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Risky behaviors, interpersonal conflict, and their relation to fluctuations in adolescents’ diurnal HPA rhythms

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Risky behaviors, interpersonal conflict, and their relation to fluctuations in adolescents’ diurnal HPA rhythms

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Risky Behaviors, Interpersonal Conflict, and their Relation to
Fluctuations in Adolescents’ Diurnal HPA Rhythms

Lauren A. Spies Shapiro, M.A.
Doctoral Dissertation
May 13
th
, 2014

Dissertation Committee
Gayla Margolin, Ph.D., Chair
John Brekke, Ph.D.
Frank Manis, Ph.D.
John McArdle, Ph.D.
Beth Meyerowitz, Ph.D.
2
Acknowledgements
Data collection and preparation of these two manuscripts was supported by a Ruth L.
Kirschstein National Research Service Award for Individual Predoctoral Fellowship 5 F31
MH087029-03 to Lauren A.S. Shapiro, and NIH grant!RO1 HD46807 awarded to Gayla
Margolin. Current and past members of The USC Family Studies Project team played an integral
role in collecting and organizing the data used in these studies, which include (in alphabetical
order): Brian Baucom, Diana Bennett, Larissa Borofsky, Claire Burgess, Sarah Duman, Elyse
Guran, Esti Iturralde, Ilana Kellerman, Kelly Miller, Michelle Ramos, Aubrey Rodriguez, Darby
Saxbe, Adela Timmons, and Katrina Vickerman. I would also like to acknowledge and thank the
families who participated in the USC Family Studies Project for their generosity and their
willingness to engage in study procedures.
I feel incredibly fortunate and grateful to have Gayla Margolin as a mentor. Her
consistent support, patience, and enthusiasm have allowed me to push myself and sharpen my
skills as both a researcher and a clinician, and her passion for research that promotes the
betterment of families is inspiring. I am also thankful for my committee members, John Brekke,
Frank Manis, Jack McArdle, and Beth Meyerowitz, who have challenged me and supported me
along the way, helping to enhance my research and my thinking throughout graduate school. My
dear classmates both at USC and on internship have enriched my experience, bringing much
encouragement and amusement over the years. Lastly, I would like to extend a heartfelt thank
you to my family in Chicago and Los Angeles. My parents have offered immense amounts of
support, patience, and positivity, and my sister has been an incredible listener, mentor, and when
needed, distraction. Lastly, I am so very grateful for my husband, Josh, who has been an
extraordinary source of support, laughter, warmth, and joy.
3
Table of Contents

Acknowledgements ...............................................................................................................2
Introduction to Dissertation ................................................................................................. 5

Manuscript 1: Past Year Substance Use, Sexual Risk Behaviors, Delinquent Behaviors, and
Day-to-Day Risky Behaviors in Relation to Adolescents’ Diurnal Cortisol Patterns
Abstract 8
Introduction 9
Methods 17
Results 28
Discussion 31
References 39
Tables
Table 1 50
Table 2 51
Table 3 52
Table 4 54
Table 5 55
Table 6 56
Table 7 57
Table 8 58
Figures
Figure 1 59
Figure 2 60
Figure 3 61
Figure 4 62

Manuscript 2: Conflict with Family and Friends: Associations with Inter- and Intraindividual
Differences in Adolescents’ Diurnal HPA Activity
Abstract 64
Introduction 65
Methods 78
Results 88
Discussion 92
References 100
Tables
Table 1 113
Table 2 115
Table 3 117
Table 4 118
Table 5 119
Table 6 120
Figures
Figure 1 121
Figure 2 122
4
Figure 3 123
Figure 4 124
Figure 5 125

Overall Discussion and Conclusions 126
References for Overall Discussion and Conclusions 132
Appendix A 136
Appendix B 150
Appendix C 164
Appendix D 166
Appendix E 168
Appendix F 173

5
Introduction
This dissertation applies a biopsychosocial perspective to better understand how
physiological stress activity, as measured through the hypothalamic-pituitary-adrenocortical
(HPA) axis, relates to adolescents’ experiences involving risky behaviors and interpersonal
conflict. These three constructs—diurnal hypothalamic-pituitary-adrenocortical (HPA) activity,
risk behaviors, and conflict with parents and peers—all have important implications for
adolescents’ concurrent adjustment (Hughs, Power, & Francis, 1992; Steinberg, 1999; Laursen,
1995; Furman & Burmester, 1992) and can have detrimental consequences for later mental and
physical health (Clark, Martin, & Cornelius, 2008; DeBellis, 2002; Kemeny, 2007; Tarullo &
Gunnar, 2006). To date, research examining diurnal HPA activity has focused primarily on
severe forms of risky behaviors and interpersonal conflict, such as substance abuse, psychopathic
behaviors, and maltreatment (Bruce, Fisher, Pears, & Levine, 2009; Kliewer, 2006; Trickett,
Noll, Susman, Shenk, & Putnam, 2010; Badrick et al., 2008; Gowin et al., 2013). Moreover, the
research primarily is based on either adult or child samples (Barker, Greenberg, Seltzer,
Almeida, 2012; Nater, Hoppmann, & Scott, 2013; Bevans, Cerbone, & Overstreet, 2008;
Slatcher & Robles, 2011), neglecting the transition from childhood to adulthood. The present
studies enhance our understanding of adolescents’ everyday experiences of risky behaviors and
interpersonal conflict in relation to physiology, and highlights possible points of intervention for
the promotion of both psychosocial and physical well-being in adolescents and emerging adults.
The overarching goal of these studies is to advance our knowledge of how adolescents’
diurnal patterns of the HPA axis activity relate to experiences common to adolescents.
Specifically, these studies examine the following questions: 1) What is the relation between
diurnal HPA activity and risky behaviors, both on a global level as well as from day-to-day? 2)
6
How do current experiences of interpersonal conflict and previous exposure to parent-to-youth
aggression relate to adolescents’ diurnal HPA activity? The two studies comprising this
dissertation use multilevel modeling in order to capture both inter and intraindividual differences
in diurnal cortisol as related to daily experiences. Additionally, we examined the relation
between diurnal cortisol and concurrent day-to-day experiences of risky behaviors and conflict
within the context of adolescents’ past experiences.
These studies are part of an ongoing longitudinal project that includes multiple waves of
data. We measured adolescents’ risky behaviors over the previous year as well as detailed daily
assessments across a series of days. We also examined parent-to-youth aggression across three
waves of data in addition to daily assessments of interpersonal conflict. Both studies utilize three
consecutive days of saliva samples, collected five times per day, to capture diurnal patterns of
HPA activity. The incorporation of longitudinal measures, daily home data assessments, and
three days of biological measures allowed for novel, detailed analyses of these constructs, which
are defining features of adolescence. These studies serve to broaden our understanding of diurnal
cortisol, risky behaviors, and interpersonal conflict, using a more comprehensive approach than
in prior research. The results have important implications for adolescents’ psychosocial and
physiological well-being as they launch into adulthood.
7
Running Head: RISKY BEHAVIORS AND DIURNAL COTISOL

Past Year Substance Use, Sexual Risk Behaviors, Delinquent Behaviors, and Day-to-Day Risky
Behaviors in Relation to Adolescents’ Diurnal Cortisol Patterns

Lauren A. Spies Shapiro
University of Southern California
RISKY BEHAVIORS AND DIURNAL CORTISOL 8
Abstract
Health risk behaviors such as substance use, sexual risk behaviors, and delinquent
behaviors are prevalent in adolescence, and they can lead to a range of consequences from
punishment by parents to involvement of the criminal justice system, unwanted pregnancy, or
even death. Researchers have examined the link between physiological arousal, including
hypothalamic-adrenocortical (HPA) axis functioning, and risk behaviors, but the majority of
these studies examine risk behaviors within the context of behavioral disorders in youth and
substance use in adults, neglecting investigations of more normative risk behaviors that occur in
adolescence. The present study fills a gap in the literature by addressing two primary questions:
1) how do risk behaviors common to adolescents relate to diurnal patterns of the HPA axis, and
2) to what extent are there intraindividual differences in diurnal cortisol on days immediately
following adolescents’ risky behaviors? Youth reported on their risky behaviors over the past
year as well as each day for four consecutive days as part of daily diary procedures. Concurrent
with the daily risk behavior assessments, adolescents provided saliva samples, assayed for
cortisol, five times per day across three days. Adolescents’ reports of substance use and sexual
risk behaviors over the previous year related to lower morning cortisol levels. However, daily
assessments showed heightened morning cortisol levels on days immediately following risky
behaviors. Implications related to sensation-seeking theories of risky behaviors are discussed, as
are possibilities for preventive interventions for harmful risky behaviors in adolescent
populations.
Keywords: Risky behaviors, adolescence, diurnal cortisol, sensation-seeking, delinquent
behaviors
RISKY BEHAVIORS AND DIURNAL CORTISOL 9
Introduction
Risky behaviors are expected and relatively typical for adolescents as they exert their
autonomy, demonstrate increased independence, and experiment with adult-like activities
(Hughs, Power, & Francis, 1992; Steinberg, 1999). For example, a national survey of youth
demonstrated the prevalence of risky behaviors in high school students: 70.8% had consumed
alcohol, 39.9% had used marijuana, 7.7% had rarely or never worn a seatbelt, 47.4% were
sexually active, and of those, 39.8% reported not using a condom the last time they had sex
(Centers for Disease Control, 2011). Yet, despite the normative nature of these behaviors, if a
teen engages in them to excess or simply is unlucky, the behaviors can potentially put
adolescents at risk for the leading causes of injuries and death in this age group, e.g., automobile
accidents (Centers for Disease Control, 2006). Risk behaviors also can trigger other adverse
outcomes such as early pregnancy, academic underachievement, school drop out, and ultimately
occupational underachievement (Caspi, Wright, Moffitt, & Silva, 1998; Zapata, Hillis,
Marchbanks, Curtis, & Lowry, 2008; Zimmerman & Schmeelk-Cone, 2003). The search for
explanations of why adolescents engage in risky behaviors has generated a large body of
research on links with psychological and behavioral disorders (Moffitt, 1993; Molina & Pelham,
2003; Repetti, Taylor, & Seeman, 2002). More recently, attention has been directed to the
interplay between risk behaviors and physiology to address whether physiology motivates risky
behaviors or whether risky behaviors alter physiology (Casey, Getz, & Galvan, 2008; Steinberg,
2008). In the present study, diurnal HPA axis activity is investigated in connection to overall
patterns of adolescents’ risky behaviors over the past year as well as risky behaviors on the day
immediately prior to the HPA data collection.

RISKY BEHAVIORS AND DIURNAL CORTISOL 10
Theories Linking Physiological Arousal to Risk Behaviors
Generally speaking, the concept of hypoarousal underlies explanations for high risk-
taking, although the specific type of risk-taking also is relevant. Hypoarousal, defined as overall
low levels of physiological activity and low responsiveness to external stimuli, is hypothesized to
be associated with low fearfulness and fewer perceived negative consequences for doing things
that are risky (Raine, 1993; Raine, 2005). Relatedly, engaging in risky behaviors may be a way
to counteract low states of physiological arousal, particularly if the low arousal is perceived as
unpleasant; this is referred to as sensation-seeking (Raine, Reynolds, Venables, Mednick, &
Farrington, 1998; Zuckerman, 1994). In support of the low arousal hypothesis, attenuation of
HPA activity puts children at risk for engaging in delinquent behavior through a variety of
psychobiological mechanisms, including low levels of arousal limiting the ability for emotional
learning as well as alterations in brain development, particularly in the limbic system (Susman,
2006). Low arousal also relates to a compromised ability to process environmental cues,
including threats, and is related to poorer attention overall (Ellis & Boyce, 2008; Flinn, 2006). In
particular, whereas risky activities such as breaking the law or sexual risk behaviors would be
anticipated to increase arousal, other risky behaviors, including use of drugs that are depressants,
would not be explained by the hypoarousal model.
An alternative explanation linking physiology to risky behaviors, particularly for
substance use, is self-medication. That is, for adolescents who are highly aroused, certain
activities that dampen physiological activity or activate the reward systems of the brain may
relieve adolescents from distress and its physiological correlates (Anthenelli, 2012). Posing a
self-medication explanation for substance use, DeBellis (2002) suggests that early traumatic
events lead to dysregulated biological activity, often in combination with negative affect
RISKY BEHAVIORS AND DIURNAL CORTISOL 11
disorders. He suggests that substance abuse is a means of self-medication for both negative
affect and dysregulated biological stress.
Diurnal Cortisol and its Relation to Arousal and Stress
The HPA axis, which regulates chemical reactions in response to stressors, can provide
important clues regarding the connection between physiological arousal and risky behaviors.
Cortisol, a hormone secreted within the HPA axis, functions to restore homeostasis following
exposure to stressors, increasing blood sugar to help the body confront environmental demands
(Coderre, Srivastava, & Chiasson, 1991). The HPA axis is a unique physiological measure, as
not only is it activated in response to immediate stressors, but it also exhibits a diurnal rhythm.
The diurnal pattern of cortisol is characterized by a peak within 30 minutes of awakening and a
subsequent decline throughout the day, reaching an evening nadir (Kirschbaum & Hellhammer,
1989). More specifically, a strong morning awakening response, a sharp decline thereafter
followed by a gradual decline, and low evening cortisol levels are considered characteristic of
“healthy” diurnal cortisol patterns (Smyth et al., 1997).
Researchers have measured several aspects of the diurnal cortisol pattern, including the
cortisol awakening response (CAR) and the daily slope in order to capture the diurnal pattern
(Fairchild et al., 2008; Klimes-Dougan et al., 2001). Common measurements of the CAR
include the total morning cortisol output, measured with the total area under the curve with
respect to ground for morning cortisol levels (CAR AUCg; Nelemans, Hale, Branje, van Lier,
Jansen, Platje et al., in press), and the total morning cortisol increase above the awakening level,
which is the area under the curve with respect to increase (CAR AUCi; Pruessner, Kirschbaum,
Meinlshcmid, & Hellhammer, 2003). Although the exact purpose of the CAR is not entirely
clear, researchers suggest that the surge in HPA activity in the morning helps prepare individuals
RISKY BEHAVIORS AND DIURNAL CORTISOL 12
to meet the demands of the day ahead (Fries, Dettenborn, & Kirschbaum, 2009). Capturing HPA
activity from morning to nighttime, the daily cortisol slope provides an index of the steepness of
an individual’s diurnal cortisol pattern. It is often calculated by subtracting the evening cortisol
level from the peak morning cortisol level (Jarcho, Slavich, Tylova-Stein, Walkowitz, & Burke,
2013).
When faced with chronic activation due to ongoing stressors, the HPA axis
downregulates over time to protect the body from harmful consequences, known as allostatic
load (McEwen & Stellar, 1993). For example, repeated experiences of extreme childhood
stressors relate to the downregulation of the HPA axis, which protects the body from physical
harm, including immunosuppression and damage to hippocampal cells (McEwen, 2007;
McEwen, 2008; Trickett, Noll, Susman, Shenk, & Putnam, 2010). Diurnal cortisol patterns are
uniquely helpful in providing evidence of allostatic load and irregular diurnal cortisol patterns,
which can be observed as deviations from the predicted diurnal pattern. Furthermore, in
comparison to cortisol reactivity, diurnal cortisol is not dependent on immediate reactions to
individual stressors. Allostatic load observed in diurnal cortisol is characterized by weaker
morning awakening responses and flatter daily slopes (Skinner, Shirtcliff, Haggerty, Coe, &
Catalano, 2011). Many physical health problems, psychological disorders, and chronic stressors
are linked to flatter diurnal cortisol patterns, indicative of less “healthy” diurnal cortisol activity
(Adam & Kumari, 2009; Klimes-Dougan et al., 2001; Van den Bergh, Calster, Puissant, &
Huffel, 2008).
Risk Behaviors and Diurnal HPA Activity
The majority of research examining HPA activity and risk behaviors in youth focuses on
children with symptoms of behavioral disorders, including conduct disorder, oppositional defiant
RISKY BEHAVIORS AND DIURNAL CORTISOL 13
disorder, and other externalizing symptoms, as opposed to normative adolescent risk-taking.
These studies largely find overall flatter diurnal cortisol slopes (Fairchild et al., 2008; Shirtcliff,
Granger, Booth, & Johnson, 2005), although there are exceptions (Marsman et al., 2008).
Studies in adults largely focus on individuals with substance use disorders, finding primarily
elevated CARs in those who abuse substances (Badrick, Bobak, Britton et al., 2008; Wisniewski
et al., 2006).
In support of the hypoarousal theories of risky behaviors, studies have found evidence of
the link between risky behaviors and hypoarousal of the HPA axis in youth, although these
studies only examine externalizing symptoms. For example, Shirtcliff and colleagues (2005)
found lower morning cortisol levels in boys ages 6 to 16 who engaged in externalizing behaviors,
measured through participants’ self-report. Similarly, a study of 6 to 11 year-olds demonstrated
that boys with disruptive behavior disorders showed lower cortisol levels while at the study
clinic than healthy control boys (Dorn, Kolko, Susman, et al., 2009). A study of males ages 14
to 18 with and without conduct disorder demonstrated higher evening cortisol levels in males
with conduct disorder, indicative of flatter diurnal slopes; no differences were found in the
morning awakening response between males with and without conduct disorder diagnoses
(Fairchild et al., 2008).
However, not all research consistently points to flatter diurnal cortisol patterns in
individuals who exhibit risky behaviors. For example, a study of diurnal cortisol in youth (mean
age = 11.1) with internalizing and externalizing symptoms demonstrated that girls with
externalizing symptoms showed a stronger morning awakening response than girls with no
symptoms or those with comorbid internalizing symptoms; this difference was not found in boys,
pointing to potential sex differences in the link between risk behaviors and HPA activity
RISKY BEHAVIORS AND DIURNAL CORTISOL 14
(Marsman et al., 2008). An examination of substance use in an adult sample demonstrated
stronger morning awakening responses in heavy-drinking adult females, even though heavy-
drinking males showed flatter cortisol slopes (Badrick et al., 2008). Also demonstrating stronger
CARs in substance users, a study found elevated morning serum cortisol levels in both male and
female adults who used heroine and cocaine (Wisniewski et al., 2006). Importantly, research
demonstrates immediate stress-like cortisol responses to taking substances, including alcohol and
nicotine (Mendelson, Ogata, & Mello, 1971; Granger et al., 2007). Thus, there is evidence in the
literature for both heightened and blunted HPA activity in relation to substance use and risky
behaviors, with variation across age, type of risk behavior, and study methodology.
Still, there is a very limited understanding of how risky behaviors common to adolescents
relate to diurnal cortisol patterns both generally and from day-to-day. Further research is needed
in order to consolidate the discrepant findings in the literature and to expand our knowledge of
how different types of risk behaviors relate to HPA functioning, particularly in adolescents.
Studies to date include substantial methodological differences, including variations in cortisol
collection method (serum versus salivary), time at which morning cortisol was measured, and
number of samples collected (Shirtcliff et al., 2005; Dorn et al., 2009; Badrick et al., 2008).
Furthermore, the limited range of behaviors measured in individual studies impedes our
understanding of how different types of risky behaviors may differentially relate to diurnal
cortisol patterns. Another important consideration is that risk behaviors are largely measured on
average, over a period of time (Dorn et al., 2009; Van Goozen, Matthys, Cohen-Kettenis,
Buitelaar, & Van Engeland, 2000; Marsman et al., 2008). Active engagement in these behaviors
directly prior to saliva collection could alter diurnal cortisol levels, obscuring the link between
risk behaviors and diurnal cortisol. Consideration of day-to-day fluctuations in diurnal cortisol
RISKY BEHAVIORS AND DIURNAL CORTISOL 15
patterns on days following greater versus fewer risk behaviors is important, as it can shed light
on adolescents’ immediate physiological experiences following risky behaviors. It is particularly
valuable to develop our understanding of the relation between HPA activity and risky behaviors
in adolescent populations, as risky behaviors are particularly prevalent during this time, and even
though they are quite normative, they can lead to serious negative consequences. Moreover,
adolescents show a distinct diurnal rhythm of the HPA axis, allowing for the examination of
CARs and daily cortisol slopes in this age group (Walker, Walder, & Reynolds, 2001). !
Present Study
The overarching goal of the present study was to broaden our understanding of how
adolescents’ risky behaviors relate to their diurnal cortisol patterns. In contrast to studies
examining adults or children, the present study focuses on adolescents because they engage in
significantly more risky behaviors that can have potentially detrimental consequences. We also
examined the risk behaviors that are most common to adolescents, which include substance use,
sexual risk behaviors, aggressive delinquent behaviors (e.g., getting into a physical fight), and
criminal delinquent behaviors (e.g., risky driving) (Centers for Disease Control, 2011).
Specifically, we examined adolescents’ diurnal cortisol patterns across three days in relation to
1) risk behaviors over the past year, including substance use, sexual risk behaviors, aggressive
delinquent behaviors, and criminal delinquent behaviors, and 2) day-to-day risk behaviors. The
present study is the first to our knowledge that hones in on both previous-year and day-to-day
risk behaviors for a detailed examination of their relation to adolescents’ diurnal cortisol
patterns.
The first question of focus utilized three days of saliva sampling and reports of risk
behaviors over the past year to test two hypotheses. First, we hypothesized that sexual risk
RISKY BEHAVIORS AND DIURNAL CORTISOL 16
behaviors, aggressive delinquent behaviors, and criminal delinquent behaviors over the past year
would relate to weakened, or lower, CARs (both CAR AUCg and CAR AUCi) and flatter overall
diurnal cortisol slopes. Second, given previous findings of substance use and increased CARs in
adults (Wisniewski et al., 2006), we hypothesized that substance use over the past year would
relate to greater CARs (both CAR AUCg and AUCi) in our adolescent sample. Our second
research question examined whether there were day-to-day fluctuations in diurnal cortisol
patterns on days following adolescents’ risk behaviors. To answer this question, we utilized the
three days of diurnal cortisol collection as well as daily reports of risk behaviors on each day
prior to cortisol collection. We lagged the daily reports to examine previous-day risk behaviors
with the next day’s diurnal cortisol indices. In line with the sensation-seeking theory, we
predicted that adolescents’ risk behaviors would relate to temporarily heightened cortisol levels,
as indexed by higher CARs. Lastly, given the limited literature on sex differences related to
these research questions, we included exploratory analyses of sex differences in the relation
between risky behaviors and diurnal cortisol patterns.
In order to carefully examine the associations between risky behaviors and diurnal
cortisol, all of our analyses adjusted for important covariates that have been shown to alter
cortisol levels. Specifically, we adjusted for the time at which the awakening sample was
collected, as earlier wake-up times relate to heightened CARs (Federenko et al., 2004).
Additionally, we adjusted for adolescents’ age to account for fluctuations in the diurnal cortisol
pattern across adolescence (Walker et al., 2001). Medications that alter cortisol levels were
accounted for, as were cotinine levels, a measure of nicotine intake, given their relation to
heightened cortisol levels (Granger, Hibel, Fortunato, & Kapelewski, 2009; Mendelson, Sholar,
Goletiani, Siegel, & Mellow, 2005). In order to isolate the relation between risky behaviors and
RISKY BEHAVIORS AND DIURNAL CORTISOL 17
diurnal cortisol, we adjusted for symptoms of depression given their association with both flat
diurnal rhythms as well as risky behaviors (Debellis, 2002; Doane et al., 2013; Lehrer, Shrier,
Gortmaker, & Buka, 2006). Moreover, for the day-to-day assessments of risky behaviors and
diurnal cortisol, we accounted for the previous day’s cortisol measure to hone in on the link
between risky behaviors and the following day’s cortisol pattern independent of the previous
day’s diurnal cortisol activity. Whether the participants ate, drank, exercised or had mouth sores
prior to saliva sample collection was measured due to the potential for increased cortisol levels
(Kivlighan et al., 2004; Schwartz, Granger, Susman, Gunnar, & Laird, 1998). We also included
the previous night’s hours of sleep, as greater hours of sleep relate to higher morning cortisol
concentrations (Wust et al., 2000b). Lastly, in our analyses utilizing the daily risk behavior data,
we adjusted for whether the participants completed the daily questionnaires within 24 hours of
the day described to account for noncompliance.
Method
The present study uses data from the most recent wave of a longitudinal study examining
family conflict, violence exposure, and adolescent adjustment. Participants are from a
community sample of families that were recruited through flyers, newspaper advertisements, and
word-of-mouth. The overall project includes two cohorts of participants; the first cohort is
comprised of families who had a child age 9-10 at the start of data collection (n=119), and the
second cohort began the study at the third wave of data collection, four years later (n=70).
Inclusion criteria for families recruited into the second cohort were that they have a child in
middle school, and participation in both cohorts required that families lived together for at least 3
years prior to entering the study, had two parental figures, and that the whole family could
complete procedures in English (for further details, see Margolin, Vickerman, Oliver, & Gordis,
RISKY BEHAVIORS AND DIURNAL CORTISOL 18
2000). The present study utilized data from wave 5 of the longitudinal project, which were
collected between 7 and 10 years, M=8.11, SD=.73, after wave 1 for cohort 1 and between 2 and
5 years, M=3.5, SD=.69, after the first point of data collection for cohort 2.
Participants
Participants were 99 adolescents (46 female) from the longitudinal study described
above, with 67 participants from cohort 1 and 32 participants from cohort 2. A total of 131
participated in wave 5 procedures. To be included here, the adolescents needed to provide saliva
samples used for diurnal cortisol assays; of the 32 who did not provide saliva, all completed a
portion of the online questionnaires via Qualtrics. However, 8 were away at college and unable
to complete the saliva collection procedures, and 24 participated in the in-lab procedures but did
not complete the saliva procedures. The participants’ age ranged from 14 to 21, M=18.18
(SD=1.08). The sample was diverse; 32.3% identified their ethnicity as Hispanic/Latino.
Participants identified their race as Asian/Pacific Islander (5.1%), 20.2% Black/African
American, 37.4% Caucasian, 29.3% more than one race, or 8.1% other/unknown. Roughly 8%
of the participating families fell below the national poverty line for family size; mean income for
the sample was $90,927 (SD=$76,179). Mean years of parental education was 14.6 (SD=2.8),
with a range of 2 to 20 years of education. Forty-one were high school students, 48 were college
students or were enrolled to start college the following fall, and 10 reported that they were
neither in high school nor college. Also, of the 99 participants in the present study, 25 reported
that they were currently employed, including both full and part-time employment.
Between-group comparisons of adolescents who did (n=99) versus did not (n = 32)
complete the saliva collection procedures, but who did participate in wave 5 of the longitudinal
study, demonstrated no significant differences in ethnicity, race, age, annual income, or parents’
RISKY BEHAVIORS AND DIURNAL CORTISOL 19
level of education. Similarly, comparisons between those who did versus did not provide saliva
samples in wave 5 revealed no differences in overall level of risk behaviors over the past year,
nor any differences in specific types of risk behaviors including substance use, sexual risk
behaviors, aggressive delinquent behaviors, and criminal delinquent behaviors. Analogous
comparisons were conducted between the two cohorts. Cohort 1 was significantly older than
cohort 2, t(97)=7.77, p <.001. Cohort 2 demonstrated greater total morning cortisol output,
t(97)=-2.25, p=.03, and steeper peak-to-bed cortisol slopes, t(97)=-2.82, p=.006, than cohort 1.
There were no significant differences between the two cohorts in risk behavior endorsement,
total morning cortisol increase, ethnicity, race, annual income, or parents’ level of education.
Procedures
This study includes three types of data collection: 1) three days of diurnal saliva sample
collection with five preset collection times on each day, 2) 10 days of once-per-day daily home
data collection with three of those days overlapping with the cortisol samples, and 3) in-lab self-
report questionnaires. Participants attended a laboratory-based meeting prior to any home data
collection. In that meeting, participants were instructed in the daily questionnaire procedures
and actually completed one questionnaire. They also received both verbal and written
instructions in the saliva collection procedures. A visual representation of the timeline for our
home data collection procedures is presented in Figure 1. We compensated participants after the
in-lab visits ($50) as well as after completion of the home data collection procedures ($160).
Cortisol collection through saliva samples. Participants collected five saliva samples
per day across three consecutive weekdays: one upon awakening, a second 20 minutes later, a
third 40 minutes after awakening, a fourth at 4pm, and a fifth at 9pm—times chosen to capture
the diurnal rhythm of salivary cortisol (Smyth et al., 1997). Saliva collection was to occur on
RISKY BEHAVIORS AND DIURNAL CORTISOL 20
weekdays because participants were more likely to wake up early on these days and to engage in
a more standardized routine (e.g., attending school or work). Participants chose three
consecutive, typical weekdays on which to collect saliva samples, allowing for at least one day
of daily questionnaires to precede the first day of saliva collection.
The experimenter instructed the participants that for three days in a row, an alarm on their
watch would alert them to engage in the saliva collection procedures, and that concurrent with
the saliva collection, they were to complete a brief questionnaire. We provided participants with
a “Spit Kit” that included 15 salivary oral swabs and saliva storage tubes, 15 questionnaires to
complete at the time of sample collection, a thermal lunch box, an ice pack, and a digital watch
with alarms set to the pre-determined sampling times. Alarms were set to allow for an
awakening sample, a sample 20 min post-awakening, 40-min post-awakening, at 4:00pm, and
9:00pm. Experimenters set the three morning alarms at individualized times for each participant
for the awakening cortisol sample and the two following morning samples, with all alarms set so
that participants would collect their first sample by 8:00am. The alarm for samples 1, 4, and 5
were set 10-minutes prior to the collection time to signal participants to rinse their mouths with
water, which was necessary to ensure the validity of the samples. For each saliva sample,
participants placed the oral swab under their tongue in the front of their mouth for two minutes,
timed by the provided watches. Participants also completed the brief questionnaire during the 2-
minute collection time for each sample to assess covariates such as whether they ate, drank,
exercised, or had any mouth sores prior to saliva collection to be used as covariates for analyses.
Participants placed the saliva samples in the freezer immediately after collection, or if they were
at school or away from home, they put the sample in the provided thermal lunch box with ice
packs.
RISKY BEHAVIORS AND DIURNAL CORTISOL 21
Prior to the saliva collection days, we instructed participants both verbally and in writing
to “…not consume caffeine or alcohol for 24 hours prior to saliva collection… not eat or drink
anything (including water) or brush your teeth prior to completing the first three saliva
collections… do not eat or drink anything for one hour prior to the collection of the saliva
samples in the afternoon and evening.”!!The night before participants were to begin their saliva
collection procedures, an experimenter called the participant to remind them that they would
begin saliva collection the following day, and again offered to answer any questions regarding
the procedures.!
Following the three days of collection, adolescents returned their Spit Kits and frozen
saliva samples to the lab or an experimenter picked up the saliva collection supplies and samples
from the participant’s home. Once returned to the lab, saliva samples were frozen at -20 degrees
Celsius, after which they were shipped in dry ice for assay at Salimetrics, LLC (State College,
PA). A high-sensitive enzyme immunoassay was used for sample analyses, conducted in
duplicate for reliability. The inter-assay correlation was r(1,432)=.98, p<.0001. The mean
value was used for all analyses; repeated analyses were conducted for sample pairs that differed
more than 7%. The last sample of each of the three days was also assayed for cotinine, a
byproduct of nicotine, in order to assess and adjust for smoking (Bramer & Kallungal, 2003).
Because tobacco products heighten cortisol responses, we followed recommendations to test for
cortisol elevations that were due to smoking (Granger et al., 2007).
Daily home questionnaires. Each day for 10 consecutive days, participants received an
email prompt at 5:00pm to follow a survey link via Qualtrics to a web-based questionnaire. This
questionnaire included items about their day, including 18 items regarding whether they engaged
in risk behaviors, e.g., “drank alcohol,” “stole or tried to steal something,” and “drove a car or
RISKY BEHAVIORS AND DIURNAL CORTISOL 22
other vehicle when I had been drinking alcohol or had used drugs.” Participants also reported
the number of hours they slept the previous night. Participants who did not wish to complete the
questionnaires online were provided with paper versions of the daily questionnaires, completed
by 13 of the 99 participants. The participants completed the first of the 10 daily questionnaires in
the lab and provided data for the prior day and evening. Each daily questionnaire was time-
stamped through the Qualtrics program, so that we could monitor exactly when the questionnaire
was completed. Most of the questionnaires (86.5%) were completed within 24 hours of
questionnaire link receipt and 90.5% of all questionnaires were completed within 48 hours. Of
the completed home data questionnaires for the present study, 42.2% of questionnaires were
completed between 5:00pm and midnight of the night it was received, and 11.5% were
completed between midnight and 9am the next morning; 31.5% were completed before midnight
the day following questionnaire receipt. A smaller proportion of questionnaires were completed
between two and four days following the day described, comprising 14.8% of the home data
questionnaires.
Measures
Diurnal cortisol. We examined diurnal cortisol patterns through several indices: (a) total
morning cortisol output, calculated as the area under the curve with respect to ground (CAR
AUCg), (b) morning awakening response, calculated as the area under the curve with respect to
increase (CAR AUCi) (Fairchild et al., 2008; Pruessner et al., 2003), and (c) daily slope,
calculated as the difference between the peak of morning samples (either sample two or three)
and the evening sample. Each of these indices—CAR AUCg, measuring total morning cortisol
output, CAR AUCi, measuring morning cortisol increase above the awakening sample, and daily
slope, measuring the diurnal cortisol slope across the day—were normally distributed and did not
RISKY BEHAVIORS AND DIURNAL CORTISOL 23
require log-transformation. They were calculated from the raw cortisol values. Of the 1,485
samples we aimed to collect (15 for each of 99 participants), there were 26 samples that could
not be assayed due to insufficient saliva quantity, and an additional 27 missing saliva samples,
leaving 53 total missing samples (.04%) that were statistically accounted for in the analyses
using full information maximum likelihood estimations (FIML; Schafer & Graham, 2002).
Previous-year risky behaviors. Risky behaviors during the prior year were assessed
through an adapted version of the CDC Youth Risk Behavior Surveillance Questionnaire
(YRBS; Grunbaum et al., 2003; Appendix A). Participants responded to questions on: alcohol
and drug use (11 items), sexual risk behaviors (3 items), aggressive delinquent behaviors (5
items) and non-aggressive criminal delinquent behaviors (12 items). Although the original
YRBS questionnaire included assessments over the past 30 days, we inquired about the
frequency of behaviors over the past year to assess a longer period of time and to capture a
greater range of risk behaviors. Additionally, because the response scales were not consistent
across items (e.g., responses included anchors from 0 to > 10 days as well as 0 to ≥ 40 times), we
computed z-scores for each item and then calculated the mean of the items for each of the four
risk behavior types.
Daily risky behaviors. Daily risk behaviors were assessed through the home data
questionnaires that participants completed each evening of saliva collection days as well as the
day prior to the first saliva collection day. Adolescents reported on 18 items assessing risk
behaviors they exhibited that day, including substance use, e.g., “Today I used an illegal drug
(such as marijuana, cocaine, ecstasy) or prescription drug not prescribed for me,” delinquent
behaviors, e.g., “Today I broke, damaged, or destroyed something belonging to others on
purpose,” and endangering behaviors, “Today I drove or rode in a car without wearing a seat belt
RISKY BEHAVIORS AND DIURNAL CORTISOL 24
or drove over the speed limit” (Appendix B). Adolescents responded on a 4-point scale ranging
from not at all to a lot. For the analyses, the mean of each of the 18 risk behaviors each day was
used. Although we collected 10 days of daily risk data, for the present study we utilized the
three days of data that fell on the saliva collection days in addition to the day prior to saliva
collection for each participant, resulting in a total of four days of daily risk behavior data. We
took the mean of these items to measure daily risk due to the low base-rate of any one item
across the four days.
Covariates for cortisol analyses. Covariates for the cortisol analyses included time of
the awakening sample, participants’ age, current medications, cotinine, depressive symptoms,
whether the participant ate, drank, exercised, or had a mouth sore before sample collection, hours
of sleep, and whether the daily questionnaire was completed within 24 hours. Time of
awakening sample collection, current medications, and whether the participant ate, drank,
exercised, or had mouth sores prior to sample collection were assessed with the questionnaire
participants completed during each saliva collection (Appendix C). Age was calculated with the
lab visit date and the participants’ birthdate that the participants provided upon entering the
larger study. We assessed the number of hours that the participants slept the previous night in
the emailed daily questionnaire. To determine whether the daily questionnaires were completed
within 24 hours, we utilized the computerized time-stamp of when each questionnaire was
completed.
Most participants collected their first saliva sample before 9:00am (97.8%). All but one
of the wake-up times ranged from 4:56am to 9:30am, with one outlying participant waking up at
2:00pm. Participants reported eating, drinking, or exercising within 30 minutes of sample
collection for 21.7% of the samples. A portion of participants (14.1%) reported use of
RISKY BEHAVIORS AND DIURNAL CORTISOL 25
medications, with six reporting use of oral contraceptives, 4 using asthma medications, one using
a thyroid medication, and two using medication for ADHD.
All analyses adjusted for the amount of cotinine in the participants’ saliva, even if there
were merely trace amounts due to second-hand smoke. Only 14.3% of participants had a
complete absence of cotinine in their saliva samples. Within our sample, there were eight
adolescents who had cotinine levels on at least one of the three days that were above the
threshold to designate someone as a “smoker” (>10ng/ml; Caraballo, Giovino, & Pechacek,
2004); cotinine for these eight individuals was adjusted for in the same way as it was for all
participants.
Given findings relating depressive symptoms to distinct cortisol patterns as well as to risk
behaviors (Debellis, 2002), we assessed depressive symptoms to allow us to isolate the relation
between diurnal cortisol patterns and risky behaviors. Adolescents completed the Beck
Depression Inventory (BDI; Beck, Rush, Shaw, & Emery, 1979; Appendix D) during their visit
to the lab. The measure included 20 items assessing a range of depressive symptoms on a four-
point scale (e.g., 0: “I have not lost interest in other people or activities” to 3: “It’s hard to get
interested in anything”); the suicide ideation item was omitted in this study. For participants in
this study, the mean score was 7.61 (SD = 7.06). There were no significant sex differences in
BDI scores in our sample.
Analytic Strategy
First, we utilized Spearman correlations due to non-normal distributions to examine the
interrelationships among the cortisol indices, risk behavior measures, and covariates, conducted
with SPSS Version 19. Furthermore, we conducted t-tests in order to examine sex differences in
the observed variables. In order to test hypotheses 1 and 2, we ran multilevel regression models
RISKY BEHAVIORS AND DIURNAL CORTISOL 26
with Mplus Version 7.11 for Mac (Muthén & Muthén, 2012). To test our first hypothesis
examining the relation between previous-year risky behaviors and diurnal cortisol patterns, we
used a series of multilevel regression models that included the diurnal cortisol indices as the
dependent variable, and each type of previous-year risk behavior as the independent variable.
Each cortisol index (CAR AUCg, CAR AUCi, and daily slope) and each type of risk behavior
(substance use, sexual risk behaviors, aggressive delinquent behaviors, criminal delinquent
behaviors, and total risk behaviors) were run in separate models. In each of the models, we
included the within-level covariates (i.e., time of awakening sample, cotinine, whether the
participant ate, drank, exercised, or had mouth sores, hours of sleep, and whether the
questionnaire was completed within 24 hours) on level one, and the previous year risk behavior
type as well as the between-level covariates (i.e., age, medications, and depressive symptoms) on
level two. The covariates that were not significant predictors of any of the cortisol indices,
which included depression, whether the participant ate, drank, exercised, or had mouth sores,
hours of sleep, and whether the daily questionnaires were completed within 24 hours, were then
excluded from the model, leaving the following model:
Level 1: Y
ij
= β
0j
+ β
1j
(time of awakening sample) + β
2j
(cotinine) + e
ij

