aspects of daily living including vocational pursuits.

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Tenth, persistent research efforts will assist in advancing a systems-wide approach
that bodes well to prevention efforts within campus settings (Wechsler et al., 2000a).
Life purpose, optimism, and a sense of coherence within a college population (Adams et
al., 2000) deserve further exploration in regard to their relationship with well-being and
to the process of resilience in overcoming the risks of familial substance abuse. Further
studies may advance our understanding of how a state of “mindfulness” interacts with
psychosocial adaptation to CID within our student populations.

Within a college population, it is critically important to understand how cohesive
relationships and network of friends inﬂuence students’ risks for personal substance use.
Much of our current research focuses on risk factors related to genetic family only.
Although genetic factors are undeniably strong, there are signiﬁcant non-genetic familial
inﬂuences that are currently unexplored. And, in advancing our understanding of genetic
inﬂuences, it is advisable to isolate collegiate CoAs (Perkins & Berkowitz, 1991) to study
patterns of personal substance use and familial substance abuse and how these factors are
related to resilience (Keeling, 2000), psychosocial adaptation, and academic
achievement.

Finally, it is recommended that all professionals embrace and engage the
collective works of neighboring professions as we add to the movements of spirituality
and holistic health in counseling, allied health, and medicine. For example, occupational
therapists have a long—standing and shared history in shaping and improving the lives of
individuals with CID. Occupational therapists approach the human adaptive process by
applying therapeutically-driven, adaptive activities focused toward clients’ active

processes of “doing” and “becoming”. Perhaps, the joint efforts of rehabilitation

169

counselors and occupational therapists can serve to compliment one another in
understanding more fully how the power of the human will and spirit, or personal
causation, (Miller & Walker, 1993) promotes psychosocial adaptation (Livneh, 2001)
which in turn, activates and engages the functional performance of individuals in
attaining occupational adaptation following a period of illness, injury, disability, or
disease (Kielhofner, 1980a; Kielhofner, 1980b; Kielhofner & Burke, 1980; Kielhofner,
Burke, & Igi, 1980; Schkade & Schultz, 1992; Schultz & Schkade, 1992). Through
combined viewpoints, researchers are presented with a prime opportunity to align their
efforts in comprehending the “total” processes of psychosocial and occupational
adaptation to an even more comprehensive model of holistic adaptation (Schkade &
Schultz, 1992; Schultz & Schkade, 1992) and total rehabilitation (Wright, 1980). This
broader view of spirit-mind-boay adaptation seeks to deﬁne the shared adaptive processes
for individuals and families with disabilities in achieving a life that is ﬁlled with optimal
satisfaction, balance, health, and well-being. Reilly’s (1962) notable quote which stands
as a cornerstone of the occupational therapy profession and has signiﬁcant relevancy to
the ﬁeld of rehabilitation counseling says that “Man, through the use of his hands as they
are energized by mind and will, can inﬂuence the state of his own health” (p. 2.).
Conclusion

Through the cyclical nature of life, ever-changing conditions, and uncertainty, we
grow to adapt to our environments by continually accessing and expending the resources
we need to overcome adversity. Many times, our successes are driven by the tangible
and intangible resources we use to accomplish our goals. We strive to reach our goals,

through not only the informed choices we make but, through the internal forces that

170

motivate us to acquire precious and vital resources that are otherwise unavailable to us
without these engaging efforts. When we increase our comprehension of the intrinsic
nature of our human will and volitional system combined with the use of human
adaptation strategies we can equip our clients with disabilities toward maximizing their
psychosocial, occupational, and vocational performance capabilities within their vital
spheres of inﬂuence. When this happens, we will be one step closer to integrating
rehabilitation across disciplines.

In conclusion, this study was rich with intent, meaning, and purpose. Overall, it
accomplished much more than was anticipated and served to tie major concepts together
in a new way. The overriding results show that adversity provides opportunities to
develop resilience which is integral to human adaptation. The insights gained by this
study can enrich our perspectives toward disability, ability, and rehabilitation. This study
also demonstrated a cutting-edge approach which is in tune with state-of-the-art research
on the topics of campus substance abuse and familial substance abuse.

