Download Memmo (2006) Thriving in Adolescence

THE STRUCTURE AND CORRELATES OF EXTERNAL
THRIVING IN LATER ADOLESCENCE:
CONCEPTUAL CONSIDERATIONS AND EMPIRICAL FINDINGS
by
Marina C. Memmo, M.S.
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
(Human Development and Family Studies)
at the
UNIVERSITY OF WISCONSIN – MADISON
2006
© Copyright by Marina C. Memmo 2006
All Rights Reserved
i
Abstract
The scientific study of adolescence has recently been described as entering a
new phase of research and discovery that holds the promise of integrating basic and
applied scholarship in the field. As part of this new era, researchers and practioners
have expressed greater interest in understanding the personal and ecological basis of
positive development, or thriving, during adolescence. This study considers how
thriving has been defined in the literature, and provides empirical support for the
conceptualization and operationalization of external thriving as a multidimensional
construct that includes both the presence of adaptive behaviors and the absence of
externalizing problem behaviors. In addition, the study also examines the contextual
correlates of external thriving based on the theoretical distinction between adaptive
and maladaptive developmental processes. Adaptive developmental processes are
designated as social assets, while maladaptive developmental processes are
designated as contextual risks. The findings indicate that the contextual correlates of
external thriving are best represented by a combination of both social assets and
contextual risks, but that adaptive behaviors are more strongly associated with social
assets and problem behaviors are more strongly associated with contextual risks.
These findings are interpreted as indicating that adaptive behaviors and problem
behaviors are primarily associated with qualitatively different types of developmental
processes. Implications of the findings for future research and practice are discussed.
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Acknowledgements
This work would not have been possible without the caring and support of many
people. In particular I would like to thank:
Professor Stephen A. Small for believing in me at the beginning, for giving
me the priceless opportunity to learn and grow under his guidance, and for always
encouraging and inspiring me to continue my work.
Dr. Glenis Benson for being a true friend, and providing such a wonderful mix
of humor, caring, support and amazing insight into the minds and hearts of people.
Dr. Gay Eastman for our many wonderful discussions, and her willingness to
share her wisdom and experience as both a professional and a parent.
Dr. William Aquilino, Dr. Arthur Reynolds, Dr. Richard S. Zeldin and Dr.
Susan Riesch for sharing their knowledge, expertise and understanding of research.
Dr. Colin Brenan and the good folks at Biotrove, Inc. for giving me a quiet
place to work, and for always greeting me with kind words and good advice.
Very special thanks are due to Dr. Karl Yoder, who admirably rose to the occasion
and made me realize how important it is to pick the right partner in life, and
Fiorella Memmo, because my accomplishments are always hers as well.
This Thesis is Dedicated to my children, Erminia and Eli
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TABLE OF CONTENTS
PAGE
ABSTRACT......................................................................................................i
ACKNOWLEDGEMENTS ............................................................................ii
CHAPTER
I. Overview ................................................................................................ 1
II. Review of the Literature......................................................................... 6
What is Positive Youth Development? ............................................ 8
Defining Adaptive Behaviors............................................................ 9
Defining Problem Behaviors...........................................................12
The Relationship Between Adaptive Behaviors
and Problem Behaviors ...................................................................15
Positive Development and Thriving ...............................................19
Thriving as a Multidimensional Construct.....................................21
III. The Developmental Context ................................................................24
The Role of Contextual Risks in Adolescent Development...........26
Risk Factors from a PYD Perspective.............................................29
The Role of Social Assets in Adolescent Development ................32
The Role of Theory in Understanding Youth Development .........37
IV. Restatement of Purpose........................................................................43
V. Method..................................................................................................46
The Survey and Procedure ..............................................................46
Participants.......................................................................................47
Missing Data ....................................................................................48
Measures ..........................................................................................50
Demographic Variables ...........................................................50
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Characteristics of the Adolescent ............................................52
Problem Behaviors...................................................................54
Adaptive Behaviors..................................................................56
Psychological Health Indicators ..............................................59
Characteristics of the Social Context ......................................61
Social Assets ............................................................................62
Contextual Risks ......................................................................65
Summary of Variables Used in the Study...............................68
VI. Results – Part I......................................................................................71
The Structure of Thriving in Adolescence ..............................71
Preliminary Analysis................................................................71
Analysis of First-Order Factor Structure.................................74
Factor Identification.................................................................75
Correlations Among the Primary Factors ...............................79
Analysis of the Second-Order Factor Structure .....................81
Summary of Findings ..............................................................83
Measures of External Thriving, Adaptive Behaviors
and Problem Behaviors............................................................84
VII. Results – Part II ....................................................................................86
The Contextual Correlates of Thriving ...................................86
Zero-Order Correlations ..........................................................87
Hierarchical Regression Analysis for External Thriving........90
Hierarchical Regression Analysis for Adaptive Behaviors ....97
Hierarchical Regression Analysis for Problem Behaviors ...100
Summary of Findings ............................................................102
VIII. Results – Part III.................................................................................105
Mediation Analysis ................................................................105
Summary of Findings ............................................................111
IX. Discussion...........................................................................................113
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The Structure of Thriving ......................................................116
The Correlates of External Thriving .....................................122
The Correlates of Adaptive Behaviors
and Problem Behaviors..........................................................127
The Mediating Role of Adaptive Behaviors .........................134
Limitations and Future Directions.........................................135
Implications for Policy and Practice .....................................140
Conclusion..............................................................................142
X. References ..........................................................................................144
APPENDIX A: DANE COUNTY 2000 YOUTH SURVEY...........161
APPENDIX B: MISSING DATA TABLES.....................................182
APPENDIX C: FACTOR RELIABILITY........................................187
APPENDIX D: MEASUREMENT CONSTRUCTION ..................197
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LIST OF TABLES
TABLE
PAGE
1.
Summary of Demographic Characteristics of Respondents........................52
2.
Summary of Measures Reflecting Characteristics of the Adolescent .........69
3.
Summary of Measures Reflecting Characteristics of the Social Context ...70
4.
Percent of Students Exhibiting Involvement in Selected Outcomes...........73
5.
Pattern Matrix for the Full Sample...............................................................76
6.
Structure Matrix for the Full Sample ...........................................................77
7.
Factor Identification with Representative Items ..........................................79
8.
Factor Correlation Matrix for the First Order Factors .................................80
9.
Higher Order Factor Structure......................................................................82
10. Summary of Risk and Asset Indicators in Relation to Outcomes ...............88
11. Means and Standard Deviations of the Outcomes by
Demographic Variables ................................................................................91
12. Comparisons Between Demographic and Full Risks and
Assets Model.................................................................................................92
13. Reduced Risks and Assets Model vs. Model with Nonlinear
Associations ..................................................................................................94
14. Regression Results for Risks and Social Assets Indicators of
Adaptive Behaviors ......................................................................................98
15. Regression Results for Risks and Social Assets Indicators of
Problem Behaviors..................................................................................... 101
16. Coefficients in the Hierarchical Regression Analysis for
Problem Behaviors..................................................................................... 108
17. Results of Significance Tests of the Indirect (Mediated) Relationship.... 111
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LIST OF TABLES AND FIGURES IN APPENDICIES
TABLE
PAGE
B1. Percent of Missing Data by Item Number ............................................ 182
B2. Parameter Estimates by Missing Data Options .................................... 183
C1. Correlations Among Problem Behaviors, Personal Assets,
and Psychological Distress Indicators .................................................. 188
C2. Rotated Factor Matrix for Sample 1...................................................... 193
C3. Rotated Factor Matrix for Sample 2...................................................... 194
C4. Correlations of Varimax Factor Loadings from Separate
Analyses................................................................................................. 195
C5. Congruence of Varimax Factor Loadings from Separate
Analyses................................................................................................. 195
C6. Modified Factorial Congruence of the Varimax Factor Loadings
from S1 and S2 ...................................................................................... 195
D1. Initial Reliability Analysis for Indicators of Thriving.......................... 199
D2. Reliability Analysis of Final Indicators of Thriving ............................ 200
D3. Reliability Analysis of Adaptive Behaviors Items ............................... 200
D4. Reliability Analysis of Problem Behaviors Items ................................ 200
D5. Summary of Dependent Measures........................................................ 201
FIGURE
D1. Distribution of External Thriving ......................................................... 201
D2. Distribution of Adaptive Behaviors ...................................................... 202
D3. Distribution of Problem Behaviors ....................................................... 202
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CHAPTER 1
Overview
In recent years, the positive youth development (PYD) approach has gained
considerable momentum within the field of adolescent development and problem
prevention. Initially begun as a national mobilization campaign aimed at redirecting
concern about youth problems into a public commitment to youth development
(Pittman & Fleming, 1991), the underlying principles of the PYD approach have
more recently been integrated into the contemporary scientific study of adolescence
by prominent scholars in the field (Steinberg & Lerner, 2004). One outcome of this
new perspective has been an increased focus on community based research and
programming aimed at identifying the individual and ecological basis of healthy,
positive development among youth (Eccles & Gootman, 2002; Lerner, et al., 2005).
However, in comparison to more established approaches, the PYD approach is still a
relatively recent phenomena, and there remains much to be done in order to
successfully advance this new agenda.
The present study aims to inform such efforts by examining how adaptive
behaviors and problem behaviors interrelate with one another and are associated with
multiple aspects of the social environment among older adolescents. It is believed that
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a better understanding of the correlates of positive development among youth who are
nearing adulthood will provide valuable insight into what young people need to
experience, and avoid, in order to thrive developmentally. However, one of the
current challenges to gaining such understanding is the problem of how to
conceptualize and empirically study positive development, or thriving, in
adolescence.
PYD scholars generally define positive adolescent development as a two
dimensional construct. For example, Scales, Benson, Leffert and Blyth (2000) define
thriving as “…a concept that incorporates not only the absence of problem behaviors
or other signs of pathology, but also signs or indicators of healthy development”
(p.28). Similarly, Roth, Brooks-Gunn, Murray and Foster (1998) define positive
development as “the engagement in prosocial behaviors and avoidance of healthcompromising and future-jeopardizing behaviors” (p. 426). Despite acknowledging
that the construct incorporates both the presence of positive behaviors and the
absence of negative behaviors, researchers have generally considered only positive
attributes in their measures of thriving. For example, the seven indicators of thriving
proposed by scholars at the Search Institute (Scales et al., 2000), and the Five C’s of
positive development studied by Lerner and his associates (Lerner et al., 2005) both
reflect positive outcomes only.
It is argued that if the goal of research is to gain a better understanding of the
ecological basis of thriving during adolescence, one must account for both domains of
behavior as indicated by the definitions of positive adolescent development just cited.
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This means that a measure of positive development during adolescence must include
variation in problem behaviors as well as adaptive behaviors. Furthermore, it is
argued that in specifying an ecological model of the factors associated with positive
development, researchers must consider how contextual risks as well as social assets
are related to a youth’s overall level of thriving.
Prevention scholars have used factor analysis procedures to study the cooccurrence of problem behaviors expressed by adolescents, and have discovered that
they often load together on a single general factor, or on several primary factors that
are then related at a higher level (for a review, see Willoughby, Chalmers & Busseri,
2004). Similarly, PYD scholars have examined the dimensional relations among
positive attributes of adolescents and found that they too co-occur and can be related
under a higher order construct (Lerner, et al., 2005). The limitation these research
perspectives share is that both consider only one aspect of a youth’s character, either
the positive or the negative, while ignoring the other. Several scholars have argued
for a need to integrate these two perspectives into a unified approach for studying
adolescents (Catalano, Hawkins, Berglund, Pollard & Arthur, 2002; Small &
Memmo, 2004). This approach would recognize that while negatively related,
problem behaviors and positive outcomes are not mutually exclusive, and do not
necessarily share the same etiology.
In the present study, the structure of thriving is examined through an
exploratory factor analysis of a wide variety of youth characteristics that include both
adaptive behaviors and problem behaviors. The goal of the analysis is to determine
4
whether a general problem behavior factor and a general adaptive behavior factor can
be identified, and whether these factors are inversely related under a single higher
order thriving construct. The results of the analysis then provides the basis of a two
dimensional measure of external thriving in adolescence that serves as the dependant
variable in the second part of the study.
In the second part of the study, a variable focused approach (see Masten, et
al., 1999) is used to examine the environmental correlates of external thriving. The
aim of this analysis is to determine what type of environment factors (i.e., social
assets or contextual risks) demonstrate the strongest relationship to external thriving,
and whether the same factors are equally important to each of its components (i.e.,
adaptive behaviors and problem behaviors). It was expected that a combination of
risks and social assets would be significantly related to external thriving overall, but
when examined individually, social assets would demonstrate a stronger association
with adaptive behaviors, and contextual risks would demonstrate a stronger
association with problem behaviors.
The final part of the study considers whether the data supports the assumption
of the PYD approach that social assets foster positive youth attributes, which in turn
reduce the likelihood that a youth will engage in problem behaviors. Although the
data used in this study is cross-sectional, empirical support for this assumption would
strengthen the case for the PYD perspective, and provide a good rationale for
examining the mediating role of adaptive behaviors under more sophisticated,
longitudinal conditions. The assumption is tested by examining the extent to which
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adaptive behaviors appear to mediate the relationship between social assets and the
expression of problem behaviors among adolescents who are nearing adulthood and
will soon be beyond the direct control of parents and conventional social institutions.
Specifically, the current investigation addresses the following three questions:
1. Do problem behaviors and adaptive behaviors co-occur to the extent that a
general problem behavior factor and a general adaptive behaviors factor can
be identified through a factor analysis procedure? And if so, are these two
primary factors inversely related under a single higher order thriving
construct?
2. What is the nature of the environmental correlates of thriving, and are the
same factors equally important to each of its components (i.e., adaptive
behaviors and problem behaviors)?
3. To what extent do adaptive behaviors appear mediate the relationship between
social assets and problem behaviors among older adolescents?
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CHAPTER 2
Review of the Literature
In a defining statement of the PYD agenda, Pittman and Irby (1996) make the
case that youth problem prevention, youth development, and community development
must be viewed as inseparable goals. They argue that addressing youth problems is
critical, but prevention alone is not enough. What also is needed is a civic
commitment to strengthening the potential of all young people to develop into caring
and competent adults. They illustrate their point with the example of the employer,
who does not hire someone based on what he or she is not (i.e., not a drug user, not in
a gang, not a teen parent, etc.), but rather looks for someone with skills, values,
attitudes, knowledge and commitments that will contribute to the success of the
workplace.
The notion of inseparable goals challenges scholars and researchers to view
youth development from a broader perspective: one that simultaneously takes into
consideration multiple dimensions of a youth’s character, as well as his or her
developmental context. Currently, progress in the field of adolescent development
and problem prevention is marked by research advances on several fronts (Small &
Memmo, 2004). One is in the area of prevention, where the primary focus is to
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eliminate problem behaviors that may cause an individual harm or undermine his or
her future health and well-being (e.g., see Durlak, 1997). Another is in the area of
positive youth development (PYD), which emphasizes promoting values and
competencies (i.e., adaptive behaviors) that increase a youth’s potential for future
success (Benson, Leffert, Scales & Blyth, 1998). Informing both of these
perspectives is research on resilience, which seeks to identify the personal and
ecological basis of positive adaptation despite the experience of severe risk or
adversity (e.g., see Masten, 2001). Although related in many ways, each of these
perspectives informs us about a different and important aspect of youth development,
but none tells the whole story. In order to obtain a broader understanding of the
individual and ecological basis of healthy adolescence, one must attend to and
integrate the lessons of each.
For example, Roth et al. (1998) define positive development as “the
engagement in prosocial behaviors and avoidance of health-compromising and futurejeopardizing behaviors” (p. 426). They also describe a framework that includes both
resilience and competency building components, which they believe has the potential
to help young people navigate the transition from adolescence to early adulthood
without engaging in unhealthy and risky behaviors. This framework suggests that
researchers should focus on three things: (a) What causes adolescents to follow
different developmental paths? (b) What factors can alter a healthy or risky
developmental trajectory? and (c) How might risk and protective factors interact to
facilitate or hinder healthy adolescent development?
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Attention to multiple research strategies to achieve the multidimensional but
inseparable goals of positive adolescent development is needed in order to obtain a
broader understanding of the individual and the ecological influences on healthy
adolescence. Unfortunately, most researchers continue to focus exclusively on either
problem behaviors or adaptive behaviors, and seldom consider them jointly. With this
in mind, the following review examines how scholars and researchers have defined
and studied adaptive and problem behaviors in the past, and considers how such
efforts might be used to operationalize a broader concept of positive youth
development, or thriving, than is generally found in the literature.
What is Positive Adolescent Development?
Defining positive adolescent development requires a judgment about the
quality of a youth’s development that depends upon how people in different cultures
and different historical periods value, perceive and interpret a person’s character and
competencies. Masten (2001) writes that in studies of resilience, researchers have
used different criteria to judge good adaptation, including demonstrating an
observable track record of meeting the major age expectations of a given society
within a particular historical period (e.g., Elder, 1998), the absence of psychopathology or problem behaviors (Conrad & Hammen, 1993), and sometimes a
combination of both (Dubow, Edwards & Ippolito, 1997).
9
Moore and Halle (2000) state what may seem to be a simple and obvious
answer to the question of how positive adolescent development might be defined in
contemporary American society. Most parents want their teenagers to avoid drugs,
violence, crime, and unsafe sex. They also want young people to stay in school, to be
happy and emotionally healthy, to have positive relationships with other people, and
to contribute to the community. In recent years, PYD scholars have proposed various
lists of attributes that are more or less similar to this. While the particular attributes
vary to some extent, one thing all scholars appear to agree upon is that positive
adolescent development must be understood as more than simply the absence of
problem behaviors (e.g., see Benson, et al., 1998; Eccles & Gootman, 2002; Lerner,
Fisher & Weinberg, 2000; Lerner, Brentano, Dowling & Anderson, 2002; Pittman,
Irby & Ferber, 2000).
Defining Adaptive Behaviors
Although still a relatively recent phenomena, the fundamental principles of
the PYD approach have recently been identified by prominent scholars as part of a
new era of scientific research and discovery in adolescent development (Steinberg &
Lerner, 2004). Despite this advance, several challenges continue to exist for the
perspective (see Zeldin, 2000). One is the need to establish a consensus with regard
to what adaptive behaviors are most essential to promote in order to best prepare
youth for the future (Pittman, et al., 2000; Roth, et al., 1998). Scholars have
10
described such characteristics as “Internal Assets” (Benson et al., 1998), “Personal
Assets” (Eccles & Gootman, 2002), and markers of “Positive Well-Being” (Moore &
Glei, 1995), and have conceptualized them as both indicators of a youth’s current
developmental status, as well as predictors of his or her future developmental status.
The set of 20 Internal Assets identified by scholars at the Search Institute is
probably the best known and most widely disseminated list of positive youth
attributes that has emerged from the field of PYD (see Benson et al., 1998; Scales &
Leffert, 1999). Benson et al. (1998) define Internal Assets as “competencies, skills
and self-perceptions that young people develop gradually over time” (p.143). The
assets are grouped into four categories that reflect critical dimensions of youth
development. These are: (a) Commitment to Learning (e.g., achievement motivation
and school engagement), (b) Positive Values (e.g., caring and integrity), (c) Social
Competencies (e.g., interpersonal and cultural competence), and (d) Positive Identity
(e.g., self esteem and sense of purpose). Although they reflect what many people
would regard as positive outcomes, the 20 Internal Assets have served primarily as
either descriptive variables or independent variables in research conducted by the
scholars at the Search Institute (e.g., Leffert et al., 1998; Scales et al, 2000).
Other scholars have also identified positive youth attributes, or personal
assets, that are associated with better long-term developmental outcomes for the
individual. For example, in their report on behalf of the National Research Council’s
Committee on Community-Level Programs for Youth, Eccles and Gootman (2002)
identify twenty-eight characteristics, grouped within four domains of development
11
(i.e., physical, intellectual, psychological and emotional, and social) that were derived
from an extensive consideration of theory, practical wisdom, and empirical research.
Among the characteristics they identify are regular physical exercise (physical
development), academic success in school (intellectual development), prosocial and
culturally sensitive values (psychological and emotional development), and a
commitment to civic engagement (social development). The authors argue that while
individuals do not need to possess every characteristic in order to develop in a
positive manner, more is better than fewer, and life is generally easier to manage if
one has personal assets in all four domains.
Researchers have also empirically examined various indexes of positive
attributes in relation to demographic, personal and contextual factors (e.g., see Moore
& Glei, 1995; Scales et al., 2000). The indicators used in these measures are intended
as alternatives to similar measures of problem behaviors typically used to describe
youth (Theokas et al., 2005). Scales et al. (2000) identify seven categories of behavior
they view as representing core adolescent tasks. These are: (a) school success, (b)
leadership, (c) helping others, (d) maintenance of physical health, (e) delay of
gratification, (f) valuing diversity, and (g) overcoming adversity. Again, the authors
do not presume this list exhausts the range of possible outcomes that could be used to
describe successful youth. They do argue, however, that these actions and behaviors
are generally indicative of positive development during adolescence.
More recently, Lerner et al. (2005) published findings from the first wave of a
longitudinal study seeking to identify the individual and ecological basis of positive
12
development during adolescence. In this study, positive attributes are identified in
terms of the Five C’s: Competence, Confidence, Connection, Character,
Caring/Compassion, and a Sixth C: Contribution (see also Roth & Brooks-Gunn,
2003). Based on ideas derived from developmental contextualism (Lerner, 2002),
these attributes are conceptualized as outcomes that emerge from a life-long thriving
process in which the inherent plasticity of human nature is directed by adaptive
developmental regulations toward a state of idealized adulthood. Using crosssectional data from the first wave of the study, the authors tested the structural
relations among the Five C’s with their fifth grade sample. Although they report
finding some support for their conceptualization of positive youth development, they
also acknowledge that further work is needed to revise and improve the model.
Defining Problem Behaviors
Advocates of the PYD approach often criticize the hegemony of prevention
research in the study of adolescent development. In general, they argue that a focus
on problem prevention is the legacy of a deficit view of adolescence, which serves to
stigmatize youth and detract from a more productive agenda that would concentrate
on gaining a better understanding of young people as resources to be developed rather
than problems to be solved (e.g., see Benard, 1993; Benson, 1997; Lerner, et al.,
2002; Theokas, et al., 2005). Nevertheless, problem prevention is an integral part of
13
the inseparable goals of PYD, and there is much to be gained from attending to the
lessons learned from over three decades of prevention research.
For example, there is a fairly good consensus among researchers, scholars,
and practioners that externalizing behaviors such as substance abuse, unprotected
sexual activity, aggression and delinquency are among those which are especially
problematic to the health and well-being of contemporary adolescents (Donovan &
Jessor, 1985; Herrenkohl et al., 2003; Huizinga, Loeber, Thornberry, & Cothern,
2000; Jessor, 1998; Kirby, 2001; Newcomb & Bentler, 1988). Other problem
behaviors that reflect psychological dysfunction or internalizing symptoms include
anxiety, eating disorders, depression and suicidal ideation (Archibald, Graber &
Brooks-Gunn, 1999; Compas, Conner & Hinden, 1998; Gutman & Sameroff, 2004;
Hershberger & D’Augelli, 1995; Perkins & Hartless, 2002).
What makes these behaviors problematic is that they all violate either a
person’s physical health or the current laws or norms of society. For example, in
recent years, social and demographic changes have shifted expectations regarding the
timing of parenthood to the late twenties and early thirties, largely because in today’s
world it takes that much longer to complete one’s education, establish a career and
find a life partner (Arnett, 2000). These changes mean that even in the best of
circumstances young people must rely on the support of others, such as parents and
teachers, for a longer period of time in order to make a successful transition into
adulthood. However, problem behaviors such as teen pregnancy, substance abuse and
various forms of psychological dysfunction create their own need for support that
14
may strain social and family resources and take away from the support a young
person could otherwise have used for more productive purposes (Osgood, Foster,
Flanagan & Ruth, 2005). As a result, the transition into adulthood is made all the
more difficult for young people who must cope with both the consequences of
problem behaviors and the loss of missed opportunity.
Years of research in the field of prevention have greatly advanced our
understanding of such problems and their ramifications for the future well-being of
youth. Scholars have learned that problem behaviors often co-occur within an
individual and that risky lifestyles during adolescence can have potentially disastrous
results for a youth’s future development (Brener & Collins, 1998; Donovan, Jessor &
Costa, 1988; Elliot, 1993; Graber & Brooks-Gunn, 1996; Huizinga, et al., 2000;
Newcomb & Bentler, 1988). Researchers have studied the co-occurrence of problem
behaviors in adolescence using factor analytic methods, and generally find support for
the presence of a general problem behavior factor (see Willoughby, et al., 2004).
Regardless of whether or not this reflects a “problem behavior syndrome” (Jessor &
Jessor, 1977), the evidence is clear that covariation in adolescent problem behaviors
is considerable (see Steinberg & Avenevoli, 1998). Finally, a substantial body of
research has been accumulated over the years demonstrating that it is possible and
vital to reduce problem behaviors if young people are to have a productive and
healthy future (e.g., see Alford, 2003; Coie, et al., 1993; Durlak, 1997).
15
The Relationship Between Adaptive Behaviors and Problem Behaviors
A primary assumption of the PYD approach is that helping youth to achieve
their full potential is the best way to prevent them from experiencing problems
(Pittman & Zeldin, 1995). Demonstrating that it is possible to prevent problem
behaviors by promoting adaptive behaviors would be a significant advantage for the
PYD perspective. Lerner, et al. (2005) state that a young person who exhibits many
positive youth development outcomes “should show negligible or low levels of risk
behaviors as well as internalizing problems” (p. 24). The idea that positive outcomes
directly reduce negative outcomes is also inherent in the Search Institute’s 40
Developmental Asset framework. Half of the assets in this framework (i.e., the 20
Internal Assets) represent prosocial values and competencies (e.g., commitment to
learning, positive values, social competencies, and positive identity) that are expected
to prevent problem behaviors and enhance future well-being (see Benson, 1997;
Scales & Leffert, 1999).
However, findings from both resilience and prevention research suggest that
the relationship between positive youth characteristics and problem prevention may
not be so straightforward. For example, Luthar (1993) reviews research indicating
that resilient youth who demonstrate positive adaptation with respect to behavioral or
cognitive abilities often do so at the expense of increased vulnerability to emotional
distress or social relationships. She concludes that resilience is best viewed as domain
specific, with definitions that emphasize particular types of resilience (e.g., academic
16
resilience, social resilience or emotional resilience) being of greater utility than
notions of an “overall” resilience phenomenon. Furthermore, in a study comparing
affluent suburban adolescents to inner-city adolescents, Luthar and D’Avanzo (1999)
found that higher levels of both problem behaviors and competence could co-occur
within the same group of adolescents. In this study, suburban adolescents reported
higher rates of substance use, more psychological anxiety, and were more likely to be
absent from school than their inner-city peers, despite being rated by their teachers as
more competent and having fewer problems (e.g., acting out, frustration tolerance).
In a study examining the Search Institute’s 40 Developmental Asset
framework in relation to problem behaviors, Leffert et al. (1998) found that certain
internal assets (i.e., restraint, peaceful conflict resolution, school engagement and
resistance skills) were more strongly related than others to an index measuring
multiple types of problem behaviors. When particular externalizing problem
behaviors (i.e., substance use, delinquency, violence, sexual intercourse and
gambling) were considered individually, only restraint (i.e., “young person believes it
is important not to be sexually active or to use alcohol or other drugs”, Scales &
Leffert, 1999, p.3) demonstrated a consistent relationship across multiple types of
outcomes. Apart from these four variables, the remaining internal assets did not make
a meaningful unique contribution to the explained variance in any of the externalizing
problem behaviors that were examined. This includes internal assets that reflect
general positive qualities such as caring, sense of equality and social justice, integrity,
honesty and personal responsibility.
17
These findings make more sense when one considers them in light of research
investigating the mediating role of certain types of adaptive behaviors. Botvin and
Griffin (2004) review several research studies that examined the relationship between
developmental competencies targeted by the Life Skills Training program and
substance use outcomes (i.e., alcohol and tobacco use). The purpose of these studies
was to identify what might be the “active ingredients” of the intervention. The Life
Skills Training program is a school-based curriculum that seeks to prevent problem
behaviors by fostering domain general competencies (e.g., personal self management
and social competence) and domain specific knowledge, attitudes and expectations
(e.g., expectations regarding the social benefits of tobacco use).
In two studies (Epstein, Griffin, & Botvin, 2000; Griffin, Espstein, Botvin &
Spoth, 2001), the researchers found that the relationship between general social
competence (i.e., confidence, assertiveness and good communication skills) and
substance use was mediated by a youth’s expectancies regarding the social benefits of
drinking and smoking. In other words, youth who were more socially competent were
less likely to perceive a social benefit from drinking and smoking, which in turn
reduced the likelihood that they would drink or smoke. In the case of one study, the
reviewers report that the relationship was fully mediated (i.e., Griffin, et al., 2001).
This means that the direct relationship between general social competence and
substance use behavior was no longer significant once a youth’s expectations
regarding the social benefits of drinking and smoking were considered in the analysis.
The reviewers also report on several other studies that demonstrate a similar pattern
18
of results. That is, domain general competencies were consistently found to protect
against substance use by influencing domain specific knowledge, expectations and
resistance skills.
In general, while most research findings support the conclusion that positive
and negative outcomes are inversely related, they also indicate that competence does
not necessarily protect a person from emotional difficulties. Furthermore, among
characteristics identified as positive, some are more likely than others to be directly
related to fewer problem behaviors. Such characteristics are perhaps best understood
as domain specific protective processes (i.e., knowledge, attitudes and expectations
relating specifically to problem behaviors), which appear to be influenced to some
extent by domain general values and skills. If such is the case, then the mediated
relationship is more important than the main effect when examining the protective
function of domain general values and skills in relation to problem behaviors. These
findings also illustrate that while inversely related, positive behaviors and problem
behaviors are not mutually exclusive. Researchers should not expect to find a perfect
inverse correlation between the two. This is because most individuals tend to exhibit
some combination of both.
Finally, one must recognize that any behavior can become a problem when
taken to such an extreme that it interferes with the well-being of the person and/or the
group. For example, religiosity may be regarded as a virtue to some, but a religious
fanaticism that leads to the annihilation of cultural objects, sources of knowledge, free
expression, one’s own life and the lives of others is clearly a problem. Ultimately, our
19
fullest understanding of adolescent development will come in the course of studying
the balance that makes up the whole person, rather than through an exclusive focus on
positive or negative facets of a person’s character.
