University of Iowa
Iowa Research Online
Theses and Dissertations
Spring 2015
Estimated effects of perceived sleep deprivation on
psychological well-being during college
Maria Ann Richter
University of Iowa
Copyright 2015 Mia Ann Richter
This dissertation is available at Iowa Research Online: http://ir.uiowa.edu/etd/1740
Recommended Citation
Richter, Maria Ann. "Estimated effects of perceived sleep deprivation on psychological well-being during college." PhD (Doctor of
Philosophy) thesis, University of Iowa, 2015.
http://ir.uiowa.edu/etd/1740.
Follow this and additional works at: http://ir.uiowa.edu/etd
Part of the Education Commons
ESTIMATED EFFECTS OF PERCEIVED SLEEP DEPRIVATION ON
PSYCHOLOGICAL WELL-BEING DURING COLLEGE
by
Maria Ann Richter
A thesis submitted in partial fulfillment
of the requirements for the Doctor of
Philosophy degree in Educational
Policy and Leadership Studies
in the Graduate College of
The University of Iowa
May 2015
Thesis Supervisors: Professor Ernest T. Pascarella
Professor Michael Teague
Copyright by
MARIA ANN RICHTER
2015
All Rights Reserved
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
PH.D. THESIS
This is to certify that the Ph.D. thesis of
Maria Ann Richter
has been approved by the Examining Committee
for the thesis requirement for the Doctor of Philosophy
degree in Educational Policy and Leadership Studies at the
May 2015 graduation.
Thesis Committee:
Ernest T. Pascarella, Thesis Supervisor
Michael Teague, Thesis Supervisor
Clar Baldus
Christopher Morphew
Joseph Coulter
To grapple with what constitutes optimal functioning is, at the most basic level, to broach
ultimate questions of why we are here and how we should live.
ii
ACKNOWLEDGMENTS
I approached this dissertation with curiosity, joy, wonderment, and awe. As I cross this
finish line, my supporting crew circles around in celebration. This is my chance to publically
acknowledge my team.
To my committee: thank you for matching my pace and enthusiasm every step of this
journey. Ernie, Teague, Clar, Joe, and Chris, you know who you are and you know what you do
for aspiring students. Thank you all for your hard work and positive energy. Thank you for
believing in me and sharing in my excitement. We make a great team!
To my mom and dad: thank you for always encouraging and nurturing my desire to
learn, explore, and read. You consistently demonstrate how hard work and self-discipline make
any goal a reality. Your support, laughter, patience, and kindness will stay with me forever.
It’s my pleasure to share this honor with you. This is yours much as it is mine. I hope this
achievement inspires you both to continue expanding and growing at every stage in your lives.
To my partner and sidekick, Nate: thank you for teaching me how to laugh at myself
and slow down a little. Goodness knows I sometimes need it. Your strict adherence to the
“best” makes life easy: I always know what you’re going to provide. Knowing I could
aggressively pursue this goal with your support does not go unnoticed.
To my circle of friends: I could not have imagined such rich friendships would someday
be mine. Mary, Jay, Laila, Merrilee, Bob, Jesse, Flora, JK, Clint, Crabby, and SD, our energy
and positive flow made this journey a wonderful experience. Thank you all for your interest
and joy.
Last but not least, I would like to thank my yellow Labrador, Buddy. Buds, you’re the
ultimate dissertation pal. Always willing to go for a walk, hold hands, or simply sit and feel the
sunshine, you never once told me you were too busy to listen. Thank you.
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I love you all, and I’m thrilled to see what’s next! Onto the next one!
iv
ABSTRACT
This study examined the effects of perceived sleep deprivation on psychological wellbeing using multiple linear regression techniques on a longitudinal, multi-institutional sample
of students at four-year universities and colleges. Using a College Outcomes model as a
theoretical foundation, this study examined perceived sleep deprivation’s influence on
psychological well-being at the end of four academic years, while controlling for institutional
and student background characteristics that are theoretically associated with psychological
well-being. Pre-test and post-test data from the Wabash National Study of Liberal Arts
Education (WNS) created findings suggesting sleep deprivation is positively related to total
psychological well-being and the six subscales composing the complete measure (selfacceptance, autonomy, environmental mastery, positive relationships with others, purpose in
life, and personal growth). This study contributes to college outcome models by supporting the
claims for the importance of healthy, habitual sleep in relation to student’s ability to achieve
overall psychological well-being, as well as the six subscales of the total model. This study has
implications for higher education and public health policy, including practical applications for
those involved with higher education, including students, staff, faculty, and administrators.
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PUBLIC ABSTRACT
The purpose of this study is to better understand the relationships between sleep and
well-being. An important type of well-being is psychological well-being. When we are
psychologically well, we have more positive life outlooks, better relationships with our friends
and family, think more highly of ourselves, feel more capable to deal with life’s adversities,
trust more in who we are, and overall, just feel better. In general, when people feel that they
have psychological well-being, they also feel that their lives are more pleasant to live. This is a
good thing, and we as human beings should seek ways to maximize our psychological wellbeing.
An important question this study addresses is the relationship between your sleep and
your psychological well-being. How do you feel when you have a full night’s sleep? Are you
rested, alert, clear-minded, and ready to take on your day? Now, compare this to how you feel
when you are sleep deprived. We all know what it feels like to have enough sleep, or
unfortunately, to be sleep deprived. Intuitively, we know when people lack sleep they feel a
decline in their psychological well-being. Perhaps lack of sleep makes an individual less
positive, damages relationships with others, or causes a person to feel less able to deal with
life’s stresses.
This study seeks to better understand the unique relationship between sleep deprivation
and psychological well-being. By better understanding if and how sleep deprivation influences
psychological well-being, individuals can realize the importance of consistent, restful sleep.
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TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION ................................................................................................ 1
Eudaimonia ............................................................................................................................... 1
Purpose and Research Questions ............................................................................................ 10
Overview of Methods ............................................................................................................. 11
Significance of the Study........................................................................................................ 11
Definition of Terms ................................................................................................................ 12
Assumptions, Limitations, and Delimitations ........................................................................ 13
Conclusion .............................................................................................................................. 14
Chapter Organization.............................................................................................................. 14
CHAPTER 2: REVIEW OF THE LITERATURE .................................................................... 16
College Impact Research ........................................................................................................ 16
Theoretical Framework........................................................................................................... 20
Research on Psychological Well-being .................................................................................. 24
Student Background Characteristics ....................................................................................... 25
Participation in College Experiences and Effects on Psychological Well-being ................... 31
Sleep Deprivation and Psychological Well-being .................................................................. 43
Summary................................................................................................................................. 47
Conclusion .............................................................................................................................. 48
CHAPTER 3: METHODOLOGY ............................................................................................. 51
Purpose and Research Questions ............................................................................................ 51
Research Design ..................................................................................................................... 52
Research Variables ................................................................................................................. 54
Data Analysis .......................................................................................................................... 62
Limitations .............................................................................................................................. 64
Conclusions ............................................................................................................................ 65
CHAPTER 4: RESULTS OF THE STUDY .............................................................................. 69
Descriptive Statistics .............................................................................................................. 70
Correlations ............................................................................................................................ 72
Multiple Linear Regression .................................................................................................... 73
Results Summary .................................................................................................................... 81
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CHAPTER 5: DISCUSSION, IMPLICATIONS, AND CONCLUSIONS ............................... 93
Summary and Discussion of Results ...................................................................................... 93
Theoretical Implications ......................................................................................................... 99
Policy Implications ............................................................................................................... 100
Limitations ............................................................................................................................ 104
Implications for Future Research Endeavors........................................................................ 104
Conclusions .......................................................................................................................... 105
REFERENCES ..................................................................................................................... 93107
viii
LIST OF TABLES
Table
1. Definitions of Theory-Guided Dimensions of Well-Being ................................................ 15
2. Variable Relationship with total Psychological Well-being, Personal Growth, Positive
Relationships with Others, Self-Acceptance, Autonomy, Environmental Mastery, and
Purpose in Life ................................................................................................................... 49
3. Variable Definitions ............................................................................................................ 66
4. Descriptive Statistics for All Variables ............................................................................... 82
5. Correlation Matrix ............................................................................................................... 84
6. Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation
on Total Psychological Well-being ..................................................................................... 85
7. Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation
on Self-Acceptance .............................................................................................................. 86
8. Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation
on Positive Relationships with Others ................................................................................. 87
9. Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation
on Environmental Mastery .................................................................................................. 88
10. Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation
on Autonomy ...................................................................................................................... 89
11. Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation
on Purpose in Life .............................................................................................................. 90
12. Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation
on Total Personal Growth .................................................................................................. 91
13. Multiple Linear Regression Model Estimating Effects of Changed Perceived Sleep
Deprivation from high-school to end of fourth year College on Total Psychological
Well-being .......................................................................................................................... 92
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LIST OF FIGURES
Figure
1. Theoretical model................................................................................................................. 57
x
CHAPTER 1
INTRODUCTION
Eudaimonia
Research delving into well-being, specifically psychological well-being, has increased in
recent decades, capturing the attention of epidemiologists, social scientists, policy makers, and
economists (Diener, Suh, Lucas, & Smith, 1999; Herzog & Strevey, 2008; Huppert, 2005;
Kahneman, Diener, & Schwarz, 1999; Marks & Shah, 2005; Mulgan, 2006; Ryan & Deci, 2001;
Ryff, Keyes, & Shmotkin, 2002; Steptoe, O’Donnell, Marmot, and Wardle, 2007). Two types of
psychological well-being have emerged from this research: eudaimonic and hedonic.
Eudaimonic well-being is concerned with human actualization, purposeful engagement in life,
and the realization of human potential (Keyes, Shmotkin, & Ryff, 2002; Steptoe, O’Donnell,
Marmot, & Wardle, 2007; Ryan & Deci, 2001; Ryff, 1989a; Ryff & Keyes, 1995; Ryff, Keyes,
& Hughes, 2003; Waterman, 1993). Eudaimonic well-being distinctly differs from hedonic wellbeing, as hedonic well-being is concerned with the pursuit of pleasure and happiness
(Czikszentmihalyi, 1990, 1996; Seligman, 2002, 2011; Waterman, 2008). While eudaimonic
well-being emphasizes individuals’ evaluations of functioning in life, hedonic well-being
emphasizes an individual’s evaluations of feelings regarding life (Keyes & Annas, 2009; Ryan,
Huta, & Deci, 2008). For the purpose of this dissertation, eudaimonic well-being will be the type
of psychological well-being researched.
Eudaimonic psychological well-being has been a topic of discussion for centuries. In his
Nichomanchean Ethics of 300 B.C., Aristotle explained eudaimonia as the realization of one’s
true potential (Aristotle,1925; Keyes, Shmotkin, & Ryff, 2002; Kraut, 2008; Ryff, 1989a; Ryff,
Singer, & Love, 2004; Ryff, 2013 ). According to Aristotle’s explanation of eudaimonia,
1
individuals enter life with unique capacities called one’s daimon (Broadie, 1991). Actualization
of one’s daimon is one of the highest virtues attainable to humans, and this pursuit allows one to
experience eudaimonia (Ryff, 2014). Our purpose in life is to realize our unique capacities,
strive for their realization, and thereby find the meaning of our lives by actualizing our true
potentials (Hudson, 1996). Striving to actualize potential makes one psychologically well, as the
purpose of life is to fulfill one’s unique capacities as a human being. The writings of Aristotle
claimed the highest of all human goods is not happiness, feeling good, or satisfying one’s
numerous appetites. In fact, certain aspects of positive functioning require effort and discipline
which may be at odds with short term happiness (Waterman, 1984; Waterman, 2007; Waterman,
2008). Instead, well-being is found by pursing activities of the soul that are in accord with
virtue.
Since Aristotle’s first arguments, articulating the components of psychological well-being
has continued to captivate scholars and researchers seeking to move eudaimonic from the
philosophical to the empirical realm (Coan, 1977; Diener, 1999; Waterman, 1993; Waterman,
2007; Ryff, 2014). Well-being is a multifaceted concept with many dimensions, and attempts to
establish constitutional criteria of well-being have been diverse, value-laden, and lacking
empirical support (Boehm & Kubzansky, 2012; Siefert, 2005). Empirical endeavors to
operationalize eudaimonic well-being began in the 1980s, stemming from earlier theories of
clinical and adult developmental psychology (Keyes, Shmotkin, & Ryff, 2002). One
operationalization of eudaimonic well-being was developed by Dr. Carol Ryff. Dr. Ryff’s
operationalization of psychological well-being aimed to measure thriving vis-à-vis existential
challenges humans face in their lifetimes. Today, her definition and operationalization of
2
psychological well-being is the most accepted, respected, and utilized measure of psychological
well-being in the world (Boehm & Kubzansky, 2012; Ryff & Keyes, 1995).
Psychological Well-being Operational Definition
Eudaimonic psychological well-being is operationalized by the Ryff Scales of
Psychological Well-Being (SPWB) (Ryff, 1989; Ryff & Keyes, 1995). The SPWB is a
theoretically grounded 54-item instrument based on a convergence of multiple frameworks of
positive functioning (Ryff, 1989, 1995; Ryff and Keyes, 1995). It measures six unique
dimensions of psychological well-being: (1) self-acceptance, (2) personal growth, (3) purpose in
life, (4) positive relations with others, (5) environmental mastery, and (6) autonomy (Ryff, 1989;
Ryff & Keyes, 1995; Keyes, Shmotkin, & Ryff, 2002).
Self-acceptance measures one’s positive evaluation of oneself. Having self-acceptance is
to have a positive opinion of one’s self, recognizing both one’s negative and positive
characteristics. Personal growth measures a sense of continued development and origination as
an individual. Having a sense of personal growth is to recognize the self as able to improve thru
time as well as continued seeking to fulfill one’s highest potential. Purpose in life measures
one’s belief that one is living a purposeful and meaningful life. Having purpose in life includes
nurturing goals for one’s life while simultaneously finding meaning in what one does. Positive
relations with others measures the quality of relationships one possesses with others. Having
positive relations means maintaining warm and trusting relationships with others while
recognizing and considering the needs of the self and others.
Environmental Mastery measures an individual’s ability to effectively manage their life
as well as the surrounding world. Possessing environmental mastery also includes identification
of environments to which one is well-suited. Finally, Autonomy measures an individual’s sense
3
of self-determination. Having autonomy is being able to independently regulate one’s behavior
without undue influence from outside pressure.
Psychological Well-being and College Students
Psychological well-being is often thought of as a hallmark of the liberal arts educational
experience because educational encounters and experiences allow students to search for meaning
and direction in their lives (Seifert, 2005; Stull, 1962). The liberal arts education is also aimed at
helping students realize their true potentials and actualize their human existence. According to
Stull (1962), the purpose of the liberal arts is to make students intelligent and responsible agents
who are able to participate richly in the good life. Eudaimonic psychological well-being is
concerned with accessing the good life, and the liberal arts education is aimed at helping students
reach this goal. Thus, as the liberal arts education is concerned with preparing students for
participation in the good life, it is also concerned with students’ eudaimonic psychological wellbeing.
Notwithstanding the liberal arts’ interest in psychological well-being, there is a need to
better understand the factors that influence student’s psychological well-being. To date, scant
research has examined psychological well-being among college students and how different
college experiences alter psychological well-being (Bowman, 2010). Thankfully, an increased
focus on student’s psychological well-being in college has the attention of researchers, policymakers, educators, parents, and students, asking for better clarification to the pathway of
psychological well-being. Research has attempted to evaluate some student factors (i.e. sex, race
or ethnicity) as well as various health factors influencing psychological well-being. Still, the
research to date is incomplete. To better understand psychological well-being of college
students, research must move forward to focus on what occurs during college that contributes to
4
or detracts from psychological well-being. How does the college environment influence
psychological well-being of students? Specifically, how does perceived sleep deprivation of
students in the college environment influence their psychological well-being? In the general
population, associations between sleep and psychological well-being have only recently began to
garner attention (Hamilton, Nelson, Stevens, & Kitzman, 2007; Howell, Digdon, Buro, 2010;
Wood, Joseph, Lloyd, & Atkins, 2009). Very little attention has been paid to the relationship
between sleep deprivation and psychological well-being in college students. The lack of
empirically driven research to better understand the relationship between sleep deprivation and
psychological well-being in students of higher education drives this study. The purpose of this
dissertation is to create a better understanding of the relationship between sleep deprivation and
psychological well-being, as measured by the SPWB, in college students.
Psychological Well-being Impacts
As previously mentioned, studies with a college student sample utilizing the SPWB as the
dependent measure are not abundant. However, many researchers using other valid populations
have utilized the SPWB as the dependent measures of interest. For the purpose of this
dissertation, any empirically sound study utilizing SPWB as a dependent measure may be
considered for inclusion to provide a more comprehensive and complete picture of psychological
well-being in the adult population.
Numerous empirical studies have been conducted utilizing the SPWB. These studies
evaluated how psychological well-being varied by age, gender, and socioeconomic status
(Clarke, Marshall, Ryff, & Rosenthall, 2000; Marmot, Ryff, Bumpass, Shipley, & Marks, 1997;
Ryff, 1989; Ryff & Keyes, 1995). SPWB has also been used to examine psychological wellbeing as an outcome of life transitions and experiences as well as the role of psychological well-
5
being in understanding resiliency when facing adversity (see Kling, Ryff, & Essex, 1997; Kling,
Seltzer, & Ryff, 1997; Ryff, Lee, Essex, & Schmutte, 1994; Ryff, Singer, Love, & Essex, 1998;
Singer, Ryff, Carr, & Magee, 1998).
Other researchers seeking to discern individuals’ level of functioning in their daily lives
have utilized the SPWB. Applying the health model of human functioning, these researchers
viewed health as the presence of high levels of physical and psychological well-being (Keyes &
Shapiro, 2004). From the health model perspective, studies of the United States adult population
have shown a lack of psychological well-being is associated with increased burden as measured
by lost work productivity, disability and cardiovascular disease, chronic physical illness,
healthcare utilization, and decreased psychosocial functioning (Keyes, 2007). Studies have also
shown psychological well-being makes a contribution to many outcomes in adult life, including
improved physical health, increased life satisfaction, and increased social support (Bowman &
Kitayama, 2009; Ryff, 2008). Clearly, the presence or absence of psychological well-being
(PWB) changes individual functional status.
College Outcomes
Various theoretical models to explain college outcomes (such as psychological wellbeing) have been created by vetted researchers (Chapman & Pascarella, 1983; Donaldson &
Grahm, 1999; Hossler, Braxton, & Coppersmith, 1989; Jackson, 1982). A common feature of
these different models is their consideration of individual characteristics, the institutional
environment, and final outcomes. Psychological well-being has been the outcome measure in
many previous process-oriented studies as well as life experience studies, with researchers
utilizing various forms of these college outcomes models to better understand psychological
well-being, finding both student’s background characteristics and the institution’s environment
6
influence student’s psychological well-being (see Heidrich & Ryff, 1993; Ryff & Essex, 1992;
Ryff, Lee, Essex, & Schumutte, 1994; Schmutte & Ryff, 1994; Tweed & Ryff, 1991; Van Riper,
Ryff, & Pridham, 1992). However, researchers to date have not extensively studied the influence
of the college institution’s environment and context on psychological well-being. Few studies
have specifically addressed the college environment and its role in formulation of a student’s
psychological well-being. Even fewer studies have specifically addressed the role of sleep
deprivation in college students as it relates to their psychological well-being.
A model of college outcomes is further explained in Chapter 2. Also, previous research
on college student’s and psychological well-being is reviewed. Additionally, because
psychological well-being in college students has not been extensively studied, relevant research
on other adult populations and psychological well-being are included. This inclusion helps to
better understand the unique factors that contribute to an individual’s psychological well-being
and create a more refined understanding of the contextual influences of psychological wellbeing.
Sleep Deprivation
The independent variable of interest in this study is perceived sleep deprivation. Sleep
deprivation is common and critically relevant to our society (Bonnet, Balkin, Dinges, Roehrs,
Rogers, & Wesensten, 2005). Oddly, humans are the only animals that choose to sleep less than
our biological clocks and needs for sleep indicate (Turek, 2005). As clinically described by the
International Classification of Diseases, sleep deprivation is recognized by the diagnosis of
insufficient sleep syndrome (Bonnet et al, 2005). Between 1/4 and 1/3 of American adults
complain of sleep deprivation and nearly 70 million Americans suffer from chronic sleep
disorders and sleep deprivation (Steptoe, O’Donnell, Marmot, and Wardle, 2007; US Department
7
of Health and Human Services, 1993). Our culture and society today implies: (1) sleep
deprivation is the norm instead of the exception, (2) places value on attempting to minimize
sleep time, and (3) continues to operate with the philosophy that sleep deprivation is not
important and can be overcome by sheer force of will (Bonnet & Arand, 1995; Turek, 2005;
Wolverton, 2013). These implications are dangerous. These cultural and societal views must be
addressed with empirical evidence supporting the importance of regular and sufficient sleep.
This study seeks to add to this empirical effort.
Past research addressing sleep deprivation has focused on the detrimental effects of
leading a sleep deprived life. Sleep deprivation has been associated with impaired cognitive
function, increased work absenteeism and accidents, decrease in vitality, reduced mental health
status, premature mortality, decreased social functioning, lower physical health, decreased
general quality of life, acute illness, and chronic illness (Lund, Reider, Whiting, & Prichard,
2005; Steptoe, O’Donnell, Marmot, and Wardle, 2007). Additionally, strong associations have
been found between sleep deprivation and anxiety, depression, somatic pain, decreased academic
performance, increased risk-taking behavior, increased drug use, and willingness to drive while
drowsy (Lund et al, 2005). Cleary, the determinants of sleep deprivation are numerous. Sleep
deprivation is often experienced by individuals undergoing life transitions or persons
experiencing elevated levels of stress. College students qualify for both of these sleep
deprivation risk factors.
College years are a time of life transition which provides a new degree of personal
freedom previously unexperienced, allowing for volition in lifestyle habits without parental
influence. College may also be a time of stress for many students. Sleep is a lifestyle factor often
altered in college, partially due to stress, social experiences, drinking, partying, and academic
8
demands (Pilcher, Ginter, & Sadowsky, 1997). College students frequently report insufficient
sleep as well as feeling sleep deprived (Lau, Wong, Ng, Hui, Cheung, & Mok, 2013; Lund,
Reider, Whiting, & Prichard, 2010; Pilcher & Walters, 2010; Suen, Ellis, & Tam, 2008). Sleep
deprived students report increased physical health complaints as well as increased anxiety,
depression, anger, fatigue, and confusion. Lower quality sleepers also report decreased positive
affect, decreased life satisfaction, increased missing of class, increased use of prescription over
the counter drugs and recreational drugs to regulate sleep, increased alcohol consumption, and
increased daytime sleepiness (Lund, Reider, & Prichard, 2005).
Sleep deprivation in college students is a problem that has been recognized for quite
some time. Hicks, Fernandez, and Pellegrini (2001) conducted two decades of research
addressing changes in sleep satisfaction of university students. To conduct their study, they
asked students to complete a questionnaire of their sleep satisfaction in 1978, 1988, and 2000.
Rating of sleep satisfaction included a measure of perception of quantity as well as perception of
deprivation. In 1978, of 1489 students sampled, 24% of respondents were dissatisfied with their
sleep. In 1988, of 734 students sampled, 53% were dissatisfied with their sleep. In a 2000-2001
update, 71% of 1462 students sampled reported dissatisfaction with their sleep. Put another way,
students in 2000 were 2.96 times more likely to report sleep dissatisfaction than students in 1978.
Authors of this research “…believe that this is an alarming trend, and effort to reverse it is
needed” (2001).
The connection between sleep deprivation and deleterious outcomes is clear, and authors
have called for research to continue to investigate the relationship between sleep and well-being
(Pilcher, Ginter, and Sadowsky, 1997; Turek, 2005). However, limited research exists
investigating the relationship between sleep deprivation and psychological well-being in college
9
students. This study will fill a gap in the empirical understanding of psychological well-being by
researching how sleep deprivation influences psychological well-being at the end of four years of
college.
Purpose and Research Questions
The purpose of this study is to investigate the influence of sleep deprivation on students’
psychological well-being, while controlling for the effects of pre-college psychological wellbeing, student background characteristics, theoretically relevant measures of other collegiate
experiences, and other the institutional environmental characteristics. The study utilizes multiple
linear regression to answer the primary research questions for this study:
1) How is the perception of sleep deprivation related to psychological well-being over
four years in college students?
a. How is the perception of sleep deprivation related to self-acceptance in
college students?
b. How is the perception of sleep deprivation related to positive relations with
others in college students?
c. How is the perception of sleep deprivation related to environmental mastery in
college students?
d. How is the perception of sleep deprivation related to autonomy in college
students?
e. How is the perception of sleep deprivation related to purpose in life in college
students?
f. How is the perception of sleep deprivation effect personal growth in college
students?
10
2) Do changes in perceptions of sleep deprivation from high school to college influence
psychological well-being?
Overview of Methods
This study utilizes data from the Wabash National Study of Liberal Arts Education
(WNS), a multi-institution longitudinal dataset. The institutional sample in the WNS dataset
includes 17, four-year institutions located in 11 states from 4 regions in the United States. This
study uses a longitudinal design in which the significant effects come from within subject change
over time, controlling for time-invariant individual differences of participating subjects. Because
this study is interested in analyzing a relationship in a model which incorporated theory driven
independent variables and control variables, regression analysis is the appropriate statistical
method for determining the relationship between sleep deprivation and psychological well-being.
Given the continuous nature of the dependent measure, multiple linear regression is used to
estimate student’s perceptions of sleep deprivation on psychological well-being. Finally, several
potentially confounding variables are controlled for in the upcoming analyses, thus ensuring that
significant effects on psychological well-being were more likely attributed to perceived sleep
deprivation but not individual demographic factors or psychological well-being at baseline.
The dependent measure in the study is previously operationalized in this chapter. The
regression model includes four categories of independent variables: background characteristics,
pre-college psychological well-being, institutional characteristics, and sleep deprivation.
Significance of the Study
This study is significant because longitudinal analysis of sleep deprivation’s impact on
psychological well-being of college students over four years is yet to be completed. Further
understanding of the college experience and its relationship to psychological well-being is
11
necessary. Research advancing the understanding of psychological well-being must continue
account for all levels of analysis of adaptive human functioning. Multidisciplinary research has
been called for to create studies of development which attend to the context of people’s lives,
with psychological well-being serving as a vital thread throughout (Ryff, 2013). These areas of
psychosocial and contextual inquiry also link to research on health and biological regulations.
This study strives to do just that.
This study also adds to research literature and philosophical argumentation that life is a
pursuit requiring effort, and good lives come from frequently challenging and frustrating
engagement in living. The extant formulation of human health needs refinement to broaden the
meaning of health to include the core features of psychological well-being, as the social sciences
have articulated and studied. This study helps answer the call by Ryff and Singer (2003) for
“new research probing the connections between the health of the mind, broadly defined, and the
health of the body” (p. 272).
Definition of Terms
Daimon. An ideal in the sense of an excellence. A perfection toward which one strives. Gives
meaning and direction to one’s life (Ryff, 1989, p. 1070).
Eudaimonia. (eu-DIE-mo-NIA): “The feelings accompanying behavior in the direction of, and
consistent with, one’s true potential” (Waterman, 1984, p. 16). Fulfilling one’s potential and
identifying meaningful life pursuits (Waterman, 2007).
Psychological well-being (PWB). Eudaimonic psychological well-being. Emphasis is placed on
an individuals’ evaluation of functioning in life. Composed of six component parts: (1) purpose
in life, (2) personal growth, (3) self-acceptance, (4) positive relations, (5) environmental mastery,
12
and (6) autonomy. Together, these theoretically based components comprise a multidimensional
conceptualization of eudaimonic well-being.
Assumptions, Limitations, and Delimitations
All researchers approach their studies with assumptions that may affect the research
findings. Assumptions are parts of the research that the researcher takes for granted. A series of
assumptions are made to proceed with the following study. First, the theoretical models selected
are assumed to be sound. Second, based on previous empirical assessments, the secondary
dataset’s measures and variables were assumed to be reliable and valid. Third, the study
participants were assumed to have completed the instruments honestly and to the best of their
abilities and to have reported accurate answers to best represent themselves. Fourth,
respondent’s answers are assumed to be their perceptions and are not guaranteed to be 100%
accurate representations of the truth. Fifth, empirical studies utilized for literature review are
assumed to have been completed in sound fashion. Finally, when analyzing the data, the data are
assumed to follow the requirements of multiple linear regression (Chapter 3).
Limitations do exist in the following study. First, because a secondary data set is utilized,
the variables and their definitions have been previously fixed. The variables to create questions
could not be changed. For example, students were not asked the number of hours of sleep
obtained nightly, nor were they asked to provide common reasons for feeling sleep deprived.
These questions would have provided additional information pertinent to the study. Second,
because the dataset was previously fixed, certain variables previously linked to psychological
well-being could not be included, nor were reasonable proxies available for substitution in the
extant dataset.
13
Finally, the scope of the study is intentionally limited. The topic of sleep deprivation and
psychological well-being is vast. The purpose of this study is to answer the research questions
posed. A full account of sleep and sleep deprivation is beyond the scope of this study. With that
in mind, information detailing the science behind sleep and sleep deprivation is limited, and
instead the focus is specifically on sleep deprivation as it may impact psychological well-being
in college students.