It is essential that we incorporate a more holistic approach to serving individuals
and families with disabilities through inclusion of an interdisciplinary perspective within
our research efforts. We must continue to build bridges across major domains of thought,
incorporating transdisciplinary methods in our efforts to shape our paradigms toward
disability. As much as we need to marry theory with practice and art with science, so we
need to direct our future research endeavors to the uniﬁcation of the mind with the body
and ultimately to Spirit. It is imperative that we begin to bridge our knowledge of the
objective and subjective worlds to the holistic world in our attempts to manifest the larger

mysteries of life as evidenced through human behavior.

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Appendices

172

Appendix A

Printed Invitation — Distributed in Class

173

Appendix A: Printed Invitation — Distributed in Class
Research Study Opportunity!

You are invited to participate in an online research study entitled “Relationships Between
Health Values, Spiritual Well-Being, and Resilience Among College Students Reporting
Personal Substance Use and Familial Substance Abuse: An Exploratory Study”. I am a
doctoral candidate in the department of Counseling, Educational Psychology, and Special
Education (CEPSE) at MSU. For the past 3 ‘/2 years, I have taught 12 sections of CEP
261 Substance Abuse. As a result of my teaching experiences, I became very interested
in knowing more about the adaptive qualities and processes of college students in relation
to substance abuse.

The research project will assist me in completing my Ph.D. in Rehabilitation Counselor
Education (RCE). Graduates from the RCE program educate rehabilitation counselors to
assist individuals with disabilities, including substance abuse. The results of the study
will also provide valuable information for substance abuse professionals in understanding
and assisting college students and families affected by substance abuse. It is a unique
opportunity and your contribution to the study can impact future research in these areas.

In appreciation of your participation, you will have the opportunity to select one of the
following free gifts:

0 A free chair or hand massage (or make-up session) from Douglas J Salon
0 A free 7-day VIP membership to Powerhouse Gym
0 Coupons for free and discounted items from Bruegger’s Bagels

All you need to do is take the survey and print out the last page and bring it to class. I
will visit your class again in a few weeks to collect the printed page and distribute the
coupon for your free gift selection.

Following today’s visit, you will receive an electronic message through the ANGEL
system. The message will include a letter of informed consent that you are asked to read
prior to taking the survey. You must be 18 years of age or older to participate. The
survey will take approximately 15-20 minutes of your time. Please be aware that
although the survey is anonymous, it requires a reporting of information that is sensitive
in nature. Therefore, you may wish to seek privacy when you take it. For more
information, please click on the following link:

www.msu.edu/~dfarrell
(or paste into your Internet browser)
Sincerely,

Debra J. Farrell, ABD, OTR, ADS, CAC-I
Doctoral Candidate, CEPSE, Michigan State University
dfarrell@msu.edu

 

I74

Appendix B

Electronic Invitation — Sent via ANGEL

175

Appendix B: Electronic Invitation — Sent via ANGEL

Dear Students of CEP 261,

Thank you for allowing me to visit your classroom today to invite you to participate in an
online research study entitled “Relationships Between Health Values, Spiritual Well-
Being, and Resilience Among College Students Reporting Personal Substance Use and
Familial Substance Abuse: An Exploratory Study”.

Please open and read the attachment that includes a letter of informed consent outlining
important information regarding your voluntary participation in the study.

The survey will take approximately 15-20 minutes to complete. You must be 18 years of
age or older to participate. Please note that although the survey is anonymous, it requires
a reporting of information that is sensitive in nature. Therefore, you may wish to seek
privacy when you take it. For more information, please click on the following link:

www.msu.edu/~dfarrell
(or paste into your Internet browser)

Sincerely,

Debra J. Farrell, ABD, OTR, ADS, CAC-I
Doctoral Candidate, CEPSE, Michigan State University

176

Appendix C

Letter of Informed Consent — Attached to ANGEL Message

177

Appendix C: Letter of Informed Consent — Attached to ANGEL Message

“Relationships Between Health Values, Spiritual Well-Being, and Resilience Among
College Students Reporting Personal Substance Use and Familial Substance Abuse: An
Exploratory Study”

Michigan State University

Department of Counseling, Educational Psychology, and Special Education (CEPSE)
Ofﬁce of Rehabilitation and Disability Studies

4th ﬂoor, Erickson Hall

East Lansing, MI 48824

Dear Prospective Survey Participant:

You are invited, along with approximately 275 other students, to participate in an online
research study entitled “Relationships Between Health Values, Spiritual Well-Being, and
Resilience Among College Students Reporting Personal Substance Use and Familial
Substance Abuse: An Exploratory Study”. The results of this study are important to
helping substance abuse and rehabilitation professionals identify key psychosocial factors
that are integral to the process of adaptation for college students and families of substance
abuse.