Positive Development and Thriving
Along with an emphasis on promoting prosocial values and competencies that
increase a youth’s potential for future success, the PYD approach has also introduced
a new vocabulary for discussing the development of young people (Lerner, Dowling
& Anderson, 2003). Among these new constructs is the concept of thriving as both an
indicator of well-being (Scales, et al., 2000) and as a life-span developmental process
(Lerner et al., 2003). As an indicator of well-being, thriving has been defined as,
“…a concept that incorporates not only the absence of problem behaviors or other
signs of pathology, but also signs or indicators of healthy development” (Scales et al.,
2000, p.28). This definition of thriving is so similar to the definition of positive
development by Roth et al. (1998) cited earlier that one may conclude that the two are
conceptually the same.
However, Lerner and his colleges (see Lerner et al., 2003) have recently
expanded the concept of thriving to reflect a life-span developmental process that is
characterized by relative plasticity and adaptive developmental regulations between
the developing person and his or her social context. Relative plasticity refers to the
potential for systematic change in structure or function. From a life-span
20
developmental perspective, it means that at any point in time a person has the
capacity for developmental change, although to what extent is generally constrained
by a person’s past development and the nature of his or her current developmental
context. Adaptive developmental regulations refer to the interrelationships between
the individual and his or her context that maintain and perpetuate healthy, positive
functioning. Lerner et al. (2003) write that ultimately, “a youth that is thriving is
engaged in person-context regulatory processes that will eventuate in healthy and
productive adult personhood” (p.177).
As a process concept, thriving can only be evaluated over time. However, at
any particular point in time, a person’s level of thriving is indexed by his or her
current state of well-being or positive development (Lerner, et al., 2002). This
returns us to the definitions of positive development and thriving provided previously.
While both of these conceptual definitions include the absence of problem behaviors,
when operationalized, researchers have created measures of thriving that focus
exclusively on the presence of adaptive behaviors without any reference to the
absence of problem behaviors. As described earlier, all seven items in the thriving
index constructed by Scales et al (2000) reflect positive outcomes, as do all the Five
C’s considered by Lerner and his associates (see Lerner et al., 2005). While current
conceptual definitions of positive development or thriving reflect the inseparable
goals of PYD (i.e., youth development and problem prevention), current operational
definitions and the empirical study of this construct do not.
21
An exception to this was a study by Moore and Glei (1995) who used an
ecological model to examine predictors of both “missteps” and “positive well-being”
during the adolescent years. The “missteps” measure included five problem
behaviors: (a) hard drug use, (b) running away from home, (c) sexual activity, (d)
pregnancy, and (e) dropping out of school. The “positive well being” measure
included six positive or prosocial behaviors: (a) life satisfaction, (b) low/no
depression, (c) community involvement, (d) religiosity, (e) closeness with parents,
and (f) placement of importance on correcting social inequalities. Although the two
measures were created using different methods, and were not combined to create an
overall measure of thriving, in this study at least, positive development was
operationalized as a multidimensional construct.
Thriving as a Multidimensional Construct
If it is assumed that an employer does not hire someone based on what he or
she is not (i.e., not a drug user, not in a gang, not a teen parent, etc.), but rather looks
for someone with skills, values, attitudes, knowledge and commitments that will
contribute to the success of the workplace. It might be equally true that an employer
may not choose to hire someone if he or she discovers that the applicant is a drug
addict, a gang member or a teen parent. The reason for this is that employers, like
most people, judge a person’s character and qualifications based on multiple types of
information, both positive and negative. One may argue that failing to disclose a
22
problem behavior that may seriously undermine a person’s job performance is
misleading. In a similar way, one may argue that failing to consider problem
behaviors in an assessment of thriving is also misleading.
This is not to say that good measures of adaptive behaviors are unnecessary.
The very opposite is true. Much can be learned from studies that focus particular
attention on the development of adaptive behaviors during adolescence and their role
in the transition to adulthood. One of the lessons learned from prevention research is
that you can discover a great deal about the etiology of a particular outcome when
you take a more focused perspective. However, it is argued that the PYD perspective
challenges researchers to consider their findings within the context of an overall view
of youth development that is as accurate and balanced as possible.
In this study, thriving is conceptualized as a multidimensional construct that is
reflected by a person’s behaviors, attitudes and values. The expression of adaptive
behaviors (e.g., prosocial activities, educational commitment and social
responsibility) contributes to thriving, while the expression of problem behaviors
(e.g., substance abuse, risky sex, and aggression) detracts from it. Furthermore,
although they both contribute to judgments about thriving, adaptive behaviors and
problem behaviors are conceptualized as being theoretically distinct constructs.
Therefore, the absence of a problem does not qualify as a adaptive behavior. Nor does
the absence of an adaptive behavior qualify as a problem outcome. One may argue,
and rightly so, that a youth who lacks adaptive behaviors cannot develop in a healthy
manner. However, the term “failure to thrive” has been used at times to describe a
23
similar situation (see Reiff, 1991). Therefore, in this study the absence of an adaptive
behavior (e.g., school failure) is regarded as a “failure to thrive.” It is believed that
maintaining this distinction is important, because the absence of a behavior indicates
the absence of a developmental process, whereas the presence of a behavior (good or
bad) indicates that either an adaptive or maladaptive developmental process is at
work.
24
CHAPTER 3
The Developmental Context
The notion of inseparable goals challenges scholars to view youth
development from a broader perspective: one that takes into consideration multiple
dimensions of a youth’s character and multiple aspects of his or her developmental
context. In the previous chapter, the first two goals of PYD (i.e., youth development
and problem prevention) were examined with respect to the concept of thriving. This
chapter focuses on the third goal of PYD, community development, and moves the
focus of consideration away from the developing person to the developmental
context.
Benson (1998) writes that family support, community support and
socialization consistency are all critical components of a developmental infrastructure
that is essential for preventing problem behaviors and promoting positive outcomes
among children and adolescents. In his view, the role of community is more than
providing young people with things to do and places to be, it includes adults who are
willing to share the responsibilities of childrearing with families by establishing
affirming relationships with young people, acting on their behalf, and articulating a
consistent message regarding social values on a day to day basis.
25
Two integrally related perspectives on community development currently
prevail among PYD scholars. The first considers community from the
phenomenological point of view of the developing person and is concerned with
identifying features of the social context that young people need to experience and
avoid in order to thrive developmentally. The second is concerned with how to
identify, mobilize and organize resources, partnerships and supports in order to create
and sustain the contexts that foster positive development. PYD scholars have referred
to the first perspective as the “vision” of positive youth development, and the second
as how “the vision becomes reality” (Camino & Zeldin, 2002, p. 77).
Both aspects of community development are vital, but from the standpoint of
the present study it is the community from the phenomenological point of view of the
developing person that is most relevant to understanding the individual and ecological
basis of thriving in adolescence. According to Bronfenbrenner (1979), this is what
matters most for behavior and development. Furthermore, Bronfenbrenner stresses
that the meaning a person attaches to a particular context and his or her role in it
exerts a far stronger influence on development than what might be described as
“objective” reality. A similar sentiment echoes throughout the PYD literature. It may
explain why a deficit approach to problem prevention generally fails (i.e., nobody
likes to be told they need fixing) and why entrusting young people with meaningful
roles and tasks is so important to positive development.
The following review examines what developmental scholars have learned
about the nature of contextual influences on youth development in terms of both the
26
risks youth face that contribute to the development of problem behaviors, as well as
the social assets they need in order to develop in a positive manner. Much of the work
of integrating the vast research base that has accumulated over the past decades
regarding positive influences on youth development has been done by PYD scholars
seeking ways to foster positive development in community settings (e.g., see Eccles
& Gootman, 2002; Zeldin, 1995; Zeldin, Kimbal & Price, 1995). However, not all of
the findings pertinent to youth development, especially with respect to the interactive
nature of risk processes, have been included in these assessments. The review ends by
considering how developmental theory can help researchers make a conceptual
distinction between risks and social assets, which are generally confounded in most
research on adolescent development.
The Role of Contextual Risks in Adolescent Development
In prevention and resilience research, the concept of risk has been
instrumental in guiding researchers seeking to identify the individual and ecological
basis of problem development and the factors that contribute to good adaptation
despite the experience of severe adversity (e.g., Coie et al., 1993; Fraser, 2004;
Hawkins, Catalano & Miller, 1992; Masten, 2001; O’Conner & Rutter, 1996). Risk
factors are most commonly defined as individual or environmental hazards that: (a)
increase the likelihood that a problem behavior will occur, (b) contribute to a more
serious state of dysfunction, and/or (c) maintain a problem condition (Coie et al.,
27
1993; Fraser, 2004; Small & Memmo, 2004). It should be noted that this definition
does not distinguish between risk markers, which are variables that index a host of
related risks, and risk processes, which are the causal mechanisms that are directly
responsible for the development of a problem behavior (O’Conner & Rutter, 1996).
With respect to individual level risks, Fraser (2004) writes that while prior
problem behaviors are the best predictors of future problem behaviors, the presence
of a “risky” behavior should be viewed as a cue that something in the social context is
amiss. To prevent problem behaviors, researchers must rely on risk factors that exist
before a person gets into trouble. Individual level variables, such as genetic
predispositions for psychopathology, adverse temperament, and medical problems in
childhood, place children at risk for problematic outcomes (Deater-Deakard, Dodge,
Bates & Pettit, 1998; O’Conner & Rutter, 1996). However, researchers stress that it
is the interaction between individual and contextual factors that most likely accounts
for a person’s overall level of risk and the likelihood that he or she will express
problem behaviors (Sameroff & Fiese, 2000; Werner & Smith, 1982).
Contextual risk factors have been identified at all levels of the social
environment (i.e., family, peer group, neighborhood and school). This means that
even if a person is able to avoid risks in one context, he or she may be exposed to
them in another. Therefore, scholars advocate targeting multiple settings in order to
maximize the likelihood of preventing problem behaviors (Bogenschneider, Small &
Riley, 1990; Small & Luster, 1994). In addition, researchers have found that different
combinations of risks tend to cluster to produce the same form of problem behavior.
28
For example, Sameroff, Seifer, Barocas, Zax and Greenspan (1987) examined 10
correlates of socioeconomic status (SES) to determine whether poor development
(i.e., low IQ) was a function of low SES or the compounding of multiple
environmental stressors. When the data was cluster analyzed, they found that the
same outcomes were associated with different combinations of risk factors, with
certain clusters having no overlapping variables whatsoever.
Prevention researchers have also found that while certain risk factors can be
attributed to particular types of problem outcomes (e.g., unsafe sexual activity that
leads to pregnancy), this is generally rare. Rather, risk factors tend to have a
generalized impact, resulting in many problem behaviors sharing fundamental risk
factors in common. In a overview of the field of prevention science, Coie, et al.
(1993) provide a list of thirty risk factors that have been found to exert a generic
influence on multiple types of problem outcomes. These include risks associated with
family circumstances (e.g., family conflict and disorganization), emotional difficulties
(e.g., child abuse and stressful life events), school problems (e.g., scholastic
demoralization), neighborhood circumstances (e.g., neighborhood disorganization,
and racial injustice), and problems with peers (e.g., peer rejection, alienation, and
negative peer influence). Sociocultural risk markers, such as extreme poverty, single
parent families, and maternal youthfulness also fall into this generic risk category.
Finally, researchers have learned a great deal about how risks interact with
one another to increase the likelihood of problem behaviors. For example, researchers
have demonstrated that exposure to multiple risk factors has been found to have a
29
cumulative effect on the expression of multiple types of problem outcomes, including
externalizing symptoms and suicidal ideation (Appleyard, Byron, van Dulmen &
Stroufe, 2005; Deater-Deakard et al., 1998; Sameroff et al., 1987; Perkins & Hartless,
2002). Furthermore, studies suggest that the impact of multiple risks may have more
than an additive effect (Rutter, 1979). For example, Pollard, Hawkins and Arthur
(1999) examined the impact of cumulative risk on five types of problem behaviors
among adolescents (i.e., alcohol use, marijuana use, taking a gun to school, being
arrested, and attacking with intent to harm). Across all problem outcomes, they found
a substantial effect for increased risk exposure, with clear curvilinear associations
most strongly expressed for outcomes with low baseline prevalence rates (i.e.,
marijuana use, taking a gun to school and attacking with intent to harm). They
conclude that exposure to high levels of risk may be more relevant for extreme forms
of deviance in comparison to more widespread behaviors such as alcohol use.
Risk Factors from a PYD Perspective
PYD scholars have been criticized for a tendency to focus primarily on
positive outcomes while overlooking the fact that adolescents often face risks that
may jeopardize their health and well being if not also addressed (Small & Memmo,
2004). However, it is perhaps more accurate to say that PYD scholars generally deal
with the influence of risks in one of two ways: (a) they address risks in terms of a
need to maintain a safe haven within which development can take place (Eccles &
30
Gootman, 2002; Zeldin, 1995; Zeldin, et al., 1995), or (b) they invert the risk
continuum so that low levels of risk are conceptualized as social assets (e.g., see
Leffert et al., 1998; Scales & Leffert, 1999, Taylor et al., 2005).
In describing the features of positive developmental settings, PYD scholars
often cite physical and psychological safety as the most basic condition required for
positive development to take place (Eccles & Gootman, 2002; Zeldin, 1995; Zeldin,
et al., 1995). For example, Eccles and Gootman (2002) describe how unsafe health
conditions, exposure to hazardous materials, and exposure to infectious agents all
undermine a youth’s capacity to develop in a positive manner. More importantly, they
point out that various forms of victimization, such as abuse and experiencing,
witnessing or being threatened with violence (e.g., harassment) may lead to a variety
of problem outcomes such as: (a) symptoms of posttraumatic stress, (b) retributive
violence, and (c) gang formation or gang membership. Adults who are in charge of
youth are encouraged to take extra steps to maintain the safety of the developmental
setting, and take actions that will avert potential threats. They argue that such risks
not only lead adolescents to cope in maladaptive ways, but also interferes with the
allocation of attention to intellectual, psychological, emotional and social
development.
In addition to overt threats to development, Eccles and Gootman (2002) point
out that chaotic and poorly managed social settings undermine cognitive functioning
and development, especially with respect to academic, social and emotional tasks. In
order to maintain control, they advocate clear and consistent rules for behavior,
31
boundary setting and appropriate levels of adult supervision. On a similar note, they
argue that deviant social norms increase the likelihood that adolescents will engage in
problem behaviors. Sources of deviant norms include association with deviant peers,
media influences, and the collective effect of grouping together youth who are all
heavily involved in problem behaviors (Dishion, McCord & Poulin, 1999). To offset
the negative influences from these quarters, adults are encouraged to pay close
attention to exactly what social norms are being created and reinforced in their
programs, to model and clearly express social norms that are conventionally positive,
and to promote to whatever extent is possible a “group” culture that emphasizes
prosocial and responsible behavior.
As noted earlier, the second way risks have been handled by PYD researchers
is by inverting the risk continuum so that the absence of risks are operationalized as
social assets (e.g., see Leffert et al., 1998; Scales et al, 2000). For example, the
Search Institute operationalizes the social asset “Positive peer influence” by reverse
scoring an item that asks how many of a youth’s best friends engage in problem
behaviors. In contrast, Jessor, Turbin and Costa (1998) use two indicators of peer
influence. The first, “Friends as models for problem behavior”, is classified as a risk
factor, and asks how many of a youth’s best friends engage in problem behaviors. The
second, “Friends as Models for Conventional Behavior”, is classified as a protective
factor (i.e., a social asset), and asks respondents to report on the proportion of friends
who take part in conventional activities, such as school clubs and church groups.
32
It is argued that the approach taken by Jessor, et al. (1998) is more accurate
and less likely to obscure the relationship between different types of peer influence
and youth outcomes. The absence of a risk should not quality as a social asset any
more than the absence of a problem behavior qualifies as a positive outcome. For
example, nobody writes down on their resume that their friends are not gang
members. On the other hand, a person might indicate that they attended a prestigious
university or worked with top scholars in their field. The reason for this is that the
former tells you nothing about the types skills or competencies a person might have,
whereas the latter provides some indication that a person may have learned something
of value.
The Role of Social Assets in Adolescent Development
Over the past several years, PYD scholars have done a tremendous service to
the field by synthesizing research on adolescent development in order to identify the
correlates of the social context that consistently demonstrate a positive association
with better youth outcomes (Eccles & Gootman, 2002; Scales & Leffert, 1999;
Zeldin, 1995; Zeldin, et al., 1995). Along with this tremendous effort, PYD scholars
also introduced the term “assets” (Benson, 1990) to refer to the correlates of positive
adjustment, just as the term “risks” is used refer to the correlates of dysfunction.
Although most of the work on identifying social assets was done with the intention of
developing community programs that might actually improve the lives of young
33
people, the results and insights provided by these scholars is relevant to most
developmental contexts.
In a pioneering effort seeking to address the question of what day-to-day
experiences a young person needs in order to acquire desirable behaviors, attitudes
and skills, scholars at the Center for Youth Development and Policy Research
synthesized the work of numerous research roundtables, youth-focused task force
reports, and more than 200 research studies (Zeldin, 1995; Zeldin, et al., 1995). As a
result of this effort, they identified five critical features of the social context that were
consistently associated with positive youth outcomes. They divided these features
into the two broad domains of Opportunity and Support. The Opportunity domain
includes: (a) challenging and relevant chances for formal and informal instruction and
training, and (b) new roles and responsibilities (e.g., contribution and service). The
Support domain includes: (a) ongoing contact with people and social networks that
provide emotional support, (b) motivational supports such as high expectations, and
(c) strategic supports, such as options assessment and planning, and access to
resources. To summarize their findings, these scholars report that what young people
need on a daily basis is access to safe places, challenging experiences and caring
people (Zeldin, 1995; Zeldin & Price, 1995).
In 1990, the Search Institute published its first report centered on the concepts
of Internal and External Assets (Benson, 1990). In 1999, scholars at the Search
Institute conducted an extensive literature review in order to revise the framework, to
provide a scientific basis for the elements it incorporates, and to respond to criticisms
34
about the quality of the concepts and how they were measured (Scales & Leffert,
1999). The complete Assets framework includes 40 Developmental Assets, which are
divided into 20 External and 20 Internal Assets. Internal assets reflect characteristics
of the adolescent, and were discussed in the previous chapter. The 20 External Assets
of the framework are defined as “the positive developmental experiences of
relationships and opportunities that adults provide” (Benson et al., 1998, p.143), and
are grouped into the following four categories: (a) Support (e.g., family life provides
high levels of love and support), (b) Empowerment (e.g., young people are given
useful roles in the community), (c) Boundaries and Expectations (e.g., clear rules and
consequences), and (d) Constructive Use of Time (e.g., young person spends three or
more hours a week in lessons or practice in music, theatre, or other arts).
Recently, the entire item set used to measure these 40 Developmental Assets
was examined via exploratory factor analysis in order to determine whether empirical
support could be found for the conceptual structure of the asset framework (Theokas,
et al., 2005). In the course of the analysis, the 40 Developmental Assets were reduced
to 14 primary dimensions and two higher order dimensions. The 14 primary
dimensions of the empirical model did not support the structure of the original
framework, although the higher order analysis indicated a distinction between
characteristics of the person and characteristics of the social context. The structure of
the social context that emerged from the exploratory factor analysis included six
primary factors, three of which were general factors representing the ecological
domains of family, school and community, and three were specific factors relating to
35
adult mentors, parent involvement and contextual safety. A seventh contextual factor,
relating to rules and boundaries, had a moderate but unique loading on the higher
order dimension representing characteristics of the person.
These findings are significant in several ways. First, they demonstrate that
characteristics of the adolescent and characteristics of the social context, while
related, represent distinct domains. Second, the presence of both specific and general
social context factors suggests that the variables were measured at different levels,
and the factor analysis responded to the higher internal consistency among multiple
items measuring a single narrow construct by differentiating them apart from the rest.
This differentiation, however, was resolved in the higher order analysis. Third, the
findings indicate that multiple items pertaining to the social context will tend to
cluster along ecological dimensions in an exploratory factor analysis. The three
general factors identified in the analysis reflect the different ecological domains of
family, school, and community. This indicates that the internal consistency of a social
context (e.g., high or low in social assets) outweighs the co-occurrence of risks or
social assets across different contexts. Finally, the unique loading of “Rules and
Boundaries” on the dimension representing characteristics of the person illustrates a
point made by Gorsuch (1988), who advises against mixing independent and
dependant variables in the same analysis, as they may produce factors that cross the
two domains.
Finally, in a report from the National Research Council’s Committee on
Community-Level Programs for Youth, Eccles and Gootman (2002) discuss the
36
essential features of a positive developmental setting for youth. The Committee used
a combination of theory, practical wisdom, and empirical research to identify eight
features of the social context that were viewed as particularly important to fostering
positive development. In addition, the Committee went a step further, and also
identified contextual features they refer to as “opposite poles” that serve to undermine
the effectiveness of a developmental context.
The eight features identified by the Committee are: (a) Physical and
Psychological Safety (e.g., practices that increase safe peer group interaction), (b)
Appropriate Structure (e.g., clear and consistent rules), (c) Supportive Relationships
(e.g., warmth and closeness), (d) Opportunities to Belong (e.g., social engagement
and integration regardless of gender, ethnicity, sexual orientation or disabilities), (e)
Positive Social Norms (e.g., injunctions, ways of doing things, values and morals), (f)
Support for Efficacy and Mattering (e.g., making a real difference in one’s
community) (g) Opportunities for Skill Building (e.g., exposure to intentional
learning experiences), and (h) Integration of Family, School and Community efforts
(e.g., concordance among family, school and community).
It has already been proposed that most of the “opposite poles” identified by
Eccles and Gootman (2002) really represent risks. Furthermore, many of the
strategies they advocate for offsetting the negative impact of risk processes reflect
findings from the fields of prevention and resilience research with respect to the
effect of protective factors on reducing or buffering against risk. Protective factors,
such as appropriate supervision and clearly expressed behavioral expectations, are
37
generally classified as social assets from a PYD perspective, because of their positive
association with better youth outcomes. However, unlike social assets which act on
the developing person to create new structures of knowledge or skill, protective
factors act on other factors in the social context to either buffer against risks, or to
disrupt the mediational chain through which risks operate (Bronfenbrenner, 1998;
MacKinnon & Dwyer, 1993).
The Role of Theory in Understanding Youth Development
Since the early 1990s, a developmental systems perspective has served as the
principle theoretical foundation for the study of youth development and problem
prevention (Benson, et al., 1998; Bogenschneider, 1996; Catalano et al. 2002; Lerner
et al., 2005; Sameroff & Fiese, 2000; Steinberg & Lerner, 2004). However, while
acknowledging the importance of theory in explaining youth development (e.g., see
Lerner et al, 2002), PYD scholars are just beginning to use theory as a guide to
determining what a youth needs to experience and avoid in order to thrive, and more
importantly why certain risks and assets appear to exert such powerful influences on
youth outcomes (e.g., Eccles & Gootman, 2002).
Part of the reason for this is that the PYD approach emerged largely from the
bottom up. That is, in response to a recognition on the part of youth practioners that
the view of adolescence derived from the grand developmental theories of the past
were not only inaccurate, but presented a portrait of youth that led scholars and
38
researchers to overemphasize their deficits and ignore their potential (Lerner, et al.,
2003). What follows is an attempt to examine the concepts of contextual risks and
social assets from a theoretical point of view in order to illustrate the differences
between them, and to emphasize the importance of keeping the two constructs
distinct.
Central to both ecological systems theory (Bronfenbrenner, 1989) and the
theory of development contexualism (Lerner, 2002) is the view that reciprocal
person-context interactions function as the principle mechanisms of human
development. Lerner uses the term “developmental regulation” to broadly describe
how the person and context adjust to one another over time (Lerner, et al., 2002).
Bronfenbrenner takes a more focused approach, emphasizing the impact of particular
forms of enduring interaction, which he refers to as “proximal processes”, that take
place within the immediate context and are responsible for producing specific
outcomes (see Bronfenbrenner, 1989/1992; Bronfenbrenner & Morris, 1998).
Like the process of natural selection, proximal processes are value free. They
do not operate in a teleological fashion, but are given direction and meaning as a
function of the conditions that are present within a person’s social ecology (see
Proposition II, in Bronfenbrenner & Morris, 1998). When proximal processes result
in developmental regulations that are mutually beneficial to both the individual and
mainstream society, they are judged as “adaptive” and the individual is described as
being engaged in a thriving process that leads to an “adult status marked by making
culturally valued contributions to self, others, and institutions” (Csikszentmihalyi &
39
Rthunde, 1998; cited in Lerner et al., 2002, p.15). On the other hand, proximal
processes may also result in developmental regulations that are detrimental to both
the individual and the social context (e.g., see Patterson, 1986 and Sameroff, 1995).
In such situations, one can argue that developmental regulations may be considered
maladaptive, and the individual may be viewed as engaged in a degenerative process
that leads to an adult status marked by a state of dependency and dysfunction.
It is proposed that this difference in the nature of developmental processes is
what distinguishes a contextual risk from a social asset. In order to avert harm to both
the person and society, it is important to identify and then reduce or eliminate
maladaptive developmental processes (i.e., risks) that increase the likelihood that a
person will develop problem behaviors. On the other hand, in order to promote the
welfare of both the person and society, it is important to identify and then enhance or
foster adaptive developmental processes that are related to an individual’s increased
capacity to develop prosocial values and skills.
However, in research and practice, this distinction is often obscured, largely
because of a tendency on the part of researchers to operationalize the absence of a
risk as an asset (e.g., Leffert et al., 1998), and the absence of an asset as a risk (see
Masten, 2001). A corollary of this practice is that the failure to develop a problem
behavior is often defined as a positive outcome, while the failure to develop an
adaptive behavior is viewed as a problem. For example, school failure is often
considered a problem behavior that is associated with other problem behaviors such
as substance use and teen pregnancy (Allen, Philliber, Herrling & Kuperminc, 1997;
40
Coley & Chase-Landsdale, 1998; Leffert et al., 1998; Steinberg & Avenevoli, 1998;
Steinberg, Brown & Dornbush, 1996). However, the “risks” for school failure
generally include a lack of resources, a lack of bonding with teachers, a lack of
parental and cultural support, and a lack of academic motivation (Richman, Bowen &
Woolley, 2004; Luthar & Ansary, 2005). In recent years, the focus of research on
school failure has switched to a focus on the correlates of school connectedness and
success (Brewster & Bowen, 2004; Whitlock, 2006). The corresponding change in
how the “problem” is perceived has changed the discourse from one of identifying
risks to one of promoting the social assets a young person needs in order to be
academically successful (Catalano, Haggerty, Oesterle, Fleming & Hawkins, 2004;
Hall, Yohalem, Toman & Wilson, 2002). What young people need in order to
succeed in school has not changed. What has changed is how the issue is framed and
addressed.
Theory also tells us that the multiple layers of the developmental system are
intricately related in a highly complex matrix of reciprocal influences (Lerner, 2002).
This means that a certain degree of relatedness can nearly always be found between
elements at different levels of the system. Therefore, unless the role of intervening
variables is explicitly considered, it is quite possible to find a significant relationship
between two variables that are not proximal to one another or causally related. School
failure may be a risk factor for teen pregnancy (e.g., see Corcoran, Franklin &
Bennett, 2000), but failing a math test does not make you pregnant, and school
success cannot be considered a form of contraception. In cases such as this,
41
researchers generally acknowledge the importance of intervening variables (e.g., the
correlates of school bonding, or exposure to deviant norms), but seldom examine their
role explicitly. As a result, practioners are provided with information that helps them
to identify who is most likely to need assistance, but are left to fill in the gaps
regarding what sort of intervention is most likely to be effective (Derzon & Lipsey,
1999).
The challenge for researchers is to first identify the processes that are most
directly related to the particular behavior in question (i.e., the immediate proximal
process responsible for the behavior) and then move back through the web of
mediated influences to find points or nodes that are responsive to intervention (i.e.,
processes or variables that can be influenced). Providing practioners and policy
makers with this sort of information allows them to decide what types of strategies
are most likely to be effective in their particular community or setting. It also means
that deciding between a prevention or positive youth development approach is
ultimately a false choice, because the best chance for success requires one to be open
and willing to use strategies from both approaches (Small & Memmo, 2004).
In the previous section, characteristics of the person were classified as either
problem behaviors or adaptive behaviors with the stipulation that a behavior had to be
present in order to qualify for a particular class. That is, the absence of an outcome
does not count as an adaptive or problem behavior. In this section, a similar logic is
used with regard to contextual variables. In this study social assets are defined as
contextual variables that are associated with adaptive developmental regulations, and
42
risks are defined as contextual variables that are associated with maladaptive
developmental regulations. As with characteristics of the individual, a contextual
variable must be present in order to qualify as a social asset or contextual risk. This is
a far more constrained view of assets and risks than what is generally found in the
literature where the absence of an asset is often operationalized as a risk and the
absence of a risk is frequently operationalized as an asset. However, it is believed that
such a view is necessary in order to illuminate the association between adaptive and
maladaptive processes and the expression of problem behaviors, adaptive behaviors
and thriving.
43
CHAPTER 4
Restatement of Purpose
The purpose of this study was to examine how adaptive behaviors and
problem behaviors are related to one another and in turn are associated with multiple
aspects of the social environment among older adolescents. It is believed that a better
understanding of the correlates of positive development among youth who are nearing
adulthood will provide valuable insight into what young people need to experience
and avoid in order to thrive developmentally.
The results of the study are presented in three parts, each corresponding to one of the
following research questions:
1. Do problem behaviors and adaptive behaviors co-occur to the extent that a general
problem behavior factor and a general adaptive behavior factor can be identified
through a factor analysis procedure? And if so, are these two primary factors
inversely related under a single higher order thriving construct?
2. What is the nature of the environmental correlates of thriving, and are the same
factors equally important to each of its components (i.e., adaptive behaviors and
problem behaviors)?
44
3. To what extent do adaptive behaviors appear to mediate the relationship
between social assets and problem behaviors among older adolescents?