Conclusion
Limited research exists that evaluates college student’s psychological well-being as
operationalized in this study. Even fewer studies have attempted to look at the influence of sleep
deprivation on student’s psychological well-being. A clear need in higher education literature
exists to better understand the effect sleep deprivation might have on college outcomes,
including psychological well-being. This research helps alleviate this need.
Chapter Organization
This dissertation is divided into five chapters. Chapter 1 introduces psychological wellbeing, including an operational definition as well as its importance. From this discussion, the
need for additional research on psychological well-being, especially research addressing the
influence sleep deprivation upon psychological well-being is proposed. Next, the purpose and
central argument of the present study are presented. Chapter 2 reviews the literature on
psychological well-being and provides the theoretical foundation for the research. Chapter 3
reviews the methodological approach utilized to analyze the research, including study design,
sample, instruments, and variables. It also includes a brief description of the data analysis.
Chapter 4 provides the study’s complete data analysis. Chapter 5 is comprised of the results,
discussion of the implication, and other issues to guide future research.
14
Table 1
Definitions of Theory-Guided Dimensions of Well-Beingª
Self-acceptance
High scorer: Possesses a positive attitude toward the self; acknowledges and accepts
multiple aspects of self, including good and bad qualities; feels positive about past life.
Low scorer: Feels dissatisfied with self; is disappointed with what has occurred with
past life; is troubled about certain personal qualities; wishes to be different than what he
or she is.
Positive relations with others
High scorer: Has warm, satisfying, trusting relationships with others; is concerned about
the welfare of others; capable of strong empathy, affection, and intimacy; understands
give and take of human relationships.
Low scorer: Has few close, trusting relationships with others; finds it difficult to be
warm, open, and concerned about others; is isolated and frustrated in interpersonal
relationships; not willing to make compromises to sustain important ties with others.
Autonomy
High scorer: Is self-determining and independent; able to resist social pressures to think
and act in certain ways; regulates behavior from within; evaluates self by personal
standards.
Low scorer: Is concerned about the expectations and evaluations of others; relies on
judgments of others to make important decisions; conforms to social pressures to think
and act in certain ways.
Environmental mastery
High scorer: Has a sense of mastery and competence in managing the environment;
controls complex array of external activities; makes effective use of surrounding
opportunities; able to choose or create contexts suitable to personal needs and values.
Low scorer: Has difficulty managing everyday affairs; feels unable to change or
improve surrounding context; is unaware of surrounding opportunities; lacks sense of
control over external world.
Purpose in life
High scorer: Has goals in life and a sense of directedness; feels there is meaning to
present and past life; holds beliefs that give life purpose; has aims and objectives for
living.
Low scorer: Lacks a sense of meaning in life; has few goals or aims, lacks sense of
direction; does not see purpose of past life; has no outlook or beliefs that give life
meaning.
Personal growth
High scorer: Has a feeling of continued development; sees self as growing and
expanding; is open to new experiences; has sense of realizing his or her potential; sees
improvement in self and behavior over time; is changing in ways that reflect more selfknowledge and effectiveness.
Low scorer: Has a sense of personal stagnation; lacks sense of improvement or
expansion over time; feels bored and uninterested with life; feels unable to develop new
attitudes or behaviors.
ª Ryff and Keyes (1995, p.1072)
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CHAPTER 2
REVIEW OF THE LITERATURE
Chapter 2 reviews the theory and research related to the study of psychological wellbeing beginning with an explanation of the conceptual framework. Next, a description of the
theoretical model pertaining to psychological well-being is included. Following theoretical
explanation, a review of empirical studies evaluating characteristics and experiences associated
with psychological well-being is included. A brief discussion of sleep deprivation follows.
Finally, empirical studies are reviewed that have evaluated characteristics and experiences
associated with sleep deprivation and psychological well-being in college students.
The purpose of this literature review is to provide a strong rationale for this study. This
review of the current literature on psychological well-being identified common themes, including
the role of background characteristics as well as the strong influence of both the environment and
experiences on psychological well-being. This literature review also identifies important gaps in
the literature that should be addressed while providing certainty for the decision to utilize Ryff’s
model of psychological well-being. Finally, this literature review provides sound argumentation
supporting this current study on sleep deprivation and psychological well-being.
College Impact Research
To better understand psychological well-being, researchers have sought to identify
influential traits of individuals, particular life experiences, or contextual situations which alter
psychological well-being status. This inquiry identified key influences on psychological wellbeing. While psychological well-being has been largely studied in older populations and
nationally representative surveys in the United States, the study of psychological well-being in
16
college student populations is underdeveloped. Nonetheless, psychological well-being in college
student populations can and should be studied. The application of college student outcomes
models is appropriate when studying psychological well-being in college students as higher
education researchers consider psychological well-being to be an outcome of a liberal arts
education and student outcome models provide the theoretical framework necessary to explore
this area of research (King, Kendall-Brown, Lindsay, VanHecke, 2007; Seifert, Goodman,
Lindsay, Jorgensen, Wolniak, Pascarella, & Blaich, 2007). While providing theoretical support
for the study of college outcomes, these models also help identify key variables to be included in
college outcomes research models. For the purpose of this study, the relevant student outcome
model is discussed below.
Student Outcomes Model
Scholars have created various models to analyze student outcomes of the college
experience (Astin, 1970a, 1970b; Hossler & Gallagher, 1987; Pascarella, 1984; Pascarella &
Terenzini, 2005; Paulsen, 1990). These models consistently consider three factors: individual
characteristics, institutional environments, and outcomes. Individual characteristics are traits or
states pertinent to the unique individual. Some examples of individual characteristics include:
race and ethnicity, sex or gender, socioeconomic status, academic ability, and parental
educational attainment. Institutional characteristics pertaining to the unique institution may
include: type, selectivity, and environment. Outcomes are the effects rendered by the college
experience. Some examples include: leadership ability, moral development, graduation, career
opportunity, and psychological well-being.
Because the college environment is composed of various programmatic and structural
characteristics influential to growth, experiences, and development of undergraduate students, it
17
is necessary to account for potentially-biasing background characteristics, pre-college
experiences, and college-level experiences by using college impact methodology. Importantly,
college impact models that measure student change do not utilize any one student development
theory or dimension. Instead, college impact models concentrate on the origins and processes by
which a student changes during college (Pascarella & Terenzini, 2005). Also, college impact
models are both less grounded in prior developmental theory and less specific than theories of
college student development (Pascarella & Terenzini, 2005). College impact research frequently
incorporates conceptual frameworks pertaining to student background characteristics,
institutional characteristics, measures of student involvement, and various indicators of college
effects (Weidman, 1989). The following discussion describes one model frequently used by
researchers in higher education to better understand various outcomes of college. This model
serves as the conceptual framework of this study.
Astin’s I-E-O. A pioneering college impact model was created by Astin (1970a, 1970b,
1993). The Input-Environment-Outcome (I-E-O) model states that the effects of college can be
isolated and measured with the creation of an analytical model composed of three distinct blocks:
inputs, environments, and outcomes. Within the model, inputs include student’s preexisting
characteristics and precollege experiences. Inputs also include various demographic
characteristics, such as race, ethnicity, gender, attitudes, values, academic preparation, personal
and social attributes, and familial background characteristics (i.e., socioeconomic status and
parental education). Both academic and social precollege experiences are considered inputs.
Environment considers the college experiences to which students are exposed and features that
shape these college experiences, including academics, peers, courses, curricula, co-curricular
programs, social networks, extracurricular activities, and faculty influences (Pascarella &
18
Terenzini, 2005). Finally, outcomes include measures of student’s characteristics following
immersion in the college environment, including knowledge, skills, attitudes, beliefs, interests,
values, and behaviors of the students upon graduation from college. When utilized in a
longitudinal fashion, this model assesses the effect of the college environment on student change.
Application of this model is powerful, as it acknowledges psychological well-being’s alteration
by student’s background characteristics as well as the many features of the college environment.
Higher education literature clearly demonstrates that college outcomes are largely
determined by effort put forth by students in the educational process, meaning college outcomes
depend immensely on students’ engagement in educationally purposeful activities (Astin, 1993;
Hu & Kuh, 2003; Kuh, Hu, & Vesper, 2000; Pascarella & Terenzini, 2005). Astin’s I-E-O
model posits that student engagement or involvement in college activities influences college
outcomes.
Astin’s I-E-O model is particularly applicable to this study, as research has shown
psychological well-being develops in part through a combination of personality characteristics
and life experiences (Helson & Srivastava, 2001; Ryff & Singer, 1996). Placing psychological
well-being within the context of the individual’s life allows for description of the whole person,
“and the multiple forces, internal and external, impinging on them. Going for the whole story,
despite the complexities involved, will help generate findings that are responsive to the
variability in the world around us” (Ryff, 2008, pp. 412-413). Psychological well-being is
multidimensional, with transactional, personal, and environmental determinants and is
profoundly influenced the context of people’s lives (Edwards, Ngcobo, Edwards, & Palavar,
2005; Ryff and Singer, 1998; Ryff, 2008). Thus, it is reasonable to suspect that psychological
19
well-being may be related to student engagement after controlling for student background
characteristics.
To date, research on psychological well-being in college students has largely focused on
adjustment processes for students in college. These adjustment processes are thought to be
related to the student’s unique characteristics as well as the college environment (Bowman,
2010; Gloria, Castellanos, Orozco, 2005; Locks, Hurtado, Bowman, & Oseguera, 2008;
Mendoza-Denton, Downey, Purdie, Davis, & Pierzak, 2002; Mounts, 2004). The importance of
incorporating contextual factors into the study of psychological well-being is called for,
considering both micro-level and macro-level influences on psychological well-being (Ryff,
2008). Adjustment processes in college are largely influenced by contextual factors. Thus, the
contextual relevancy of psychological well-being is fulfilled with the application of Astin’s
model. This conceptual understanding guides the research design and data analysis here.
While the college outcome models position psychological well-being as a valid college
outcome, no studies have specifically investigated the relationship of sleep deprivation and
psychological well-being as an outcome of the four year college experience. My research
contributes to the literature on psychological well-being by examining the relationship between
sleep deprivation and psychological well-being in college students after four years of enrollment.
Theoretical Framework
Psychological well-being
As mentioned in Chapter 1, the theoretical grounding for Ryff Scale of Psychological
Well-being (SPWB) begins with Aristotle’s Nichomanchean Ethics (Aristotle, 350 B.C.) In his
Nichomancheean Ethics, Aristotle discussed the Greek term for “the good life” or “happiness”,
eudaimonia. In Aristotle’s analysis, eudaimonia is activity of the soul in accordance with
20
human’s special virtue: rational activity. Thus, eudaimonia is the striving toward excellence of
one’s unique potential; it is not a state “arising” within someone, but instead is purposeful action
(Ross, 1925; Ryff & Annas, 2009). Ultimately, then, happiness and the good life, known as
eudaimonia, is the ongoing active exercising of moral and intellectual virtue to do what we are
meant to do in the best way possible.
Having defined eudaimonia, Dr. Ryff next moved to operationalize eudaimonic
psychological well-being. Different from other forms of psychological well-being (i.e.,
subjective or hedonic), eudaimonic psychological well-being aimed to measure the development,
growth, and self-realization of the unique individual as the measurable signs of psychological
well-being (Ryff & Singer, 1998; Ryff, 1989a). This conception of psychological well-being is
theoretically and empirically distinct from theories of life satisfaction, happiness, locus of
control, and self-esteem (Ryff & Keyes, 1995). Rooted in Aristotle, Ryff’s theory of
psychological well-being next emerged from theorists and psychologists whom focused on three
areas of interest: (1) life-span developmental theories, (2) clinical theories of personal growth,
and (3) mental health literature.
A brief discussion of these three areas of interest is important for further understanding of
eudaimonic psychological well-being. To begin, life span theories, including Erik Erikson’s
psycho-social stage model (1959), Neugarten’s (1973) description of the executive process of
personality, and Buhler’s (1935) basic life tendencies, informed unique challenges and tasks of
human life (Ryff, 1982; Ryff, 1989a). Together, these theories helped formulate and clarify
positive functioning, providing an attentiveness to different periods of the human life cycle.
Thus, growth of the young adult was distinguished from an aged individual. These differences
21
allowed for differentiation of changing roles across human life cycles, including changing
psychological and biological processes (Ryff, 1989a).
Second, clinical theories of personal growth were included in the formulation of
eudaimonic psychological well-being. The growth and development of humans was explored by
Maslow (1968), who articulated the construct of self-actualization. Jung (1933) explored the
process of individuation, while Rodgers (1961) further addressed full functioning in life, and
what it means to become a person. Allport (1961) contributed psychological explanation for the
human maturation process and advanced psychological functioning. These pioneering theorists
and their work were culled for application to eudaimonic well-being. Dr. Ryff identified
commonalities from the work of these authors, gathering overlapping ideas which were
applicable to her conceptualization of psychological well-being.
Finally, mental health literature was utilized to elaborate the meaning of positive
functioning in human life. Specifically incorporated was Jahoda’s (1958) criterion of what it
meant to be psychologically healthy. Jahoda argued the absence of mental disease was not a
workable definition of “mental health”. Instead, six criteria of mental health were created, a
revolutionary idea for its time. Dr. Ryff chose to incorporate aspects of Jahoda’s research, thus
allowing eudaimonic psychological well-being to focus on the presence of wellness instead of
the absence of disease.
While important to the advancement of the field of psychological well-being, these prior
conceptions of positive functioning lacked the necessary, valid, and reliable assessment
procedure warranted for operationalization of a construct making these empirically unviable. In
order to translate these ideas to an empirical level, an operational measure of psychological wellbeing was created.
22
Incorporation of the previously discussed theoretical and psychological perspectives
allowed Ryff to operationalize the concept of eudaimonic psychological well-being by focusing
on primary points of convergence within these perspectives (Dierendonck, 2005; Ryff, 1989a;
Ryff & Singer, 1998; Ryff & Singer, 2008 Ryff, Singer, & Love, 2004). Ryff’s model has six
dimensions, with each dimension articulating different challenges individuals may encounter
while striving forward to positive functioning in their lives (Ryff, 1989a; Ryff & Keyes, 1995).
The six dimensions of eudaimonic psychological well-being are: autonomy, environmental
mastery, personal growth, purpose in life, positive relations with other, and self-acceptance
(Ryff, 1989a). The six dimensions are operationally defined with structured self-report scales.
Individually and in totality, these dimensions contribute to an assessment of a person’s individual
level of positive psychological functioning and well-being (Dierendonck, 2005; Ryff, 1989b;
Ryff, Singer, & Love, 2004). Prior to the creation of the SPWB, psychological well-being was
faced with the absence of a theoretical framework or conceptual rationale. Additionally, an
implicit negativism to psychology dominated the field (Ryff, 1989a). The development and
evaluation of these self-report scales to measure psychological well-being followed the
construct-orientation approach to personality assessment and the empirical translation is
supported by the presence of psychological theory that specifies the constructs of interest (Ryff
and Singer, 2008; Wiggins, 1980). The SPWB is theoretically grounded and implicitly positive.
Finally, the SPWB is presently regarded as the best objective, standardized measure of
psychological well-being (Conway & Mcleod, 2002). For these reasons, the SPWB has been
selected as the instrument to measure the dependent variable of interest.
For the purposes of this dissertation, the term “psychological well-being” is utilized with
definitive theoretical grounding as well as a specific definition: Ryff’s definition of eudaimonic
23
psychological well-being as measured by the SPWB. Additionally, the abbreviation “PWB”
refers exclusively to the eudaimonic psychological well-being as operationalized by the SPWB.
Brief, further explanation of the six subscales is appropriate at this point in the study. To
begin, self-acceptance asks whether one holds a positive attitude toward oneself and one’s life.
Self-acceptance also emphasizes the need to have positive self-regard, a form of long-term selfevaluation involving awareness and acceptance of both personal strengths and weaknesses.
Positive relations with others emphasizes the importance of warm, trusting interpersonal
relationships. Autonomy refers to the ability to resist social pressures to think and/or act in a
certain way. Autonomy also tasks the individual to not look for others for approval, but to look
within for personal standards of evaluation. Environmental mastery includes manipulation of,
participation in, and control over the environment, including mastery and competence within
one’s environment. It also refers to an individual’s ability to choose or create environments
suitable to one’s unique psychic condition. Purpose in life for an individual is found when they
have goals, intentions, and a sense of directedness, combining to provide feelings of
meaningfulness and cohesion of the various parts of one’s life. Finally, personal growth of an
individual realizes the importance of continued development of one’s potential, to grow and
expand as a person at all stages of life. Personal growth is explicitly concerned with the selfrealization of the individual. Together, these six dimensions compose the eudaimonic theory of
psychological well-being in an integrative model, operationalized by the SPWB (Ryff & Singer,
2008).
Research on Psychological Well-being
Utilizing current literature on psychological well-being, the influence of background
(pre-college) characteristics on PWB was identified and examined. These characteristics
24
included: sex, race, parental income, parental education level, and grant status. Institutional
characteristics were next reviewed. These institutional characteristics included the environment,
experiences, and student behaviors exhibited while at the institution. Institutional (i.e.
environmental, experiential, behavioral) characteristics selected for inclusion were: type of
institution attended, amount of exercise participated in, perceived health status, number of
cigarettes smoked, frequency of alcoholic beverage consumption, hours spent in co-curricular
involvement, grade-point average, academic major, hours spent socializing, hours worked,
perceived quality of relationships with peers, involvement in a religious group, involvement in a
fraternity or sorority, and perceived sleep deprivation. Table 2 provides a summary of variables’
relationships with PWB. Following this review, the dependent variable and college outcome of
interest, psychological well-being is discussed. Because research on college students is limited,
other empirically valid studies utilizing subjects other than college students were included in this
literature review.
Student Background Characteristics
Reviewing past research on psychological well-being helps provide contextual and
factual information as well as perspective. Scientific products utilizing PWB have emerged in
six thematic areas: (1) how well-being changes across adult development and later in life, (2)
personality correlates of well-being, (3) well-being and experiences in life, (4) well-being as
related to work and communal activities, (5) connections between well-being and health,
including biological risk factors, and (6) clinical and intervention studies (Ryff, 2014). These
diverse areas of study help illustrate the growing interest in better understanding humans as
proactive, meaning-making creatures who actively navigate the challenges of life. Also of
importance to this study are reviews of previous studies utilizing PWB as the dependent variable
25
of interest. Helping to guide the selection of variables for this study was the fact that past studies
of PWB have typically utilized sociodemographic variables as controls in regression analyses to
predict well-being as a function of proximal experiential and interpretive variables (Ryff, Magee,
Kling, & Wing, 1999; Ryff, Singer, & Love, 2004). To date, a formidable body of empirical
research has examined how PWB varies by socio-demographic factors, including: age, gender,
socio-economic status, race, ethnicity, or culture (Ryff & Singer, 2002a; Ryff and Singer 1998;
Ryff, Keyes, & Hughes, 2003).
Demographic factors
SES: Education, Occupation, and Income
Does PWB accrue disproportionately in the lives of individuals with greater access to
resources? Evidence indicates PWB is affected by macro level social factors, particularly those
social structures that incorporate hierarchically ordered status and access to resources (i.e.,
occupational, educational, or income classes) (Clarke, Marshall, Ryff, & Rosenthal, 2000;
George, 1996). Socioeconomic status (SES) is frequently defined by three related yet distinct
parts: a person’s income, educational, and employment status (Dahl & Kjaersgaard, 1993;
Feinstein, 1993; Marmot, Ryff, Bumpass, Shipley, & Marks, 1997; Ryff, Magee, Kling, & Wing,
1999; Ryff & Singer, 1996). Because SES is a macro-level social structure, it is plausible that
PWB will be affected by it. Prior research has shown PWB to be influenced by SES, with PWB
increasing as SES increases (Keyes & Shapiro, 2004). Not surprisingly, lower SES is associated
with reduced PWB (Marmot, Fuhrer, Ettner, Marks, Bumpass, & Ryff, 1998; Ryff and Singer,
2002a; Ryff, 2013; Subramanian, Kim, & Kawachi, 2005). These models show a clear SES
gradient in PWB for both men and women.
26
PWB is positively correlated with both occupational status and educational attainment
(Marmot et al, 1997; Ryff et al, 1999). In turn, occupational status and educational attainment
are positively linked with income. Total PWB (all scales summed together), increases as years of
educational attainment increase (Keyes & Ryff, 1998). Each individual dimension of PWB also
increases as educational attainment increases (Keyes & Shapiro, 2004; Marmot et al, 1997).
Reynolds and Ross (1998) argued that education functions to pass on status, position, and good
incomes to adult children of high status parents. Thus, education is important because of its
relationship with advantageous family backgrounds and its provision of abilities, skills, and
resources that will eventually impact PWB. Additionally, the growing body of evidence linking
socioeconomic status to health outcomes also suggests a link between one’s location in the
socioeconomic hierarchy and psychological well-being (Adler, Boyce, Chesney, Cohen,
Folkman, Kahn, & Syme, 1994; Mirowsky & Ross, 2003).
Pertinent to the current study, parental education level, occupation, and income have
been linked to students’ PWB. In her study examining the social correlates of PWB among
undergraduates, Daraei’s findings indicated statistically significant differences in PWB of
students according to educational levels of parents, occupations of fathers, and family income
levels (2013). Daraei’s findings are consistent with Ryff and Singer’s (2008) results finding
socioeconomic status to be a good predicator of PWB. Additionally, a longitudinal cohort of
Swedish women showed women in higher SES strata to have higher PWB than those in lower
strata (Johansson, Huang, & Lindfors, 2007). Importantly, those in the lower SES groups were
shown to possess worse PWB than individuals in higher SES groups (Marmot et al. 1997).
Further evidence exists for the claim that PWB is strongly positively correlated with
educational attainment (Keyes, Shmotkin, & Ryff, 2002; Marmot, Ryff, Bumpass, Shipley, &
27
Marks, 1997; Ryff & Singer, 1998; Ryff & Singer, 2008). The association is most strongly
evident for personal growth and purpose in life (Ryff & Singer, 2008). Midlife and older adults
with higher levels of educational attainment have increased likelihood of positive PWB (Keyes,
Shmotkin, & Ryff, 2002). For example, the Wisconsin Longitudinal Study (WLS) showed
individuals with more education have higher profiles of well-being (Ryff & Singer, 2002a; Ryff,
Magee, Kling, & Wing, 1999). The MIDUS national survey also found PWB to be
compromised among individuals with less education (Marmot et al, 1998; Marmot, Ryff,
Bumpass, Shipley, & Marks, 1997). The National Survey of Families and Households found
educational differences influencing purpose in life: the less education an individual possesses,
the lower purpose in life one possessed (Bumpass & Aquilino, 1995). Finally, studies of social
inequality have shown those with disadvantaged educational status have lower PWB (Marmot,
Ryff, Bumpass, Shipley, & Marks, 1997; Ryff, Magee, Kling, & Wing, 1999). These studies all
correlated increased educational attainment with increased PWB.
In addition to educational attainment, gainful occupational status has been positively
correlated with PWB (Marmot et all, 1997). To illustrate, the Whitehall studies of British civil
servants found a social gradient in PWB, with higher levels at higher grades of employment
(Marmot, Shipley, & Rose, 1984; Marmot, Smith, Patel, North, Head, White, Brunner, &
Feeney, 1991; Marmot et al, 1997). These studies support the claim that occupational status has
a causal connection with health and PWB in adult life (Marmot et al, 1997).
The final component of SES, income, next deserves attention. Income, which is to a
great extent a consequence of educational achievement, may influence PWB. As previously
stated, a social gradient exists whereby higher levels of income are associated with higher levels
of PWB (Ryff & Singer, 1998). For example, parental income may be translated into material
28
goods or experiences that enhance PWB of the child (Ryff et al., 1999). To date, researchers
acknowledge it is only possible to speculate about the underlying mechanisms by which income
facilitates positive PWB. Perhaps it is the culmination of income, material goods, status, and
opportunities as “important protective factors in the face of stress, challenge and adversity”
which improve PWB (Ryff & Singer, 1996, p. 18). Nonetheless, viewed from the perspective of
growing scientific literature linking social class standing to health, it is reasonable to hypothesize
lower position in the social hierarchy equals lower likelihood of PWB (Marmot, Ryff, Bumpass,
Shipley, & Marks, 1997; Ryff & Singer, 1996; Bluestone, 2014).
Because of the strong relationship between PWB and components of SES, any model
looking at the influence of variables on PWB must control for SES. Thus, pre-test variables of
parental income, parental education, and grant status ( a function of family income and debt)
were included in the regression model.
Gender/Sex
Gender differences in PWB exist and have shown replicative consistency across many
studies (Ryff et al, 2003). For example, women have higher profiles on positive relations with
others and personal growth than men (Ryff, 1989; Ryff & Keyes, 1995; Ryff & Singer, 1998;
Ryff & Singer, 2002; Ryff, Keyes, & Hughes, 2003). Importantly, when given opportunities for
higher education, women reveal comparable, or even advantaged, total profiles of PWB when
compared to men (Ryff, Magee, Fling, Wing, 1999). As men age, they increase in
environmental mastery and positive relations with others at a faster rate than women (Ryff &
Singer, 2002). As distinctions in levels of PWB exist as influenced by gender, a variable has
been included for gender (male or female) in the following study.
Race/Ethnicity
29
Demands for better understanding of PWB pushed for the examination of racial and
ethnic minority college student functioning. Specifically, Gloria et al. (2005) sought to better
understand the degree that perceived educational barriers, cultural fit, and coping responses
predicted PWB of Latina undergraduates. Controlling for background and educational
characteristics, the authors found problem-focused responses strongly predicted PWB. Here,
problem-focused responses were defined as coping strategies to deal with adverse events. Even
in the face of educational barriers, a positive coping strategy for these Latina students boosted
their PWB.
As previously mentioned, studies have shown increases and decreases in PWB for
individuals undergoing life-transitions (Kling, Seltzer, & Ryff, 1997; Marks & Lambert, 1998;
Smider, Essex, & Ryff, 1996; Rafanelli, Grandi, Conti, and Belluardo 1998). One potential
resource for accomplishing the life transition from high-school to college and from adolescence
into adulthood is positive PWB (Bowman, 2010). College students of color, racial, or ethnic
minority may undergo more difficult college transitions than their white peers, and as PWB is
linked to successful life transitions, it is possible that students of racial and ethnic minority may
experience lower levels of PWB as they navigate this life transition (Cho, Hudley, Lee, Varry, &
Kelly, 2008; Fischer, 1997; Hurtado, Carter, Spuler, 1996; Terenzini, Rendon, Upcraft, Millar,
Allison, Gregg, & Jalomo, 1994; Zwerling & London, 1992).
Despite evidence suggesting potential decrements in PWB because of race and ethnicity,
PWB has also been bolstered in racial and ethnic groups whom experience greater adversity.
Ryff, Keyes, and Hughes (2003) showed Black and Latino adults to have higher psychological
well-being than their white counter parts. Minority status, across multiple racial/ethnic groups,
was found to be a positive predictor of PWB, highlighting themes of psychological strength
30
perseverance despite race-related adversity (Ryff et al, 2003). Ethnic minority status may be a
positive predictor of PWB (Ryff, Singer, & Love, 2004). Because of the relationship between
PWB and race, any model looking at the influence of variables on PWB must control of race.
Thus, a variable on race is included in the present study.
ACT/SAT Composite Score
The American College Testing (ACT) exam and the Scholastic Aptitude Test (SAT)
scores are among the most significant predictors of college grade point average (GPA), which is
a reason institutions of higher education rely on these scores to predict college outcomes (Taylor,
Vatthauer, Bramoweth, Ruggero, & Roane, 2013). The ACT and SAT are utilized as college
admissions tests because they are specifically designed to predict the success a high school
student will have academically in college (Taylor, Vatthauer, Bramoweth, Ruggero, & Roane,
2013). This pre-college measure of academic ability is also an indication of the institution’s
selectivity (Pascarella & Terenzini, 2005; Volkwein & Sweitzer, 2006). ACT and SAT scores
have also been correlated with higher SES of students (Wai, 2013). As ACT and SAT scores are
indicators of future college academic success, college selectivity, and student SES, and PWB has
been linked to these indicators, a variable controlling for ACT or SAT is included in the present
study.
Participation in College Experiences and Effects on Psychological Well-being
Institutional Characteristics
Psychological well-being is not only influenced by students’ background characteristics,
but also by the institution and its characteristics. The institution creates the environment in
which the student will have experiences which in turn influence their PWB. PWB must be
considered and explained by combinations of both biological and environmental influences,
31
including both normative and non-normative life experiences. The science of PWB must capture
the variety and complexity of adult life (Ryff, 2008). PWB has multidimensional personal,
transactional and environmental determinants (Edwards, Ncgobo, Edwards, & Palavar, 2005).
These facts make inclusion of variables addressing environmental and experiential components
of student’s college lives important for this study.
Certain experiences in the college journey will affect student PWB, and others will not.
A thorough search of relevant databases revealed few studies that specifically utilized the SPWB
as the dependent variable of interest in studies with college student samples. These studies
sought to better understand how the college environment intermixed with specific independent
variables of interest to best understand how the college student’s PWB may be altered. These
studies also utilized conceptual frameworks similar to this study, Astin’s I-E-O Model of college
impact. Any model looking at the influence of variables on PWB must control for institutional
environmental influences. These influences are described below.