Participants must be at least 18 years of age or older. Your participation is completely
voluntary. All of your information is anonymous, no IP addresses will be collected, and
no identiﬁers will ever be a part of the data. Your privacy will be protected to the
maximum extent allowable by law. The results of this study may be published in a short
article in a professional journal.

By taking the survey, you will be agreeing to any risks or beneﬁts of the study. Risks for
this study are minimal and include possible psychological or emotional discomfort posed
by responding to questions about sensitive topics of substance use and abuse. In this
event, please contact the MSU Counseling Center at voice (517) 335-8270 and TTY
(517) 353-7278 to request a consultation.

Potential beneﬁts of the study include a free gift of your choosing. The choices are as
follows:

0 A free chair or hand massage (or make-up session) from Douglas J Salon
0 A free 7-day VIP membership to Powerhouse Gym
0 Coupons for free and discounted items from Bruegger’s Bagels

All you need to do is take the survey, print out the last page, and bring it to class. The

project investigator will visit your class again in a few weeks to collect the printed page
and distribute a coupon for your free gift selection.

178

Appendix C (continued): Letter of Informed Consent — Attached to ANGEL Message

Please remember, your participation is completely voluntary. You may choose not to
participate at all, or you may refuse to participate in certain procedures or answer certain
questions or discontinue your participation at any time without consequences. You may
also decline to answer speciﬁc questions should you ﬁnd them intrusive.

If you have any questions about the study please contact this researcher at
dfarrell@msu.edu, ofﬁce phone (517) 355-1838 or Dr. Michael Leahy, at
leahvm@msu.edu, ofﬁce phone (517) 432-0605. If you have questions or concerns
regarding your rights as a survey participant, you may contact anonymously if you wish —
Dr. Peter Vasilenko, Director of the Human Subject Protection Programs at Michigan
State University, by phone: (517) 355-2180; fax: (517) 432-4503; email: irb@msu.edu;
or regular mail at: HRPP, 202 Olds Hall, East Lansing, Michigan, 48824-1047.

 

Thank you for your time and valuable contribution to college students and families of
substance. If you have questions regarding the research, please feel free to contact us at
the numbers listed below. Survey results are anticipated to be available after September
1, 2007. You may discuss the results by initiating contact with us after that date.

The survey will take approximately 15-20 minutes to complete. The survey includes
responding to questions related to alcohol and drug use. Therefore, you may wish to seek
privacy when you take it. For more information on this study, please click on the
following link:
www.msu.edu/~dfarrell
(or paste into your Internet browser)

By completing the online survey, you indicate your voluntary agreement to participate in
this research and have your answers included in the data set.

Sincerely,

Project Investigator:

Debra J. Farrell, ABD, OTR, ADS, CAC-I
Doctoral Candidate

dfarrell@msu.edu

(517) 355-1838

Responsible Project Investigator:
Michael Leahy, Ph.D.

Professor and Research Adviser
lealn/m@msu.edu

(517) 432-0605

 

179

Appendix D

First Follow-Up Message — Sent via ANGEL

180

Appendix D: First Follow-Up Message — Sent via ANGEL

Title: Free Gift Coupons Delivered Next Week

Hello Section _,

This is Debra Farrell. I will deliver the ﬁrst round of coupons to your classroom next
week. I will award free gift coupons to all of you who have participated in the survey
thus far. In fact, I have extra coupons and enhanced coupons to award you. Everyone
who completes the survey will receive the Bruegger’s Bagel coupon, along with your
choice of a 14-day (increased from 7-day) VIP membership to Powerhouse Gym or an
Express Chair or Express Hand Massage from Douglas J Salon.

You can access the survey by clicking on the following address:

www.msu.edu/~dfarrell
(or paste into your Internet browser)

Thanks everyone for your participationll Data collection for the survey is going great
thanks to all of you!! See you soon!!