Level 2: β
0j
= γ
00
+ γ
01
(risk behavior) + γ
02
(medication) + γ
03
(age) + u
0j

With this series of models, we were able to examine the relation between each type of previous-
year risk behavior and each diurnal cortisol index. In order to better interpret the models and to
better pinpoint where the differences were in the diurnal cortisol pattern, we conducted post-hoc
tests of the models with each of the five cortisol samples; the individual cortisol samples were
log-transformed due to non-normality.
RISKY BEHAVIORS AND DIURNAL CORTISOL 27
For our second hypothesis examining day-to-day variation of diurnal cortisol patterns
following reported risky behaviors, we used a similar model with a lagged measure of the
previous day’s risk behaviors. Importantly, the models used previous-day reports of risky
behaviors (e.g., reported on day 1) in relation to the current day’s cortisol index (e.g., collected
on day 2). Similar to our analyses for Hypothesis 1, for Hypothesis 2 we first included all
within-level and between-level covariates in the models. A crucial addition to the covariates in
the models testing day-to-day variation for Hypothesis 2 was the inclusion of the previous-day’s
cortisol index. This helps to isolate the relation between previous-day risk behaviors and diurnal
cortisol. Depressive symptoms, whether the participant ate, drank, exercised, or had mouth sores
prior to sample collection, hours of previous night’s sleep, and completion of the daily
questionnaire within 24 hours had no relation to the diurnal cortisol indices, and were
consequently not included in the model for reasons of parsimony. These models included
previous-day risk behaviors, time of awakening sample, cotinine levels, and the previous day’s
cortisol index on level one, and medication and age as level two covariates:
Level 1: Y
ij
= β
0j
+ β
1j
(previous-day risk behavior) + β
2j
(time of awakening sample) +
β
3j
(cotinine) + β
4j
(previous-day cortisol index) + e
ij

Level 2: β
0j
= γ
00
+ γ
01
(medication) + γ
02
(age) + u
0j

Again, to better interpret the model results, we conducted post-hoc analyses examining the
models with each of the five saliva samples, which were log-transformed.
To examine sex differences we conducted models testing cross-level interactions in order
to examine whether sex moderated the within or between-level associations in the above models.
Additionally, for all multilevel models, FIML estimations were used for all missing data in order
to utilize the complete data set.
RISKY BEHAVIORS AND DIURNAL CORTISOL 28
Results
Descriptive Statistics
Descriptive statistics for the cortisol indices, risk behavior variables, and covariates are
presented in Table 1 both separately for males and females as well as for the complete sample.
There were no significant sex differences in the cortisol, risk, or covariate variables. Table 2
presents the Spearman correlations among each of the cortisol indices, risk behavior variables,
and covariates in addition to the mean of each cortisol sample across the three days. The
correlations ranged from moderate to high (rs=.21-.97). Sexual risk behaviors over the past year
were negatively correlated with the CAR AUCg, CAR AUCi, and the cortisol daily slope.
Substance use was positively correlated with sexual risk behaviors over the past year, and
aggressive delinquent behaviors were positively correlated with substance use and criminal
delinquent behaviors over the past year. Furthermore, mean daily risk behaviors across the four
days were positively correlated with substance use, sexual risk behaviors, and criminal
delinquent behaviors, but not aggressive delinquent behaviors, over the past year.
Table 3 displays the descriptive statistics for past-year risk behavior items by risk
behavior type. The majority (91.9%) of the sample reported engaging in at least one of the
previous-year risk behaviors: 75.8% endorsed at least one type of substance use, compared with
39.9% of a national sample reporting lifetime marijuana use, and 70.8% of a national sample
reporting lifetime alcohol use; 49.5% of the present sample endorsed sexual risk behavior, which
is comparable to 40.8% of a national sample reporting not using a condom during their last
sexual intercourse; 36.4% endorsed aggressive delinquent behavior, comparable to 32.8% of a
national population reporting getting into a physical fight over the past year; 65.6% of the
present sample endorsed at least one criminal delinquent behavior (CDC, 2011). Approximately
RISKY BEHAVIORS AND DIURNAL CORTISOL 29
3 of 5 (61.6%) participants engaged in two or more types of risky behaviors over the past year.
Table 4 displays the daily risk behavior items as well as their frequency of endorsement. Over
the course of the four days of daily data collection (the day before and the three days during
saliva sample collection), 21 adolescents reported at least one risky behavior, and 23.5% of the
assessed days included endorsement of at least one risky behavior.
Further correlations were conducted to examine the relations between both cortisol
samples and summary cortisol indices (i.e., CAR AUCg, CAR AUCi, and daily slope) across the
three days. Table 5 displays the intercorrelations among the five saliva samples across all three
of the saliva collection days. These correlations ranged from moderate to high (rs=.21-.74). All
cortisol values were positively correlated with their respective sample number on the other two
days, with the exception of day 2 sample 4 and day 3 sample 4, which were not significantly
correlated. Intercorrelations between CAR AUCg, CAR AUCi, and daily slope across the three
days are presented in Table 6. Each of the cortisol indices was positively correlated with the
same index on the other two days, with the exception of the CAR AUCi on days 2 and 3.
Past-Year Risky Behaviors and Diurnal Cortisol
In the models testing our first hypothesis examining whether there were main effects of
different types of risk behaviors over the past year on diurnal cortisol indices, we found two
significant main effects: both substance use and sexual risk behaviors were related to lower CAR
AUCg. Table 7 displays the model statistics for each type of risk behavior (substance use, sexual
risk, aggressive delinquent, and criminal delinquent behaviors, as well as total risk behaviors) on
each of the primary cortisol indices (CAR AUCg, CAR AUCi, and daily slope). Additionally,
we conducted post-hoc analyses with each of the five cortisol samples across the three days, also
displayed in Table 7. First, we found a negative main effect of substance use behaviors on total
RISKY BEHAVIORS AND DIURNAL CORTISOL 30
morning cortisol output (CAR AUCg), such that high levels of substance use over the past year
related to lower CAR AUCg. This finding was consistent with the negative main effect of
substance use over the past year on cortisol samples 1 and 2. Figure 2 depicts the raw mean
diurnal cortisol values of those in the lower, middle, and upper third of the sample for reported
substance use, without adjusting for any covariates. This figure illustrates the low morning
cortisol levels in those reporting the most substance use.
Similarly, there was a negative main effect of sexual risk behaviors on CAR AUCg;
greater sexual risk behaviors related to lower morning cortisol output. There was also a
significant negative main effect of sexual risk behaviors on cortisol daily slope. These findings
are also reflected in the negative main effect of sexual risk behaviors on cortisol samples 1, 2, 3,
and 4, shown in Table 7. Figure 3 displays the mean raw cortisol values for those in the lower,
middle, and upper third of the sample for reported sexual risk behaviors, which illustrates the
lower morning cortisol levels and flatter daily slopes in those reporting the greatest number of
sexual risk behaviors. There were no significant differences in CAR AUCi or daily slope for
past year substance use, aggressive delinquent behaviors, criminal delinquent behaviors, or total
risk behaviors. There were no significant differences in CAR AUCg for aggressive delinquent,
criminal delinquent, or total risk behaviors, and there were no significant differences in CAR
AUCi for sexual risk behaviors.
Daily Risky Behaviors and Diurnal Cortisol
Hypothesis two investigated whether there were day-to-day differences in diurnal cortisol
following risk behaviors. The models revealed one significant intraindividual difference when
examining previous-day risk behaviors and total morning cortisol output (CAR AUCg). Table 8
displays the model statistics for previous-day risk behaviors and the primary diurnal cortisol
RISKY BEHAVIORS AND DIURNAL CORTISOL 31
indices (CAR AUCg, CAR AUCi, and daily slope) as well as for previous-day risk behaviors
and each of the five cortisol samples. Greater risk behaviors the previous day related to greater
CAR AUCg the following morning. This finding is further supported by the significant relation
between previous-day risk behaviors and intraindividually higher concentrations of cortisol
sample 2. Figure 4 depicts the heightened raw cortisol levels the morning following risky
behaviors, displaying the mean cortisol values of 1) individuals on days following no risk
behaviors and 2) individuals on days following any endorsement of risk behaviors. There were
no significant findings for previous-day risk behaviors and CAR AUCi or cortisol daily slope.
Sex Differences
Hypothesis 3 predicted sex differences in the relation between risk behaviors and diurnal
cortisol, which were not supported in our study. Sex was examined for both main effect
differences as well as for cross-level interactions with daily risky behaviors and diurnal cortisol
indices. There were no significant main effects of sex on any of the diurnal cortisol indices
(CAR AUCg, CAR AUCi, or daily slope), nor were there any significant cross-level interactions
between sex and daily risk behaviors on the cortisol indices; power analyses suggest that these
nonsignificant results are not a result of insufficient statistical power.
Discussion
These findings expand our knowledge of the physiological correlates for specific risk
behaviors in adolescence, both cumulatively and on a daily basis. In partial support of sensation-
seeking theory, this study provides evidence for overall lower morning levels of cortisol in
adolescents who engage in frequent substance use and sexual risk behaviors, as well as daily
increases in morning cortisol levels following adolescents’ day-to-day risk behaviors. Moreover,
the vast majority of participants in our sample endorsed at least one type of risk behavior over
RISKY BEHAVIORS AND DIURNAL CORTISOL 32
the past year, underscoring the frequency of risk behaviors in adolescent samples. We found
partial support for our first hypothesis examining previous-year risk behaviors and diurnal
cortisol. As predicted, sexual risk behaviors related to lower morning cortisol levels, but only for
CAR AUCg, and not CAR AUCi. In contrast to our hypothesis, substance use also related to
lower morning CAR AUCg. Additional novel findings supported our second hypothesis, as we
found evidence of intraindividual differences in diurnal cortisol on days following risk
behaviors. Specifically, youth showed higher total morning cortisol output (CAR AUCg) after
engaging in risk behaviors the previous day, which is suggestive of a temporary physiological
“boost” the morning after exhibiting risk behaviors. We did not find evidence of our third
hypothesis examining sex differences in the relation between risk behaviors and diurnal cortisol.
The CAR and Previous-Year Risky Behaviors
Participants who engaged in frequent substance use and sexual risk behaviors over the
past year demonstrated lower morning awakening responses, as measured with CAR AUCg. In
contrast to aggressive and criminal delinquent behaviors, both substance use and sexual risk
behaviors can be construed as pleasurable activities. Additionally, both behaviors are
significantly related to measures of the behavioral approach motivational system (BAS; Carver
& White, 1994; Voigt, Dillard, Braddock, Anderson, Sapory, & Stephenson, 2009), and both are
closely tied to dopaminergic systems (Lovallo, 2006; Melis & Argiolas, 1995). In contrast,
aggressive and criminal delinquent behaviors may be reflective of peer pressure or avoidance of
negative consequences, e.g., peer rejection or feelings of vulnerability, without the goal of
enjoyment-seeking. As such, these delinquent behaviors may be qualitatively different from
substance use and sexual risk behaviors. Moreover, substance use and sexual risk behaviors
were more commonly endorsed in our sample than were aggressive and criminal delinquent
RISKY BEHAVIORS AND DIURNAL CORTISOL 33
behaviors, with greater variability. This may have limited our ability to detect significant
differences in diurnal cortisol related to aggressive and criminal delinquent behaviors.
The finding that substance use and sexual risk behaviors relate to overall lower morning
cortisol output (CAR AUCg) in youth provides support for the hypoarousal described in the
sensation-seeking theory—that individuals who demonstrate hypoarousal are more inclined to
engage in risk behaviors (Raine et al., 1998). Interestingly, our results were significant for the
CAR AUCg, which is thought to be a more reliable measurement for trait characteristics of the
HPA axis than CAR AUCi (Hellhammer et al, 2007). Our findings suggest that substance use
and sexual risk behaviors relate to lower overall morning levels of cortisol, but not to smaller
morning increases. That is, individuals who engage in substance use and sexual risk behaviors
may still exhibit a morning awakening increase in cortisol, captured by the CAR AUCi, but, as
indicated when we tested differences in the awakening cortisol sample, they start the day with
lower overall cortisol levels and have lower morning cortisol output (CAR AUCg). Taken
together, these results emphasize that adolescents who engage in greater substance use and
sexual risk behaviors exhibit lower overall morning cortisol levels, indicative of low trait
morning cortisol output.
Intraindividual Elevations in Morning Cortisol Following Risky Behaviors
An important contribution of the present paper is the finding of heightened total morning
cortisol output (CAR AUCg) on days following adolescents’ risk behaviors. These results also
map onto sensation-seeking theory, which suggests that individuals with low levels of arousal
engage in risk behaviors to initiate arousal (Raine et al., 1998). Although the term “sensation-
seeking” suggests more immediate, reactive arousal, the results here demonstrated heightened
arousal as higher cortisol levels the morning following risky behaviors. This extends the
RISKY BEHAVIORS AND DIURNAL CORTISOL 34
findings of immediate physiological responses to risky activities (e.g., Mendelson et al., 1971;
Granger et al., 2007; Sinha et al., 2003; Wolfing, Flor, & Grusser, 2008) to greater arousal the
following morning.
Beyond sensation-seeking theory, there are several other potential explanations for our
finding of heightened cortisol output the morning following risk behaviors. This may be a result
of lasting physiological arousal from the previous day’s risk behavior, excitement while thinking
about the previous day’s risk behavior, or anxiety upon awakening in anticipation of the
consequences of the previous day’s behavior (Fries et al., 2009). Moreover, adolescents may
feel less prepared for the day ahead following yesterday’s risky behaviors, prompting anxiety
and higher morning cortisol levels. Future research is needed to further explore the reason for
heightened morning cortisol levels following days of risky behaviors, which may be reflective of
exhilaration and excitement or, alternatively, anxiety related to potential negative consequences
of yesterday’s behavior. Importantly, because we cannot determine causality or the direction of
effects related to risky behaviors and CARs, future research is needed to help clarify this
association.
It is striking that the direction of results in the present study varied depending on the
timeframe of focus. Global assessment of risk behaviors and diurnal cortisol demonstrated
weaker CAR AUCgs, whereas day-to-day assessments of risk behaviors and diurnal cortisol
demonstrated heightened CAR AUCgs following these behaviors. Through examining both
general risk behaviors over the past year in combination with daily risk behaviors, this study was
able to capture a clearer picture of both immediate and cumulative associations between risky
behaviors and physiology.
RISKY BEHAVIORS AND DIURNAL CORTISOL 35
The issue of directionality raises important questions for the interpretation of this study’s
results, as does the possibility of third variables that may influence both risk behaviors and
diurnal cortisol. This study did not allow us to test whether youth who experience blunted
diurnal cortisol patterns turn to risk behaviors, or whether risk behaviors lead to physiological
blunting. Moreover, early adverse childhood experiences may lead to both risk behaviors and
physiological blunting. For example, a study examining childhood adversity demonstrated that
rates of unwanted pregnancy in adolescence, an index of sexual risk behaviors, increased
incrementally with each additional adverse childhood experience (Hillis et al., 2005). Similarly,
rates of substance use increase with exposure to stressful childhood experiences (Dube et al.,
2003). However, childhood trauma also is associated with blunted diurnal cortisol levels over
time (Trickett et al., 2010). In addition to childhood adversity, there are other important
constructs related to both daily activities and risk behaviors, such as interactions with deviant
peers, that also warrant investigation (Prinstein, Boergers, & Spirito, 2001). Future studies
would benefit from the incorporation of a number of constructs related to risky behaviors in
adolescents.
Limitations
There are a number of limitations to this study that should be mentioned. First,
our measure of daily risk behaviors may have underestimated the number of risk behaviors that
the adolescents in our sample exhibited. We required adolescents to collect saliva on weekdays
in order to maximize consistency and to ensure that they would be more likely to wake up before
8:00am. However, many risk behaviors tend to occur over the weekend when young people are
likely to be out late, partying, and spending time with friends. Still, the base rate of risk
behaviors during the week was sufficient enough for the detection of significant findings. Future
RISKY BEHAVIORS AND DIURNAL CORTISOL 36
studies would benefit from comparing risk behaviors and diurnal cortisol during the week and on
the weekend in order to assess diurnal HPA activity in relation to the full range of adolescents’
risk behaviors. Another consideration is that in order to maximize the validity of the cortisol
sampling, participants were instructed to abstain from certain risky behaviors, including smoking
and drinking alcohol. This may have reduced the mean levels of risk behaviors that we were
able to observe.
Another important consideration is that use of different substances results in a variety of
physiological changes; in our study, we examined all forms of substance use over the past year
as one measure. The most common substance used in the sample was alcohol (64.2%
endorsement), followed by marijuana (39.8% endorsement), both of which are depressants.
However, a wide variety of drug classes were included in our yearly measure of substance use.
It would be helpful to examine larger samples, and in turn, larger base-rates of substance use in
order to investigate differences between substances in relation to diurnal cortisol. Likewise,
when examining daily risk behaviors in the present study, we needed to include all exhibited risk
behaviors in our measure due to the low base-rate of endorsement. However, different types of
risky behaviors may differentially relate to day-to-day diurnal cortisol fluctuations and, with
more days, it might be possible analyze daily cortisol by category of risk.
Implications and Future Directions
The results of this study have important implications for understanding the physiological
correlates of risky behaviors in adolescents and emerging adults, as well as for the measurement
thereof. Engaging in an array of risk behaviors is, and will likely always be, a prevalent feature
of adolescence (Steinberg, 1999). Whereas prior research focused on diurnal cortisol and more
extreme forms of risky behaviors such as substance abuse and behaviors associated with conduct
RISKY BEHAVIORS AND DIURNAL CORTISOL 37
disorder (Fairchild et al., 2008; Wisniewski et al., 2006), our study demonstrated that even
common risky behaviors are related to alterations in adolescents’ diurnal cortisol patterns.
Accordingly, a thorough understanding of both salient health consequences in addition to the
more subtle physiological sequelae of substance use, sexual risk behaviors, and delinquent
behaviors could help mitigate some of the detrimental consequences of these behaviors. It is
crucial, however, to discern the point at which risk behaviors are harmful to youth, and not
merely reflective of developmentally normative experimentation. That is, many adolescents
engage in risk behaviors without substantial negative consequences, and pathologizing normative
adolescent experimentation would be a mistake. Yet, the present study demonstrated altered
diurnal cortisol patterns in those who engage in common risky behaviors, raising the question of
how we determine whether risky behaviors are “detrimental.”
Our findings highlight several potential avenues for future research. For example,
intervention studies have demonstrated changes in diurnal cortisol rhythms from pre to post-
intervention (Ciccetti, Rogosch, Toth, & Sturge-Apple, 2011; van Andel, Jansen, Grietens,
Knorth, & van der Gaag, in press). Preventive interventions that reduce risk behaviors and also
increase HPA regulation, such as psychotherapy, pharmacotherapy, exercise, or diet
modifications may offer important tools for reducing harmful levels of adolescent risk behaviors.
Another important extension of this research would be to examine daily cortisol levels along
with other physiological indices. For example, Popma and colleagues (2007) found an
association between testosterone and aggressive behaviors at low levels of cortisol, but not at
high levels of cortisol (Popma, Vermeiren, Geluk et al., 2007). Alpha-amylase, an index of the
sympathetic nervous system, has also been implicated as a potential moderator for disruptive
behaviors (Vries-Bouw, Jansen, Vermeiren et al., 2012). There is a dynamic interplay of
RISKY BEHAVIORS AND DIURNAL CORTISOL 38
hormones, brain activity, and enzymes both within the immediate physiological stress response
and within individuals’ diurnal rhythms. Investigating interactions between multiple
physiological indices might better explain their association with behaviors.
Conclusions
A thorough understanding of adolescents’ risky behaviors is vital for promoting adaptive
adolescent development as well as for the reduction of potentially dangerous consequences of
risky behaviors. The present study offers a biological lens through which to view these
behaviors, supporting that certain risk behaviors relate to overall lower morning levels of
cortisol, but temporarily is associated with a morning cortisol increase the following morning.
This study also underlines the value of examining both day-to-day risky behaviors and broader
measures of risky behaviors to allow for a more detailed investigation of their relation to
adolescents’ physiology. Furthermore, comprehensive investigations of diurnal HPA
functioning and daily risk behaviors, in conjunction with potential protective activities, could
help identify ways to minimize some of the negative consequences of risky behaviors.
Continued research is needed to better understand the distinction between normative risk-taking
behaviors in adolescence and hazardous risk-taking, and to examine whether various types of
risky behaviors have differential associations with physiology.