Transition in Life Status
PWB has been linked to certain life events, including discrete life transitions and chronic
life challenges (Kling, Ryff & Essex, 1997; Kwan, love, Ryff, & Essex, 2003; Kling, Seltzer, &
Ryff, 1997). Longitudinal studies have found eudaimonic well-being to be dynamic, exhibiting
cross-time changes as individuals negotiate various life transitions (Kwan, Love, Ryff, & Essex,
2003; Ryff, Singer, & Love, 2004; Ryff, 2008). When individuals are faced with a life
transition, they are given the opportunity for personal growth and attainment of higher levels of
development (Maslow, 1968, Rogers, 1961; Ryff, 1985). As informed by Astin’s I-E-O Model,
the experience of college marks a significant life transition for the student involved as entrance
and experience within university is a major life transition. Here students leave home, lose
32
support networks, adjust to new learning environments, navigate financial support, embark on
more serious personal relationships, determine educational direction, encounter challenging
classes, and navigate new social contexts (Arkoff, Meredith, Bailey, Cheang, Dubanoski, Griffin,
& Niyekawa, 2006; Galambos, Lascano, Howard, & Maggs, 2013). Logically, their PWB may
be altered, influenced, or changed by this life transition.
Inclusion of select empirical studies demonstrating life transitions’ influence on PWB
support the claim that college as life transition will alter student’s PWB. First, Marks and
Lambert (1998) showed that people who get married experienced greater increased in
psychological well-being than individuals who were currently married or who chose to remain
single. This change in status from “single” to “married” provided some inflationary action to the
individual’s sense of PWB. While not measuring for change in marital status in this current
study, this research demonstrates how a change in life status can inflate PWB.
A second example of change in life status that bolstered PWB was shown in elderly
women. In this study, elderly women who elected to move to a new communal residential living
situation exhibited greater increases in PWB than women whom chose to remain in their same
residential situation where they had care-giving obligations (Kling, Seltzer, & Ryff 1997).
Potential reasons for increases in PWB in this elderly population included: more community
involvement, increased social support, and relief from stressful obligations of providing longterm care-giving responsibilities. In a similar study, Smider, Essex, and Ryff (1996) found
elderly women whom moved into new homes showed higher levels of PWB if they were also
resilient in the face of their negative circumstance. This study linked resilience to positive scores
of PWB.
33
A final example of improved PWB because of successful life transition was found in a
group of psychiatric patients undergoing treatment for depression. The patient group was
divided into treatment and non-treatment groups. The treatment group received information
about Ryff’s dimensions of PWB with specific counseling addressing strategies to improve their
own PWB as well as avoid experiences that would potentially damage their PWB. The nontreatment group did not receive this counseling. Patients who received counseling specifically
pertaining to PWB had better outcomes than patients whom did not receive this treatment
(Rafanelli et al., 1998).
Studying PWB in college students is particularly relevant because college is a time of life
transition for many students (Terenzini, Rendon, Upcraft, Millar, Allison, Gregg, & Jalomo,
1994). Students adjusting to the college environment are experiencing their own transition much
like the individuals previously discussed. The empirical examples provided show how life
transition can bolster PWB. As PWB can be altered by life transitions, and college is a time of
life transition, it is logical to deduce that the college life transition will alter the student’s PWB in
some way.
Institutional Type
A desired personal outcome of a liberal arts education is PWB (King, Kendall Brown,
Lindsay, & VanHecke, 2007). The distinctiveness of the liberal arts outcome is found in the
integrated connections existing between outcomes which span cognitive, interpersonal, and
intrapersonal domains (Pascarella & Siefert, 2009). One purpose of a liberal arts education is to
improve the overall goods for the student participating in the educational experience. It has been
argued that PWB is a good that should be procured by a liberal arts student. The claims made
concerning institutional type suggest that the college environment may enhance or inhibit
34
student’s PWB. Thus, capturing the institutional type characteristic is important for this study.
For these reasons, institutional type was included to gauge institutional differences which may
have been missed by other college experience variables. Specifically, this study chose to look at
the liberal arts college, eliminating research institution and community college participants from
the sample.
Behavioral Characteristics
The science of PWB must capture the variety and complexity of adult life by considering
combinations of both biological and environmental influences, including both normative and
non-normative life experiences (Ryff, 2008). The exact nature of the relationship between
physical health and PWB has not been fully explored, although aspects of PWB have been linked
to health behaviors, with research suggesting dimensions of PWB are correlated with indicators
of physical health (Hamilton, Nelson, Stevens, & Kitzman, 2007; Heidrich, 1993; Ryff, 2013).
The core hypothesis of positive health’s relationship to PWB is “that the experience of wellbeing contributes to the effective functioning of multiple biological systems, which may help
keep the organism from succumbing to disease, or, when illness or adversity occurs, may help
promote rapid recovery” (Ryff, Singer, & Love, 2004, p. 1383). These researchers provide
evidence that the eudaimonic life, as measured by SPWB, can affect some physical
characteristics related biological functioning and health.
Health Status
Physical health status has been directly linked to PWB. To date, many studies of PWB
have focused on adults to identify health factors which influence or are influenced by PWB.
Research has shown PWB is positively and consistently associated with various measures of
physical health, including protective biological correlates (Lindfors & Lundberg, 2002; Steptoe,
35
O’Donnell, Marmot, & Wardle, 2007; Ryff et al, 2006; Ryff, 2008: Ryff, Singer, & Love, 2004).
The biological correlates related to PWB include low cortisol output, diminished cardiovascular
stress response, increased antibody response to vaccination, and increased immune efficiency.
Increased immune efficiency associated with PWB is manifested by lower levels of
inflammatory cytokines, particularly IL-6, which is an integral part of the stress response (Black,
2003; Ryff, Singer, & Love, 2004). Presence of IL-6 is linked to atherosclerosis, insulin
resistance, type II diabetes, and metabolic syndrome in humans (Black, 2003). Specifically,
individuals with higher levels of purpose in life have significantly lower levels of IL-6 (Ryff,
Singer, & Love, 2004).
Individuals with higher PWB also have lower cardiovascular risk factors, including lower
weight, lower waist to hip ratios, lower glycosylated hemoglobin, and increased HDL (Ryff,
Singer, & Love, 2004; Teague et al, 2013). These cardiovascular biomarkers are predictive of
downstream coronary heart disease, diabetes, and atherosclerosis as well as psychosocial and
socio-demographic factors, including sense of control, coping, social support, and socioeconomic status (Badimon, Fuster, & Badimon, 1992; Corti, 1995; Feldman & Steptoe, 2003;
Seeman, Mendes de Leon, Berkman, &Ostfeld, 1993). Additionally, PWB has been linked to
better self-reported health, lower morbidity, less pain, and increased longevity (Chida & Steptoe,
2008; Diener & Chan, 2011; Pressman & Cohen, 2005). Clearly, PWB is consequential for
health, as it promotes effective simultaneous regulation of many physiological systems of the
human body (Ryff and Singer, 2008). When addressing PWB and physical health, Friendman
and Kern (2011) demand more inclusive and complex models to further construct the elaborate
causal pathways linking health to PWB. This study seeks to do just that.
36
In conclusion, these finding lend strong support to the hypothesis that PWB is related to
more established markers of physical health. Because PWB has been linked to health, specific
health related variables are included in this study. Exposure to morbidity or mortality inducing
agents may biologically harm the individual and lower their PWB and, as biological well-being
is intertwined to PWB, must be considered when researching PWB. These variables included
here are: alcohol consumption, frequency of exercise, smoking habit, and frequency of feeling
sleep deprived. As these variable changes one’s health status, and health status influences PWB,
they must be controlled for as to not confound the relationship between sleep deprivation and
PWB.
Exercise, Alcohol, and Smoking
Higher levels of PWB are found in individuals who engage in diverse types of exercise
(Edwards, Edwards, & Basson, 2004). Individuals who engage in regular physical activity score
higher on all six dimensions of PWB than non-frequent exercisers (Edwards, Nbcobo, Edwards,
& Palavar, 2005). Research has shown PWB is promoted by regular exercise occurring for 30
minutes/day, at least three times per week (Edwards, 2002, Fox, 2000). For example,
participation in regular exercise was found to increase PWB in their analysis of MIDUS data
(Grzywacz & Keyes, 2004). Specifically, regular, vigorous exercise enhanced PWB.
Longitudinal studies have shown emerging adults decrease their physical activity consumption
during college years, thus potentially lowering their PWB (Nelson, Neumark-Sztainer, Hannan,
Sirard, & Story, 2006; Nelson, Story, Neumark-Sztainer, & Lytle, 2008). Because physical
activity has been correlated to PWB, a control variable for physical activity has been introduced
in the model.
37
Data from the National Longitudinal Study of Adolescent Health found emerging
adulthood is a time for significant increase in high-risk alcohol use, including binge drinking
behavior (American College Health Association, 2007; Harris, Gordon-Larsen, Chantala, &
Urdry, 2006). Alcohol use is common in undergraduates (Rhoades & Maggs, 2006). As
excessive alcohol consumption negatively influences the biological system and PWB has been
linked to biological functioning, a variable to control for binge drinking status has been included
in the following study.
Data from the National Longitudinal Study of Adolescent Health found emerging
adulthood as a time for significant increases in tobacco use, including chewing and smoking
(American College Health Association, 2007).
The chemicals found in tobacco are highly
addictive and cause a host of health problems (Teague, Mackenzie, & Rosenthal, 2013). Despite
the deleterious physical effects of smoking, smokers often claim there are compensatory
psychological benefits which may accompany the use of cigarettes including a sense of
relaxation and increased alertness (Costa & McCrae, 1981; Teague et al, 2013). Many smokers
claim smoking to be an aid or comforter, turning to smoking when unhappy, distressed, or
nervous (CDC, 2014). Smokers often report increases in depression, anxiety, or other unpleasant
symptoms when quitting smoking (CDC, 2014). As smoking negatively influences the
biological system and PWB has been linked to biological functioning, a variable to control for
smoking status has been included in the following study.
Spirituality
Social scientists have linked PWB to the psychological construct of spirituality (Kirby et
al, 2004; Wink & Dillon, 2003; Van Dierendonck, 2004). Utilizing data from the 2005 National
Survey of Midlife in the United States (MIDUS), Greenfield, Vaillant, and Marks (2009) found
38
higher levels of spirituality were associated with better levels of PWB in all six dimensions. The
sizes of associations between spirituality and PWB vary among the six dimensions of PWB. For
example, the association between spirituality and positive relations with others, self-acceptance,
and positive affect were greater than the association between spirituality and autonomy
(Greenfield, Vaillant, & Marks, 2009). In a smaller study, spirituality was positively linked with
personal growth (Wink & Dillon, 2003). Thus, spirituality promotes some aspects of PWB more
strongly than others (Ellison & Fan, 2008; Maselko & Kubzansky, 2006; Greenfield, Vaillant, &
Marks, 2009). As spiritualty has been shown to improve PWB, it is appropriate to include a
variable for spirituality in the following study.
Social Relationships and Social Support
Social engagement is a key aspect of well-being. A positive association exists between
social relationships, social support, and psychological functioning as people have a fundamental
need for close relationships, and social relationships are one of the strongest correlates of
positive PWB (Baumeister & Leary, 1995; Bradburn, 1969; Diener & Seligman, 2002). Social
engagement is also beneficial for PWB as it provides people the opportunity to provide help to
others. The process of helping others increases PWB while providing an opportunity to be
recognized and appreciated by others (Gencoz & Ozalal, 2004).
Pertinent to this study, both Chow (2007) and Daraei (2013) empirically demonstrated
that student’s PWB was positively affected by their positive relationships with family members
and friends. In addition to social relationships, social support has been associated with PWB.
Gencoz and Ozalal (2004) studied the indirect and direct effects of social support on PWB in
undergraduate students, finding social support to positively increase PWB. It has been suggested
that life outside the classroom contributes to large individual differences in social relationships
39
and support, thus becoming a more powerful predictor of variations in PWB (Ryff and Heidrich,
1997). It is possible that social engagement, support, and relationships found in the college
environment play a key role in the PWB of college students. For this reason, variables
addressing hours spent socializing, co-curricular involvement, and perceived quality of
relationships with peers were included in this study.
Fraternity or Sorority Membership
Fraternity and sorority (Greek) membership has been shown to increase levels of
academic effort, involvement with organizations, increased interpersonal skills, and interaction
with other students (Pike & Askew, 1990; Pike, 2000). Pike (2000) also examined the direct and
indirect effects of fraternity and sorority membership on student involvement, reporting
significantly higher levels of social involvement in Greek than non-Greek students. Pike (2003)
also reported fraternity and sorority members were at least as engaged as their non-Greek
counterparts, and senior Greek members tended to be significantly more involved than nonGreek seniors. Additionally, Greek members reported significantly greater gains in social
development than students who were not members of fraternity and sorority (Pike, 2003).
Martin, Hevel, Asel, and Pascarella (2011) showed that membership in a fraternity or
sorority did not have a significant unique influence on students’ growth along key educational
outcomes in the first year of college. As far as can be determined, their study was the first to
explore the effect of fraternity or sorority membership on PWB. As their study addressed only
the first year of college, these researchers called for future research to investigate whether
fraternity or sorority membership affected PWB as the student progresses in college. This study
helps fill this gap.
40
Additionally relevant to the study of fraternity and sorority membership is the research of
Ryff and Heidrich (1997). In their study of young adults, participants responded to items dealing
with the level of participation in extracurricular and social activities, including fraternity and
sorority membership. These young adult’s ratings of their participation in activities were
significant predictors of all six measure of current psychological well-being, as increased activity
involvement increased scores of total PWB.
Considering the opportunity to form social relationships within Greek membership, and
the influence social relationships have on PWB, it is important to consider Greek membership in
this study. My general effects model controls for fraternity and sorority membership.
Hours Worked
Since the 1990s, there has been a dramatic increase in the number of students combining
employment and academic studying, with estimates placing between 41% and 54% of students
working while studying (Broadbridge & Swanson, 2006; Woodward, 2003). Researchers have
explored the reasons for increased employment status in college students, with most students
stating financial necessity as their primary reason for working while studying (Lucas & Ralston,
1997; Smith & Taylor, 1997; Woodward, 2003). Students also reported working because they
enjoyed the social aspects, educational benefits, good experiences for current studies, and
potential future employment benefits (Broadbridge & Swanson, 2006).
Limited research has examined how work influences PWB (Ryff, 2013). A few things
have been discovered that do link work to PWB. Paid work has been found to increase personal
growth in men, while unpaid work lowers their self-acceptance and environmental mastery
(Lindfors, Berntsson, & Lundberg, 2006). Ryff & Heidrich’s (1997) study found meaningful
work to strongly predict positive PWB in older individuals. Gilbreath and Benson’s (2004)
41
exploratory study of 167 workers discovered supervisor’s behavior made a statistically
significant contribution to employees’ PWB. In their study, Cancer and Zizek surveyed 470
employees from 320 randomly selected organizations to better understand how working
conditions affected their PWB. Their research showed differences between various groups of
employees regarding how they perceived different aspects of PWB. In sum, operational heads
highly valued positive relations with others and autonomy and had higher total PWB scores.
Contrarily, lower level workers achieved the lowest aggregate value of PWB. This study
demonstrates that workers in different levels of an organization may possess different levels of
PWB.
While the links between work and PWB have not been fully understood, it is clear that
work does influence PWB. It is also known that many college students maintain on-and-off
campus work positions during their college careers. Because of the potential effect of work
status on PWB, a variable has been created to include both on-and-off campus work in the study.
Grade Point Average (GPA) and College Major
Academic performance is an important college outcome, having both intrinsic and
extrinsic value to students. Grade point average (GPA) predicts future financial success, affects
psychosocial well-being, relates to earnings increases post-graduation, and is linked to
internship, graduate college, and career opportunities (Baird, 1985; Erikson, 1963; Filer, 1981;
Hu, 2005; Jones & Jackson, 1990; Pascarella & Terenzini, 2005; Wise, 1975). Yet measuring
student outcomes in college is a challenging endeavor, and GPA is no different. The
relationships between PWB and GPA and PWB and college major have been shown to be either
positive or nonexistent.
42
Research has shown that academic pressure has a negative effect on PWB.
Mukhopadhyay and Kumar (1999) demonstrated that academic pressure is associated with stress
and lower psychological well-being in students. It is well documented that stressful life events
affect psychological well-being (Cohen, Janicki, Denise, & Gregory, 2007; Thoits, 2006).
College major is a significant college experience (Astin, 1993). Literature suggests students
enrolled in science, technology, engineering, and mathematics (STEM) have more time intensive
and stressful majors (Gilmer, 2007; MacPhee, Farro, & Canetto, 2013). STEM majors also
persist more frequently when provided with social support, mentoring, and self-efficacy
(MacPhee et al, 2013; Shapiro & Sax, 2011). Additionally, college major is a very significant
part of the college experience and must be considered when investigating college experience’s
influence on outcomes (Astin, 1993).
The relationship between academic pressure and PWB has also been shown to have no
significant effect. For example, Ryff and Heidrich’s (1997) study of young adult students
revealed no significant effects for school ratings. Participants were asked to answer 15 items
dealing with school achievement, including GPA and academic major. These questions revealed
no significant effects on PWB. While the relationships between GPA and PWB and college
major and PWB are still unclear, it is important to include variable for both GPA and college
major. Presumably, both GPA and college major are important parts of the college environment,
playing key roles for the students’ transitional life experience, stress experience, and goals. For
these reasons, both GPA and college major are controlled for in this study.
Sleep Deprivation and Psychological Well-being
While research clearly links sleep duration to psychopathology, mortality, poor cognitive
performance, depression, anxiety, and stress, the link between sleep duration and dimensions of
43
PWB is less clear (Gelman & King, 2001; Hamilton, Nelson, Stevens, & Kitzman, 2007;
Hamilton, Karlson, & Catley, 2007; Meltzer & Mindell, 2007; Polimeni, Richdale, & Fancis,
2007). It is hypothesized that a person’s typical sleep duration may relate to the eudaimonic
facets of well-being as they reflect a value of continued striving that necessitates energetic
engagement, which would be enhanced by adequate sleep. Adequate sleep may also offset net
effects of demands placed on the human system, its overall health, and the resilience of the
human organism, including PWB (Hamilton, Karlson, & Catley, 2007). Researchers have long
assumed a main function of sleep, especially slow-wave sleep, is to repair minor daily damage as
well to restore health and vigor (Adam & Oswald, 1997). Additionally, sleep may function as a
biobehavioral resource as well as a moderator of responses to life’s challenges, as restorative
sleep facilitates systemic resilience to stress and inadequate sleep is a diathesis to stress-related
dysfunction (Hamilton et al, 2007). As previously detailed, PWB is found by living life in an
actualized way as well as facing life’s challenges. In order to achieve PWB, energetic reserves
must be available and accessible. Little doubt exists about the fundamental importance of
sufficient, restorative sleep in maintaining health, renewing and replenishing depleted energy
(Drake, Roehrs, & Roth, 2003; Hamilton et al, 2007; Jean-Louis, Kripke, & Ancoli-Isreal, 2000;
Lund, Reider, Whiting, & Prichard, 2010). As sleep deprivation robs the body of necessary
renewal and replenishment of reserves, the possibility of PWB diminishing in the face of sleep
deprivation is very real.
While sparse, some studies have correlated sleep with PWB. To begin, in their study of
135 older women, Ryff, Singer, and Love (2004) found women reporting higher levels of
environmental mastery had longer periods of sleep duration, increased time in bed, faster onset
of REM sleep, and longer duration of REM sleep. Individuals with increased purpose in life had
44
less body movement during sleep. Some of the findings by Ryff, Singer, and Love (2004) were
supported by a second study which correlated PWB with longer sleep duration and increased
REM sleep (Hamilton, Nelson, Stevens, & Kitzman, 2007). In their study, Hamilton et. al
(2007) surveyed 502 community residents about both sleep habits and SPWB. This study
proposed a theoretical framework defining sleep as a resource related to stress management and
self-regulation, meaning individuals who were not sleep deprived were provided with a greater
resource with which they could navigate life’s various stresses and demands. This theory of
sleep as an energetic resource places sleep within a self-regulatory framework, providing an
empirical link between sleep and PWB (Zohar, Tzischinsky, Epstein, & Lavie, 2005). Optimal
sleepers, as shown by increased sleep duration and REM sleep, were associated with better
PWB, including greater environmental mastery, personal growth, positive relations with others,
self-acceptance, and purpose in life.
Ryff, Singer, and Love (2004) approached the link of PWB and sleep from a biological
perspective. According to these authors, a key hypothesis of positive health is a state of wellbeing which is accompanied by optimal functioning of an organism’s physiological systems
(Ryff & Singer, 1998; Ryff, Singer, & Love, 2004; Singer & Ryff, 2001). This biopsychosocial
interplay comprises part of the mechanistic processes that delay morbidity and maintains
functional capacity by increasing periods of life lived in a quality state (Ryff, Singer, & Love,
2004). Eudaimonic well-being states an individual will have purposeful, active, and striving life
experiences, often in the face of adversity. Eudaimonic living prompts greater biological
activation and a pattern of arousal that contributes to physiological toughening. This arousal
includes a resistance to catecholamine depletion and a suppression of damaging pituitary
adrenal-cortical response (Dienstbier, 1989; Ryff, Singer, & Love, 2004). The role of sleep in
45
human existence is unequivocally important. The fundamental importance of sufficient,
restorative sleep in maintaining physical and mental health has been well documented (Lund,
Reider, Whiting, & Prichard, 2010). The hypothetical restorative function of sleep as necessary
for proper PWB is indeed plausible.
College Students, Sleep Deprivation, and Psychological Well-being
College students are sleep deprived (Pilcher & Walters, 1997; Lund, Reider, Whiting, &
Prichard, 2010). This deprivation may be due to the large amount of flexibility in their sleep
schedules, lack of adult supervision, erratic schedules, alcohol consumption, or drug use (Lund et
al, 2010). Studies indicate that sleep is frequently a problem for college-age students, including
mounting evidence that this age group most commonly suffers from disordered sleep patterns
that result in chronic sleep debt, or sleep deprivation (Gibson, Powles, & Thabane, 2006; Hicks,
Mistry, Lucero, Lee, Pellegrini, 1989; Hicks & Pellegrini, 2001; Miller, Shattuck, & Matsangas,
2010). Loss of sleep at traditional college age is additionally troublesome because research
suggests sleep is even more critical for young adults as their bodies and brains experience rapid
growth and development (Carskadon, 1990, 2002; Dahl & Lewin, 2002). The dangers of
problematic sleep in college students is supported by continued research demonstrating that
insufficient sleep deteriorates optimal health and results in a variety of negative consequences
(Vail-Smith, Felts, & Becker, 2009). College students suffering from sleep deprivation are at
risk for problems far more serious than simply struggling to function in daily activities, as sleep
deprivation is a risk factor for major mood and substance abuse disorders (Lund et al, 2010;
Neckelmann, Mykletun, & Dahl, 2007; Roane & Taylor, 2008). Colleges must be proactive in
identifying sleep difficulties as well as articulating the importance of sufficient, restorative sleep
for college students’ PWB.
46
Few studies have specifically researched sleep deprivation’s effects on PWB in college
student populations. Clearly, one area of research to be further developed is the relationship of
sleep deprivation and PWB in college students. In summary, few researchers have investigated
whether sleep deprived individuals suffer from decreased PWB. The purpose of this study is to
further examine the relationship between sleep deprivation and dimensions of PWB in college
students.
Summary
The purpose of this study is to examine the effects of theoretically and empirically
selected college experiences on student’s PWB. Specifically, this study examines the effects of
sleep deprivation on student’s PWB. While few studies have examined PWB in college students,
a very limited number of studies have examined the impact of college experiences and sleep
deprivation on college students’ PWB.
Astin’s I-E-O college impact model contributes to the theoretical guidance of this study,
as well as Ryff’s model of PWB. Combined, these models allow for inputs, experiences, and
outputs to specifically examine the unique relationship of sleep deprivation and psychological
well-being while controlling for a battery of potentially confounding variables.
The literature reviewed in this chapter allowed for selection of appropriate control
variables. To clearly understand if sleep deprivation influences PWB, potential effects of other
confounding variables must be controlled. These potential confounding variables include
students’ background characteristics. Also potentially confounding are other variable sets,
including pre-college PWB, institutional characteristics, and college student characteristics and
experiences which may be associated with PWB.
47
Clearly, the literature on PWB in college student populations is incomplete. This study
will expand PWB research by evaluating the relationship between sleep deprivation and PWB.
To understand sleep deprivation’s influence on PWB, it is necessary to focus on sleep
deprivation and evaluate this relationship with a pre-test/post-test design.
Conclusion
Chapter one introduced the importance of PWB. Chapter two further reviewed the
literature on PWB and provided theoretical foundations for the research. Chapter 3 will detail
the methodological approach for analysis of the data and describe the study design, sample,
instruments, and variables.
48
Table 2
Variable Relationship with total Psychological Well-being, Personal Growth, Positive
Relationships with Others, Self-Acceptance, Autonomy, Environmental Mastery, and Purpose in
Life
Independent Variable
Sex
Race
Sleep Deprivation
Frequency of Exercise
Overall Health
Parental Educational Attainment
Financial Grant
Parental Income
Smoking
Alcohol Consumption
Co-curricular involvement
Major
Grade point average
Association with Dependent Variable
Females have higher scores of Positive
Relationships with Others. Education boosts
female total PWB over equally educated male
total PWB. Males score higher in
Environmental Mastery than females.
Total PWB is bolstered in racial and ethnic
groups whom experience adversity. Minority
status boosted total PWB.
Sleep deprivation is linked to a host of health
related detriments, including physical, social,
psychological, and emotional deficits
Total PWB increases in individuals whom
regularly engage in regular physical activity.
Total PWB is linked to various measures of
physical health as well as biological correlates.
Increased overall health boosts total PWB as
well as Purpose in Life.
Total PWB, as well as all six subscales of
PWB, increase as educational attainment
increases.
A component of Socioeconomic status. PWB
increases as financial status increases.
Individuals in economic need suffer lower
levels of total PWB.
A component of Socioeconomic status.
Individuals of higher economic status have
higher scores of total PWB.
Studies have yet to link PWB to smoking.
Studies have yet to link PWB to alcohol
consumption
Involvement in co-curricular activities
increases Total PWB as well as all six
subscales.
Studies have yet to directly link PWB to
Academic Major.
Studies have yet to directly link PWB to GPA.
49
Table 2. cont.
Socialization Hours
Yet to be linked directly to PWB.
Work Hours
Meaningful work increases Personal Growth in
males, while unpaid work decreases selfacceptance and environmental mastery. Total
PWB increases for both working men and
women in work scenarios.
Higher quality relationships increase Total
PWB and all six subscales.
Perceived Quality of Relationships with Peers
Religious Group Involvement
Fraternity or Sorority Membership
Involvement in religious activities boosts Total
PWB and all six subscales.
Involvement in fraternity or sorority life has no
impact on Total PWB or six subscales.
50
CHAPTER 3
METHODOLOGY
This chapter explains the research methodology employed for the study. It also describes
the procedures used, including survey design. The following sections compose this chapter:
purpose and research questions, research design, research variables, and short data analysis
overview. The chapter concludes with a brief section on limitations of the study, and previews
directions for the remainder of the study.
Purpose and Research Questions
The purpose of this study is to examine the effects of sleep deprivation on students’
psychological well-being and the six subscales composing psychological well-being (PWB).
The research questions of this study are:
1) How is the perception of sleep deprivation related to psychological well-being over
four years in college students?
a. How is the perception of sleep deprivation related to self-acceptance in
college students?
b. How is the perception of sleep deprivation related to positive relations with
others in college students?
c. How is the perception of sleep deprivation related to environmental mastery in
college students?
d. How is the perception of sleep deprivation related to autonomy in college
students?
51
e. How is the perception of sleep deprivation related to purpose in life in college
students?
f. How is the perception of sleep deprivation related to personal growth in
college students?
2) Do changes in perceptions of sleep deprivation from high school to college influence
psychological well-being?
These questions are examined by using data from the WNLS.
Research Design
A multi-institutional longitudinal data set, the Wabash National Study of Liberal Arts
Education (WNS) provide data for this study. The Center of Inquiry in the Liberal Arts at
Wabash College and the Center for Research on Undergraduate Education (CRUE) at the
University of Iowa coordinated the WNS. WNS is a large, longitudinal investigation of the
effects of liberal arts colleges and liberal arts experiences on personal, psychological, cognitive,
and other outcomes. These outcomes are theoretically linked to a liberal arts education.
Primarily geared to investigate liberal arts education outcomes, the dataset does contain a
multitude of variables, allowing researchers to explore many varied outcomes. The WNS data
set contains the relevant and necessary variables identified and discussed in the previous
literature review to evaluate psychological well-being. Therefore, it is an ideal dataset for this
research.
Institutional Sample
The institutional sample of the WNS was selected from more than 60 colleges and
universities responding to a national invitation to participate in the study. From these 60
respondents, The Center for Inquiry and CRUE selected 17 four-year colleges and universities
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from the Northeast, Southeast, Midwest, and Pacific Coast regions of the United States. These
17 institutions were selected to best represent nationwide differences found in colleges and
universities in the United States. These differences include: type, control, size, location, and
residential preferences. Given the primary purpose of the WNS, liberal arts colleges were
intentionally over-sampled within the sample. The Carnegie Classification of Institutions
classified seven of the participating institutions as research universities, nine as regional
universities not granting a doctorate, and twenty-nine as liberal arts colleges (Carnegie
Classification, 2013).