Sincerely,

Debra J. Farrell, ABD, OTR, ADS, CAC-I
Doctoral Candidate, CEPSE, Michigan State University

181

Appendix E

Second Follow-Up Message — Sent via ANGEL

182

Appendix E: Second Follow-Up Message — Sent via ANGEL

Title: Survey Will Close Soon

Dear Students of CEP 261,

The online research study entitled, “Relationships Between Health Values, Spiritual
Well-Being, and Resilience Among College Students Reporting Personal Substance Use
and Familial Substance Abuse: An Exploratory Study” will close on Monday, April 2,
2007 at 8:00 am. Students who have not already taken the survey have approximately 10
days left. To access the survey, please click on the following link:

www.msu.edu/~dfarrell
(or paste into your Internet browser)

 

In appreciation of your participation, you will have the opportunity to select one of the
following free gifts:

0 A free chair or hand massage (or make-up session) from Douglas J Salon
0 A free 14-day VIP membership to Powerhouse Gym

Everyone who participates in the survey will also receive:

0 Coupons for free and discounted items from Bruegger’s Bagels
0 Additional coupons for discounted ice cream and discounted MSU T-shirts

All you need to do is take the survey and print out the last page and bring it to class. I
will arrange delivery of a ﬁnal round of coupons. Thank you so much everyone! You
participation has made this study a great success!

Sincerely,

Debra J. Farrell, ABD, OTR, ADS, CAC-I

Doctoral Candidate, CEPSE, Michigan State University
dfarrell@msu.edu

183

Appendix F

Initial Page of Survey — Posted on AF S Space

184

Appendix F: Initial Page of Survey — Posted on AFS Space

Thank you for your interest in participating in

Relationships Between Health Values, Spiritual Well-Being, and
Resilience Among College Students Reporting Personal
Substance Abuse and Familial Substance Abuse:

An Exploratory Study

Take Me To The Study
mm

185

Appendix G

Welcome Page of Survey — Posted on Website

186

Appendix G: Welcome Page of Survey — Posted on Website

College Student Survey

This research study involves taking a 15-20 minute survey. The study focuses on helping
substance abuse and rehabilitation professionals identify key psychosocial factors that are
integral to the process of adaptation for college students and families of substance abuse.

This research is completely voluntary. You must be 18 years of age or older to
participate. Your privacy will be protected to the maximum extent allowable by law.
You can skip any question that you do not wish to answer. You can also end your
participation at any time. If you experience psychological or emotional discomfort,
please contact the MSU Counseling Center at voice (517) 335-8270 and TTY (517) 353-
7278 to request a consultation.

Please be sure you have read the letter of informed consent prior to taking this survey.
The letter was sent to you through your ANGEL account for the CEP 261 Substance
Abuse course. If you have any questions about the study please contact this researcher,
Debra Farrell, at (517) 355-1838, dfarrell@suedu, or Dr. Michael Leahy, at (517) 432-
0605, leahvm@msu.edu.

 

If you have questions or concerns regarding your rights as a survey participant, you may
contact anonymously if you wish — Dr. Peter Vasilenko, Director of the Human Subject
Protection Programs at Michigan State University, by phone: (517) 355-2180; fax: (517)
432-4503; email: irb@¢nsu.edu; or regular mail at: HRPP, 202 Olds Hall, East Lansing,
Michigan, 48824-1047.

 

Please be sure and print out or email yourself the last page of the survey as evidence of
your participation. You will need this printed page to collect your free gift/award.

Prior to taking the survey, you may wish to zoom your screen in or out to improve the
readability of the survey questions. Also, please be sure to check your answers prior to
advancing each page. The survey does not allow you to scroll back through previously
completed pages.

187

Appendix H

College Student Survey — Posted on Website

188

Appendix H: College Student Survey — Posted on Website

College Student Survey

1. Please indicate your gender:

0
0

Male
Female

2. Please indicate your age in years (type in):

Please Note: You must be at least 18 years of age to participate in the survey.

 

3. Please indicate your academic rank in college:

OOOOOOOOO

1St year undergraduate
2nd year undergraduate
3rd year undergraduate
4th year undergraduate
Fifth-year undergraduate
Graduate student
Lifelong learner
Adult specialization
Other, please specify

 

4. Please indicate your race/ethnicity:

(check all that apply)

OOOOOO

White-not Hispanic (includes Middle Eastern)
Black-not Hispanic

Hispanic or Latino

Asian or Paciﬁc Islander

American Indian or Alaskan Native

Other, please specify

 

189

Appendix H (continued): College Student Survey — Posted on Website
5. What is your approximate GPA (cumulative grade point average) right now?