The first research question will be addressed through an exploratory factor
analytic procedure, which examines the structural relations among a variety of
adaptive behaviors and problem behaviors. Prevention scholars have studied the
interrelationships among problem behaviors in adolescents through factor analysis
procedures, and have discovered that they often cluster together either along a single
general problem behavior factor, or along several primary factors that are then related
at a higher level (for a review, see Willoughby, et al., 2004). Similarly, the structural
relations among positive attributes of adolescents have also been examined and found
to be related under a higher order construct (Lerner, et al., 2005). The limitation these
research perspectives share in common is that both consider only one aspect of
adolescent development (i.e., either positive or negative outcomes) while ignoring the
other. It is expected that these two dimensions that have been observed individually in
previous research studies will be replicated in the present study, and will be
negatively correlated under a higher order thriving construct.
In the second part of the study, hierarchical regression analyses are conducted
to determine what type of environment factors (i.e., social assets or contextual risks)
demonstrate the strongest relationship to external thriving, and whether the same
factors are equally important to each of its components (i.e., adaptive behaviors and
problem behaviors). It is expected that a combination of risks and social assets will be
45
significantly related to thriving in general. However, based on the proposed
theoretical distinction between contextual risks and social assets, it is further expected
that when examined empirically, social assets will demonstrate a stronger association
with adaptive behaviors, and contextual risks will demonstrate a stronger association
with problem behaviors.
Finally, the study examines whether there is empirical support for a central
assumption of the PYD approach that social assets foster positive youth attributes,
which in turn reduces problem behaviors. This is done by examining the extent to
which adaptive behaviors appear to mediate the relationship between social assets and
the expression of problem behaviors within this sample of older adolescents. It was
expected that the mediation hypothesis would be supported, although it is recognized
that such support is only circumstantial given the cross-sectional nature of the present
data.
46
CHAPTER 5
Method
The Survey and Procedure
The data for this study came from the 2000 Dane County Youth Assessment.
The instrument used in the assessment is a 160-item self-report survey that has been
administered to middle and high school students living in Dane County at five-year
intervals since 1980. The Teen Assessment Project (TAP) on behalf of the Dane
County Youth Commission and the school district of Beloit, Wisconsin collected the
data for this study during the 1999 – 2000 school year. The primary purpose of the
assessment is to provide participating school districts, program administrators, local
policy makers, and parents with information about the needs, interests, and behaviors
of local youth in order to guide program development and local policy decisions. In
addition, the survey is used to track changes over time in several countywide
indicators of youth behavior. Before each administration, the psychometric properties
of items on the survey are evaluated and revised based on data from previous years, in
order to improve their effectiveness and efficiency in providing useful information to
school and community leaders at the next time of assessment.
47
Prior to the 2000 assessment, the survey underwent its fourth revision overall,
and its second revision under the direction of researchers at the Teen Assessment
Project at the University of Wisconsin-Madison. Most of the 160 items in the 2000
survey were drawn from well-validated instruments used in pervious research studies
(see Small & Rogers, 1995). Others were retained, because they had proven their
effectiveness throughout 20 years of use in Dane County. All 7th through 12th grade
students who were present in school on the day the survey was administered were
asked to take part. Several weeks prior to the administration of the survey, parents
were sent a letter by mail informing them that the survey would be taking place and
were asked to notify the school if they did not wish their child to participate. The
survey was completely anonymous. To safeguard students’ identity all responses
were recorded on standardized answer sheets that were sealed in an envelope and
delivered to the University of Wisconsin - Madison to be coded and analyzed.
Participants
Student population estimates are based upon school enrollment data for the
1999-2000 school year available from the Wisconsin Department of Public
Instruction1. In Dane County, twelve public school districts and one private high
school participated in the survey. Approximately 75% of the population in these
districts and school (N=23,508) returned valid surveys for a total of 17,465 cases
1
Available at http://www.dpi.state.wi.us
48
processed. A total of 1,107 surveys (approximately 5% of the population) were
judged invalid and removed prior to data analysis. In the Beloit school district 74% of
the school district population (N=2744) returned valid surveys for a total of 2,030
cases processed.
This study focuses exclusively on the responses of 11th and 12th grade students
between the ages of 16 and 19 or older. Initially, the number of qualified respondents
was 5,330. However, after a preliminary examination of the data, an additional 92
cases (< 2%) were eliminated from this group for one of the following reasons: (a)
they had missing data on more than 64 of the survey items, (b) the information they
provided was inconsistent within the survey (e.g., indicating they first used tobacco at
an age that was older than their current age), or (c) because they responded to a trick
question or “lie scale” in a manner that indicated they were either not paying attention
or not being truthful on the survey items. The analysis reflects the responses of the
remaining students (n = 5,238).
Missing Data
For the most part, missing data was not a problem for the respondents in the
study. The amount of missing data per participant ranged from zero to 64 items, with
66% of the respondents having no missing data, and 90% having missing information
on less than six of the 160 survey items. The per-item rate of missingness was
directly related to the survey length. That is, less than one percent of responses in the
49
first half of the survey were missing compared to items in the second half of the
survey for which missing data increased steadily from less than 1% to less than 9%
for the last few items (See Table B1 in Appendix B). This was interpreted as
indicating that the missing data was missing at random (MAR) and was not due to the
underlying values of the variables. According to Rubin’s (1976) MAR mechanism,
missing values on a variable can be related to other measurable variables, but must be
unrelated to the underlying values of the variable itself.
In a multivariate analysis involving a large number of items, case deletion
procedures can result in an unacceptably high proportion of respondents being
discarded even if the per-item rate of missingness is low. This is because few
respondents have complete data. Furthermore, those who do may not be
representative of the entire sample, which introduces a potential bias in the results
(Schafer & Olsen, 1998; see Appendix B for the impact on parameter estimates). In
such situations, methodologists recommend maximum-likelihood estimation for data
that is MAR above more common methods such as pairwise deletion, listwise
deletion, or mean substitution (Peugh & Enders, 2004).
In this study, missing data was imputed using the expectation maximization
(EM) algorithm in SPSS 13.0. EM is an iterative maximum-likelihood procedure in
which a cycle of calculating means and covariances followed by data imputation is
repeated until a stable set of estimated missing values is reached. Once the missing
values have been imputed, multivariate analyses can then be performed as with
complete data.
50
Measures
Demographic Variables
Three of the demographic variables described below were included as control
variables in the subsequent hierarchical regression analysis. These are gender, race
and parents’ education. The remaining demographic variables are included in order to
provide a description of the characteristics of the adolescents who participated in the
study. The descriptive statistics of the sample are presented in Table 1. Summaries of
the other measures used in the study are provided in Tables 2 and 3 which appear at
the end of this chapter. The complete survey with the exact wording and response
options for every item is provided in Appendix A.
Personal Attributes: On six items in the questionnaire respondents were asked
to indicate their gender, race/ethnic background, age, grade in school, special
education placement, and sexual orientation. Qualified respondents were limited to
youth in the 11th and 12th grade who were between the ages of 16 and 19 or older.
Special education placement included none, part-time, or full-time education classes
or program. Race/ethnic background was initially divided into eight categories, which
were later collapsed into five (i.e., Black, Hispanic, White, Mixed Race, and Other)
due to the low number of respondents for certain minority groups in the study.
Similarly, the categories for sexual minority status were collapsed into four groups
51
based on the degree to which a youth questioned his or her sexual orientation, or
stated a confirmed sexual minority identity. The four groups for sexual minority
status are: Never, Sometimes, Always and Confirmed.
Size of Community: Respondents were asked to indicate which of five options
best described where they lived most of the time. These were: “Metropolitan Area”,
“Smaller city”, “In a small town or village”, “In the country”, “On a working farm”.
Higher scores indicate a larger community. The last two categories were combined to
indicate that the respondent lived in a rural area.
Parents’ Education. Respondents were asked to indicate how much education
each of their parents completed on a scale ranging from “elementary or junior high
school” to “professional or graduate degree”. Respondents who did not know the
educational attainment of one parent were scored for the parent they did report on.
Otherwise, the individual items for each parent were averaged to form a single
measure (2-items, α = .75). Higher scores indicate higher average level of educational
attainment by parents.
Family Structure: Respondents were asked to indicate whom they lived with
most of the time from a variety of options that covered a wide range of possible
family structures. This item was recoded to indicate the number of adults with whom
the respondent lives: i.e., two adults (two parents, parent and stepparent), one adult
(mother only, father only, shared custody), non-parental adults (foster home, relative),
or no adults (alone/with friends). Higher values corresponded to more adults at home.
52
Table 1.
Summary of Demographic Characteristics of Respondents (In Percentages)
Gender
Special Education
Male
Female
48.5
51.5
White
Other
Black
Mixed Race
Hispanic
82.3
5.8
5.2
4.2
2.6
11th
12th
52.4
47.6
Metro Area
Small City
Small Town
Rural Area
59.0
17.9
14.7
8.4
None
Part- time
Full-time
92.1
5.7
2.2
Two Parents
Single Parent
Adult – Not Parent
No Adults
77.1
19.6
2.2
1.1
Race
Grade
Community
Family Structure
Questions Sexual Orientation
Never
Sometimes
Always
Confirmed
83.4
7.8
6.2
2.6
Mean (SD)
Age
Parents Education*
16.8(.70)
3.8(1.7)
*The mean indicates approximately a “4-year college graduate”.
Characteristics of the Adolescent
Twenty-five measures reflecting a variety of adolescent behaviors and
attitudes were selected to represent the domains of problem behaviors, adaptive
behaviors and psychological health respectively. While this sampling of variables in
no way exhausts the possible range of outcomes that could be used to characterize
youth along these dimensions, all of the variables that were included represent
53
attributes or characteristics of the person that have been identified in the past as
representative of each of these three domains. The selection of problem behaviors
was based on externalizing behaviors that involve legal or normative transgressions
that have been traditionally studied by researchers. These include drug use, sexual
activity, aggression and various forms of delinquency (see Jessor, 1998).
The variables representing adaptive behaviors were identified based on the
framework developed by the National Resource Council’s Committee on
Community-Level Programs for Youth (Eccles & Gootman, 2002). Within this
framework, 28 youth attributes that reflect good adaptation are grouped into four
developmental domains (i.e., physical development, intellectual development,
psychological/ emotional development, and social development). The variables in this
analysis represent a sample of the personal assets of adolescents that were identified
by the Committee from each of the four domains. They include characteristics such as
academic success, civic engagement, prosocial and culturally sensitive values, good
health habits, and attachment to prosocial institutions.
The Committee’s framework also identifies good mental health as a personal
asset within the category of psychological and emotional development (see Eccles &
Gootman, 2002). Therefore, indicators of psychological health were also included in
the analysis. However, for the most part the indicators reflect symptoms of
psychological dysfunction (e.g., anxiety, depression, suicidal ideation, and eating
disorders) rather than psychological well-being. Consequently, they were all coded to
reflect a greater intensity of internalizing problems. According to the PYD
54
perspective, a youth who displays many positive developmental outcomes should
exhibit low levels of internalizing problems. Therefore, these indicators are expected
to relate negatively to the indicators of adaptive behaviors.
Problem Behaviors
Tobacco, Alcohol and Marijuana Use. The standardized scores of a two item
index of tobacco use (α = .91, r = .84), a three item index of alcohol use (α = .88) and
an single item measure of yearly marijuana use were averaged to create a measure of
the three substances most commonly used by high school students (see Luthar &
D’Avanzo, 1999). For each measure, respondents were asked how often they used
these substances either in the past year, or in the past month/30 days. Response
options for the individual items differ, but all range from no use to greater intensity of
use. For example, ranging from 0= “never” to 5= “10 or more times a month” or
“daily”. Higher values corresponded to more frequent use (3-items, α = .79).
Hard Drug Use. The raw scores of five items measuring yearly inhalant,
hallucinogen, cocaine/crack, stimulant, and unauthorized prescription drug use were
averaged to form a measure of respondent’s use of hard drugs in the past year. The
response options for all items ranged from 0=“never” to 5=“daily”. Higher values
correspond to greater use (5-items, α = .71).
55
Sexual Risk Taking. The standardized scores of two items measuring early
sexual initiation and unprotected sexual intercourse were averaged to form a measure
of risky sexual activity. Respondents were asked to indicate their age at first sexual
intercourse and how consistently they used contraception (1= “always” to 6=“never”).
Respondents were given a score of zero if they reported they were not sexually active.
Higher values indicate earlier and greater sexual risk taking (2-items, α = .78, r =
.64).
Aggressive Behavior. The standardized scores of five indicators of
aggression were averaged to create an aggression index. The items were measured on
different scales, with higher scores indicating greater aggression. The five items were
as follows: physical fight with weapons in the past year (0= “never” to 7 =“12 or
more times”), physical fight without weapons in the past year (0= “never” to
5=“More than 10 times”), gang involvement (0= “never asked or pressured to join a
gang” to 3= “currently in a gang”), carried a weapon into the school building in the
past month (0=”never” to 4=“6 or more days”), how often suspended from school in
the past year (0= never to 3=“3 times or more”). School suspension was included in
the aggressive behavior measure because in Wisconsin aggressive or violent behavior
is by far the most common reason why students are suspended from school (see Legal
Action of Wisconsin, 2002). Higher values on the index indicate a greater likelihood
of aggressive behavior (5-items, α = .71).
56
Driving While Intoxicated. On a single item respondents were asked to
indicate if they had driven a motorized vehicle after drinking alcohol in the past
month. Response options ranged from 0=“no” to 4=“6 times or more”.
Drug Use During School Hours. On a single item respondents were asked to
indicate how often in the past 30 days did they drink alcohol or use drugs during
school hours. Response options ranged from 0=“never” to 5=“5 or more times”.
Shoplifting. On a single item respondents were asked to indicate if they had
ever shoplifted. The three response options were: “no” ,“longer than a year ago” and
“in the past year”. More recent shoplifting was scored as more problematic.
Vandalism. On a single item respondents were asked to indicate if they had
ever vandalized public or private property. The three response options were: “no”,
“longer than a year ago” and “in the past year”. More recent vandalism was scored as
more problematic.
Adaptive Behaviors
Commitment to Learning. The standardized scores of six items were averaged
to create an index of a youth’s commitment to learning. The items were measured on
different scales, with higher scores indicating a greater commitment to learning. The
six items and the response range were as follows: average grade in courses at school
(7=“mostly As” to 0= “mostly below D), plans after finishing high school (4= “go to
a 4-year college” to 0=”drop out before I finish high school”), time spent doing
57
homework (6= “20+ hours per week” to 0=”none”), how much they worried about
getting good grades (4= “very much” to 0=”not at all”), how often in the past month
did they skip a day of school without a valid excuse (4=“never” to 0=”4 or more
times”), and if they enjoyed going to school (4=“strongly agree” to 0= “strongly
disagree”) (6-items, α = .70).
Civic Engagement. How much time a youth spent engaged in volunteer
activities was used as a measure of civic engagement. On two items, respondents
were asked to indicate how much time they spent in volunteer activities on a yearly
(e.g., “not interested” to “weekly”) and weekly basis (i.e., “none” to “more than 20
hours per week”). The measure was calculated as the average of the standardized
scores for these items. Higher values indicate more time spent volunteering and
greater civic engagement (2-items, α = .82, r = .69).
Prosocial and Culturally Sensitive Values. On five items respondents were
asked to indicate on a 4-point scale the extent to which they “agree” or “disagree”
with statements reflecting prosocial values. Three statements were culturally focused
(e.g., “I would like to learn more about other races or cultures”), one reflects
sensitivity toward sexual minorities (i.e., “I could never stay friends with someone
who told me he or she was gay or lesbian”) and one reflected a general attitude
toward behaving in a civil manner (i.e., “It feel it is important to always be
considerate and respectful of others”). These items were coded such that higher
values indicated greater prosocial and culturally sensitive values (5-items, α = .64).
58
Number of Prosocial Activities. Youth were asked to indicate how much time
they spent in activities sponsored by conventional social institutions. Response
options ranged from 0=“none” to 6=“more than 20 hours per week”. Six activities
were considered. These were: school sports or extracurricular activities, music or
dance lessons, nonschool sports or activities (e.g., Boy or Girl Scouts, 4-H), activities
sponsored by a religious institution, time at a community or youth center and
leadership activities. The number of prosocial activities was determined by counting
the number of activities that a youth reported being involved in for at least one hour a
week. Scores ranged from 0 to 6. Higher values on this measure are viewed as
indicating a greater attachment to conventional institutions.
Most time Spent in a Single Activity. This measure is also based on the six
items previously described for the measure of the “number of prosocial activities”,
but reflects only the greatest amount of time a youth reports spending in any single
activity. Response options ranged from 0 =“none” to 6=“more than 20 hours per
week”. Higher values on this measure are also viewed as indicating a greater
attachment to conventional institutions.
Regular Aerobic Exercise. On a single item respondents were asked to
indicate how often in the past seven days did they exercise or were physically active
for at least twenty minutes. Response options ranged from 0=”not at all” to 7=”7 or
more times”. Aerobic exercise is viewed as a health promoting behavior (see Murphy,
Lamonda, Carney & Duncan, 2004), with higher values indicating better health
behavior.
59
Consistent Use of a Seatbelt. On a single item respondents were asked to
indicate how often they used a seatbelt when driving or riding in a motor vehicle.
Response options ranged from 0=”never” to 4=”always”. Consistent use of a seatbelt
is viewed as a health promoting behavior (see Murphy et al., 2004), with higher
values indicating better health behavior.
Consistent Use of a Protective Helmet. On a single item respondents were
asked to indicate how often they wore a protective helmet for sports (e.g., biking).
Response options ranged from 0=”never” to 4=”always”. Consistent use of a helmet
is viewed as a health promoting behavior (see Murphy et al., 2004), with higher
values indicating better health behavior.
Values Sexual Restraint. On a single item respondents were asked indicate on
a 4-point scale the extent to which they “agree” or “disagree” with the statement “I
believe teenagers should not be having sexual intercourse”. Higher scores indicate
stronger values regarding sexual restraint.
Psychological Health Indicators
Negative Self Regard. On a single item respondents were asked to indicate on
a 4-point scale the extent to which they “agree” or “disagree” with the statement “I
am comfortable with who I am”. Higher scores indicate greater negative self regard.
Unhealthy Dieting Practices. On a single item respondents were asked to
indicate whether they engaged in various types of unhealthy dieting practices (e.g.,
60
vomiting, using laxative or diet pills). Response options include using none, one, or
multiple types of weight loss methods. Scores were assigned based on the number of
unhealthy weight loss methods respondents report using, with higher values
indicating a greater number of unhealthy dieting practices.
Worry About Peer Relations. On a single item respondents were asked to
indicate on a 5-point scale how much they worried about “Not fitting in with the other
kids at school”. Response options ranged from 0=“not at all” to 4=”very much”.
Higher scores indicate greater anxiety about peer relations.
Worry About Personal Appearance. On a single item respondents were asked
to indicate on a 5-point scale how much they worried about “How I look (my general
appearance..)”. Response options ranged from 0=“not at all” to 4=”very much”.
Higher scores indicate greater anxiety about personal appearance.
Reports Needing Help for Weight Control. On a single item respondents were
asked to indicate on a 3-point scale whether they currently feel the need for help
regarding weight control. Response options ranged from 0=“no help needed” to
2=”already receiving help”. Higher scores indicate that weight control is a major
concern.
Reports Needing Help for An Eating Disorder. On a single item respondents
were asked to indicate on a 3-point scale whether they currently feel the need for help
regarding an eating disorder (e.g., excessive dieting or self-induced vomiting).
Response options ranged from 0=“no help needed” to 2=”already receiving help”.
Higher scores indicate an eating disorder is a major concern.
61
How Often Depressed in the Past Month. On a single item respondents were
asked to indicate whether they felt depressed or very sad in the past month. Response
options ranged from 0=”no” to 4=”yes, all the time”. Higher scores indicate a greater
sense of sadness or depression.
How Often Suicidal in the Past Month. On a single item respondents were
asked to indicate whether they seriously thought about killing themselves in the past
month. Response options ranged from 0=”no” to 4=”yes, all the time”. Higher scores
indicate more intense suicidal ideation
Characteristics of the Social Context
The identification of social assets was based on the essential opportunities and
supports young people need in order to develop in a positive manner (Zeldin, 1995).
Opportunities include the chance to take on new roles and responsibilities, to actively
learn, and to be of service to others. Supports include emotional support from friends
and caring adults, motivational support in terms of boundaries and expectations, and
strategic support in terms of guidance and decision making. An effort was made to
include supports and opportunities from a range of ecological niches, and to measure
positive developmental processes (e.g., discussing future plans with an adult) as well
as the availability and quality of resources and opportunities.
The risk factors selected for the study have been identified in previous
research studies as either increasing the likelihood that a problem behavior would
62
occur or have been characterized as representing a significant threat to development.
In general they fall into two classes: (a) stressful life events such as experiencing
abuse, harassment, racism, and various forms of victimization (see Masten, et al,
1999), and (b) associations with deviant peers and adults who model problem
behaviors (Deater-Dekard et al., 1998; Dishion et al., 1999). For the most part, each
risk was measured by a single item. However, many of these items have been used in
previous research examining multiple correlates of problem outcomes and were found
to be unique predictors of problem behaviors (Memmo, 1997). A summary of the
measures is provided in Table 3 at the end of this section.
Social Assets
High Quality School Curriculum. Two items asked respondents to indicate on
a 4-point scale the extent to which they “agree” or “disagree” with the statements “I
am getting the education and skills I need to be successful after I graduate from high
school” and “I believe I am getting a good, high quality education at my school”.
Higher scores indicate a better assessment of the school curriculum (2-items, α = .85,
r = .74).
Caring and Supportive Adults. The standardized scores of seven items
reflecting respondents perceptions of caring, support, and monitoring from nonparental adults and neighbors were averaged to form an index of how much
respondents felt they mattered to adults in their school and community. For all of the
63
items, respondents were asked to indicate on a four-point scale the extent to which
they 3=“strongly agree” or 0=“strongly disagree” with various statements such as
“my teachers care about me an how well I do in school”, school personnel “are
helpful when I need them”, “I can count on police if I…need help”, neighborhood
adults “keep an eye on…teens”, can be counted on “…to help me”, and “would
probably tell my parents [if I were doing something wrong]”, and “people in my
community know and care about each other”. Items were scored so that higher values
would indicate that non-parental adults are perceived as more supportive (7-items, α
= .70).
Counseling by a School Adult. On a single item respondents were asked to
indicate how often in the past year did they have a good talk with an adult at school
about their future plans (e.g., college or employment plans). Response options ranged
from 0=”never” to 4=”very often”. Higher values indicate more frequent counseling
by an adult at school.
Supportive Non-Parental Adults. On a single item respondents were asked to
indicate the number of non-parental adults they felt they could rely on if they had a
problem and needed help. Response options ranged from 0=”no other adults
available” to 4=”4 or more adults”. Higher scores indicate a greater number of adults
are available.
Perceived Availability of Diverse Community Resources. On nine items
respondents were asked to indicate whether they felt their community has “about the
right amount”, “doesn’t have enough” or has “too much” of various resources and
64
opportunities to which youth might become involved. The nine items include: (a)
organized team sports, (b) social activities just for fun, (c) performing arts, (d)
organized activity clubs, (e) youth leadership activities, (f) volunteer projects, (g)
community or youth center, (h) peer helping programs, and (i) employment programs.
Each item was dichotomized, such that indicating the community had “about the right
amount” was given a score of one, and the other responses were given a score of zero.
An index was then calculated by summing across all scores. The index ranged from 0
to 9, with higher values reflecting youth perceiving a greater number of diverse
community resources being available (9-items, α=.80).
Parental Monitoring. Four items were averaged to form an index representing
the degree to which respondents felt their parents monitored their whereabouts, plans,
and activities. Response options ranged from 0=“never” to 4=“very often” for
statements about parents such as, “I talk to them about the plans I have with my
friends”, and “When I go out they ask me where I’m going”. Reporting “no adults at
home” was scored as zero. Higher values indicate greater parental awareness and
monitoring (4-items, α = .85).
Parental Support. Two items were averaged to form an index representing the
degree to which a youth perceived care and support from parents. Response options
ranged from 0=“never” to 4=“very often” for the following statements, “My parent(s)
are there when I need them” and “My parent(s) care about me”. Reporting “no adults
available” was scored as zero. Higher values indicate greater perceived care and
support from parents (2-items, α = .75, r = .60).
65
Counseling by a Parent. On a single item respondents were asked to indicate
how often in the past year did they have a good talk with at least one parent about
their future plans (e.g., college or employment plans). Response options ranged from
0=”never” to 4=”very often”. Reporting “no adults available” was scored as zero.
Higher values indicate more frequent counseling by a parent.
Parents’ Value Teen Restraint. Three items were averaged to form an index
representing the degree to which parents clearly express their disapproval of teen
sexual activity and substance use. Respondents were asked to indicate whether their
parents think it is wrong for teens my age to “have sexual intercourse”, “drink
alcohol”, and “smoke/chew tobacco”. Response options were scored on a 5-point
scale that ranged from “strongly agree”, “agree”, “not sure”, “disagree” or “strongly
disagree”. Higher values indicate that a youth has a clearer understanding of their
parents’ stronger disapproval (3-items, α = .78).
Peer Support. On a single item respondents were asked to indicate on a fourpoint scale the extent to which they “agree” or “disagree” with the statement, “My
friends help me to stay out of trouble”. Higher scores were viewed as indicating a
greater perceived support from peers.
Contextual Risks
Association with Deviant Peers. The standardized scores of five items
reflecting respondents’ perceptions of their friends’ behavior were averaged to create
66
a peer risk index. Three items asked respondents to indicate on a four point scale the
extent to which they “strongly agree” to “strongly disagree” with statements about
their friends involvement in various problematic or illegal activities. The items
included “most of my friends do not have sexual intercourse”, “use alcohol and
drugs”, and “smoke or use tobacco”. On the fourth item, respondents were asked to
indicate if they had ridden in a motorized vehicle with a teen driver who was drinking
alcohol (0=”no” to 4=”6 times or more) in the past month. On the final item,
respondents were asked to indicate their primary source of alcohol. This item was
scored such that high scores reflect friends or older friends as the primary source of
alcohol. Specifically, the item was scored as follows: 0=”I don’t drink”, 1=”parents”,
2=”self/other”, 3=”friends/older friends/legal adult”. Higher scores on this measure
reflect having friends and associates who are perceived as engaging in and supporting
more deviant behavior (5-items, α = .80).
History of Sexual Abuse. On a single item respondents were asked to indicate
whether they had ever been sexually abused by an adult. Response options included
“no”, “was abused, but abuse has stopped”, and “currently being abused”. Current
abuse was coded as a higher level of risk than past abuse.
History of Physical Abuse. On a single item respondents were asked to
indicate whether they had ever been physically abused by an adult. Response options
included “no”, “was abused, but abuse has stopped”, and “currently being abused”.
Current abuse was coded as a higher level of risk than past abuse.
67
Witness to Abuse. On a single item respondents were asked to report whether
they had ever personally witnessed someone being beaten or physically abused, either
in their home, school, community or different combinations of each. Responses were
recoded to reflect the number of locations in which the respondent witnessed abuse
taking place, and ranged from zero to three.
Victim of a Crime. On a single item respondents were asked to indicate if he
or she had ever been the victim of a crime. Response options ranged from “no” to “3
times or more”. More frequent victimization was coded as greater victimization risk.
Harassment from Kids at School. On a single item respondents were asked to
indicate how often in the past year they had experienced some form of harassment
(e.g., teased, threatened, chased or cornered) from a student at school. Response
options ranged from “never” to “4 times or more”. Higher values indicate more
frequent harassment.
Unsafe Neighborhood. On a single item respondents were asked to indicate on
a 4-point scale the extent to which they “agree” or “disagree” with the statement that
their neighborhood is a safe place to live. Higher values on this item indicate less
agreement and greater safety risk.
Racial Bias from a Teacher. On a single item respondents were asked to
indicate on a 4-point scale the extent to which they “agree” or “disagree” with a
statement about being treated unfairly by teachers due to their race. Higher scores
indicate greater agreement and perceived racial bias.
68
Adults at Home Smoke. On a single item respondents were asked to indicate
the number of adults in their household who currently smoke cigarettes. Response
options ranged from 0=“none” to 4=“four or more”.
Criminal Activity in the Family. On a single item respondents were asked to
indicate if a family member (other then yourself) had been charged with criminal
activity in the past year. Response options were dichotomous, either 0=“no” or
1=”yes”.
Rides with an Adult Who is Driving While Intoxicated (DWI). On a single
item respondents were asked to indicate if in the past month they had ridden in a
motorized vehicle with an adult who was drinking alcohol. Response options ranged
from 0=“no” to 4=“6 times or more”. Higher values represent greater frequency of
occurrence.
Summary of Variables Used in the Study
The following tables provide a summary of the variables used in the study.
The items representing characteristics of the person were grouped into three types: (a)
problem behaviors, (b) adaptive behaviors, and (c) indicators of psychological health.
In Table 2, the alpha, mean and standard deviation for these groups of variables are
also presented. For example, the eight variables measuring different types of problem
behaviors were standardized and averaged to form an eight item index of problem
behaviors (α = .79). The reported mean and standard deviation are for this index.
69
Table 2.
Summary of Measures Reflecting Characteristics of the Adolescent
Characteristics of the Adolescent
#Items
Alpha
Problem Behaviors
8a
Tobacco, alcohol & marijuana use
Hard drug use
Sexual risk taking
Aggressive behavior
Driving while intoxicated
Drug use during school hours
Shoplifting
Vandalism
Mean
SD
.79
.00
.63
3
5
2
5
1
1
1
1
.79
.71
.78
.71
n/a
n/a
n/a
n/a
.00
.09
.00
.00
.31
.40
.66
.52
.84
.28
.91
.69
.79
1.17
.77
.79
Adaptive Behaviors
9a
.73
.00
.56
Commitment to learning
Civic engagement
Prosocial and culturally sensitive values
Number of prosocial activities
Most time in a single activity
Regular aerobic exercise
Consistent use of a seatbelt
Consistent use of a protective helmet
Values sexual restraint
6
2
5
1
1
1
1
1
1
.70
.82
.64
n/a
n/a
n/a
n/a
n/a
n/a
.00
.00
2.19
1.49
2.72
3.70
3.03
.92
1.25
.63
.92
.49
1.28
1.77
2.44
1.27
1.35
.93
Psychological Health Indicators
8a
.71
.00
.58
Negative self regard
Unhealthy dieting practices
Worry about peer relations
Worry about personal appearance
Reports needing help for weight control
Reports needing help for an eating disorder
How often depressed past month
How often suicidal past month
1
1
1
1
1
1
1
1
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
.72
.40
.79
1.71
.21
.06
1.27
.23
.71
.74
1.05
1.27
.45
.26
1.05
.62
a
The number of variables in the group, and the mean of their average standardized scores
70
Table 3.