RISKY BEHAVIORS AND DIURNAL CORTISOL 39
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RISKY BEHAVIORS AND DIURNAL CORTISOL 50

!
Table 1

Descriptive Statistics of Cortisol Indices and Individual Samples, Risk Behavior Measures, and
Covariates

Males
M(SD)
Females
M(SD)
Total
M(SD)
CAR AUCg .31 (.21) .34 (.24) .33 (.23)
CAR AUCi .03 (.13) .07 (.19) .05 (.16)
Daily cortisol slope .46 (.41) .53 (.39) .50 (.40)
Cortisol 1: awakening sample .42 (.35) .41 (.42) .41 (.38)
Cortisol 2: awake+20min sample .49 (.34) .55 (.44) .52 (.39)
Cortisol 3: awake+40min sample .46 (.43) .53 (.39) .49 (.41)
Cortisol 4: 4pm sample .19 (.33) .19 (.36) .19 (.34)
Cortisol 5: 9pm sample .15 (.33) .13 (.29) .14 (.31)
Past-year substance use .56 (.64) .38 (.53) .48 (.60)
Past-year sexual risk behaviors .77 (1.02) .65 (1.00) .72 (1.01)
Past-year aggressive delinquent behaviors .09 (.26) .03 (.11) .06 (.20)
Past-year criminal delinquent behaviors .29 (.37) .20 (.43) .25 (.40)
Daily risk behaviors .04 (.08) .03 (.10) .04 (.08)
Time of awakening 7.43 (1.04) 7.35 (.86) 7.39 (.96)
Age 18.14 (1.14) 17.93 (.99) 18.06 (1.09)
Taking medication 9.43% 19.56% 14.14%
Cotinine 7.80 (28.62) 1.25 (6.50)

4.76 (21.62)
Depressive symptoms 6.52 (7.47) 8.15 (6.73) 7.27 (6.75)
Ate/drank/exercised/mouth sores 25.2% 17.7% 21.7%
Hours of previous night’s sleep 7.29 (1.73) 7.34 (1.69) 7.31 (1.71)
Completed daily questionnaire within 24 hours 88.9% 92.0% 90.4%
Note. CAR=Cortisol awakening response. AUCg=Area under the curve with respect to ground.
AUCi=Area under the curve with respect to increase. There were no significant sex differences
in the presented variables. Cortisol concentration values are in µg/dL.
RISKY BEHAVIORS AND DIURNAL CORTISOL 51

Table 2

Intercorrelations among Model Variables

*p<.05, **p<.01, ***p<.001
Note. All correlated values comprise the mean of each participants’ values across days. Cort=cortisol sample. SU=past-year substance use. SR=past-year sexual risk.
AD=past-year aggressive delinquent behaviors. CD=past-year criminal delinquent behaviors. Time refers to the time of first sample. Meds=medications. Cot=cotinine.
Dep=depressive symptoms. Comp=pre-sample compliance (ate/drank/exercised/had mouths sores prior to sample collection). Sleep=hours of sleep. In 24=completed daily
questionnaires within 24 hours. !

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
1. CAR AUCg -
2. CAR AUCi .34** -
3. Daily Slope .83*** .45*** -
4. Cort 1 .77*** -.24* .59*** -
5. Cort 2 .97*** .37*** .80*** .72*** -
6. Cort 3 .90*** .50*** .81*** .55*** .83*** -
7. Cort 4 .39*** -.02 .18 .32** .36*** .36*** -
8. Cort 5 .30** -.07 .00 .25* .25* .28** .55*** -
9. SU -.02 -.02 .03 -.01 -.04 -.03 .09 .05 -
10. SR -.29*** -.29** -.22* -.18 -.30** -.23* -.16 -.01 .44*** -
11. AD -.04 -.04 .10 -.07 .01 -.06 -.01 -.09 .25* .13 -
12. CD .02 .02 .09 .01 .02 -.03 .01 -.04 .36*** .16 .34** -
13. Daily Risk -.13 -.13 -.15 -.02 -.14 -.20 -.01 .03 .31** .36** .15 .23* -
14. Time -.07 .02 -.15 -.08 -.05 -.09 .26* .25* .17 .06 .03 .10 .00 -
15. Age -.26* -.10 -.21* -.20 -.21* -.26** .06 -.04 .10 .21* .04 -.10 .01 .29** -
16. Meds .10 .19 .14 .00 .10 .13 .08 -.01 -.02 -.09 .17 -.02 -.18 .11 -.11 -
17. Cot .05 -.08 -.02 .02 .08 .03 .25* .25* .15 .07 .11 .03 .12 .26* .26* -.07 -
18. Dep .17 .27** .25* .04 .17 .21* .17 -.01 .25* -.17 .13 .25* -.06 .01 -.05 .21* -.08 -
19. Comp -.02 -.11 -.04 .04 -.02 -.08 .01 .07 .05 -.02 .04 .12 .11 .13 .09 .06 .08 .11 -
20. Sleep -.01 .02 -.02 -.03 .02 -.01 -.17 .03 -.07 -.03 .05 -.22* .00 .18 .05 .17 .01 .00 .03 -
21. In 24 -.09 .09 .00 -.12 -.09 -.01 -.09 -.29** -.25* -.10 .18 -.18 -.06 -.13 -.02 .09 -.14 .09 .04 .07 -
RISKY BEHAVIORS AND DIURNAL CORTISOL 52
Table 3
!
Descriptive Statistics for Past Year Risk Behavior Items

Item
Reported
max
Reported
max label
Possible
max
Possible
max label Mean (SD)
% of
sample
endorsing
behavior
Substance Use Risk Behaviors
Rode in a car driven by a friend drinking alcohol 5 > 10 times 5 > 10 times .64 (1.24) 28.6
Drove a car when drinking alcohol 4 20-39 days 5 > 10 times .21 (.64) 13.5
Had at least one drink of alcohol 5 ≥ 40 days 5 ≥ 40 days 1.54 (1.60) 64.2
Had ≥ 5 drinks of alcohol 5 ≥ 40 days 5 ≥ 40 days .79 (1.28) 37.4
Smoked at least one cigarette 5 ≥ 40 days 5 ≥ 40 days .66 (1.44) 23.4
Chewed tobacco 2 3-9 days 5 ≥ 40 days .07 (.34) 4.8
Smoked tobacco 4 20-39 days 5 ≥ 40 days .35 (.83) 16.7
Used marijuana 5 ≥ 40 days 5 ≥ 40 days 1.10 (1.66) 39.8
Used ecstasy 5 ≥ 40 days 5 ≥ 40 days .11 (.53) 7.2
Taken Rx drugs to get high 4 20-39 days 5 ≥ 40 days .19 (.64) 10.4
Taken Rx drugs AND alcohol to get high 3 ≥ 40 days 5 ≥ 40 days .08 (.37) 5.6
Sexual Risk Behaviors
Number of people with whom you’ve had sex 5 6 or more 5 6 or more 1.02 (1.28) 54.0
Used drugs or alcohol before sex 5 > 10 times 5 > 10 times .48 (1.05) 22.2
Had sex without a condom 5 > 10 times 5 > 10 times .95 (1.66) 31.5
Aggressive Delinquent Behaviors
Snatched someone’s purse or wallet 3 5-10 times 5 ≥ 20 times .02 (.27) .8
Cruel to an animal 1 1 times 5 ≥ 20 times .02 (.13) 1.6
Used verbal threats to get something from a kid 3 5-10 times 5 ≥ 20 times .09 (.44) 4.0
Used physical force to get something from a kid 3 5-10 times 5 ≥ 20 times .06 (.35) 4.0
Threatened to beat up a kid to scare him/her 4 11-20 times 5 ≥ 20 times .13 (.54) 7.1
Carried a weapon such as a gun, knife, or club 5 > 10 days 5 > 10 days .42 (1.22) 12.9
Carried a weapon at school 2 3-9 times 5 > 40 times .06 (.29) 4.0
In a physical fight 7 ≥ 12 times 7 ≥ 12 times .38 (.99) 20.0
RISKY BEHAVIORS AND DIURNAL CORTISOL 53
In a physical fight on school property 4 6 or 7 times 7 ≥ 12 times .07 (.42) 4.0
In a physical fight and needed medical treatment 1 1 time 4 ≥ 6 times .02 (.13) 1.6
General Delinquent/Criminal Behaviors
Broke, damaged, destroyed family belongings 3 5-10 times 5 > 20 times .18 (.51) 12.8
Broke, damaged, destroyed school belongings 3 5-10 times 5 > 20 times .10 (.41) 6.4
Broke, damaged, destroyed others’ belongings 3 5-10 times 5 > 20 times .14 (.53) 8.0
Stole or tried to steal something worth ≤ $5 5 > 20 times 5 > 20 times .62 (1.23) 25.6
Stole or tried to steal something worth ≥ $5 5 > 20 times 5 > 20 times .38 (.98) 17.6
Broke into a building or car to steal something 2 2-4 times 5 > 20 times .06 (.28) 5.6
Took something from a store or restaurant 5 ≥ 40 times 5 ≥ 40 times .38 (.89) 24.0
Took money from parents’ purse or dresser 3 5-10 times 5 > 20 times .33 (.75) 19.0
Took anything else at home not belonging to you 3 5-10 times 5 > 20 times .44 (.74) 31.0
Took things without asking at school 3 10-19 times 5 ≥ 40 times .21 (.56) 15.9
Suspended, expelled, or sent home from school 2 2-4 times 5 > 20 times .12 (.43) 7.9
Sprayed paint or tagged 5 > 20 times 5 > 20 times .20 (.72) 9.5
Set fire to property or tried to 0 0 times 5 > 20 times .00 (.00) 0.0
Number of non-parking traffic citations 3 4-6 times 5 > 10 times .29 (.57) 23.0

Note. All minimum reported values were 0.

RISKY BEHAVIORS AND DIURNAL CORTISOL 54
Table 4

Descriptive Statistics for Daily Risk Items

Item Max
Mean
(SD)
% of sample
reporting
behavior
% of days
behaviors
were reported
Drove or rode in a car without wearing a seatbelt or drove over the speed limit 3 .18 (.51) 22.2 13.1
Broke, damaged, or destroyed something belonging to others on purpose 1 .01 (.08) 1.0 .62
Stole or tried to steal something 1 .01 (.06) 2.0 .62
Broke into a building or car (or tried to break in) to steal something or just look
around
1 .00 (.06) 1.0 .31
Rode in a car or other vehicle driven by someone who had been drinking alcohol
or had used drugs
1 .01 (.10) 3.0 .93
Drove a car or other vehicle when I had been drinking alcohol or had used drugs 1 .01 (.11) 1.0 1.2
Drank alcohol 2 .02 (.18) 3.0 1.2
Smoked a cigarette, cigar, pipe, or chewed tobacco 2 .02 (.18) 3.0 1.8
Used an illegal drug (such as marijuana, cocaine, ecstasy) or prescription drug
not prescribed for me
2 .04 (.23) 5.1 3.4
Cut class (high school) 1 .02 (.13) 1.0 .62
Got in trouble at school/ send out of class or to an administrator (high school) 1 .03 (.16) 2.0 .93
Cheated on/plagiarized school assignment or test (high school) 1 .04 (.19) 4.0 1.2
Tardy for class/classes (high school) 2 .12 (.36) 8.1 4.0
Got a dress code violation (high school) 1 .03 (.16) 2.0 2.8
Did not go to class when I was supposed to be there (not due to illness or
emergency) (college)
3 .04 (.35) 1.0 .93
Cheated on/plagiarized school assignment or test (college) 0 .00 (.00) 0.0 0.0
Did not go to work when I was scheduled to be there (not due to illness or
emergency)
1 .01 (.12) 1.0 .31
Got in trouble at work 3 .05 (.36) 2.0 .62
Note. All minimum reported values were 0. The anchors for the items were as follows: 0=Not at all, 1=A little, 2=Somewhat, 3=A lot.
All values are based on the four days of home data collection utilized in the present study.
RISKY BEHAVIORS AND DIURNAL CORTISOL 55
Table 5

Intercorrelations among Five Cortisol Samples for Days 1, 2, and 3

*p < .05, **p < .01, ***p < .001
Note. D=day, S=sample number. Samples 1-3 were collected at awakening, awakening+20min, and awakening+40min. Sample 4
was collected at 4pm, and sample 5 was collected at 9pm.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. D1S1 -
2. D1S2 .57*** -
3. D1S3 .41*** .74*** -
4. D1S4 .25* .26* .22* -
5. D1S5 .22* .25* .20 .42*** -
6. D2S1 .43*** .34** .37*** .07 .23* -
7. D2S2 .43*** .51*** .43*** .22* .24* .55*** -
8. D2S3 .34** .50** .39*** .26* .16 .31** .72*** -
9. D2S4 .20 .17 .25* .41*** .17 .09 .21* .19 -
10. D2S5 .15 .14 .22* .34** .47*** .07 .18 .14 .43*** -
11. D3S1 .34** .20 .30** .18 .25* .57*** .45*** .26* .17 .20 -
12. D3S2 .27* .33** .48*** .17 .10 .57*** .53*** .39*** .24* .15 .73*** -
13. D3S3 .17 .34** .56*** .05 -.01 .32** .37*** .27*** .20 .14 .50*** .76*** -
14. D3S4 .06 .04 .10 .29** .34** .18 .16 .03 .20 .22* .30** .25* .21* -
15. D3S5 .06 .05 .13 .34** .45*** .11 .25* .14 .19 .36** .23* .15 .10 .46*** -
RISKY BEHAVIORS AND DIURNAL CORTISOL 56

Table 6

Intercorrelations among Cortisol Indices for Days 1, 2, and 3
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!

* p < .05, ** p < .01, *** p < .001
Note. D=day. CAR=Cortisol awakening response. AUCg=Area under the curve with respect to ground. AUCi=Area under the curve
with respect to increase.

1 2 3 4 5 6 7 8 9
1. D1 CAR AUCg -
2. D2 CAR AUCg .54*** -
3. D3 CAR AUCg .43*** .59*** -
4. D1 CAR AUCi .34** .10 .18 -
5. D2 CAR AUCi .19 .35** -.02 .33*** -
6. D3 CAR AUCi .25* .59*** .32** .46*** .18 -
7. D1 Slope .82*** .37*** .38*** .42*** .21* .39*** -
8. D2 Slope .42*** .80*** .42*** .23* .45*** .18 .32** -
9. D3 Slope .43*** .32** .74*** .34** .07 .40*** .57*** .38*** -
RISKY BEHAVIORS AND DIURNAL CORTISOL 57
Table 7

Main Effects of Past-Year Risky Behaviors on Diurnal Cortisol Indices

*p<.05, ** p<.01, ***p<.001
Note. Coeff=Coefficient. SE=Standard error. All above models adjusted for the following covariates: time of awakening, age,
medications, and cotinine. Each risk behavior type was run in a separate model.

Diurnal Cortisol Indices Individual Cortisol Samples
Risky Behaviors
CAR AUCg
Coeff (SE)
CAR AUCi
Coeff (SE)
Daily Slope
Coeff (SE)
Awakening
Sample
Coeff (SE)
20min Post-
Awakening
Coeff (SE)
40-min Post-
Awakening
Coeff (SE)
4pm Sample
Coeff (SE)
9pm
Sample
Coeff (SE)
Substance use -.23 (.08)** .004 (.09) -.16 (.11) -.41 (.13)** -.41 (.14)** -.29 (.15) -.10 (.05) .03 (.11)
Sexual risk -.26 (.11)* -.21 (.13) -.24 (.11)* -.32 (.12)** -.46 (.12)*** -.35 (.13)** -.28 (.09)** -.06 (.11)
Aggressive Delinquent .02 (.14) -.10 (.10) .04 (.13) -.16 (.22) -.17 (.26) -.15 (.22) .00 (.15) -.04 (.14)
Criminal Delinquent .03 (.07) .01 (.11) .09 (.07) -.03 (.09) .00 (.11) -.06 (.11) .03 (.11) .07 (.12)
Total risk behaviors -.17 (.11) -.11 (.10) -.11 (.11) -.34 (.15)* -.39 (.18)* -.32 (.18) -.13 (.12) .00 (.14)
RISKY BEHAVIORS AND DIURNAL CORTISOL 58
Table 8

Previous-Day Risky Behaviors and Intraindividual Differences in Diurnal Cortisol Indices

* p < .05, ** p < .01, *** p < .001
Note. CAR=Cortisol awakening response. AUCg=Area under the curve with respect to ground. AUCi=Area under the curve with
respect to increase. Coeff=Coefficient. SE=Standard error.

CAR AUCg
Coeff (SE)
CAR AUCi
Coeff (SE)
Daily Slope
Coeff (SE)
Awakening
Sample
Coeff (SE)
20min Post-
Awakening
Coeff (SE)
40-min Post-
Awakening
Coeff (SE)
4pm Sample
Coeff (SE)
9pm
Sample
Coeff (SE)
Previous-
day risk .23 (.11)* -.15 (.14) .12 (.08) .02 (.06) .15 (.07)* .01 (.06) .11 (.07) .12 (.06)
Level 1 Covariates
Smoking
(Cotinine)
.07 (.03)* .06 (.04) .03 (.02) -.12 (.10) .06 (.02)* .02 (.04) .08 (.04)* .09 (.06)
Previous-
day cortisol
-.32 (.05)*** -.58 (.06)*** -.41 (.09)*** -.44 (.05)*** -.42*** (.08) -.56 (.05)*** -.45 (.06)*** -.54 (.05)***
Time of
awakening
-.04 (.06) -.02 (.04) -.07 (.05) .06 (.09) .05 (.05) -.02 (.04) .07 (.06) .05 (.03)
Level 2 Covariates
Age -.26 (.11)* -.03 (.16) -.31 (.11)** -.20 (.11) -.28 (.12)* -.27 (.12)* .10 (.12) -.01 (.11)
Medication .08 (.12) .23 (.12) .18 (.14) .02 (.09) .11 (.10) .15 (.11) .05 (.11) -.05 (.09)
! 59
!

Alarms Time Saliva Collection Activity
1. Alarm 1 T1 (prior to 8am) Wake-up and Rinse Mouth
2. T1+10 min Saliva Sample 1 2-min questionnaire
3. Alarm 2 T1+30 min Saliva Sample 2 2-min questionnaire
4. Alarm 3 T1+50 min Saliva Sample 3 2-min questionnaire
5. Alarm 4 3:50pm Rinse Mouth
6. 4:00pm Saliva Sample 4 2-min questionnaire
7. 5:00pm Home Data Link Emailed
8. Alarm 5 8:50pm Rinse Mouth
9. 9:00pm Saliva Sample 5 2-min questionnaire

Figure 1. Daily home data collection procedures for saliva samples and daily risk behavior data.
! 60
!
!
!
!
Figure 2. Diurnal cortisol of participants in the lower, middle, and upper third of reported
substance use behaviors over the past year.
! !

0
0.1
0.2
0.3
0.4
0.5
0.6
Awakening Awake + 20 Awake + 40 4pm 9pm
Low Substance Use
Mid Substance Use
High Substance Use
Cortisol Concentration (µg/dL)
! 61
Figure 3. Diurnal cortisol of participants in the lower, middle, and upper third of reported sexual
risk behaviors over the past year. !

0!
0.1!
0.2!
0.3!
0.4!
0.5!
0.6!
Awakening! Awake!+!20! Awake!+!40! 4pm! 9pm!
Low!Sexual!Risk!Bx!
Mid!Sexual!Risk!Bx!
High!Sexual!Risk!Bx!
Cortisol Concentration (µg/dL)
! 62

Figure 4. Diurnal cortisol patterns of youth who did versus did not engage in previous-day risk
behaviors.

0!
0.1!
0.2!
0.3!
0.4!
0.5!
0.6!
Awakening!Awake!+!20!Awake!+!40! 4pm! 9pm!
No Previous-Day Risk
Behavior
Any Previous-Day Risk
Behavior
Cortisol Concentration (µg/dL)
! 63
Running head: INTERPERSONAL CONFLICT AND DIURNAL CORTISOL

Conflict with Family and Friends: Associations with Inter- and Intraindividual Differences in
Adolescents’ Diurnal HPA Activity