(using the drop down menu, please choose the number that most closely matches
your GPA)

4.0
3.9
3.8
3.7
3.6
3.5
3.4
3.3
3.2
3.1
3.0
2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.1
2.0
1.9
1.8
1.7
1.6
1.5 or below

OOOOOOOOOOOOOOOOOOOOOOOOOO

6. During the college school year, please describe your primary living situation:

Single sex residence hall

Co-ed residence hall

Greek housing (fraternity or sorority)
non-Greek shared living

Off-campus with roommate/s
Off-campus with parent/s
Off-campus alone

Substance-free residence hall

Other, please specify

OOOOOOOOO

 

190

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

7. Please indicate the degree of your agreement to the following statements by
clicking on the appropriate number that corresponds to the answer key.

Please note that (1) indicates “Not Agree” and (4) indicates “Strongly Agree”.
Not Agree Strongly Agree
1 2 3 4
I am willing to make sacriﬁces to be healthy.

1 2 3 4

A big part of happiness is health.
1 2 3 4

Health isn’t one of my big concerns.
1 2 3 4

I would rather have ﬁm than be healthy.
1 2 3 4

191

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

8. Please read the following statements. Below each statement you will ﬁnd ﬁve
numbers, ranging from (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor
Disagree, (4) Agree, and (5) Strongly Agree.

Click on the number which best indicates your feelings about that statement. For
example, if you strongly disagree with a statement, circle “1”. If you neither
agree nor disagree, circle “3”, and if you strongly agree, circle “5”, etc.
Strongly Disagee Neither Strongly Agree

1 2 3 4 5
There is not much I can do to help myself.

I 2 3 4 5

Often, there is no way I can complete what I have started.
1 2 3 4 5

I can’t begin to understand my problems.
1 2 3 4 5

I am overwhelmed when I have personal difﬁculties and problems.
1 2 3 4 5

I don’t know how to begin to solve my problems.
1 2 3 4 5

There is not much I can do to make a difference in my life.
1 2 3 4 5

192

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey
9. Continue using same directions as previous page.
REMINDER DIRECTIONS:
Please read the following statements. Below each statement you will ﬁnd ﬁve
numbers, ranging from (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor
Disagree, (4) Agree, and (5) Strongly Agree.
Click on the number which best indicates your feelings about that statement. For
example, if you strongly disagree with a statement, circle “1”. If you neither
agree nor disagree, circle “3”, and if you strongly agree, circle “5”, etc.
Strongly Disagree Neither Strongly Agree
1 2 3 4 5
I haven’t yet found my life’s purpose.

1 2 3 4 5

I don’t know who I am, where I came from or where I am going.
1 2 3 4 5

I have a lack of purpose in my life.
1 2 3 4 5

In this world, I don’t know where I ﬁt in.
1 2 3 4 5

I am far from understanding the meaning of life.
1 2 3 4 5

There is great void in my life at this time.
1 2 3 4 5

193

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

10. Please read the following statements. Below each statement you will ﬁnd seven
numbers, ranging from (1) Strongly Disagree on the left to (7) Strongly Agree on
the right.

Click on the number which best indicates your feelings about that statement. For
example, if you strongly disagree with a statement, circle “1”. If you are neutral,
circle “4”, and if you strongly agree, circle “7”, etc.
Strongly Disagree Neutral Strongly Agree

1 2 3 4 5 6 7
When I make plans, I follow through with them.

1 2 3 4 5 6 7

I usually manage one way or another.
1 2 3 4 5 6 7

I feel proud that I have accomplished things in my life.
1 2 3 4 5 6 7

I usually take things in my stride.
l 2 3 4 5 6 7

I am friends with myself.
1 2 3 4 5 6 7

I feel that I can handle many things at a time.
1 2 3 4 5 6 7

I am determined.
1 2 3 4 5 6 7

I have self-discipline.
1 2 3 4 5 6 7

194

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey
11. Continue using same directions as previous page.
REMINDER DIRECTIONS:
Please read the following statements. Below each statement you will ﬁnd seven
numbers, ranging from (1) Strongly Disagree on the left to (7) Strongly Agree on
the right.
Click on the number which best indicates your feelings about that statement. For
example, if you strongly disagree with a statement, circle “1”. If you are neutral,
circle “4”, and if you strongly agree, circle “7”, etc.
Strongly Disagree Neutral Strongly Agree
1 2 3 4 5 6 7
I keep interested in things.