Summary of Measures Reflecting Characteristics of the Social Context
Characteristics of the Social Context
#Items
Alpha
Mean
SD
2
7
1
1
9
4
2
1
1
1
.85
.70
n/a
n/a
.80
.85
.75
n/a
n/a
n/a
1.90
1.73
1.55
2.78
5.24
3.00
3.44
2.80
2.86
1.79
.72
.49
1.17
1.38
2.63
.94
.83
1.20
.91
.81
5
1
1
1
1
1
1
1
1
1
1
.80
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
.00
.06
.13
.72
.52
.57
.49
.81
.47
.16
.37
.74
.25
.37
.87
.88
.96
.77
.72
.78
.35
.88
Social Assets
High quality school curriculum
Caring and supportive adults
Counseling by a school adult
Supportive non-parental adults
Diverse community resources
Parental monitoring
Parental support
Counseling by a parent
Parents value teen restraint
Peer support
Contextual Risks
Association with deviant peers
History of sexual abuse
History of physical abuse
Witness to abuse
Victim of a crime
Harassment from kids at school
Unsafe neighborhood
Racial bias from a teacher
Adults at home smoke
Criminal activity in the family
Rides with an adult who is DWI
71
CHAPTER 6
Results – Part I
The Structure of Thriving in Adolescence
The structure of thriving in adolescence was examined through an exploratory
factor analysis of a wide variety of youth characteristics that include both adaptive
and problem behaviors. The goal of the analysis was to determine whether a general
problem behavior factor and an adaptive behavior factor could be identified, and
whether these factors were inversely related under a single higher order thriving
construct. Demonstrating that an empirical relationship of this sort exists would
support the argument that thriving in adolescence should be conceptualized and
studied as a multidimensional construct that includes variation in both adaptive
behaviors and problem behaviors.
Preliminary Analysis
The set of variables included in the analysis are summarized in Table 2 in the
chapter on methods. Eight to nine variables were selected from three domains of
72
adolescent outcomes: problem behaviors, adaptive behaviors and psychological
health indicators. When conducting an exploratory factor analysis, sampling more
than six variables from a particular domain increases the likelihood that the procedure
will respond by giving finer differentiation at the primary level of the factors
(Gorsuch, 1988). Therefore, it was expected that the analysis would return more than
three factors with eigenvalues greater than one (Kaiser, 1960), but that an oblique
rotation of the axes would demonstrate that the factors at the primary level were
related under a secondary or higher-order dimension.
The internal reliabilities for the three groups of variables were .79, .73 and .71
for problem behaviors, adaptive behaviors, and psychological health respectively.
An examination of the correlation matrix (see Appendix C) indicated that each
variable was at least moderately correlated (i.e., r = .30) in the expected direction with
at least one other variable in the same domain. It should be noted that given the large
size of the sample, correlations as low as .03 are significant at the p < .05 level.
Table 4 considers the actual level of reported involvement by respondents for
each of the indicators included in the study. Examining the general intensity of
involvement across all indicators is an important preliminary step in determining the
significance of the factor analysis results. This is because the correlation, which is the
starting point for the analysis, is a measure of linear association that does not take into
account the actual magnitude of the variables being compared. Therefore, unless there
is a reasonably high level of reported involvement in a set of behaviors to begin with,
finding that several items measuring multiple types of behaviors load together on the
73
Table 4. Percent of Students Exhibiting Involvement in Selected Outcomes
Characteristics of the Adolescent
Percentages
Male
Female
All
Drinks beer/wine at least once a montha
Uses hallucinogens at least once a yeara
Is sexually activea
Has been in a fight with weapons in the past yeara
Has driven while intoxicated in the past month
Has used drugs during school hours – past month
Has shoplifted in the past year
Has vandalized property in the past year
51.9
13.0
42.4
15.4
20.3
18.1
20.7
25.6
43.2
10.1
45.9
5.5
15.2
10.8
15.5
12.3
45.6
11.5
44.2
10.3
17.7
14.3
18.0
18.7
Adaptive Behaviors
Gets A’s & B’s or better in schoola
Volunteers at least one hour per weeka
Is interested in other races/cultures (agree)a
Participates in 1 activity for at least 5-9 hrs/wk
Participates in 3 activities for at least 1 hr/wk
Exercises at least 3 times a week
Always uses a seatbelt
Always uses a protective helmet
Values sexual restraint (agrees)
45.7
13.8
50.7
58.3
15.0
73.9
48.0
9.6
30.0
59.5
24.3
71.8
53.1
23.7
54.6
58.6
7.9
39.5
52.8
19.2
61.6
55.6
19.4
63.9
53.5
8.7
34.8
Psychological Health Indicators
Is comfortable with self (disagree)
Vomits, uses diet pills or weight loss products
Worries quite a bit or very much about peer relations
Worries quite a bit or very much about “how I look”
Reports needing help for weight control
Reports needing help for an eating disorder
Depressed most or all of the time – past month
8.0
2.4
6.5
17.3
8.5
1.7
7.9
11.8
10.8
10.2
38.6
29.2
7.6
15.4
10.0
6.7
8.3
28.2
19.1
4.7
11.8
1.9
1.5
1.7
Problem Behaviors
Suicidal most or all of the time – past month
a
Representative item; (n = 5238)
74
same general latent factor is not grounds for concluding that these behaviors actually
co-occur in any substantive way. (Willoughby, et al., 2004).
With respect to problem behaviors, the overall rates of high intensity
involvement for males and females combined appear to fall into two classes. The first
is a rate of about 45% for sexual activity or drinking alcohol on a regular basis. The
second is a rate of about 10% to 20% for other types of problem behaviors. With
respect to adaptive behaviors, more than half of all youth report they do well in
school (53%), are interested in learning about other cultures (62%), participate in at
least one extracurricular activity for a least five hours each week (56%), exercise
regularly (64%) and always use as seatbelt (54%). Rates of high levels of
psychological distress range from 28% of youth who worry excessively about their
physical appearance to less than 2% of youth who report being frequently suicidal.
Overall, only a small proportion of youth report high levels of psychological distress,
although a fair proportion of females in particular report worrying excessively about
how they look (39%) and feel the need for help with weight control (29%).
Analysis of the First-Order Factor Structure
The reliability of the primary factors was established in a separate analysis
(see Appendix C). The results of the analysis indicated a high level of congruence for
six primary factors across two independent subsamples of respondents. One item
relating to regular helmet use was dropped from the analysis, because it had a low
75
communality in one subsample (.171) where it did not load meaningfully on any of
the factors.
An exploratory factor analysis of the 24 remaining variables reflecting
characteristics of the adolescent was conducted on the full sample of respondents (n =
5238). The factor solution yielded six factors with eigenvalues greater than 1.0,
accounting for 53% of the variance (det. = .003, KMO = .839; Bartlett: χ2 =
29922.995, df = 276, p < .001). These factors were extracted using principle axis
factoring (PAF) and their orthogonality was tested by allowing them to correlate
using an oblique rotational procedure (i.e., Promax, Kappa = 4).
Factor Identification
The factors were interpreted based on the strength of the item loadings on
both the pattern and structure matrices. All items with factor loadings greater than or
equal to .30 were considered salient to the identification of a factor. Despite being a
fairly liberal criterion, this did not prove to be a problem when applied to the pattern
matrix, which exhibited a simple structure with all items loading on a single factor
only (see Table 5). Several secondary item loadings were observed on the structure
matrix (see Table 6). However, these were all consistent with the conceptual meaning
of the factor and how it was expected to relate to the items in the analysis.
Because the factors are allowed to correlate when rotated obliquely, the
variance a given variable shares with a particular factor is divided into two types:
76
Table 5. Pattern Matrix for the Full Sample (24 items, Promax, Kappa = 4)
Item Description
Tobacco, Alcohol & Marijuana Use
Drug use during school hours
Hard drug use
Driving while intoxicated
Shoplifting
Vandalism
Sexual activity
Values sexual restraint
Aggressive behavior
Most time in a single activity
Number of prosocial activities
Regular aerobic exercise
Unhealthy dieting practices
Needs help – Eating disorder
Needs help – Weight control
Worry about appearance
Worry about peer relations
Negative self regard
Prosocial/Culturally Sensitive Values
Civic Engagement
Commitment to learning
Consistent use of a seatbelt
How often suicidal past month
How often depressed past month
F1
F2
F3
F4
F5
F6
.980
.666
.576
.561
.459
.425
.397
-.364
.346
.849
.639
.488
.591
.562
.513
.675
.544
.407
.639
.467
.378
.350
.685
.580
Com
.806
.384
.347
.271
.267
.244
.333
.225
.324
.668
.624
.210
.370
.292
.357
.530
.308
.273
.337
.354
.519
.337
.469
.478
Blank < .3
variance that is unique to the factor and variance that is shared in common with other
factors. The squared item loadings on the structure matrix represent both unique
and common variance (i.e., the total proportion of variance in a given measure that is
related to a particular factor). In contrast, the squared item loadings on the pattern
matrix represent unique variance only (i.e., the proportion of variance in a given
measure related to a particular factor after the common variance has been removed).
77
Table 6. Structure Matrix for the Full Sample (24 items, Promax, Kappa = 4)
Factor 1
Tobacco, Alcohol & Marijuana Use
Drug use during school hours
Hard drug use
Commitment to learning
Sexual activity
Driving while intoxicated
Aggressive behavior
Shoplifting
Consistent use of a seatbelt
Values sexual restraint
Vandalism
Load
.884
.611
.566
-.556
.535
.512
.497
.493
-.472
-.444
.435
Factor 2
Most time in a single activity
Number of prosocial activities
Civic Engagement
Commitment to learning
Regular aerobic exercise
.813
.746
.444
.441
.402
Factor 3
Unhealthy dieting practices
Needs help – Weight control
Needs help – Eating disorder
Worry about appearance
How often depressed past month
How often suicidal past month
.601
.574
.535
.418
.388
.315
Factor 4
Worry about appearance
Worry about peer relations
Negative self regard
How often depressed past month
Needs help – Weight control
Load
.704
.541
.402
.357
.355
Factor 5
Commitment to learning
Number of prosocial activities
Prosocial/Culturally Sensitive Values
Civic Engagement
Consistent use of a seatbelt
Tobacco, Alcohol & Marijuana Use
Values sexual restraint
Aggressive behavior
Sexual activity
Shoplifting
Vandalism
Most time in a single activity
.635
.552
.551
.539
.507
-.362
.357
-.354
-.352
-.329
-.321
.315
Factor 6
How often suicidal past month
How often depressed past month
Negative self regard
Aggressive behavior
.682
.633
.338
.309
Based on the original frameworks used to select the variables, an examination
of the item loadings on the pattern (unique loadings) and structure (salient loadings)
matrices indicated that all eight items reflecting problem behaviors were primarily
78
related along a single dimension. Every problem indicator had both a unique and
salient positive loading on the first factor. Many problem indicators also had salient
secondary negative loadings on the fifth factor, indicating that these items were
negatively related to the factor.
With respect to adaptive behaviors, the items were split between the second
and fifth factors. While several of the items had salient loadings on both factors, each
item had unique loadings on only one of the two. The exception to this was the item
relating to sexual restraint, which had a salient loading with other adaptive behaviors
on the fifth factor, but did not load uniquely on either the second or fifth factor.
Rather, this item shared variance that was both unique and salient with the first factor.
This suggests that valuing restraint is a adaptive behavior that is specifically related to
problem behaviors.
The items relating to psychological distress were distributed across the third,
fourth and sixth factors. As with the items reflecting adaptive behaviors, certain
indicators had salient loadings on all three factors, indicating that they share variance
in common. However, each indicator also had a unique loading on only one of the
three factors. This indicates that while related, each of the three dimensions primarily
reflects a somewhat different aspect of psychological distress.
The first factor was identified as Problem Behaviors, while the second and
fifth factors were identified as Prosocial Activities and Adaptive Behaviors
respectively. The particular designations assigned to the three primary factors
representing psychological distress required greater interpretation and are therefore
79
presented with some reservations. Factor 3 was interpreted as reflecting symptoms
associated specifically with a Poor Body Image. Factor 4 was interpreted as reflecting
symptoms associated with Normative Anxiety (i.e., stress associated with a desire to
conform to social norms). Factor 6 was interpreted as representing Negative Affect.
Table 7 provides a summary of the names and representative items for each factor.
Table 7. Factor Identification with Representative Items
Label
F1 – Problem Behaviors
F2 – Prosocial Activities
F3 – Poor Body Image
F4 – Normative Anxiety
F5 – Adaptive Behaviors
F6 – Negative Affect
Representative Item
Tobacco, Alcohol and Marijuana Use
Most time spent in a single activity
Unhealthy dieting practices
Worry about appearance
Commitment to learning
How often suicidal in the past month
Pattern
Loading
.980
.849
.591
.675
.378
.685
Structure
Loading
.884
.813
.601
.704
.635
.682
Correlations Among the Primary Factors
The factor correlation matrix from the first-order factor analysis supports the
hypothesis that the primary factors are interrelated. However, an examination of the
correlations among the factors indicates that while the relationship between adaptive
behaviors and problem behaviors is fairly straightforward, the relationship between
adaptive behaviors and indicators of psychological distress is more complicated (see
Table 8). The general Adaptive Behaviors factor and the specific Prosocial Activities
factor were well correlated in a positive direction (r = .45), indicating that the two can
80
Table 8. Factor Correlation Matrix for the First Order Factors
Factors
F1 – Problem Behaviors
F2 – Prosocial Activities
F3 – Poor Body Image
F4 – Normative Anxiety
F5 – Adaptive Behaviors
F6 – Negative Affect
F1
F2
F3
F4
F5
F6
1.00
-.30
1.00
.20
-.12
1.00
-.24
.12
.38
1.00
-.50
.45
.02
.33
1.00
.31
-.15
.46
.18
-.17
1.00
be subsumed under a higher order dimension. In addition, the general Problem
Behaviors factor was negatively correlated with both Adaptive behaviors (r = -.50)
and Prosocial Activities (-.30), suggesting that it too is related, although negatively, to
the same latent construct.
However, the relationships between the primary factors reflecting
psychological distress and those reflecting adaptive behaviors and problem behaviors
did not emerge as expected. The relationship between the factor reflecting Normative
Anxiety (F4) and the factors reflecting Adaptive Behaviors (F5) and Problem
Behaviors (F1) was the reverse of what was anticipated. The factor for Adaptive
Behaviors was associated with greater Normative Anxiety (r = . 33), while the factor
for Problem Behaviors was associated with less Normative Anxiety (r = -.24).
81
Analysis of Second-Order Factor Structure
A second factor analysis was conducted to examine the structure of the higher
order relationships among the primary factors. The six first order factors included
three general factors (Problem Behaviors, Adaptive Behaviors and Normative
Anxiety) and three specific factors (Prosocial Activities, Poor Body Image and
Negative Affect). The correlation matrix relating these dimensions was used as the
input for the second order analysis. Using the matrix command in SPSS, a data file
was created based on the correlation matrix of the first order factors which was then
analyzed using an exploratory factor analysis procedure.
The results of the analysis are presented in Table 9. Both Kaiser’s Rule and
Cattell’s scree plot test indicated that two higher order factors, accounting for 64% of
the variance, should be retained. There was a clear drop in the eigenvalues between
the second and third factors (i.e., F2 = 1.71 and F3 = .776). The first higher order
factor was found to be positively associated with adaptive behaviors and normative
anxiety, and negatively associated with problem behaviors. Because of its positive
association with Normative Anxiety, the first factor was identified as External
Thriving. The second factor was found to be primarily associated with poor body
image, and included both negative affect and normative anxiety. This factor was
identified as Severe Psychological Distress manifested as a potential eating disorder.
When an oblique rotation was applied to the two higher order factors, they
were found to be uncorrelated (r = -.02). Thus, the type of psychological distress
82
Table 9. Higher Order Factor Structure – PAF, Promax (4)
Second Order Factors
First Order Factors
S-Matrix
P-Matrix
Com
1
Adaptive Behaviors
Problem Behaviors
Prosocial Activities
Normative Anxiety
.817
-.658
.495
.393
.818
-.655
.493
.403
.674
.460
.252
.404
2
Poor Body Image
Negative Affect
Normative Anxiety
.815
.563
.492
.814
.558
.500
.668
.387
*
Note: The correlation between the two second order factors = -.02
indicated by F2 is largely independent of External Thriving among youth in this
sample. The variance in Normative Anxiety was split between the two uncorrelated
factors. This suggests that Normative Anxiety may be associated with serious
psychological dysfunction, but is not a good indicator of the construct. Rather, it is
more likely that Normative Anxiety represent a more common and mild form of
internal distress. This view is supported by the correlations in Table 8, in which
Normative Anxiety demonstrates a relatively weak association with Negative Affect
(r = .18), despite the strong association between Negative Affect and Poor Body
Image (r = .46)
83
Summary of Findings
The goal of the factor analysis was to determine whether a general problem
behavior factor and a general adaptive behaviors factor could be identified, and
whether these factors were inversely related under a single higher order thriving
construct. Overall, the results support this view with respect to problem behaviors
and adaptive behaviors, but not with respect to indicators of mild or severe
psychological distress.
In the first order analysis, a general problem behavior factor was identified
among the six primary factors. In addition, two adaptive behaviors factors were also
identified at the primary level, one of which was a specific adaptive behaviors factor
(i.e., prosocial activities) and the other a general adaptive behaviors factor. The
degree of correlation between the these factors (r = .45) indicates that the two are
actually related under a single latent construct, but that the factor analysis responded
to the high internal consistency between the two variables for prosocial activities by
differentiating them apart from the rest. In the higher order analysis, these three first
order factors loaded together under a single higher order construct that was positively
related to adaptive behaviors and negatively related to problem behaviors. This higher
order construct is identified as External Thriving.
Prior to the analysis, the expectation was that thriving would incorporate a
negative relationship to psychological distress. However, among youth in the present
study this was not found to be the case. Moderate levels of Normative Anxiety were
84
positively related to External Thriving, and Severe Psychological Distress was found
to be largely independent of External Thriving. Thus, External Thriving does not
appear to provide a protective function against either forms of psychological distress
among youth in this sample.
Measures of External Thriving, Adaptive Behaviors and Problem Behaviors
A measure of external thriving was constructed to reflect the higher order
construct that was indicated by the negative relation between the general adaptive
behaviors factor and general problem behavior factor from the previous analysis. The
measure does not include items relating to severe psychological dysfunction, because
this dimension was found to be essentially unrelated to thriving in the previous
analysis. Furthermore the items relating to Normative Anxiety were also not included,
because they were found to be related to thriving in a manner that was conceptually at
odds with the definition of positive development. The decision to exclude behaviors
that are inwardly directed (i.e., internalizing) requires that the measure of thriving be
conceptually qualified as an indicator of external thriving. In other words, it reflects
behaviors and attitudes that a young person presents to the world and by which his or
her positive development is generally evaluated by society.
The standardized scores of six items reflecting adaptive behaviors (α = .71)
and six items reflecting problem behaviors (α = .74) were averaged to create a two
dimensional, twelve-item thriving index (α = .81). The individual subscale scores for
85
adaptive behaviors and problem behaviors were also computed. Each respondent
received an external thriving score, a score for adaptive behaviors, and a score for
problem behaviors. It should be noted that in the measure of external thriving, all of
the items reflecting problem behaviors were reverse scored so that higher values
indicated fewer problems, which is consistent with the definition of thriving used in
this study. However, for the measure of problem behaviors, the items were scored
positively, so that higher values indicate a greater intensity of problem behavior. A
more detailed description of the three measures and how they were constructed is
provided in Appendix D.
86
CHAPTER 7
Results – Part II
The Contextual Correlates of Thriving
In the second part of the study, a variable focused approach (see Masten, et
al., 1999) is used to examine the contextual correlates of thriving. In contrast to a
person focused approach, which examines differences between different groups of
people (e.g., via ANOVA), a variable focused approach is best suited for detecting
specific linkages between different variables by examining the pattern of covariance
between particular domains of outcome and specific predictors. The aim of this
analysis is to determine what type of environment factors (i.e., social assets or
contextual risks) demonstrate the strongest relationship to thriving, and whether the
same factors are equally important to each of its components (i.e., adaptive behaviors
and problem behaviors). Based on the theoretical distinction between risks and assets
made earlier, it was expected that a combination of contextual risks and social assets
would be significantly related to thriving overall, but when examined individually,
social assets would demonstrate a stronger association with adaptive behaviors, and
contextual risks would demonstrate a stronger association with problem behaviors.
87
Demonstrating that an empirical distinction of this sort exists would support
the proposal that risks and assets represent different types of processes (maladaptive
vs. adaptive), which are primarily related to different types of outcomes (problem
behaviors vs. adaptive behaviors). It also supports the argument that when specifying
an ecological model of the factors associated with positive youth development,
researchers should consider how contextual risks as well as social assets are related to
a youth’s overall level of thriving.
Zero-Order Correlations
A summary of the twenty-one measures reflecting social assets and contextual
risk factors that were included in the analysis is provided in Table 3 in the methods
chapter. The dependent measures of external thriving (12-items, α =.81), adaptive
behaviors (6-items, α =.71) and problem behaviors (6-items, α =.74) are described at
the end of the Chapter 6 and in Appendix D. The zero-order correlations between
each independent variable and the three dependent measures are presented in Table
10. When the independent variables were examined individually in relation to each of
the dependent variables, a clear pattern emerged. All of the items relating to social
assets were positively correlated with external thriving and adaptive behaviors, and
negatively correlated with problem behaviors. Similarly, all of the items relating to
contextual risks were negatively correlated with external thriving and adaptive
behaviors, and positively correlated with problem behaviors.
88
Table 10. Summary of Risk & Asset Indicators in Relation to Outcomes
Zero-Order Correlations
Adaptive Problem
Thriving Behaviors Behaviors
#Items
Alpha
Contextual Risks
Association with deviant peers
History of sexual abuse
History of physical abuse
Witnessing abuse
Victim of a crime
Harassment from kids at school
Racial bias from a teacher
Unsafe neighborhood
Adults at home smoke
Criminal activity in the family
Riding with an adult who is DWI
5
1
1
1
1
1
1
1
1
1
1
.80
-
-.65
-.08
-.19
-.31
-.27
-.08
-.13
-.17
-.28
-.26
-.26
-.49
-.02
-.10
-.19
-.16
-.02
-.12
-.16
-.26
-.20
-.18
.64
.12
.23
.35
.31
.12
.11
.13
.23
.25
.28
Social Assets
High quality school curriculum
Caring and supportive adults
Counseling by a school adult
Supportive non-parental adults
Diverse community resources
Parental monitoring
Parental support
Counseling by a parent
Parents value teen restraint
Peer support
2
7
1
1
9
4
2
1
4
1
.85
.70
.80
.85
.75
.78
-
.32
.35
.16
.17
.08
.48
.23
.29
.41
.33
.31
.34
.22
.18
.04
.45
.20
.31
.40
.31
-.24
-.28
-.06
-.13
-.10
-.40
-.21
-.20
-.33
-.28
r > .03 are significant at the p < .01 level ; n = 5238
Despite the consistency in the pattern of the relationships, the magnitude of
the correlations between certain contextual variables and outcomes was found to be
quite low (i.e., r < .10). In particular, “diverse community resources”, “history of
sexual abuse”, and “harassment from kids at school” demonstrated little or no relation
89
to either external thriving or adaptive behaviors, although they demonstrated a
slightly stronger association with problem behaviors. One item, “counseling by a
school adult”, did not demonstrate a significant relation to problem behaviors,
although it was moderately related to adaptive behaviors (r = .22).
On the other hand, several items demonstrated good associations (r >.40) with
at least two of the three outcomes of interest. These were “association with deviant
peers”, “parental monitoring”, and “parents value teen restraint”. Several items also
demonstrated moderate associations (r > .30) with at least two of the three outcomes
in the study. These were “high quality school curriculum”, “caring and supportive
adults”, “counseling by a parent”, “peer support” and “witnessing abuse”. The
general finding across most indicators was that social assets were more strongly
correlated with adaptive behaviors than with problem behaviors, and contextual risks
were more strongly correlated with problem behaviors than with adaptive behaviors.
In an ideal situation, the independent variables in a regression analysis are
unrelated. However, several variables in this study measure the same ecological
domain (e.g., parenting), which increases the likelihood that they are related. To
assess the degree of multivariate multicollinearity among the independent variables,
the tolerance for each risk and asset was examined. The tolerances ranged from .63
for “parental monitoring” to .90 for “riding with an adult who is driving while
intoxicated (DWI)”. This is well above the standard minimum of .20 that is generally
recommended for estimating the importance of the individual variables in the
regression model and maintaining the stability of the regression coefficients.
90
Hierarchical Regression Analysis for External Thriving
The correlates of external thriving were examined in a hierarchical multiple
regression procedure with external thriving as the dependent variable, and risks and
assets as the independent variables. On the first step of the regression analysis, three
demographic variables representing gender, race and parents education were entered
as control variables. This was done, because a preliminary examination of the data
indicated that group differences on these variables accounted for a significant
proportion of the total variance in all three outcomes of interest (see Table 11). The
categories for race were entered as four dummy variables with white youth as the
reference category. As a whole, the demographic variables (Model 1) accounted for a
significant proportion of the total variance in external thriving (F = 143.376, df = 6,
5231, p < .000; R2 = .141). While all three demographic variables were significant,
gender and parents education were especially relevant in relation to external thriving
(see Table 12). In particular, external thriving was found to be higher among females
and among youth with better-educated parents.
On the second step of the analysis, the full set of risk and social asset
indicators were entered. As illustrated in Table 12, the full risk and assets model
(Model 2A) accounted for a substantial proportion of the variance in external thriving
(F = 344.136, df = 21, 5210, p < .001; ∆R2 = .499) above what was already accounted
for by the demographics model (Model 1). At this point, the relative contributions of
the independent variables were considered. A variable was regarded as a unique
91
Table 11. Means and Standard Deviations of the Outcomes by Demographic Variables
Parents Education
Lower 20%
Middle 20%
Upper 20%
Eta2
External thriving
Adaptive behaviors
Problem Behaviors
-.22 (.60)
-.29 (.62)
.15 (.77)
.00 (.55)
.00 (.62)
.01 (.65)
.23 (.50)
.30 (.59)
-.16 (.55)
.08
.11
.03
Race
Afr.Amer
White
Hispanic
Eta2
External thriving
Adaptive behaviors
Problem Behaviors
-.19 (.55)
-.18 (.60)
.21 (.72)
(n = 273)
.02 (.57)
.02 (.64)
-.02 (.65)
(n = 4309)
-.11 (.58)
-.13 (.57)
.09 (.79)
(n = 134)
.01
.01
.01
Male (n = 2538)
Female (n = 2700)
Eta2
-.14 (.58)
-.19 (.62)
.08 (.72)
.13 (.53)
.18 (.60)
-.08 (.59)
.05
.08
.01
Gender
External thriving
Adaptive behaviors
Problem Behaviors
Note: Outcome measures are standardized to the mean of the full sample (n = 5238)
Eta2 = the proportion of total variance accounted for by group differences (p < .001 for all)
independent predictor of external thriving if a significant change in R2 occurred when
the variable was entered last into the equation. Six variables that failed to meet this
criteria were removed from the model in a backward elimination process which
considered both the magnitude of relationship between the variable and external
thriving, and the relationship of the variable to other independent variables in the
model.
On the first two steps of the elimination process, the variables relating to
“harassment from kids at school” and “sexual abuse” were removed. The beta
coefficients for both variables were not significant, their zero-order correlation with
92
Table 12. Comparisons Between Demographic and Full Risks and Assets Model
Model 1 (Tot R2 = .141)
Demo (∆R2 = .141)
Model 2A: (Tot R2 = .640)
Full R & A (∆R2 = .499)
Correlations
Variables in the Model
Gender
African American
Mixed Race
Other Race
Hispanic
Parents Education
Beta
Part
∆R
.24
-.05
-.05
.03
-.01
.28
.24
-.05
-.05
.03
-.01
.27
.06
.00
.00
.00
.00
.08
Correlations
2
Beta
Part
∆R2
Tol
.16
-.03
.01
.02
-.02
.11
.15
-.02
.01
.02
-.02
.10
.02
.00
.00
.00
.00
.01
.87
.88
.96
.93
.96
.83
-.46
.16
.12
.09
-.06
.07
-.06
.05
-.05
.05
-.04
.04
-.04
-.03
-.03
-.02
-.02
.01
-.01
-.01
.00
-.37
.13
.11
.08
-.06
.06
-.05
.05
-.05
.04
-.04
.03
-.03
-.03
-.03
-.02
-.02
.01
-.01
-.01
.00
.14
.02
.01
.01
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.66
.63
.80
.75
.90
.65
.80
.81
.84
.67
.81
.85
.87
.84
.79
.75
.87
.84
.89
.64
.90
Association with deviant peers
Parental monitoring
Parents value teen restraint
High quality school curriculum
Riding with an adult who is DWI
Caring & supportive adults
Witnessing abuse
Counseling by a school adult
Victim of a crime
Counseling by a parent
Adults at home smoke
Supportive non-parental adults
Racial bias from a teacher
Criminal activity in the family
History of physical abuse
Peer support
Diverse community resources
Unsafe neighborhood
History of sexual abuse
Parental support
Harassment from kids at school
Note1: Bold indicates p is not statistically significant; (n = 5238)
external thriving was low (i.e. < .10), and their tolerance was high (>.80). Together
these indicated that these two variables were not relevant to the model. On
subsequent steps, the variables relating to “parental support”, “unsafe neighborhood”,
93
“diverse community resources”, and “peer support” were also removed. These four
variables all demonstrated suppression effects in which the sign of their beta
coefficient was opposite to that of their zero-order correlation. The reversal of the
sign of the beta coefficient indicates that a variable has more in common with the
error variance in the other independent variables than the reliable variance in external
thriving.
To help clarify the relationship between these four variables and external
thriving with respect to the other independent variables in the model, the correlations
between the four variables and the other independent variables were examined.