Lauren A. Spies Shapiro
University of Southern California

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 64
Abstract
Adolescence is marked by greater conflict with parents and substantial importance of
peer relationships. Previous studies have related extreme experiences of conflict, such as abuse,
to HPA axis functioning. Despite the evidence that interpersonal conflict and the diurnal pattern
of the HPA axis are both linked to important developmental outcomes, few studies have
examined their relation in detail within adolescent samples. The present study addresses this gap
in research, examining several questions: 1) How do daily experiences of conflict relate to
between-person and within-person differences in adolescents’ cortisol awakening responses
(CARs)? 2) How does adolescents’ history of exposure to parent-to-youth aggression relate to
their CARs? 3) Does parent-to-youth aggression history moderate the relation between current
experiences of daily conflict and the CAR? For each of these questions, we examined sex
differences. Participants provided saliva samples over three days in addition to daily reports of
interpersonal conflict with parents and peers. Multilevel models revealed that greater average
daily conflict with fathers related to blunted CARs in males, but not females, indicative of HPA
attenuation, whereas daily conflict with peers related to heightened CARs in females, but not
males. Day-to-day analyses indicated that youth exhibited heightened CARs on the same day as
conflict with mothers, and weaker CARs the day following conflict with mothers. Consistent
with the attenuation theory and allostatic load, adolescents with more parent-to-youth aggression
in their history showed flatter CARs. Future directions and implications of these findings for
adolescent psychosocial development and physical health are discussed.
Key words: Adolescence, diurnal cortisol, interpersonal conflict, family aggression
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 65
Introduction
Adolescence is a time of figuring out how one is unique as well as where one fits in, both
of which bring about significant shifts in the adolescent’s social world. As adolescents exert
their individuality and independence, family relationships become less hierarchical and also
more conflict prone (Laursen, 1995; Smetana, Yau, Reestrepo, & Braeges, 1991a). Relatedly, as
adolescents search for their identity and seek support for their ideas, peer relationships often
assume center stage (Furman & Burmester, 1992; Larsen & Richards, 1991). Adolescence
typically is described as a stressful time of life (Arnett, 1999), due in part to the conflicts related
to the evolving relationships with family and peers, as well as to other teenage pressures
associated with school and future plans.
Stressors evoke a variety of reactions linked to adolescents’ regulatory biological stress
systems, which also are undergoing change during this developmental stage (Blumberg et al.,
2004; Liu et al., 2012). The hypothalamic-pituitary-adrenocortical (HPA) axis, part of the
biological ‘fight vs. flight’ response system, is said to be particularly responsive to social threats
(Gunnar and Adam, 2012). However, the question of how social stressors map onto the HPA
axis is quite complicated, with the timing of the stressors a key point to consider. In general, the
HPA axis shows a normal circadian rhythm associated with the regulation of overall metabolic
functions, such as alertness and appetite (Adam, 2012). Overlaid on that daily rhythm, the HPA
axis also responds to acute stressors by initiating a cascade of hormones that results in the
secretion of cortisol into the blood stream within a short time after exposure to the stressful event
(Lovallo & Thomas, 2000). However, prolonged and chronic stressors can change the set point
of the normative fluctuations in the overall daily pattern of the HPA axis (McEwen, 1998).
Thus, short-term versus long-term social stressors can evoke different patterns in HPA activity.
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 66
It is important to understand how repeated stressors relate to the overall daily HPA rhythm, as
poorly regulated diurnal cortisol rhythms are linked with maladaptive mental and physical health
outcomes (Doane et al., 2013; McEwen, 1998; Repetti, Robles, & Reynolds, 2011).
The present study aims to understand the impact of both proximal and distal social
stressors on adolescents’ diurnal HPA activity by examining the following questions: 1) How do
current, proximal experiences of conflict relate to adolescents’ diurnal cortisol patterns? 2) How
do previous, distal experiences of parent-to-youth aggression over the past several years relate to
adolescents’ diurnal cortisol patterns? 3) Is the association between daily conflict and diurnal
cortisol different in adolescents with a history of exposure to parent-to-youth aggression?
Importantly, for each of these questions, we examined the presence of sex differences. Although
there is a sizable literature examining interpersonal conflict and cortisol activity, that literature
focuses largely on conflict with family members (e.g., Davies, Sturge-Apple, Cicchetti, &
Cummings, 2008; Slatcher & Robles, 2011), or focuses on extreme forms of conflict such as
abuse (Bruce, Fisher, Pears, & Levine, 2009; Trickett, Noll, Susman, Shenk, & Putnam, 2010).
This study examines everyday conflict and includes conflict with peers as well as parents.
Diurnal HPA Activity and the Cortisol Awakening Response (CAR)
The circadian rhythm of the HPA axis generally shows an immediate release of cortisol
into the blood stream soon after awakening with a spike in cortisol within 30 to 40 minutes,
followed by a downward slope throughout the day, reaching its lowest levels at nighttime
(Edwards, Evans, Hucklebridge, & Clow, 2001). Alterations in overall HPA axis activity are
best studied through this diurnal pattern of cortisol (Wolf, Nicholls, & Chen, 2008).
The Cortisol Awakening Response (CAR), the term used to describe the rapid spike in
cortisol following awakening, coincides with rapid switches in activation between cortical and
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 67
sub-cortical brain regions as the individual attains consciousness and then alertness, and is
evidenced in children, adolescents, and adults (Clow, Hucklebridge, Stalder, Evans, & Thorn,
2010; Pruessner et al., 1997). The hippocampus and other brain regions within the limbic system
initiate the CAR, and there is a certain degree of genetic influence contributing to trait
differences in CAR rhythms across persons (Wust et al., 2000a). The CAR relates to increased
interest in exploration and cognitive processes associated with learning (De Kloet, 1991; De
Kloet, Vreugdenhil, Oitzl, & Joels, 1998; Gunnar & Vazquez, 2001). The CAR also is
connected to situational factors (Hellhammer et al., 2007), and heightened CAR is associated
with anticipation of the upcoming demands the day (Fries, Dettenborn, & Kirschbaum, 2009;
Rohleder, Beulen, Chen, Wolf, & Kirschbaum, 2007). The CAR is commonly measured through
two indices: 1) the total cortisol increase above the awakening cortisol level, measured with the
area under the curve with respect to increase (CAR AUCi), and 2) the total morning cortisol
output, measured with the area under the curve with respect to ground (CAR AUCg; Pruessner,
Kirschbaum, Meinlschmid, & Hellhammer, 2003). Based on a meta-analysis (Chida & Steptoe,
2008), both CAR AUCi and CAR AUCg were associated with general life stress and job stress.
Working adults, for example, exhibited higher morning cortisol levels on Mondays in
comparison to Sundays, and Monday morning cortisol levels were associated with higher job
demands and greater anticipation of work (Michalianou, Devereux, Rydstedt, & Cropley, 2011).
Similarly, ballroom dancers exhibited stronger CARs on the mornings of dance competitions
(Rohleder et al., 2007).
In adults and youth, prolonged stress is linked with alterations in HPA activity, although
the direction of adaptation is not always consistent (McEwen, 1998). In order to protect the body
from the negative health impact of prolonged HPA activation due to chronic stressors, the system
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 68
downregulates in order to stave off further harm (Susman, 2006). This process, whereby chronic
activation of the HPA axis ultimately wears down its ability to effectively respond to stress is
referred to as allostatic load (Repetti et al., 2011). In addition to attenuating responses to
immediate stressors (Ouellet-Morin et al., 2011), allostatic load also is evidenced in weaker
CARs and overall flatter daily slopes (Taylor, Karlamangla, Friedman, & Seeman, 2011; Repetti
et al., 2011; Trickett et al., 2010). The Chida and Steptoe (2009) meta-analysis shows that CAR
AUCi was inversely related to fatigue and burn-out and CAR AUCg was inversely related to
post-traumatic stress. Notably, allostatic load is related to numerous negative health
consequences, including suppression of the pro-inflammatory response and damage to
hippocampal cells (Tarullo & Gunnar, 2006; Kemeny, 2007).
Interpersonal Conflict, Cortisol, and the Importance of Adolescence
Interpersonal experiences in adolescence can involve varying levels of conflict, which
has been shown to initiate a response of the hypothalamic-adrenocortical (HPA) axis in both
laboratory and field-based studies (Granger, Schwartzman, Lehoux, Cooperman, & Ikede 1998;
Adam, 2006). Less is known, however, about the relation between interpersonal conflict and the
diurnal rhythm of the HPA axis, particularly in adolescent samples. With a prefrontal cortex that
is not fully matured and with less capacity to calmly assess stressful situations than adults
(Casey, Getz, & Galvan, 2008), adolescents may show heightened biological stress responses
and may have greater difficulty recovering from stress in comparison to adults. Highlighting the
need to examine these constructs in adolescent samples, there are inconsistent findings related to
differences in the diurnal cortisol rhythm of adolescents in comparison to that of children. For
example, one finding implicates heightened CARs in post-menarche female adolescents (Oskis,
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 69
Loveday, Hucklebridge, Thorn, & Clow, 2009), and another longitudinal study found flatter
daily slopes as children reached adolescence (Shirtcliff et al., 2013).
Another important consideration is sex, as female adolescents tend to experience greater
interpersonal engagement with peers and as well as more stress with peers than do males (Rose
& Rudolph, 2006). Moreover, there is evidence of heightened cortisol reactivity to social
rejection in females compared to males (Stroud, Salovey, & Epel, 2002). Within the context of
family conflict, there is evidence that parents tend to treat their adolescent sons and daughters
differently (McKinney & Renk, 2008). Also, male and female youth may respond differently to
conflict with their mothers versus fathers, for example, fathers’ harsh parenting is related to
greater overall aggressive behavior in their sons, but not their daughters (Chang, Schwartz,
Dodge, & McBride-Chang, 2003). This may translate into sex differences in diurnal cortisol
related to daily interpersonal stressors with family and peers, which we examine in the present
study. Examinations of sex differences related to HPA functioning in male versus female
adolescents are inconsistent, with some studies finding heightened cortisol levels throughout the
day in females compared to males (Gunnar, Wewerka, Frenn, Long, & Griggs, 2009; Klimes-
Dougan, Hastings, Granger, Usher, & Zahn-Waxler, 2001; Netherton, Goodyer, Tamplin, &
Herbert, 2004), and other studies finding no evidence of sex differences in diurnal HPA activity
(Knutsson et al., 1997; Kudielka & Kirschbaum, 2003).
The study of how social stressors map onto HPA axis responses is relatively new, and
there is much still to be learned about the complex interplay between past and present social
stressors and their relation to HPA activity. Furthermore, given the literature indicating that
adolescents experience greater conflict with mothers than with fathers (Smith & Forehand, 1986)
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 70
and place a high value on fitting in with peers, there are multiple social domains to consider
when examining adolescents’ diurnal cortisol in relation to daily social interactions.
Proximal Stressors and Diurnal Cortisol
There is consistent evidence of HPA reactivity in response to in-lab interpersonal
stressors for children, adolescents, and adults (Gunnar, Talge, & Herrera, 2009). Laboratory
paradigms that include some form of threat to social relationships or social standing reliably
evoke HPA responses in the majority of children and adolescents, as evidenced with parent-child
or interparental conflict tasks (Byrd-Craven, Granger, & Auer, 2011; Davies, Sturge-Apple,
Cicchetti, & Cummings, 2008), social evaluation tasks (e.g., giving a speech in front of a panel
of judges in the Trier Social Stress Test; Harkness, Stewart, Wynne-Edwards, 2011), as well as
in-lab rejection by peer confederates (Stroud et al., 2009). These studies examine in-lab
reactivity to stressors, only measuring cortisol reactivity over a short period of time—they do not
examine the diurnal cortisol pattern.
The small handful of studies that do examine diurnal HPA activity in relation to proximal
stressors across multiple days generally show overall elevations in diurnal cortisol levels with
greater reports of same-day interpersonal stressors. In one study, the CAR was viewed as a
marker for stress related to the upcoming day, a concept referred to as the anticipation
hypothesis, which is based on the idea that the CAR helps prepare individuals to confront the
day’s stressors (Schulz, Kirschbaum, Pruszner, & Hellhammer, 1998). To test the anticipation
hypothesis as well as whether there is a day-specific association between increased CARs and
daily stressors, Powell and Schlotz (2012) examined adults’ subjective anticipation of stress in
the morning, daily stressors and distress throughout the day, and the CAR on two consecutive
days. Results showed that individuals felt less distress in response to daily stressors when they
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 71
exhibited higher (versus lower) CARs, but only for CARs and stressors on the same-day—they
did not find an association between the previous day’s CAR and the next day’s stressors (Powell
& Schlotz, 2012). A separate study of adults’ self-reported social stressors and concurrent
cortisol levels, each captured 10 times per day across 5 days, showed elevated cortisol levels at
the same time as the social stressors, indicative of HPA upregulation (Jacobs et al., 2007). A
very similar investigation of adults’ self-reported stressors and daily cortisol collected over 4
days found greater total daily cortisol output (measured with cortisol AUCg across the entire
day) on the same day as greater reports of arguments and stress in the home (Stawski, Cichy,
Piazza, & Almeida, 2013). These results support the notion that elevated diurnal cortisol levels,
particularly in the CAR, may help individuals cope with same-day stressors, and also highlight
the utility of examining the link between CARs and stressors across several days to better
understand their day-to-day dynamics.
In contrast to findings of cortisol elevations concurrent with daily stressors, an
investigation of young children found that greater conflict in the home related to attenuated
same-day cortisol levels. An objective measure of stress in the home through one day of coded
parent-child conflict, recorded by the Electronically Activated Recorder, was collected
concurrently with one of two days of saliva sample collection. Findings demonstrated that
parent-child conflict related to lower average morning cortisol levels and flatter daily cortisol
slopes across the two cortisol collection days in children as young as 3 years old (Slatcher &
Robles, 2011). The above studies are very unique in that the cortisol and conflict are sampled on
the same days, spanning from one to four days, thereby addressing the question of whether
cortisol and conflict actually covary from day to day.

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 72
Chronic, Severe Interpersonal Stressors and Diurnal Cortisol Patterns
Studies that examine distal or chronic interpersonal stressors in relation to diurnal cortisol
activity tend to focus on either extreme forms of stressors that occurred in the past, such as
childhood trauma (Bruce et al., 2009; Kliewer, 2006; Trickett et al., 2010), or chronic,
substantial stressors in the home or at school (Vaillancourt et al., 2008; Wolf et al., 2008). The
majority of studies support an attenuation effect of chronic stressors, indicative of allostatic load.
Common measurements of chronic or severe interpersonal stressors include retrospective reports
through interviews (Wolf et al., 2008) and self-report questionnaires (Bevens, Cerbone, &
Overstreet, 2008; Maldonado et al., 2008; Vaillancourt et al., 2008), with one study examining
past trauma with coded child welfare records (Bruce et al., 2009). Children with a history of
chronic or traumatic stressors, such as parents’ divorce, poor parent-child relationship quality, or
high general stress in the home exhibit diurnal HPA attenuation, evidenced by lower morning
cortisol levels as indicated by one or two morning cortisol samples (Booth, Granger, & Shirtcliff,
2008; Maldonado et al., 2008), with other studies primarily indicating flatter daily cortisol slopes
through measurement of a morning sample in addition to a sample later in the day (Vaillancourt
et al., 2008; Wolf et al., 2008).
However, not all investigations of distal or chronic stressors consistently point to diurnal
HPA attenuation. Diurnal cortisol in relation to youths’ ongoing exposure to peer aggression
demonstrated mixed findings depending on participants’ sex; adolescent boys who experienced
verbal peer aggression over the past three months showed higher cortisol levels as measured by
one morning and one nighttime sample, whereas girls demonstrated the opposite pattern, with
hyposecretion of cortisol at morning and night (Vaillancourt et al., 2008). Adding another level
of complexity regarding the nature of a specific stressor, a comparison of physical abuse versus
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 73
neglect in preschool-aged foster children demonstrated lower morning cortisol levels in those
who experienced physical abuse, whereas children who experienced emotional neglect showed
higher morning cortisol levels, suggestive of HPA upregulation (Bruce et al., 2009).
These studies found evidence for poorly regulated HPA patterns related to distal social
stressors that occurred within varying timeframes, ranging from within the three months prior to
saliva collection (Vaillancourt et al., 2008), to measures of childhood trauma that may have
occurred many years in the past (Bruce et al., 2009). Accordingly, some of these stressors may
have resolved by the time of saliva sampling (e.g., stress in the home due to parents’
unemployment or health problems may have dissipated; Wolf et al., 2008), whereas other
stressors may be ongoing at the time of saliva sampling (e.g., Vaillancourt et al., 2008).
Helping to clarify the link between interpersonal stressors across varying timeframes,
Trickett and colleagues (Trickett et al., 2010) documented the development of attenuation
through repeated cortisol measurements over many years. Victims of childhood sexual abuse
(ages 6 through 16) compared to a control group first showed higher levels of serum cortisol,
possibly indicative of stress sensitization, based on two morning samples during a laboratory
visit; however, over five follow-up assessments across nearly 20 years, the sexual abuse victims
exhibited blunted morning cortisol, indicative of attenuation. Similarly, the Miller, Chen, and
Zhou (2007) meta-analyses found that stressors characterized by social threat, such as divorce,
were associated with higher morning and evening cortisol levels; yet consistent with the
attenuation literature, cortisol levels were lower in studies with longer periods of time following
stressor onset. In light of the well-documented link between traumatic interpersonal stressors and
poorly regulated diurnal cortisol patterns, Hostinar & Gunnar (2013) suggest that it also is
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 74
important to examine the relationship between more commonplace interpersonal stressors and
diurnal HPA patterns in adolescents.
Diurnal Cortisol and the Combined Role of Distal and Proximal Conflict and Aggression
Since proximal instances of conflict, particularly those related to family stressors, often
occur in a context of chronic and repeating family stress, it is somewhat puzzling to think about
the HPA activation related to proximal conflict in combination with more chronic experiences of
conflict. To date, we have found very few studies that examined diurnal cortisol patterns in
relation to both current as well as past life stressors. According to Bevans and colleagues (2008),
current stressors (such as death of a family member, losing a close friend, or switching schools)
related to elevated morning cortisol levels in school-aged children; however, exposure to those
current stressors in combination with a history of trauma exposure was associated with blunted
morning cortisol levels and heightened evening levels, i.e., flat diurnal cortisol patterns. The
authors posit that initial exposure to stressors relates to HPA upregulation, with downregulation
occurring over time and with repeated stressors. Notably, both current and past stressors in this
study identified more severe, punctuated stressors, such as parents’ divorce or death of a family
member.
Another important consideration for examinations of diurnal cortisol patterns in relation
to current conflict within the context of past conflict is the assessment of interindividual and
intraindividual differences in diurnal cortisol. Several studies address both between-person and
within-person variations in mothers’ diurnal cortisol. An investigation of mothers of children
and adults with and without autism spectrum disorders (ASD) assessed the mothers’ cortisol four
times per day across four days in conjunction with daily assessments of their child’s display of
behavior problems. Data were also collected regarding the child’s prior behavior problems over
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 75
the past six months. A test of interindividual differences showed that mothers of youth with
versus without ASD had overall lower levels of cortisol throughout the day. Examinations of
intraindividual differences revealed that mothers of youth who generally exhibited greater
behavioral problems over the past six months showed a blunted CAR on days following greater
reported behavior problems in their children (Seltzer et al., 2010). Utilizing the same
procedures, a second investigation demonstrated both blunted CARs and flatter diurnal cortisol
slopes in mothers of youth with serious mental illness following a day that was rated as highly
stressful (Barker, Greenberg, Seltzer, & Almeida, 2012). A third study found that mothers with
high parenting stress had significantly higher CARs the morning of workdays in comparison to
non-work days (Hibel, Mercado, & Trumbell, 2012). All studies of adults, these findings are
difficult to generalize to adolescent populations. Nonetheless, they illustrate the utility of
examining both between-person and within-person variations in diurnal cortisol related to same-
day and previous-day stressors to more comprehensively understand individuals’ physiology
both generally and in terms of day-to-day fluctuations.
In sum, both the timing and the severity of stressors appear to be important influences in
diurnal HPA activity. Not surprisingly, timing is somewhat confounded with severity in that the
chronic stressors tend to be maltreatment, sexual abuse, and emotional neglect, whereas the
proximal stressors tend to be arguments, school and work stressors. Adam (2012) presents a
model for multiple time frames of change in the HPA axis related social and emotional stressors.
We address three of those time frames. Specifically we look at (a) within-person, day-to-day
changes in diurnal patterns associated with day-to-day changes in experienced conflict; that is,
cortisol samples and conflict are assessed on the same days, (b) between–person differences in
diurnal patterns related to average proximal interpersonal conflict, and (c) between-person, trait-
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 76
like irregularities in diurnal cortisol related to distal parent-to-youth aggression over the past
several years. We also look at the interaction between the influences of distal parent-to-youth
aggression and day-to-day conflict. Although there are many stressors throughout adolescence,
we focus on interpersonal conflict because of its prominent role in adolescents’ daily lives
(Furman & Burmester, 1992; Larsen & Richards, 1991; Laursen, 1995). We include conflict with
peers as well as parents to get a more complete picture of the scope of interpersonal stressors
experienced by adolescents. The types of conflicts assessed over the past several years and
concurrent with the days of cortisol collection were quite similar, including both verbal conflicts
as well as physical forms of aggression. We used two cortisol indices of the CAR: (a) CAR
AUCi to assess the morning cortisol increase above the awakening level, and (b) CAR AUCg, to
assess the total morning cortisol output.
The Present Study
The present study investigates how proximal experiences of conflict with parents and
peers and distal parent-to-youth aggression exposure relate to individuals’ diurnal cortisol
patterns. This study also investigates whether adolescents who experienced current conflict with
others, in combination with previous ongoing conflict with parents, might show differing diurnal
cortisol patterns, as well as whether there are sex differences in the relation between conflict and
diurnal cortisol. Based on the somewhat limited findings of heightened cortisol for proximal
stressors, we hypothesized that daily experiences of conflict with parents and peers would relate
to elevated daily cortisol levels (Hypothesis 1). In Hypothesis 1, we examined proximal daily
conflict and diurnal cortisol in three ways in order to assess both between-person and within-
person differences: 1) mean daily conflict with mothers, fathers, and peers across the 4 days
surrounding saliva collection, 2) same-day conflict with mothers, fathers, and peers, and 3)
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 77
previous-day conflict with mothers, fathers, and peers. Analyses of same-day conflict related to
the CAR allow us to examine whether youth display physiological “anticipation” of conflict that
same day. Examination of previous-day conflict in relation to the CAR indicates whether
previous-day conflict and the associated physiological arousal carry over to the following
morning. Given the lack of research comparing different types of interpersonal conflict (i.e.,
conflict with parents vs. peers), there were no specific hypotheses for differential effects
depending on the person with whom there was conflict. In line with attenuation theories, we
hypothesized that adolescents who reported greater exposure to parent-to-youth aggression over
the past several years would demonstrate blunted, or smaller, CARs (Hypothesis 2). We further
hypothesized that conflict history would moderate the association between current reported
conflict and diurnal cortisol activity. Specifically, given the literature indicative of diurnal HPA
attenuation, we predicted that adolescents with greater distal parent-to-youth aggression would
demonstrate blunted diurnal cortisol patterns after proximal experiences of interpersonal conflict,
characterized by smaller CARs, in comparison to those with less parent-to-youth aggression in
their history (Hypothesis 3). Lastly, we conducted exploratory analyses to examine sex
differences in the relation between interpersonal stressors, both proximal and distal, and
adolescents’ diurnal cortisol.
This study extends the previous literature by examining more commonplace interpersonal
conflict in the lives of adolescents, using daily data collection methods to investigate both
between-person and intraindividual differences in diurnal cortisol indices, and taking
interpersonal conflict and aggression history into account when examining these associations.
We chose daily data collection methods because daily questionnaires are designed to minimize
recall errors and, more importantly, capture within-person variability in interpersonal conflict
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 78
across days (Almeida, Wethington, & Chandler, 1999; Bolger, Davis, & Rafaelik, 2003; Laurent
& Powers, 2006).
Method
Overview
The present study uses data from a longitudinal study that examines exposure to violence
as well as risk and resilience factors for adolescent adjustment. Participants are a community
sample of families that were recruited through flyers, newspaper advertisements, and word-of-
mouth. The larger study includes two cohorts of participants; the first cohort includes families
with a child age 9-10 at the start of data collection (n=119); the second cohort began the study at
the third wave of data collection, approximately 4 years later (n=70), and part of their inclusion
criteria was that they have a child in middle school. Participation in both cohorts required that
families lived together for at least 3 years before entering the study, had two parental figures, and
could complete procedures in English (for further details, see Margolin, Vickerman, Oliver, &
Gordis, 2010). The present study utilized data from waves 3, 4, and 5 of the overall project. The
parent-to-youth aggression history variables were collected in waves 3, 4, and 5, and the daily
conflict and diurnal cortisol data were collected in wave 5. In wave 5, there were a total of 131
adolescents.
Participants
The participants for the present study are a group of 99 (46 female) non-clinical
adolescents with an age range of 14 to 21 years, M=18.18, SD=1.08. The inclusion criterion for
the present study was that they needed to participate in the saliva collection procedures in wave
5. As noted above, we also utilized data on parent-to-youth aggression history from the current
wave (wave 5) as well as the previous two waves of the overall project (waves 3 and 4). The
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 79
sample for the study is diverse, with 32.3% identifying themselves as Hispanic/Latino; race is
5.1% Asian/Pacific Islander, 20.2% Black/African American, 37.4% Caucasian, 29.3% more
than one race, and 8.1% other/unknown. Families reported a mean combined income of $90,927
(SD=$76,179); 26% reported incomes <$50,000; 43% were between $50,000 and $100,000, and
31% were >$100,000. Eight percent of the participating families reported incomes below the
national poverty level for family size. Parents’ education ranged from 2 to 20 years, M=14.6,
SD=2.8. The mean lag time between the first wave of data collection used in this study (wave 3)
and the second wave of data collection in this study (wave 4) was 2.18 years (SD=.69), and the
mean lag time between wave 4 and wave 5 was 2.21 years (SD=1.02).
In order to address the possibility of selective attrition, we conducted a number of
comparisons between the 99 participants who provided saliva samples and those who did not, but
who still participated in the overall longitudinal study. Based on the total participants in wave 5,
we compared those who did (99) versus did not (32) provide saliva samples and found no
significant differences in ethnicity, race, annual income, parents’ level of education or in parent-
to-youth aggression history. We also tested for differences between the two cohorts in our
sample of 99 participants. Participants from cohort 1 were significantly older than those in
cohort 2, t(97)=7.77, p <.001, and cohort 2 had greater total morning cortisol output (CAR
AUCg) than cohort 1, t(97)=-2.25, p=.03. Furthermore, cohort 1 reported greater average
parent-to-youth aggression history than cohort 2, t(97)=2.00, p <.05. There were no significant
differences between the two cohorts in daily reports of conflict with mothers, fathers, or peers,
CAR AUCi, ethnicity, race, annual income, or parents’ level of education.

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 80
Procedures
The present study included three primary forms of data collection: 1) in-lab self-report
questionnaires, 2) at-home data collection of saliva samples across 3 days, and 3) at-home data
collection of interpersonal conflict experiences across 10 days, 3 of which overlapped with the
saliva collection. We compensated participants for their time both after the in-lab visits ($50) as
well as after completion of the home data procedures ($160). At the end of the wave 5 lab visit,
the experimenter instructed participants in the daily data collection procedures, explaining both
the saliva sample and home data collection.
Daily data collection procedures: saliva samples. The experimenter instructed
participants to collect five saliva samples each day over a period of three days. The saliva
samples were collected at the following specified times to capture diurnal physiological activity:
1) upon awakening or by 8am, 2) 20 min following the awakening sample, 3) 40 min following
the awakening sample, 4) at 4pm, and 5) at 9pm to capture the CAR as well as the decline in
cortisol throughout the day. The present study focused on the first three samples collected in
order to examine the CAR. To collect saliva samples, each adolescent participant was provided
with a “Spit Kit” including oral swabs (15 total), a digital watch with alarms set, a thermal bag,
an icepack, 15 saliva storage tubes, and three plastic bags. The adolescents were provided with a
digital watch programmed to alert the participant to saliva sample collection procedures
throughout the day. The experimenter asked the participants about their normal time of
awakening, and set the watch alarms to allow for the specified times of saliva collection. The
alarms for samples one, four, and five were set 10 minutes before the scheduled saliva sampling
time to allow the participants to rinse their mouths with water to clear away debris, and then to
collect saliva samples 10 minutes after rinsing. If the participant reported normally waking up
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 81
after 8:00am, the experimenter asked the participant to wake up at 7:50am, rinse their mouth, and
to collect the first saliva sample at 8:00am for the three days of saliva collection.
We instructed participants to begin saliva collection on a Monday, Tuesday, or
Wednesday to allow for three consecutive weekdays of saliva collection. We assessed cortisol
on weekdays to help maximize compliance for the collection of morning saliva samples and also
to assess diurnal cortisol on days that would be more likely to involve a routine, such as
attending school or work. We told participants not to eat, drink, or exercise for 1 hour prior to
saliva collection, or to smoke or drink alcohol for 24 hours prior to saliva collection. Youth
received verbal and written instructions about how to collect saliva, detailing that they were to
place the oral swab under their tongue in the front of their mouth for two minutes, at which time
they completed a 2-minute saliva questionnaire assessing the current time and whether they ate,
drank, exercised, or had mouth sores before collection of the current sample.
After each saliva collection, participants placed the saliva sample swabs into individual
storage tubes, put the tubes into a plastic bag, and placed the samples in their freezer. If they
were away from home, we instructed participants to place the samples in the thermal bag with
the provided ice pack. Once the 3 days of saliva collection were complete, the participants either
brought their samples back to the lab, or an experimenter picked up the samples from their home
to store in our lab freezer. Saliva samples were stored at -20 degrees Celsius, shipped in dry ice
to Salimetrics, LLC (State College, PA), and assayed for cortisol and cotinine. A high-sensitive
enzyme immunoassay was used for sample analyses, which were conducted in duplicate for
reliability. The inter-assay correlation was r(1,432)=.98, p<.0001. The mean value was used
for all analyses, and repeated analyses were conducted for sample pairs that differed more than
7%.
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 82
Participants were quite compliant with the procedures for collecting saliva. Of the 1,485
samples we aimed to collect (15 for each of 99 participants), only 27 samples were missing, i.e.,
not collected by the participants. An additional 26 samples could not be assayed due to
insufficient saliva quantity. In the analyses, we accounted for the 53 total missing samples
(.04%) with full information maximum likelihood estimation (FIML; Schafer & Graham, 2002).
Furthermore, 98% of the awakening saliva samples were collected before 9:00 am.
Daily data collection procedures: interpersonal conflict. In addition to the at-home
saliva collection, we asked participants to complete daily interpersonal conflict questionnaires
each evening for 10 days. Participants completed their first daily questionnaire during their lab
visit, reporting on the previous day. These questionnaires assessed the adolescents’ conflictual
interactions with family and peers each day; as a control for overall amount of interaction, we
also inquired about positive social interactions with family and peers. Missing data were
statistically accounted for with FIML.
Measures
Diurnal cortisol. In the present study, the primary measures of interest to capture
adolescents’ awakening cortisol response were: 1) the CAR AUCi, or the total amount that
participants’ cortisol levels changes over time from their awakening cortisol level, and 2) the
CAR AUCg, or the total volume of cortisol released over the awakening period. Both were
calculated as the area under the curve with respect to the three morning samples (Pruessner et al.,
2003).
Daily interpersonal conflict. We examined adolescents’ daily experiences of
interpersonal conflict through daily home data questionnaires, which were 18-30 minute
Qualtrics online surveys emailed to the participants each evening at 5pm (see Appendix B). In
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 83
this study, we used a total of 4 days of daily conflict data, with conflict data collected on each
saliva collection day as well as the day prior to saliva collection. The questionnaire asked
participants to think about their day and to report on interpersonal conflict they experienced
during the prior 24 hours. There were 8 items assessing conflict with mothers and the same 8
items for conflict with fathers (e.g., “How much did your dad seem angry with you?”).
Respondents rated how often each item occurred on a 4-point Likert scale, ranging from 0, Not at
all, to 3, A Lot. An additional 20 items assessed daily conflict with friends (e.g., “Yelled or
criticized me,” “Posted something mean or hurtful/embarrassing so that others can see on
Facebook or MySpace”), and respondents rated how often each item occurred on a 3-point Likert
scale, ranging from 0, Not at all, to 2, A Lot. For analyses including daily reports of conflict, we
utilized the mean score across the four days surrounding the saliva collection days in addition to
the day-to-day conflict scores as measures of proximal interpersonal conflict. This allowed us to
examine both between-person and within-person differences in diurnal cortisol related to
proximal conflict with mothers, fathers, and peers. Of the four days of daily interpersonal
conflict analyzed here, we received 327 of the 396 possible days, and the missing days were
statistically accounted for with FIML. Importantly, 86.5% of the questionnaires were completed
within 24 hours of the day of reporting.
Parent-to-youth aggression history. Adolescents completed an adapted version of the
Conflict Tactics Scale–Child (Straus, Hamby, Finkelhor, Moore, & Runyan, 1998)

two times,
once to report on mother-to-youth aggression and once to report on father-to-youth aggression.
(see Appendix E). In waves 4 and 5, six items assessed physical aggression, e.g., “slapped you,”
and “pushed, grabbed, or shoved you,” and four items assessed severe psychological aggression,
e.g., “threatened to lock you out of the house,” and “told you that you would not be part of the
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 84
family anymore.” Wave 3 included seven total items (four physical and three psychological
aggression items). Adolescents reported on the frequency of each item over the previous year
(0=Never, 1=Once, 2=Twice, 3=3-5 times, 4=6-10 times, 5=11-20 times, and 6=More than 20
times). At each wave of data collection, we calculated the mean item score, and then we
calculated the mean of scores across all three waves.
Covariates. In order to isolate the associations between interpersonal conflict and
diurnal cortisol, we adjusted for important covariates that relate to altered cortisol levels. We
assessed the time that the awakening sample was collected with the brief saliva questionnaire, as
earlier wake-up times are related to heightened CARs (Federenko et al., 2004). We also adjusted
for the adolescents’ age using their birthdate and date of participation in wave 5 procedures
because of potential fluctuations in the diurnal cortisol pattern across adolescence (Walker,
Walder, & Reynolds, 2001). We accounted for participants’ report of whether they were
currently taking any medications, measured with the saliva sample questionnaires. The fifth
saliva sample of each of the three days of saliva collection was assayed for cotinine, a byproduct
of nicotine, in order to adjust for cortisol fluctuations due to smoking (Granger, Blair,
Willoughby, Kivlighan, & Hibel, 2007).
For the daily assessments of interpersonal conflict and diurnal cortisol, we adjusted for
the previous day’s cortisol measure in order to isolate the link between interpersonal conflict and
the examined cortisol index. Whether the participants ate, drank, exercised or had mouth sores
prior to saliva sample collection, assessed with the brief saliva questionnaire, was also adjusted
for due to the potential for increased cortisol levels (Kivlighan et al., 2004; Schwartz, Granger,
Susman, Gunnar, & Laird, 1998). Moreover, greater hours of sleep relate to higher morning
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 85
cortisol levels, so we included the previous night’s hours of sleep, assessed via the daily
questionnaire, as a covariate (Wust et al., 2000b).
To account for noncompliance in our analyses utilizing the daily interpersonal conflict
data, we adjusted for whether the participants completed the daily questionnaires within 24 hours
using the computerized time stamp from the daily questionnaires; only 30 of the 327 (9.2%) of
the questionnaires were completed more than 24 hours following the day they reported on. Of
the completed home data questionnaires, 42.2% were completed between 5:00pm and midnight
the night it was received, 11.5% were completed between midnight and 9am the next morning,
31.5% were completed before midnight the next day, and 14.8% of the questionnaires were
completed between two and four days following the day described. Lastly, we included daily
positive interactions with mothers, fathers, and peers as covariates for the daily conflict analyses
in order to help isolate interpersonal conflict from general social interaction. For mothers and
fathers, the measure included the mean of 2 items assessing positive interactions, using a Likert
scale ranging from 0, Not at all, to 3, A Lot. The two items were 1) “How much did you enjoy
being with him/her,” and 2) “How much did you feel supported by him/her.” For peers, we used
the mean of 5 items with a Likert scale from 0, Not at all, to 2, A Lot, assessing peer support and
enjoyable peer interactions.
Analytic Strategy
We first examined descriptive statistics of the examined variables, using t-tests (SPSS
Version 19) to examine whether there were male-female differences. We also conducted
Spearman correlations, due to non-normal distributions, to examine the interrelationships among
conflict variables, individual cortisol samples and diurnal cortisol indices, as well as each of the
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 86
covariates. To test our three hypotheses, we utilized multilevel regression models with Mplus
Version 7.11 for Mac (Muthén & Muthén, 2012).
In order to test Hypothesis 1, which examined the relation between proximal experiences
of conflict and diurnal cortisol patterns, we conducted three sets of multilevel regression models.
The first set examined the mean reported conflict across the four days surrounding the saliva
collection for mothers, fathers, and peers. These models tested for between-person differences in
the relation between proximal conflict experiences and diurnal cortisol indices. These models
first included time of awakening sample, age, medications, cotinine, whether they ate, drank,
exercised, or had mouth sores prior to sample collection, hours of sleep, and whether the
questionnaire was completed within 24 hours as covariates. Nonsignificant covariates were
excluded, and the models ultimately used for these analyses included time of awakening sample
and cotinine on level 1, and mean daily conflict, age, and medications on level 2. Then, to test
sex differences in proximal interpersonal conflict in relation to diurnal cortisol, we included sex
and the interaction between sex and mean interpersonal conflict on level 2.
The second set of multilevel regressions for Hypothesis 1 examined within-person
differences in diurnal cortisol in relation to same-day conflict with mothers, fathers, and peers.
Specifically, these models examined whether there were intraindividual differences in diurnal
cortisol indices depending on the amount of interpersonal conflict youth experienced that same
day. The third set of multilevel regressions in Hypothesis 1 examined within-person differences
in diurnal cortisol as related to previous-day conflict with mothers, fathers, and peers, utilizing
lagged measures of the previous-day’s conflict. These analyses tested for intraindividual
differences in diurnal cortisol on days following interpersonal conflict. These models also first
included time of awakening sample, age, medications, cotinine, whether they ate, drank,
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 87
exercised, or had mouth sores prior to sample collection, hours of sleep, and whether the
questionnaire was completed within 24 hours as covariates. Additionally, the previous-day
cortisol index was included as a within-level covariate in order to adjust for differences in
diurnal cortisol that were due to the previous day’s cortisol activity. The level of positive
interactions (mothers, fathers, and peers) for both same-day and previous-day analyses was also
included in order to isolate interpersonal conflict from social contact. Nonsignificant covariates
were excluded for reasons of parsimony, and the following model was used to test the second set
of analyses in Hypothesis 1:
Level 1: Y
ij
= β
0j
+ β
1j
(daily conflict variable) + β
2j
(time) + β
3j
(cotinine) +
β
4j
(previous-day cortisol index) + e
ij