Participant Sample
At time of selection, Fall 2006, study participants were first-year, full-time
undergraduates. First-year students attending research universities were randomly selected. All
first-year students at regional and liberal arts colleges and universities selected for the study were
invited to participate. Students were offered a $50 stipend for each data collection time. The
initial data collection occurred in the fall of 2006. At that time, 4,501 students participated in the
WNS. In the follow-up data collection gathered in the spring of 2007, 3,081 students
participated. Final data collection occurred in the spring of 2010 in which 2,212 students
participated.
Ethical Considerations
WNS data are secondary in nature. Therefore, every participating institution received
Institutional Review Board (IRB) approval prior to commencing in the study. Students were
informed of the purpose of the study: to gather a national, longitudinal study examining how
college affects students, seeking to find ways to improve the college experience. Participants
53
were also ensured of strict confidentiality; any background information or answers provided
would remain confidential and unrecorded on personal institutional records.
Data Collection Procedures
Initial data collection occurred in early fall of 2006 during the freshman students’ first
semester of college. This experience took 90-100 minutes for completion. The instruments
administered included a pre-college survey instrument. This instrument asked questions
pertaining to student demographics, family characteristics, high school/pre-college experiences,
and college aspirations. Students also completed instruments addressing cognitive and
psychosocial measures.
As previously stated, the second data collection occurred in the spring of 2007 and the
last collection occurred in the spring of 2010. These follow up collections lasted approximately
120 minutes each. The cognitive and psychosocial instruments from the initial collection in fall
2006 were repeated in spring 2007 and spring 2010. In these follow up collections, students
additionally completed the National Survey of Student Engagement (NSSE) and the WNS
Student Experiences Survey (WSES). These two additional surveys contain measures relating to
various college experiences, engagement, and college-related activities. The survey scales and
items selected for use in this specific study are described in depth in the upcoming research
variables section of this chapter.
Finally, it should be noted that both the initial and follow-up data collections were
administered by ACT Inc.
Research Variables
Measures
54
A primary strength of the WNLS dataset is the pretest and posttest measures and
outcomes. This pretest/posttest design allows for measurement of total and direct effects of the
collegiate experience. Included in the Wabash study were many surveys and standardized
instruments. Clear description and identification of both dependent and independent variables is
necessary to explicate their utility and composition. The following section does just this.
Operational definitions of all variables are found in Table 3 located at the end of this chapter.
Dependent Variable
The dependent variable selected for this study was psychological well-being as measured
by Ryff’s Scales of Psychological Well-being (SPWB). This variable was a measured in both
the 2006 and 2010 data collection periods. This scale consisted of 54 questions, with 6 subscales
of 9 questions per subscale. Questions, when summed, address overall PWB. When broken into
constituent parts, questions address the six subscales of PWB: (1) autonomy, (2) selfacceptance, (3) positive relations with others, (4) purpose in life, (5) personal growth, and (6)
environmental mastery. Scale construction was modeled on the construct-oriented approach to
personality assessment, emphasizing the importance of psychological theory when
operationalizing each particular construct (Jackson, 1976; Ryff, 1989a; Wiggins, 1973). Thus,
Ryff’s theoretical model was translated to the empirical level. The psychometric properties of
the scale have been evaluated and supported by many publications (Clarke, Marshall, Ryff,
Wheaton, 2001; Gallagher, Lopez, Preacher, 2009; Ryff & Keyes, 1995; Ryff & Singer, 2006;
Springer & Hauser, 2006). It has withstood extensive psychometric scrutiny, thus supporting the
foundational evidence of the scale’s reliability and validity (Ryff, 2013).
Independent Variables
55
Astin’s I-E-O model, Ryff’s theoretical grounding of PWB, and the support of the
previous literature review provide a strong framework for selection of independent variables.
Figure 1 pictorially represents the framework of the study,
Student characteristics
As informed by the literature review, certain student characteristics were selected. They
were: sex, race, parental education attainment, financial aid grant, parental income, ACT/SAT
scores, students’ major, GPA, work status, involvement in fraternity or sorority, and involvement
in religious group.
Sex, a dichotomous variable, was coded 1=male and 0=female. Person of color variable
was coded with white as the reference group (1 = Caucasian/white; 0 = Black, non-Hispanic,
Asian/Pacific Islander, Hispanic, American Indian/Alaska Native).
Parental education of mother and father was combined into one variable. The variable
was then transformed to a binary variable, with students with parents whose parents did have a
bachelor’s degree (or more) as the reference group. Education of parent was coded 1 = graduated
college (or more), 0 = did not graduate. Financial aid in the form of a Pell grant was included
because it is related to SES, undergraduate debt, and decision to work while in college. A
dichotomous measure representing whether or not a student received financial aid in the form of
a federal grant was included to proxy for SES. Federally determined income thresholds
56
Figure 1. Theoretical model
Precollege 2006
2010
2010
Pretest RYFF
ACT
Sleep Deprivation
Race
Gender
Psychological wellbeing
Parental Education
Parental Income
Alcohol Consumption
Grant
Smoking Frequency
Sleep Deprivation
Physical Activity
(End of fourth year)
GPA
Major
Extracurricular
Involvement
Institutional Type
Frat/Sorority
pe
Work Status
Spirituality
Socialization Hours
Quality of Relationships
57
determine student eligibility for these grants. This measure has been utilized within other
college impact analytical models because of its dependable representation of financial resources
that may be available to the particular student (see Salisbury, Paulsen, & Pascarella, 2010).
Students who received a grant were the reference group (1 = received a grant, 0 = did not receive
a grant). Additionally, a standardized continuous variable was created to measure the student’s
parental income.
Student’s precollege academic achievement was measured by the ACT composite score
(SAT equivalent score). Institutions participating in the WABASH study provided students’
ACT scores.
End of college major was included, as measured by the NSSE student survey, asking for
primary major or expected area of primary major. This continuous measure asked respondents to
report number of courses taken or are taking with general areas during their college experience.
The continuous variable was recoded into a dichotomous variable, with STEM majors as the
reference group (1 = science major, engineering major, math; 0 = major other than math, science,
or engineering).
Student GPA was also included as striving for goals, such as a certain GPA, can
influence PWB. Student’s provided a self-reported GPA of courses taken up to the survey date.
This interval variable ranging from ‘A’ to ‘C- or lower’ was recoded into a continuous variable
ranging from ‘C- or lower’ to ‘A’.
Though limited empirical evidence was discovered suggesting student employment in
college has an influence on PWB, a continuous measure of the total hours a student worked onor off- campus was included in the model. The response set ranged in five-hour increments from
‘0 hours’ to ‘more than 30 hours.’ Calculation of the midpoint of each response set allowed for
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transformation into a more traditional continuous measure. Working during college may provide
costs (i.e., time and stress) and benefits (i.e., financial return and social inclusion) for students
(Broadbridge & Swanson, 2006). The various costs and benefits associated with work are also
associated with PWB. For this reason, total time spent working (both on and off campus) was
included in the model. This single item continuous variable asked students to select the number
of hours spent per week working for pay.
As shown in the previous literature review, involvement in religious or spiritual activities
may improve PWB. For this reason, a measure of membership in religious congregations or
religious groups while at college served as proxy for spiritual involvement. This dichotomous
variable utilized religiously involved students as the reference group (1 = involved; 0 = not
involved).
Involvement in co-curricular activities has previously been shown to influence personal
growth, positive relationships with others, and purpose in life in a study of first year college
students (Bowman, 2010). One may hypothesize that co-curricular activities may detract from
available time for study and add stress to a student’s life, or contrarily, co-curricular involvement
may provide meaningful, satisfying relationships or necessary stress-relief. Thus, PWB may be
lessened or increased by co-curricular involvement. For these reasons, involvement in cocurricular activities was included. This single item continuous variable asked students to select
the number of hours per week spent participating in co-curricular activities (1 = 0 hours, to 8 =
more than 30 hours).
An additional measure of the perceived quality of relationships with peers was included
to further measure the influence of relationships and their benefits upon PWB. This continuous
variable asked students to rank their perceived quality of relationships with other students.
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Scores ranged from “1 = unfriendly, unsupportive, and sense of alienation” to “7 = friendly,
supportive, and sense of belonging”.
A measure of time dedicated to socialization and relaxation was included. This singleitem continuous variable asked student’s to report how many hours in a typical week they spent
socializing or relaxing, with a response set ranging in five-hour increments from ‘0-hours’ to
‘more than 30 hours’ (1 = 0 hours, to 8 = more than 30 hours). Calculation of the midpoint of
each response set transformed the response sets into a more traditional continuous measure. As
provided in the previous literature review, socialization is positively correlated with PWB, thus
justifying inclusion of this variable. Student involvement in fraternity or sorority life has been
shown to potentially affect PWB. For this reason, those involved in a fraternity or sorority were
the reference group (1 = involved in fraternity or sorority, 0 = not involved with a fraternity or
sorority).
Institutional characteristics
Institutional characteristics included in this study relate to the institution’s structure as
well as environment. The type of institution attended is part of the institution’s structure.
Students who attended a liberal arts college were coded as the reference group (1 = attended a
liberal arts college; 0 = attended a research university or regional institution). Community
colleges were dropped from the analysis because they were 2 year institutions, and could not
provide data in 2010.
Psychological well-being
As stated above, the operational definition of the Ryff Scale of Psychological Well-being
is identical to the dependent variable. Inclusion of this variable serves as the pre-test control
variable measured prior to the first year of college. A pretest captures a baseline for PWB and
60
minimizes selection bias. Student’s pre-college PWB also helps demonstrate how other college
factors may influence PWB over the course of a four year college experience. Thus, variation in
the PWB post-test measured at the conclusion of four years of college can be more confidently
attributed to variation found in independent variable measures of selected college experiences
because the pretest score of PWB was controlled for prior to entering college (Astin & Lee,
2003; Pascarella, 2006).
Student Health Behaviors
Many health behaviors correlate with each other. Thus, PWB could be influenced by
health behaviors other than sleep deprivation. However, since sleep deprivation is linked to
these other health behaviors, simply considering its effects PWB in isolation could yield a
specious association. Consequently, the study also controlled for the effects of various other
health behaviors in addition to sleep deprivation. These included how participants rated their
overall health, how frequently they engaged in aerobic exercise, frequency of weekly binge
drinking episodes, and number of cigarettes smoked daily. These variables were treated as
standardized continuous variables.
Sleep deprivation is the independent variable of interest. As sleep deprivation has been
shown to decrease PWB and proper sleep has been shown to increase PWB, a variable for sleep
deprivation was included in the study. This variable was treated as a standardized continuous
variable.
In combination, the psychological well-being pretest control, student characteristics,
institutional characteristics, and institutional environment focus the design of this study on
estimating the effects of sleep deprivation on student’s psychological well-being during four
years of college.
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Data Analysis
Missing Data
The first step in data analysis was addressing missing data. As Allison (2002) states,
missing data is a problem because “all standard statistical methods presume that every case has
information on all the variables to be included in the analysis” (p. 1). Missing data occurs when
a particular case (participant) lack data for a variable. Listwise deletion was used to handle the
missing data in this study. If any missing data for the variables included in the model occurred,
the cases (participants) were deleted from the data analysis. Listwise deletion is a common
strategy to address the problem of missing data. Additionally, the data analysis employed
ensured the data was Missing Completely At Random (MCAR), thus ensuring that the
probability of the missing data for any variable is unrelated to that variable or the values of other
variables (Allison, 2002). MCAR ensures that no systematic difference between participants
with complete data and those with missing data.
Descriptive Statistics
After addressing missing data, the next step in data analysis includes descriptive analysis
of all variables included in the model. First, means and standard deviations or frequencies on all
variables are calculated. Next, a correlation matrix is created to investigate the relationship
between all variables. Together these analyses provide an overall picture of the variables and
sample.
Multiple Linear Regression
Regression analysis serves three purposes: description, control, and prediction (Kutner,
Nachtscheim, & Neter, 2004). Regression analysis is appropriate for determining the
relationship between sleep deprivation and PWB because this study is interested in describing a
62
relationship while incorporating control variables. Given the continuous nature of the dependent
variable, ordinary least squares (OLS) is most appropriate to estimate the effects of college
experiences on PWB.
Five assumptions must be met when using a standard linear model for a method of
analysis. These assumptions validate how the dependent measure is estimated from the value of
the independent measures (Allison, 1999). The five assumptions are: (1) the dependent measure
is a linear function of the independent measure, (2) the variance of the error terms cannot depend
on the independent measures, (3) the error term has a normal distribution, (4) the mean of the
error term is zero, and (5) the value of the error term in one case is uncorrelated with the value of
the error term for any other case (Allison, 1999). Performance of OLS produces unbiased
estimates and coefficients when these five assumptions are satisfied.
To explore whether sleep deprivation predicted PWB multiple linear regression is
utilized. Ordinary least squares regression is used to estimate the general effects of sleep
deprivation on psychological well-being. Covariates are entered simultaneously into the model,
including sleep deprivation and variables reflecting pre-college factors and college experiences
selected by preliminary identification of variables substantially predictive of college outcomes.
These covariates were discussed in this chapter as well as Chapter Two. For ease of
interpretation, all continuous measures are standardized, allowing coefficients to represent effect
sizes.
Standardized Variables
All continuous variables are standardized prior to multiple linear regression analysis.
This transformed the variables to a z-score, with a standard deviation of 1 and a mean of 0.
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Standardization places all continuous variables on the same scale, thus making comparison
easier.
Weighting
Data are adjusted to represent the population from which the sample was drawn. They
are also weighted to correct for any potential bias in the standard errors of statistics due to
oversampling. Potential response bias by sex, race, or academic ability is addressed by creating
a weighting algorithm. Using information provided by participant institutions on race/ethnicity,
sex, and ACT or SAT score, follow-up participants are weighted up to each institution’s firstyear undergraduate population by race (White or non-White), sex (male or female), and ACT (or
equivalent) quartile. Applying weights in this manner effectively makes the overall sample more
similar to the populations from which it came, although it cannot adjust for nonresponse bias.
Statistical Program
All data analyses are conducted using the STATA software package.
Limitations
Every study has important limitations to note. First, this study relied on student selfreported behaviors. Uncertainty may exist regarding the accuracy of self-reported behaviors, but
there is little reason to believe participants were deceptive in their responses given that there
were no adverse consequences for participation in the study. Overall, it is generally accepted in
the field of academic research that student self-report data are deemed adequate for use in
empirical studies (Cole & Gonyea, 2010; Gonyea, 2005). Secondly, the measurement level is
another limitation. This study requires participants to answer questions concerning their
thoughts, behaviors, and intentions at a global level. Clearly, situated measures would provide
more precise estimates of specific behaviors, but these data do reflect an averaged set of
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thoughts, behaviors, and intentions across time. A final limitation may be representativeness,
given attrition and nonresponse bias often associated with longitudinally designed studies.
Conclusions
This study uses extant data from the WNS to examine the relationship between sleep
deprivation and PWB in college students. Guided by Astin’s I-E-O and Ryff’s Theory of
Psychological Well-being, this study uses multiple linear regression to discern if sleep
deprivation influenced PWB. The powerful nature of the WNS data set allows control for
selected student background characteristics, pre-college psychological well-being, and many
other variables potentially associated with PWB. Use of advance data analysis techniques help
clarify the relationship between dependent and independent variables. Analysis and diagnostics
further clarify the magnitude of the influence sleep deprivation has on PWB. The analyses
techniques help elucidate this important and potentially powerful relationship. The following
chapters will describe the data analysis as well as the presentation of results, discussion,
implications, and final conclusions.
65
Table 3
Variable Definitions
Variable
Sex
Person of color
ACT composite score
Liberal arts college
Psychological Well-being
Perceived Sleep Deprivation
Perceived Sleep Deprivation Change
Perceived Sleep Deprivation Low
Perceived Sleep Deprivation High
Exercise Frequency
Overall Health
Parental Educational Attainment
Financial Grant
Operational definition
1 = male, 0 = female
1 = white/Caucasian, 0 = Black/non-Hispanic,
American Indian/Alaska Native, Asian/Pacific
Islander, Hispanic
Composite ACT or SAT equivalent score
converted to an ACT metric (information provided
by the institution)
1 = attended a liberal arts college, 0 = attended a
research university or regional institution
Pre-college scores on a 54-item inventory
measuring six areas of psychological well-being:
autonomy, environmental mastery, personal
growth, positive relations with others, purpose in
life, and self-acceptance.
A standardized continuous variable asking, “How
often do you feel that you are ‘sleep deprived’ (i.e.,
don’t get enough sleep to function effectively)?
(5 = almost always, 0 = never)
A standardized continuous variable measuring the
perceived change in sleep deprivation from high
school to college (5= almost always,0 = never)
A dummy variable created asking, “How often do
you feel that you are ‘sleep deprived’ (i.e., don’t
get enough sleep to function effectively)?
1 = occasionally, 0 = sometimes, never
A dummy variable created asking, “How often do
you feel that you are ‘sleep deprived’ (i.e., don’t
get enough sleep to function effectively)?
1 = frequently, always, 0 = sometimes, never
A standardized continuous variable asking “how
frequently do you engage in aerobic exercise”
(1 = 0 hours/week, 5 = 7 hours/week)
A standardized continuous variable asking,
“overall, how would you rate your health?”
(1 = very poor, 5 = excellent)
1 = at least bachelor’s degree, 0 = less than
bachelor’s degree
1 = received a financial aid grant, 0 = did not
receive a financial aid grant
66
Table 3 (cont.)
Variable Definitions
Variable
Parental Income
Operational definition
A standardized continuous variable asking, “what
is your best estimate of your parent’s total annual
income and your total annual income? (1 = less
than $14,999, 9 = $300,000 or more).
Smoking
A standardized control variable asking, “how many
cigarettes do you smoke a day?”
(1 = I don’t smoke cigarettes”, 5 = 2 or more
packs)
A standardized continuous variable asking, “In a
typical one-week period this year in college, how
often did you consume alcoholic beverages?”
(1 = 0, 8 = more than 7 times/week).
A standardized continuous variable asking, “About
how many hours in a typical week do you spend
doing the following: participating in co-curricular
activities (organizations, campus publications,
student government, fraternity or sorority,
intercollegiate or intramural sports, etc.)?
(1 = 0 hours, 8 = more than 30 hours).
1 = science, engineering, math, 0 = other
A standardized continuous variable asking “what
have most of your grades been up to now at this
institution?”
(1 = C- or lower, 8 = A).
A standardized continuous variable asking, “about
how many hours in a typical week do you spend
doing the following: Relaxing and socializing?
(watching tv, partying, etc.)
(1 = 0 hours, 8 = more than 30 hours).
A standardized continuous variable asking students
how many hours they worked both on and off
campus in the average week. (1=0 hours/week,
8=32.5 hours/week)
A standardized continuous variable asking,
students to mark the box best representing the
quality of relationships with other students,
(1 = Unfriendly, Unsupportive, Sense of
Alienation,
7 = Friendly, Supportive, Sense of Belonging)
1 = involvement in a religious group,
0 = no involvement in a religious group
Alcohol Consumption
Co-curricular involvement
Major
Grade point average
Socialization Hours
Work Hours
Perceived Quality of Relationships with
Peers
Religious Group Involvement
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Table 3 (cont.)
Variable Definitions
Variable
Fraternity or Sorority Membership
Assessments All
Operational definition
1 = involvement in a fraternity or sorority,
0 = no involvement in fraternity or sorority
1 = completes assessments at T1 and T3,
0 = completed assessments at T1, T2, and T3
68
CHAPTER 4
RESULTS OF THE STUDY
This chapter details the results of this study, which seeks to evaluate the relationship
between students’ perceptions of sleep deprivation and psychological well-being (PWB). After
implementing controls for a variety of variables theoretically associated with PWB, the research
addresses the following questions:
1) How is the perception of sleep deprivation related to psychological well-being over
four years in college students?
a. How is the perception of sleep deprivation related to self-acceptance in
college students?
b. How is the perception of sleep deprivation related to positive relations with
others in college students?
c. How is the perception of sleep deprivation related to environmental mastery in
college students?
d. How is the perception of sleep deprivation related to autonomy in college
students?
e. How is the perception of sleep deprivation related to purpose in life in college
students?
f. How is the perception of sleep deprivation related to personal growth in
college students?
2) Do changes in perceptions of sleep deprivation from high school to college influence
psychological well-being?
69
Data from the Wabash National Study (WNS) was utilized in this study and was analyzed
using multiple linear regression to evaluate the relationship between perceived sleep deprivation
and PWB. The descriptive statistics of each variable were first evaluated. Next, correlation
matrixes were run to evaluate the extent to which variables were correlated with each other.
Finally, many multiple linear regression models were used to address the research questions. A
number of important sampling and statistical adjustments and controls, including sample
weighting and clustering effects, were utilized to more accurately measure the effects of
perceived sleep deprivation on student’s PWB. All continuous dependent and independent
variables were standardized, allowing the coefficients to represent effect sizes.
This chapter presents the results of the descriptive analysis, correlation matrixes, and
multiple linear regressions. These data demonstrate the impact perceptions of sleep deprivation
have on PWB in college students after four years of enrollment.
Descriptive Statistics
After deleting missing data (n=472), the sample included 1760 participants. Table 4
shows the descriptive statistics for each variable. These statistics provide the necessary context
for understanding the sample population.
To begin, 666 participants (37.84) were male, and 1,094 (62.16%) were female. Racially,
the majority of participants were White (80.39%). Less than half of participants (42.05%)
indicated at least one parent had a graduate degree. Only a small number of students received
financial aid on the form of a federal grant (12.16%). Participant average ACT/SAT score was
27.218. About one quarter of participants majored in STEM fields (26.02%), while the majority
of participants majored in other fields (73.98%). Purposeful sampling provided for oversampling
70
of liberal arts colleges (55.39%), while others attended research, regional college, or university
(44.61%).
Students also provided information on their health status as well as extracurricular
activities while enrolled in college. In the sample, 381 participants (21.648%) were members of
either a fraternity or sorority. Six hundred and fifty-two participants (37.04%) reported being a
member of a religious or spiritual group.
Descriptive analysis of the independent variable of interest, sleep deprivation, provides
useful contextual information. Sleep deprivation was divided into two dummy variables,
occasionally sleep deprived and frequently/always sleep deprived. This division allowed for
finer discrimination of levels of sleep deprivation’s relationship to PWB. Upon entering college,
students reported feeling both occasionally sleep deprived (42.56%) as well as frequently and
always sleep deprived (36.99%). This also meant some students reported never feeling sleep
deprived (20.46%) upon entering college. After four years of college, students again reported
both feeling occasionally sleep deprived (38.98%) as well as frequently and always sleep
deprived (39.60%). This also meant some students reported never feeling sleep deprived at the
end of four years of college (21.43%). At the end of four years of college, fewer students
reported feeling occasionally sleep deprived, while more students reported feeling frequently or
always sleep deprived.
These descriptive statistics provide an overview of the student population included in this
study. It is clear that sleep deprivation is prevalent in this sample, which is comparable and
consistent with what is found in the literature. Students are sleep deprived. Therefore, using
these student demographics and institutional characteristics as strategically selected control
variables makes conceptual sense while strengthening the theoretical framework of the study.
71
Correlations
To understand the relationship between each variable, correlation matrixes were created.
Table 5 includes the correlations of all variables included in the study. Correlations range from –
1 to +1. Generally, as a correlation approaches either -1 or +1, the relationship between
variables increases in strength. A correlation less than +/- 0.30 is a weak correlation, while
falling between +/-0.30 and +/- 0.70 is considered a moderate correlation. Any correlation of +/0.70 is rated as a strong correlation. None of the variables included in this study were strongly
correlated, though some variables had moderate correlations. For example, the Ryff Total Score
at Time 1 was moderately positively correlated with Ryff Total Score at Time 3. This
pretest/posttest correlation is logical. Also moderately correlated were Ryff Total Score and
perceived quality of relationships (0.37). Overall health at Time 3 was moderately positively
correlated with Ryff Total Score (0.3103), exercise at Time 3 (0.366), and Exercise at Time 1
(0.4391). Parental income was moderately positively correlated with white racial identification
(0.3186) and parental attainment of a graduate degree (0.3502), while also being moderately
negatively correlated with acceptance of a federal grant (-0.4352). Finally, ACT/SAT ability
was positively correlated with reported grades at Time 3 (0.3776).
The correlation matrix also provided information about the independent variable of
interest, sleep deprivation. Specifically, students who reported frequently or always being sleep
deprived at Time 1 were negatively correlated to students who reported occasional sleep
deprivation at Time 1 (-0.6595). Simply, students who were frequently or always sleep deprived
were not occasionally sleep deprived; they could not be both. In a similar fashion, students who
reported frequently or always feeling sleep deprived at Time 3 were negatively correlated with
students whom reported occasional sleep deprivation at Time 3 (-0.6472). Again, students were
72
either frequently and always sleep deprived or occasionally sleep deprived, not both. This makes
logical sense, and the correlation matrix confirms this relationship.
This summary provides an important overview of the relationships found between
variables in this study.
Multiple Linear Regression
The main research question sought to evaluate whether a relationship existed between
perceived sleep deprivation and psychological well-being (PWB). Sub-questions examined the
relationship between perceived sleep deprivation and the six subscales composing total PWB.
For this analysis, all continuous variables were standardized, clustered, and weighted (as
described in previously in Chapter 3). Below, each research question is answered individually.
Research question 1: How is the perception of sleep deprivation related to total
psychological well-being over four years in college students?
Table 6 summarizes the regression estimates for the effects of perceived sleep deprivation
and other independent variables on end of fourth year total PWB. As shown in Table 6, high
levels of sleep deprivation have a significant and negative effect on PWB even with a battery of
control variables. Student’s perception of high levels of sleep deprivation after four years of
college was negatively statistically significant (b = -.3575, p <.01), with high levels of perceived
sleep deprivation being negatively related to total PWB.
The pretest measure of total PWB had a significant positive effect on total PWB
following four years of college (b = .4241, p <.001). This is unsurprising because the precollege
pretest measure of PWB should theoretically account for a significant proportion of the variance
of end of four years total PWB. Students whom reported high levels of overall health upon
entering college had a significant negative effect on total PWB (b = -.0598, p < .01), while
73
students who reported high levels of overall health at the end of four years of college had a
significant positive effect of total PWB (b = .1607, p <.001). Working on or off campus had a
significant positive effect on total PWB (b = .0350, p < .025), while high school ACT/SAT
ability had a significant negative effect on total PWB (b = -0.0569, p < 0.001). Finally, students
who reported high levels of perceived quality of relationships with their peers had significant
positive effects on total PWB (b = .2382, p <.001).
Research question “a”: How is the perception of sleep deprivation related to selfacceptance in college students?
Table 7 summarizes the regression estimates for the effects of perceived sleep deprivation
and other independent variables on end of fourth year self-acceptance. As shown in Table 7,
neither high nor low levels of perceived sleep deprivation had significant effects on selfacceptance.
While perceived levels of sleep deprivation did not affect self-acceptance, other variables
in the study did. First, the pretest measure of self-acceptance had a significant positive effect on
self-acceptance following four years of college (b = .4302, p <.001). This is unsurprising
because the precollege pretest measure of self-acceptance should theoretically account for a
significant proportion of the variance at the end of four years self-acceptance. Also of interest,
overall health at Time 3 significantly positively affected self- acceptance (b= .1778, p < .001), as
did working on or off campus (b= .0360, p < .025), increased consumption of alcohol (b= .0378,
p < .025), earning higher grades (b= .1027, p < .025), and having higher perceptions of quality
relationships with peers (b= .2110, p < .001).
Research question “b”: How is the perception of sleep deprivation related to positive
relations with others in college students?
74
Table 8 summarizes the regression estimates for the effects of perceived sleep deprivation
and other independent variables on end of fourth year positive relationship with others scale. As
shown in Table 8, high levels of perceived sleep deprivation had a significantly negative effect
on positive relationships with others. At the end of four years of college (Time 3), students
whom reported frequent or always perceived sleep deprivation had a significant negative effect
on positive relationships with others (b= -.2734., p < .025).
In addition to perceived levels of sleep deprivation, other variables in the study also
effected positive relationships with others. First, the pretest measure of positive relationships
with others had a significant positive effect on positive relationships with others following four
years of college (b = ..4093, p <.001). This is unsurprising because the precollege pretest
measure of positive relationships with others should theoretically account for a significant
proportion of the variance of end of four years positive relationships with others. Also of interest,
being male had a significant negative effect on positive relationships with others (b= -.1624, p <
.001). Additionally, increased overall health at Time 3 significantly positively affected positive
relationships with others (b= .0761, p < .001) as did increased perceptions of quality
relationships with peers (b= .3318, p < .001).
Research question “c”: How is the perception of sleep deprivation related to
environmental mastery in college students?
Table 9 summarizes the regression estimates for the effects of perceived sleep deprivation
and other independent variables on end of fourth year environmental mastery. As shown in Table
9, high levels of perceived sleep deprivation had a significantly negative effect on environmental
mastery. At Time 3, students who reported occasional perceived sleep deprivation had a
significantly negative effect on their environmental mastery (b= -.2575, p < .010). Additionally,
75
students at Time 3 who reported frequent or always perceived sleep deprivation had a significant
negative effect on environmental mastery (b= -.6073., p < .001).