Parental support was correlated with two other parenting variables, “parental
monitoring” (r = .44) and “counseling by parents” (r = .42), both of which where
unique independent predictors of external thriving. When either of these variables
were removed, the relationship between parental support and external thriving
became both positive and significant. Similarly, “peer support” was correlated with
the independent predictor “association with deviant peers” (r = -.43). When this
variable was removed, the relationship between peer support and external thriving
also became both positive and significant. Finally, when both “caring and supportive
adults” and “high quality school curriculum” were removed from the equation, the
sign of the beta coefficient associated with both “unsafe neighborhood” and “diverse
community resources” was consistent with their zero order correlations. However,
the magnitude of the beta coefficients for both were not significant.
94
Table 13. Reduced Risks and Assets Model vs. Model with Nonlinear Associations
Model 2B (Tot R2 = .639)
Reduced R & A (∆R2 = .498)
Model 3 (Tot R2 = .652)
Nonlinear (∆R2 = .013)
Correlations
Beta
Part
∆R
Gender
African American
Mixed Race
Other Race
Hispanic
Parents Education
.16
-.03
.01
.02
-.02
.11
.15
-.02
.01
.02
-.02
.10
.02
.00
.00
.00
.00
.01
Association with deviant peers
Parental monitoring
Parents value teen restraint
High quality school curriculum
Riding with an adult who is DWI
Witnessing abuse
Caring & supportive adults
Counseling by a school adult
Counseling by a parent
Victim of a crime
Adults at home smoke
Racial bias from a teacher
Supportive non-parental adults
History of physical abuse
Criminal activity in the family
-.45
.15
.12
.08
-.06
-.06
.06
.05
.05
-.05
-.04
-.03
.03
-.03
-.03
-.39
.13
.11
.07
-.06
-.05
.05
.05
.04
-.05
-.04
-.03
.03
-.03
-.03
.16
.02
.01
.01
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
Variables in the Model
Correlations
Part ∆R2
Tol
.16
-.03
.00
.02
-.02
.11
.16
-.03
.00
.02
-.02
.10
.03
.00
.00
.00
.00
.01
.92
.88
.96
.93
.96
.83
-.44
.14
.13
.08
-.05
-.05
.06
.05
.05
-.05
-.04
-.04
.04
-.03
-.03
-.38
.12
.11
.07
-.05
-.05
.05
.05
.04
-.04
-.04
-.03
.03
-.03
-.03
.15
.01
.01
.01
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.75
.67
.80
.77
.90
.80
.70
.82
.71
.86
.82
.89
.87
.87
.85
-.10
.02
-.10
.02
.01
.00
.86
.84
2
Beta
Quadratic deviant peers
Deviant peers x monitoring
Note: Bold indicates p is not statistically significant; (n = 5238)
As indicated in Table 13, the reduced risks and social assets model (Model
2B) was significant (F = 479.646, df = 15, 5216, p < .001), and accounted for a
similar proportion of the variance in external thriving (∆R2 = .498) as the full risks
and social assets model (Model 2A, ∆R2 = .499). Together, Model 1 (demographics)
95
and Model 2B (reduced risks and social assets) accounted for nearly two thirds of the
variance in external thriving (R2 = .64). When examined without the demographic
indicators, the reduced risks and social assets model (Model 2B) accounted for only
slightly less of the variance than the two models together (R2 = .60), this indicates
that the two models share in common about 10% of the variance in external thriving.
Of particular interest to this study was the nature of the indicators that
remained in the model. The primary question guiding the analysis was whether the
unique correlates of external thriving are best understood as a combination of risks, a
combination of assets, or both. The answer appears to be both. Of the 15 remaining
items, 8 were designated as contextual risks, and 7 were designated as social assets
prior to the analysis. By far, the single indicator that demonstrated the strongest
relationship to the criterion was the main effect for “association with deviant peers”,
which accounted for approximately 15% of the variation in external thriving (see
Table 13). This finding is consistent with previous research (e.g., Leffert et al., 1999),
and indicates that during adolescence peers can have a powerful influence on
behavior, which can be especially problematic when that influence is negative or
deviant.
Both the second and third strongest predictors were social assets relating to
parenting (i.e., “Parental monitoring” and “Parents value teen restraint”), which
together accounted for about 3% of the unique variance in external thriving.
Although the remaining risks and assets were significant, the magnitudes of their
unique contributions were less then 1% for each variable. However, it must be
96
remembered that these values reflect only the unique contribution of each item, and
do not reflect the items total contribution to the explained variance.
Given the strong effect that association with deviant peers has with external
thriving, the decision was made to further explore this relationship by examining two
nonlinear associations. The first was the quadratic association of increased exposure
to deviant peers, and the second was the interaction between exposure to deviant
peers and parental monitoring. To reduce the correlation between the main effect for
association with deviant peers and the interaction term (r = .94), the variable for
parental monitoring was centered prior to computing the interaction. A variable is
centered by subtracting the average score on the variable from the score of every
case. A linear transformation of this sort does not in any way alter the main effect
relationship between the parental monitoring and external thriving. It does, however,
eliminate the high correlation between the product term and the main effect for
association with deviant peers (r = -.07).
When entered together, the nonlinear associations (Model 3) accounted for a
significant proportion unique variance in external thriving beyond what was
accounted for by the two previous models (F = 93.636, df = 2, 5214, p < .001; ∆R2 =
.013). When examined individually, each was also significant, although the quadratic
association of increased exposure to deviant peers accounted for a greater proportion
of the unique variance in external thriving than the interaction term when entered last
into the analysis (see Table 13). The sign of the regression coefficient for the
quadratic association was negative, indicating that increasing exposure to deviant
97
peers was related to an exponential decline in external thriving. On the other hand, the
sign of the interaction term was positive, indicating that higher levels of parental
monitoring dampens, but does not eliminate the negative relationship between
association with deviant peers and external thriving.
Hierarchical Regression for Adaptive Behaviors
Two separate hierarchical regression analyses were conducted to determine
which of the social assets and contextual risk factors related to external thriving, were
also important in relation to the individual subcales of adaptive behaviors and
problem behaviors, respectively. In the first analysis, the 17 significant predictors of
external thriving were entered as independent variables in a hierarchical regression
analysis with adaptive behaviors as the criterion. The same was done in a second
hierarchical regression analysis with problem behaviors as the criterion. As with the
analysis for external thriving, the three demographic variables representing gender,
race and parents’ education were entered on the first step in each analysis. Since all
seventeen independent variables had already demonstrated a significant association
with external thriving, they were entered into the analyses as a block on the second
step and the results were evaluated in terms of the relative importance of each
indicator to the explanation of the total variance in the dependent variable after
controlling for the other independent variables in the model.
98
Table 14. Regression Results for Risk and Social Asset Indictors of Adaptive Behaviors
Total R2 = .529 (∆R2 = .332)
Correlations
Variables in the Model
SE (B)
Part
∆R2
Tol
.30
-.06
.00
.11
-.06
.07
.01
.03
.03
.03
.04
.00
.22
-.02
.00
.04
-.01
.17
.05
.00
.00
.00
.00
.03
.92
.88
.96
.93
.96
.83
-.27
.11
.10
.06
.09
.10
.03
-.04
.02
-.02
-.02
.03
.01
-.01
-.01
-.02
.00
.01
.01
.01
.01
.01
.01
.01
.01
.00
.01
.01
.02
.01
.01
.01
.02
.01
-.27
.14
.12
.09
.08
.07
.05
-.04
.04
-.02
-.02
.02
.02
-.01
-.01
-.01
.00
.07
.02
.02
.01
.01
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.75
.80
.67
.82
.77
.70
.71
.82
.87
.89
.86
.87
.86
.84
.90
.85
.80
Beta
B
Gender
African American
Mixed Race
Other Race
Hispanic
Parents Education
.23
-.02
.00
.04
-.01
.18
Association with deviant peers
Parents value teen restraint
Parental monitoring
Counseling by a school adult
High quality school curriculum
Caring & supportive adults
Counseling by a parent
Adults at home smoke
Supportive non-parental adults
Racial bias from a teacher
Quadratic deviant peers
History of physical abuse
Victim of a crime
Deviant peers x monitoring
Riding with an adult who is DWI
Criminal activity in the family
Witnessing abuse
-.31
.16
.15
.10
.10
.08
.05
-.04
.04
-.03
-.02
.02
.02
-.01
-.01
-.01
.00
Note: Bold indicates p is not statistically significant; (n = 5238)
In the regression analysis for adaptive behaviors, the reduced risk and social
assets model was significant, with the independent variables accounting for about a
third of the variance in the subscale beyond what was accounted for by the
demographic variables alone (F = 216.027, df = 17, 5214, p < .000; ∆R2 = .332). The
results of the regression analysis for adaptive behaviors are presented in Table 14. It
99
should be noted that even after controlling for the independent variables in the model,
the demographic variables associated with gender and parents education in particular
continued to demonstrate a significant unique association with adaptive behaviors.
Specifically, females and youth with better-educated parents reported more adaptive
behaviors than males and youth with less well educated parents.
To evaluate the importance of each independent variable, the indicators were
examined with respect to the amount of unique variance each explained when entered
last into the equation. Five variables were found to be uniquely associated with at
least 1% of the variance in adaptive behaviors beyond what was already accounted
for by the other variables in the model. These were: (a) association with deviant peers
(β = -.31), (b) parents value restraint (β = .16), (c) parental monitoring (β = .15), (d)
counseling about the future by an adult at school (β = .10), and (d) having a high
quality school curriculum (β = .10). With the exception of “association with deviant
peers” all of these items represent social assets. This is interpreted as indicating that
social assets are especially important in relation to a youth’s expression of adaptive
behaviors.
In addition, several items that were significantly associated with external
thriving were not significantly related to adaptive behaviors. For the most part, these
reflected various types of contextual risks, and included: (a) riding with an adult who
was driving while intoxicated, (b) criminal activity of a family member, and (c)
witnessing abuse. In addition, the interaction between association with deviant peers
and parental monitoring was not significant. This indicates that a high level of
100
parental monitoring does not significantly alter the negative relationship between
increased exposure to deviant peers and the expression of adaptive behaviors.
Hierarchical Regression for Problem Behaviors
In the hierarchical regression analysis for problem behaviors, the reduced
contextual risk and social assets model was significant, with the independent
variables accounting for more than half of the variance in the subscale beyond what
was accounted for by the demographic variables alone (F = 370.884, df = 17, 5214, p
< .000; ∆R2 = .520). The results of the regression analysis for problem behaviors are
presented in Table 15. It should be noted that after controlling for the independent
variables in the model, none of the demographic variables demonstrated a strong
unique association with problem behaviors. In fact, parents’ education, which was
quite important in the relationship with adaptive behaviors, was no longer
significantly related to problem behaviors after the relationship with the other
independent variables was controlled.
To evaluate the importance of each independent variable, the indicators were
examined with respect to the amount of unique variance each explained when entered
last into the equation. As with the analysis for adaptive behaviors, five variables were
found to be uniquely associated with at least 1% of the variance in problem behaviors
beyond what was already accounted for by the other variables in the model. These
were: (a) the linear and quadratic association with deviant peers (βL= .46, βQ= .16),
101
Table 15. Regression Results for Risk and Social Asset Indictors of Problem Behaviors
Total R2 = .570 (∆R2 = .520)
Correlations
Variables in the Model
SE (B)
Part
∆R2
Tol
-.08
.10
-.02
.01
.09
-.01
.01
.03
.03
.03
.04
.00
-.06
.03
-.01
.00
.02
-.02
.00
.00
.00
.00
.00
.00
.92
.88
.96
.93
.96
.83
.41
.16
.07
.07
-.07
.06
.12
-.05
-.05
-.05
.07
.03
-.02
.02
-.01
-.03
.01
.01
.01
.01
.01
.01
.01
.02
.01
.01
.01
.02
.01
.01
.01
.00
.01
.01
.40
.14
.09
.09
-.08
.08
.06
-.06
-.05
-.04
.04
.04
-.03
.03
-.02
-.02
.01
.16
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.75
.86
.86
.80
.67
.90
.87
.80
.84
.77
.85
.89
.71
.82
.87
.70
.82
Beta
B
Gender
African American
Mixed Race
Other Race
Hispanic
Parents Education
-.06
.03
-.01
.00
.02
-.02
Association with deviant peers
Quadratic deviant peers
Victim of a crime
Witnessing abuse
Parental monitoring
Riding with an adult who is DWI
History of physical abuse
Parents value teen restraint
Deviant peers x monitoring
High quality school curriculum
Criminal activity in the family
Racial bias from a teacher
Counseling by a parent
Adults at home smoke
Supportive non-parental adults
Caring & supportive adults
Counseling by a school adult
.46
.16
.10
.09
-.10
.08
.07
-.07
-.06
-.05
.04
.04
-.04
.03
-.02
-.02
.01
Note: Bold indicates p is not statistically significant
(b) being the victim of a crime (β = .10), (c) witnessing physical abuse (β = .09), (d)
parental monitoring (β = -.10), and (d) riding with an adults who is driving while
intoxicated (β = .08). It should be noted that with the exception of “parental
102
monitoring” all of these items represent contextual risks. This is interpreted as
indicating that contextual risks are especially important in relation to a youth’s
expression of problem behaviors.
Only one variable that was significantly associated with external thriving was
not significantly related to problem behaviors. This was the item relating to
counseling by an adult at school. In addition, unlike its relation to adaptive behaviors,
the interaction between parental monitoring and association with deviant peers was
significant in the analysis for problem behaviors. That is, high levels of parental
monitoring dampened the relationship between association with deviant peers and a
youth’s expression of problem behaviors. However, the strongest association by far
was for the combined linear and quadratic effect for associating with deviant peers.
When entered together last in the analysis, these two accounted for more than 19% of
the variance in problem behaviors above what was already accounted for by both the
demographic and other independent variables in the model (F=1172.989, df = 2,
5214, p < .000; ∆R2 = .194).
Summary of Findings
The aim of the hierarchical regression analysis was to determine what type of
environment factors (i.e., social assets or contextual risks) demonstrate the strongest
relationship to external thriving, and whether the same factors are equally important
to each of its components (i.e., adaptive behaviors and problem behaviors). It was
103
expected that a combination of risks and social assets would be significantly related
to external thriving overall, but when examined individually, social assets would
demonstrate a stronger association with adaptive behaviors, and contextual risks
would demonstrate a stronger association with problem behaviors.
In general, the findings indicate that these expectations were confirmed. With
respect to external thriving, fifteen of the twenty-one contextual factors that were
included in the analysis demonstrated a significant unique association with external
thriving, and together accounted for nearly two thirds of the variance in the outcome
(R2 = .64). Prior to the analysis, eight of the fifteen correlates were designated as
risks, and seven were designated as social assets. Thus, it appears that the correlates
of external thriving are best understood as a combination of contextual risks and
social assets. By far, the single indicator that demonstrated the strongest relationship
to external thriving was the main effect for “association with deviant peers”, which
accounted for approximately 16% of the variation in the outcome after controlling for
the other variables in the analysis. A post hoc examination of the relationship between
this variable and external thriving indicated that the relationship is more than linear,
increasing exponentially as the intensity of involvement with deviant peers increases.
However, it was also found that this relationship could be mitigated to a certain extent
by high levels of parental monitoring.
In separate regression analyses using the same set of independent variables, it
was found that the variables that were most strongly associated with adaptive
behaviors differed from those that were most strongly associated with problem
104
behaviors. In particular, social assets (e.g., parental monitoring, parents who value
teen restraint, and counseling by a school adult) were found to be especially relevant
to adaptive behaviors, while contextual risks (e.g., high intensity of involvement with
deviant peers, being the victim of a crime, and witnessing abuse) and were more
strongly associated with problem behaviors. This supports the argument that different
factors may be important to the development or maintenance of different types of
outcomes among adolescents. One noteworthy difference between the two regression
results was that the strength of the combined main effect and quadratic relationship
for association with deviant peers, which was the strongest correlate of both problem
behaviors and adaptive behaviors, was found to be much weaker in relation to
adaptive behaviors (∆R2 = .07) than in relation to problem behaviors (∆R2 = .18).
This suggests that deviant peers are more likely to encourage problem behaviors than
to discourage adaptive behaviors.
105
CHAPTER 8
Results – Part III
Mediation Analysis
The final analysis is largely an exploratory exercise to determine whether
support exists for the view that social assets are indirectly related to problem
behaviors through their positive relationship to adaptive behaviors. The rationale for
this analysis is as follows. A primary assumption of the PYD approach is that social
assets foster positive youth attributes, which in turn reduce problem behaviors. While
it is better to test this assumption using longitudinal data, one can make the argument
that if correct, the indirect effect of social assets should be observed concurrently in
the behavior and attitudes of older adolescents who have had time to internalize the
values and constraints of society, who are generally more self directed, and who will
soon be moving beyond the direct influence of conventional social institutions such as
the family, school and community based youth organizations.
In order to examine mediation, a causal sequence of this sort must be
hypothesized whereby an independent variable is said to produce changes in a
mediating variable that in turn affects the dependent variable. However, attributing
106
a direction of causality to variables in anything other than a controlled experiment is
always problematic, because even if the data is longitudinal, the possibility exists that
some third factor not measured in the study is the true causal agent in the sequence.
Whenever mediation is tested in a study based on a nonexperimental design,
what is being examined is the significance of the indirect relationship between an
independent variable and a dependent variable via a third variable that is related to
both. With respect to the assumption of the PYD approach, demonstrating that a
significant proportion of the relationship between social assets and problem behaviors
can be accounted for by adaptive behaviors would provide some initial support for the
hypothesized mediating relationship. A failure to find such a relationship would
indicate that at least among the youth in this study, the mediating role of adaptive
behaviors does not appear to be evident.
Throughout the course of the previous analyses, several of the statistical
conditions required for testing mediation were satisfied (see Baron & Kenny, 1986).
A set of social assets was identified that demonstrated a significant relationship to
both adaptive behaviors and problem behaviors. In addition, the dimensional analysis
indicated that adaptive behaviors are negatively related to problem behaviors. In this
part of the study, the relationship between adaptive behaviors and problem behaviors
is again examined, but in the context of the covariates from the previous analyses
(i.e., contextual risks and social assets). An indication that mediation is occurring
would be a reduction in the strength of the relationship between social assets and
problem behaviors when adaptive behaviors are included in the analysis. If the
107
strength of this relationship is no longer significant, then full mediation is implied. If
the relationship is reduced but not eliminated, then the effect is one of partial
mediation.
The magnitude of the reduction (c – c’) is the size of the indirect relationship
(see MacKinnon & Dwyer, 1993). However, to determine whether the amount of
reduction is significant requires a measure of the standard error of the difference. The
value of the standard error for the indirect relationship was derived by Sobel (1982),
and is based on the fact that the magnitude of the indirect relationship is exactly
equivalent to the product of two unstandardized regression coefficients (a*b): (a) the
regression coefficient relating the mediator to the dependent variable after adjusting
for the independent variable and any covariates, and (b) the regression coefficient
relating the independent variable to the mediator after adjusting for the same set of
covariates. As long as multiple regression is used, there is no missing data, and the
same covariates are in the equation, the two will be exactly equal (Kenny, 2006).
Hierarchical Regression with Adaptive Behaviors as the Mediator
To examine the mediating role of adaptive behaviors, a hierarchical regression
analysis was conducted with problem behaviors as the dependent variable. The first
two steps of the analysis were identical to the analysis presented in Table 15. On the
third step, the variable assessing adaptive behaviors was included. The results for
adaptive behaviors were significant (F = 188.822, df = 1, 5213, p < .000; ∆R2 = .015)
108
Table 16. Coefficients in the Hierarchical Regression Analysis for Problem Behaviors
Step 2: IV & Covariates
Step 3: Add Mediator
∆R2 = .015
Tot R2 = .585
2
Tot R = .570
SE(B3)
∆B
-.05
-.03
-.03
-.01
-.01
-.01
.01
.01
.01
.01
.00
.01
-.02
-.02
-.02
-.01
-.00
-.02
-.02
.03
-.01
.01
.02
.02
-.02
.09
-.02
.03
.08
.01
.01
.03
.03
.03
.04
.00
-.06
-.01
.00
-.02
-.01
.02
.01
.01
.01
.01
.01
.02
.01
.02
.01
.01
.01
.41
.15
.10
.09
.08
.07
-.06
.04
.03
.03
.02
.36
.15
.07
.07
.06
.13
-.05
.07
.03
.02
.02
.01
.01
.01
.01
.01
.02
.01
.02
.01
.01
.01
-.05
-.01
.00
.00
.00
.01
.00
.00
.00
.01
.00
---
-.18
-.18
.01
---
Beta
B2
SE(B2)
Beta
Independent Variables
Parental monitoring
Parents value teen restraint
High quality school curriculum
Counseling by a parent
Supportive non-parental adults
Caring & supportive adults
-.10
-.07
-.05
-.04
-.02
-.02
-.07
-.05
-.05
-.02
-.01
-.03
.01
.01
.01
.01
.00
.01
-.07
-.04
-.03
-.03
-.02
-.01
Covariates
Gender
African American
Mixed Race
Other Race
Hispanic
Parents Education
-.06
.03
-.01
.00
.02
-.02
-.08
.10
-.02
.01
.09
-.01
.01
.03
.03
.03
.04
.00
Association with deviant peers
Quadratic deviant peers
Victim of a crime
Witnessing abuse
Riding with an adult who is DWI
History of physical abuse
Deviant peers x monitoring
Criminal activity in the family
Racial bias from a teacher
Counseling by a school adult
Adults at home smoke
.46
.16
.10
.09
.08
.07
-.06
.04
.04
.01
.03
.41
.16
.07
.07
.06
.12
-.05
.07
.03
.01
.02
---
---
Mediator
Adaptive Behaviors
Note: Bold indicates p is not statistically significant; (n = 5238)
B3
109
and are presented in Table 16. According to Baron and Kenny (1986), an independent
variable must be significantly related to both the mediator and the dependent variable
in order for mediation to exist. While all seven social assets were significantly related
to adaptive behaviors (see Chapter 7, Table 14), the social asset reflecting
“counseling by an adult at school” was not significantly related to problem behaviors
(see Chapter 7, Table 15). Therefore, only six variables satisfied all of the conditions
for mediation. These were: (a) parental monitoring, (b) parents value teen restraint,
(c) high quality school curriculum, (d) counseling by parents, (e) caring and
supportive adults, and (f) supportive non-parental adults.
Including adaptive behaviors in the regression equation reduced the size of the
unstandardized regression coefficients for all of these variables (see ∆B in Table 16).
Furthermore, two social assets that were significantly related to problem behaviors in
the previous step were no longer significant when adaptive behaviors were entered
into the equation. This indicates that adaptive behaviors fully mediated the
relationship between these variables and problem behaviors. The two variables were
“caring and supportive adults” and “supportive non-parental adults”. It is interesting
to note that when adaptive behaviors were controlled, the difference in problem
behaviors relating to gender was no longer significant, and the main effect of
association with deviant peers was substantially reduced. Equally interesting is that
adaptive behaviors functioned as a suppressor variable with respect to “counseling by
an adult at school” by reducing the error variance in the covariate such that the
proportion of unique variance was now significant.
110
The significance of the indirect relationships associated with each of the six
independent variables in the analysis were tested using the Sobel method for
calculating the standard error of the mediated effect. Several versions of the Sobel test
statistic are available. The specific test statistic used in this analysis is sometimes
referred to as the Goodman (I) test, and includes the additional term in the
denominator that reflects the product of the variance of the two coefficients in the
computation of the standard error (see MacKinnon & Dwyer, 1993; Preacher &
Hayes, 2006). The magnitude of the indirect effect was significant for all six
variables. This indicates that adaptive behaviors at least partially mediate the
relationship between social assets and problem behaviors. Table 17 lists the results of
the analysis, as well as the percent of the total relationship that was mediated. Note
that the columns in Table 17 labeled “indirect 1” and “indirect 2” represent the two
different methods for calculating the size of the indirect relationship and are
equivalent. The values differ slightly from Table 16 due to rounding differences.
The column labeled “percent mediated” refers to the proportion of the total
effect that is mediated, which is determined by dividing the indirect effect by the total
effect (i.e., the value of the regression coefficient relating the independent variable to
the dependent variable when the mediator is not in the equation). This proportion
provides a relative measure of the strength of the mediated relationship. For example,
62% of the relationship between caring and supportive adults and the expression of
problem behaviors was mediated by a youth’s level of adaptive behaviors, after
controlling for the other variables in the analysis.
111
Table 17. Results of the Significance Tests of the Indirect (Mediated) Relationship
DV = Problem Behaviors
Independent Variables
Total
B(c)
Direct
B(c')
Indirect 1
(c - c')
Percent
Mediated
Parental monitoring
Parents value teen restraint
High quality school curriculum
Counseling from parents
Supportive non-parental adults
Caring & supportive adults
-.071
-.048
-.047
-.020
-.011
-.030
-.052
-.028
-.031
-.015
-.007
-.012
-.019
-.020
-.016
-.005
-.003
-.019
26.8
42.2
33.9
26.5
31.3
61.8
B(a)
-.185
Adaptive behaviors
DVM = Adaptive behaviors
Independent Variables
B(b)
Indirect 2
(a*b)
SE(a*b)
Z-Value
Parental monitoring
Parents value teen restraint
High quality school curriculum
Counseling from parents
Supportive non-parental adults
Caring & supportive adults
.104
.110
.086
.029
.018
.101
-.019
-.020
-.016
-.005
-.003
-.019
.002
.002
.001
.003
.001
.001
-9.468
-10.037
-7.455
-4.507
-3.703
-6.131
Note: Z-Values are all significant at p < .001
Summary of Findings
The findings indicate that among youth in this sample, there is some empirical
support for the mediating role of adaptive behaviors in the relationship between social
assets and problem behaviors. The indirect relationship for all six social assets was
significant, indicating that adaptive behaviors mediate a significant proportion of the
112
relationship between these variables and the expression of problem behaviors.
Adaptive Behaviors fully mediated the relationship between two social assets:
“support from non-parental adults”, and “caring and supportive adults”, both of which
are community level variables. The strength of the mediated relationship was greatest
for the variable “caring and supportive adults”. Sixty-two percent of the relationship
between this variable and the expression of problem behaviors was mediated by a
youth’s level of adaptive behaviors after controlling for the other variables in the
analysis.
113
CHAPTER 9
DISCUSSION
The primary purpose of this study was to gain a better understanding of the
structure and contextual correlates of positive development, or thriving, among older
adolescents. Lerner et al. (2004) identify “thriving” as part of the new vision and
vocabulary of PYD that has emerged in recent years for discussing America’s young
people. As a multidimensional construct, thriving focuses our attention on what we
want and do not want for young people, allows us to judge how well they are doing
developmentally, and challenges us to consider what all adolescents need to
experience and avoid in order to develop in a healthy and productive manner. It is
believed that an examination of the correlates of thriving among youth who are
nearing adulthood provides valuable insight into what may be key characteristics of
the individual and the social context that are important to measure and track in studies
examining the development of thriving over time.
However, one of the challenges to gaining a better understanding of the
ecological basis of thriving is the discrepancy between how the construct is generally
defined and how it has been operationalized in past research. Although scholars have
defined thriving as both the presence of adaptive behaviors and the absence of
114
problem behaviors, researchers have generally considered only positive attributes in
their assessments of thriving (e.g., Scales et al., 2000; Lerner et al., 2005). It is
believed that failing to consider problem behaviors in an assessment of thriving not
only undermines the original definition of the construct, but also has the potential to
overestimate the quality of a youth’s development.
An important contribution of this study is its attention to the development of
the person as a whole. That is, rather than focusing exclusively on either positive or
negative attributes of the individual, this study begins with the premise that adaptive
behaviors and problem behaviors, while inversely related, are not mutually exclusive
and do not necessarily share the same etiology. As a result, the factor analyses was
able to go beyond replicating previous research on the co-occurrence of adaptive
behaviors and externalizing problem behaviors and demonstrate that these two
dimensions can be subsumed under a higher order thriving construct that provides a
more complete view of what young people are like. The findings indicate that many
adolescents who engage in problem behaviors also exhibit positive qualities that are
valued in our society. This supports the view that a two part strategy that focuses on
enhancing adaptive behaviors and reducing problem behaviors has the best chance of
increasing the potential for all youth to make a successful transition into adulthood.
The study also raises questions about how different attributes of the individual
are expected to relate to one another. In particular, with regard to the association
between internalizing problem behaviors and external thriving, the findings suggest
that these dimensions are either unrelated or positively related in a manner that is
115
inconsistent with expectations based on the literature. While more work is needed to
clarify these results, they nevertheless illustrate the importance of examining general
assumptions about the relations among diverse attributes of the individual.
By limiting how key constructs such as “risks” and “assets” are defined, this
study also adds to our understanding of how specific forms of interaction between the
developing person and the social context are related to different types of youth
outcomes. It is believed that maintaining such distinctions is important, because the
absence of a behavior indicates the absence of a developmental process, while the
presence of a behavior (good or bad) indicates that an adaptive or maladaptive
developmental process is at work. Adaptive developmental processes (i.e., social
assets) were found to be primarily associated with the expression of adaptive
behaviors. This suggests that such resources and experiences are particularly relevant
to the development of positive outcomes among adolescents. Similarly, maladaptive
developmental processes (i.e., contextual risks) were found to be primarily associated
with the expression of problem behaviors. The close relationship between traumatic
experiences or deviant associations and the development of problem behaviors among
youth provides a clear indication of the need to attend to risks if the goal is to prevent
negative outcomes.
Finally, this study sought to bridge the gap between developmental theory and
its application to real world issues. Rather than relying on empirical definitions,
contextual risks and social assets were identified based on what developmental theory
tells us about the sorts of experiences that promote the development of both positive
116
and negative forms of social and mental behavior in a person. Furthermore, by
focusing primarily on contextual factors, the correlates of thriving, adaptive behaviors
and problem behaviors examined in this study provides information to practioners
that is relevant to not only identifying those youth who may be in need of assistance,
but also helps to identify what aspects of the social context may wish to target in
order to maximize the likelihood of influencing youth outcomes.