1 2 3 4 5 6 7

I can usually ﬁnd something to laugh about.
I 2 3 4 5 6 7

My belief in myself gets me through hard times.
1 2 3 4 5 6 7

I can usually look at a situation in a number of ways.
1 2 3 4 5 6 7

My life has meaning.
1 2 3 4 5 6 7

When I am in a difﬁcult situation, I can usually ﬁnd my way out of it.
1 2 3 4 5 6 7

l have enough energy to do what I have to do.
1 2 3 4 5 6 7

195

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

The following question pertains to the types of substances you have used over your

lifetime.

 

l2. OVER YOUR LIFETIME, what types of drugs have you ever used?
Some examples are included for each category.

(check all categories that apply)

OOOOOO

OOOOOOOOO

Tobacco (cigarettes, cigars, smokeless tobacco)

Alcohol (beer, wine, liquor)

Marijuana (pot, hash, hash oil)

Caffeine (coffee, tea, chocolate, soda)

Cocaine (crack, rock, freebase)

Amphetamines (Adderall, Ritalin, Provigil, diet pills, speed, meth,
crank)

Designer (intentional use only) (Ecstasy, rooﬁes, GHB, Liquid X)
Hallucinogens (acid, mushrooms, angel dust)

Tranquilizers or Sleeping Pills (Special K, Valium)

Barbiturates (Amytal, Phenobarbitol)

Narcotics (heroin, morphine, Vicodin, Oxycontin, codeine)
Analgesics or OTC medications (aspirin, Tylenol, NSAIDs)
Inhalants (solvents, nitrous, glue, gasoline)

Anabolic steroids (Android, Anadrol, Durabolin, Oxandrin, Winstrol)
Other, please specify

 

196

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

The following question pertains to the types of substances you have used during the
past 12 months.

 

l3. DURING THE PAST 12 MONTHS, what types of drugs have you used?
Some examples are included for each category.
(check all categories that apply)

Tobacco (cigarettes, cigars, smokeless tobacco)

Alcohol (beer, wine, liquor)

Marijuana (pot, hash, hash oil)

Caffeine (coffee, tea, chocolate, soda)

Cocaine (crack, rock, freebase)

Amphetamines (Adderall, Ritalin, Provigil, diet pills, speed, meth,
crank)

Designer (intentional use only) (Ecstasy, rooﬁes, GHB, Liquid X)
Hallucinogens (acid, mushrooms, angel dust)

Tranquilizers or Sleeping Pills (Special K, Valium)

Barbiturates (Amytal, Phenobarbitol)

Narcotics (heroin, morphine, Vicodin, Oxycontin, codeine)
Analgesics or OTC medications (aspirin, Tylenol, NSAIDs)
Inhalants (solvents, nitrous, glue, gasoline)

Anabolic steroids (Android, Anadrol, Durabolin, Oxandrin, Winstrol)
Other, please specify

OOOOOO

OOOOOOOOO

 

I97

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

14.

The following series of questions concerns information about your potential
involvement with DRUGS during the past 12 MONTHS. These drugs “DO NOT
include your use of ALCOHOLIC BEVERAGES”.

The various classes of drugs may include: cannabis (e.g. marijuana, hash),
solvents, tranquilizers (e.g. Valium), barbiturates, cocaine, stimulants (e.g.
speed), hallucinogens, (e.g. LSD) or narcotics (e. g. heroin).

For each item, carefully read the statement and decide if your answer is “Yes” or
“No”. If you have difﬁculty with a statement, choose the response that is mostly
right.

Have you used drugs other than those required for medical reasons?

0 Yes
0 No

(Mandatory Question: You may choose “not” to answer this question. If this is
the case, you will also be choosing to discontinue your participation in the
remainder of the survey and you will need to exit the survey by closing your
Internet browser).

198

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

15. The following questions concern information about your potential involvement
with “or use” of DRUGS during the past 12 MONTHS. These drugs “DO NOT
include your “use” of ALCOHOLIC BEVERAGES”.

 

YES NO
1 2
Have you used more than one drug at a time?
1 2
Are you always able to stop using drugs when you want to?
1 2
Have you had blackouts or ﬂashbacks as a result of drug use?
1 2
Do you ever feel bad or guilty about your drug use?
1 2
Have family members ever complained about your involvement with drugs?
1 2
Have you stayed away from your family because of your drug use?
1 2
Have you engaged in illegal activities to obtain drugs?
1 2
Have you ever experienced withdrawal symptoms (felt sick) when you stopped
taking drugs?
1 2

Have you had medical problems as a result of your drug use (e. g. memory loss,
hepatitis, convulsions, bleeding, etc.)?
1 2

199

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

The following series of questions pertain to your use of ALCOHOLIC
BEVERAGES.

Please Note: A drink is deﬁned as one can or bottle of beer (12 02.), or one glass of
wine (4 02.), or one can or bottle of wine cooler (12 02.), or one straight shot of liquor
(l 1/2 oz. -— 80 proof spirits), or one (single shot) mixed drink.

16. How often do you have a drink containing alcohol?

Never

Monthly or less

2 to 4 times a month

2 to 3 times per week
4 or more times a week

00000

17. How many drinks containing alcohol do you have on a typical day when you are
drinking?

lor2
3or4
50r6
7to9
IOormore

00000

18. For women, how often do you have four or more drinks on one occasion?
For men, how often do you have ﬁve or more drinks on one occasion?

Never

Monthly or less

2 to 4 times a month

2 to 3 times per week
4 or more times a week

00000

200

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey
19. Please mark the answer that is correct for you.

NEVER LESS than 2 to 4 times a 2 to 3 times a 4 or more
MONTHLY MONTH WEEK times a WEEK
1 2 3 4 5

How often during the last year have you found that you were not able to stop
drinking once you had started?
1 2 3 4 5

How often during the last year have you failed to do what was normally expected
of you because of drinking?
1 2 3 4 5

How often during the last year have you needed a drink in the morning to get yourself
going after a heavy drinking session?

1 2 3 4 5
How often during the last year have you had a feeling of guilt or remorse after
drinking?

1 2 3 4 5

How often during the last year have you been unable to remember what happened the
night before because of your drinking?
1 2 3 4 5

201

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

20. Please mark the answer that is correct for you.

 

NO YES, but not in the YES, during the
last year last year
1 2 3

Have you or someone else been injured as a result of your drinking?
1 2 3

Has a relative, friend, doctor, or other health worker been concerned about your
drinking or suggested you cut down?
1 2 3

202

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

Next, you will be asked a few remaining questions pertaining to “SUBSTANCE
ABUSE” patterns that may or may not be present in your genetic (or blood) and non-
genetic (or non-blood) family members.

Please read the following deﬁnition of “SUBSTANCE ABUSE” (established by the
American Psychiatric Association in the Diagnostic and Statistical Manual of Mental
Disorders or DSM-IV-TR) to assist you in identifying any past or present problems:

“A maladaptive pattern of substance use leading to clinically signiﬁcant impairment or distress,
as manifested by one (or more) of the following four criteria, occurring within a 12-month period:

Recurrent substance use resulting in a failure to fulﬁll major role obligations at work,
school, or home (e. g. repeated absences or poor work performance related to
substance use; substance-related absences, suspensions, or expulsionsﬁ'om school,
neglect of children or household)

Recurrent substance use in situations in which it is physically hazardous (e. g. driving
an automobile or operating a machine when impaired by substance use)

Recurrent substance-related legal problems (e. g. arrests for substance-related
disorderly conduct)

Continued substance use despite having persistent or recurrent social or interpersonal
problems caused or exacerbated by the effects of the substance (e. g. arguments with
spouse about consequences of intoxication, physical ﬁghts) ”

21. Using the deﬁnition for substance abuse provided above, do any of your

Genetic [Blood] Relatives

01'

Non-Genetic [Non-Blood] Relatives (including “adoptive, step-relatives, or
cohabitating relatives, other non-legal family members, or “very” close friends)
have a past or present problem with substance abuse?

0 Yes
0 No

(Mandatory Question: You may choose “not” to answer this question. If this is
the case, you will also be choosing to discontinue your participation in the
remainder of the survey and you will need to exit the survey by closing your
Internet browser).

203

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

The next two questions refer to your Genetic [Blood] Relatives.

 

22. For each category, please indicate how many of your

Genetic [Blood] Relatives

have a past or present problem with substance abuse.