Level 2: β
0j
= γ
00
+ γ
01
(medication) + γ
02
(age) + u
0j

To examine sex differences in daily interpersonal conflict as related to diurnal cortisol, we
included sex on level 2 and conducted a cross-level interaction between sex and the level 1
relation between the CAR and the daily conflict variable.
For hypothesis 2, which examined the relation between parent-to-youth aggression
history and the diurnal cortisol indices (CAR AUCi and CAR AUCg), we used multilevel
models with the within-person covariates on level one (time of awakening sample, cotinine,
whether the participant ate, drank, exercised, or had mouth sores, hours of sleep, and whether the
questionnaire was completed within 24 hours), and parent-to-youth aggression history as well as
the between-level covariates (age and medications) on level 2. The covariates that were
nonsignificant were excluded for reasons of parsimony, resulting in the following model:
Level 1: Y
ij
= β
0j
+ β
1j
(time of awakening sample) + β
2j
(cotinine) + e
ij

Level 2: β
0j
= γ
00
+ γ
01
(parent-to-youth aggression) + γ
02
(medication) + γ
03
(age) + u
0j
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 88
These models allowed us to examine the relation between parent-to-youth aggression history and
the CAR AUCi and CAR AUCg. The two diurnal cortisol indices were run in separate models
with parent-to-youth aggression history. To assess sex differences for distal interpersonal
conflict, we then included sex and the interaction between sex and parent-to-youth aggression
history on level 2.
For hypothesis 3, which examined whether conflict history moderates the association
between daily conflict and diurnal cortisol indices, we tested a cross-level interaction between
parent-to-youth aggression history and daily conflict with mothers, fathers, and peers. This set
of analyses examined whether there were differential associations between daily conflict (mean
conflict across the 4 days, same-day conflict, and previous-day conflict) and the diurnal cortisol
indices depending on the level of parent-to-youth aggression history. Lastly, we examined sex
differences in a 3-way, cross-level interaction to assess whether males versus females
demonstrated differing CARs in relation to both proximal and distal conflict. For these analyses,
sex and the interaction between sex and aggression history were included on level two, and then
we specified a cross-level interaction with the level 1 association between the daily conflict
measures and the cortisol indices.
Results
Descriptive Statistics
Table 1 displays the descriptive statistics for all study variables, including the covariates,
separately for males and females as well as for the entire sample. There were no significant
mean sex differences among the variables. The Spearman correlations for all of the variables are
displayed in Table 2, and the significant correlations ranged from moderate to high (rs=-.21 to
.96). The mean of daily conflict with mothers was positively correlated with the mean of daily
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 89
conflict with fathers, and likewise, the mean of positive interactions with mothers was positively
correlated with the mean of positive interactions with fathers. Mean conflict with fathers was
also negatively correlated with CAR AUCi. Additionally, participants’ age was negatively
correlated with CAR AUCg. Table 3 displays the intercorrelations between CAR AUCi and
CAR AUCg across the three days of saliva collection. Both CAR indices were positively
correlated with their respective index on the other two days with the exception of CAR AUCi for
days 2 and 3.
Proximal daily conflict and between-person differences in diurnal cortisol. Table 4
presents the models run to test the between-person differences in diurnal cortisol related to
proximal interpersonal conflict (mean conflict across 4 days) with mothers, fathers, and peers
(Hypothesis 1, part 1). For these analyses, we used the series of multilevel models specified
above. The results demonstrated that conflict with fathers and peers over the four days was
related to adolescents’ cortisol awakening. Testing sex as a moderator, we found a significant
interaction between sex and average daily conflict with fathers for both the CAR AUCi and CAR
AUCg, although no significant interactions for conflict with mothers. Probing of the interaction
revealed that males who experienced greater average daily conflict with fathers showed smaller
CAR AUCis, γ (SE) =-.26 (.13), p=.03, and smaller CAR AUCgs, γ (SE) =-.53 (.13), p<.001,
whereas girls did not show differences in their CAR AUCi, γ (SE) =-.54 (.45), p=.25, or in their
CAR AUCg, γ (SE) =-.26 (.37), p=.49, related to average daily conflict with their fathers. Figure
1 displays the mean raw cortisol levels of males’ and females’ cortisol concentrations plotted at
levels above versus below the mean of conflict with fathers. Males who report high conflict with
fathers appear to have blunted CARs compared to the males reporting low conflict, whereas the
females’ morning awakening response appears unaffected by conflict with their fathers.
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 90
Sex also significantly moderated the relation between average peer conflict across the 4
days and CAR AUCi and CAR AUCg. To probe the significant interactions, we tested the
relation between mean conflict with peers and diurnal cortisol for males and females separately,
and found that for females, greater peer conflict related to significantly smaller CAR AUCi, γ
(SE) =-.67 (.31), p=.03, but greater CAR AUCg, γ (SE) =.58 (.27), p=.04. As illustrated in
Figure 2, the girls reporting high conflict with their peers have less steep morning cortisol
increases but greater total morning cortisol output. For boys, there was no significant association
between mean peer conflict and CAR AUCi, γ (SE) =.004 (.20), p=.99, or CAR AUCg, γ (SE)
=.14 (.12), p=.27. As seen in Figure 2, boys’ morning cortisol rise was not distinguishable by
level of peer conflict.
Proximal daily conflict and within-person differences in diurnal cortisol. The second
and third components of Hypothesis 1 examined within-person differences in diurnal cortisol on
the day of as well as the day following conflict with mothers, fathers, and peers. Again, we used
a series of multilevel regression models with each model testing day-to-day differences in
diurnal cortisol indices, adjusting for time of awakening sample, cotinine, and the previous day’s
cortisol index on level one, and age and medications on level two.
As detailed in Table 5, separate tests of the relation between same-day conflict with
mothers, fathers, and peers and participants’ CAR AUCi and CAR AUCg showed significance
for mothers but not fathers or peers. Specifically, on days that youth experienced greater conflict
with mothers, they exhibited greater CAR AUCi and CAR AUCg. As shown in the Figure 3
graph of mean raw cortisol values for those who experienced no versus any same-day conflict
with their mothers, higher morning cortisol values are exhibited by those who experienced same-
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 91
day conflict with their mothers. The cross-level interaction between sex and same-day conflict
with mothers, fathers, or peers was not significant for the two cortisol indices.
Next, to test the relation between previous-day interpersonal conflict and diurnal cortisol,
we ran additional models including a lagged measure of participants’ daily reports of conflict
from the previous day, adjusting for previous-day cortisol indices. As presented in Table 5, on
days following conflict with mothers, participants demonstrated significantly smaller CAR
AUCis. The graph of mean raw cortisol values for individuals who experienced no versus any
previous-day conflict in Figure 4 show lower CAR AUCis in those who experienced previous-
day conflict with mothers. There were no significant intraindividual differences in CAR AUCg
with previous-day reports of conflict with mothers, nor were there significant differences in CAR
AUCi or CAR AUCg in relation to experiences of previous-day conflict with fathers or peers.
Additionally, the cross-level interactions between sex and previous-day conflict with mothers,
fathers, and peers were not significant for either of the cortisol indices.
Main effects of parent-to-youth aggression history. Table 6 displays results of the
multilevel regression model testing the relation between history of parent-to-youth aggression
and diurnal cortisol patterns (Hypothesis 2). Here we combine mothers and fathers for parsimony
and due to the significant correlation (r
s
= .56) between mothers’ and fathers’ aggression toward
the youth over the past three waves of data collection. A negative main effect of parent-to-youth
aggression was found for CAR AUCi. Specifically, youth with greater parent-to-youth
aggression histories demonstrated blunted CAR AUCi. In Figure 5 depicting the mean raw
cortisol concentrations of those who reported no aggression history, low (below-the-mean)
aggression history, and high (above-the-mean) aggression history, youth reporting high parent-
to-youth aggression histories exhibit lower CAR AUCi. We did not find a significant main
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 92
effect of parent-to-youth aggression history on the CAR AUCg. The interaction between sex and
parent-to-youth aggression history was not significant for CAR AUCi or CAR AUCg.
Cross-level interactions of parent-to-youth aggression history and current daily
conflict. To test our third hypothesis examining parent-to-youth aggression history as a
moderator of the relation between daily conflict and diurnal cortisol patterns, we used another
series of multilevel regression models to examine potential cross-level interactions. With
combined parent-to-youth aggression history as the potential moderator, we tested cross-level
interactions with mothers, fathers, and peers for (a) mean proximal conflict, (b) same-day
proximal conflict, and (c) previous-day proximal conflict. None of the cross-level interactions
reached statistical significance and thus we do not have evidence of the moderation effect. The
3-way cross-level interactions between sex, parent-to-youth aggression history, and the parent
and peer daily conflict variables also were not significant for either of the cortisol indices.
Power analyses suggest that the nonsignificant results are not due to insufficient statistical
power.
Discussion
The present study is the first to our knowledge that examined adolescents’ experiences of
current conflict with parents and peers as well as past parent-to-youth aggression exposure in
relation to diurnal cortisol patterns. By including a longitudinal measure of parent-to-youth
aggression history as well as detailed measures of current experiences of interpersonal conflict,
we were able to investigate how common experiences of social conflict in adolescents’ lives
relate to their daily physiology.
Hypothesis 1, that proximal conflict with parents and peers would relate to elevated
cortisol levels, was partially supported. We found elevations in the CAR in our examination of
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 93
same-day conflict with mothers, and, for females only, in our examination of proximal mean
peer conflict; this may reflect adolescents’ anticipation of conflict or of a demanding upcoming
day. However, we also found that previous-day conflict with mothers related to blunted CARs,
and that proximal mean conflict with fathers related to blunted CARs in males, but not in
females. These findings implicate HPA attenuation in relation to certain proximal social
stressors. We also found evidence for the attenuation theory when testing Hypothesis 2, as our
results demonstrated that greater reports of parent-to-youth aggression history related to blunted
morning cortisol levels. Our third hypothesis, examining parent-to-youth aggression history as a
moderator of the relation between proximal interpersonal conflict and diurnal cortisol, was not
supported. Our findings of both blunted and elevated CARs depending on the timeframe
examined in addition to our findings related to sex differences highlight the complex interplay
between daily social conflict and adolescents’ morning cortisol levels.
Proximal Conflict and Interindividual Differences in Diurnal Cortisol
We found evidence for between-person differences in the CAR related to mean proximal
conflict with fathers and peers, and our results differed for males and females. In support of
Hypothesis 1, experiences of proximal conflict with peers related to elevated CARs, indicative of
HPA upregulation, in female but not male adolescents. Female adolescents place particularly
high importance on their friendships and fitting in compared to their male counterparts
(Benenson & Benarroch, 1998), and conflict with friends may carry more significance for
females than males. Moreover, there is evidence that females appraise peer conflict as more
stressful than do males (Rose & Rudolph, 2006). Accordingly, during times when girls are
fighting with their friends, they may wake up with greater anticipation of what the day ahead
holds, and, in turn, heightened levels of cortisol.
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 94
Our result showing that mean daily conflict with fathers related to weaker CARs in
males, however, was counter to Hypothesis 1. An important consideration for interpreting this
result is that males tend to experience harsher parenting from their fathers than from their
mothers (McKee et al., 2007). Moreover, research has demonstrated that fathers tend to engage
in firmer and less affectionate parenting practices with their sons in comparison to their
daughters starting at a young age (Siegal, 1987). As such, males may exhibit heightened HPA
axis activity related to conflict with fathers, which over time, could be manifest in weaker CARs.
Yet, given that the measured proximal conflict in our study was concurrent and ongoing, it is
difficult to say that these interpersonal stressors relate to HPA downregulation due to chronic
activation over time. One possibility is that the reported conflict with fathers was merely a
snapshot of ongoing, chronic interpersonal conflict at home, prompting the downregulation of
HPA activity in males (Susman, 2006). Alternatively, these male adolescents my have started
with lower CARs, and a third variable may relate to both lower CARs and conflict with fathers.
Proximal Conflict and Intraindividual Differences in Diurnal Cortisol
Another novel finding is the intraindividual differences in diurnal cortisol related to
same-day and previous-day conflict with participants’ mothers. Similar to other studies showing
elevated CARs on days involving social stressors (Jacobs et al., 2007; Stawski et al., 2013), the
heightened CARs may be an indication of greater anticipation or apprehension for the day ahead,
and this apprehension may have potentiated conflict with mothers (Rohleder et al., 2007; Stalder,
Evans, Hucklebridge, & Clow, 2010). Additionally, because the CAR helps individuals prepare
for the day’s demands (Fries et al., 2009; Rohleder et al., 2007), particularly heightened CARs
may also be indicative of a generally arduous day, making youth more prone to conflictual
interactions with their mothers.
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 95
Of particular interest is that youth demonstrated lower CARs on days following reports of
conflict with their mothers, pointing to HPA downregulation. Our finding of lower CAR AUCis
on days following conflict with mothers is consistent with research linking general feelings of
fatigue, burnout, or exhaustion to weakened CARs (Chida & Steptoe, 2009), but extends this
finding to a more detailed, micro level. Moreover, our finding of blunted CARs in youth
following conflict with mothers echoes the findings of blunted CARs in mothers following
stressful days with their children (Barker et al., 2012; Seltzer et al., 2010). This raises important
questions about physiological “recovery” from mother-to-youth conflicts.
One possibility for finding evidence of intraindividual differences in the CAR for
mothers but not fathers or peers is that youth may interact with their mothers most consistently
throughout the day, allowing for greater day-to-day variability in experiences of conflict with
mothers. In fact, adolescents in our study reported significantly more conflict with their mothers
than fathers across the four assessed days. Additionally, there is evidence that mothers spend
more time with their adolescents than do fathers, and that mothers carry greater responsibility for
adolescents’ activities and discipline (Phares, Fields, & Kamboukos, 2009). Although
adolescents may spend substantial time with peers throughout the day or after school or work,
they may spend more cumulative time each day in the presence of their mothers. Furthermore,
relationships with peers do not have the same hierarchical structure as do relationships between
adolescents and their parents, and the consequences of same-day or previous-day conflict with
peers may be qualitatively different. Because parent-adolescent conflict resolution tends to
involve less compromise, more submission, and more disengagement on the part of the
adolescent in comparison to conflict with peers (Laursen, 1993b), day-to-day conflict with
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 96
parents, and the resolution thereof, may differ from that with peers. This could partially explain
day-to-day HPA fluctuations related to conflict with mothers but not peers.
Parent-to-Youth Aggression History and Blunted Diurnal Cortisol Activity
The result that higher reports of parent-to-youth aggression history related to lower CARs
supports and extends the trauma literature linking traumatic stress to flatter diurnal cortisol
slopes to also include more common experiences of conflict and aggression (Hart, Gunnar, &
Cicchetti, 1996; Kaufman, 1991; Kliewer, 2006; Booth, Granger, & Shirtcliff, 2008; Susman,
2006). Here, participants reported on parent-to-youth psychological and physical aggression that
was substantial, but that would not necessarily fit the criteria for trauma or abuse. This supports
the notion that even common experiences of parent-to-youth aggression over time, which more
than half of our sample experienced at some level, relate to blunted diurnal cortisol patterns as
indicated by weaker CARs.
Our third hypothesis tested the idea that current conflict would be perceived differently
based on adolescents’ prior history of conflict with parents. That is, high levels of prior conflict
might condition adolescents to respond in a certain way to ongoing conflicts and, based on the
attenuation hypothesis, would most likely downregulate their HPA activity. Certainly the current
conflicts might be a more novel experience for those who did not have much conflict in their
history. Contrary to our hypothesis, the moderation effect was not supported. Moreover, our
measure of proximal conflict was uncorrelated with our measure of aggression history,
suggesting that the reported proximal conflict is not necessarily a repeated stressor for those who
also reported parent-to-youth aggression history. Whereas the family history variable
represented physical and severe psychological aggression, the current conflict variable generally
assessed irritation, anger and criticism, although one item also assessed physical aggression. The
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 97
finding here of momentary, within-person elevations in the CAR on days that involved conflict
with mothers hints at a potential mechanistic link between ongoing interpersonal stressors and
blunted diurnal HPA activity over time. It is possible that youth currently demonstrating
heightened CARs related to conflict with their mothers may also experience HPA
downregulation and blunted CARs over time although that remains to be tested.
Clinical Implications
This study found evidence of alterations in diurnal HPA activity related to stressors that
are commonplace in adolescence, and raises important questions about the point at which
experiences of interpersonal conflict are detrimental versus helpful for adaptive development
(Laursen & Collins, 1994). These results also have important implications for understanding
adolescents’ health. For example, poorly regulated HPA activity may play a role in the
connection between exposure to aggression and frequent somatic complaints in youth (Margolin
et al., 2010). Moreover, poorly regulated HPA activity may be a harbinger of chronic health
problems in the future. Although youth who experience frequent conflict with others may not
currently exhibit chronic health problems, blunted HPA activity over time relates to health
concerns later in life (Repetti et al., 2011). Our results also have implications for adolescents’
academic and occupational adjustment. As Bevans and colleagues (2008) noted, weak CARs
may impede youths’ attentional and learning capacity throughout the day. Our findings of
weaker CARs in relation to conflict exposure may play a role in the relation between stressful
home environments and increased risk of academic failure (Cahill, Kaminer, & Johnson, 1999).
Limitations
There are several important limitations in the present study that warrant attention. First,
we did not have information about the time at which the daily conflict with parents and peers
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 98
occurred, complicating the interpretation of associations between the CAR and same-day
interpersonal conflict. For example, it is possible that conflict with a participant’s mother
occurred in the morning and initiated an HPA stress response that was captured in the CAR
AUCi. Alternatively, the conflict may have occurred in the evening, indicating that the CAR
preceded the same-day conflict. Although we were able to address some patterns of effects
through the same-day and prior-day analyses, future research assessing social stressors multiple
times per day could better capture the direction of effects.
Additionally, we may not have been able to capture the full range of interpersonal
conflict experiences as they relate to diurnal cortisol. Because we only measured adolescents’
saliva on weekdays to help maximize compliance, it is possible that participants experienced a
greater degree of social interaction with people close to them on weekends when they were not at
school or at work. It may be helpful for future studies to include both weekday and weekend
measures of diurnal cortisol. In addition, although our focus was on conflict as a stressful
interpersonal situation, there are a variety of stress-evoking interpersonal (e.g., talking to a
member of the opposite sex) and non-interpersonal (taking an exam, speaking out in class,
performing in sports) circumstances that can trigger an HPA response. With the HPA axis
reactive to an array of emotion-laden experiences as well as physical activity, we tried to adjust
for some of these circumstances (e.g., assessing exercise; positive interpersonal interaction).
Yet, it was difficult to account for all experiences that can influence HPA activity. Moreover,
personality differences, which play a role in social interactional style (Eaton & Funder, 2003),
may also relate to variations in diurnal cortisol patterns. For example, Davies and colleagues
noted differential cortisol reactivity in toddlers with inhibited versus bold temperaments in
response to parental conflict (Davies, Sturge-Apple, & Cicchetti). Similar to differences found
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 99
in cortisol reactivity, temperamental differences, which we did not assess, may also relate to
variations in diurnal cortisol rhythms.
Despite these limitations, the present study used a novel approach to provide important
insight into adolescents’ day-to-day physiology in relation to both distal and proximal conflict
with others. In fact, although our study required much time and focus on the part of the
adolescents, participants were surprisingly compliant with our complex procedures. This
highlights that frequent contact with participants and clear communication of procedures allows
for the collection of rich, detailed, ecologically valid data.
Conclusion
The present study highlights that commonplace interpersonal conflict in adolescents’
lives relates to variations in not only overall diurnal patterns, but also variations in day-to-day
diurnal cortisol. By examining both previous and current interpersonal conflict across social
realms, we were able to find that proximal experiences of conflict relate to both elevated and
attenuated CARs depending on when, i.e., same-day versus previous-day, and with whom the
conflict occurred, whereas distal conflict with parents relates to attenuated diurnal cortisol
patterns. Our results highlight the intricate day-to-day fluctuations in diurnal cortisol based on
experiences of conflict in adolescents’ lives, which is important given the connection of the HPA
axis to health and well-being during adolescence and beyond. Understanding the role of
everyday experiences in adolescents’ physiology may help mitigate some of the deleterious
effects of poorly regulated HPA activity, and may also promote the identification of protective
factors for positive adolescent development.

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 100
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!
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 113

Table 1
Descriptive Statistics for Cortisol Measures, Conflict Variables, and Covariates
Note. CAR=Cortisol awakening response. AUCi=Area under the curve with respect to increase. AUCg=Area under the curve with
respect to ground. Agg Hx=Aggression history. Positive=Positive interactions. There were no significant sex differences in the
presented variables. Cortisol concentration values are in µg/dL.

Males Females Total Sample
Mean (SD) % Sample
Endorsement
Mean (SD) % Sample
Endorsement
Mean (SD) % Sample
Endorsement
Cortisol morning AUCi .03 (.13) - .07 (.19) - .05 (.16) -
Cortisol morning AUCg .31 (.21) - .34 (.24) - .33 (.23) -
Daily cortisol slope .46 (.41) - .53 (.39) - .50 (.40) -
Cortisol 1: awakening .42 (.35) - .41 (.42) - .41 (.38) -
Cortisol 2: awake+20min .49 (.34) - .55 (.44) - .52 (.39) -
Cortisol 3: awake+30min .46 (.43) - .53 (.39) - .49 (.41) -
Cortisol 4: 4pm .19 (.33) - .19 (.36) - .19 (.34) -
Cortisol 5: 9pm .15 (.33) - .13 (.29) - .14 (.31) -
Parent-to-youth agg hx .17 (.23) 62.5 .13 (.22) 55.6 .15 (.23) 57.8
Daily conflict with mother
1.59 (2.24) 45.3
1.80 (2.18) 52.2 1.69 (2.20) 48.5
Daily conflict with father
1.15 (2.03) 35.8
1.01 (1.67) 36.9 1.09 (1.86) 36.4
Daily conflict with peers
1.30 (1.45) 69.9
1.05 (1.26) 67.4 1.19 (1.36) 68.7
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 114

Table 1, Continued

Descriptive Statistics for Cortisol Measures, Conflict Variables, and Covariates

Note. CAR=Cortisol awakening response. AUCi=Area under the curve with respect to increase. AUCg=Area under the curve with
respect to ground. Agg Hx=Aggression history. Positive=Positive interactions. There were no significant sex differences in the
presented variables. Cortisol concentration values are in µg/dL.

Males Females Total Sample
Mean (SD) % Sample
Endorsement
Mean (SD) % Sample
Endorsement
Mean (SD) % Sample
Endorsement
Time of awakening sample 7.43 (1.04) - 7.35 (.86) - 7.39 (.96) -
Age 18.14 (1.14) - 17.93 (.99) - 18.06 (1.09) -
Medications - 9.43 - 19.56 - 14.14
Cotinine 7.80 (28.62) 86.9 1.25 (6.50) 84.2 4.76 (21.62) 85.7
Ate/drank/exercised/sores - 25.2 - 17.7 - 21.7
Hours of sleep 7.29 (1.73) - 7.34 (1.69) - 7.31 (1.71) -
Completed daily questionnaire
within 24 hours
- 88.9 - 92.0 - 90.4
Daily mother positive 1.86 (.91) 64.1 1.97 (.81) 73.9 1.91 (.86) 68.7
Daily father positive 1.89 (.99) 60.3 1.95 (.76) 65.2 1.92 (.88) 62.7
Daily peer positive .59 (.45) 81.2 .70 (.44) 82.6 .64 (.45) 81.8
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 115
Table 2

Intercorrelations among Model Variables

1 2 3 4 5 6 7 8 9 10 11
1. CAR AUCi -
2. CAR AUCg .34** -
3. Cort 1 -.24* .77*** -
4. Cort 2 .37*** .97*** .72*** -
5. Cort 3 .50*** .90*** .55*** .83*** -
6. Cort 4 -.02 .39*** .32** .36*** .36*** -
7. Cort 5 -.07 .30** .25* .25* .28** .55*** -
8. Prnt Agg hx -.14 -.03 .07 -.04 -.12 -.07 .13 -
9.Mom Daily -.09 .01 .08 -.02 -.05 .00 -.09 .15 -
10. Dad Daily -.27* -.16 -.01 -.16 -.25* -.06 .09 .15 .34** -
11. Peer Daily .08 .20 .12 .19 .20 -.02 .03 .10 .22 -.08 -
12. Time .02 -.07 -.08 -.05 -.09 .26* .25* .10 .02 .17 -.10
13. Age -.10 -.26* -.20 -.21* -.26** .06 -.04 -.01 -.24* .01 -.31**
14.Meds .19 .10 .00 .10 .13 .08 -.01 -.21 .06 .06 .01
15. Cot -.08 .05 .02 .08 .03 .25* .25* .25* -.02 -.07 .03
16.Comp -.11 -.02 .04 -.02 -.08 .01 .07 .15 .10 .16 .06
17.Sleep .02 -.01 -.03 .02 -.01 -.17 .03 -.10 .05 .13 .18
18. In 24 .09 -.09 -.12 -.09 -.01 -.09 -.29** -.12 .17 .11 .08
19. Mom Pos -.12 -.14 -.08 -.10 -.08 -.04 .02 -.02 -.45*** .00 -.18
20. Dad Pos .11 -.02 -.04 .04 -.06 -.02 -.11 .05 -.15 -.04 -.10
21. Peer Pos .09 .08 -.01 .05 .14 -.02 .01 -.10 .09 -.04 .64***

*p < .05, ** p < .01, *** p < .001
Note. All correlated values comprise the mean of each participants’ values across days. Cort=Cortisol sample. Prnt=Parent. Hx= History.
Daily=Mean daily conflict across the four days surrounding saliva collection. Time=Time of first sample. Meds=Medications. Cot=Cotinine.
Comp=Pre-sample compliance (ate/drank/exercised/had mouths sores prior to sample collection). Sleep=Hours of sleep. In 24=Completed daily questionnaires
within 24 hours. Pos=Positive daily interactions.!