The three analyses presented in this study represent steps along the way
toward a better understanding of the underlying structure of thriving and the nature of
its correlates. For the most part, they were conducted in order to illustrate what are
believed to be important conceptual considerations regarding the nature of
developmental outcomes and the environmental processes that are associated with
them. Unfortunately, the data used in this study were not originally collected with
these specific intentions in mind, and as a result, the depth and breadth of certain
measures of the constructs examined in this study are limited. While a wide variety of
developmental processes are considered, it is likely that other processes that were not
examined (e.g., connections to prominent members of the community) will prove to
be important in future assessments of the ecological basis of thriving in adolescence.
The Structure of Thriving
In this study, the structure of thriving as a multidimensional construct was
considered by examining the co-occurrence of multiple types of problem behaviors,
117
adaptive behaviors, and symptoms of internal distress among adolescents using an
exploratory factor analysis procedure. In previous studies, researchers have used
similar techniques to examine the co-occurrence of problem behaviors (see
Willoughby et al., 2004) and adaptive behaviors (Lerner, et al., 2005) among
adolescents, but none have considered the two simultaneously, or have considered
them together with indicators of psychological dysfunction. At the primary level of
the factors, the findings from this study generally replicated those of previous
researchers. Various types of adaptive behaviors and problem behaviors were found
to co-occur to the extent that a general adaptive behavior factor and a general problem
behavior factor could be identified. In addition, by examining these factors in the
same analysis, it was possible to demonstrate that these two dimensions are inversely
related under a higher order thriving construct.
The general problem behavior factor identified in this study includes many
problem behaviors that have been identified in past research as being particularly
detrimental to the health and well-being of adolescents. These include: substance use,
driving while intoxicated, shoplifting, risky sexual activity and aggressive behavior.
The youth in this sample report rates of high intensity involvement in these problem
behaviors that fall within a range of 10% to about 40%. These rates of involvement,
along with the findings that these problem behaviors share from 18% to 78% of their
total variance with the problem factor, indicates that these types of problem behaviors
co-occur to some extent among youth in this sample. This finding replicates previous
118
findings regarding the co-occurrence of problem behaviors among adolescents in
general (see Willoughby et al., 2004).
With respect to adaptive behaviors, apart from two recent studies (Lerner, et
al., 2005; Theokas, et al., 2005), researchers have not examined the co-occurrence of
adaptive behaviors among youth. However, these two studies indicate that a general
higher order latent factor reflecting a variety of adaptive behaviors factor can be
identified using both confirmatory and exploratory factor analysis methods. In the
present study, both a general adaptive behavior factor and a more specific adaptive
behavior factor were identified. The correlation between these two factors (r = .45)
indicates that they can be subsumed under a higher order adaptive behaviors
construct.
The adaptive behavior factors identified in this study include values and
activities that are associated with multiple domains of positive youth development
(see Eccles & Gootman, 2002). The general factor includes: prosocial and culturally
sensitive values, civic engagement, commitment to learning, and health maintenance
behaviors. The more specific factor includes: number of prosocial activities, longest
time spent in a particular activity, and physical exercise. More than half of the youth
in the sample reported high levels of involvement on many of these variables, and the
amount of variance each variable shared with the factor was generally greater than
25% of its total. These findings indicate that among youth in this sample, adaptive
behaviors are even more likely to co-occur among adolescents than are problem
behaviors. This is significant both to our understanding of what young people are
119
really like and how they may serve as resources within the community. It also
supports the arguments made by PYD scholars that an overemphasis on problem
behaviors has the tendency to unfairly characterize young people as inherently
problem prone (e.g., see Lerner et al., 2002).
The primary purpose of the factor analysis was to determine whether a higher
order thriving construct could be identified from among the primary factors. The
negative correlation between the general problem behaviors factor and the general
adaptive behaviors factor indicated that the two shared about 25% of their variance in
common. The secondary analysis indicated that the two were inversely related under
a higher order latent construct identified as External Thriving. Thus, the findings
support the conceptual definition of thriving as “the engagement in prosocial
behaviors and avoidance of health-compromising and future-jeopardizing behaviors”
(Roth et al., 1998; p. 426). However, when the findings for the particular internalizing
problem behaviors examined in this study were considered, the idea that positive
youth attributes are inversely related to signs of pathology (Scales et al, 2000) or
internalizing problems (Lerner, et al, 2005) was not supported. Here the findings
indicate that symptoms of internal distress were either unrelated to external thriving,
or were more likely to occur as external thriving increased.
One possible explanation for the lack of a relationship between the two higher
order factors representing External Thriving and Severe Psychological Distress is that
only a small fraction of youth in this study reported experiencing high levels of the
form of psychological distress that is indicated by the factor. For example, very few
120
youth overall reported they engaged in unhealthy dieting practices (6.7%), felt the
need for help with an eating disorder (4.7%), or reported feeling suicidal most of all
of the time in the past month (1.7%). These variables all had the highest loading on
their primary factors, which then loaded together on the higher order factor for Severe
Psychological Distress. It is proposed that this higher order factor may not
characterize the co-occurrence, but rather abstinence or noninvolvement of youth in
these behaviors (see Willoughby et al., 2004). If this is the case, then the lack of a
relationship between External Thriving and Severe Psychological Distress may
simply indicate that adolescents in this sample are equally unlikely to exhibit the form
of internal distress that is indicated by the factor at all levels of External Thriving.
With respect to Normative Anxiety, the pattern of results was more
complicated, and more problematic from a PYD perspective. The variance in the
primary factor reflecting Normative Anxiety was split between the two higher order
factors, External Thriving and Severe Psychological Distress, and was found to be
positively associated with both. Previously it was suggested that Normative Anxiety,
while correlated with other symptoms of psychological dysfunction, might represent a
mild form of internal distress that is commonly experienced by adolescents. This is
view is supported to some extent by the relatively weak association between
Normative Anxiety and Negative Affect (r = .18), despite the strong association
between Negative Affect and Poor Body Image (r = .46).
Nevertheless, the positive loading of Normative Anxiety on the higher order
factor representing External Thriving is not consistent with views expressed by PYD
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scholars, who propose that adolescents who display many positive youth development
outcomes should show negligible or low levels internalizing problems (Lerner, et al.,
2005). Rather, it is more in accord with findings from research on resilience, where it
has been observed that resilient youth who demonstrate good adaptation with respect
to behavioral or cognitive abilities often do so at the expense of increased
vulnerability to emotional distress or social relationships (see Luthar, 1993). This
suggests that the relationship between Normative Anxiety and External Thriving that
was observed in this study may be influenced by a youth’s level of risk. At high
levels of risk, the stress of resisting negative influences, such as the influence of
deviant peers, may increase both a youth’s sense of Normative Anxiety and his or her
level of External Thriving.
With respect to the particular variables in the analysis, the most interesting
finding was for the item relating to sexual restraint. Unlike the other adaptive
behaviors examined in the study, sexual restraint shared both common and unique
variance with the factor representing problem behaviors, but shared only common
variance with the factor representing adaptive behaviors. Sexual restraint is the only
adaptive behavior in this study that can be classified as a domain specific protective
process (i.e., an attitude or expectation relating specifically to a problem behavior) as
opposed to a domain general value or skill (e.g., culturally sensitive values). Its
unique and negative association with problem behaviors supports the view that
certain youth characteristics identified as positive are more likely than others to be
directly related to fewer problem behaviors (See Botvin and Griffin, 2004).
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Overall, the findings indicate that thriving can be both conceptualized and
operationalized as a least a two dimensional construct that includes the presence of
adaptive behaviors and the absence of (externalizing) problem behaviors. However,
more work is needed in order to determine how External Thriving is related to
indicators of internal distress. It is possible that with better measures of multiple types
of psychological dysfunction a clearer relationship between External Thriving and
internal distress will be identified. In particular, a good measure of stress associated
with an adolescent’s sense of self in relation to peer acceptance and social
relationships would help to clarify the relationship between a desire to fit in with
peers and the expression of problem behaviors in an assessment of a youth’s overall
level of external thriving.
The Correlates of External Thriving
In the second part of the study, a series of regression analyses were conducted
to examine the contextual correlates of external thriving, adaptive behaviors, and
(externalizing) problem behaviors. The aim of these analyses was to determine what
type of environmental factors (i.e., social assets or contextual risks) demonstrate the
strongest relationship to external thriving, and whether the same factors are equally
important to each of its components (i.e., adaptive behaviors and problem behaviors).
Prior to the analysis, a conceptual distinction was made between contextual
factors that represent risks and those that represent social assets. In particular,
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contextual risks were defined as indicating the presence of maladaptive
developmental processes. These include stressful life events such as experiencing
abuse, harassment, racism, and various forms of victimization (see Masten, et al,
1999), as well as associations with deviant peers and adults who model problem
behaviors (Deater-Dekard et al., 1998; Dishion et al., 1999). It is believed that the
presence of such processes leads directly to a youth’s expression of problem
behaviors, but their absence does not benefit a person in any way.
On the other hand, social assets were defined as indicators of adaptive
developmental processes that serve a positive function in the lives of young people by
either acting on other factors in the social environment to maintain the safety and
stability of the developmental context (i.e., protective function) or by acting on the
individual to create new structures of culturally valued skill or knowledge (i.e.,
generative function). Thus, social assets both protect and nurture the development of
young people. Exposure to social assets of both types is necessary to thriving overall,
but as Bronfenbrenner (1998) points out, processes that function to ensure the
stability of time and place, such as parental monitoring, do not in themselves serve a
generative function. In order for development to take place, a young person must
engage in a learning experience. That is, he or she must experience the generative
processes associated with social assets such as those that provide instruction, training,
and the opportunity to try out new roles and responsibilities (Zeldin, 1995) in order to
develop adaptive behaviors and skills. It is believed that the presence of social assets
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is essential to the promotion of thriving among adolescents, and their absence results
in a failure to thrive in developmentally valued ways.
Of the 21 variables that were selected to represent contextual risks and social
assets, 11 were classified as risks prior to the analyses, and 10 were classified as
social assets. An examination of the zero-order correlations between these variables
and each of the dependent variables (i.e., external thriving, adaptive behaviors and
problem behaviors) indicated that both the direction and strength of the relationship
between each predictor and outcome was consistent with the conceptual distinction
between a risk and a social asset that is made in this study. That is, variables that
were classified as risks were found to be positively related to problem behaviors, and
negatively related to adaptive behaviors and thriving. Similarly, variables that were
classified as assets were found to be negatively related to problem behaviors, and
positively related to adaptive behaviors and thriving. In addition, assets were
generally more strongly related to adaptive behaviors than problem behaviors, and
risks were generally more strongly related to problem behaviors than to adaptive
behaviors.
In the hierarchical regression analysis, the full set of 21 risks and assets were
first considered. An examination of the individual predictors resulted in six variables
being eliminated from the model. Two of these variables were judged as essentially
irrelevant in association with external thriving, but the other four demonstrated
suppression effects, in which the sign of their beta coefficient was opposite to that of
their zero-order correlation. This indicates that their relationship to thriving was not
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only fully mediated by other predictors in the analysis, but that they were also
controlling for part of the error term in these predictors as well.
For example, “peer support” was more strongly correlated with “association
with deviant peers” (r = -.43) than with thriving (r = .33). When “association with
deviant peers” was removed from the model, the suppression effect on “peer support”
was eliminated and its relationship to thriving became significant. The strength of the
zero-order correlation indicates that peer support is important to external thriving.
However, in a multivariate context, the variable for “peer support” did not add more
to the explained variation in external thriving above what was already explained by
the variable reflecting negative peer influence. A similar pattern emerged with respect
to “parental support”. The positive association between this variable and thriving was
mediated by “parental monitoring” and “counseling by a parent”, both of which
reflect specific behaviors on the part of parents. This suggests that in a multivariate
context, the best predictors of youth outcomes may be measures that focus on what
people actually do (e.g., provide counseling), as opposed to measures that reflect a
youth’s general feelings or judgments about these people.
The final reduced risks and assets model included 15 independent predictors
of thriving. Prior to the analysis, eight of the 15 independent predictors of thriving
were designated as risks, and seven were designated as social assets. The results
suggest that the correlates of thriving are best understood as a combination of
contextual risks and social assets. The variables with the strongest relationship to
thriving reflected peer risks and parenting assets. It seems that even among older
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adolescents, what parents do (i.e., parental monitoring) and the values they express
(i.e., valuing teen restraint) are important to a youth’s level of thriving.
However, the single indicator that demonstrated the strongest relationship to
external thriving was the main effect for “association with deviant peers”, which
accounted for approximately 16% of the variation in the outcome. The negative
impact that associating with deviant peers has on a youth’s development is well
established in the literature (Dishion, et al, 1999; Jessor, Turbin & Costa, 1998;
Leffert et al. 2000). Consequently, a post hoc analysis was conducted to further
explore this relationship. An important finding from prevention research is that a high
level of exposure to risk often has a cumulative effect on the expression of problem
behaviors (e.g., Pollard, Hawkins & Arthur, 1999). Therefore, it was decided to test
the quadratic effect of increased exposure to deviant peers. In addition, it was
believed that high levels of parental monitoring, which have been found to moderate
the effects of exposure to risks in previous research studies (e.g., Luster & Small,
1997), might serve to moderate the negative relationship between association with
deviant peers and thriving. Therefore, the interaction effect of parental monitoring
and association with deviant peers was also tested.
The findings indicated that both the cumulative effect of increased exposure to
deviant peers and the moderating effect of parental monitoring were significant. Of
the two, the cumulative effect of association with deviant peers was the strongest,
indicating that having friends who model and support multiple types of problem
behaviors is associated with an exponential decline in a youth’s level of external
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thriving. High levels of parental monitoring appear to dampen this relationship to a
certain degree, but cannot eliminate it completely. This suggests that if the goal of a
program or social policy is to promote thriving in adolescence, a focus on protective
factors might not be enough to offset the negative impact of having friends who
model and support problem behaviors. In this case, the best strategy may be to
remove the risk completely by permanently removing him or her from the influence
of deviant peers.
The Correlates of Adaptive Behaviors and Problem Behaviors
In distinguishing between a risk and an asset, it was proposed that risks would
demonstrate a stronger association with problem behaviors, and assets would
demonstrate a stronger association with adaptive behaviors. In the first part of the
analysis, it was found that a combination of risks and assets were uniquely related to
external thriving. To determine whether these correlates were primarily associated
with the variability in thriving that is related to the presence of adaptive behavior or
problem behaviors, two additional regression analyses on each of the components of
thriving were conducted.
The regression analyses which considered the correlates of external thriving in
relation to the individual dimensions of adaptive behaviors and problem behaviors
provides empirical support for a conceptual distinction between contextual risks and
social assets that was proposed in the review of the literature. The findings indicate
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that the principle correlates of positive outcomes differ from those of problem
behaviors. In particular, contextual factors designated as social assets were more
strongly associated with positive outcomes, while the contextual factors designated as
risks were more strongly associated with the expression of problem behaviors. This
suggests that research findings based on a single component of thriving (e.g., problem
behaviors) may not readily generalize to the other component of thriving (i.e.,
adaptive behaviors). It also indicates that the distinction between social assets and
contextual risks is more than just a matter of semantics, but reflect different types of
processes that are important to the development or maintenance of different types of
outcomes among adolescents.
For example, social assets that were important in relation to adaptive
behaviors, such as “counseling by a school adult” and “caring and supportive adults”,
represent the types of community connections and supports that PYD scholars
emphasize are essential to promoting positive outcomes in youth (Benson, 1998;
Zeldin, 1995). While positive parenting practices continue to be the primary
correlates of adaptive behaviors, experiences that go beyond the family may be what
makes the difference in the lives of young people who may not receive such support
at home.
In this study, having better-educated parents maintained a significant unique
association with a youth’s expression of adaptive behaviors, even after the
contributions of the other variables in the analysis were controlled. Bronfenbrenner
(1998) writes that in order for parents to enable their children to acquire new
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knowledge or skill, they must either possess such knowledge or skills themselves, or
have access to resources outside the family that can provide their children with the
experiences they need to develop such competencies. The findings from this study
suggest that having resources outside the family increases the likelihood that a youth
will exhibit adaptive behaviors. In future studies, testing the extent to which such
resources can compensate for what parents may lack would provide practioners with
valuable information about who might benefit the most from a community based
intervention.
The results of the separate regression analysis illustrate how problem
behaviors and adaptive behaviors are primarily associated with not only different
aspects of the social context, but with qualitatively different types of developmental
processes. The distinction between contextual risks and social assets made in this
study was primarily based on what developmental theory tells us about the role of
proximal processes (both good and bad) in the development of specific forms of
behavior (Bronfenbrenner & Morris, 1998). Two variables in particular illustrate this
difference most clearly. In the analysis for adaptive behaviors, the variable
“counseling by a school adult” had a meaningful unique association with adaptive
behaviors. However, this same variable was found to be unrelated to the expression
of problem behaviors. On the other hand, the variables “witnessing abuse” and
“riding with an adult who is DWI” both had meaningful unique associations with
problem behaviors, but both were also unrelated to the expression of adaptive
behaviors.
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These three variables all represent proximal processes that a young person
may experience that are likely to result in the development of new structures of
thought, behavior, and skill. Some, such as “counseling by a school adult” are
adaptive and are associated with the development of positive outcomes. Others, such
as “witnessing abuse” are maladaptive and are associated with the development of
problem behaviors. What is most intriguing is the specificity with which these
processes appear to operate. The maladaptive processes associated with the
expression of problem outcomes are for the most part unrelated to the expression of
adaptive behaviors. Similarly, the adaptive process associated with the development
of positive outcomes is largely unrelated to the expression of problem behaviors.
This pattern of findings suggests that researchers should reconsider what they
mean by a “generic” risk or asset (see Coie, et al., 1993). A particular risk may be
generic in the sense that it is related to the development of a host of problem
behaviors, but it may be specific in the sense that it is unrelated to the development of
adaptive behaviors. This is the essence of the slogan “risk free is not fully prepared”
(Pitman, Irby & Ferber, 2000). Reducing risks may decrease a youth’s chances of
developing problem behaviors, but it may have little or no impact on promoting
positive outcomes. On the other hand, failing to attend to risks that demonstrate little
association with adaptive behaviors may be fatal to adolescents (e.g., “driving with an
adult who is DWI”). Risks often represent threats that can have immediate negative
consequences that go beyond long term developmental considerations. Therefore, if
the choice is between reducing risks or building assets in a high risk environment, it
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might be argued that priority should be given to reducing risks first in order to create
a safe haven in which development can take place.
The risks in this study were identified as markers of maladaptive
developmental processes that were expected to be most strongly associated with the
development of problem behaviors. The results of the analyses support this view. On
the other hand, the social assets in this study were identified as markers of adaptive
developmental processes that were expected to be most strongly associated with the
development of adaptive behaviors. The results of the analyses support this view as
well. However, among the variables examined in this study, two in particular
demonstrated a consistent relationship to both adaptive and problem behaviors. These
were: “association with deviant peers” and “parental monitoring”.
Having friends to model and support deviant behavior is clearly an advantage
to the development of problem behaviors among adolescents. However, this same
factor also appears to be a disadvantage to the development of adaptive behaviors as
well. It may be that youth who lack adaptive behaviors, and have a tendency to be
problem prone, gravitate to peers who share many of the same characteristics. Once
such relationships are established, the reciprocal nature of peer interactions may
reinforce and accentuate this pattern. For example, research findings reported by
Dishion et al. (1999), indicate that when engaged in conversation, delinquent youth
react positively primarily to deviant talk, whereas prosocial children tend to ignore
deviant talk in favor of normative discussions. The authors suggest that high-risk
youth use deviant talk as a tool to establish friendships. In doing so, however, the
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positive social reactions such talk elicits then serves as a form of “deviance training”
in which laughter, social attention, and interest reinforce both deviant behavior and
the sorts of values and meanings a young person develops. However, while
modeling, reinforcement, and the perceived social benefits of “fitting in” can be used
to explain the relationship between deviant peers and the expression of problem
behaviors, they do not explain how deviant peers may undermine a youth’s
expression of adaptive behaviors.
Eccles and Gootman (2002) propose that association with deviant peers
introduces a competing influence on an adolescent’s internalization of the prosocial
norms advocated by conventional institutions. That is, the density of reinforcement
from deviant peers can be so high that is seriously undermines efforts by adults to
guide young people to develop in a positive manner. The internalization of any
behavior, value, or skill requires enduring forms of interaction that must be
maintained on a daily basis (Bronfenbrenner & Morris, 1998). Therefore, an
investment of time and energy is needed regardless of whether the behavior or skill is
adaptive or problematic. This may explain the cumulative effect that high intensity
involvement with deviant peers has on the expression of problem behaviors. High
intensity involvement results in stronger internalization and greater automaticity in
the expression of problem behaviors. It may be that deviant peers actively discourage
the development of adaptive behaviors, simply because such behaviors are associated
with conventional institutions that are in general opposed to the norms and activities
of deviant groups. However, even if deviant peers do not actively discourage a youth
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from developing adaptive behaviors, the norms and behaviors they introduce
compete, and thus detract from, the time and energy a youth would otherwise have
available to develop adaptive behaviors and skills.
That is, the trade-off for associating with deviant peers may not only include
developing problem behaviors, but may also include failing to engage in the types of
activities that support positive development as well. If this is the case, then a future
examination of the contextual correlates of adaptive behaviors should consider the
extent to which “association with deviant peers” moderates the relationship between
social assets and adaptive behaviors. Under conditions of high intensity involvement
with deviant peers, social assets many have little effect on the development of
adaptive behaviors, primarily because youth are not being exposed to them at a
sufficient level for them to have a developmental impact.
The other variable to demonstrate a consistent relationship to both adaptive
and problem behaviors was “parental monitoring”. Parental monitoring was found to
reduce the likelihood of problem behaviors in general, protect against the influence of
deviant peers, and increase the likelihood that a youth will develop adaptive
behaviors. The protective function of parental monitoring is fairly self-evident. By
definition, monitoring requires vigilance against risks with the intention of doing
something about them if discovered. However, the generative function of parental
monitoring is less clear, and is perhaps best explained by the multivariate relationship
among the different parenting variables that were observed in this study. For
example, parental monitoring was found to fully mediate the effect of parental
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support on external thriving. Therefore, it is proposed that in addition to monitoring
per se, “parental monitoring” in this study may serve as a marker for a host of other
parenting practices as well. In future studies, it would be useful to include more
parenting variables such as “parental guidance” that assess what parents actually do
to promote positive outcomes.
The Mediating Role of Adaptive Behaviors
The present study examined the extent to which the relationship between
social assets and problem behaviors is mediated by a young person’s expression of
adaptive behaviors. Demonstrating that a relationship of this sort is indicated would
add strength to the PYD position that investing in social assets in order to promote
positive outcomes can be an effective means of preventing problem behaviors as
well. Six social assets were significantly related to both adaptive behaviors and
problem behaviors. In the mediational analysis, adaptive behaviors were found to
mediate a significant proportion of the relationship between all six social assets and
problem behaviors. For two social assets in particular, “support from non-parental
adults” and “caring and supportive adults”, the relationship was fully mediated.
Both of these variables reflect community contributions to positive youth
development, and their protective function appears to be largely realized through their
relationship to adaptive behaviors. Adding adaptive behaviors to the model also helps
to explain why social assets may seem to have a weak association with problem
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behaviors in studies that include both adaptive behaviors and social assets as
independent variables in a model that considers main effects only. When examining
the relative contribution of social assets in such models, PYD scholars must be
cognizant of potential mediating effects of the other variables that are also present.
Finally, while adaptive behaviors made a significant unique contribution to
the explained variance in problem behaviors, the magnitude of this contribution was
still relatively small compared to the combined contribution of the risks that were
also included in the analysis. Thus, it appears that regardless of how well a youth
might be doing in certain aspects of his or her development, positive attributes cannot
entirely overcome the negative impact of experiencing multiple risks. In this analysis,
risks such as “association with deviant peers”, “being the victim of a crime”,
“witnessing abuse” and “driving with an adult who is DWI” still accounted for a
meaningful proportion of the variance in problem behaviors, even when a youth’s
level of adaptive behavior was taken into consideration. Therefore, promoting social
assets in order to promote adaptive behaviors appears to be only a partial solution if
the goal is to prevent problem behaviors. A more comprehensive strategy requires
finding ways to eliminate or reduce risks as well.
Limitations and Future Directions
As was stated previously, the data used in this study were not collected with
the specific intention of examining the distinctions and exploring the relationships
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that are the focus of this study. As a result, the depth and breadth of the measures
used to represent certain important constructs was often limited. Furthermore, by
relying on cross-sectional data, the conclusions regarding how different types of
developmental processes relate to particular outcomes must be considered with
caution. Reciprocal influences are equally possible, as is the influence of third
variables that were not considered. In addition, the present study relies exclusively on
self-report data from a single informant (i.e., the adolescent) using a single
measurement instrument (i.e., the survey). This introduces the potential of reporting
bias and common method variance, both of which serve to obscure the true reliability
of the findings. In future studies, additional perspectives and more objective measures
of the social context in particular should be used in conjunction with self-reports as
checks and balances to the information provided by the adolescent.
The idea of multiple sources of information extends to the need to identify a
sample of youth who display a wider variety of demographic profiles. The sample
used in this study represents youth from a single midwestern county. The
demographic profile of youth in the study reflects the largely homogenous and
predominately white, middle class characteristics of youth in this area. Therefore, the
results may not generalize to other regions, socioeconomic mixes, or ethnic group
distributions. Future studies of thriving will need to focus on a more nationally
representative sample of youth, or on identifiably different groups of individuals (e.g.,
inner city minority youth, religiously conservative youth, recent Latino immigrants,
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etc.) to determine the extent to which the findings replicate across different groups of
adolescents.
The survey covered a wide range of issues, but in many cases only one item
was available to measure important constructs. While this appears not to have been a
problem with respect to the measures of risks, for variables such as “peer support”
and “teen values sexual restraint”, it would have been better to have had broader
measures that included more items. The measures of risks in this study primarily
represent the experience of specific events or features of the context (e.g., witnessing
abuse or indicating the number of adults at home who smoke). Since they represent
observations of environmental conditions rather than psychological constructs, they
are more likely to be reliably reported. However, given the importance of deviant
peers in this study, a more comprehensive measure reflecting association with
conventional peers would have been beneficial. In addition, more items reflecting a
youth’s values regarding problem behaviors in addition to sexual restraint, such as
valuing restraint toward drug use or using physical force, may have provided better
insight into the relationship between internalized values and a youth’s expression of
problem behaviors. Finally, more extensive measures of psychological health and
dysfunction would have helped to clarify some of the issues raised by this study
regarding the relationship between internalizing problem behaviors and external
thriving.
The ability to support and illustrate untested ideas using existing data is one
strategy for determining whether there is any merit to continuing a particular line of
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investigation. It is believed that despite their limitations, the findings from this study
offer support for the concepts and distinctions that were proposed based on theory
and prior research. However, these ideas still need to be tested under more controlled,
longitudinal conditions. For example, one might conclude from the results of the
mediation analysis that adaptive behaviors mediate the relationship between social
assets and problem behaviors. However, there are five other models that could also be
tested with these three variables. It may be that social assets mediate the relationship
between adaptive behaviors and problem behaviors, or problem behaviors mediate the
relationship between adaptive behaviors and social assets. If one were to examine all
six possible mediational models with these data, it is likely that all will yield a result
of partial mediation. The trouble with concurrent data is that all one can say is that the
variables share variance in common, but one cannot truly know the causal direction of
the relationships among one’s variables.
In addition, in order for mediation to work, there has to be some temporal
component. That is, if “A” causes “B” which causes “C”, there has to be some time
between A and B, and some time between B and C, in order for the causal effects to
work. Over time, one would expect some attenuation of the relationships, as random
effects get larger. However, when all of the data is collected simultaneously (i.e., as
in the present cross-sectional data set), this natural attenuation cannot be observed,
and the effects may be somewhat inflated. Therefore, one must be cautious when
considering the plausibility of the size of the mediated effect. However, despite these
limitations, the results of the mediation analysis provide some indication that there is
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an effect that is worth future consideration, especially with respect to the indirect
relationship between problem behaviors and “caring and supportive adults”. Had the
analysis failed to find a significant mediated relationship, a fundamental assumption
of the PYD approach would have been compromised.
Future research is needed to confirm the findings of this study using data that
can tap into how thriving develops over time, and is influenced by changes that occur
in the individual, in the social context, and in the interaction between the two. In
addition, analyzing the data with a focus on the how individuals who exhibit various
patterns of adjustment differ with respect to the risks or assets they experience would
also add to a better understanding of the correlates of thriving.
For example, in this study a dimensional or variable centered approach was
used to study thriving. However, thriving as a multidimensional construct readily
lends itself to future research using a person centered approach as well. Such research
could divide youth into quadrants based on their expression of high or low levels of
adaptive behaviors or problem behaviors. This would provide greater insight into the
contextual factors that are associated with youth who are thriving (i.e., high in
adaptive behaviors, low in problem behaviors), failing to thriving (i.e., low in
adaptive behaviors, low in problem behaviors), problem prone (i.e., low in adaptive
behaviors, high in problem behaviors) and enigmatic (i.e., high in adaptive behaviors,
high in problem behaviors). The present variable centered approach cannot
distinguish, for example, between enigmatic youth and youth who are failing to
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thrive, although it is highly likely that these individuals will display markedly
different profiles with respect to the contextual factors they experience.
Implications for Policy and Practice
For the most part, the principles of the PYD approach reflect the interests of
parents, practioners, and policy makers who continue to search for ways to help
young people successfully navigate the transition into adulthood without engaging in
unhealthy and risky behaviors (Roth et al., 1998). The concept of thriving instantiates
these interests, but only if it is defined and operationalized as a multidimensional
construct. As stated earlier, thriving focuses our attention on what we want for young
people, allows us to judge how well they are doing developmentally, and challenges
us to consider what all adolescents need to experience and avoid in order to develop
in a healthy and productive manner. Failing to consider problem behaviors in an
assessment of thriving creates the potential of overestimating the quality of a youth’s
development, and may lead practioners and policy makers to overlook the importance
of contextual factors that may seriously undermine an adolescent’s future prospects.
In this study, external thriving was related to both contextual risks and social
assets. Therefore, if the goal is to promote external thriving, practioners and policy
makers must attend to both types of processes. This means drawing upon what has
been learned from research on both prevention and PYD regarding the best strategies
for addressing each. At times, the messages from these two approaches will converge.
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While at other times it may require knowledge derived from one or the other,
especially if the goal is to target a particular adaptive or maladaptive process in order
to achieve a particular end.