(type in the appropriate number for each category)

Children

Mother

Father

Brothers (includes half-brothers)
Sisters (includes half-sisters)
Maternal Grandparents

Paternal Grandparents

Aunts or Uncles

First Cousins

23. What types of drugs have posed a past or present problem with your

Genetic [Blood] Relatives?

(check all categories that apply)

OOOOOO

OOOOOOOOO

Tobacco (cigarettes, cigars, smokeless tobacco)

Alcohol (beer, wine, liquor)

Marijuana (pot, hash, hash oil)

Caffeine (coffee, tea, chocolate, soda)

Cocaine (crack, rock, freebase)

Amphetamines (Adderall, Ritalin, Provigil, diet pills, speed, meth,
crank)

Designer (intentional use only) (Ecstasy, rooﬁes, GHB, Liquid X)
Hallucinogens (acid, mushrooms, angel dust)

Tranquilizers or Sleeping Pills (Special K, Valium)

Barbiturates (Amytal, Phenobarbitol)

Narcotics (heroin, morphine, Vicodin, Oxycontin, codeine)
Analgesics or OTC medications (aspirin, Tylenol, NSAIDs)
Inhalants (solvents, nitrous, glue, gasoline)

Anabolic steroids (Android, Anadrol, Durabolin, Oxandrin, Winstrol)
Other, please specify

 

204

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

The ﬁnal two questions refer to your Non-Genetic [Non-Blood] Relatives and family
members bearing “no formal legal” relationship but nonetheless considered family by
you.

 

24. For each category, please indicate how many of your NonGenetic [Non-Blood]
Relatives have a past or present problem with substance abuse.

REMEMBER: Non-Genetic [Non-Blood] Relatives include: “very” close friends,
and adoptive, step, or cohabitating” family members and family members bearing
“no formal legal” relationship but nonetheless considered family by you.

For each category, please add up and type in the appropriate number.

Romantic Partner or Spouse

Very Close Friends

Children (Step, Adoptive, or Cohabitating)
Mother (Step, Adoptive, or Cohabitating)

Father (Step, Adoptive, or Cohabitating)

Brothers (Step, Adoptive, or Cohabitating)

Sisters (Step, Adoptive, or Cohabitating)
Grandparents (Step, Adoptive, or Cohabitating)
Aunts or Uncles (Step, Ad0ptive, or Cohabitating)

205

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

25. What types of drugs have posed a problem (past or present) for your

Non-Genetic [Non-Blood] Related family members (“very” close friends,
adoptive, step, cohabitating, or other non-legal family members)?

(check all categories that apply)

OOOOOO

OOOOOOOOO

Tobacco (cigarettes, cigars, smokeless tobacco)

Alcohol (beer, wine, liquor)

Marijuana (pot, hash, hash oil)

Caffeine (coffee, tea, chocolate, soda)

Cocaine (crack, rock, freebase)

Amphetamines (Adderall, Ritalin, Provigil, diet pills, speed, meth,
crank)

Designer (intentional use only) (Ecstasy, rooﬁes, GHB, Liquid X)
Hallucinogens (acid, mushrooms, angel dust)

Tranquilizers or Sleeping Pills (Special K, Valium)

Barbiturates (Amytal, Phenobarbitol)

Narcotics (heroin, morphine, Vicodin, Oxycontin, codeine)
Analgesics or OTC medications (aspirin, Tylenol, NSAIDs)
Inhalants (solvents, nitrous, glue, gasoline)

Anabolic steroids (Android, Anadrol, Durabolin, Oxandrin, Winstrol)
Other, please specify

 

206

 

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

© Copyright, 1987 Gail M. Wagnild & Heather M. Young. Used by permission.
All rights reserved. “The Resilience Scale” is an international trademark of Gail
M. Wagnild & Heather M. Young.

RS-l 5 is a modiﬁed version of the Resilience Scale, developed and tested by
James T. Neill and Katica L. Dias and appears in the (2001) article, Adventure
education and resilience: The double-edged sword. Journal of Adventure
Education and Outdoor Learning, 1(2), 35-42.

207

 

Appendix H (continued): College Student Survey — Posted on Website

College Student Survey

This completes the survey!
Please print or email yourself a copy of this page to present as evidence of your
participation in receiving your gift/award. Please remember, your survey responses are

anonymous and receiving your gift/ward will not require use of your name or identity.

Thank you for taking the time to participate!
Your answers are greatly appreciated!

208

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209

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