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 116
Table 2, Continued

Intercorrelations among Model Variables

12 13 14 15 16 17 18 19 20 21
12. Time -
13. Age .29** -
14. Meds .11 -.11 -
15. Cot .26* .26* -.07 -
16. Comp .13 .09 .06 .08 -
17. Sleep .18 .05 .17 .01 .03 -
18. In 24 -.13 -.02 .09 -.14 .04 .07 -
19. Mom Pos .10 .38** .12 .14 -.03 .11 -.10 -
20. Dad Pos .14 .43** .10 .10 -.02 .13 -.09 .70*** -
21. Peer Pos -.20 -.17 .00 -.17 -.09 .10 .26* .04 .11 -

*p < .05, ** p < .01, *** p < .001
Note. All correlated values comprise the mean of each participants’ values across days. Cort=Cortisol sample. Prnt=Parent. Hx= History.
Daily=Mean daily conflict across the four days surrounding saliva collection. Time=Time of first sample. Meds=Medications. Cot=Cotinine.
Comp=Pre-sample compliance (ate/drank/exercised/had mouths sores prior to sample collection). Sleep=Hours of sleep. In 24=Completed daily
questionnaires within 24 hours. Pos=Positive daily interactions.!
!

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 117

Table 3

Intercorrelations among Cortisol Indices for Days 1, 2, and 3
!
!
!
!
!
!
!
!
!
!
!
!
!
* p < .05, ** p < .01, *** p < .001
Note. D=Day. CAR=Cortisol awakening response. AUCg=Area under the curve with respect to ground.
AUCi=Area under the curve with respect to increase.
1 2 3 4 5 6
1. D1 CAR AUCg -
2. D2 CAR AUCg .54*** -
3. D3 CAR AUCg .43*** .59*** -
4. D1 CAR AUCi .34** .10 .18 -
5. D2 CAR AUCi .19 .35** -.02 .33*** -
6. D3 CAR AUCi .25* .59*** .32** .46*** .18 -
INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 118
Table 4

Multilevel Models of Mean Daily Conflict with Mothers, Fathers, and Peers in relation to Diurnal Cortisol Indices

Conflict with Mothers Conflict with Fathers Conflict with Peers
CAR AUCi
Coeff(SE)
CAR AUCg
Coeff(SE)
CAR AUCi
Coeff(SE)
CAR AUCg
Coeff(SE)
CAR AUCi
Coeff(SE)
CAR AUCg
Coeff(SE)
Mother Conflict -.25 (.21) -.01 (.23) - - - -
Father Conflict - - -.74 (.07)*** -.72 (.05)*** - -
Peer Conflict - - - - -.69 (.09)*** .67 (.10)***
ConflictXSex .14 (.16) -.03 (.19) .60 (.08)*** .55 (.06)*** .57 (.07)*** -.53 (.08)***
Level 1 Covariates
Time -.05 (.03) -.04 (.07) -.05 (.03) -.04 (.07) -.05 (.03) -.04 (.07)
Cotinine .07 (.04) .04 (.03) .07 (.04) .03 (.03) .07 (.04) .04 (.03)
Level 2 Covariates
Age -.07 (.19) -.24 (.10)* -.05 (.09) -.13 (.06)* -.08 (.09) -.01 (.06)
Meds .23 (.16) .05 (.12) .17 (.09) .06 (.07) .12 (.08) .04 (.06)
!
* p<.05, ** p<.01, *** p<.001
Note. Each diurnal cortisol index was run in a separate model. Conflict=Mean daily conflict. Time=Time of awakening sample.
ConflictXSex=Conflict by sex interaction. Meds=Medications.

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 119
Table 5
Multi-level Models of Daily Conflict and Within-Person Differences in Diurnal Cortisol Indices

Conflict with Mothers Conflict with Fathers Conflict with Peers
CAR
AUCi
Coeff(SE)
CAR
AUCg
Coeff(SE)
CAR
AUCi
Coeff(SE)
CAR
AUCg
Coeff(SE)
CAR
AUCi
Coeff(SE)
CAR
AUCg
Coeff(SE)
CAR
AUCi
Coeff(SE)
CAR
AUCg
Coeff(SE)
CAR
AUCi
Coeff(SE)
CAR
AUCg
Coeff(SE)
CAR
AUCi
Coeff(SE)
CAR
AUCg
Coeff(SE)
Same-day
conflict
.25(.07)** .25(.08)** - - -.05(.05) .11(.08) - - .01(.12) -.11(.14) - -
Previous-day
conflict
- - -.11(.05)* .01(.07) - - .07(.05) .02(.04) - - .10(.11) .01(.15)
Level 1 Covariates
Cotinine .08(.04)* .04(.03) .08(.04)* .04(.03) .08(.04)* .04(.03) .08(.04)* .04(.03) .08(.04)* .04(.03) .07(.03)* .04(.02)
Previous-day
cortisol index
-.62(.06)** -.33(.04)** -.61(.06)** -.34(.03)** -.61(.06)** -.33(.03)** -.61(.06)** -.33(.03)** -.61(.05)** -.34(.03)** -.59(.06)** -.33(.04)**
Time -.05(.04) -.04(.07) -.02(.04) -.02(.07) -.03(.04) -.03(.07) -.05(.04) -.02(.07) -.03(.03) -.02(.06) -.03(.03) -.02(.06)
Level 2 Covariates
Age -.03(.16) -.25(.11)* -.04(.17) -.26(.10)* -.03(.17) -.26(.10)* -.02(.16) -.26(.10)* -.03(.16) -.26(.11)* -.04(.16) .26(.10)*
Medication .22(.12) .07(.12) .22(.12) .08(.12) .22(.12) .08(.12) .23(.12) .08(.12) .22(.12) .08(.12) .23(.12) .08(.12)
!
*p<.05, ** p<.001
Note. Coeff = Coefficient. SE = Standard error. Time = Time of awakening sample. Same-day and previous-day conflict were run in
separate models for mothers, fathers, and peers and for each of the cortisol indices.

INTERPERSONAL CONFLICT AND DIURNAL CORTISOL! 120
Table 6

Multilevel Models of Parent-to-Youth Aggression History and Diurnal Cortisol Indices

CAR AUCi
Coeff (SE)
CAR AUCg
Coeff (SE)
CAR AUCi
Coeff (SE)
CAR AUCg
Coeff (SE)
Level 1
Time of awakening sample -.05 (.03) -.04 (.07) -.02 (.01) -.04 (.07)
Cotinine .07 (.04) .04 (.03) .01 (.01) .04 (.03)
Level 2
Parent-to-youth aggression history -.25 (.12)* .10 (.11) -.17 (.17) -.26 (.30)
Age -.05 (.19) -.25 (.10)* -.01 (.01) -.20 (.10)
Medications .25 (.15) .05 (.11) .05 (.03) .04 (.10)
Sex - - -.03 (.02) -.17 (.10)
Sex-by-AggHx Interaction - - .06 (.11) .39 (.31)

*p<.05, ** p<.01
Note. Coeff=Coefficient. SE=Standard error. AggHx=Parent-to-youth aggression history.
! 121

!
!
!
!
!
!
!
!
!

Figure 1. Diurnal cortisol patterns of males versus females reporting on conflict with their
fathers above versus below the mean across the 4 days of daily data collection.

!
!
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Low Conflict
Girls
High Conflict
Girls
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Low Conflict
Boys
High Conflict
Boys
Cortisol Concentration (µg/dL) Cortisol Concentration (µg/dL)
Females
Males
! 122
!
!
!
!
!

Figure 2. Diurnal cortisol patterns of males versus females reporting on conflict with their peers
above versus below the mean across the 4 days of daily data collection.

Cortisol Concentration (µg/dL)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Low Conflict
Girls
High Conflict
Girls
Cortisol Concentration (µg/dL)
Females
Males
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Low Conflict
Boys
HighConflict
Boys
! 123

Figure 3. Mean raw cortisol values across the day of participants who did versus did not
experience same-day conflict with their mother.
! !
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Awake Awake+20 Awake+40 4pm 9pm
No Same-Day Conflict
with Mom
Any Same-Day Conflict
with Mom
Cortisol Concentration (µg/dL)
! 124

Figure 4. Raw cortisol values across the day of participants who did versus did not experience
previous-day conflict with their mother.
!

!

!
!
!
!

Cortisol Concentration (µg/dL)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Awake Awake+20 Awake+40 4pm 9pm
No Previous-Day
Conflict w/ Mom
Any Previous-
Day Conflict with
Mom
! 125

Figure 5. Diurnal cortisol patterns of youth with no, low (below the mean) and high (above the
mean) histories of Parent-to-Youth aggression exposure across the previous three waves of data
collection.

Cortisol Concentration (µg/dL)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
No Aggression Hx
Low Aggression Hx
High Aggression Hx
! 126
Discussion
The results of these two studies not only broaden our understanding of the relations
between adolescents’ diurnal cortisol and their experiences involving risky behaviors and
interpersonal conflict, but also highlight important methodological and theoretical directions for
future research. In both studies, the direction of effects for diurnal cortisol indices varied
depending on the timeframe in which each construct was examined, as well as whether we
examined within versus between-person differences in diurnal cortisol. This brings the
complexities of HPA functioning and allostatic load in relation to day-to-day experiences to the
forefront. Moreover, these studies examined common experiences in adolescence with a range
of severity, raising questions about the point at which normative experiences become
problematic for adolescent development.
Manuscript 1 highlighted differences in general versus day-to-day assessments of
experiences common to adolescents, honing in on risky behaviors in relation to diurnal cortisol.
Specifically, we examined different types of risk behaviors, including substance use, sexual risk
behaviors, aggressive delinquent behaviors, and criminal delinquent behaviors in relation to
adolescents’ diurnal cortisol. We also conducted more proximal assessments of risk behaviors to
examine potential intraindividual differences in diurnal cortisol relating to day-to-day risky
behaviors. Results showed that although overall engagement in substance use and sexual risk
behaviors related to lower morning cortisol levels, we found heightened morning cortisol levels
on days following engagement in risky behaviors, which maps onto the sensation-seeking theory
of risky behaviors (Raine, Reynolds, Venables, Mednick, & Farrington, 1998). These results
raise the possibility of day-to-day, within-person increases in CARs following risky behaviors,
! 127
i.e., HPA upregulation, as a potential mechanistic clue linking risky behaviors to overall HPA
downregulation.
Likewise, Manuscript 2 sought to examine ongoing as well as day-to-day experiences of
interpersonal conflict and aggression in relation to diurnal cortisol in adolescents. Moreover, we
examined whether previous experiences of conflict and aggression over several years moderated
the relation between current conflict and diurnal cortisol patterns. We found evidence for both
heightened and dampened diurnal cortisol related to proximal everyday conflict, with differences
accounted for by sex as well as the person with whom there was conflict. Proximal conflict with
peers related to heightened CARs in females, but not males, and proximal conflict with fathers
related to blunted CARs in males, but not females. Examinations of intraindividual differences
in diurnal cortisol also showed both heightened and blunted CARs; youth exhibited heightened
CARs on days of conflict with their mothers, and blunted CARs on days following conflict with
their mothers. Consistent with the attenuation theory, we found that overall experiences of
parent-to-youth aggression were associated with blunted CARs. As with our findings in
Manuscript 1, day-to-day elevations in CARs suggest a potential mechanism linking everyday
conflict, as well as the associated HPA upregulation, to the eventual downregulation of diurnal
HPA activity.
Diurnal Cortisol in Relation to Distal versus Proximal Experiences
In both manuscripts, our distal measures of risk behaviors and interpersonal aggression
related to lower morning cortisol levels. Our findings underline the importance of incorporating
both proximal and distal assessments of constructs in examinations of diurnal cortisol in order to
capture the complexity of these relations. As with most constructs examined in developmental
research, interpersonal experiences, risky behaviors, and physiology are dynamic and change
! 128
throughout adolescent development (Hughs, Power, & Francis, 1992; Laursen, 1995; Furman &
Burmester, 1992; Repetti, Robles, & Reynolds, 2011; Steinberg, 1999). Further complicating the
assessment of these constructs over time is that their interrelations differ across development.
For example, infants exposed to family stressors demonstrate sensitized HPA reactivity, and as
children get older they start to show blunted HPA reactivity to similar stressors (Gunnar, Larson,
Hertsgaard, Harris, & Brodersen, 1992; Davies, Sturge-Apple, Cicchetti, & Cummings, 2007).
The future challenge involved in making sense of how diurnal cortisol relates to these constructs
is to not only take a snapshot of their correlates, or even to examine both proximal and distal
measures of risky behaviors and conflict, but also to examine their relation to diurnal cortisol
over time in future studies to gain a more thorough understanding of developmental change.
Allostatic Load in Adolescents
Given the findings of blunted morning cortisol levels in our sample, we can speculate that
allostatic load may be manifest in the diurnal HPA rhythms of adolescents, as has been suggested
in other studies of adolescents (e.g., Trickett et al., 2010). Yet, the two studies also demonstrated
momentary elevations in CARs subsequent to risky behaviors and prior to experiences of
conflict. These momentary elevations raise the possibility that over time, elevated levels of
corticosteroids may contribute to overall blunted CARs. Even still, intraindividual examinations
of CARs may show momentary elevations surrounding certain behaviors, but individuals’ CARs
may nonetheless be “blunted” in comparison to “healthy” adolescents.
Moreover, allostatic load appears in different ways across stages of development. For
example, there are precursors to allostatic load, which may be observed as prolonged immune
responses and elevated cortisol levels, as well as later outcomes resulting from allostatic load,
such as obesity and disease (Repetti, Robles, & Reynolds, 2011). Adolescents in our sample
! 129
who demonstrated weakened CARs likely fall somewhere in between the precursors and the
detrimental outcomes on the spectrum of allostatic processes, but much more research is needed
to clarify the actual physical vulnerabilities of adolescents who exhibit blunted HPA patterns.
Repeated assessments of diurnal cortisol over time would help clarify the dynamic process by
which stressors relate to diurnal cortisol rhythms.
Normative versus Problematic Behaviors in Adolescents in Relation to Allostatic Load
Overload of the physiological stress system happens with severe and chronic stressors,
but researchers have yet to identify the point at which allostatic load begins to emerge given
individual differences and variations in environmental stressors. Early identification of allostatic
load is viewed as an important step in developmental research to promote the prevention of and
interventions for resulting health complications (Cicchetti, 2011). A helpful way to better
understand allostatic load is to focus not only on the extreme forms of stressors and behaviors
that influence HPA activity, but to also examine more subtle forms of these behaviors, which we
achieved in the present study.
A vital question that these two manuscripts raise is whether there is a point at which risky
behaviors and interpersonal conflict, which are normative elements of adolescent development,
can be deemed problematic for adaptive adjustment and physiology. In adolescents, there is a
greater level of maturation in the socioemotional network of the brain, which involves social and
emotional reactivity, relative to the cognitive-control network, related to inhibition and planning;
as such, risky behaviors and interpersonal conflict are inevitable parts of development
(Steinberg, 2007). Key considerations for evaluating the level of harm incurred by these
behaviors are the context in which these events are occurring, the consequences of these events,
as well as the frequency and intensity of the events. For example, having sex without a condom
! 130
may carry a different level of risk for a 20-year-old in a long-term, committed relationship than
for a 15-year-old who is intoxicated at a party. A better understanding of physiological
differences in adolescents exhibiting severe and frequent versus normative risky behaviors and
interpersonal conflict would make us better equipped to identify youth who are truly “at-risk”
both psychosocially and physiologically.
Limitations and Future Directions
Both studies highlighted several limitations that are important to consider when
interpreting the results, and these limitations can be addressed in future research. Some
limitations that both studies shared were their use of weekday, versus both weekday and
weekend assessments of cortisol, and also the limited information regarding when the particular
risky behavior or conflict occurred. Use of detailed ecological momentary assessments (EMA)
throughout the day in conjunction with cortisol sampling would help clarify the connection
between experiences and cortisol fluctuations. While future studies can take these
methodological considerations into account, there are also a number of theoretical future
directions that would broaden our understanding of how risky behaviors and interpersonal
conflict fit within adolescents’ stress physiology.
First, concurrent assessment of a number of physiological indices would help us uncover
the mechanistic links between conflict, risk behaviors, and diurnal cortisol, particularly if these
assessments were repeated over time. Given that corticosteroids can cross the blood-brain
barrier (Gunnar & Quevedo, 2007), incorporation of brain imaging would allow us to examine
hippocampal volume and other limbic structures that are directly implicated in diurnal HPA
rhythms (Fries, Dettenborn, & Kirschbaum, 2009). Moreover, examining levels of other
hormones that researchers have related to aggressive behaviors, such as oxytocin and
! 131
testosterone (Malik, Zai, Abu, Nowrouzi, & Beitchman, 2012; Popma et al., 2007), would help
us elucidate the intricate interrelationships among risky behaviors, conflict, and physiology.
Identification of protective factors would also greatly benefit this line of research. There
is evidence that intervention can alter diurnal HPA rhythms (Brotman, Gouley, Huang et al.,
2007; Fisher, Stoolmiller, Gunnar, & Burraston, 2007), and expanding our knowledge to address
prevention could help cultivate adolescents’ resilience. Of interest, there have been no studies
examining daily subjective experiences of heightened versus dampened CARs or flat versus
steep diurnal cortisol patterns, which could potentially open up avenues for biofeedback or
behavioral activation interventions. Furthermore, isolating interventions that target both problem
behaviors as well as regulation of adolescents’ diurnal physiology could have powerful
implications for adolescent health promotion.
Overall Conclusions
Taken together, these studies deepen our understanding of diurnal cortisol in adolescents,
uncovering connections to proximal, distal, and day-to-day experiences of risky behaviors and
interpersonal conflict. Overall, our findings revealed blunted morning cortisol levels in relation
to ongoing risk behaviors and interpersonal conflict, with intraindividual bursts and blunting of
morning cortisol levels in our day-to-day examinations. Both ongoing as well as day-to-day
assessments were crucial to identify the intricate relations between diurnal cortisol and common
adolescent behaviors. Future studies would benefit from detailed assessments of frequency and
severity of risky behaviors and interpersonal conflict in conjunction with their physiological
correlates over time. This would propel us toward a clearer understanding of the dynamic
interplay among these characteristic features of adolescent development.

! 132
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Appendix A
YRBS Youth Report Wave 5 (revised 6.20.08)

1. How do you describe your health in general? (FSP:!1,!YRBS:!not!included)!

A. Excellent
B. Very good
C. Good
D. Fair
E. Poor
F. Very poor

2. During the past year, how many times did you skip class, classes, or a day of school without your
parent’s permission? (FSP:!3,!YRBS:!not!included)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

3. During the past year, how many times did you cheat on tests? (FSP:!5,!SRA:!13,!YRBS:!not!included)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

4. During the past year, how many times have you broken, damaged, or destroyed something belonging
to your parents, or other people in your family, on purpose? (SRA:!1,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

5. During the past year, how many times have you broken, damaged, or destroyed something belonging
to a teacher or a school, on purpose? (SRA:!2,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

6. During the past year, how many times have you broken, damaged, or destroyed other things
belonging to other kids or adults, not counting things that belong to your family or school, on purpose?
(SRA:!3,!YRBS!not!included)!

! 137
A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

7. During the past year, how many times have you stolen, or tried to steal, something worth $5 or less?
(SRA:!4,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

8. During the past year, how many times have you stolen, or tried to steal, something worth more than
$5? (SRA:!5,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

9. During the past year, how many times have you broken into a building or car (or tried to break in) to
steal something or just look around? (SRA:!6,!YRBS!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

10. During the past year, how many times did you purposely take something from a store or restaurant
without paying? (FSP:!7!added$restaurant,!SRA:!7,!YRBS:!not!included)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

11. During the past year, how many times have you taken some money at home that did not belong to
you, like from your mother’s purse or your parents’ dresser? (SRA:!8,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
! 138
F. More than 20 Times

12. During the past year, how many times have you taken anything else at home that did not belong to
you? (SRA:!9,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

13. During the past year, how many times did you take things without asking, from other kids, teachers
or other adults at school? (FSP:!9,!YRBS:!not!included)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

14. During the past year, how many times have you been suspended, expelled, or sent home from
school for bad behavior? (SRA:!27,!(added!suspended,!expelled),!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

15. During the past year, how many times have you written things or sprayed paint on walls, sidewalks,
or cars where you were not supposed to do that? (SRA:!28,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

16. During the past year, how many times have you purposely set fire to a building, car, or other
property, or tried to do so? (SRA:!30,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

17. During the past year, how many times have you snatched someone’s purse or wallet, or picked
someone’s pocket? (SRA:!34,!YRBS:!not!included)!
! 139

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

18. During the past year, how many times have you been cruel to an animal, or purposely hurt an
animal? (SRA:!36,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

19. During the past year, how many times have you used verbal threats to get money or something from
another kid, or to get the kid to agree to something? (SRA:!38,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

20. During the past year, how many times have you used physical force to get money or something from
another kid, or to get the kid to agree to something? (SRA:!39,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

21. During the past year, how many times have you threatened to beat up another kid (or helped a
friend threaten to beat up another kid) just to scare the kid? (SRA:!40,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 to 4 Times
D. 5 to 10 Times
E. 11 to 20 Times
F. More than 20 Times

22. Have you EVER been asked to join a gang? (New!question:!not!in!FSP,!YRBS,!or!SRA)!

A. Yes
B. No

23. Have you EVER been in a gang? (New!question:!not!in!FSP,!YRBS,!or!SRA)!
! 140

A. Yes
B. No

24. Are you currently in a gang? (New!question:!not!in!FSP,!YRBS,!or!SRA)
A. Yes
B. No

25. How often do you wear a seat belt when riding in a car driven by someone else?!(FSP:!10,!C.!occasionally!is!
added,!YRBS:!9)!

A. Never
B. Rarely
C. Occasionally
D. Sometimes
E. Most of the time
F. Always

The next questions ask about drinking alcohol. This includes drinking beer, wine, wine coolers,
and liquor such as rum, gin, vodka, or whiskey. For these questions, drinking alcohol does not
include drinking a few sips of wine for religious purposes.

26. During the past year, how many times did you ride in a car or other vehicle driven by a friend or
acquaintance who had been drinking alcohol? (FSP:!12,!YRBS:!does!not!include!during!past!year)

A. 0 Times
B. 1 Time
C. 2 or 3 Times
D. 4 or 5 Times
E. 6 to 10 Times
F. More than 10 Times

27. During the past 30 days, how many times did you ride in a car or other vehicle driven by a friend
or acquaintance who had been drinking alcohol? (FSP:!does!not!include!past!30!days,!YRBS:!10,!scale:!E.!6!or!more!times,!no!F)!
(Change!scale??)!

A. 0 Times
B. 1 Time
C. 2 or 3 Times
D. 4 or 5 Times
E. 6 to 10 Times
F. More than 10 Times

28. During the past year, how many times did you drive a car or other vehicle when you had been
drinking alcohol? (FSP:!14,!YRBS:!does!not!include!during!past!year)!

A. 0 Times
B. 1 Time
C. 2 or 3 Times
! 141
D. 4 or 5 Times
E. 6 to 10 Times
F. More than 10 Times

29. During the past 30 days, how many times did you drive a car or other vehicle when you had been
drinking alcohol? (FSP:!does!not!include!30!days,!YRBS:!11,!scale:!E.!6!or!more!times,!no!F)!

A. 0 Times
B. 1 Time
C. 2 or 3 Times
D. 4 or 5 Times
E. 6 to 10 Times
F. More than 10 Times

30. During your life, on how many days have you had at least one drink of alcohol (not including a few
sips for religious purposes)? (FSP:!15,!YRBS:!39,!scale!is!different:!F.!40!to!99!days,!G.!100!or!more!days)!

A. 0 days
B. 1 or 2 days
C. 3 to 9 days
D. 10 to 19 days
E. 20 to 39 days
F. 40 or more days

31. During the past year, on how many days have you had at least one drink of alcohol? (FSP:!16,!YRBS:!does!
not!include!past!year)!

A. 0 days
B. 1 or 2 days
C. 3 to 9 days
D. 10 to 19 days
E. 20 to 39 days
F. 40 or more days

32. During the past 30 days, on how many days have you had at least one drink of alcohol? (FSP:!17,!YRBS:!
41)!(typo!on!FSP!version:!C.!should!be!3!to!5!days)!

A. 0 days
B. 1 or 2 days
C. 3 to 5 days
D. 6 to 9 days
E. 10 to 19 days
F. 20 to 29 days
G. All 30 days

33. During the past year, on how many days did you have 5 or more drinks of alcohol in one day? (FSP:!20,!
YRBS:!does!not!include!past!year)!

A. 0 days
B. 1 or 2 days
C. 3 to 9 days
D. 10 to 19 days
E. 20 to 39 days
F. 40 or more days

34. During the past 30 days, on how many days did you have 5 or more drinks of alcohol in one day?
(FSP:!20,!YRBS:!42)!!
! 142

A. 0 days
B. 1 day
C. 2 days
D. 3 to 5 days
E. 6 to 9 days
F. 10 to 19 days
G. 20 or more days

35. How old were you when you had your first drink of alcohol other than a few sips? (FSP:!18,!YRBS:!40)!

A. I have never had a drink of alcohol other than a few sips
B. 17 years or older
C. 15 or 16 years old
D. 13 or 14 years old
E. 11 or 12 years old
F. 9 or 10 years old
G. 8 years old or younger

36. Have you EVER tried cigarette smoking, even one or two puffs? (FSP:!21,!YRBS:!28)!

A. Yes
B. No

37. During the last year, on how many days did you smoke at least one cigarette? (FSP:!23,!YRBS:!does!not!include!
past!year)!

A. 0 days
B. 1 or 2 days
C. 3 to 9 days
D. 10 to 19 days
E. 20 to 39 days
F. 40 or more days

38. During the past 30 days, on how many days did you smoke cigarettes? (FSP:!24,!YRBS:!30)!
!
A. 0 days
B. 1 or 2 days
C. 3 to 5 days
D. 6 to 9 days
E. 10 to 19 days
F. 20 to 29 days
G. All 30 days

39. During the past year, on how many days did you use chewing tobacco, snuff, or dip, such as
Redman, Levi Garrett, Beechnut, Skoal, Skoal Bandits, or Copenhagen? (FSP:!25,!asks!ever,!YRBS:!36,!asks!past!30!days)!

! 143
A. 0 days
B. 1 or 2 days
C. 3 to 9 days
D. 10 to 19 days
E. 20 to 39 days
F. 40 or more days

40. During the past year, on how many days did you smoke cigars, cigarillos, or little cigars? (FSP:!26,!asks!
ever,!YRBS:!38,!asks!past!30!days)!
A. 0 days
B. 1 or 2 days
C. 3 to 9 days
D. 10 to 19 days
E. 20 to 39 days
F. 40 or more days

The next questions ask about marijuana use. Marijuana is also called grass, pot, or weed.

41. During your life, how many times have you EVER used marijuana? (FSP:!27,!YRBS:!44)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 to 99 Times
G. 100 or more times

42. How old were you when you tried marijuana for the first time? (FSP:!28,!YRBS:!45)!

A. I have never tried marijuana
B. 17 years or older
C. 15 or 16 years old
D. 13 or 14 years old
E. 11 or 12 years old
F. 9 or 10 years old
G. 8 years old or younger

43. During the past year, how many times did you use marijuana? (FSP:!29,!YRBS:!does!not!include!past!year)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
E. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

44. During the past 30 days, how many times did you use marijuana? (FSP:!30,!YRBS:!46)!

A. 0 Times
B.1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
! 144
F. 40 or more Times

45. During your life, how many times have you EVER used ecstasy (also called MDMA)? (FSP:!31,!YRBS:!54,!
ever!not!included)!
!
A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

46. During the past year, how many times have you used ecstasy? (FSP:!32,!YRBS:!not!included)!
!
A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

47. During your life, how many times have you used any other illegal drugs, including any form of
cocaine (powder, crack, freebase, etc.), sniffing glue, heroin (aka smack, junk, or China white),
methamphetamines ( aka speed, crystal, crack, or ice), or steroid pills or shots? (FSP:!34,!YRBS:!combination!of!48,!
50,!52,!53,!55)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

48. How many times have you EVER taken prescription drugs to get high (not for medical purposes)? (FSP:!
35,!YRBS:!not!included)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

49. During the past year, how many times have you taken prescription drugs (not for medical purposes)
to get high? (FSP:!36,!YRBS:!not!included)
A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

! 145

50. How many times have you EVER taken prescription drugs (not for medical purposes) along with
alcohol or other drugs to get high? (FSP:!37,!YRBS:!not!included)

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

51. During the past year, how many times have you taken prescription drugs (not for medical purposes)
along with alcohol or other drugs to get high? (FSP:!38,!YRBS:!not!included)!