For example, in this study, negative peer influence was strongly related to
both adaptive behaviors and problem behaviors. By attending to the lessons learned
from prevention research, the cumulative effect of negative peer influence was also
uncovered. It is likely that removing a youth from the influence of deviant peers will
improve thriving by reducing the chances that a youth will develop problem
behaviors. However, it is unlikely that this strategy alone will encourage the
development of adaptive behaviors. To encourage adaptive behaviors, it will be
necessary to target assets as well. Therefore, opportunities will need to be created for
the young person to be exposed to social assets, such as the positive influence of
conventional peers.
While a willingness to use multiple strategies is necessary, it is important to
begin by using what resources are available as other resources are being developed.
For example, if a community has had success with a particular problem prevention
program, it makes sense to continue with that program as other programs aimed at
fostering adaptive behaviors are developed. However, it is important to continually
track how changes in the types of resources or programs available to youth are related
to their level of thriving overall, and to their expression of adaptive behaviors and
problem behaviors as well. It may be that adding or eliminating certain programs has
little or no impact on thriving, while the impact of reducing or adding others is much
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greater. Keeping track of how changes in the social context relate to changes in the
young person will help practioners and policy makers to judge where it is best to
allocate resources.
Finally, it is important to consider multiple goals when evaluating the success
of a particular program or policy. For example, a policy of building assets to increase
adaptive behaviors, with the intention of ultimately reducing problem behaviors as
well, may actually have only a moderate impact on reducing problem behaviors.
However, this does not mean that such a policy is not worth pursuing. Successfully
promoting adaptive behaviors serves a valuable function in the lives of young people
in its own right, and policy makers and practioners should view this as an
achievement. The difficulties associated with targeting risks that may lie beyond the
capacity of social interventions to address, especially those that related to exposure to
negative influences within the peer group or family, may make it impossible to
eliminate the potential for youth to develop problem behaviors. However, the cost of
failing to reduce problem behaviors is greatly reduced if the result of a social
intervention is the successful promotion of adaptive behaviors.
Conclusion
As the scientific study of adolescence moves into a new phase of discovery,
the focus of research will come to be increasingly aimed at meeting the practical
concerns of parents, practioners and policy makers in understanding the fundamental
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basis of healthy, positive development for all youth, and in doing so will inform basic
research on the mechanisms that underlie human development in general (Steinberg
& Lerner, 2004). The agenda outlined by the three inseparable goals of the PYD,
which includes problem prevention, youth development and community
development, challenges scholars and researchers to view youth development from a
broader perspective: one that simultaneously takes into consideration multiple
dimensions of a youth’s character, as well as his or her developmental context.
The aim of the present study was to inform such efforts by critically
examining how thriving during adolescence should be understood, operationalized
and studied as a multidimensional construct that includes both positive and negative
attributes of the individual. It is believed that research aimed at gaining a better
understanding of the ecological basis of thriving in adolescence instantiates the
multiple goals of positive youth development, and provides a more holistic view of
the developing person. Throughout this study, emphasis was place on the importance
of maintaining conceptual distinctions and limiting the meaning of key constructs
used to describe characteristics of youth, the social context, and developmental
processes in particular. While progress is being made in the field in this regard, a
great deal of consensus building remains to be done. Eventually, such distinctions
will need to be established if progress in the field is to continue to go forward, and a
better understanding of the nature of development during adolescence is to be gained.
144
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APPENDIX A - DANE COUNTY 2000 YOUTH SURVEY
Section 1: About Yourself
1.
Are you male or female?
0 = Male
1 = Female
2.
To what racial or ethnic group do you belong?
0 = Native American Indian
1 = Black/African American
2 = Hispanic
3 = Hmong
4 = Asian (Not Hmong)
5 = White (Not Hispanic)
6 = Mixed race (e.g. both African American and White)
7 = Other
3.
How old are you?
0 = 10 or younger
1 = 11
2 = 12
3 = 13
4 = 14
5 = 15
6 = 16
7 = 17
8 = 18
9 = 19 or older
4.
What is your current grade in school?
0 = 7th grade
1 = 8th grade
2 = 9th grade
3 = 10th grade
4 = 11th grade
5 = 12th grade
6 = Other
5.
Are you currently enrolled in a special education class or program?
0 = No
1 = Yes, full time
2 = Yes, part time
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6.
What is the average grade you usually get in your courses at school?
0 = Mostly As
1 = About half As & half Bs
2 = Mostly Bs
3 = About half Bs & half Cs
4 = Mostly Cs
5 = About half Cs & half Ds
6 = Mostly Ds
7 = Mostly below D
7.
What do you think you will do after you finish high school? Pick only one.
0 = I will probably drop out before I finish high school
1 = Go to a 2-year college
2 = Go to a 4-year college or university
3 = Get a full-time job
4 = Join the military (Army, Navy, Air Force, etc.)
5 = Get married and be supported by my husband/wife
6 = Don't know yet
7 = Other
Section 2: Your Living Situation
8.
Which best describes where you live the majority of the time?
0 = In the Madison metropolitan area (Madison, Middleton, Monona, Fitchburg)
1 = In a smaller city (Stoughton, Sun Prairie, Verona)
2 = In a small town or village
3 = In the country, not on a working farm
4 = On a working farm
9.
Whom do you live with most of the time?
0 = Two parents (biological or adoptive)
1 = Mother and stepfather
2 = Father and stepmother
3 = With mother only
4 = With father only
5 = Half the time with my mother, half the time with my father (shared custody)
6 = With parent and another adult (non-relative)
7 = Group home or foster home
8 = With a relative (aunt, uncle, grandparents, etc.)
9 = I live alone or with friends
163
10. Does your mother (or other adult female you live with) work for pay?
0 = I don't live with my mother or an adult female
1 = Employed full time (32 hrs. or more per week)
2 = Employed part time (less than 32 hours per week)
3 = Homemaker/not working outside the home
4 = Unemployed for less than one year, but looking for work
5 = Unemployed for more than one year, but looking for work
6 = Full-time student
7 = Retired/disabled
8 = Other
11. Does your father (or other adult male you live with) work for pay?
0 = I don't live with my father or an adult male
1 = Employed full time (32 hrs. or more per week)
2 = Employed part time (less than 32 hours per week)
3 = Homemaker/not working outside the home
4 = Unemployed for less than one year, but looking for work
5 = Unemployed for more than one year, but looking for work
6 = Full-time student
7 = Retired/disabled
8 = Other
12. How much education did your mother/stepmother complete? Give your best
guess if not sure.
0 = Elementary or junior high school
1 = High school
2 = Some college or technical school
3 = Graduated from a 2-year college or technical school
4 = Graduated from a 4-year college
5 = Some school beyond 4-year college
6 = Professional or graduate degree (Ph.D., M.D., M.A., law degree, etc.)
7 = Don't know
13. How much education did your father/stepfather complete? Give your best guess
if not sure.
0 = Elementary or junior high school
1 = High school
2 = Some college or technical school
3 = Graduated from a 2-year college or technical school
4 = Graduated from a 4-year college
5 = Some school beyond 4-year college
6 = Professional or graduate degree (Ph.D., M.D., M.A., law degree, etc.)
7 = Don't know
164
14. Are your parents divorced or separated?
0 = Never married
1 = Never divorced or separated
2 = Divorced/separated within the last year
3 = Divorced/separated more than one year ago
15. Since the time you began school (1st grade) how many times have you had to
change schools because you moved?
0 = None
3 = 3 times
1 = Once
4 = 4 times
2 = Twice
5 = 5 or more times
16. During the past year, have you run away from home and stayed away at least
overnight?
0 = No
1 = Yes, 1 time
2 = Yes, 2 times
3 = Yes, 3 times
4 = Yes, 4 times
5 = Yes, 5 or more times
Section 3: How Do You Spend Your Time?
17. Did you have a job this past summer?
0 = No, I did not want to work
1 = No, I looked but could not find a job
2 = Yes, but only for less than three weeks
3 = Yes, most of summer, 5-15 hours per week
4 = Yes, most of summer, 16-30 hours per week
5 = Yes, most of summer, more than 30 hours per week
18. Do you currently work at a paid job?
0 = No, not looking for one
1 = No, I am looking for a job but can't find one
2 = Yes, I do occasional jobs (baby-sitting, snow shoveling, lawn work, etc.)
3 = Yes, I usually work between 1-15 hours a week
4 = Yes, I usually work between 15-30 hours a week
5 = Yes, I usually work more than 30 hours a week
165
19. Are you currently involved in any volunteer activities?
0 = No, and I'm not interested in volunteering
1 = No, but I am interested in knowing more about volunteering
2 = Yes, I am currently volunteering on a weekly basis
3 = Yes, I am currently volunteering on a monthly basis
4 = Yes, I am currently volunteering a few times a year
5 = I have volunteered in the past, but I am not currently volunteering.
How many hours per week do you spend:
(Response options )
0 = None;
4 = 10-15 hrs/wk;
1 = <1 hr /wk;
5 = 16-20 hrs/wk;
2 = 1-4 hrs/wk;
6 = > 20 hrs/wk
3 = 5-9 hrs/wk;
20. In school sport or extracurricular activities
21. Doing homework or studying
22. In music or dance lessons
23. In nonschool sports or other activities such as Boy or Girl Scouts, 4-H or other
such youth activities
24. Involved in activities sponsored by a religious institution
25. Doing things with your family (other than watching T.V.)
26. At a community or youth center
27. Volunteering
28. Youth leadership activities (student counsel, peer helping programs, etc.)
166
29. If you DO NOT regularly participate in after school programs/activities, which
is the MOST IMPORTANT reason you don't? (choose only one.)
0 = I am in after school programs in my school
1 = I am not aware of any after school programs
2 = Programs cost too much
3 = Transportation problems getting home after school
4 = Programs don't interest me
5 = I have a job after school
6 = I have family responsibilities after school (e.g. baby-sitting a younger
sibling)
7 = Other
30. How much time do you spend using the internet?
0 = None, I don't have access
1 = Have access but never use it
2 = Less than 1 hour per week
3 = 1-2 hours per week
4 = 3-4 hours per week
5 = 5 or more hours per week
Section 4: Student Worries
How much do you worry about the following?
(Response options )
0 = Not at all;
1 = A little;
2 = Some;
3 = Quite a bit;
4 = Very much
31. Getting good grades
32. Being pressured into having sex
33. Being pressured into drinking or doing other drugs
34. Being picked on or physically hurt by another teen
35. Getting AIDS or a sexually transmitted disease
36. Not fitting in with the other kids at school
37. How I look (my general appearance; e.g. that I am too fat, or too short, etc.)
38. That I might get pregnant or get someone else pregnant
167
39. How well my parents get along with each other
Section 5: Drug Use Past Year
Please indicate how often you have used the following during the past year:
(Response options )
0 = Not at all;
3 = 1 to 3 times a week;
1 = Once or twice;
4 = 4 to 6 times a week;
2 = 1 to 3 times a month;
5 = Daily
40. Smoking Tobacco (cigarettes, cigars, pipe)
41. Other tobacco (snuff, chewing)
42. Beer/wine/wine coolers
43. Hard Liquor
44. Marijuana (grass, pot, hash)
45. Alatrix (trix, trixie)
46. Inhalants (i.e. sniffing fumes to get high)
47. Hallucinogens (LSD, acid STP, psilocybin, mushrooms, mescaline, peyote, PCP,
angel dust, etc.)
48. Cocaine/crack
49. Stimulants (e.g. amphetamines, speed, crank, crystal meth)
50. Steroids ( or other performance enhancing drugs)
51. Unauthorized prescription drugs (e.g. Ritalin or Valium prescribed for someone
else)
168
52. During the past month, have you had 5 or more alcoholic drinks at one time? (A
"drink" is a glass of wine or beer, a bottle or can of beer, a shot of liquor, or a
mixed drink).
0 = Never
1 = Yes, once
2 = Yes, twice
3 = Yes, 3 to 5 times
4 = Yes, 6 to 9 times
5 = Yes, 10 or more times
53. If you or your friends go out and consume alcohol, how often do you use a
designated driver?
0 = I don't drink alcohol
1 = Never use designated driver
2 = Rarely
3 = Sometimes
4 = Often
5 = Always
54. During the past month, have you ridden in a motorized vehicle (i.e., car, truck,
motorcycle, snowmobile) with a teen driver who was drinking alcohol?
0 = No
1 = Yes, once
2 = Yes, twice
3 = Yes, 3-5 times
4 = Yes, 6 times or more
55. During the past month have you ridden in a motorized vehicle (i.e. car, truck,
motorcycle, snowmobile) with an adult driver who was drinking alcohol?
0 = No
1 = Yes, once
2 = Yes, twice
3 = Yes, 3-5 times
4 = Yes, 6 times or more
56. During the past month have you driven a motorized vehicle (i.e. car, truck,
motorcycle, snowmobile) after drinking alcohol?
0 = No
1 = Yes, once
2 = Yes, twice
3 = Yes, 3-5 times
4 = Yes, 6 times or more
169
57. If you drink alcohol, where do you usually get the alcohol that you drink?
0 = I don't drink
1 = I take it from my parents or from my friend's parents without them knowing
2 = I get it from friends my own age
3 = I buy it myself at a local store, tavern or bar
4 = I ask someone of legal age to buy it for me
5 = Older friends give it to me
6 = My parents give it to me
7 = Other
58. If you smoke or chew tobacco, how old were you when you first started?
0 = 10 or younger
5 = 15
1 = 11
6 = 16
2 = 12
7 = 17
3 = 13
8 = 18
4 = 14
9 = 19 or older
59. How frequently have you smoked cigarettes during the past 30 days?
0 = Not at all
1 = Less than once cigarette per day
2 = One to five cigarettes per day
3 = About one half pack per day
4 = About one pack per day
5 = About one and one half packs per day
6 = Two packs or more per day
60. During the past 30 days, did you ever drink alcohol or use drugs during school
hours?
0 = Never
1 = Yes, once
2 = Yes, twice
3 = Yes, 3 times
4 = Yes, 4 times
5 = Yes, 5 or more times
170
Section 6: About Your Health
61. During the past 30 days, did you use any of the following methods to loose
weight or keep from gaining weight?
0 = No, I did not use any of the methods listed
1 = Skipped meals or fasted
2 = Used diet pills or diet products (like Slimfast)
3 = Vomited
4 = Used laxatives
5 = Used more than one of the methods listed
62. In the past 7 days how often have you exercised or been physically active (e.g.
rollerbladed, played basketball, done aerobics) for at least 20 minutes?
0 = Not at all
4 = 4 times
1 = Once
5 = 5 times
2 = Twice
6 = 6 times
3 = 3 times
7 = 7 or more times
63. When was the last time you were seen by a doctor or other health professional
(NOT including the school nurse)?
0 = In the last year
1 = 1 year ago
2 = 2 years ago
3 = 3 to 4 years ago
4 = 4 to 6 years ago
5 = Over 6 years ago
64. How often do you wear a seatbelt when driving or riding in a motor vehicle?
0 = Never
1 = Rarely
2 = Sometimes
3 = Often
4 = Always
65. In the past year have you worked at a place that allows employees or customers to
smoke in your presence?
0 = No
1 = Yes, at my current job
2 = Yes, at a previous job
171
66. How often do you wear a protective helmet for sports (e.g. for biking,
rollerblading, skateboarding etc.)?
0 = Never
1 = Rarely
2 = Sometimes
3 = Often
4 = Always
Section 7: Health and Social Service Needs
The following is a list of problems which young people may experience. Please
indicate whether or not they have ever been a problem for you.
(Response options )
0 = No problem;
1 = Somewhat of a problem;
3 = A serious problem; 4 = A very serious problem
2 = A moderate problem;
67. Not being able to get affordable medical treatment or dental care
68. Not being able to get birth control information or supplies
69. Not being able to get professional counseling for personal or family problems
Listed below are issues that concern some young people. Please indicate whether or
not you currently feel the need for help on any of these concerns
(Response options )
0 = No help needed;
1 = Help needed;
2 = Already receiving help
70. Pregnancy
71. Weight control
72. Relationship with parents
73. Sexual orientation (questions or other issues about being gay, lesbian, or bisexual)
74. Alcohol/drug problems of a family member
75. Personal alcohol/drug problems
172
76. An eating disorder (e.g. excessive dieting or self-induced vomiting)
77. How to quit smoking cigarettes, or chewing tobacco
Section 8: Personal Issues
78. Other than your parents, how many adults in your life could you turn to if you
had a problem and needed help? (e.g. your grandparents, aunt/uncle, a teacher,
coach, priest, rabbi, etc.)
0 = No other adults available
1 = At least one other adult
2 = At least two other adults
3 = At least three other adults
4 = 4 or more other adults
79. During the past month, have you felt depressed or very sad?
0 = No
1 = Yes, once in a while
2 = Yes, some of the time
3 = Yes, most of the time
4 = Yes, all of the time
80. During the past month, have you seriously thought about killing yourself?
0 = No
1 = Yes, once or twice
2 = Yes, some of the time
3 = Yes, most of the time
4 = Yes, all of the time.
81. If you have ever been sexually active, how old were you the first time you
voluntarily had sexual intercourse?
0 = I have never had sexual intercourse
1 = 11 years old or younger
2 = 12 years old
3 = 13 years old
4 = 14 years old
5 = 15 years old
6 = 16 years old
7 = 17 years old
8 = 18 years old
9 = 19 years old or older
173
82. If you have had sexual intercourse, how often do you and/or your partner use
some form of birth control?
0 = I do not have sexual intercourse
1 = Never
2 = Rarely
3 = Sometimes
4 = About half the time
5 = Most of the time
6 = Always
83. Have you ever been pregnant or made someone pregnant?
0 = No
1 = Yes, within the past year
2 = Yes, more than a year ago
84. Do you ever feel confused about whether you are lesbian, gay or bisexual?
0 = Always
1 = A lot of the time
2 = Sometimes
3 = Rarely
4 = Never
5 = I consider myself to be lesbian, gay or bisexual
85. Have you ever been sexually abused by an adult?
0 = No
1 = I am currently being sexually abused
2 = I was sexually abused, but the abuse has stopped
86. Have you ever been physically abused by an adult?
0 = No
1 = I am currently being physically abused
2 = I was physically abused, but the abuse has stopped
87. During the past year, how many times were you in a physical fight in which
weapons were present?
0 = 0 times
4 = 6 or 7 times
1 = 1 time
5 = 8 or 9 times
2 = 2 or 3 times
6 = 10 or 11 times
3 = 4 or 5 times
7 = 12 or more times
174
88. During the past 12 months, how many times were you in a physical fight (no
weapons present)?
0 = 0 times
3 = 4 or 5 times
1 = 1 time
4 = 6 to 10 times
2 = 2 or 3 times
5 = More than 10 times
89. Have you ever personally witnessed someone being beaten or physically abused?
0 = Never
1 = Yes, in my home
2 = Yes, in my school
3 = Yes, in my town
4 = Yes, in my home and at school
5 = Yes, in my school and town
6 = Yes, in all three of these places
90. Are you a member of a gang?
0 = No, never been asked or pressured to join
1 = No, but have been asked or pressured to join
2 = Was in a gang, but am no longer
3 = Yes, currently in a gang
91. Have you ever been the victim of a crime?
0 = No,
1 = Yes, once
2 = Yes, twice
3 = Yes, 3 times or more
92. During the past month, how many days did you carry a weapon into the school
building?
0 = 0 days
1 = 1 day
2 = 2 or 3 days
3 = 4 or 5 days
4 = 6 or more days
93. In the past year, have you been suspended from school?
0 = No, never
1 = Yes, once
2 = Yes, twice
3 = Yes, three times or more
175
94. In the past month, how many times have you skipped a day of school (been absent
when you were not sick or did not have another valid excuse)?
0 = Never
1 = Once
2 = Twice
3 = 3 times
4 = 4 or more times
95. Have you ever shoplifted?
0 = No
1 = Yes, in the past year
2 = Yes, longer than a year ago
96. Have you ever vandalized public (including school) or private property?
0 = No
1 = Yes, in the past year
2 = Yes, longer than a year ago
Section 9: About Your School
Please indicate how much you agree or disagree with the following statements
(Response options )
0 = Strongly Agree;
1 = Agree;
2 = Disagree;
3 = Strongly Disagree
97. I enjoy going to school
98. The rules in my school are enforced fairly
99. I am getting the education and skills I need to be successful after I graduate from
high school
100. I believe I am getting a good, high quality education at my school
101. There are places in my school where I don't feel safe
102. Kids at school treat me unfairly because of my race or ethnicity.
103. My teachers care about me and how well I do in school
104. The teachers in my school sometimes treat me unfairly because of my race.
176
105. Generally, counselors, nurses, social workers, and psychologists at my school
are helpful when I need them
106. How often in the past year have you experienced some form of sexual
harassment from a student at school? (Sexual harassment is unwanted sexual
attention such as sexual comments, jokes, graffiti, touching, blocking or
cornering).
0 = Never
1 = Once
2 = 2 or 3 times
3 = 4 times or more
107. How often in the past year have you experienced some other form of harassment
from a student at school? (e.g. teased, threatened, chased or cornered because
of your race, how you look, how you dress, your sexual orientation, a disability,
etc).
0 = Never
1 = Once
2 = 2 or 3 times
3 = 4 times or more
108. In the past year, how often have you had a good talk with an adult at school
about your future plans (e.g., college or employment plans)?
0 = Never
1 = Rarely
2 = Sometimes
3 = Often
4 = Very Often
Section 10: Opinions About Your Community
Please indicate how much you agree or disagree with the following statements
(Response options )
0 = Strongly Agree;
1 = Agree;
2 = Disagree;
3 = Strongly Disagree
109. I can count on police if I am having a problem or need help.
110. Adults in my community keep an eye on what teens are up to
111. If I had a problem, there are neighbors whom I could count on to help me.
177
112. If I were doing something wrong, adults in my community would probably tell
my parent(s).
113. People in my community know and care about each other
114. My neighborhood is a safe place to live.
115. People sometimes treat me unfairly because of my race or ethnicity.
116. There are opportunities for youth from different races and cultures to talk with
and do things with each other
117. Have you ever been teased, threatened, or harassed about being gay, lesbian or
bisexual? (e.g. called names like "fag" or "dyke", chased, cornered, etc.)
0 = Never
1 = Rarely
2 = Occasionally
3 = Often
4 = Very often
Please indicate whether you think your community has about the right amount, too
much, or too little of each of the following activities.
(Response options )
0 = Don't have enough;
1 = Have about the right amount;
2 = Have too much
118. Organized team sports
119. Social activities just for fun like dances, lock-ins, ski trips, etc.
120. Performing arts (music, dance, plays, etc.)
121. Organized activity clubs (school clubs, 4-H, Boy Scouts, Girl Scouts, etc.)
122. Youth leadership activities to help develop youth programs and rules and
regulations that affect young people (student council, youth boards, advisory
committees, etc.).
123. Opportunities for young people to get involved in community volunteer
programs and projects.
124. Community youth center or neighborhood center
178
125. Peer-helping programs (kids helping other kids) (i.e., peer education, peer
listeners, peer counseling, peer tutoring).
126. Employment programs to help teens find part-time work or summer jobs.
Section 11: Your Family
Indicate how much of the following are true about the adults you live with (e.g. your
parent(s) or guardian)
(Response options )
0 = Never;
1 = Rarely;
5 = No adults at home
2 = Sometimes;
3 = Often;
4 = Very Often;
127. I tell them whom I'm going to be with before I go out.
128. I talk to them about the plans I have with my friends.
129. When I go out, they ask me where I'm going
130. They usually know what I am doing after school.
(Answer these questions about either your parent(s) or the adults you live with.)
Indicate how much of the following are true for you.
(Response options )
0 = Never;
1 = Rarely;
5 = No adults at home
2 = Sometimes;
131. My parent(s) are there when I need them
132. My parent(s) care about me
3 = Often;
4 = Very Often;
179
In your family, in the past year has there been...
(Response options )
0 = No;
1 = Yes;
133. A family member (other than yourself) charged with criminal activity?
134. How many adults in your household currently smoke cigarettes?
0 = None
1 = One
2 = Two
3 = Three
4 = Four or more.
(Answer these questions about either your parent(s) or the adults you live with.)
How often in the past year have you had a good talk with at least one parent or an
adult you live with about each of the following?
(Response options )
0 = Never;
1 = Rarely;
5 = No adults at home
2 = Sometimes;
3 = Often;
4 = Very Often;
135. Risks of drinking or taking other drugs
136. Whether or not it's okay for teenagers to have sex.
137. Birth Control
138. The dangers or risks of getting AIDS/HIV or other sexually transmitted diseases
139. Your personal problems
140. Your future plans (e.g. college or employment plans)
180
Please indicate how much you agree or disagree with the following statements
(Response options )
0 = Strongly Agree;
1 = Agree;
2 = Disagree;
3 = Strongly Disagree
141. My parent(s) think it is wrong for teens my age to have sexual intercourse
142. My parent(s) think it is wrong for teens my age to drink alcohol.
143. My parent(s) think it is wrong for teens my age to smoke/chew tobacco.
Section 12: Your Friends
Please indicate how much you agree or disagree with the following statements
(Response options )
0 = Strongly Agree;
1 = Agree;
2 = Disagree;
3 = Strongly Disagree
144. My friends help me to stay out of trouble
145. Most of my friends do not have sexual intercourse
146. Most of my friends do not drink or do drugs
147. Most of my friends do not smoke cigarettes or chew tobacco
Section 13: Your Views and Opinions
Please indicate how much you agree or disagree with the following statements
(Response options )
0 = Strongly Agree;
1 = Agree;
2 = Disagree;
3 = Strongly Disagree
148. I make a real effort to get along with people of different races or cultures
149. Sometimes I treat other people worse because of their race or the color of their
skin
150. I would like to learn more about people of other races or cultures
181
151. I could never stay friends with someone who told me he or she was gay or
lesbian
152. I am comfortable with who I am
153. I feel it is important to always be considerate and respectful of others
154. I believe teenagers should not be having sexual intercourse.
How much of a problem are each of the following?