In addition to perceived levels of sleep deprivation, other variables in the study also
effected environmental mastery. First, the pretest measure of environmental mastery had a
significant positive effect on environmental mastery following four years of college (b = .3551, p
<.001). This is unsurprising because the precollege pretest measure of environmental mastery
should theoretically account for a significant proportion of the variance of end of four years
environmental mastery. Environmental mastery was also significantly positively affected by
increased overall health at Time 3 (b= .1570, p < .001), working on or off campus (b= .0629, p <
.001), consumption of alcohol (b= .0813, p < .01), earning a higher GPA (b= .1296, p < .001),
and perceptions of positive relationships with peers (b= .2294, p < .001). Having a higher
ACT/SAT statistically negatively influenced environmental mastery (b= .0667, p < .001).
Research question “d”: How is the perception of sleep deprivation related to
autonomy in college students?
Table 10 summarizes the regression estimates for the effects of perceived sleep
deprivation and other independent variables on end of fourth year autonomy. As shown in Table
10, perceived sleep deprivation had a significantly negative effect on autonomy. Students who
reported occasional sleep deprivation at the end of college had statistically significant negative
effects on autonomy (b= -.1958, p < .025). Also, students whom reported frequent or always
perceived sleep deprivation at the end of college had a significantly negative effect on their
autonomy (b= -.2070, p < .025).
In addition to perceived levels of sleep deprivation, other variables in the study also
effected autonomy. First, the pretest measure of autonomy had a significant positive effect on
76
autonomy following four years of college (b = .5103, p <.001). This is unsurprising because the
precollege pretest measure of autonomy should theoretically account for a significant proportion
of the variance of end of four years autonomy. Additionally, alcohol consumption had a
significantly positively effect on autonomy (b= .0727, p < .025). Also of interest, both high
school ACT/SAT (b= -.0586, p < .01) and religious group involvement (b= -.0781, p < .01) had
significant negative effects on autonomy
Research question “e”: How is the perception of sleep deprivation related to
purpose in life in college students?
Table 11 summarizes the regression estimates for the effects of perceived sleep
deprivation and other independent variables on end of fourth year purpose in life. As shown in
Table 11, perceived sleep deprivation had a significantly negative effect on purpose in life.
Students who reported frequent or always perceived sleep deprivation at the end of college had a
significantly negative effect on their purpose in life (b= -.2380, p < .01).
In addition to perceived levels of sleep deprivation, other variables in the study also
effected purpose in life. First, the pretest measure of purpose in life had a significant positive
effect on purpose in life following four years of college (b = .3564, p <.001). This is
unsurprising because the precollege pretest measure of purpose in life should theoretically
account for a significant proportion of the variance of end of four years purpose in life. Also of
interest, being male significantly negatively affected purpose in life (b= -.1320, p < .025) as did
increased time spent socializing (b= -.0968, p < .025). Both high levels of perceived overall
health (b= .1346, p < .001) and perceived quality of relationships with peers (b= .1562, p < .001)
had significant positive effects of purpose in life in college students at the end of four years of
college.
77
Research question “f”: How is the perception of sleep deprivation effect personal
growth in college students?
Table 12 summarizes the regression estimates for the effects of perceived sleep
deprivation and other independent variables on end of fourth year personal growth. As shown in
Table 12, perceived sleep deprivation had no effect on personal growth.
While perceived levels of sleep deprivation did not statistically significantly affect
personal growth, other variables in the study did. First, the pretest measure of personal growth
had a significant positive effect on personal growth following four years of college (b = .3941, p
<.001). This is unsurprising because the precollege pretest measure of personal growth should
theoretically account for a significant proportion of the variance of end of four years personal
growth. Also of interest, high levels of perceived overall health upon entering college (Time 1)
statistically negatively affected personal growth (b= -.0926, p < .025), while high levels of
perceived overall health at the end of four years of college (Time 3) statistically positively
affected personal growth (b= .1173, p < .001). Perceived high quality relationships with peers
(b= .1322, p < .001) had significant positive effects personal growth, while higher levels of
hours spent socializing (b= -.0832, p < .001) had a significant negative effect on personal
growth.
These findings suggest that perceived sleep deprivation has a significant relationship with
PWB and certain subscale measures, even with controlling for background characteristics and
institutional characteristics. That is, students whom perceive that they are sleep deprived show
decreases overall PWB, positive relationships with others, autonomy, environmental mastery and
purpose in life. Only self-acceptance and personal growth were unaltered by perceived sleep
deprivation at the end of four years in college. Additionally, this quasi pretest/posttest design
78
offered much control for selection effects, thus permitting the effects of the in college
experiences and college environmental factors to be estimated in a statistically stronger, less
biased, and more controlled manner.
Research question “2”: Do changes in perceptions of sleep deprivation from high
school to college influence psychological well-being?
Table 13 summarizes the regression estimates for the effects of changes in perceived
sleep deprivation from high school to college and other independent variables on end of fourth
year PWB. As shown in Table 13, changes in perceived sleep deprivation had a negative
statistically significant effect on PWB (b= -.1566, p < .01). Thus, if a student reported a change
in perceived sleep deprivation from high school to the end of four years of college their PWB
decreased at the end of four years of college. The model measuring perceived change in sleep
deprivation from high school to college is very similar to the model measuring perceived sleep
deprivation effects on total psychological well-being over four years of college. Like perceived
sleep deprivation’s effects on total PWB over four years in college, perceived change from high
school to end of college PWB showed statistically significant effects from perceiving sleep
deprivation at Time 1 (b= -.0763, p < .001), positive perceived health at Time 1 (b= -.0577, p <
.01), positive perceived health at Time 3 (b= .1578, p < .001), working on or off campus (b=
.0340, p < .025), ACT/SAT ability (b= -.0569, p < .001), and perceived quality of relationships
with peers (b= .2349, p < .001).
These findings suggest that changes in perceived sleep deprivation from high school to
end of the fourth year of college have a significant relationship with PWB. That is, students who
perceived that their sleep deprivation increased from high school to the end of college
experienced a decrease in total PWB. Additionally, this quasi pretest/posttest design offered
79
much control for selection effects, thus permitting the effects of the in college experiences and
college environmental factors to be estimated in a statistically stronger, less biased, and more
controlled manner.
Diagnostic Results
Each regression model underwent a series of regression diagnostics looking for
specification errors. The command linktest allowed me to test for these errors. Every model had
a statistically significant a_hat, while every model also had a_hatsq that was not statistically
significant. Thus, the models created were not misspecified. Also of interest was goodness-offit. The command lfit returned goodness-of-fit values that were not significant, indicating the
data fit the models well.
No zero cell counts existed, meaning there were no instances where the dependent
variables of interest fit completely within on independent variable category. Degree of
collinearity was tested in each model using the command collin. None of the models returned a
tolerance value within a range of concern.
Diagnostic tests to test for influential observers, including outliers, nonnormality, and
influential cases, were also administered. Testing for influential cases can be done by
conducting influential case diagnostics using the residuals, leverage, deviance, and DBETA. The
command extremes allowed for evaluation of the 5 lowest and 5 highest values of the
standardized residuals, leverage, deviance, and DBETA. The regression models did not return
observations with high residuals or deviance. The DBETA values, which indicate the amount of
influence a case may have on the model, were lower than 1, and not a cause for concern.
80
Results Summary
This chapter presented descriptive statistics, multiple linear regressions, and diagnostic
tests of the study. Missing cases were analyzed, allowing the conclusion that the cases were
Missing Completely at Random (MCAR), and that no apparent systematic biases existed within
the study. Listwise deletion was utilized to delete missing cases, and provided a sample of 1,760
participants. Descriptive statistics were next run to provide an overview of the participants in the
study. Additionally, a correlation matrix was created to provide an overview of the relationships
between the variables in the study.
Multiple linear regression was utilized to examine the relationships between perceived
sleep deprivation and PWB in college students. In general, even when controlling for
background and institutional characteristics, perceived sleep deprivation lowers PWB and its six
constituent subscales, with higher levels of perceived sleep deprivation further depressing PWB
and subscale scores. These results indicate that perceived sleep deprivation can significantly
influence students’ total PWB, as well as positive relationships with others, autonomy,
environmental mastery, and purpose in life. Additionally, a change in perception of sleep
deprivation from high school to end the of college diminishes overall PWB. All regression
models had several control variables that were statistically significant, indicating that both
institutional and background characteristics also had an effect on total PWB as well as its
subscales. The quasi-pretest-posttest design of this study offers considerable control for
selection effects, therefore allowing the effects of environmental factors and in-college
experience to be estimated in a statistically strong, less biased, and more controlled way.
Chapter 5 will discusses the results of this study. In addition, evaluation of theoretical
and political implications, study limitations, and final conclusions of this study are included.
81
Table 4
Descriptive Statistics for All Variables
Total (n=1,760)
Frequency, (%)
Mean, (SD)
Sex
Male
Female
666 (37.84)
1,094 (62.16)
White
1415 (80.39)
Race
Black/nonHispanic
American Indian, Asian,
Hispanic
Occasionally Sleep Deprived Time 1
Frequently/Always Sleep Deprived Time 1
Occasionally Sleep Deprived Time 3
Frequently/Always Sleep Deprived Time 3
Received Pell
Grant
Yes
No
Exercise frequency
Perceived health Time 1
Perceived health Time 3
Parental Income
Parental educational
attainment
Graduate Degree
Less than graduate degree
Worked on or off campus
ACT/SAT score
Smoking
frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts
institution
Yes
No
345 (19.61)
749 (42.55)
651 (36.98)
686 (38.97)
697 (39.60)
214 (12.15)
1,546 (87.85)
3.40 (2.4)
3.57 (2.1)
4.34 (0.621)
5.71 (1.84)
740 (42.045)
1,020 (57.95)
9.246 (8.717)
27.248 (4.29)
1.07 (0.304)
1.635 (1.049)
0.018 (1.01)
975 (55.39)
785 (44.61)
82
Table 4 (cont.)
Total (n=1,760)
Frequency, (%)
Mean, (SD)
STEM Major
Yes
No
Fraternity or Sorority
Member
Yes
No
End of College GPA
Socialization frequency
Religious group involvement
Yes
No
Perceived quality of relationships with peers
458 (26.02)
1,302 (73.98)
381 (21.64)
1,379 (78.36)
6.424 (1.327)
3.934 (1.598)
652 (37.04)
1,108 62.96)
6.018 (1.099)
83
84
19. Cocurricular involvement
20. Attended liberal arts
21. STEM major
22. Frat./Sorority member
23. GPA
24. Socialization Frequency
25. Religious involvement
26. Relationship quality
Table 5 (cont.)
Correlation Matrix
19
1
0.164
-0.0044
0.222
-0.0063
-0.0204
0.0647
0.1737
1. Ryff Total T3
2. Ryff Total T1
3. Male
4. Occasional SD T1
5. Frequent/always SD T1
6. Occasional SD T3
7. Frequent/always SD T3
8. White
9. Received Pell Grant
10. Exercise Frequency
11. Overall health T1
12. Overall health T3
13. Parental Income
14. Parental education
15. Worked on or off campus
16. ACT/SAT
17. Smoking frequency
18. Alcohol consumption
19. Cocurricular involvement
20. Attended liberal arts
21. STEM major
22. Frat./Sorority member
23. GPA
24. Socialization Frequency
25. Religious involvement
26. Relationship quality
Table 5
Correlation Matrix
1
-0.0462
0.0664
0.0027
-0.0292
-0.0999
-0.0033
20
1
1
0.5335
-0.0844
0.0242
-0.0848
0.0523
-0.1761
0.0287
-0.0243
0.093
0.1708
0.3101
0.0572
0.0458
0.0438
-0.0118
-0.0742
0.0052
0.1144
0.0013
-0.0171
0.0357
0.1814
-0.0778
0.0628
0.3734
22
1
0.0322
-0.1197
-0.0134
-0.0856
0.0311
-0.0609
0.1193
0.1213
0.0416
0.0649
0.0237
-0.0609
0.0768
0.0712
0.1837
0.1279
0.052
0.1167
0.0365
-0.0819
0.1525
-0.0381
-0.0189
3
23
24
1
-0.6895
0.0826
-0.1142
0.0776
0.0208
0.0162
0.0715
0.0447
0.0175
0.0072
0.0026
0.044
-0.024
-0.0259
-0.0151
0.0372
0.0448
-0.0004
0.0304
0.0039
0.006
0.0186
4
25
1
-0.0983
0.2964
-0.1108
-0.0114
-0.0445
-0.1591
-0.0962
-0.0073
0.0412
-0.0022
-0.0044
-0.0019
0.0346
-0.0061
-0.0891
-0.0494
-0.0026
-0.002
-0.0155
-0.015
-0.0369
5
1
1
-0.0539
1
-0.0228 -0.0694
1
-0.0039 -0.05 -0.0195
1
0.0116 -0.0947 0.1333 -0.0766
0.06 -0.0188 0.1237
0.007 0.1686
21
1
-0.1018
0.0548
-0.161
0.0072
-0.097
0.0242
0.0202
0.126
0.2305
0.1758
0.0295
0.0372
0.0081
0.0534
-0.063
-0.0445
0.0432
-0.0561
-0.0118
-0.019
0.0946
-0.043
0.0814
0.1686
2
1
26
1
-0.6472
0.0425
-0.0478
0.0629
0.0211
0.0189
0.0366
0.0037
-0.0191
0.0105
0.0156
-0.0172
-0.0151
0.0562
-0.0518
0.0269
0.0534
0.0315
-0.0051
0.0317
6
8
9
10
11
12
13
14
1
-0.0889
1
0.0898 -0.1929
1
-0.1124 0.1097 -0.0415
1
-0.1141 0.1373 -0.0869 0.2553
1
-0.1288 0.1272 -0.1024 0.3633 0.4391
1
-0.0953 0.3186 -0.4352 0.1112 0.1451 0.1619
1
-0.0237
0.119 -0.2042 0.0721 0.0903 0.0951 0.3502
1
0.0561 -0.1002 0.1399 -0.029 -0.0615 -0.0557 -0.2194 -0.169
-0.0553 0.2714 -0.2726 -0.0075 0.0907 0.0944 0.2954 0.2492
0.0202 0.0194 0.0804 -0.0869 -0.0932 -0.1593 -0.0288 -0.0103
0.0101 0.1271 -0.1023 0.1316 0.0729 0.0394 0.1949 0.0743
-0.0252 0.0363 -0.012
0.333
0.098 0.1367 0.0337 0.0521
-0.0611 0.0608
0.159
0.05 0.0196 -0.0361 -0.1147 -0.023
0.0228 0.0221 -0.0542 0.0307 0.0558
0.049 0.0476 0.0221
-0.0025 0.0441 0.0366 0.0198 -0.0582 -0.026 -0.0117 -0.0564
-0.1058 0.1915 -0.1505 0.0483 0.0584 0.1387 0.1259 0.1223
-0.0662 0.0576 -0.0706 -0.0255 0.0164 -0.029 0.1212 0.0863
0.0019 0.0557 0.0134 -0.0163 0.0122 0.0245
0.007 0.0616
-0.0405 0.0978 -0.0202 0.1227 0.1182 0.1785 0.0573 -0.0021
7
1
-0.2284
0.0301
-0.0724
-0.0123
-0.0026
-0.0928
0.034
-0.0943
-0.106
-0.0295
0.0236
15
17
18
1
-0.0028
1
0.1247 0.2295
1
-0.0294 0.0275
0.055
-0.1593 0.1237
0.05
0.1544 -0.0469 -0.0487
-0.1338 0.0562
0.095
0.3776 -0.075 -0.0172
0.1706 0.0868 0.2797
0.1187 -0.1029 -0.1705
-0.0384 -0.0735 0.0559
16
Table 6
Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation on Total
Psychological Well-being (r2= 0.4513)
Coef.
Psychological Well-being Total Time 1
Male
Sleep Deprived Occasionally Time 1
Sleep Deprived Frequently/Always Time 1
Sleep Deprived Occasionally Time 3
Sleep Deprived Frequently/Always Time 3
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts institution
STEM Major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
0.4241
-0.1065
0.0266
0.1112
-0.1946
-0.3574
-0.0382
-0.0355
0.0323
-0.0598
0.1607
0.0193
0.0109
0.0350
-0.0569
0.0462
0.0569
0.0586
0.0182
-0.0041
-0.0521
0.1048
-0.0604
0.0402
0.0303
SE
0.0192
0.0463
0.0507
0.0822
0.0983
0.0995
0.0591
0.1089
0.0301
0.0161
0.0186
0.0293
0.0940
0.0123
0.0120
0.0549
0.0294
0.0431
0.0564
0.0322
0.0445
0.0475
0.0278
0.0402
0.0303
pvalue
0.000
0.035
0.607
0.195
0.065
0.002
0.527
0.748
0.299
0.002
0.000
0.518
0.908
0.012
0.000
0.412
0.071
0.193
0.751
0.899
0.258
0.043
0.045
0.369
0.000
85
Table 7
Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation on
Self-Acceptance (r2= 0.4122)
Self-acceptance Time 1
Male
Sleep Deprived Occasionally Time 1
Sleep Deprived Frequently/Always Time 1
Sleep Deprived Occasionally Time 3
Sleep Deprived Frequently/Always Time 3
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts institution
STEM major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
Coef.
SE
Pvalue
0.4310
-0.1496
0.0908
0.1037
-0.0852
-0.2054
-0.0322
0.0031
0.0209
-0.0523
0.1778
0.0221
-0.0140
0.0360
-0.0322
0.0625
0.0378
0.0627
0.0165
-0.0125
-0.0086
0.1027
0.0064
-0.0415
0.2110
0.0258
0.0641
0.0430
0.0736
0.0747
0.0869
0.0442
0.1615
0.0217
0.0243
0.0163
0.0161
0.0604
0.013
0.0172
0.0313
0.01381
0.0265
0.0504
0.0471
0.05669
0.0373
0.0212
0.0473
0.0177
0.000
0.033
0.051
0.193
0.271
0.031
0.476
0.985
0.349
0.047
0.000
0.188
0.819
0.014
0.080
0.063
0.015
0.031
0.748
0.794
0.881
0.014
0.766
0.393
0.000
86
Table 8
Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation on
Positive Relationships with Others (r2= 0.4551)
Coef.
Positive Relationships With Others Time 1
Male
Sleep Deprived Occasionally Time 1
Sleep Deprived Frequently/Always Time 1
Sleep Deprived Occasionally Time 3
Sleep Deprived Frequently/Always Time 3
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts institution
STEM major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
0.4093
-0.1624
0.09
0.1223
-0.1467
-0.2734
-0.0465
0.0414
0.0397
-0.0036
0.0761
-0.0055
0.0712
-0.0017
-0.0325
0.0539
0.0816
0.0336
-0.0307
0.006
-0.0376
0.0506
-0.0441
0.0451
0.3318
SE
0.0223
0.0313
0.0519
0.0494
0.108
0.097
0.0368
0.151
0.0235
0.0282
0.0141
0.0354
0.0637
0.012
0.0226
0.024
0.0436
0.0375
0.0734
0.0373
0.0562
0.0228
0.0309
0.0518
0.0491
P-value
0.000
0.000
0.102
0.025
0.193
0.012
0.224
0.787
0.111
0.898
0.000
0.877
0.281
0.886
0.169
0.039
0.080
0.383
0.682
0.862
0.247
0.041
0.173
0.396
0.000
87
Table 9
Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation on
Environmental Mastery (r2= 0.4656)
Environmental Mastery Time 1
Male
Sleep Deprived Occasionally Time 1
Sleep Deprived Frequently/Always Time 1
Sleep Deprived Occasionally Time 3
Sleep Deprived Frequently/Always Time 3
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts institution
STEM major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
Coef.
SE
0.3551
-0.0954
0.0317
0.139
-0.2575
-0.6073
-0.0111
-0.0792
0.0614
-0.0369
0.157
-0.0003
-0.0039
0.0629
-0.0667
0.0073
0.0813
0.0491
-0.0305
0.0509
-0.0627
0.1296
-0.0111
-0.0723
0.2294
0.0223
0.046
0.0523
0.0623
0.0825
0.0815
0.0572
0.0706
0.0324
0.0215
0.0224
0.0281
0.0808
0.0106
0.0094
0.0483
0.0267
0.0427
0.0493
0.0373
0.0495
0.0312
0.02325
0.049
0.0243
P-value
0.000
0.055
0.553
0.040
0.007
0.000
0.848
0.279
0.077
0.105
0.000
0.989
0.962
0.000
0.000
0.881
0.008
0.267
0.545
0.192
0.223
0.001
0.639
0.159
0.000
88
Table 10
Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation on
Autonomy (r2= 0.3478)
Coef.
Autonomy Time 1
Male
Sleep Deprived Occasionally Time 1
Sleep Deprived Frequently/Always Time 1
Sleep Deprived Occasionally Time 3
Sleep Deprived Frequently/Always Time 3
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts Institutions
STEM Major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
0.5103
0.0724
-0.0344
0.0128
-0.1958
-0.2076
-0.1445
0.0989
0.0001
-0.0498
0.0863
0.0251
0.0286
-0.024
-0.0586
0.0391
0.0727
0.0633
0.1027
-0.0601
-0.0764
0.0224
-0.0696
-0.0781
0.0482
SE
0.0252
0.0390
0.0360
0.1036
0.0729
0.0841
0.0798
0.0965
0.0294
0.0217
0.0363
0.0241
0.0625
0.0442
0.0180
0.0597
0.0251
0.0465
0.0598
0.0656
0.0970
0.0404
0.0602
0.0256
0.0270
P-value
0.000
0.082
0.354
0.903
0.016
0.025
0.089
0.320
0.996
0.036
0.030
0.313
0.653
0.595
0.005
0.522
0.011
0.192
0.105
0.373
0.442
0.586
0.266
0.008
0.093
89
Table 11
Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation on
Purpose in Life (r2= 0.3279)
Coef.
Purpose in Life Time 1
Male
Sleep Deprived Occasionally Time 1
Sleep Deprived Frequently/Always Time 1
Sleep Deprived Occasionally Time 3
Sleep Deprived Frequently/Always Time 3
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts institution
STEM major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
0.3564
-0.132
0.0246
0.0886
-0.1101
-0.238
-0.0207
-0.0314
0.037
-0.0298
0.1346
0.0535
-0.0745
0.0543
-0.0573
0.0319
-0.0301
0.0223
-0.0466
0.0332
0.0045
0.1249
-0.0968
-0.0273
0.1562
SE
0.019
0.0533
0.07
0.0902
0.0859
0.0772
0.0618
0.0675
0.041
0.0136
0.0273
0.023
0.1115
0.0297
0.0248
0.0647
0.0457
0.0343
0.0483
0.0472
0.0842
0.054
0.0333
0.0498
0.0234
P-value
0.000
0.025
0.730
0.341
0.218
0.007
0.742
0.648
0.380
0.044
0.000
0.034
0.513
0.087
0.034
0.629
0.520
0.524
0.348
0.492
0.957
0.034
0.010
0.591
0.000
90
Table 12
Multiple Linear Regression Model Estimating Effects of Perceived Sleep Deprivation on
Personal Growth (r2= 0.2779)
Personal Growth at Time 1
Male
Sleep Deprived Occasionally Time 1
Sleep Deprived Frequently/Always Time 1
Sleep Deprived Occasionally Time 3
Sleep Deprived Frequently/Always Time 3
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts institution
STEM Major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
Coef.
SE
0.3941
-0.0964
-0.1378
-0.0043
-0.127
-0.1156
0.0944
-0.0085
-0.0031
-0.0926
0.1173
-0.0019
0.0579
0.0495
-0.007
0.0134
0.0167
0.0465
0.0786
-0.0435
-0.04
0.06189
-0.0832
0.0143
0.1322
0.0192
0.0538
0.0777
0.109
0.0906
0.0908
0.0697
0.0552
0.0374
0.0351
0.0122
0.0354
0.0969
0.0227
0.0276
0.0525
0.0288
0.0367
0.0586
0.0278
0.06244
0.0501
0.028
0.0276
0.0314
P-value
0.000
0.092
0.095
0.969
0.180
0.221
0.195
0.879
0.935
0.018
0.000
0.965
0.559
0.045
0.801
0.801
0.569
0.224
0.199
0.138
0.531
0.235
0.009
0.611
0.001
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Table 13
Multiple Linear Regression Model Estimating Effects of Changed Perceived Sleep Deprivation
from high-school to end of fourth year college on Total Psychological Well-being (r2=
0.2779)
Psychological Well-being Total Time 1
Male
Perceived sleep deprivation change
Perceived sleep deprivation Time 1
White
Received Pell Grant
Exercise frequency
Overall perceived health Time 1
Overall perceived health Time 3
Parental income
Parental educational attainment
Worked on or off campus
ACT/SAT score
Smoking frequency
Alcohol consumption frequency
Cocurricular involvement
Attended liberal arts institution
STEM major
Fraternity or sorority membership
End of college GPA
Socialization frequency
Religious Group Involvement
Perceived quality of relationships with peers
Coef.
SE
0.4264
-0.1039
-0.1566
-0.0763
-0.045
-0.0129
0.031
-0.0577
0.1578
0.0262
0.0089
0.034
-0.0569
0.0443
0.057
0.0589
0.0193
-0.0052
-0.0557
0.1001
-0.064
-0.0355
0.2349
0.019
0.0445
0.0045
0.019
0.0582
0.01015
0.0297
0.0166
0.018
0.0301
0.0925
0.0131
0.0118
0.056
0.0289
0.0455
0.0576
0.0342
0.0414
0.0451
0.028
0.0394
0.0301
P-value
0.000
0.033
0.003
0.001
0.450
0.900
0.312
0.003
0.000
0.397
0.924
0.020
0.000
0.441
0.066
0.214
0.742
0.881
0.198
0.041
0.036
0.381
0.00
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CHAPTER 5
DISCUSSION, IMPLICATIONS, AND CONCLUSIONS
This study focused on PWB and its relationship to perceived sleep deprivation. Scientific
studies utilizing PWB have emerged in six thematic areas: (1) how well-being changes across
adult development and later in life, (2) personality correlates of well-being, (3) well-being and
experiences in life, (4) well-being as related to work and communal activities, (5) connections
between well-being and health, including biological risk factors, and (6) clinical and intervention
studies (Ryff, 2014). No studies have evaluated the effects of perceived sleep deprivation on
PWB at the end of four years of college. Using data from the Wabash National Study (WNS),
this study examined the effects of perceived sleep deprivation on total PWB and its six subscales,
while controlling for the effects of pre-college PWB, student background characteristics, and
institutional characteristics.
Chapter 5 begins with a summary and discussion of the results from the analysis found in
Chapter 4. Also examined are the implications of this study for the advancement of theory
concerning the relationship between perceived sleep deprivation and PWB. Policy and practical
implications of this study are discussed, followed by summarization of study limitations. The
chapter concludes with implications for future research and final concluding thoughts.
Summary and Discussion of Results
The purpose of this study was to better understand the influence of perceived sleep
deprivation on students’ PWB. Specifically, this study used multiple linear regression to address
the primary research question, and sub-questions, of this study:
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1) How is the perception of sleep deprivation related to psychological well-being over
four years in college students?
a. How is the perception of sleep deprivation related to self-acceptance in
college students?
b. How is the perception of sleep deprivation related to positive relations with
others in college students?
c. How is the perception of sleep deprivation related to environmental mastery in
college students?
d. How is the perception of sleep deprivation related to autonomy in college
students?
e. How is the perception of sleep deprivation related to purpose in life in college
students?
f. How is the perception of sleep deprivation related to personal growth in
college students?
2) Do changes in perceptions of sleep deprivation from high school to college influence
psychological well-being?
Research Methods Review
Using secondary data from a multi-institutional longitudinal dataset, several multiple
linear regressions were implimented to answer the studies’ research questions. This data set, the
Wabash National Study of Liberal Arts Education (WNS), was a sizable, longitudinal
investigation focused on the effects of both liberal arts colleges and education. The WNS was
pre and post-test in design, allowing for these effects to be attributed to liberal arts colleges and
the educational experiences at these institutions. The WNS contained a copious number of
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variables, allowing interested researchers access to investigate many important college outcomes.
Included in the WNS data set were institutional characteristics, student background
characteristics, a perceived sleep deprivation variable, and the Ryff Scale of Psychological Wellbeing (RSPWB). Because this dataset contained all relevant variables, it was an appropriate
selection for this research.
Participants in the WNS were first-year, freshman, full-time undergraduates at the initial
data collection (“Time 1”) and time of selection in fall of 2006. All students at both regional and
liberal arts institutions were extended invitations for participation, while participants at larger
research institutions were randomly selected. Time 1 data collection yielded 4,501 student
participants. Final data collection (“Time 3”) occurred in the spring of 2010, the fourth, senior
year for participants. Time 3 data collection yielded 2,212 student participants, a 49% response
rate.
These data were utilized in a series of multiple linear regressions to evaluate the
relationship between perceived sleep deprivation and total PWB, as well as the six subscales of
PWB: personal growth, positive relationships with others, self-acceptance, autonomy,
environmental mastery, and purpose in life. Also examined was the potential effect of change in
perceived sleep deprivation from high-school to college, and this change’s effect of total PWB.
The analysis controlled for the effects of pre-college PWB, specific student background
characteristics, student behavioral characteristics, and select institutional characteristics.