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

52. During the past year, on how many days have you carried a weapon such as a gun, knife, or club?
(FSP:!40,!YRBS:!does!not!include!past!year)
A. 0 days
B. 1 day
C. 2 or 3 days
D. 4 or 5 days
E. 6-10 days
F. More than 10 days

53. During the past 30 days, on how many days have you carried a weapon such as a gun, knife, or
club? (FSP:!does!not!include!30!days,!YRBS:!12:!scale!is!different:!E.!6!or!more!days,!no!F)!

A. 0 days
B. 1 day
C. 2 or 3 days
D. 4 or 5 days
E. 6-10 days
F. More than 10 days

54. During the past year, on how many days have you carried a weapon such as a gun, knife, or club on
school property? (FSP:!not!included,!YRBS:!does!not!include!past!year)

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

55. During the past 30 days, on how many days have you carried a weapon such as a gun, knife, or
club on school property? (FSP:!not!included,!YRBS:!14)!

A. 0 days
B. 1 day
C. 2 or 3 days
! 146
D. 4 or 5 days
E. 6 or more days

56. During the past year, on how many days did you not go to school because you felt you would be
unsafe at school or on your way to or from school? (FSP:!not!included,!YRBS:!does!not!include!past!year)

A. 0 Times
B. 1 or 2 Times
C. 3 to 9 Times
D. 10 to 19 Times
E. 20 to 39 Times
F. 40 or more Times

57. During the past 30 days, on how many days did you not go to school because you felt you would be
unsafe at school or on your way to or from school? (FSP:!not!included,!YRBS:!15)!

A. 0 days
B. 1 day
C. 2 or 3 days
D. 4 or 5 days
E. 6 or more days

58. During the past year, how many times has someone threatened or injured you with a weapon such
as a gun, knife or club on school property? (FSP:!not!included,!YRBS:!16)!

A. 0 times
B. 1 time
C. 2 or 3 times
D. 4 or 5 times
E. 6 or 7 times
F. 8 or 9 times
G. 10 or 11 times
H. 12 or more times

59. During the past year, how many times has someone stolen or deliberately damaged your property
such as your car, clothing, or books on school property? (FSP:!not!included,!YRBS:!17)

A. 0 times
B. 1 time
C. 2 or 3 times
D. 4 or 5 times
E. 6 or 7 times
F. 8 or 9 times
G. 10 or 11 times
H. 12 or more time

60. During the past year, how many times have you been in a physical fight? (FSP:!42,!YRBS:!18:!used!YRBS!scale)!

A. 0 times
B. 1 time
C. 2 or 3 times
D. 4 or 5 times
! 147
E. 6 or 7 times
F. 8 or 9 times
G. 10 or 11 times
H. 12 or more time

61. During the past year, how many times were you in a physical fight in which you were injured and
had to be treated by a doctor or nurse? (FSP:!not!included,!YRBS:!19)!

A. 0 times
B. 1 time
C. 2 or 3 times
D. 4 or 5 times
E. 6 or more times

62. During the past year, how many times were you in a physical fight on school property? (FSP:!not!
included,!YRBS:!20)!

A. 0 times
B. 1 time
C. 2 or 3 times
D. 4 or 5 times
E. 6 or 7 times
F. 8 or 9 times
G. 10 or 11 times
H. 12 or more time

63. During your life, with how many people have you had sex (intercourse, oral sex, anal sex)? (FSP:!48,!
YRBS:!60,!does!not!say!include!intercourse,!oral!sex,!anal!sex)!

A. None
B. 1 person
C. 2 persons
D. 3 persons
E. 4 to 5 persons
F. 6 or more persons

64. During the last year, with how many people have you had sex (intercourse, oral sex, anal sex)? (FSP:!
49,!YRBS:!not!included)!

A. None
B. 1 person
C. 2 persons
D. 3 persons
E. 4 to 5 persons
F. 6 or more persons

65. During your life, how many times have you EVER used alcohol or drugs before you had sex
(intercourse, oral sex, anal sex)? (FSP:!50,!YRBS:!62,!question!is!different:!“Did!you!drink!alcohol!or!use!drugs!before!you!had!sexual!
intercourse!the!last!time?”!answer!options!are!yes/no)!
! 148

A. 0 Times
B. 1 Time
C. 2 or 3 Times
D. 4 to 6 Times
E. 7 to 10 Times
F. More than 10 Times

66. During the past year, how many times did you use alcohol or drugs before you had sex (intercourse,
oral sex, anal sex)? (FSP:!51,!YRBS:!does!not!include!last!year)!

A. 0 Times
B. 1 Time
C. 2 or 3 Times
D. 4 to 6 Times
E. 7 to 10 Times
F. More than 10 Times

67. During your life, how many times have you EVER had sex (intercourse, oral sex, anal sex) without
using a male condom? (FSP:!52,!YRBS:!63,!question!is!different:!“The!last!time!you!had!sexual!intercourse,!did!you!or!your!partner!use!a!
condom?”)!

A. 0 Times
B. 1 Time
C. 2 or 3 Times
D. 4 to 6 Times
E. 7 to 10 Times
F. More than 10 Times

68. During the last year, how many times have you had sex (intercourse, oral sex, anal sex) without
using a male condom? (FSP:!53,!YRBS:!not!included)!

A. 0 Times
B. 1 Time
C. 2 or 3 Times
D. 4 to 6 Times
E. 7 to 10 Times
F. More than 10 Times

69. During the last year, how many traffic citations (not including parking tickets) have you had? (New!
question:!not!in!YRBS,!SRA,!or!FSP)!
A. 0
B. 1
C. 2 or 3
D. 4 to 6
E. 7 to 10
F. More than 10

70. Have you EVER been stopped in your car by the police after you had been drinking alcohol? (New!
question:!not!in!YRBS,!SRA,!or!FSP)!
A. 0 Times
B. 1 Time
C. More than one time

! 149
71. Have you EVER been in a car accident when you were the passenger? (New!question:!not!in!YRBS,!SRA,!or!FSP)!
!
A. 0 Times
B. 1 Time
C. More than one time

72. Have you EVER been in a car accident when you were the driver? (New!question:!not!in!YRBS,!SRA,!or!FSP)!

A. 0 Times
B. 1 Time
C. More than one time

73. Have you EVER been in a car accident after drinking alcohol when you were the driver? (New!question:!not!
in!YRBS,!SRA,!or!FSP)!

A. 0 Times
B. 1 Time
C. More than one time
!

! 150
Appendix B
Office Use Only: Postmarked ____/____/____ Rec’d ____/____/____ ! In Lab ID # ______-___

Day # ____ Youth Home Data Form – Wave 5 (6.18.09)

Date described ____/____/____ Sun Mon Tue Wed Thu Fri Sat
Date filled out ____/____/____ Time filled out ______:_____ A.M. / P.M.

Did it happen? If so, how did it make you feel?
Who was it?

Check all that apply
With my group of friends today…
Not at
all
Some A lot
Very
bad
A little
bad
Not
good or
bad
A little
good
Very
good

Boyfriend/
Girlfriend or
Dating
Partner
Any Other
Friend
1. ! A friend was annoyed with me ! ! ! ! ! ! ! ! ! !
2. ! I was annoyed with a friend ! ! ! ! ! ! ! ! ! !
3. ! A friend was angry with/yelled at or criticized me ! ! ! ! ! ! ! ! ! !
4. ! I was angry with/yelled at or criticized a friend ! ! ! ! ! ! ! ! ! !
5. !
A friend sent a mean or hurtful text message or e-
mail to me
! ! ! ! ! ! ! ! ! !
6. !
I sent a mean or hurtful text message or e-mail to
a friend
! ! ! ! ! ! ! ! ! !
7. !
A friend posted/wrote something mean or
hurtful/embarrassing about me so that others can
see (e.g., Facebook or MySpace)
! ! ! ! ! ! ! ! ! !
8. !
I posted/wrote something mean or
hurtful/embarrassing about a friend so that others
can see (e.g., Facebook or MySpace)
! ! ! ! ! ! ! ! ! !
9. !
A friend said something mean or hurtful to me in a
face-to-face conversation or on the phone
! ! ! ! ! ! ! ! ! !
10. !
I said something mean or hurtful to a friend in a
face-to-face conversation or on the phone
! ! ! ! ! ! ! ! ! !
11. ! A friend helped me out/did me a favor ! ! ! ! ! ! ! ! ! !
12. ! I helped out a friend/did her/him a favor ! ! ! ! ! ! ! ! ! !
13. A friend was jealous or resentful of me
14. I was jealous or resentful of a friend
! 151
Did it happen? If so, how did it make you feel?
Who was it?

Check all that apply
With my group of friends today…
Not at
all
Some A lot
Very
bad
A little
bad
Not
good or
bad
A little
good
Very
good

Boyfriend/
Girlfriend or
Dating
Partner
Any Other
Friend
15. A friend lied to me ! ! ! ! ! ! ! ! ! !
16. I lied to a friend ! ! ! ! ! ! ! ! ! !
17. A friend accused me of flirting with someone ! ! ! ! ! ! ! ! ! !
18. I accused a friend of flirting with someone ! ! ! ! ! ! ! ! ! !
19.
A friend talked about me behind my back or
spread rumors about me
! ! ! ! ! ! ! ! ! !
20.
I talked about a friend behind his/her back or
spread rumors about him/her
! ! ! ! ! ! ! ! ! !
21. A friend swore or cursed at me
22. I swore or cursed at a friend
23. A friend ignored me ! ! ! ! ! ! ! ! ! !
24. I ignored a friend ! ! ! ! ! ! ! ! ! !
25. A friend did something to make me feel jealous ! ! ! ! ! ! ! ! ! !
26. I did something to make a friend feel jealous ! ! ! ! ! ! ! ! ! !
27. A friend slapped me or pulled my hair ! ! ! ! ! ! ! ! ! !
28. I slapped a friend or pulled his/her hair ! ! ! ! ! ! ! ! ! !
29. A friend pushed, grabbed, shoved, or hit me ! ! ! ! ! ! ! ! ! !
30. I pushed, grabbed, shoved, or hit a friend ! ! ! ! ! ! ! ! ! !
31. A friend stood up for me ! ! ! ! ! ! ! ! ! !
32. I stood up for a friend ! ! ! ! ! ! ! ! ! !
33.
A friend humiliated or embarrassed me in front of
others
! ! ! ! ! ! ! ! ! !
! 152
Did it happen? If so, how did it make you feel?
Who was it?

Check all that apply
With my group of friends today…
Not at
all
Some A lot
Very
bad
A little
bad
Not
good or
bad
A little
good
Very
good

Boyfriend/
Girlfriend or
Dating
Partner
Any Other
Friend
34.
I humiliated or embarrassed a friend in front of
others
! ! ! ! ! ! ! ! ! !
35. A friend made unwanted sexual advances ! ! ! ! ! ! ! ! ! !
36. I made unwanted sexual advances toward a friend ! ! ! ! ! ! ! ! ! !
37. A friend told others things I said in confidence ! ! ! ! ! ! ! ! ! !
38. I told others things a friend said in confidence ! ! ! ! ! ! ! ! ! !
39.
A friend drove recklessly while angry with me or
argued with me while he/she was driving
! ! ! ! ! ! ! ! ! !
40.
I drove recklessly while angry with a friend or
argued with a friend while I was driving
! ! ! ! ! ! ! ! ! !
41.
A friend made me feel my thoughts and feelings
are important
! ! ! ! ! ! ! ! ! !
42.
I made a friend feel his/her thoughts and feelings
are important
! ! ! ! ! ! ! ! ! !
43.
A friend tried to pressure me to do something I
didn’t want to do
! ! ! ! ! ! ! ! ! !
44.
I tried to pressure a friend to do something s/he
didn’t want to do
! ! ! ! ! ! ! ! ! !
45. I hung out/enjoyed being with a friend ! ! ! ! ! ! ! ! ! !
46. I felt supported by a friend ! ! ! ! ! ! ! ! ! !

This scale consists of a number of words that describe different feelings and emotions. Read each item and then circle the
appropriate answer next to that word. Indicate to what extent you have felt this way during the past day.

! 153
Today, I felt…
Very slightly or
not at all
A little Moderately Quite a bit Extremely
47. !
Interested
1 2 3 4 5
48. !
Distressed
1 2 3 4 5
49. !
Excited
1 2 3 4 5
50. !
Upset
1 2 3 4 5
51. !
Strong
1 2 3 4 5
52. !
Guilty
1 2 3 4 5
53. !
Scared
1 2 3 4 5
54. !
Hostile
1 2 3 4 5
55. !
Enthusiastic
1 2 3 4 5
56. !
Proud
1 2 3 4 5
57. !
Irritable
1 2 3 4 5
58. !
Alert
1 2 3 4 5
59. !
Ashamed
1 2 3 4 5
60. !
Inspired
1 2 3 4 5
61. !
Nervous
1 2 3 4 5
62. !
Determined
1 2 3 4 5
63. !
Attentive
1 2 3 4 5
64. !
Jittery
1 2 3 4 5
65. !
Active
1 2 3 4 5
66. !
Afraid
1 2 3 4 5
67. !
Sad
1 2 3 4 5
68. !
Fearless
1 2 3 4 5
69. !
Cheerful
1 2 3 4 5
70. !
Daring
1 2 3 4 5
71. !
Lonely
1 2 3 4 5
72. !
Happy
1 2 3 4 5
73. !
Angry
1 2 3 4 5
! 154
Today, I felt…
Very slightly or
not at all
A little Moderately Quite a bit Extremely
74. !
Bored
1 2 3 4 5
75. !
Calm
1 2 3 4 5
76. !
Miserable
1 2 3 4 5
77. !
Bold
1 2 3 4 5
78. !
Tired
1 2 3 4 5
79. !
Mad
1 2 3 4 5

Not at all A little Somewhat A lot Not Applicable
# I saw, talked with, or spent time with my MOM
or someone like a MOM today
! ! ! ! !

Think about your MOM or someone like a mom… Not at all A little Somewhat A lot Not Applicable
80. !
How much did you enjoy being with her?
! ! ! ! !
81. !
How much conflict did you and she have?
! ! ! ! !
82. !
How much did she annoy you?
! ! ! ! !
83. !
How much did she hurt your feelings?
! ! ! ! !
84. !
How much did your mom push or shove or
physically hurt you?
! ! ! ! !
85. !
How much did your mom seem angry with
you?
! ! ! ! !
86. !
How much did she yell at or criticize you?
! ! ! ! !
87. !
How much did she seem distant or
withdrawn?
! ! ! ! !
88. !
How much did she seem too busy or
distracted to talk with you?
! ! ! ! !
89.
How much did you feel supported by her?
! ! ! ! !

! 155

Not at all A little Somewhat A lot Not Applicable
# I saw, talked with, or spent time with my DAD or
someone like a DAD today
! ! ! ! !

Today my DAD or someone like a DAD… Not at all A little Somewhat A lot Not Applicable
90.
How much did you enjoy being with him?
! ! ! ! !
91.
How much conflict did you and he have?
! ! ! ! !
92.
How much did he annoy you?
! ! ! ! !
93.
How much did he hurt your feelings?
! ! ! ! !
94.
How much did your dad push or shove or
physically hurt you?
! ! ! ! !
95.
How much did your dad seem angry with you?
! ! ! ! !
96.
How much did he yell at or criticize you?
! ! ! ! !
97.
How much did he seem distant or withdrawn?
! ! ! ! !
98.
How much did he seem too busy or distracted to
talk with you?
! ! ! ! !
99.
How much did you feel supported by him?
! ! ! ! !

Not at all A little Somewhat A lot Not Applicable
# I saw, talked with, or spent time with my
BROTHER/SISTER
! ! ! ! !

Today my BROTHER/SISTER… Not at all A little Somewhat A lot Not Applicable
100.
How much did you enjoy being with him/her?
! ! ! ! !
101.
How much conflict did you and s/he have?
! ! ! ! !
102.
How much did s/he annoy you?
! ! ! ! !
103.
How much did s/he hurt your feelings?
! ! ! ! !
104.
How much did your brother or sister push or
shove or physically hurt you?
! ! ! ! !
105.
How much did s/he seem angry with you?
! ! ! ! !
106.
How much did s/he yell at or criticize you?
! ! ! ! !
107.
How much did s/he seem distant or withdrawn?
! ! ! ! !
108.
How much did s/he seem too busy or distracted
to talk with you?
! ! ! ! !
109.
How much did you feel supported by him/her?
! ! ! ! !
! 156

Not at all A little Somewhat A lot Not Applicable
# I saw, talked with, or spent time with my parents
today
! ! ! ! !

Thinking about your parents today… Not at all A little Somewhat A lot Not Applicable
110.
How much did your parents enjoy being each
other today?
! ! ! !! !!
111.
How much conflict did they have with one
another?
! ! ! !! !!
112.
How much did they seem to annoy each other?
! ! ! !! !!
113.
How much did they hurt each other’s feelings?
! ! ! !! !!
114.
How much did your parent or parents push or
shove or physically hurt each other?
! ! ! !! !!
115.
How much did your parents seem angry with
each other today?
! ! ! !! !!
116.
How much did they yell at or criticize each
other?
! ! ! !! !!
117.
How much did they seem distant or withdrawn
from each other?
! ! ! !! !!
118.
How much did they seem too busy or distracted
to talk with each other?
! ! ! !! !!
119.
How much did they seem kind to each other
today?
! ! ! !! !!
120.
How much did your parents enjoy being each
other today?
! ! ! !! !!
121.
How much conflict did they have with one
another?
! ! ! !! !!

Today I…
Not at
all
A little
Some A lot
122. Worked out/exercised ! ! ! !
123. Played sports ! ! ! !
124.
Drove or rode in a car without wearing a seat belt or drove over the
speed limit
! ! ! !
! 157

Today I…
Not at
all
A little
Some A lot
125.
Broke, damaged, or destroyed something belonging to others on
purpose
! ! ! !
126. Stole or tried to steal something ! ! ! !
127.
Broke into a building or car (or tried to break in) to steal something or
just look around
! ! ! !
128. Spent time volunteering/involved in community service ! ! ! !
129.
Rode in a car or other vehicle driven by someone who had been
drinking alcohol or had used drugs
! ! ! !
130.
Drove a car or other vehicle when I had been drinking alcohol or had
used drugs
! ! ! !
131. Drank alcohol ! ! ! !
132. Smoked a cigarette, cigar, pipe, or chewed tobacco ! ! ! !
133.
Used an illegal drug (such as marijuana, cocaine, ecstasy) or
prescription drug not prescribed for me
! ! ! !
134. Did an extracurricular activity (e.g., music, drama, etc) !
!
! !

Please answer the following if you are still in high school, if
not skip to #
Did it happen?
Today I…
Not at
all
A little
Some A lot
135. Did homework or studied ! ! ! !
136. Failed or got a bad grade on homework, a paper, project, or quiz/test ! ! ! !
137. Got a good grade or did well on homework, a paper, project, or quiz/test ! ! ! !
138. Got recognition for something I did in school ! ! ! !
139. Cut class/classes ! ! ! !
140. Got in trouble at school ! ! ! !
141. Enjoyed my time at school ! ! ! !
142. Cheated on/plagiarized school assignment or test ! ! ! !

! 158
Please answer the following if you are in college (or trade
school/certificate program), if not skip to #

Did it happen?
Today I…
Not at
all
A little
Some A lot
143. Did homework or studied ! ! ! !
144. Failed or got a bad grade on homework, a paper, project, or quiz/test ! ! ! !
145. Got a good grade or did well on homework, a paper, project, or quiz/test ! ! ! !
146. Got recognition for something I did in school ! ! ! !
147. Cut class/classes ! ! ! !
148. Enjoyed my time at school ! ! ! !
149. Cheated on/plagiarized school assignment or test ! ! ! !

Please answer the following if you have a job

Did it happen?
Today I…
Not at
all
A little
Some A lot
150. Got recognition for something I did at work ! ! ! !
151.
Did not go to work when I was scheduled to be there (not due to illness
or emergency)
! ! ! !
152. Got in trouble at work ! ! ! !
153. Enjoyed my time at work ! ! ! !
154. Felt like I did a good job at work ! ! ! !
155. Got along with people at work ! ! ! !
156. Felt irritated with someone at work ! ! ! !
157. Messed up on something at work ! ! ! !
158. Somebody criticized me at work ! ! ! !

Sleep
159. How many hours did you sleep last night? ______________
! 159

Not at
all
A little Some A lot
160. Did you have trouble falling asleep or staying asleep? ! ! ! !
161. Did you feel rested when you woke up this morning? ! ! ! !

Nutrition
Today. . . None 1 2 3
4 or
more
162. How many servings of fruits or vegetables did you have (e.g., 1 serving = 1 piece of fruit)? ! ! ! ! !
163. How many Cokes/Pepsi/Sprite or other sodas did you drink? ! ! ! ! !
164. How many meals did you have from a fast food restaurant? ! ! ! ! !
165. Did you eat breakfast this morning?
Yes
!
No
!
!!!!!!!
!!!!!

Please indicate whether or not today you worried about:!
!

Today, how much did you worry about… Not at all A little Somewhat A lot Not Applicable
Your mom or someone like a mom ! ! ! ! !
Your dad or someone like a dad ! ! ! ! !
Your relationship with a friend ! ! ! ! !
Your relationship with a boyfriend or girlfriend ! ! ! ! !
Your brother or sister ! ! ! ! !
Another family member ! ! ! ! !
Money problems ! ! ! ! !
Grades or school work ! ! ! ! !
Your health ! ! ! ! !
! 160

Today, how much did you worry about… Not at all A little Somewhat A lot Not Applicable
Being left out of something you want to do ! ! ! ! !

Health or health habits (smoking, alcohol, drugs, eating) of your mom (or
someone like a mom to you): What worried you?
! ! ! ! !

Health or health habits (smoking, alcohol, drugs, eating) of your dad (or
someone like a dad to you) What worried you?
! ! ! ! !

Your own health or health habits (smoking, alcohol, drugs, eating) What
worried you?
! ! ! ! !

Someone else’s health or health habits (smoking, alcohol, drugs, eating)
Who?
What worried you?
! ! ! ! !
Some activity…such as a sports, music, dance or drama performance ! ! ! ! !
Being teased or bullied ! ! ! ! !
Sexually transmitted diseases or pregnancy ! ! ! ! !

Your parents finding out that you messed up in some way or did something
they don’t approve of
! ! ! ! !
Your safety or being in a physically dangerous situation ! ! ! ! !
The safety of someone else ! ! ! ! !
Being hassled by police ! ! ! ! !
Your appearance ! ! ! ! !
Hanging out with friends who could get me into trouble ! ! ! ! !
An upcoming event ! ! ! ! !
Something you read ! ! ! ! !
Something you heard ! ! ! ! !
!
!
Please indicate whether or not today you had pleasant or positive thoughts about:
Today, how much did you have pleasant thoughts
about…
Not at all A little Somewhat A lot Not Applicable
Your mom or someone like a mom ! ! ! ! !
! 161
Today, how much did you have pleasant thoughts
about…
Not at all A little Somewhat A lot Not Applicable
Your dad or someone like a dad ! ! ! ! !
Your relationship with a friend ! ! ! ! !
Your relationship with a boyfriend or girlfriend ! ! ! ! !
Your brother or sister/step-brother or step-sister ! ! ! ! !
Another family member ! ! ! ! !
Grades or school work ! ! ! ! !
Work outside of school ! ! ! ! !
Your health ! ! ! ! !
Being asked to do something you want to do ! ! ! ! !
Some activity…such as a sports, music, dance or drama performance ! ! ! ! !
Your appearance ! ! ! ! !
A creative project ! ! ! ! !
An upcoming event ! ! ! ! !
Something you read ! ! ! ! !
Something you heard ! ! ! ! !

Below are some things that people do because these things make them feel good or these things help them to deal with some concern
or hassle. Please indicate whether or not you did each thing today, and whether or not this activity made you feel any better or worse.

Did you do this? How did it make you feel?

Activity
Not at all A little Somewhat A lot
A lot
better
+2
Somewhat
better
+1
No better
or worse
0
Somewhat
worse
-1
A lot
worse
-2
Shared my problem with others, enlisted their
support, encouragement and advice
! ! ! ! ! ! ! ! !
Tried to show support or offered support to
someone else
! ! ! ! ! ! ! ! !
Focused on solving the problem ! ! ! ! ! ! ! ! !
Worked hard and achieved ! ! ! ! ! ! ! ! !
Kept worrying ! ! ! ! ! ! ! ! !
! 162
Worried about my future in general and my
personal happiness
! ! ! ! ! ! ! ! !
Spent time with people I like ! ! ! ! ! ! ! ! !
Talked to friends ! ! ! ! ! ! ! ! !
Talked to relatives ! ! ! ! ! ! ! ! !
Was concerned with what others think ! ! ! ! ! ! ! ! !
Hoped for the best about something or that a
miracle would happen
! ! ! ! ! ! ! ! !
Did not do anything about a problem, gave
up
! ! ! ! ! ! ! ! !
Made myself better by letting off steam,
crying screaming
! ! ! ! ! ! ! ! !
Took out my frustration on others ! ! ! ! ! ! ! ! !
Ate more ! ! ! ! ! ! ! ! !
Ate less ! ! ! ! ! ! ! ! !
Used alcohol, cigarettes or drugs ! ! ! ! ! ! ! ! !
Organized group action to deal with
concerns, attended group meeting or rallies
! ! ! ! ! ! ! ! !
Consciously blocked out a problem;
pretended it didn’t exist
! ! ! ! ! ! ! ! !
Was hard on myself, saw myself as
responsible for a problem
! ! ! ! ! ! ! ! !
Kept my concerns and feelings to myself, ! ! ! ! ! ! ! ! !
Avoided other people ! ! ! ! ! ! ! ! !
Prayed for help and guidance; read a holy
book
! ! ! ! ! ! ! ! !
Looked on the bright side of things, reminded
myself that there are others who are worse
off, tried to stay cheerful
! ! ! ! ! ! ! ! !
Found ways to relax by reading a book or
magazine
! ! ! ! ! ! ! ! !
Found ways to relax by watching TV ! ! ! ! ! ! ! ! !
Played a solitary sport or worked out or did
something to stay fit
! ! ! ! ! ! ! ! !
Played a sport or game with someone ! ! ! ! ! ! ! ! !
Worked on a computer ! ! ! ! ! ! ! ! !
Interacted with friends on a computer ! ! ! ! ! ! ! ! !
Talked with someone on the phone or face to
face
! ! ! ! ! ! ! ! !
! 163
Did volunteer work ! ! ! ! ! ! ! ! !
Went shopping ! ! ! ! ! ! ! ! !
Cooked something for myself or someone
else
! ! ! ! ! ! ! ! !
Hung out with people I like ! ! ! ! ! ! ! ! !
Hung out with friends who did something
mean
! ! ! ! ! ! ! ! !
Hung out with people who did something
illegal (stole, defaced property)
! ! ! ! ! ! ! ! !