(Response options )
0 = Not problem for me;
1 = Somewhat of a problem;
2 = A serious problem;
155. Entertainment and other recreational activities cost too much
156. Recreation, school or community centers are not open when wanted
157. Lack of transportation to and from recreational activities
158. Summer or part-time jobs are not available
159. Knowing how or where to apply for jobs
160. Not having enough experience, skills or training to get hired
182
APPENDIX B – MISSING DATA TABLES
Table B1. Percent of Missing Data by Item Number
Item#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
N
11
49
0
0
26
9
9
17
18
29
21
22
24
48
5
9
19
8
19
24
11
10
7
13
19
13
13
24
216
13
8
6
6
8
15
9
15
17
40
22
%
.21
.94
.00
.00
.50
.17
.17
.32
.34
.55
.40
.42
.46
.92
.10
.17
.36
.15
.36
.46
.21
.19
.13
.25
.36
.25
.25
.46
4.12
.25
.15
.11
.11
.15
.29
.17
.29
.32
.76
.42
Item#
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
N
4
3
5
2
0
1
2
1
2
2
2
4
33
7
6
17
68
13
9
11
41
15
13
9
37
36
6
39
23
10
9
9
10
19
13
12
26
21
15
17
%
.08
.06
.10
.04
.00
.02
.04
.02
.04
.04
.04
.08
.63
.13
.11
.32
1.30
.25
.17
.21
.78
.29
.25
.17
.71
.69
.11
.74
.44
.19
.17
.17
.19
.36
.25
.23
.50
.40
.29
.32
Item#
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
N
26
40
50
34
24
17
5
11
21
9
15
1
10
17
25
23
27
46
42
55
66
91
68
127
85
72
78
69
132
152
133
160
181
135
173
213
173
234
238
240
%
.50
.76
.95
.65
.46
.32
.10
.21
.40
.17
.29
.02
.19
.32
.48
.44
.52
.88
.80
1.05
1.26
1.74
1.30
2.42
1.62
1.37
1.49
1.32
2.52
2.90
2.54
3.05
3.46
2.58
3.30
4.07
3.30
4.47
4.54
4.58
Item#
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
N
257
279
277
292
303
340
204
207
211
211
226
228
319
257
257
273
270
273
272
308
295
297
295
344
350
366
360
366
382
394
407
368
363
454
414
417
414
417
419
437
%
4.91
5.33
5.29
5.57
5.78
6.49
3.89
3.95
4.03
4.03
4.31
4.35
6.09
4.91
4.91
5.21
5.15
5.21
5.19
5.88
5.63
5.67
5.63
6.57
6.68
6.99
6.87
6.99
7.29
7.52
7.77
7.03
6.93
8.67
7.90
7.96
7.90
7.96
8.00
8.34
183
Table B2. Parameter Estimates by Missing Data Options
Listwise Deletion
Missing
Original Data
(n=3436)
Item#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
N
11
49
0
0
26
9
9
17
18
29
21
22
24
48
5
9
19
8
19
24
11
10
7
13
19
13
13
24
216
13
8
6
6
8
15
9
15
17
40
22
4
%
.21
.94
.00
.00
.50
.17
.17
.32
.34
.55
.40
.42
.46
.92
.10
.17
.36
.15
.36
.46
.21
.19
.13
.25
.36
.25
.25
.46
4.12
.25
.15
.11
.11
.15
.29
.17
.29
.32
.76
.42
.08
Mean
.52
1.96
6.86
4.48
.97
5.28
4.15
1.13
2.73
1.60
1.22
3.73
3.87
.83
1.00
.28
2.59
1.93
1.53
2.27
2.64
.69
.63
.60
2.05
.17
.66
.46
1.87
2.77
2.80
.47
.54
.39
.89
.79
1.71
.88
.86
1.47
.24
SE
.007
.009
.010
.007
.004
.021
.018
.011
.008
.019
.019
.025
.027
.018
.021
.013
.016
.014
.020
.026
.018
.017
.016
.014
.017
.009
.014
.013
.016
.024
.014
.012
.013
.011
.017
.015
.018
.017
.017
.026
.012
SD
.50
.64
.73
.50
.28
1.54
1.34
.80
.56
1.40
1.38
1.78
1.96
1.28
1.49
.95
1.19
1.00
1.45
1.90
1.29
1.24
1.16
.99
1.25
.62
.98
.91
1.13
1.70
1.03
.88
.92
.80
1.24
1.05
1.27
1.21
1.19
1.90
.84
Mean
.52
1.97
6.86
4.49
.98
5.40
4.24
1.15
2.75
1.57
1.18
3.77
3.94
.81
.90
.24
2.62
1.94
1.54
2.32
2.68
.68
.61
.60
2.06
.15
.66
.44
1.94
2.82
2.82
.46
.54
.37
.88
.79
1.76
.88
.85
1.47
.22
SE
.009
.010
.012
.009
.004
.025
.022
.014
.009
.023
.022
.030
.033
.022
.024
.015
.020
.017
.025
.032
.021
.021
.020
.017
.021
.009
.016
.015
.019
.029
.017
.014
.015
.013
.020
.017
.021
.021
.020
.032
.014
SD
.50
.57
.72
.50
.24
1.48
1.27
.82
.52
1.33
1.28
1.74
1.93
1.28
1.41
.87
1.17
.99
1.44
1.89
1.24
1.21
1.14
.98
1.22
.56
.96
.87
1.12
1.68
1.01
.84
.90
.75
1.20
1.02
1.25
1.21
1.17
1.89
.80
MLE – Imputed
(n=5238)
Mean
.52
1.96
6.86
4.48
.97
5.28
4.15
1.13
2.73
1.60
1.23
3.73
3.87
.83
1.00
.28
2.59
1.93
1.53
2.27
2.64
.69
.63
.61
2.05
.17
.67
.46
1.89
2.77
2.80
.47
.54
.39
.89
.79
1.71
.88
.86
1.47
.24
SE
.007
.009
.010
.007
.004
.021
.018
.011
.008
.019
.019
.025
.027
.018
.021
.013
.016
.014
.020
.026
.018
.017
.016
.014
.017
.009
.014
.013
.015
.023
.014
.012
.013
.011
.017
.015
.018
.017
.016
.026
.012
SD
.50
.64
.73
.50
.28
1.54
1.34
.80
.56
1.39
1.38
1.77
1.96
1.28
1.49
.95
1.19
1.00
1.45
1.90
1.29
1.24
1.16
.99
1.25
.62
.98
.91
1.12
1.70
1.03
.88
.92
.80
1.23
1.05
1.27
1.21
1.19
1.90
.84
184
Table # B2. Continued
Missing
Item
42
43
44
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
N
3
5
2
1
2
1
2
2
2
4
33
7
6
17
68
13
9
11
41
15
13
9
37
36
6
39
23
10
9
9
10
19
13
12
26
21
15
17
26
40
%
.06
.10
.04
.02
.04
.02
.04
.04
.04
.08
.63
.13
.11
.32
1.30
.25
.17
.21
.78
.29
.25
.17
.71
.69
.11
.74
.44
.19
.17
.17
.19
.36
.25
.23
.50
.40
.29
.32
.50
.76
Original Data
Mean
1.36
1.05
.92
.03
.16
.06
.08
.06
.11
1.01
1.24
.44
.31
.37
1.53
2.15
.67
.40
.40
3.70
.28
3.03
.54
.92
.22
.18
.20
.21
.22
.03
.10
.06
.06
.06
.12
2.78
1.26
.23
2.14
.90
SE
.016
.015
.020
.004
.007
.005
.006
.006
.006
.020
.019
.013
.011
.012
.018
.042
.016
.016
.010
.034
.010
.018
.011
.019
.010
.009
.009
.006
.007
.003
.005
.004
.004
.004
.005
.019
.015
.009
.036
.021
SD
1.16
1.08
1.43
.27
.52
.36
.44
.45
.45
1.43
1.38
.93
.79
.88
1.26
3.03
1.18
1.17
.74
2.44
.75
1.27
.81
1.35
.69
.62
.66
.45
.48
.22
.35
.28
.28
.26
.36
1.38
1.05
.62
2.62
1.52
Listwise Deletion
(n=3436)
Mean
1.38
1.03
.90
.03
.15
.04
.07
.05
.10
1.00
1.23
.44
.31
.36
1.57
2.11
.66
.38
.40
3.71
.25
3.07
.54
.90
.17
.18
.19
.20
.22
.03
.10
.05
.05
.05
.12
2.84
1.25
.21
2.02
.83
SE
.019
.018
.024
.004
.008
.005
.007
.007
.007
.024
.023
.016
.013
.015
.021
.051
.020
.019
.013
.042
.012
.021
.014
.023
.010
.011
.011
.008
.008
.004
.006
.005
.004
.004
.006
.023
.017
.010
.043
.024
SD
1.12
1.04
1.38
.21
.47
.30
.39
.43
.41
1.41
1.34
.93
.78
.88
1.26
2.97
1.16
1.13
.74
2.44
.68
1.24
.82
1.33
.61
.62
.65
.44
.47
.21
.34
.27
.25
.26
.35
1.34
1.02
.59
2.49
1.44
MLE - Imputed
(n=5238)
Mean
1.36
1.05
.93
.03
.16
.06
.08
.06
.11
1.02
1.24
.44
.31
.37
1.54
2.16
.67
.40
.40
3.70
.28
3.03
.54
.92
.22
.18
.20
.21
.22
.03
.10
.06
.06
.06
.12
2.78
1.27
.23
2.14
.90
SE
.016
.015
.020
.004
.007
.005
.006
.006
.006
.020
.019
.013
.011
.012
.017
.042
.016
.016
.010
.034
.010
.018
.011
.019
.010
.009
.009
.006
.007
.003
.005
.004
.004
.004
.005
.019
.014
.009
.036
.021
SD
1.16
1.08
1.43
.27
.52
.36
.44
.45
.46
1.43
1.38
.93
.79
.88
1.26
3.03
1.18
1.17
.74
2.44
.75
1.27
.81
1.35
.69
.62
.66
.45
.48
.22
.35
.28
.28
.26
.36
1.38
1.05
.62
2.62
1.52
185
Table B2. Continued
Missing
Item
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
N
50
34
24
17
5
11
21
9
15
1
10
17
25
23
27
46
42
55
66
91
68
127
85
72
78
69
132
152
133
160
181
135
173
213
173
234
238
240
257
279
%
.95
.65
.46
.32
.10
.21
.40
.17
.29
.02
.19
.32
.48
.44
.52
.88
.80
1.05
1.26
1.74
1.30
2.42
1.62
1.37
1.49
1.32
2.52
2.90
2.54
3.05
3.46
2.58
3.30
4.07
3.30
4.47
4.54
4.58
4.91
5.33
Original Data
Mean
.08
.28
.06
.13
.23
.51
.72
.15
.52
.10
.11
.94
.66
.52
1.56
1.44
1.92
1.88
.81
.47
1.84
.49
1.94
.51
.57
1.55
1.62
1.70
1.73
1.76
1.57
.81
.57
1.27
.25
.68
.40
.58
.73
.67
SE
.005
.010
.003
.005
.012
.014
.012
.008
.012
.008
.006
.019
.011
.011
.011
.011
.011
.011
.012
.010
.011
.011
.011
.013
.013
.016
.012
.012
.013
.012
.011
.010
.011
.012
.010
.007
.007
.007
.006
.007
SD
.37
.70
.25
.37
.89
1.04
.87
.56
.88
.55
.44
1.39
.77
.79
.82
.82
.76
.79
.86
.75
.78
.77
.81
.94
.96
1.17
.85
.84
.90
.84
.81
.72
.82
.84
.72
.47
.49
.49
.45
.47
Listwise Deletion
(n=3436)
Mean
.07
.25
.05
.12
.18
.46
.71
.11
.49
.07
.09
.90
.66
.52
1.55
1.45
1.95
1.91
.78
.42
1.85
.43
1.96
.50
.56
1.56
1.65
1.73
1.74
1.79
1.58
.78
.50
1.27
.23
.71
.40
.59
.75
.70
SE
.006
.011
.004
.006
.013
.017
.015
.008
.015
.008
.007
.023
.013
.013
.014
.014
.012
.013
.014
.012
.013
.012
.013
.016
.016
.020
.014
.014
.015
.014
.013
.012
.013
.014
.012
.008
.008
.008
.007
.008
SD
.35
.67
.23
.36
.78
.97
.86
.46
.86
.46
.39
1.36
.77
.79
.80
.80
.73
.76
.83
.70
.75
.71
.77
.93
.96
1.17
.82
.82
.89
.81
.78
.68
.74
.82
.70
.46
.49
.49
.43
.46
MLE - Imputed
(n=5238)
Mean
.09
.28
.06
.13
.23
.51
.72
.15
.52
.10
.11
.95
.66
.52
1.56
1.44
1.92
1.88
.81
.47
1.84
.49
1.94
.52
.57
1.55
1.62
1.70
1.72
1.76
1.56
.81
.57
1.28
.25
.68
.40
.58
.72
.67
SE
.005
.010
.003
.005
.012
.014
.012
.008
.012
.008
.006
.019
.011
.011
.011
.011
.010
.011
.012
.010
.011
.011
.011
.013
.013
.016
.012
.011
.012
.011
.011
.010
.011
.011
.010
.006
.007
.007
.006
.006
SD
.37
.69
.25
.37
.89
1.05
.87
.56
.88
.55
.44
1.39
.77
.79
.82
.81
.76
.78
.86
.75
.78
.77
.81
.94
.96
1.17
.84
.83
.89
.83
.80
.72
.81
.83
.71
.46
.48
.48
.44
.46
186
Table B2. Continued
Missing
Item
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
N
277
292
303
340
204
207
211
211
226
228
319
257
257
273
270
273
272
308
295
297
295
344
350
366
360
366
382
394
407
368
363
454
414
417
414
417
419
437
%
5.29
5.57
5.78
6.49
3.89
3.95
4.03
4.03
4.31
4.35
6.09
4.91
4.91
5.21
5.15
5.21
5.19
5.88
5.63
5.67
5.63
6.57
6.68
6.99
6.87
6.99
7.29
7.52
7.77
7.03
6.93
8.67
7.90
7.96
7.90
7.96
8.00
8.34
Original Data
Mean
.57
.54
.57
.52
3.09
2.66
3.34
2.93
3.27
3.63
.15
1.45
1.28
.99
.93
1.83
2.81
2.66
2.86
3.08
.46
1.21
1.43
1.78
1.45
2.23
2.44
1.76
2.13
.72
2.39
1.25
.61
.36
.27
.23
1.71
1.73
SE
.007
.007
.007
.007
.016
.017
.015
.017
.014
.012
.005
.017
.018
.018
.017
.019
.017
.017
.016
.015
.011
.012
.014
.014
.015
.010
.011
.012
.013
.010
.010
.014
.009
.009
.008
.007
.008
.008
SD
.50
.50
.50
.50
1.16
1.23
1.03
1.19
1.02
.86
.36
1.23
1.24
1.24
1.20
1.33
1.22
1.16
1.11
1.05
.80
.83
.98
1.00
1.03
.70
.79
.83
.89
.73
.72
.96
.65
.60
.55
.51
.54
.53
Listwise Deletion
(n=3436)
Mean
.58
.55
.59
.53
3.15
2.69
3.41
2.97
3.32
3.72
.15
1.46
1.27
.96
.89
1.85
2.88
2.69
2.87
3.11
.42
1.21
1.42
1.81
1.47
2.24
2.48
1.74
2.15
.71
2.42
1.24
.60
.33
.23
.21
1.73
1.75
SE
.008
.008
.008
.009
.019
.020
.016
.020
.016
.012
.006
.021
.021
.021
.020
.023
.020
.020
.019
.018
.013
.014
.017
.017
.017
.011
.013
.014
.015
.012
.012
.016
.011
.010
.009
.008
.009
.009
SD
.49
.50
.49
.50
1.10
1.20
.96
1.15
.96
.71
.35
1.22
1.23
1.22
1.17
1.32
1.17
1.15
1.11
1.03
.76
.81
.97
.98
1.02
.67
.74
.81
.86
.71
.69
.94
.64
.58
.51
.48
.52
.51
MLE – Imputed
(n=5238)
Mean
.56
.54
.57
.52
3.08
2.65
3.33
2.92
3.26
3.63
.16
1.44
1.28
.99
.94
1.82
2.80
2.66
2.86
3.08
.47
1.21
1.44
1.78
1.46
2.22
2.44
1.76
2.12
.72
2.39
1.25
.61
.36
.27
.24
1.71
1.72
SE
.007
.007
.007
.007
.016
.017
.014
.016
.014
.012
.005
.017
.017
.017
.016
.018
.017
.016
.015
.014
.011
.011
.013
.014
.014
.009
.011
.011
.012
.010
.010
.013
.009
.008
.007
.007
.007
.007
SD
.48
.49
.48
.49
1.14
1.22
1.02
1.18
1.01
.85
.35
1.20
1.21
1.21
1.18
1.30
1.20
1.14
1.09
1.03
.78
.81
.96
.98
1.01
.68
.77
.80
.87
.71
.70
.93
.63
.58
.54
.49
.53
.52
187
APPENDIX C – FACTOR RELIABILITY
The aim of this analysis was to establish a set of reliable factors that would
replicate across two independent random samples. Therefore, the full sample was
divided into random subsamples, and tests were performed for each to determine
whether the correlation matrices were significant and the sample sizes were sufficient
given the number of variables in the analysis. Separate factor analyses were then
conducted for each subsample and the results were compared. Correlation and
congruence coefficients were calculated as indicators of construct comparability
across the two samples. The item correlation matrix for the full sample is presented
in Table C1.
Initial Analyses of the Separate Samples
In order to provide a measure of factor reliability, the full sample of 5,238
respondents was randomly divided into three subsamples (S1, S2 & S3; n = 1,746).
Two of these subsamples (S1 & S2) were used to test the reliability of the factors in
this analysis. The third (S3) was reserved for later study. Separate factor analyses
were conducted for each sub-sample, and comparisons were made between the results
in order to guide decisions regarding the number of factors to retain and the
meaningfulness of particular variables. As a further check of factor reliability, the
correlation and congruence coefficients of the Varimax factor loading from the
separate factor analyses of the two samples were computed.
188
189
190
In the preliminary analysis of the 25 items, Barlett’s test was significant for
both samples (S1, χ2 = 10443.6; S2, χ2 = 10645.5; S1 & S2, df = 300, p < .000)
indicating that the correlation matrix was significant. The value of the determinant of
the correlation matrices for both S1 and S2 indicated that the items were highly
correlated, but that extreme multicollinearity was not a problem (S1, det = .0024 >
.00001; S2, det = .0022 > .00001). The size of the subsamples was also sufficient
given the number of variables in the analysis (S1, KMO = .835 > .50; S2, KMO =
.839 > .50).
The factor structure of the 25 indicators measuring characteristics of the
individual was then analyzed separately for S1 (n=1,746) and S2 (n=1,746) using
principle axis factoring (PAF). This extraction method was selected because it was
believed that other sources of variation exist that are not measured by the indicators in
the analysis. With PAF, factors are extracted solely on the basis of the common
variance that exists among the indicators. In contrast, a principle components (PC)
analysis makes the assumption that all of the variance (common, error & unique
variance) in an item can be modeled by a linear combination of the other items in the
analysis.
The results for the separate analyses were similar. In both cases, the solution
yielded 7 factors with initial eigenvalues greater than 1.0 (S1, 56% of the variance;
S2, 57% variance). In accordance with Kaiser’s Rule (Kaiser, 1960) these seven were
retained and the factor axis were rotated using an orthogonal rotational procedure
(i.e., Varimax) to aid in their interpretability. The Varimax rotation of the factor axes
191
maximizes the variance of the squared loadings of a factor (column) on all the
variables (rows) in a factor matrix, which has the effect of differentiating the original
variables by extracted factor. As a result, each factor will tend to have either large or
small loadings of any particular variable. This generally yields results that make it as
easy as possible to identify each variable with a single factor.
A variable that shared at least 9% of its variance with a particular factor was
regarded as making a meaningful contribution to that factor. That is, all items with
factor loadings greater than or equal to .30 were considered in the interpretation of the
factor. This criterion resulted in several variables having multiple loadings on
different factors. However, unlike confirmatory methods, exploratory factor analysis
does not require the assumption of simple structure or strict dimensionality in order to
interpret the factors. Simple structure, in which an item loads high on only one factor
and low on all others, was introduced by Thurstone as a means of side-stepping the
rotational problem in exploratory factor analysis and the extent to which it is a
reasonable solution is still debated (see Zumbo, Sireci & Hambleton, 2003).
An examination of the item loadings indicated that six of the seven factors
were highly similar across the two samples. However, one factor was different in
each. In S1, the item representing “sexual restraint” formed its own factor on which
several problem behaviors (i.e., “tobacco, alcohol & marijuana use”, “sexual
activity”, and “shoplifting”) had negative secondary loadings. A similar pattern
occurred in S2, the item for “prosocial and culturally sensitive values” formed its own
factor on which two other adaptive behaviors (i.e., “commitment to learning” and
192
“civic engagement”) had positive secondary loadings. In addition, the communality of
the item representing “consistent use of a protective helmet for sports” was found to
be relatively low in S1 (.171), where it did not load meaningfully on any of the
factors.
Evaluation of Factor Reliability
The previous findings indicated that factor reliability across the two samples
could be improved by eliminating the item relating to helmet use and reducing the
total number of factors to six. Therefore, the analysis was repeated with 24 items and
six factors extracted. For both S1 and S2, the six factors accounted for 54% of the
variance in the items. For S2, removing the item relating to helmet use reduced the
number of factors with eigenvalues greater than one to six rather than seven. The
factors were again rotated using a Varimax rotational procedure. The rotated factor
matrices of the separate factor analyses for S1 and S2 are presented in Tables C2 and
C3, along with the item communalities. Although the order in which the factors
emerged differed slightly, a visual comparison of the two matrices suggests a high
degree of congruence between S1 and S2 in the pattern and magnitude of the loadings
of the items.
To provide a more objective evaluation of the reliability of the factors,
correlation and congruence coefficients were calculated for the separate rotated factor
analytic solutions for S1 and S2. These were Pearson’s correlation, Tucker’s phi
congruence index, and a modified factorial congruence statistic (Herrero, Cuesta &
193
Table C2. Rotated Factor Matrix for Sample 1 (Varimax Rotation)
Item Description
Tobacco, Alcohol & Marijuana Use
Drug use during school hours
Hard drug use
Driving while intoxicated
Sexual activity
Aggressive behavior
Commitment to learning
Shoplifting
Consistent use of a seat belt
Vandalism
Values sexual restraint
Most time in a single activity
Number of prosocial activities
Regular aerobic exercise
Needs help – Weight control
Unhealthy dieting practices
Needs help – Eating disorder
Worry about appearance
Worry about peer relations
Negative self regard
Prosocial/Culturally Sensitive Values
Civic engagement
How often suicidal past month
How often depressed past month
F1
F2
F3
F4
.891
.615
.572
.482
.477
.473
-.458 .320
.457
-.431
.426
-.409
.797
.679
.433
F5
F6
.301
.436
.319
.354
.581
.517
.488
.344 .644
.529
.390
.528
.415
.389
.305
.618
.474
Communalities
.820
.402
.389
.238
.320
.379
.567
.270
.349
.251
.234
.655
.622
.231
.406
.308
.254
.541
.338
.249
.334
.359
.443
.404
Blank < .3
Fernandez, 1997). The values of these measures are presented in Tables C4-C6.
Tucker’s phi index is a generalization from the Pearson correlation that measures both
pattern and magnitude similarities between samples. It is distributed within a range of
+1 (perfect congruence) to –1 (perfect reflected congruence). Like the correlation
coefficient, the phi congruence index is a measure of linear association. Therefore, it
is possible for both indicators to be large when in actuality the factor loadings of the
194
Table C3. Rotated Factor Matrix for Sample 2 (Varimax Rotation)
Item Description
Tobacco, Alcohol & Marijuana Use
Drug use during school hours
Hard drug use
Driving while intoxicated
Shoplifting
Sexual activity
Vandalism
Commitment to learning
Values sexual restraint
Aggressive behavior
Most time in a single activity
Number of prosocial activities
Regular aerobic exercise
Unhealthy dieting practices
Needs help – Eating disorder
Needs help – Weight control
Prosocial/Culturally Sensitive Values
Consistent use of a seatbelt
Civic engagement
Worry about appearance
Worry about peer relations
Negative self regard
How often suicidal past month
How often depressed past month
F1
F2
F3
.894
.605
.571
.533
.492
.469
.459
-.438
-.432
.396
F4
F5
F6
Communalities
.434
.843
.652
.445
.359
.613
.601
.550
-.368
.323
.527
.435
.400
.598
.492
.461
.565
.437 .490
.823
.376
.370
.298
.290
.335
.257
.482
.255
.326
.734
.605
.209
.421
.374
.372
.331
.349
.314
.463
.269
.297
.418
.502
Blank < .3
items across the two samples are very different in magnitude. Therefore, a modified
congruence statistic (CT) was included as a measure of the difference in the
magnitude of the item loadings on each factor across the two samples. The formula
for CT is a generalization from the population standard deviation, and the statistic is
distributed within a range of 0 (similar solutions) to 1 (total discrepancy).
195
Table C4. Correlations of Varimax Factor Loadings from Separate Analyses (N = 24)
S2 Factors
S1 Factors
F1
F2
F3
F4
F5
F6
F1
F2
F3
.996**
-.485*
-.076
-.365
-.674**
.156
-.474*
.990**
-.349
-.128
.328
-.332
-.097
-.283
.971**
.337
-.039
.183
F4
F5
F6
-.743**
.420*
-.089
.146
.976**
-.349
-.304
-.166
.472*
.961**
-.037
.257
.180
-.313
.244
.028
-.294
.953**
**p < .01 (2-tailed); p < .05 (2-tailed)
Table C5. Congruence of Varimax Factor Loadings from Separate Analyses (Tucker’s phi, φ)
S2 Factors
S1 Factors
F1
F2
F1
F2
F3
F4
F5
F6
.996
-.327
.090
-.155
-.522
.268
-.312
.991
-.094
.080
.401
-.111
F3
.080
-.032
.976
.513
.131
.378
F4
F5
F6
-.597
.478
.071
.263
.978
-.173
-.128
.018
.590
.969
.102
.404
.291
-.086
.428
.259
.101
.961
Note: Significance tests for φ are not available. A value of +.95 is generally interpreted as
practical identity of the factors (Jensen, 1998. p.99).
Table C6. Modified Factorial Congruence of the Varimax Factor Loadings from S1 & S2 (CT)
S2 Factors
S1 Factors
F1
F2
F3
F4
F5
F1
F2
F3
F4
F5
F6
.031
.518
.414
.460
.521
.369
.513
.035
.356
.327
.264
.340
.421
.355
.051
.225
.299
.240
.538
.251
.307
.273
.047
.324
.458
.343
.203
.056
.300
.231
F6
.362
.332
.219
.249
.301
.054
Note: Values closer to zero indicate less variability (i.e., greater similarity) between the factors
196
The results for all three relational statistics indicated that all 6 factors were
practically identical between S1 and S2. The Pearson correlation and Tucker’s
congruence coefficients were all greater than .95 for the corresponding factors, while
differences in the magnitude of the item loadings were small (CTrange = .031 to .056).
Thus, it appears that individuals from both samples responded to the items in a
consistent manner, indicating that the factors are reliable and not simply the result of
spurious or chance correlations.
197
APPENDIX D – MEASUREMENT CONSTRUCTION
Based on the results of the factor analysis the measures of thriving, adaptive
behaviors and problem behaviors were constructed. Reliability analyses were
conducted on each measure to ensure that the constructs were internally consistent
and that all items contributed to the overall meaning of the measures.
Construction and Evaluation of the Dependent Measures
The measure of thriving was constructed to reflect the higher order construct
that was indicated by the negative correlation between the factors relating to adaptive
behaviors and problem behaviors in the structural analysis. The measure does not
include items relating to psychological dysfunction, because this dimension was
found to be essentially unrelated to either of the other two factors. Furthermore, the
items relating to Normative Anxiety were also not included, because they were found
to be related to adaptive behaviors and problem behaviors in a manner that was
conceptually at odds with the notion of positive development. The decision to exclude
behaviors that are inwardly directed (i.e., internalizing) requires that the measure of
thriving be conceptually qualified as an indicator of external thriving. That is, as a
measure of behaviors and attitudes that a young person presents to the world and by
which his or her positive development is generally evaluated by society.
198
To construct the measure of thriving, the eight indicators of problem
behaviors that had unique loadings on the first factor were reverse coded to reflect the
absence of the problem. These were: (a) Tobacco, Alcohol and Marijuana Use, (b)
Drug use during school hours, (c) Hard drug use, (d) Driving while intoxicated, (e)
Sexual activity, (f) Aggressive Behavior, (g) Shoplifting and (h) Vandalism. The
internal consistency of these indicators was then examined in a reliability analysis
that also included the seven adaptive behaviors indicators that were negatively related
to problem behaviors in the higher order dimension. The seven adaptive behaviors
indicators were: (a) Commitment to learning, (b) Civic engagement, (c) Prosocial and
culturally sensitive values, (d) Regular use of a seatbelt, (e) Valuing sexual restraint,
(f) Regular physical activity, and (g) Attachment to prosocial institutions as indicated
by the number of prosocial activities a youth participates in for at least one hour per
week.
The results of the reliability analysis for the 15 indicators of thriving are
presented in Table D1. The overall reliability of the scale was very good (α = .81).
However, the results indicated that internal consistency could be improved by
removing the item relating to regular physical exercise. Furthermore, in order to
provide a balanced measure of positive and negative traits, and to reduce the number
of items relating to substance use in particular, two additional items reflecting
problem behaviors were also removed. These were “Drug use during school hours”
and “Vandalism”. What remained were six items measuring problem behaviors and
six items measuring adaptive behaviors. Removing the three items had little effect on
199
the overall reliability of the scale (see Table D2). The adaptive behaviors and
problem behavior components of the measure were then examined separately. As
indicated in Tables D3 and D4, both subscales demonstrated adequate levels of
reliability (Adaptive Behaviors, α = .71; Problem Behaviors, α = .74).
Individual scores for each measure were calculated by averaging the
standardized values of the items for each scale. That is, each respondent received a
thriving score, a score for adaptive behaviors, and a score for problem behaviors. It
should be noted that in the measure of thriving, all of the items reflecting problem
behaviors were reverse scored so that higher values indicated fewer problems, which
is consistent with the definition of thriving used in this study. However, for the
measure of problem behaviors, the items were scored positively, so that higher values
indicate a greater intensity of problem behavior.
Table D1. Initial Reliability Analysis for Indicators of Thriving (14 items, Alpha = .81)
Item Description
No Tobacco, Alcohol, Marijuana Use
No Drug use during school hours
No Hard drug use
No Driving while intoxicated
No Sexual activity
No Aggressive behavior
No Shoplifting
No Vandalism
Commitment to Learning
Civic engagement
Social tolerance
Attachment to prosocial institutions
Regular use of a seatbelt
Regular physical exercise
Values sexual restraint
Item
Total R
Squared
Multiple R
Alpha if
deleted
.70
.49
.45
.42
.49
.45
.44
.38
.61
.35
.34
.40
.50
.05
.42
.60
.36
.31
.25
.31
.28
.25
.21
.41
.30
.19
.34
.30
.07
.26
.79
.80
.81
.81
.80
.81
.81
.79
.81
.81
.81
.81
.80
.83
.81
200
Table D2. Reliability Analysis for Final Indicators of Thriving (12 items, Alpha = .81)
Item Description
No Tobacco, Alcohol, Marijuana Use
No Hard drug use
No Driving while intoxicated
No Sexual activity
No Aggressive behavior
No Shoplifting
Commitment to Learning
Civic engagement
Social tolerance
Attachment to prosocial institutions
Regular use of a seatbelt
Values sexual restraint
Item
Total R
Squared
Multiple R
Alpha if
deleted
.67
.42
.40
.50
.44
.42
.62
.36
.35
.41
.51
.43
.54
.29
.24
.31
.26
.22
.40
.30
.17
.31
.30
.24
.78
.80
.80
.79
.80
.80
.78
.81
.81
.80
.79
.80
Table D3. Reliability Analysis of Adaptive behaviors Items (6 items, Alpha = .71)
Item Description
Commitment to Learning
Civic engagement
Social tolerance
Attachment to prosocial institutions
Regular use of a seatbelt
Values sexual restraint
Item
Total R
Squared
Multiple R
Alpha if
deleted
.57
.48
.36
.49
.45
.34
.33
.29
.15
.31
.23
.13
.64
.66
.70
.66
.67
.71
Table D4. Reliability Analysis of Problem Behaviors – Scored Positively (6 items, Alpha = .74)
Item Description
Tobacco, Alcohol, Marijuana Use
Hard drug use
Driving while intoxicated
Sexual activity
Aggressive behavior
Shoplifting
Item
Total R
Squared
Multiple R
Alpha if
deleted
.68
.48
.42
.47
.44
.40
.49
.29
.23
.25
.22
.20
.65
.71
.72
.71
.72
.73
201
The summary statistics for each variable is presented in Table D5. The
distribution of each variable is graphed in Figures D1 to D3. The distributions for
both External Thriving and Adaptive Behaviors are fairly normal. However, the
distribution for Problem Behaviors demonstrates a strong positive skew.
Table D5. Summary of Dependent Measures
External Thriving
Adaptive Behaviors
Problem Behaviors
#Items
Alpha
Mean (SD)
12
6
6
.81
.71
.74
.00 (.57)
.00 (.64)
.00 (.66)
Distribution of External Thriving
600
500
400
300
Frequency
200
Std. Dev = .57
100
Mean = 0.00
N = 5238.00
0
00
1.
0
.5
00
0.
Figure D1.
0
-.5
0
.0
-1
0
.5
-1
0
.0
-2
0
.5
-2
0
.0
-3
EXTERNAL THRIVING
Skew (SD)
-.90 (.03)
-.12 (.03)
1.79 (.03)
202
Distribution of Adaptive Behaviors
500
400
300
Frequency
200
100
Std. Dev = .64
Mean = 0.00
N = 5238.00
0
1.
1.
.8
.3
88
3
3
3
38
8
3
8
-.1
3
.1
.6
.1
-.6
-1
-1
-2
Adaptive Behaviors
Figure D2.
Distribution of Problem Behaviors
3000
2000
Frequency
1000
Std. Dev = .66
Mean = 0.00
N = 5238.00
0
4.
3.
25
25
25
25
5
5
Figure D3.
2.
1.
.2
- .7
PROBLEM BEHAVIORS