Background characteristics included: sex, race/ethnicity, parental education, parental income,
ACT scores, and financial aid status. Student college characteristics included: involvement in a
fraternity or sorority, religious group involvement, frequency of exercise, perception of overall
health status, on and off campus work hours, frequency of alcohol consumption, self-reported
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GPA, hours spent socializing, frequency of smoking, and the perception of the quality of
relationships with peers. The institutional characteristic addressing structure identified students
who attended a liberal arts college for this study. The independent variable of interest was
perceived sleep deprivation, which was further divided into “occasional” and “frequently or
always” sleep deprived. Finally, the pre-test measure was a variable for pre-college PWB.
Results Review
Multiple linear regressions were first conducted for total PWB as well as each subscale of
PWB. A quick summary table of results is found in Table 14. In general, students reporting
frequent or always perceiving sleep deprivation had a negative and statistically significant effect
on PWB and the six subscales, even when controlling for a grouping of student background
characteristics and institutional characteristics. Students reporting occasional perceived sleep
deprivation also had negative statistically significant effects on PWB and the six subscales, but
in less frequent and dramatic ways.
Interestingly, there were two subscales that do not appear to be affected by perceived
sleep deprivation. Both personal growth and self-acceptance were not altered by either
occasional or frequently or always perceiving sleep deprivation. While perceived sleep
deprivation had some effect on total PWB, as well as positive relationships, autonomy,
environmental mastery, and purpose in life, it did not alter personal growth or self-acceptance.
Also, change in perceived sleep deprivation from high school to the end of the fourth year of
college had a negative statistically significant effect on total PWB.
Additionally, estimates of the effects of pre-college PWB and several student background
characteristics, student college characteristics, and institutional characteristics were statistically
significant predictors of total PWB as well as the six subscales of PWB. One of the most
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frequent, positive and statistically significant relationships occurred between participant’s
perceived health at Time 3, total PWB, and various subscales. Specifically, individuals whom
reported higher levels of perceived overall health experienced a positive, statistically significant
boost to their total PWB, personal growth, positive relationships with others, self-acceptance,
environmental mastery, and purpose in life. The only subscale unaffected by increased in
perception of overall health was autonomy. These findings strongly align with previous research
linking PWB, and its six subscales, to various measures of health and perceived health . This
research further verifies the strong interplay and relationship between psychological well-being
and physical health. More research should be done to continue identifying the important links
between all aspects of a person’s health status, including physical, mental, spiritual, emotional,
and psychological health.
Another variable which frequently demonstrated positive, statistically significant effects
on total PWB and its six subscales was perceived students perception of the quality of
relationships with other students. This scale ranged from unfriendly, unsupportive, and
alienating to friendly, supportive, and belonging. Students who rated their relationships as
friendly, supportive, and belonging saw increases in total PWB, personal growth, positive
relationships with others, self-acceptance, environmental mastery, and purpose in life.
Autonomy was the only subscale unaffected by increases in ratings in quality of relationships.
These results support previous research on the importance and power of social support when
creating PWB. Future research should isolate ratings of relationship quality as an independent
variable of interest, creating a model which addresses the unique role social support, and its
relationships, play in the creation and maintenance of all parts of PWB.
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Also in line with previous research, students whom reported working on or off campus
had increases in their total PWB. The results of this study also showed work to increase selfacceptance and environmental mastery. These results are new for the college student population.
Previous research found meaningful work to increase personal work, while unpaid work
decreased self-acceptance and environmental mastery. Perhaps because this work was paid, it
increased self-acceptance and environmental mastery. Another possible explanation are the
reported ancillary benefits provided by employment during these formative years, two of which
might be increased self-acceptance and environmental mastery. Students seeking employment
should remember these possible ancillary benefits, as should university employers when creating
a positive working experience for student employees.
Other important contributions to the literature were found in this study. First, the
relationship between ACT/SAT scores and PWB was explored. No previous literature existed on
this relationship. The results of this study found ACT/SAT scores to have negative effects on
total PWB, autonomy, and environmental mastery. Second, the relationship between frequency
of alcohol consumption and PWB was addressed. No previous literature existed on this
relationship. Participants who frequently consumed alcohol received positive increases in selfacceptance, autonomy, and environmental mastery. Third, students who reported higher GPAs
were found to have increases in self-acceptance and environmental mastery. These specific
relationships had not previously been studied. Fourth, students who spent more hours socializing
experienced decreases in personal growth and purpose in life. This relationship is somewhat
contrary to previous literature suggesting the potential benefits of socializing and the subsequent,
potential social support. Perhaps this item did not serve as a proxy for social support, but merely
served as an accounting for how students choose to allocate their time. It may very well be that
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students whom spent increasing time socializing then suffered the ill effects of underprepared
studies, sleep deprivation, or stress. This may be a simple case of mismanaged time wreaking
havoc. Finally, students whom reported religious group involvement had decreases in autonomy.
Future research should take these five unique results and further explore their relationships with
PWB.
As anticipated, one of the strongest relationships was between pre-college PWB and
PWB at the end of four academic years. Having pre-college PWB had a positive and statistically
significant effect on Time 3 PWB. This relationship held true for total PWB and all six
subscales. These results direct attention to the importance of a pre-test for PWB in order to
control for students’ pre-college characteristics while allowing for better understanding of the
relationship other variables have with end of college PWB.
The findings here suggest that students’ perceptions of sleep deprivation have a
significant relationship with total PWB, as well as the subscales of positive relationships with
others, autonomy, environmental mastery, and purpose in life. These relationships were
identified while controlling for both institutional characteristics and background characteristics.
The inclusion of a pre-test created a quasi-pretest-posttest design which offered powerful control
for selection effects, thus allowing the effects of participant’s college experiences and
environmental factors to be estimated in a more controlled, statistically stronger, less biased
manner.
Theoretical Implications
College impact research has yet to delve deeply into college student’s psychological wellbeing, and no other model to date has explicitly included perceived sleep deprivation as a
standard aspect of both institutional environment and characteristic of student PWB. This study
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strongly supports the suggestion that researchers should incorporate perceived sleep deprivation
when investigating college PWB, and potentially other college outcomes of interest.
Previous research has directed attention to the relationship between PWB and health measures.
Higher education research has not taken this college outcome, and its relationship to student
health, to its fullest extent. It should. As suggested by Ryff, Singer and Love (2004), possible
psychosocial antecedents to good sleep may exist. Additionally, benefiting from the restorative
function so sleep may contribute to quality relationships, purposeful engagement, and continued
growth (Sejnowski and Destexhe, 2000). Thus, the relationship between sleep and psychological
well-being may indeed be reciprocal. This relationship between sleep and psychological wellbeing may be a key feature of the process to keep humans free from illness, disease, and
disability (Ryff, Singer, & Love, 2004). While it is not the purpose of this study to state why or
how sleep deprivation relates to PWB, it is the purpose of this study to show that this relationship
does indeed exist. The findings of this study link perceived sleep deprivation to PWB and its six
subscales, furthering the development of a theory as to the relationship between sleep and PWB.
This research furthers the theoretical implications for variables linking to the Ryff Scales
of Psychological Wellbeing (RSPWB) by incorporating perceived sleep deprivation. This
inclusion helps to further understand Ryff’s theoretical underpinnings for her scales of PWB
because sleep is a fundamental requirement for human existence, and is a health related behavior.
Just as previous research has provided support for the theoretical linkages between PWB and
physical health, so to, this research further advances the link between PWB and yet another
aspect of physical health: sleep.
Policy Implications
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This study of the relationship between perceived sleep deprivation and PWB has
important implications for policy pertaining to higher education. These implications include
practical aspects for all involved in undergraduate education, including undergraduates, faculty,
staff, student health providers, and administrators. Sleep deprivation is a problem that has both
individual and societal contexts, and prevention, intervention, and treatment must note this truth.
According to the Institute of Medicine, a multipronged effort to increase awareness and improve
diagnosis and treatment of sleep deprivation must occur. This effort must include public health
campaigns, educational strategies for health care providers, and improved surveillance of general
population (Colten & Altevogt, 2006). This effort must also be applied to college campuses,
including campaigns, educational strategies, and surveillance. Many of the 17 million US
college-aged students are currently enrolled in over 6,000 post-secondary institutions, offering a
prime opportunity for intervention in a large population of emerging adults for health promotion
efforts (Knapp, Kelly-Reid, & Whitmore, 2006; Laska, Pasch, Lust, Story, & Ehlinger, 2009; US
Census Bureau, 2008). An intervention for institutions of higher education which adds
guidelines for students to be educated on the effects of sleep would be advantageous (Taylor et
al, 2013). The need to education these student’s on a life-long healthy habit cannot be
understated.
Higher education policymakers who are interested in increasing retention and overall
health of their student body should also make efforts to improve the sleep hygiene of their
students. As it has been shown here, perceived sleep deprivation has a significant relationship
with PWB and its subscales, and this relationship should inform decision makers who are
developing policies which could impact students’ sleep. For example, policy makers could opt
to place value on an appropriate student life balance, mandating consideration for allocation of
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proper sleep requirements. Policy makers who are interested in better understanding the sleep
patterns, habits, and needs of students could invest additional funding for research on sleep
deprivation in college students. Higher education policy makers could also partner with national
and government officials whom are also seeking to better understand the importance of sleep,
sleep deprivation, and PWB.
As previously shown, PWB is a desired outcome of higher education, and can be altered
by perceived sleep deprivation. While the many dangers of sleep deprivation have been
enumerated in Chapter 2, it is worth-while to note the addition of yet another danger of sleep
deprivation: decreased total PWB, positive relationships with others, environmental mastery,
autonomy, and purpose in life. Decreases in any, let alone all, of these aspects of PWB should
be alarming to student health providers. In order to combat the deleterious effects of perceived
sleep deprivation, student health providers should provide necessary interventions to improve the
quality of student sleep on their campuses. These interventions may include orientation
informational sessions, campus wide media events promoting the value of sleep, seminars and
workshops, sleep friendly dormitories, sleeping stations on campus, and increased focus on sleep
for student mental health providers. Some interventions could relate to sleep hygiene, including
circadian rhythm management training, education of proper sleep hygiene, and white noise use
(Breus, 2005; Carskadon, 2002; Dement, 2005). Improvement in sleep hygiene may also include
limiting naps to less than 1 hour, using beds only for sleeping, and making sure the bedroom is
comfortable for sleep.
Creating a campus wide awareness of the value of sleep, and the dangers of sleep
deprivation, could also be the job of the student health providers. Educating students of their
proper sleep needs is also important. These campaigns could be modeled after successful
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alcohol, sexual activity, and drug prevention interventions found on many college campuses
today. Just as colleges choose to promote various health behaviors conducive to health and wellbeing, promotion of proper sleep and PWB must also be done.
Additionally, this research provides practical applications that institutions of higher
education can use to structure better environments and conditions to help students as they
navigate their undergraduate careers. An imperative change that must occur on college
campuses is the normative culture which promotes sleep deprivation and prohibits the acquisition
of necessary amounts of sleep for healthy functioning in undergraduate students. The levels of
sleep deprivation on college campuses are astounding and inexcusable. The current culture of
overextension, full class loads, extracurricular activities on overdrive, managing social
relationships, dating a significant other, volunteering, working, and studying begs the question:
when do they sleep? Culture places a “badge of honor”: for the 3:15 AM email to the professor,
for the “all-nighter”, for the chosen forgoing of a good night’s sleep. This is the norm. Until this
normative culture of sleep deprivation is intentionally addressed, intervened, and mitigated,
undergraduate students will struggle alone to defend their time for necessary, healthy, restorative
sleep. Exactly how institutions of higher education choose to change their sleep cultures will
need to be uniquely their own. Perhaps placing a value on sleep, and scheduling it in just like
each semester’s courses, is a place to start. Perhaps creating reading lists and assignments which
fit into allocated time requirements for all courses should be implemented. Much like student
athletes are allocated hours of practice each week, student scholars could also be guaranteed
reasonable academic loads which accommodate time for sleep. Also, providing acceptance for
sleep by removing the senseless promotion and praise for sleep deprivation must occur. Staff
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and faculty need to lead by example in this case, forgoing unhealthy behavior per role model
status.
Limitations
As previously stated in Chapter 1, all research has limitations. Because this study utilized
a secondary dataset, certain limitations were inevitable. To begin, all variables and their
definitions in this study were fixed. The independent variable of interest, perceived sleep
deprivation, was a single item in the study. The variable reported student’s perceptions of their
sleep deprivation, and no other variables providing ancillary support pertaining to sleep habits,
environments, or durations were included in the study. The true, absolute nature of study
participants’ sleep deprivations was not attainable information. Additionally, working with fixed
variables meant the study was unable to replicate all variables found in other student outcomes
models.
Secondly, the scope of the study was intentionally limited. The topic of sleep deprivation
and psychological well-being is vast. The purpose of this study was to answer the research
questions posed. A full account of sleep and sleep deprivation was beyond the scope of this
study. With that in mind, the study limited information detailing the science behind sleep and
sleep deprivation, and instead focus specifically on sleep deprivation as it may impact
psychological well-being in college students.
Implications for Future Research Endeavors
This study identified many opportunities for future research endeavors. First, research
should move forward to better understand sleep deprivation in college students. As the negative
consequences of sleep deprivation continue to receive scientific attention, the practical
implications for college students must be taken seriously. Future research should investigate
104
sleep deprivation’s influence on other college outcomes of interest. The linkage between sleep
deprivation and other outcomes should not be overlooked. Secondly, future research should
continue to investigate the relationship between perceived sleep deprivation and PWB. PWB of
college students is an important topic, one which cannot be argued against. All students should
be as psychologically well as possible. With this in mind, researchers should seek to better
understand the relationship between perceived sleep deprivation and PWB at a more nuanced
level.
Another avenue for researcher could include a qualitative inquiry into student’s sleeping
habits, sleep deprivation, and personal PWB. Perhaps a qualitative component allowing students
to describe how perceived sleep deprivation influences their PWB would illuminate how sleep
deprivation harms PWB in a way that quantitative analysis doesn’t. Also of interest may be a
sleep laboratory study which would measure sleep consumption in very exacting ways. This
level of sleep analysis could more precisely measure at what point sleep deprivation starts to
inhibit PWB as well as thresholds for PWB subscales. Future research should also seek to
understand why perceived sleep deprivation seems to not lessen the subscales of personal growth
and self-acceptance.
Conclusions
This research has identified that a relationship does exist between perceived sleep
deprivation and PWB in students after four years of higher education. Specifically, perceived
sleep deprivation lessens total PWB, autonomy, positive relationships with others, environmental
mastery, and purpose in life. This research also strengthens the proposed relationship between
physical health and psychological health. Previous research has focused on physical health and
biomarkers, and their unique impact on PWB. This research creates a new area for much needed
105
exploration: sleep’s relationship to psychological well-being. Linking the physical body to the
psychological mind is a true scientific endeavor. The findings of this study do just that. This
study also makes an important contribution to Ryff’s theory of psychological well-being,
providing support for the mind-body relationship. Future research should further investigate the
degree to which perceived sleep deprivation influences PWB, as well as other outcome variables
of interest in the field of higher education.
106
REFERENCES
Abbott, R. A., Ploubidis, G. B., Huppert, F. A., Kuh, D., & Croudace, T. J. (2010). An evaluation
of the precision of measurement of Ryff’s psychological well-being scales in a population
sample. Social Indicators Research, 97(3), 357-373. doi:10.1007/s11205-009-9506-x
Adam, K., & Oswald, I. (1977). Sleep is for tissue restoration. Journal for the Royal College of
Physicians, 11, 376-388.
Adler, N. E., Boyce, T., Chesney, M. A., Cohen, S., Folkman, S., Kahn, R. L., & Syme, S. L.
(1994). Socioeconomic status and health: The challenge of the gradient. American
Psychologist, 49(1), 15.
Ahrberg, K., Dresler, M., Niedermaier, S., Steiger, A., & Genzel, L. (2012). The interaction
between sleep quality and academic performance. Journal of Psychiatric Research, 46(12),
1618-1622. doi:http://dx.doi.org/10.1016/j.jpsychires.2012.09.008
Ahrens, C. J. C., & Ryff, C. D. (2006). Multiple roles and well-being: Sociodemographic and
psychological moderators. Sex Roles, 55(11-12), 801-815.
Alapin, I., Fichten, C. S., Libman, E., Creti, L., Bailes, S., & Wright, J. (2000). How is good and
poor sleep in older adults and college students related to daytime sleepiness, fatigue, and
ability to concentrate? Journal of Psychosomatic Research, 49(5), 381-390.
Allgöwer, A., Wardle, J., & Steptoe, A. (2001). Depressive symptoms, social support, and
personal health behaviors in young men and women. Health Psychology, 20(3), 223.
Allport, G. W. (1961). Pattern and growth in personality.
Almeida, D. M., & Horn, M. C. (2004). Is daily life more stressful during middle adulthood? (pp.
425-451). Chicago, IL, US: University of Chicago Press.
Altevogt, B. M., & Colten, H. R. (2006). Sleep disorders and sleep deprivation:: An unmet
public health problem National Academies Press.
Altun, İ., Cınar, N., & Dede, C. (2012). The contributing factors to poor sleep experiences in
according to the university students: A cross-sectional study. Journal of Research in
Medical Sciences: The Official Journal of Isfahan University of Medical Sciences, 17(6),
557.
Ando, M. (2003). The effects of short- and long-term life review interview on the psychological
well-being of young adults. Psychological Reports, 93(2), 595-602.
doi:10.2466/PR0.93.6.595-602
Aristotle. (1925). The nicomachean ethics. New York: Oxford University Press.
107
Arkoff, A., Meredith, G. M., Bailey, E., Cheang, M., Dubanoski, R. A., Griffin, P. B., &
Niyekawa, A. M. (2006). Life review during the college freshman year. College Student
Journal, 40(2), 263-269.
Arnold, K. A., Turner, N., Barling, J., Kelloway, E. K., & McKee, M. C. (2007).
Transformational leadership and psychological well-being: The mediating role of
meaningful work. Journal of Occupational Health Psychology, 12(3), 193.
Asken, M. J., & Raham, D. C. (1983). Resident performance and sleep deprivation: A review.
Academic Medicine, 58(5), 382-388.
Astin, A. W. (1970). The methodology of research on college impact, part one. Sociology of
Education, , 223-254.
Astin, A. W. (1970). The methodology of research on college impact, part two. Sociology of
Education, , 437-450.
Astin, A. W. (1973). Measurement and determinants of the outputs of higher education. Does
College Matter, , 107-127.
Astin, A. W. (1993). What matters in college.
Badimon, J. J., Fuster, V., & Badimon, L. (1992). Role of high density lipoproteins in the
regression of atherosclerosis. Circulation, 86(6 Suppl), III86-94.
Baehr, E. K., Revelle, W., & Eastman, C. I. (2000). Individual differences in the phase and
amplitude of the human circadian temperature rhythm: With an emphasis on morningness–
eveningness. Journal of Sleep Research, 9(2), 117-127.
BAEKELAND, F., & LASKY, R. (1966). Exercise and sleep patterns in college athletes.
Perceptual and Motor Skills, 23(3f), 1203-1207.
Bailey, S. L., & Heitkemper, M. M. (1991). Morningness-eveningness and early-morning
salivary cortisol levels. Biological Psychology, 32(2), 181-192.
Baldwin, D. C.,Jr, & Daugherty, S. R. (2004). Sleep deprivation and fatigue in residency
training: Results of a national survey of first- and second-year residents. Sleep, 27(2), 217223.
Barrera, M., Sandler, I. N., & Ramsay, T. B. (1981). Preliminary development of a scale of
social support: Studies on college students. American Journal of Community Psychology,
9(4), 435-447.
Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal
attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497.
108
Becker, C. M., Adams, T., Orr, C., & Quilter, L. (2008). Correlates of quality sleep and
academic performance. Health Educator, 40(2)
Black, P. H. (2003). The inflammatory response is an integral part of the stress response:
Implications for atherosclerosis, insulin resistance, type II diabetes and metabolic syndrome
X. Brain, Behavior, and Immunity, 17(5), 350-364.
Blazina, C., & Watkins Jr, C. E. (1996). Masculine gender role conflict: Effects on college men's
psychological well-being, chemical substance usage, and attitudes towards help-seeking.
Journal of Counseling Psychology, 43(4), 461.
Bonnet, M. H., & Arand, D. L. (1995). We are chronically sleep deprived. Sleep-New York-, 18,
908-911.
Bonnet, M. H., Balkin, T. J., Dinges, D. F., Roehrs, T., Rogers, N. L., & Wesensten, N. J.
(2005). The use of stimulants to modify performance during sleep loss: A review by the
sleep deprivation and stimulant task force of the american academy of sleep medicine.
Sleep, 28(9), 1163-1187.
Bowman, N. A. (2010). The development of psychological well-being among first-year college
students. Journal of College Student Development, 51(2), 180-200.
Bowman, N. A. (2013). How much diversity is enough? the curvilinear relationship between
college diversity interactions and first-year student outcomes. Research in Higher
Education, 54(8), 874-894.
Bowman, N. A., Kitayama, S., & Nisbett, R. E. (2009). Social class differences in self,
attribution, and attention: Socially expansive individualism of middle-class americans.
Personality & Social Psychology Bulletin, 35(7), 880-893. doi:10.1177/0146167209334782
[doi]
Bradburn, N. M. (1969). The structure of psychological well-being.
Brim, O. G., Ryff, C. D., & Kessler, R. C. (2004). The MIDUS national survey: An overview. In
O. G. Brim, C. D. Ryff & R. C. Kessler (Eds.), (pp. 1-34). Chicago, IL, US: University of
Chicago Press.
Briones, B., Adams, N., Strauss, M., & Rosenberg, C. (1996). Relationship between sleepiness
and general health status. Sleep: Journal of Sleep Research & Sleep Medicine,
Broadie, S. (1991). Ethics with aristotle.
Brown, F. C., Buboltz Jr, W. C., & Soper, B. (2002). Relationship of sleep hygiene awareness,
sleep hygiene practices, and sleep quality in university students. Behavioral Medicine,
28(1), 33-38.
109
Brown, S. G., Morrison, L. A., Larkspur, L. M., Marsh, A. L., & Nicolaisen, N. (2008). Wellbeing, sleep, exercise patterns, and the menstrual cycle: A comparison of natural hormones,
oral contraceptives and depo-provera. Women & Health, 47(1), 105-121.
doi:10.1300/J013v47n01_06
Buboltz Jr, W. C., Brown, F., & Soper, B. (2001). Sleep habits and patterns of college students:
A preliminary study. Journal of American College Health, 50(3), 131-135.
Buboltz, W. C., Loveland, J., Jenkins, S. M., Brown, F., Soper, B., & Hodges, J. (2006). College
student sleep: Relationship to health and academic performance. (pp. 1-39). Hauppauge,
NY, US: Nova Science Publishers.
Bühler, C. (1935). The curve of life as studied in biographies. Journal of Applied Psychology,
19(4), 405.
Bumpass, L. L., & Aquilino, W. (1995). A social map of midlife: Family and work over the
middle life course John D. and Catherine T. MacArthur Foundation Research Network on
Successful Midlife Development.
Burns, R. A., & Machin, M. A. (2009). Investigating the structural validity of Ryff’s
psychological well-being scales across two samples. Social Indicators Research, 93(2), 359375. doi:10.1007/s11205-008-9329-1
Caldwell, K., Emery, L., Harrison, M., & Greeson, J. (2011). Changes in mindfulness, wellbeing, and sleep quality in college students through taijiquan courses: A cohort control
study. The Journal of Alternative and Complementary Medicine, 17(10), 931-938.
doi:10.1089/acm.2010.0645
Carlson, E. D., & Chamberlain, R. M. (2004). The black–white perception gap and health
disparities research. Public Health Nursing, 21(4), 372-379.
Carney, C. E., Edinger, J. D., Meyer, B., Lindman, L., & Istre, T. (2006). Daily activities and
sleep quality in college students. Chronobiology International, 23(3), 623-637.
Carskadon, M. A. (1990). Patterns of sleep and sleepiness in adolescents. Pediatrician, 17(1), 512.
Carskadon, M. A. (2010). Adolescent sleep patterns: Biological, social, and psychological
influences Cambridge University Press.
Chang, E. C. (2006). Perfectionism and dimensions of psychological well-being in a college
student sample: A test of a stress-mediation model. Journal of Social and Clinical
Psychology, 25(9), 1001-1022.
110
Chang, E. C. (2006). Perfectionism and dimensions of psychological well-being in a college
student sample: A test of a stress-mediation model. Journal of Social and Clinical
Psychology, 25(9), 1001-1022. doi:10.1521/jscp.2006.25.9.1001
Chang, P. P., Ford, D. E., Mead, L. A., Cooper-Patrick, L., & Klag, M. J. (1997). Insomnia in
young men and subsequent depression: The johns hopkins precursors study. American
Journal of Epidemiology, 146(2), 105-114.
Chapman, D. W., & Pascarella, E. T. (1983). Predictors of academic and social integration of
college students. Research in Higher Education, 19(3), 295-322.
Chapman, D. W., & Pascarella, E. T. (1983). Predictors of academic and social integration of
college students. Research in Higher Education, 19(3), 295-322.
Chee, M. W., & Choo, W. C. (2004). Functional imaging of working memory after 24 hr of total
sleep deprivation. The Journal of Neuroscience : The Official Journal of the Society for
Neuroscience, 24(19), 4560-4567. doi:10.1523/JNEUROSCI.0007-04.2004
Cheng, S. H., Shih, C., Lee, I. H., Hou, Y., Chen, K. C., Chen, K., . . . Yang, Y. C. (2012). A
study on the sleep quality of incoming university students. Psychiatry Research, 197(3),
270-274.
Chida, Y., & Steptoe, A. (2008). Positive psychological well-being and mortality: A quantitative
review of prospective observational studies. Psychosomatic Medicine, 70(7), 741-756.
doi:10.1097/PSY.0b013e31818105ba [doi]
Cho, S., Hudley, C., Lee, S., Barry, L., & Kelly, M. (2008). Roles of gender, race, and SES in the
college choice process among first-generation and nonfirst-generation students. Journal of
Diversity in Higher Education, 1(2), 95.
Christian, M. S., & Ellis, A. P. J. (2011). Examining the effects of sleep deprivation on
workplace deviance: A self-regulatory perspective. Academy of Management Journal, 54(5),
913-934. doi:10.5465/amj.2010.0179
Christopher, J. C. (1999). Situating psychological well‐being: Exploring the cultural roots of its
theory and research. Journal of Counseling & Development, 77(2), 141-152.
Chu, J., & Richdale, A. L. (2009). Sleep quality and psychological wellbeing in mothers of
children with developmental disabilities. Research in Developmental Disabilities, 30(6),
1512-1522. doi:10.1016/j.ridd.2009.07.007
Clarke, P. J., Marshall, V. W., Ryff, C. D., & Rosenthal, C. J. (2000). Well-being in canadian
seniors: Findings from the canadian study of health and aging. Canadian Journal on
Aging/La Revue Canadienne Du Vieillissement, 19(02), 139-159.
111
Clarke, P. J., Marshall, V. W., Ryff, C. D., & Rosenthal, C. J. (2000). Well-being in canadian
seniors: Findings from the canadian study of health and aging. Canadian Journal on
Aging/La Revue Canadienne Du Vieillissement, 19(02), 139-159.
Clarke, P. J., Marshall, V. W., Ryff, C. D., & Wheaton, B. (2001). Measuring psychological
well-being in the canadian study of health and aging. International Psychogeriatrics,
13(S1), 79-90.
Clinkinbeard, S. S., Simi, P., Evans, M. K., & Anderson, A. L. (2011). Sleep and delinquency:
Does the amount of sleep matter? Journal of Youth and Adolescence, 40(7), 916-930.
doi:10.1007/s10964-010-9594-6
Coan, R. W. (1977). Hero, artist, sage, or saint? A survey of views on what is variously called
mental health, normality, maturity, self-actualization, and human fulfillment. Columbia U
Press.
Cohen, S., & Pressman, S. D. (2006). Positive affect and health. Current Directions in
Psychological Science, 15(3), 122-125.
Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis.
Psychological Bulletin, 98(2), 310.
Cole, J. S., & Korkmaz, A. (2013). First-year students' psychological well-being and need for
cognition: Are they important predictors of academic engagement? Journal of College
Student Development, 54(6), 557-569.
Conway, C., & MacLeod, A. (2002). Well-being: Its importance in clinical practice and research.
Clinical Psychology, 16, 26-29.
Corti, M., Guralnik, J. M., Salive, M. E., Harris, T., Field, T. S., Wallace, R. B., . . . Hennekens,
C. H. (1995). HDL cholesterol predicts coronary heart disease mortality in older persons.
Jama, 274(7), 539-544.
Costa Jr, P. T., & McCrae, R. R. (1981). Stress, smoking motives, and psychological well-being:
The illusory benefits of smoking. Advances in Behaviour Research and Therapy, 3(4), 125150.
Cui, C., Li, C., & Wang, X. (2005). The application of ryff' psychological well-being scale in
college students. [the application of ryff' psychological well-being scale in college
students.]. Chinese Mental Health Journal, 19(2), 128-130.
Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic
performance. Sleep Medicine Reviews, 10(5), 323-337.
doi:http://dx.doi.org/10.1016/j.smrv.2005.11.001
112
Dahl, E., & Kjærsgaard, P. (1993). Trends in socioeconomic mortality differentials in post‐war
norway: Evidence and interpretations. Sociology of Health & Illness, 15(5), 587-611.
Dahl, R. E., & Lewin, D. S. (2002). Pathways to adolescent health sleep regulation and behavior.
Journal of Adolescent Health, 31(6), 175-184.
Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and psychological well-being
across life's domains. Canadian Psychology/Psychologie Canadienne, 49(1), 14.
Dewald, J. F., Meijer, A. M., Oort, F. J., Kerkhof, G. A., & Bögels, S. M. (2010). The influence
of sleep quality, sleep duration and sleepiness on school performance in children and
adolescents: A meta-analytic review. Sleep Medicine Reviews, 14(3), 179-189.
doi:http://dx.doi.org/10.1016/j.smrv.2009.10.004
Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews
Neuroscience, 11(2), 114-126.
Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national
index. American Psychologist, 55(1), 34.
Diener, E., & Chan, M. Y. (2011). Happy people live longer: Subjective well‐being contributes
to health and longevity. Applied Psychology: Health and Well‐Being, 3(1), 1-43.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three
decades of progress. Psychological Bulletin, 125(2), 276.
Diener, E., & Seligman, M. E. (2002). Very happy people. Psychological Science, 13(1), 81-84.
Dienstbier, R. A. (1989). Arousal and physiological toughness: Implications for mental and
physical health. Psychological Review, 96(1), 84.
Digdon, N. L. (2010). Circadian preference and college students'beliefs about sleep education.
Chronobiology International, 27(2), 297-317.
Dinges, D. F. (1989). The nature of sleepiness: Causes, contexts and consequences. Perspectives
in Behavioral Medicine: Eating, Sleeping, and Sex, , 147-179.
Donaldson, J. F., & Graham, S. (1999). A model of college outcomes for adults. Adult Education
Quarterly, 50(1), 24-40.
Donaldson, J. F., & Graham, S. (1999). A model of college outcomes for adults. Adult Education
Quarterly, 50(1), 24-40.
Doran, S., Van Dongen, H., & Dinges, D. (2001). Sustained attention performance during sleep
deprivation: Evidence of state instability. Archives Italiennes De Biologie, 139(3), 253-267.
113
Drake, C. L., Roehrs, T., & Roth, T. (2003). Insomnia causes, consequences, and therapeutics:
An overview. Depression and Anxiety, 18(4), 163-176.
Drummond, S. P., & Brown, G. G. (2001). The effects of total sleep deprivation on cerebral
responses to cognitive performance. Neuropsychopharmacology, 25, S68-S73.
Durmer, J. S., & Dinges, D. F. (2005). Neurocognitive consequences of sleep deprivation.
Seminars in Neurology, , 25(1) 117-129.
Dusselier, L., Dunn, B., Wang, Y., Shelley II, M.,C., & Whalen, D. F. (2005). Personal, health,
academic, and environmental predictors of stress for residence hall students. Journal of
American College Health, 54(1), 15-24. doi:10.3200/JACH.54.1.15-24
Earle, A., & Heymann, S. J. (2004). Work, family, and social class. In O. G. Brim, C. D. Ryff &
R. C. Kessler (Eds.), (pp. 485-513). Chicago, IL, US: University of Chicago Press.
Edwards, D. J., Edwards, S. D., & Basson, C. J. (2004). Psychological well-being and physical
self-esteem in sport and exercise. International Journal of Mental Health Promotion, 6(1),
25-32.
Edwards, S. (2002). Promoting mental health through physical exercise.
Edwards, S. D., Ngcobo, H. S., Edwards, D. J., & Palavar, K. (2005). Exploring the relationship
between physical activity, psychological well-being and physical self-perception in different
exercise groups. South African Journal for Research in Sport, Physical Education and
Recreation, 27(1), p. 59-74.
Ellison, C. G., & Fan, D. (2008). Daily spiritual experiences and psychological well-being
among US adults. Social Indicators Research, 88(2), 247-271.
Engle-Friedman, M., Riela, S., Golan, R., Ventuneac, A. M., Davis, C. M., Jefferson, A. D., &
Major, D. (2003). The effect of sleep loss on next day effort. Journal of Sleep Research,
12(2), 113-124. doi:10.1046/j.1365-2869.2003.00351.x
Erikson, E. H. (1959). Identity and the life cycle (vol. 1). Psychological Issues.Monograph, 1
Fava, G. A., & Ruini, C. (2003). Development and characteristics of a well-being enhancing
psychotherapeutic strategy: Well-being therapy. Journal of Behavior Therapy and
Experimental Psychiatry, 34(1), 45-63.
Feinstein, J. S. (1993). The relationship between socioeconomic status and health: A review of
the literature. The Milbank Quarterly, , 279-322.
Feldman, P. J., & Steptoe, A. (2003). Psychosocial and socioeconomic factors associated with
glycated hemoglobin in nondiabetic middle-aged men and women. Health Psychology,
22(4), 398.
114
Fernandes, H. M., Vasconcelos-Raposo, J., & Teixeira, C. M. (2010). Preliminary analysis of the
psychometric properties of ryff's scales of psychological well-being in portuguese
adolescents. The Spanish Journal of Psychology, 13(2), 1032-1043.
Fernández-Castillo, A. (2013). Sleep time, test anxiety and aggressiveness in university students.
Ansiedad y Estrés, 19(1), 71-82.
Fischer, M. J. (2007). Settling into campus life: Differences by race/ethnicity in college
involvement and outcomes. The Journal of Higher Education, 78(2), 125-156.
Fleeson, W. (2004). The quality of american life at the end of the century. In O. G. Brim, C. D.
Ryff & R. C. Kessler (Eds.), (pp. 252-272). Chicago, IL, US: University of Chicago Press.
Forquer, L. M., Camden, A. E., Gabriau, K. M., & Johnson, C. M. (2008). Sleep patterns of
college students at a public university. Journal of American College Health, 56(5), 563-565.
Fox, K. (2000). Physical activity and mental health promotion: The natural partnership. Journal
of Public Mental Health, 2(1), 4-12.
Fredriksen, K., Rhodes, J., Reddy, R., & Way, N. (2004). Sleepless in chicago: Tracking the
effects of adolescent sleep loss during the middle school years. Child Development, 75(1),
84-95. doi:10.1111/j.1467-8624.2004.00655.x
Friedman, E. M., Hayney, M. S., Love, G. D., Urry, H. L., Rosenkranz, M. A., Davidson, R. J., .
. . Ryff, C. D. (2005). Social relationships, sleep quality, and interleukin-6 in aging women.
Proceedings of the National Academy of Sciences of the United States of America, 102(51),
18757-18762. doi:10.1073/pnas.0509281102
Friedman, E. M., Love, G. D., Rosenkranz, M. A., Urry, H. L., Davidson, R. J., Singer, B. H., &
Ryff, C. D. (2007). Socioeconomic status predicts objective and subjective sleep quality in
aging women. Psychosomatic Medicine, 69(7), 682-691.
doi:10.1097/PSY.0b013e31814ceada
Fukuda, K., & Ishihara, K. (2001). Age-related changes of sleeping pattern during adolescence.
Psychiatry and Clinical Neurosciences, 55(3), 231-232. doi:10.1046/j.14401819.2001.00837.x
Galambos, N. L., Dalton, A. L., & Maggs, J. L. (2009). Losing sleep over it: Daily variation in
sleep quantity and quality in canadian students' first semester of university. Journal of
Research on Adolescence, 19(4), 741-761.
Galambos, N. L., Howard, A. L., & Maggs, J. L. (2011). Rise and fall of sleep quantity and
quality with student experiences across the first year of university. Journal of Research on
Adolescence, 21(2), 342-349.
115
Galambos, N. L., Vargas Lascano, D. I., Howard, A. L., & Maggs, J. L. (2013). Who sleeps
best? longitudinal patterns and covariates of change in sleep quantity, quality, and timing
across four university years. Behavioral Sleep Medicine, 11(1), 8-22.
Galambos, N. L., Dalton, A. L., & Maggs, J. L. (2009). Losing sleep over it: Daily variation in
sleep quantity and quality in canadian students' first semester of university. Journal of
Research on Adolescence, 19(4), 741-761. doi:10.1111/j.1532-7795.2009.00618.x
Gallagher, M. W., Lopez, S. J., & Preacher, K. J. (2009). The hierarchical structure of Well‐
Being. Journal of Personality, 77(4), 1025-1050.
Garcia, D., Archer, T., Moradi, S., & Andersson-Arntén, A. (2012). Exercise frequency, high
activation positive affect, and psychological well-being: Beyond age, gender, and
occupation. Psychology, 3(4), 328-336. doi:10.4236/psych.2012.34047
Gelman, V. S., & King, N. J. (2001). Wellbeing of mothers with children exhibiting sleep
disturbance. Australian Journal of Psychology, 53(1), 18-22.
Gencoz, T., & Ozalal, Y. (2004). Direct and indirect effects of social support on psychological
well-being. Journal of Social Behavior and Personality, 32(5), 449-458.
Giannotti, F., Cortesi, F., Sebastiani, T., & Ottaviano, S. (2002). Circadian preference, sleep and
daytime behaviour in adolescence. Journal of Sleep Research, 11(3), 191-199.
Gibson, E. S., Powles, A., Thabane, L., O'Brien, S., Molnar, D. S., Trajanovic, N., . . . ChilcottTanser, L. (2006). Sleepiness” is serious in adolescence: Two surveys of 3235 canadian
students. BMC Public Health, 6(116), 1-9.
Gilbert, S. P., & Weaver, C. C. (2010). Sleep quality and academic performance in university
students: A wake-up call for college psychologists. Journal of College Student
Psychotherapy, 24(4), 295-306. doi:10.1080/87568225.2010.509245
Gilbreath*, B., & Benson, P. G. (2004). The contribution of supervisor behaviour to employee
psychological well-being. Work & Stress, 18(3), 255-266.
Gilmer, T. C. (2007). An understanding of the improved grades, retention and graduation rates of
STEM majors at the academic investment in math and science (AIMS) program of bowling
green state university (BGSU). Journal of STEM Education, 8(1-2), 11-21.
Greenfield, E. A., Vaillant, G. E., & Marks, N. F. (2009). Do formal religious participation and
spiritual perceptions have independent linkages with diverse dimensions of psychological
well-being? Journal of Health and Social Behavior, 50(2), 196-212.
Grzywacz, J. G., & Keyes, C. L. (2004). Toward health promotion: Physical and social behaviors
in complete health. American Journal of Health Behavior, 28(2), 99-111.
116
Guindon, S., O'Rourke, N., & Cappeliez, P. (2004). Factor structure and invariance of responses
by older men and women to an abridged version of the ryff scale of psychological wellbeing. Journal of Mental Health and Aging, 10(4), 301-310.
Gulec, M., Selvi, Y., Boysan, M., Aydin, A., Oral, E., & Aydin, E. F. (2013). Chronotype effects
on general well-being and psychopathology levels in healthy young adults. Biological
Rhythm Research, 44(3), 457-468. doi:10.1080/09291016.2012.704795
Haack, M., & Mullington, J. M. (2005). Sustained sleep restriction reduces emotional and
physical well-being. Pain, 119(1-3), 56-64. doi:10.1016/j.pain.2005.09.011
Hamilton, N., Nelson, C., Stevens, N., & Kitzman, H. (2007). Sleep and psychological wellbeing. Social Indicators Research, 82(1), 147-163.
Hamilton, N. A., Catley, D., & Karlson, C. (2007). Sleep and the affective response to stress and
pain. Health Psychology, 26(3), 288.
Hamilton, N. A., Nelson, C. A., Stevens, N., & Kitzman, H. (2007). Sleep and psychological
well-being. Social Indicators Research, 82(1), 147-163. doi:10.1007/s11205-006-9030-1
Harrison, Y., & Horne, J. A. (2000). The impact of sleep deprivation on decision making: A
review. Journal of Experimental Psychology: Applied, 6(3), 236.
Hastings, M., & Maywood, E. S. (2000). Circadian clocks in the mammalian brain. Bioessays,
22(1), 23-31.
Heidrich, Carol D Ryff Susan M. (1997). Experience and well-being: Explorations on domains
of life and how they matter. International Journal of Behavioral Development, 20(2), 193206.
Heidrich, S. M., & Ryff, C. D. (1993). The role of social comparisons processes in the
psychological adaptation of elderly adults. Journal of Gerontology, 48(3), P127-36.
Helson, R., & Srivastava, S. (2001). Three paths of adult development: Conservers, seekers, and
achievers. Journal of Personality and Social Psychology, 80(6), 995.
Herzog, T. R., & Strevey, S. J. (2008). Contact with nature, sense of humor, and psychological
well-being. Environment and Behavior, 40(6), 747-776.
Hicks, R. A., Mistry, R., Lucero, K., Lee, L., & PELLEGRINI, R. (1989). The sleep duration and
sleep satisfaction of college students: Striking changes over the last decade (1978-1988).
Perceptual and Motor Skills, 68(3), 806-806.
Hicks, R. A., & PELLEGRINI, R. (1991). The changing sleep habits of college students.
Perceptual and Motor Skills, 72(3c), 1106-1106.
117
Hitlin, S., & Salisbury, M. H. (2013). Living life for others and/or oneself: The social
development of life orientations. Social Science Research, 42(6), 1622-1634.
Hossler, D., Braxton, J., & Coppersmith, G. (1989). Understanding student college choice.
Higher education: Handbook of theory and research (5th ed., ) Agathon Press.
Hossler, D., & Gallagher, K. S. (1987). Studying student college choice: A three-phase model
and the implications for policymakers. College and University, 62(3), 207-221.
Howatt, W. A. (2013). Roles of internal locus of control and self-efficacy on managing job
stressors and ryff's six scales of psychological well-being Available from RIS Format UTF8.
Howell, A. J., Digdon, N. L., & Buro, K. (2010). Mindfulness predicts sleep-related selfregulation and well-being. Personality and Individual Differences, 48(4), 419-424.
doi:10.1016/j.paid.2009.11.009
Howell, A. J., Digdon, N. L., Buro, K., & Sheptycki, A. R. (2008). Relations among
mindfulness, well-being, and sleep. Personality and Individual Differences, 45(8), 773-777.
doi:10.1016/j.paid.2008.08.005
Howell, A. J., Jahrig, J. C., & Powell, R. A. (2004). Sleep quality, sleep propensity and academic
performance. Perceptual and Motor Skills, 99(2), 525-535. doi:10.2466/PMS.99.5.525-535
Hu, S., & Kuh, G. D. (2003). Maximizing what students get out of college: Testing a learning
productivity model. Journal of College Student Development, 44(2), 185-203.
Hu, S., & McCormick, A. C. (2012). An engagement-based student typology and its relationship
to college outcomes. Research in Higher Education, 53(7), 738-754.
Hudson, D. W. (1996). Happiness and the limits of satisfaction Rowman & Littlefield.
HUEN, L. E., CHAN, T. G., YU, W. M., & WING, Y. (2007). Do medical students in hong
kong have enough sleep? Sleep and Biological Rhythms, 5(3), 226-230.
Huppert, F. A., Baylis, N., & Keverne, B. (2005). The science of well-being Oxford University
Press.
Hurtado, S., Carter, D. F., & Spuler, A. (1996). Latino student transition to college: Assessing
difficulties and factors in successful college adjustment. Research in Higher Education,
37(2), 135-157.
Jackson, D. N., & Manual, J. P. I. (1976). Research psychologists press. Goshen, NY,
Jackson, G. A. (1982). Public efficiency and private choice in higher education. Educational
Evaluation and Policy Analysis, , 237-247.
118
Jahoda, M. (1958). Current concepts of positive mental health.
Jean-Louis, G., GIZYCKI, H. V., Zizi, F., & Nunes, J. (1998). Mood states and sleepiness in
college students: Influences of age, sex, habitual sleep, and substance use. Perceptual and
Motor Skills, 87(2), 507-512.
Jean-Louis, G., Kripke, D. F., & Ancoli-Israel, S. (2000). Sleep and quality of well-being. Sleep,
23(8), 1115-1121.
Jernelöv, S., Olgart Höglund, C., Axelsson, J., Axén, J., Grönneberg, R., Grunewald, J., . . .
Lekander, M. (2009). Effects of examination stress on psychological responses, sleep and
allergic symptoms in atopic and non-atopic students. International Journal of Behavioral
Medicine, 16(4), 305-310. doi:10.1007/s12529-008-9020-6
Johansson, G., Huang, Q., & Lindfors, P. (2007). A life-span perspective on women's careers,
health, and well-being. Social Science & Medicine, 65(4), 685-697.
Jones, C. R., Campbell, S. S., Cooper, F., DeSano, A., Murphy, P. J., Jones, B., . . . Ptč, L. J.
(1999). Familial advanced sleep-phase syndrome: A short-period circadian rhythm variant
in humans. Nature Medicine, 5(9), 1062-1065.
Jung, C. G. (1933). Modem man in search of a soul. New York: Hartcourt, Brace and World,
Kabrita, C. S., Hajjar-Muça, T. A., & Duffy, J. F. (2014). Predictors of poor sleep quality among
lebanese university students: Association between evening typology, lifestyle behaviors, and
sleep habits. Nature and Science of Sleep, 6, 11.
Kafka, G. J., & Kozma, A. (2002). The construct validity of ryff's scales of psychological wellbeing (SPWB) and their relationship to measures of subjective well-being. Social Indicators
Research, 57(2), 171-190. doi:10.1023/A:1014451725204
Kahneman, D., Diener, E., & Schwarz, N. (2003). Well-being: Foundations of hedonic
psychology Russell Sage Foundation.
KALES, J. D., & HEALEY, S. (1979). Prevalence of sleep disorders in the los angeles
metropolitan area. Am J Psychiatry, 36(10)
Kállay, É., & Rus, C. (2014). Psychometric properties of the 44-item version of Ryff’s
psychological well-being scale. European Journal of Psychological Assessment, 30(1), 1521. doi:10.1027/1015-5759/a000163
Karacan, I., Thornby, J. I., Anch, M., Holzer, C. E., Warheit, G. J., Schwab, J. J., & Williams, R.
L. (1976). Prevalence of sleep disturbance in a primarily urban florida county. Social
Science & Medicine (1967), 10(5), 239-244.
119
Karasawa, M., Curhan, K. B., Markus, H. R., Kitayama, S. S., Love, G. D., Radler, B. T., &
Ryff, C. D. (2011). Cultural perspectives on aging and well-being: A comparison of japan
and the united states. The International Journal of Aging and Human Development, 73(1),
73-98.
Kemp, S., Helton, W. S., Richardson, J. J., Blampied, N. M., & Grimshaw, M. (2011).
Sleeplessness, stress, cognitive disruption and academic performance following the
september 4, 2010, christchurch earthquake. Australasian Journal of Disaster and Trauma
Studies, 2011(2), 11-18.
Kenney, S. R., LaBrie, J. W., Hummer, J. F., & Pham, A. T. (2012). Global sleep quality as a
moderator of alcohol consumption and consequences in college students. Addictive
Behaviors, 37(4), 507-512.
Keyes, C. L. M., & Ryff, C. D. (1998). Generativity in adult lives: Social structural contours and
quality of life consequences.
Keyes, C. L. (2005). Mental illness and/or mental health? investigating axioms of the complete
state model of health. Journal of Consulting and Clinical Psychology, 73(3), 539.
Keyes, C. L., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The empirical
encounter of two traditions. Journal of Personality and Social Psychology, 82(6), 1007.
Keyes, C. L. M., & Annas, J. (2009). Feeling good and functioning well: Distinctive concepts in
ancient philosophy and contemporary science. The Journal of Positive Psychology, 4(3),
197-201. doi:10.1080/17439760902844228
Keyes, C. L. M., & Shapiro, A. D. (2004). Social well-being in the united states: A descriptive
epidemiology. In O. G. Brim, C. D. Ryff & R. C. Kessler (Eds.), (pp. 350-372). Chicago, IL,
US: University of Chicago Press.
Keyes, C. L. M., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The empirical
encounter of two traditions. Journal of Personality and Social Psychology, 82(6), 10071022. doi:10.1037/0022-3514.82.6.1007
Keyes, C. L. M., & Ryff, C. D. (1999). Psychological well-being in midlife. In S. L. Willis J. D.
Reid (Ed.), (pp. 161-180). San Diego, CA, US: Academic Press.
King, P. M., Brown, M. K., Lindsay, N. K., & Vanhecke, J. R. (2007). Liberal arts student
learning outcomes: An integrated approach. About Campus, 12(4), 2-9.
Kitamura, T., Kishida, Y., Gatayama, R., Matsuoka, T., Miura, S., & Yamabe, K. (2004). Ryff's
psychological well-being inventory: Factorial structure and life history correlates among
japanese university students. Psychological Reports, 94(1), 83-103.
doi:10.2466/PR0.94.1.83-103
120
Kling, K. C., Ryff, C. D., & Essex, M. J. (1997). Adaptive changes in the self-concept during a
life transition. Personality and Social Psychology Bulletin, 23(9), 981-990.
Kling, K. C., Ryff, C. D., & Essex, M. J. (1997). Adaptive changes in the self-concept during a
life transition. Personality and Social Psychology Bulletin,
Kling, K. C., Seltzer, M. M., & Ryff, C. D. (1997). Distinctive late-life challenges: Implications
for coping and well-being. Psychology and Aging, 12(2), 288.
Kraut, R. (2008). The blackwell guide to aristotle's nicomachean ethics John Wiley & Sons.
Kurina, L. M., Knutson, K. L., Hawkley, L. C., Cacioppo, J. T., Lauderdale, D. S., & Ober, C.
(2011). Loneliness is associated with sleep fragmentation in a communal society. Sleep,
34(11), 1519-1526. doi:10.5665/sleep.1390 [doi]
Kutner, M. H., Nachtsheim, C., & Neter, J. (2004). Applied linear regression models McGrawHill/Irwin.
Kwan, C. M. L., Love, G. D., Ryff, C. D., & Essex, M. J. (2003). The role of self-enhancing
evaluations in a successful life transition. Psychology and Aging, 18(1), 3.
Lachman, M. E., & Firth, K. M. P. (2004). The adaptive value of feeling in control during
midlife. In O. G. Brim, C. D. Ryff & R. C. Kessler (Eds.), (pp. 320-349). Chicago, IL, US:
University of Chicago Press.
Lack, L. C. (1986). Delayed sleep and sleep loss in university students. Journal of American
College Health, 35(3), 105-110.
Lack, L. C. (1986). Delayed sleep and sleep loss in university students. Journal of American
College Health, 35(3), 105-110. doi:10.1080/07448481.1986.9938970
Lakin-Thomas, P. L. (2000). Circadian rhythms: New functions for old clock genes. Trends in
Genetics, 16(3), 135-142.
Laska, M. N., Pasch, K. E., Lust, K., Story, M., & Ehlinger, E. (2009). Latent class analysis of
lifestyle characteristics and health risk behaviors among college youth. Prevention Science,
10(4), 376-386.
Lau, E. Y. Y., Wong, M. L., Ng, E. C. W., Hui, C. H., Cheung, S. F., & Mok, D. S. Y. (2013).
"Social jetlag" in morning-type college students living on campus: Implications for physical
and psychological well-being. Chronobiology International, 30(7), 910-918.
doi:10.3109/07420528.2013.789895
Lautenbacher, S., Kundermann, B., & Krieg, J. (2006). Sleep deprivation and pain perception.
Sleep Medicine Reviews, 10(5), 357-369.
121
Leary, O., & Donal, É. (2013). Effects of Acute Sleep Restriction on Laboratory and Ambulatory
Physiological Reactivity in Young Adults,
Lee, C. (1999). Health habits and psychological well-being among young, middle-aged and older
australian women. British Journal of Health Psychology, 4, 301-314.
doi:10.1348/135910799168650
Lim, J., & Dinges, D. F. (2008). Sleep deprivation and vigilant attention. Annals of the New York
Academy of Sciences, 1129(1), 305-322.
Lindfors, P., Berntsson, L., & Lundberg, U. (2006). Total workload as related to psychological
well-being and symptoms in full-time employed female and male white-collar workers.
International Journal of Behavioral Medicine, 13(2), 131-137.
Lindfors, P., & Lundberg, U. (2002). Is low cortisol release an indicator of positive health?
Stress and Health, 18(4), 153-160.
Lindfors, P., Berntsson, L., & Lundberg, U. (2006). Factor structure of ryff's psychological wellbeing scales in swedish female and male white-collar workers. Personality and Individual
Differences, 40(6), 1213-1222. doi:10.1016/j.paid.2005.10.016
Lohsoonthorn, V., Khidir, H., Casillas, G., Lertmaharit, S., Tadesse, M. G., Pensuksan, W. C., . .
. Williams, M. A. (2013). Sleep quality and sleep patterns in relation to consumption of
energy drinks, caffeinated beverages, and other stimulants among thai college students.
Sleep and Breathing, 17(3), 1017-1028.
Lund, H. G., Reider, B. D., Whiting, A. B., & Prichard, J. R. (2010). Sleep patterns and
predictors of disturbed sleep in a large population of college students. Journal of Adolescent
Health, 46(2), 124-132.
Marks, N., & Shah, H. (2005). A well-being manifesto for a flourishing society. In F. A.
Huppert, N. Baylis & B. Keverne (Eds.), The science of well-being (pp. 503-531) Oxford
University Press.
Marks, N. F. (1996). Flying solo at midlife: Gender, marital status, and psychological well-being.
Journal of Marriage and the Family, 58(4), 917-932.
Marks, N. F., & Lambert, J. D. (1998). Marital status continuity and change among young and
midlife adults longitudinal effects on psychological well-being. Journal of Family Issues,
19(6), 652-686.
Marks, N. F., Bumpass, L. L., & Jun, H. (2004). Family roles and well-being during the middle
life course. In O. G. Brim, C. D. Ryff & R. C. Kessler (Eds.), (pp. 514-549). Chicago, IL,
US: University of Chicago Press.
122
Markus, H. R., Ryff, C. D., Curhan, K. B., & Palmersheim, K. A. (2004). In their own words:
Well-being at midlife among high school-educated and college-educated adults. How
Healthy are we, , 273-319.
Markus, H. R., Plaut, V. C., & Lachman, M. E. (2004). Well-being in america: Core features and
regional patterns. In O. G. Brim, C. D. Ryff & R. C. Kessler (Eds.), (pp. 614-650). Chicago,
IL, US: University of Chicago Press.
Marmot, M. G., Fuhrer, R., Ettner, S. L., Marks, N. F., Bumpass, L. L., & Ryff, C. D. (1998).
Contribution of psychosocial factors to socioeconomic differences in health. Milbank
Quarterly, 76(3), 403-448.
Marmot, M. G., Shipley, M. J., & Rose, G. (1984). Inequalities in death—specific explanations
of a general pattern? The Lancet, 323(8384), 1003-1006.
Marmot, M. G., Stansfeld, S., Patel, C., North, F., Head, J., White, I., . . . Smith, G. D. (1991).
Health inequalities among british civil servants: The whitehall II study. The Lancet,
337(8754), 1387-1393.
Marmot, M., Ryff, C. D., Bumpass, L. L., Shipley, M., & Marks, N. F. (1997). Social
inequalities in health: Next questions and converging evidence. Social Science & Medicine,
44(6), 901-910.
Marsland, A. L., Cohen, S., Rabin, B. S., & Manuck, S. B. (2006). Trait positive affect and
antibody response to hepatitis B vaccination. Brain, Behavior, and Immunity, 20(3), 261269.
Martin, G. L., Hevel, M. S., Asel, A. M., & Pascarella, E. T. (2011). New evidence on the effects
of fraternity and sorority affiliation during the first year of college. Journal of College
Student Development, 52(5), 543-559.
Martin, J. S., Hébert, M., Ledoux, É., Gaudreault, M., & Laberge, L. (2012). Relationship of
chronotype to sleep, light exposure, and work-related fatigue in student workers. Chronobiol
Int, 29(3), 295-304. doi:10.3109/07420528.2011.653656
Maselko, J., & Kubzansky, L. D. (2006). Gender differences in religious practices, spiritual
experiences and health: Results from the US general social survey. Social Science &
Medicine, 62(11), 2848-2860.
Maslow, A. H., & Lowry, R. (1968). Toward a psychology of being.
Matricciani, L., Olds, T., & Petkov, J. (2012). In search of lost sleep: Secular trends in the sleep
time of school-aged children and adolescents. Sleep Medicine Reviews, 16(3), 203-211.
doi:10.1016/j.smrv.2011.03.005
123
McEwen, B. S. (2006). Sleep deprivation as a neurobiologic and physiologic stressor: Allostasis
and allostatic load. Metabolism, 55, S20-S23.
McKinzie, C., Altamura, V., Burgoon, E., & Bishop, C. (2006). Exploring the effect of stress on
mood, self-esteem, and daily habits with psychology graduate students. Psychological
Reports, 99(2), 439-448. doi:10.2466/PR0.99.6.439-448
Medeiros, A. L. D., Mendes, D. B., Lima, P. F., & Araujo, J. F. (2001). The relationships
between sleep-wake cycle and academic performance in medical students. Biological
Rhythm Research, 32(2), 263-270.
Meijer, A. M., & van den Wittenboer, G. L. H. (2004). The joint contribution of sleep,
intelligence and motivation to school performance. Personality and Individual Differences,
37(1), 95-106. doi:http://dx.doi.org/10.1016/j.paid.2003.08.002
Mellinger, G. D., Balter, M. B., & Uhlenhuth, E. H. (1985). Insomnia and its treatment.
prevalence and correlates. Archives of General Psychiatry, 42(3), 225-232.
Meltzer, L. J., & Mindell, J. A. (2007). Relationship between child sleep disturbances and
maternal sleep, mood, and parenting stress: A pilot study. Journal of Family Psychology,
21(1), 67.
Mendoza-Denton, R., Downey, G., Purdie, V. J., Davis, A., & Pietrzak, J. (2002). Sensitivity to
status-based rejection: Implications for african american students' college experience.
Journal of Personality and Social Psychology, 83(4), 896.
Millear, P. (2013). All things being equal: Do personal resources contribute equally to well-being
and mental health? Asia Pacific Journal of Counselling and Psychotherapy, 4(1), 89-101.
doi:10.1080/21507686.2013.778305
Miller, N. L., & Shattuck, L. G. (2005). Sleep patterns of young men and women enrolled at the
united states military academy: Results from year 1 of a 4-year longitudinal study. Sleep:
Journal of Sleep and Sleep Disorders Research, 28(7), 837-841.
Mirowsky, J., & Ross, C. E. (2003). Social causes of psychological distress Transaction
Publishers.
Mohan, J., Sehgal, M., & Tripathi, A. (2008). Psychological well-being, spiritual well-being and
personality. Journal of Psychosocial Research, 3(1), 81-97.
Moore, P. J., Adler, N. E., Williams, D. R., & Jackson, J. S. (2002). Socioeconomic status and
health: The role of sleep. Psychosomatic Medicine, 64(2), 337-344.
Moore, P. J., Adler, N. E., Williams, D. R., & Jackson, J. S. (2002). Socioeconomic status and
health: The role of sleep. Psychosomatic Medicine, 64(2), 337-344.
124
Mulgan, G. (2007). Good and bad power: The ideals and betrayals of government Penguin UK.
Myers, D. G. (1993). Pursuit of happiness: Discovering the pathway to fulfillment, well-being,
and enduring personal joy Harper Paperbacks.
Myers, D. G. (2000). The funds, friends, and faith of happy people. American Psychologist,
55(1), 56.
Neckelmann, D., Mykletun, A., & Dahl, A. A. (2007). Chronic insomnia as a risk factor for
developing anxiety and depression. Sleep, 30(7), 873-880.
Nes, R. B., Røysamb, E., Reichborn-Kjennerud, T., Tambs, K., & Harris, J. R. (2005).
Subjective wellbeing and sleep problems: A bivariate twin study. Twin Research and
Human Genetics, 8(5), 440-449. doi:10.1375/twin.8.5.440
Neugarten, B. L. (1973). Personality change in late life: A developmental perspective.
Nicpon, M. F., Huser, L., Blanks, E. H., Sollenberger, S., Befort, C., & Robinson Kurpius, S. E.
(2006). The relationship of loneliness and social support with college freshmen's academic
performance and persistence. Journal of College Student Retention: Research, Theory and
Practice, 8(3), 345-358.
No, a. i. (2012). Abstracts from the 19th annual conference of the international society for
quality of life research. Quality of Life Research: An International Journal of Quality of Life
Aspects of Treatment, Care & Rehabilitation, 21, 1-132. doi:10.1007/s11136-012-0248-x
Norlander, T., Johansson, Å., & Bood, S. Å. (2005). The affective personality: Its relation to
quality of sleep, well-being and stress. Social Behavior and Personality, 33(7), 709-722.
doi:10.2224/sbp.2005.33.7.709
Orzech, K. M., Salafsky, D. B., & Hamilton, L. A. (2011). The state of sleep among college
students at a large public university. Journal of American College Health, 59(7), 612-619.
Pace-Schott, E., Hutcherson, C. A., Bemporad, B., Morgan, A., Kumar, A., Hobson, J. A., &
Stickgold, R. (2009). Failure to find executive function deficits following one night's total
sleep deprivation in university students under naturalistic conditions. Behavioral Sleep
Medicine, 7(3), 136-163. doi:10.1080/15402000902976671
Padgett, R. D., Johnson, M. P., & Pascarella, E. T. (2012). First-generation undergraduate
students and the impacts of the first year of college: Additional evidence. Journal of College
Student Development, 53(2), 243-266.
Pallos, H., Gergely, V., Yamada, N., Miyazaki, S., & Okawa, M. (2007). The quality of sleep
and factors associated with poor sleep in japanese graduate students. Sleep and Biological
Rhythms, 5(4), 234-238.
125
Pascarella, E. T., Terenzini, P. T., & Feldman, K. A. (1991). How college affects students
Jossey-Bass San Francisco.
Pascarella, E. T., Terenzini, P. T., & Feldman, K. A. (2005). How college affects students
Jossey-Bass San Francisco.
Pascarella, P. (1984). The new achievers: Creating a modern work ethic Free Press.
Patrick, G., & Gilbert, J. A. (1896). Studies from the psychological laboratory of the university
of iowa: On the effects of loss of sleep. Psychological Review, 3(5), 469.
Patry, F. L. (1935). What the college student should know about present-day mental hygiene.
The Journal of Abnormal and Social Psychology, 30(1), 4-16. doi:10.1037/h0056737
Phelan, C. H., Love, G. D., Ryff, C. D., Brown, R. L., & Heidrich, S. M. (2010). Psychosocial
predictors of changing sleep patterns in aging women: A multiple pathway approach.
Psychology and Aging, 25(4), 858.
Pike, G. R. (2003). Membership in a fraternity or sorority, student engagement, and educational
outcomes at AAU public research universities. Journal of College Student Development,
44(3), 369-382.
Pilcher, J. J., & Huffcutt, A. J. (1996). Effects of sleep deprivation on performance: A metaanalysis. Sleep: Journal of Sleep Research & Sleep Medicine,
Pilcher, J. J., & Walters, A. S. (1997). How sleep deprivation affects psychological variables
related to college students' cognitive performance. Journal of American College Health,
46(3), 121-126.
Pilcher, J. J. (1998). Affective and daily event predictors of life satisfaction in college students.
Social Indicators Research, 43(3), 291-306. doi:10.1023/A:1006883231707
Pilcher, J. J., Ginter, D. R., & Sadowsky, B. (1997). Sleep quality versus sleep quantity:
Relationships between sleep and measures of health, well-being and sleepiness in college
students. Journal of Psychosomatic Research, 42(6), 583-596.
doi:http://dx.doi.org/10.1016/S0022-3999(97)00004-4
Polimeni, M., Richdale, A., & Francis, A. (2007). The impact of children’s sleep problems on the
family and behavioural processes related to their development and maintenance.
Relaunching Soon, 3(1), pp. 76-85.
Pressman, S. D., & Cohen, S. (2005). Does positive affect influence health? Psychological
Bulletin, 131(6), 925.
126
Pressman, S. D., Matthews, K. A., Cohen, S., Martire, L. M., Scheier, M., Baum, A., & Schulz,
R. (2009). Association of enjoyable leisure activities with psychological and physical wellbeing. Psychosomatic Medicine, 71(7), 725-732. doi:10.1097/PSY.0b013e3181ad7978
Promislo, M. D., Giacalone, R. A., & Welch, J. (2012). Consequences of concern: Ethics, social
responsibility, and well-being. Business Ethics: A European Review, 21(2), 209-219.
doi:10.1111/j.1467-8608.2011.01648.x
Radler, B. T., & Ryff, C. D. (2010). Who participates? accounting for longitudinal retention in
the MIDUS national study of health and well-being. Journal of Aging and Health, 22(3),
307-331. doi:10.1177/0898264309358617
Regestein, Q., Natarajan, V., Pavlova, M., Kawasaki, S., Gleason, R., & Koff, E. (2010). Sleep
debt and depression in female college students. Psychiatry Research, 176(1), 34-39.
Reid, A., & Baker, F. C. (2008). Perceived sleep quality and sleepiness in south african
university students. South African Journal of Psychology, 38(2), 287-303.
Reppert, S. M. (1998). A clockwork explosion! Neuron, 21(1), 1-4.
Roane, B. M., & Taylor, D. J. (2008). Adolescent insomnia as a risk factor for early adult
depression and substance abuse. Sleep, 31(10), 1351-1356.
Roberts, R. E. (2008). Positive well-being and sleep. Journal of Psychosomatic Research, 64(4),
417-418. doi:http://dx.doi.org/10.1016/j.jpsychores.2008.02.025
Rogers, C. (2012). On becoming a person: A therapist's view of psychotherapy Houghton Mifflin
Harcourt.
Rothenberg, S. (1997). Introducing sleep disorders. Understanding Sleep.Washington DC:
American Psychological Association,
Ruini, C., Ottolini, F., Rafanelli, C., Tossani, E., Ryff, C. D., & Fava, G. A. (2003). The
relationship of psychological well-being to distress and personality. Psychotherapy and
Psychosomatics, 72(5), 268-275. doi:71898 [doi]
Ruini, C., & Fava, G. A. (2004). Clinical implications of psychological well-being. Ricerche Di
Psicologia, 27(1), 159-175.
Ruini, C., Ottolini, F., Rafanelli, C., Tossani, E., Ryff, C. D., & Fava, G. A. (2003). The
relationship of psychological well-being to distress and personality. Psychotherapy and
Psychosomatics, 72(5), 268-275. doi:10.1159/000071898
Ruthig, J. C., Marrone, S., Hladkyj, S., & Robinson-Epp, N. (2011). Changes in college student
health: Implications for academic performance. Journal of College Student Development,
52(3), 307-320.
127
Ryff, C. D. (1989). Beyond ponce de leon and life satisfaction: New directions in quest of
successful ageing. International Journal of Behavioral Development, 12(1), 35-55.
Ryff, C. D. (1989). In the eye of the beholder: Views of psychological well-being among middleaged and older adults. Psychology and Aging, 4(2), 195.
Ryff, C. D. (1995). Psychological well-being in adult life. Current Directions in Psychological
Science,
Ryff, C. D. (2008). Challenges and opportunities at the interface of aging, personality, and wellbeing. Handbook of Personality: Theory and Research, , 399-418.
Ryff, C. D. (2013). Psychological well-being revisited: Advances in the science and practice of
eudaimonia. Psychotherapy and Psychosomatics, 83(1), 10-28.
Ryff, C. D., Keyes, C. L., & Hughes, D. L. (2003). Status inequalities, perceived discrimination,
and eudaimonic well-being: Do the challenges of minority life hone purpose and growth?
Journal of Health and Social Behavior, , 275-291.
Ryff, C. D., Magee, W. J., Kling, K., & Wing, E. H. (1999). Forging macro-micro linkages in the
study of psychological well-being. The Self and Society in Aging Processes, , 247-278.
Ryff, C. D., Schmutte, P. S., & Lee, Y. H. (1996). How children turn out: Implications for
parental self-evaluation. The Parental Experience in Midlife, , 383-422.
Ryff, C. D., & Singer, B. (1996). Psychological well-being: Meaning, measurement, and
implications for psychotherapy research. Psychotherapy and Psychosomatics, 65(1), 14-23.
Ryff, C. D., & Singer, B. (2000). Interpersonal flourishing: A positive health agenda for the new
millennium. Personality and Social Psychology Review, 4(1), 30-44.
Ryff, C. D., & Singer, B. H. (2006). Best news yet on the six-factor model of well-being. Social
Science Research, 35(4), 1103-1119.
Ryff, C. D., & Singer, B. H. (2008). Know thyself and become what you are: A eudaimonic
approach to psychological well-being. Journal of Happiness Studies, 9(1), 13-39.
Ryff, C. D., Singer, B. H., & Love, G. D. (2004). Positive health: Connecting well-being with
biology. Philosophical Transactions-Royal Society of London Series B Biological Sciences,
, 1383-1394.
Ryff, C. D. (1982). Successful aging: A developmental approach. The Gerontologist, 22(2), 209214.
128
Ryff, C. D. (1989). Happiness is everything, or is it? explorations on the meaning of
psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069-1081.
doi:10.1037/0022-3514.57.6.1069
Ryff, C. D. (1995). Psychological well-being in adult life. Current Directions in Psychological
Science, 4(4), 99-104. doi:10.1111/1467-8721.ep10772395
Ryff, C. D., & Heidrich, S. M. (1997). Experience and well-being: Explorations on domains of
life and how they matter. International Journal of Behavioral Development, 20(2), 193-206.
doi:10.1080/016502597385289
Ryff, C. D., & Singer, B. (1998). The contours of positive human health. Psychological Inquiry,
9(1), 1.
Ryff, C. D., & Singer, B. H. (2006). Best news yet on the six-factor model of well-being. Social
Science Research, 35(4), 1103-1119. doi:10.1016/j.ssresearch.2006.01.002
Ryff, C. D., Singer, B. H., Wing, E., & Love, G. D. (2001). Elective affinities and uninvited
agonies: Mapping emotion with significant others onto health. In C. D. Ryff B. H. Singer
(Ed.), (pp. 133-175). New York, NY, US: Oxford University Press.
Scanlan, J. N., Bundy, A. C., & Matthews, L. R. (2011). Promoting wellbeing in young
unemployed adults: The importance of identifying meaningful patterns of time use.
Australian Occupational Therapy Journal, 58(2), 111-119. doi:10.1111/j.14401630.2010.00879.x
Schmutte, P. S., & Ryff, C. D. (1997). Personality and well-being: Reexamining methods and
meanings. Journal of Personality and Social Psychology, 73(3), 549.
Seeman, T., Mendes de Leon, C., Berkman, L., & Ostfeld, A. (1993). Risk factors for coronary
heart disease among older men and women: A prospective study of community-dwelling
elderly. American Journal of Epidemiology, 138(12), 1037-1049.
Seifert, T. A., Goodman, K. M., Lindsay, N., Jorgensen, J. D., Wolniak, G. C., Pascarella, E. T.,
& Blaich, C. (2008). The effects of liberal arts experiences on liberal arts outcomes.
Research in Higher Education, 49(2), 107-125.
Sejnowski, T. J., & Destexhe, A. (2000). Why do we sleep? Brain Research, 886(1), 208-223.
SHANAHAN, T. L., & CZEISLER, C. A. (1991). Light exposure induces equivalent phase
shifts of the endogenous circadian rhythms of circulating plasma melatonin and core body
temperature in men*. The Journal of Clinical Endocrinology & Metabolism, 73(2), 227-235.
Shapiro, C. A., & Sax, L. J. (2011). Major selection and persistence for women in STEM. New
Directions for Institutional Research, 2011(152), 5-18.
129
Shapiro, C. M., & Dement, W. C. (1993). ABC of sleep disorders. impact and epidemiology of
sleep disorders. BMJ (Clinical Research Ed.), 306(6892), 1604-1607.
Sheldon, K. M., Kasser, T., Smith, K., & Share, T. (2002). Personal goals and psychological
growth: Testing an intervention to enhance goal attainment and personality integration.
Journal of Personality, 70(1), 5-31.
Sing, C. Y., & Wong, W. S. (2010). Prevalence of insomnia and its psychosocial correlates
among college students in hong kong. Journal of American College Health, 59(3), 174-182.
doi:10.1080/07448481.2010.497829
Singer, B., & Ryff, C. D. (1999). Hierarchies of life histories and associated health risks. Annals
of the New York Academy of Sciences, 896(1), 96-115.
Sirigatti, S., Stefanile, C., Giannetti, E., Iani, L., Penzo, I., & Mazzeschi, A. (2009). Assessment
of factor structure of ryff's psychological well-being scales in italian adolescents. Bollettino
Di Psicologia Applicata, 259(56), 30-50.
Smider, N. A., Essex, M. J., & Ryff, C. D. (1996). Adaptation to community relocation: The
interactive influence of psychological resources and contextual factors. Psychology and
Aging, 11(2), 362.
Smith, C., & MacNeill, C. (1994). Impaired motor memory for a pursuit rotor task following
stage 2 sleep loss in college students. Journal of Sleep Research, 3(4), 206-213.
Smyth, J. M., Ockenfels, M. C., Gorin, A. A., Catley, D., Porter, L. S., Kirschbaum, C., . . .
Stone, A. A. (1997). Individual differences in the diurnal cycle of cortisol.
Psychoneuroendocrinology, 22(2), 89-105.
Springer, K. W., & Hauser, R. M. (2006). An assessment of the construct validity of Ryff’s
scales of psychological well-being: Method, mode, and measurement effects. Social Science
Research, 35(4), 1080-1102.
Springer, K. W., & Hauser, R. M. (2006). An assessment of the construct validity of ryff's scales
of psychological well-being: Method, mode, and measurement effects. Social Science
Research, 35(4), 1080-1102. doi:10.1016/j.ssresearch.2005.07.004
Springer, K. W., Hauser, R. M., & Freese, J. (2006). Bad news indeed for ryff's six-factor model
of well-being. Social Science Research, 35(4), 1120-1131.
doi:10.1016/j.ssresearch.2006.01.003
Springer, K. W., Pudrovska, T., & Hauser, R. M. (2011). Does psychological well-being change
with age? longitudinal tests of age variations and further exploration of the
multidimensionality of Ryff’s model of psychological well-being. Social Science Research,
40(1), 392-398. doi:10.1016/j.ssresearch.2010.05.008
130
Stecker, T. (2004). Well-being in an academic environment. Medical Education, 38(5), 465-478.
doi:10.1046/j.1365-2929.2004.01812.x
Steptoe, A., O'Donnell, K., Marmot, M., & Wardle, J. (2008). Positive affect, psychological
well-being, and good sleep. Journal of Psychosomatic Research, 64(4), 409-415.
Steptoe, A., Wardle, J., & Marmot, M. (2005). Positive affect and health-related neuroendocrine,
cardiovascular, and inflammatory processes. Proceedings of the National Academy of
Sciences of the United States of America, 102(18), 6508-6512. doi:0409174102 [pii]
Stull, R. A. (1962). Liberal arts, our guardian: The pursuit of the good life. The Journal of
Higher Education, 33(1), 38-43.
Suen, L., Tam, W., & Hon, K. (2010). Association of sleep hygiene-related factors and sleep
quality among university students in hong kong. Hong Kong Med J, 16(3), 180-185.
Suen, L. K., Ellis Hon, K., & Tam, W. W. (2008). Association between sleep behavior and sleeprelated factors among university students in hong kong. Chronobiology International, 25(5),
760-775.
Suo, J. (2012). The feasibility of the group counseling to improve the college students'
psychological on well-being. Medicine and Society, 3, 029.
Taras, H., & Potts-Datema, W. (2005). Sleep and student performance at school. Journal of
School Health, 75(7), 248-254. doi:10.1111/j.1746-1561.2005.00033.x
Taylor, D. J., Vatthauer, K. E., Bramoweth, A. D., Ruggero, C., & Roane, B. (2013). The role of
sleep in predicting college academic performance: Is it a unique predictor? Behavioral Sleep
Medicine, 11(3), 159-172.
Taylor, D. J., & Bramoweth, A. D. (2010). Patterns and consequences of inadequate sleep in
college students: Substance use and motor vehicle accidents. Journal of Adolescent Health,
46(6), 610-612. doi:10.1016/j.jadohealth.2009.12.010
Tempesta, D., Couyoumdjian, A., Curcio, G., Moroni, F., Marzano, C., De Gennaro, L., &
Ferrara, M. (2010). Lack of sleep affects the evaluation of emotional stimuli. Brain
Research Bulletin, 82(1-2), 104-108. doi:10.1016/j.brainresbull.2010.01.014
Terenzini, P. T., Rendon, L. I., Upcraft, M. L., Millar, S. B., Allison, K. W., Gregg, P. L., &
Jalomo, R. (1994). The transition to college: Diverse students, diverse stories. Research in
Higher Education, 35(1), 57-73.
Terenzini, P. T., Rendon, L. I., Upcraft, M. L., Millar, S. B., Allison, K. W., Gregg, P. L., &
Jalomo, R. (1994). The transition to college: Diverse students, diverse stories. Research in
Higher Education, 35(1), 57-73.
131
Terenzini, P. T., Springer, L., Yaeger, P. M., Pascarella, E. T., & Nora, A. (1996). Firstgeneration college students: Characteristics, experiences, and cognitive development.
Research in Higher Education, 37(1), 1-22.
Thacher, P. V. (2008). University students and "the all-nighter": Correlates and patterns of
students' engagement in a single night of total sleep deprivation. Behavioral Sleep Medicine,
6(1), 16-31. doi:10.1080/15402000701796114
Thoits, P. A. (1985). Social support and psychological well-being: Theoretical possibilities.
Social support: Theory, research and applications (pp. 51-72) Springer.
Trockel, M. T., Barnes, M. D., & Egget, D. L. (2000). Health-related variables and academic
performance among first-year college students: Implications for sleep and other behaviors.
Journal of American College Health, 49(3), 125-131.
Trotter, M. J. (2009). Effects of participation in a mindfulness-based stress reduction program
on college students' psychological well-being. ERIC.
TSAI, L., & LI, S. (2004). Sleep education in college: A preliminary study 1. Perceptual and
Motor Skills, 99(3), 837-848.
Tsai, L., & Li, S. (2004). Sleep patterns in college students: Gender and grade differences.
Journal of Psychosomatic Research, 56(2), 231-237.
Tsai, L., & Li, S. (2004). Sleep patterns in college students: Gender and grade differences.
Journal of Psychosomatic Research, 56(2), 231-237. doi:http://dx.doi.org/10.1016/S00223999(03)00507-5
Tsui, Y. Y., & Wing, Y. K. (2009). A study on the sleep patterns and problems of university
business students in hong kong. Journal of American College Health, 58(2), 167-176.
doi:10.1080/07448480903221418
Tucker, A. M., Whitney, P., Belenky, G., Hinson, J. M., & Van Dongen, H. P. (2010). Effects of
sleep deprivation on dissociated components of executive functioning. Sleep, 33(1), 47-57.
Tugade, M. M., & Fredrickson, B. L. (2004). Resilient individuals use positive emotions to
bounce back from negative emotional experiences. Journal of Personality and Social
Psychology, 86(2), 320.
Turek, F. (2005). The prevailing culture of sleepiness. Sleep, 28(7), 798-799.
US DEPARTMENT OF HEALTH AND HUMAN SERVICES. (1993). Wake up america
national sleep alert
Vail-Smith, K., Felts, W. M., & Becker, C. (2009). Relationship between sleep quality and health
risk behaviors in undergraduate college students. College Student Journal, 43(3)
132
van Dierendonck, D. (2004). The construct validity of ryff's scales of psychological well-being
and its extension with spiritual well-being. Personality and Individual Differences, 36(3),
629-643. doi:10.1016/S0191-8869(03)00122-3
van Dierendonck, D., Díaz, D., Rodríguez-Carvajal, R., Blanco, A., & Moreno-Jiménez, B.
(2008). Ryff's six-factor model of psychological well-being, a spanish exploration. Social
Indicators Research, 87(3), 473-479. doi:10.1007/s11205-007-9174-7
van Dierendonck, D. (2004). The construct validity of ryff's scales of psychological well-being
and its extension with spiritual well-being. Personality and Individual Differences, 36(3),
629-643. doi:http://dx.doi.org/10.1016/S0191-8869(03)00122-3
Van Reeth, O., Weibel, L., Spiegel, K., Leproult, R., Dugovic, C., & Maccari, S. (2000).
Interactions between stress and sleep: From basic research to clinical situations. Sleep
Medicine Reviews, 4(2), 201-219.
Van Riper, M., Ryff, C., & Pridham, K. (1992). Parental and family well‐being in families of
children with down syndrome: A comparative study. Research in Nursing & Health, 15(3),
227-235.
Verlander, L. A., Benedict, J. O., & Hanson, D. P. (1999). Stress and sleep patterns of college
students. Perceptual and Motor Skills, 88(3), 893-898.
Villar, F., Triadó, C., Celdrán, M., & Solé, C. (2010). Measuring well-being among spanish older
adults: Development of simplified version of ryff's scale of psychological well-being.
Psychological Reports, 107(1), 265-280. doi:10.2466/02.07.08.10.21.PR0.107.4.265-280
Voelker, R. (2004). Stress, sleep loss, and substance abuse create potent recipe for college
depression. JAMA: Journal of the American Medical Association, 291(18), 2177-2179.
doi:10.1001/jama.291.18.2177
Vogel, G. W., Vogel, F., McAbee, R. S., & Thurmond, A. J. (1980). Improvement of depression
by REM sleep deprivation: New findings and a theory. Archives of General Psychiatry,
37(3), 247-253.
Vogel, G. W. (1983). Evidence for rem sleep deprivation as the mechanism of action of
antidepressant drugs. Progress in Neuro-Psychopharmacology and Biological Psychiatry,
7(2–3), 343-349. doi:http://dx.doi.org/10.1016/0278-5846(83)90122-7
Volkwein, J. F., & Sweitzer, K. V. (2006). Institutional prestige and reputation among research
universities and liberal arts colleges*. Research in Higher Education, 47(2), 129-148.
Wagner, M., & Rhee, Y. (2013). Stress, sleep, grief: Are college students receiving information
that interests them? College Student Journal, 47(1), 24-33.
133
Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A., & Stickgold, R. (2002). Practice with
sleep makes perfect: Sleep-dependent motor skill learning. Neuron, 35(1), 205-211.
doi:http://dx.doi.org/10.1016/S0896-6273(02)00746-8
Weinger, M. B., & Ancoli-Israel, S. (2002). Sleep deprivation and clinical performance. Jama,
287(8), 955-957.
Wells, T. T., & Cruess, D. G. (2006). Effects of partial sleep deprivation on food consumption
and food choice. Psychology & Health, 21(1), 79-86. doi:10.1080/14768320500102301
Wheaton, A. G., Liu, Y., Perry, G. S., & Croft, J. B. (2011). Effect of short sleep duration on
daily activities—United states, 2005-2008. JAMA: Journal of the American Medical
Association, 305(19), 1956-1957.
Wiggins, J. S. (1973). Personality and prediction: Principles of personality assessment.
Williamson, A. M., & Feyer, A. M. (2000). Moderate sleep deprivation produces impairments in
cognitive and motor performance equivalent to legally prescribed levels of alcohol
intoxication. Occupational and Environmental Medicine, 57(10), 649-655.
Wink, P., & Dillon, M. (2008). Religiousness, spirituality, and psychosocial functioning in late
adulthood: Findings from a longitudinal study.
Wirz-Justice, A., & Van den Hoofdakker, Rutger H. (1999). Sleep deprivation in depression:
What do we know, where do we go? Biological Psychiatry, 46(4), 445-453.
Wissing, M. P., & Van Eeden, C. (2002). Empirical clarification of the nature of psychological
well-being. South African Journal of Psychology, 32(1), p. 32-44.
Wittmann, M., Dinich, J., Merrow, M., & Roenneberg, T. (2006). Social jetlag: Misalignment of
biological and social time. Chronobiology International, 23(1-2), 497-509.
doi:10.1080/07420520500545979
Wolfson, A. R. (2010). Adolescents and emerging adults' sleep patterns: New developments.
Journal of Adolescent Health, 46(2), 97-99.
doi:http://dx.doi.org/10.1016/j.jadohealth.2009.11.210
Wolverton, M. (2013, Chasing slumber: In a nation chronically wanting for rest, the gravest
danger is not merely the molecular impact of sleep deprivation - but the simple fact that we
no longer realize just how tired we are. Psychology Today,
Wong, M. L., Lau, E. Y. Y., Wan, J. H. Y., Cheung, S. F., Hui, C. H., & Mok, D. S. Y. (2013).
The interplay between sleep and mood in predicting academic functioning, physical health
and psychological health: A longitudinal study. Journal of Psychosomatic Research, 74(4),
271-277. doi:10.1016/j.jpsychores.2012.08.014
134
Wong, M. L., Lau, E. Y. Y., Wan, J. H. Y., Cheung, S. F., Hui, C. H., & MOK, D. S. Y. (2013).
The interplay between sleep and mood in predicting academic functioning, physical health
and psychological health: A longitudinal study. Journal of Psychosomatic Research, 74(4),
271-277. doi:http://dx.doi.org/10.1016/j.jpsychores.2012.08.014
Wood, A. M., Joseph, S., Lloyd, J., & Atkins, S. (2009). Gratitude influences sleep through the
mechanism of pre-sleep cognitions. Journal of Psychosomatic Research, 66(1), 43-48.
Word, C. A. (2007). Sleep quality of college students and its relationship to coping styles and
well-being Available from RIS Format UTF-8.
Zhang, L., & Diao, J. (2006). A study of sleep quality and related influencing factors in college
students. [A study of sleep quality and related influencing factors in college students.].
Chinese Journal of Clinical Psychology, 14(5), 515-517.
Zhou, J., Xiang, X., & Hu, Y. (2007). A study of subjective well-being and affecting factors of
medical college students. [A study of subjective well-being and affecting factors of medical
college students.]. Chinese Journal of Clinical Psychology, 15(3), 279-281.
Zika, S., & Chamberlain, K. (1992). On the relation between meaning in life and psychological
well‐being. British Journal of Psychology, 83(1), 133-145.
Zohar, D., Tzischinsky, O., Epstein, R., & Lavie, P. (2005). The effects of sleep loss on medical
residents' emotional reactions to work events: A cognitive-energy model. Sleep: Journal of
Sleep and Sleep Disorders Research,
135