! 164
Appendix C
Activity'Questionnaire'for'Each'Saliva'Sample'
'
Please'answer'the'following'questions'while'you'are'providing'your'saliva'sample.''
ID'Number:______________'' ' ' ' ' '
' DATE:__________________'
' ' ' ' ' ' ' ' ' ' ' ' '
' ' TIME:__________________(PM/AM)!
1.!!Did!you!interact!with!a!family!member!over!the!past!30!minutes?!
a)!YES!! b)!NO!
!
2.!!If!yes,!to!what!extent!did!you!experience!conflict!with!that!family!member?!
0! ! ! 1! ! ! 2! ! ! 3! ! ! !
Not!at!all!!!!! !!!!!!A!Little! !!!!!!!!! !!!!!!!!!Some! !!!!!!!!! !!!!!!!!!A!lot! !
!
3.!!If!yes,!how!upset!did!that!make!you!feel?!
0! ! ! 1! ! ! 2! ! ! 3! !
Not!at!all!upset!!!!!!A!little!upset! !!!Fairly!upset!! !!!Really!upset!
!
4.!!Did!you!interact!with!a!peer/friend!over!the!past!30!minutes?!
a)!YES!! b)!NO!
!
5.!!If!yes,!to!what!extent!did!you!experience!conflict!with!that!peer/friend?!
0! ! ! 1! ! ! 2! ! ! 3! !
Not!at!all!!!!! !!!!!!A!Little! !!!!!!!!! !!!!!!!!!Some! !!!!!!!!! !!!!!!!!!A!lot! !
!
6.!!If!yes,!how!upset!did!that!make!you!feel?!
0! ! ! 1! ! ! 2! ! ! 3! !
Not!at!all!upset!!!!!!A!little!upset! !!!Fairly!upset!! !!!Really!upset!!
!
7.!!Did!you!interact!with!your!dating/romantic!partner!over!the!past!30!minutes?!
a)!YES!! b)!NO!
!
8.!!If!yes,!to!what!extent!did!you!experience!conflict!with!your!romantic/dating!partner?!
0! ! ! 1! ! ! 2! ! ! 3! !
Not!at!all!!!!! !!!!!!A!Little! !!!!!!!!! !!!!!!!!!Some! !!!!!!!!! !!!!!!!!!A!lot! !
!
9.!!If!yes,!how!upset!did!that!make!you!feel?!
0! ! ! 1! ! ! 2! ! ! 3! !
Not!at!all!upset!!!!!!A!little!upset! !!!Fairly!upset!! !!!Really!upset!!
!
10.!!Have!you!eaten!anything!within!the!past!hour?!
a)!YES!! b)!NO!
!
11.!!Did!you!have!anything!to!drink!within!the!past!hour?!
! 165!
a)!YES!! b)!NO!
!
12.!!Did!you!exercise!within!the!past!hour?!
a)!YES!! b)!NO!

! 166!
Appendix D
BDI-II Youth Report Wave 5/5a

Instructions: This questionnaire consists of 21 groups of statements. Please read each group of statements
carefully, and then pick out the one statement in each group that best describes the way you have been feeling
during the past two weeks, including today. Circle the number beside the statement you have picked. If several
statements in the group seem to apply equally well, circle the highest number for that group. Be sure that you do
not choose more than one statement for each group, including Item 16 (Changes in sleeping pattern) or Item 18
(Changes in Appetite).

1. Sadness
0 I do not feel sad.
1 I feel sad much of the time.
2 I am sad all the time.
3 I am so sad or unhappy that I can’t stand it.

2. Pessimism
0 I am not discouraged about my future.
1 I feel more discouraged about my future
than I used to be.
2 I do not expect things to work out for me.
3 I feel my future is hopeless and will only
get worse.

3. Past Failure
0 I do not feel like a failure.
1 I have failed more than I should have.
2 As I look back, I see a lot of failures.
3 I feel I am a total failure as a person.

4. Loss of Pleasure
0 I get as much pleasure as I ever did from
the things I enjoy.
1 I don’t enjoy things as much as I used to.
2 I get very little pleasure from the things I
used to enjoy.
3 I can’t get any pleasure from the things I
used to enjoy.

5. Guilty Feelings
0 I don’t feel particularly guilty.
1 I feel guilty over many things I have done
or should have done.
2 I feel quite guilty most of the time.
3 I feel guilty all of the time.

6. Punishment Feelings
0 I don’t feel I am being punished.
1 I feel I may be punished.
2 I expect to be punished.
3 I feel I am being punished.

7. Self-dislike
0 I feel the same about myself as ever.
1 I have lost confidence in myself.
2 I am disappointed in myself.
3 I dislike myself.

8. Self-criticalness
0 I don’t criticize or blame myself more than usual.
1 I am more critical of myself than I used to be.
2 I criticize myself for all of my faults.
3 I blame myself for everything bad that happens.

10. Crying
0 I don’t cry anymore than I used to.
1 I cry more than I used to.
2 I cry over every little thing.
3 I feel like crying, but I can’t.

11. Agitation
0 I am no more restless or wound up than usual.
1 I feel more restless or wound up than usual.
2 I am so restless or agitated that it’s hard to stay
still.
3 I am so restless or agitated that I have to keep
moving or doing something.

12. Loss of Interest
0 I have not lost interest in other people or
activities.
1 I am less interested in other people or things than
before.
2 I have lost most of my interest in other people or
things.
3 It’s hard to get interested in anything.

! 167!

13. Indecisiveness
0 I make decisions about as well as ever.
1 I find it more difficult to make decisions than usual.
2 I have much greater difficulty in making decisions
than I used to.
3 I have trouble making any decisions.

14. Worthlessness
0 I do not feel I am worthless.
1 I don’t consider myself as worthwhile and useful as
I used to.
2 I feel more worthless as compared to other people.
3 I feel utterly worthless.

15. Loss of Energy
0 I have as much energy as ever.
1 I have less energy than I used to have.
2 I don’t have enough energy to do very much.
3 I don’t have enough energy to do anything.

16. Changes in Sleeping Pattern
0 I have not experienced any change in my sleeping
pattern.
___________________________________________
1a. I sleep somewhat more than usual.
1b. I sleep somewhat less than usual.
___________________________________________
2a. I sleep a lot more than usual.
2b. I sleep a lot less than usual.
___________________________________________
3a. I sleep most of the day.
3b. I wake up 1-2 hours early and can’t get back to
sleep.

17. Irritability
0 I am no more irritable than usual.
1 I am more irritable than usual.
2 I am much more irritable than usual.
3 I am irritable all the time

18. Changes in Appetite
0 I have not experienced any change in my
appetite.
________________________________________
1a. My appetite is somewhat less than usual.
1b. My appetite is somewhat greater than usual.
________________________________________
2a. My appetite is much less than before.
2b. My appetite is much greater than usual.
________________________________________
3a. I have no appetite at all.
3b. I crave food all the time.

19. Concentration Difficulty
0 I can concentrate as well as ever.
1 I can’t concentrate as well as usual.
2 It’s hard to keep my mind on anything for very
long.
3 I find I can’t concentrate on anything.

20. Tiredness or Fatigue
0 I am no more tired or fatigued than usual.
1 I get more tired or fatigued more easily than
usual.
2 I am too tired or fatigued to do a lot of the things I
used to.
3 I am too tired or fatigued to do most of the things
I used to.

21. Loss of Interest in Sex
0 I have not noticed any recent change in my
interest in sex.
1 I am less interested in sex than I used to be.
2 I am much less interested in sex now.
3 I have lost interest in sex completely.

! !
! 168!
Appendix E
ID#!__________!
Parent'Child'Conflict—'Youth'Report'on'Father'Wave'5'(revised
6.20.08)!
!
!
DIRECTIONS: Below is a list of some things that parents sometimes do when having
an argument or disciplining a child. Please indicate if your dad has done any of these
items with you within the past year.
!
Who will you be answering these questions about?
_________________________________
!
!
! !
Neve
r!
Onc
e!
Twic
e!
3!to!
5!
time
s!
6!to!
10!
time
s!
11!to!
20!
times!
Mor
e!
than!
20!
time
s!
1. Explained why something you did was
wrong
! ! ! ! ! ! !
2. Sent you to your room ! ! ! ! ! ! !
3. Shook you ! ! ! ! ! ! !
4. Shouted, yelled, or screamed at you ! ! ! ! ! ! !
5. Spanked you with his hand ! ! ! ! ! ! !
6. Swore or cursed at you ! ! ! ! ! ! !
7.
Said he would send you away or kick
you out of the house
! ! ! ! ! ! !
8. Slapped you ! ! ! ! ! ! !
9.
Threatened to hit you, but did not
actually do it
! ! ! ! ! ! !
1
0.
Spanked you with an object ! ! ! ! ! ! !
1
1.
Took away privileges or grounded you ! ! ! ! ! ! !
1
2.
Twisted your arm behind your back ! ! ! ! ! ! !
1
3.
Called you dumb or lazy or some
other name like that
! ! ! ! ! ! !
1
4.
Purposely destroyed something that
belongs to you
! ! ! ! ! ! !
1
5.
Threw something at you out of anger ! ! ! ! ! ! !
1 Pushed, grabbed or shoved you ! ! ! ! ! ! !
! 169!
! !
Neve
r!
Onc
e!
Twic
e!
3!to!
5!
time
s!
6!to!
10!
time
s!
11!to!
20!
times!
Mor
e!
than!
20!
time
s!
6.
1
7.
Threatened to lock you out of the
house
! ! ! ! ! ! !
1
8.
Said something hurtful (e.g. about
your appearance or friends)
! ! ! ! ! ! !
1
9.
Refused to talk to you ! ! ! ! ! ! !
2
0.
Refused to hear what you wanted to
say
! ! ! ! ! ! !
2
1.
Insulted or shamed you in front of
others
! ! ! ! ! ! !
2
2.
Told you that you are not as good as
someone else (n)!
! ! ! ! ! ! !
2
3.
Told you that you are a failure or will
be a failure or won’t succeed at
anything (n)
! ! ! ! ! ! !
2
4.
Told you that you are acting like a jerk
(n)
! ! ! ! ! ! !
2
5.
Kicked you out of the car (n) ! ! ! ! ! ! !
2
6.
Kicked you out of your home (n) ! ! ! ! ! ! !
2
7.
Told you that you would not be part of
the family anymore (n)
! ! ! ! ! ! !
2
8.
Threatened to stop supporting you
financially or told you that he would
not pay for something important such
as schooling etc. (n)
! ! ! ! ! ! !
2
9.
Threatened to pull you from
extracurricular or organized activities
that you like (n)
! ! ! ! ! ! !
3
0.
Did pull you from extracurricular or
organized activities that you like (n)
! ! ! ! ! ! !
3
1.
Grounded you (didn’t let you go to
social events or fun activities) for a
month or less (n)
! ! ! ! ! ! !
3
2.
Grounded you (didn’t let you go to
social events or fun activities) for
more than a month (n)
! ! ! ! ! ! !
3
3.
Took away your driving privileges or
use of the car for a period of time (n)
! ! ! ! ! ! !
! 170!
! !
Neve
r!
Onc
e!
Twic
e!
3!to!
5!
time
s!
6!to!
10!
time
s!
11!to!
20!
times!
Mor
e!
than!
20!
time
s!
3
4.
Disciplined you by assigning extra
work chores (n)
! ! ! ! ! ! !
3
5.
Showed you that he cares about your
well-being even when disciplining you
(n)
! ! ! ! ! ! !
!
! 171!
ID#!__________!
6.'Parent'Child'Conflict—'Youth'Report'on'Mother'Wave'5'(revised
6.20.08)!

DIRECTIONS: Below is a list of some things that parents sometimes do when having
an argument or disciplining a child. Please indicate if your mom has done any of these
items with you within the past year.

Who will you be answering these questions about?
_________________________________

Never! Once! Twice!
3!to!
5!
times!
6!to!
10!
times!
11!to!
20!
times!
More!
than!
20!
times!
1.
Explained why something you
did was wrong

2. Sent you to your room
3. Shook you
4.
Shouted, yelled, or screamed at
you

5. Spanked you with her hand
6. Swore or cursed at you
7.
Said she would send you away
or kick you out of the house

8. Slapped you
9.
Threatened to hit you, but did
not actually do it

10. Spanked you with an object
11.
Took away privileges or
grounded you

12.
Twisted your arm behind your
back

13.
Called you dumb or lazy or
some other name like that

14.
Purposely destroyed something
that belongs to you

15.
Threw something at you out of
anger

16. Pushed, grabbed or shoved you
17.
Threatened to lock you out of
the house

18.
Said something hurtful (e.g.
about your appearance or
friends)

! 172!

Never! Once! Twice!
3!to!
5!
times!
6!to!
10!
times!
11!to!
20!
times!
More!
than!
20!
times!
19. Refused to talk to you
20.
Refused to hear what you
wanted to say

21.
Insulted or shamed you in front
of others

22.
Told you that you are not as
good as someone else (n)

23.
Told you that you are a failure
or will be a failure or won’t
succeed at anything (n)

24.
Told you that you are acting like
a jerk (n)

25. Kicked you out of the car (n)
26. Kicked you out of your home (n)
27.
Told you that you would not be
part of the family anymore (n)

28.
Threatened to stop supporting
you financially or told you that
she would not pay for
something important such as
schooling etc. (n)

29.
Threatened to pull you from
extracurricular or organized
activities that you like (n)

30.
Did pull you from extracurricular
or organized activities that you
like (n)

31.
Grounded you (didn’t let you go
to social events or fun activities)
for a month or less (n)

32.
Grounded you (didn’t let you go
to social events or fun activities)
for more than a month (n)

33.
Took away your driving
privileges or use of the car for a
period of time (n)

34.
Disciplined you by assigning
extra work chores (n)

35.
Showed you that she cares
about your well-being even
when disciplining you (n)

!
! 173!
Appendix F

Mplus Code

*Note. Variables within {} were each run in separate models.

Manuscript 1: Past Year Substance Use, Sexual Risk Behaviors, Delinquent Behaviors, and
Day-to-Day Risky Behaviors in Relation to Adolescents’ Diurnal Cortisol Patterns

I. Past-year risky behaviors and diurnal cortisol

DATA: file is "/Users/lauren/Desktop/1-21 analyses.csv";
VARIABLE:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayriskn dyrsklgn awakepk
awakpklg sex totrisz mdayrsk;
usevariables ={CAR, CARi, hiloslop} cot wakeup meds age {zdrugalc, zsexrisk, zdelinag,
zdelinst};
within = cot wakeup;
between = {zdrugalc, zsexrisk, zdelinag, zdelinst} age meds;
cluster = id;
missing are all (999);

DEFINE:
center cot wakeup (groupmean);
center age {zdrugalc, zsexrisk, zdelinag, zdelinst} (grandmean);

ANALYSIS:
type = twolevel;

MODEL:
%WITHIN%
{CAR, CARi, hiloslop} on cot wakeup;
[cot wakeup];

%BETWEEN%
{CAR, CARi, hiloslop} on {zdrugalc, zsexrisk, zdelinag, zdelinst} age meds;
[{CAR, CARi, hiloslop} {zdrugalc, zsexrisk, zdelinag, zdelinst} age meds];
OUTPUT: standardized tech1 tech3;
! 174!

II. Daily risky behaviors (same-day and previous-day) and diurnal cortisol

DATA: file is "/Users/lauren/Desktop/1-21 analyses.csv";
VARIABLE: names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayriskn dyrsklgn awakepk
awakpklg sex;
usevariables = {CAR, CARi, hiloslop} {dayriskn, dayrisklgn} cot wakeup age meds {CARlg,
CARilg, hiloslg};
within = {dayriskn, dayrisklgn} cot wakeup {CARlg, CARilg, hiloslg};
between = age meds ;
cluster = id;
missing are all (999);

DEFINE:
center {dayriskn, dayrisklgn} cot wakeup {CARlg, CARilg, hiloslg} (groupmean);
center age (grandmean);

ANALYSIS:
type = twolevel;

MODEL:
%WITHIN%
{CAR, CARi, hiloslop} on {dayriskn, dayrisklgn} cot wakeup {CARlg, CARilg, hiloslg};
[{dayriskn, dayrisklgn} cot wakeup {CARlg, CARilg, hiloslg}];

%BETWEEN%
{CAR, CARi, hiloslop} on age meds;
[{CAR, CARi, hiloslop} age meds];

OUTPUT: standardized tech1 tech3;

III. Sex differences: past year risky behaviors

DATA: file is "/Users/lauren/Desktop/1-21 analyses.csv";
VARIABLE:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
! 175!
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayriskn dyrsklgn awakepk
awakpklg sex;
usevariables = {CAR CARi hiloslop} {zdrugalc, zsexrisk, zdelinag, zdelinst} wakeup meds age
sex cot;
within = wakeup cot;
between = age meds sex;
cluster = id;
missing are all (999);

DEFINE:
center wakeup cot (groupmean);
center age (grandmean);
risksex={zdrugalc, zsexrisk, zdelinag, zdelinst}*sex;

ANALYSIS:
type = twolevel ;

MODEL:
%WITHIN%
{CAR, CARi, hiloslop} on wakeup cot ;
[wakeup cot ];

%BETWEEN%
{CAR, CARi, hiloslop} on {zdrugalc, zsexrisk, zdelinag, zdelinst} age sex risksex;
[{CAR, CARi, hiloslop} {zdrugalc, zsexrisk, zdelinag, zdelinst} age sex];
OUTPUT: standardized tech1 tech3;

IV. Sex differences: daily risky behaviors

DATA: file is "/Users/lauren/Desktop/1-21 analyses.csv";
VARIABLE:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayriskn dyrsklgn awakepk
awakpklg sex;
! 176!
usevariables = {CAR, CARi, hiloslop} {dayriskn, dyrsklgn} cot wakeup age meds {CARlg,
CARilg, hiloslg} sex;
within = {dayriskn, dyrsklgn} cot wakeup {CARlg, CARilg, hiloslg};
between = age meds sex;
cluster = id;
missing are all (999);

DEFINE:
center {dayriskn, dyrsklgn} cot wakeup {CARlg, CARilg, hiloslg} (groupmean);
center age (grandmean);

ANALYSIS:
type = twolevel random;
algorithm = integration;
integration = montecarlo;
MODEL:
%WITHIN%
{CAR, CARi, hiloslop}on {dyrsklgn, dayriskn} cot wakeup {CARlg, CARilg, hiloslg};
slope | {CAR, CARi, hiloslop} on {dyrsklgn, dayriskn};
[{dyrsklgn, dayriskn} cot wakeup {CARlg, CARilg, hiloslg}];
%BETWEEN%
{CAR, CARi, hiloslop} on age meds sex;
slope on sex;
[{CAR, CARi, hiloslop} age meds sex];
OUTPUT: standardized tech1 tech3;

Manuscript 2: Conflict with Family and Friends: Associations with Inter- and
Intraindividual Differences in Adolescents’ Diurnal HPA Activity

I. Proximal daily conflict and between-person differences in diurnal cortisol

data: file is "/Users/lauren/Desktop/2-5 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
fromexty sex dadday momday fromday frnday prntcons prntconm prntmlg prcons prconm
! 177!
prconslg prconmlg totdaym totdaylg;

usevariables = cot wakeup meds age {dadday, momday, frnday} {CAR, CARi};
within = cot wakeup ;
between = {dadday, momday, frnday} meds age;

cluster = id;
missing are all (999);

DEFINE:

center cot wakeup (groupmean);
center {dadday, momday, frnday} age (grandmean);

ANALYSIS:

type = twolevel;

MODEL:

%WITHIN%
{CAR, CARi} on cot wakeup;
[ cot wakeup ];

%BETWEEN%
{CAR, CARi} on {dadday, momday, frnday} meds age ;
[{CAR, CARi} {dadday, momday, frnday} meds age];

OUTPUT: standardized tech1 tech3;

II. Proximal daily conflict and within-person differences in diurnal cortisol

data: file is "/Users/lauren/Desktop/4-9 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
! 178!
fromexty sex dadday momday fromday prntcons prntconm prntmlg prcons prconm
prconslg prconmlg totdaym totdaylg;

usevariables = {momcons, momslg, dadcons, dadslg, prcons, prconslg} cot meds wakeup age
{CAR, CARi} {CARlg, CARilg};
within = { momcons, momslg, dadcons, dadslg, prcons, prconslg } cot wakeup {CARlg,
CARilg};
between = age meds;

cluster = id;
missing are all (999);

DEFINE:

center { momcons, momslg, dadcons, dadslg, prcons, prconslg } cot wakeup {CARlg,
CARilg} (groupmean);
center age (grandmean);

ANALYSIS:

type = twolevel;

MODEL:

%WITHIN%
{CAR, CARi} on { momcons, momslg, dadcons, dadslg, prcons, prconslg } cot wakeup
{CARlg, CARilg};
[{momcons, momslg, dadcons, dadslg, prcons, prconslg} cot wakeup {CARlg, CARilg}];

%BETWEEN%
{CAR, CARi} on age meds;
[{CAR, CARi} age meds];

OUTPUT: standardized tech1 tech3;

III. Main effects of parent-to-youth aggression history

data: file is "/Users/lauren/Desktop/2-5 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
! 179!
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
fromexty sex dadday momday fromday prntcons prntconm prntmlg prcons prconm
prconslg prconmlg totdaym totdaylg;

usevariables = cot wakeup meds age t345extm {CAR, CARi};
within = cot wakeup ;
between = t345extm meds age;

cluster = id;
missing are all (999);

DEFINE:

center cot wakeup (groupmean);
center t345extm age (grandmean);

ANALYSIS:

type = twolevel;

MODEL:

%WITHIN%
{CAR, CARi} on cot wakeup ;
[ cot wakeup ];

%BETWEEN%
{CAR, CARi} on t345extm meds age ;
[{CAR, CARi} t345extm meds age];

OUTPUT: standardized tech1 tech3;

IV. Cross-level interactions of parent-to-youth aggression history and current daily conflict

data: file is "/Users/lauren/Desktop/2-5 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
! 180!
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
fromexty sex dadday momday fromday prntcons prntconm prntmlg prcons prconm
prconslg prconmlg totdaym totdaylg friday ethn race;

usevariables = cot wakeup meds age {momcons, momslg, dadcons, dadslg, prcons, prconslg}
{CAR, CARi} {CARlg, CARilg};
within = cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg} {CARlg,
CARilg};
between = meds age;

cluster = id;
missing are all (999);

DEFINE:

center cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg} {CARlg, CARilg}
(groupmean);
center age (grandmean);

ANALYSIS:

type = twolevel random;
algorithm = integration;
integration = montecarlo;

MODEL:

%WITHIN%
{CAR, CARi} on cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg}
{CARlg, CARilg};
slope | {CARi, CAR} on {momcons, momslg, dadcons, dadslg, prcons, prconslg};
[cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg}];

%BETWEEN%
{CAR, CARi} on meds age t345extm;
slope on t345extm;
[{CAR, CARi} meds age];

OUTPUT: standardized tech1 tech3;

V. Sex as a moderator of conflict and diurnal cortisol
! 181!

A. Mean daily conflict and sex differences

data: file is "/Users/lauren/Desktop/2-5 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
fromexty sex dadday momday fromday prntcons prntconm prntmlg prcons prconm
prconslg prconmlg totdaym totdaylg frnday ethn race;

usevariables = cot wakeup meds age {CAR, CARi} {dadday, momday, frnday} sex sexcn;
within = cot wakeup;
between = meds age sex {dadday, momday, frnday} sexcn;

cluster = id;
missing are all (999);

DEFINE:

center cot wakeup (groupmean);
center age {dadday, momday, frnday} (grandmean);
sexcn={dadday, momday, frnday}*sex;

ANALYSIS:

type = twolevel ;

MODEL:

%WITHIN%
{CARi, CAR} on cot wakeup;
[cot wakeup ];

%BETWEEN%
{CARi, CAR} on sexcn meds age sex {dadday, momday, frnday};
[CAR meds age sexcn sex {dadday, momday, frnday}];
! 182!

OUTPUT: standardized tech1 tech3;

B. Same-day and previous-day conflict and sex differences

data: file is "/Users/lauren/Desktop/2-5 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
fromexty sex dadday momday fromday prntcons prntconm prntmlg prcons prconm
prconslg prconmlg totdaym totdaylg friday ethn race;

usevariables = cot wakeup meds age {momcons, momslg, dadcons, dadslg, prcons, prconslg}
{CAR, CARi} {CARlg, CARilg} sex;
within = cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg} {CARlg,
CARilg};
between = meds age race sex ;

cluster = id;
missing are all (999);

DEFINE:

center cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg} {CARlg, CARilg}
(groupmean);
center age (grandmean);

ANALYSIS:

type = twolevel random;
algorithm = integration;
integration = montecarlo;

MODEL:

%WITHIN%
! 183!
{CAR, CARi} on cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg}
{CARlg, CARilg};

slope | {CARi, CAR} on {momcons, momslg, dadcons, dadslg, prcons, prconslg};
[cot wakeup {momcons, momslg, dadcons, dadslg, prcons, prconslg}];

%BETWEEN%
{CAR, CARi} on meds age sex;
slope on sex;
[{CAR, CARi} meds age sex];

OUTPUT: standardized tech1 tech3;

C. Distal conflict and sex differences

data: file is "/Users/lauren/Desktop/2-5 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
fromexty sex dadday momday fromday prntcons prntconm prntmlg prcons prconm
prconslg prconmlg totdaym totdaylg friday ethn race;

usevariables = cot wakeup meds age {CAR, CARi} t345extm sex sexhx;

within = cot wakeup;
between = meds age sex t345extm sexhx;

cluster = id;
missing are all (999);

DEFINE:

center cot wakeup (groupmean);
center age t345extm (grandmean);
sexhx=t345extm*sex;

! 184!
ANALYSIS:

type = twolevel;

MODEL:

%WITHIN%
{CARi, CAR} on cot wakeup;
[cot wakeup ];

%BETWEEN%
{CARi, CAR} on sexhx meds age sex t345extm;
[CAR meds age sexhx sex t345extm];

OUTPUT: standardized tech1 tech3;

D. Three-way interaction between distal conflict, proximal conflict, and sex

data: file is "/Users/lauren/Desktop/2-5 Conflict analyses.csv";
variable:
names are id cort1 cort2 cort3 cort4 cort5 cot meds wakeup hrssleep
lncort1 lncort2 lncort3 lncort4 lncort5 hiloslg alcday drugday
alcdrugd delinday schoold delidrug totriskd CAR CARi CARilg lncort5lg
CARlg age alcdrulg delindlg schoolg delidrlg totrislg income3 crtslp12
crtslp13 ctsl12lg ctsl13lg lcrt1lg zdelinq zschool zdrugalc zsexrisk ztotrisk
froposlg meatdrex hiloslop slope15 dayrisk dayrislg lncrt1lg lncrt2lg lncrt3lg
lncrt4lg lncrt5lg slop15lg bdi momcon dadcon prntcon mompos dadpos momlg dadlg
prntlg totcon fromcon totconlg totfrolg momposlg dadposlg totpos frompos froposlg2
totposlg prntpos prntposlg zdelinag zdelinst slpqual dayrskn dayrsnlg w5extagg
w345ext w45extag w34extag awakepk awakpklg m345exts m345extm m345exms
d345exts d345exms d345extm t345exts t345exms t345extm frdcons momcons
dadcons fromcons frdslg dadslg momslg fromslg t345exch peromcs peromclg
fromexty sex dadday momday fromday prntcons prntconm prntmlg prcons prconm
prconslg prconmlg totdaym totdaylg friday;

usevariables = cot wakeup meds age {CAR, CARi} t345extm {momcons, momslg, dadcons,
dadslg, frdcons, frdslg} sex sexhx;

within = cot wakeup {momcons, momslg, dadcons, dadslg, frdcons, frdslg};
between = meds age t345extm sex sexhx ;

cluster = id;
missing are all (999);

DEFINE:

! 185!
center cot wakeup {momcons, momslg, dadcons, dadslg, frdcons, frdslg} (groupmean);
center age t345extm sexhx (grandmean);
sexhx=sex*t345extm;

ANALYSIS:

type = twolevel random;
algorithm = integration;
integration = montecarlo;

MODEL:

%WITHIN%
{CAR, CARi} on cot wakeup ;
slope | {CAR, CARi} on {momcons, momslg, dadcons, dadslg, frdcons, frdslg};
[ cot wakeup {momcons, momslg, dadcons, dadslg, frdcons, frdslg}];

%BETWEEN%
{CAR, CARi} on t345extm sex sexhx meds age ;
slope on t345extm sex sexhx;
{CAR, CARi} with slope;
[{CAR, CARi} t345extm meds age sex sexhx];

OUTPUT: standardized tech1 tech3;

Abstract (if available)

Abstract This dissertation applies a biopsychosocial perspective to better understand how physiological stress activity, as measured through the hypothalamic‐pituitary‐adrenocortical (HPA) axis, relates to adolescents’ experiences involving risky behaviors and interpersonal conflict. These three constructs—diurnal hypothalamic‐pituitary‐adrenocortical (HPA) activity, risk behaviors, and conflict with parents and peers—all have important implications for adolescents’ concurrent adjustment (Hughs, Power, & Francis, 1992

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Asset Metadata

Creator Shapiro, Lauren A. Spies (author)

Core Title Risky behaviors, interpersonal conflict, and their relation to fluctuations in adolescents’ diurnal HPA rhythms

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 06/20/2014

Defense Date 06/02/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag adolescence,delinquent behaviors,diurnal cortisol,family aggression,interpersonal conflict,OAI-PMH Harvest,risky behaviors,sensation-seeking

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Margolin, Gayla (committee chair), Brekke, John S. (committee member), Manis, Franklin R. (committee member), McArdle, John J. (committee member), Meyerowitz, Beth E. (committee member)

Creator Email lspies@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-421975

Unique identifier UC11285963

Identifier etd-ShapiroLau-2567.pdf (filename),usctheses-c3-421975 (legacy record id)

Legacy Identifier etd-ShapiroLau-2567.pdf

Dmrecord 421975

Document Type Dissertation

Format application/pdf (imt)

Rights Shapiro, Lauren A. Spies

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA