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Adolescent conduct problems and substance use: an examination of the risk pathway across the transition to high school

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Adolescent conduct problems and substance use: an examination of the risk pathway across the transition to high school

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Content 1

Adolescent Conduct Problems and Substance Use: An Examination of the Risk Pathway
across the Transition to High School

Rubin Khoddam

Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
Doctor of Philosophy in Psychology

Degree Conferral Date: August 2017

2
Table of Contents
Acknowledgments ..........................................................................................................................4
General Introduction .....................................................................................................................7
Manuscript 1: Internalizing Symptoms and Conduct Problems: Redundant, Incremental, or
Interactive Risk Factors for Adolescent Substance Use During the First Year of High School?..17
Abstract ..............................................................................................................................18
Introduction ........................................................................................................................20
Methods..............................................................................................................................22
Results ................................................................................................................................27
Discussion ..........................................................................................................................29
References ..........................................................................................................................34
Table 1. Descriptive Statistics ......................................................................................42
Table 2. Correlation Matrix .........................................................................................43
Figure 1. Independent Associations .............................................................................44
Figure 2. Incremental Association of Conduct Problems ............................................45
Figure 3. Incremental Association of Internalizing Symptomatology .........................46
Figure 4. Interactive Associations ...............................................................................47
Supplemental Table 1. Sample Sizes ............................................................................48
Manuscript 2: Alternative and Complementary Reinforcers as Mechanisms Linking Adolescent
Conduct Problems and Substance Use ...........................................................................................49
Abstract ..............................................................................................................................50
Introduction ........................................................................................................................52
Methods..............................................................................................................................56
Results ................................................................................................................................64
Discussion ..........................................................................................................................69
References ..........................................................................................................................77
Table 1. Descriptive Statistics ......................................................................................85
Table 2. Correlational Matrix......................................................................................87
Table 3. Mediation using Any Substance Use Outcome ..............................................88
Table 4. Mediation using Alcohol Use Outcome .........................................................89
Table 5. Mediation using Cigarette Use Outcome .......................................................91
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Table 6. Mediation using Marijuana Use Outcome .....................................................92
Table 7. Sex Differences ..............................................................................................94
Manuscript 3: Longitudinal Examination of Alternative and Complementary Reinforcers
Linking Conduct Problems and Substance Use Abstract ..............................................................95
Abstract ..............................................................................................................................96
Introduction ........................................................................................................................97
Methods............................................................................................................................101
Results ..............................................................................................................................106
Discussion ........................................................................................................................109
References ........................................................................................................................115
Table 1. Descriptive Statistics ....................................................................................124
Table 2. Correlation Matrix .......................................................................................126
Table 3. Path Model Results ......................................................................................127
Figure 1. Final Path Model .......................................................................................128
General Discussion .....................................................................................................................129
References for General Introduction and General Discussion ..............................................134

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Acknowledgments
The research described in this dissertation was supported by the National Institute of
Drug Abuse (Grant F31 DA039708; PI: Rubin Khoddam and Grant R01 033296; PI: Dr. Adam
Leventhal). I am extraordinarily grateful to have been entrusted with the means to investigate my
questions of interest that have futhered my training as a clinical psychological scientist.
I also extend my gratitude to my dissertation guidance committee members, Drs. Adam
Leventhal, Gayla Margolin, Steve Sussman, and John Monterosso for their support of this project
as well as my development. They have continuously challenged me to be better and to think
more broadly, thoughtfully, and in more nuanced ways. Dr. Carol Prescott has also been a great
mentor and advocate throughout my graduate training, allowing me the space to pursue my own
interests while simultaneously pushing me outside my comfort zone. Thanks are also due to the
Heal, Emotion, and Addiction Laboratory (HEAL) for their kindness, support, intellect, and
laughs. Specifically, the HEAL members on the Happiness and Health study have been critical to
the completion of this project, including Gaylene Gunning, Nafeesa Andrabi, Alicia Ramos, and
Matthew D. Stone. They have not only been an integral part of data collection, but have helped
further my professional and personal development. I would also like to extend my gratitude to
Dr. Junhan Cho, a postdoctoral fellow in HEAL, and Nicholas Jackson, a fellow graduate student
at USC, who have helped mentor me on a variety of statistical techniques included in this
dissertation as well as other published articles.
To my cohort who has been with me every step of this journey through graduate school, I
could not think of a better (and more diverse) group of individuals to have spent the past five
years with. I am so thankful for all the April Fool’s Day pranks, classroom jokes, and all around
laughs you all helped incorporate into my training. You made the past five years fly by.
5
To Dr. Adam Leventhal, my dissertation chair, research advisor, mentor, and friend, I
could not have made it through this program without you. You gave me opportunities I would
not have had otherwise. You showed me how to lead a large lab with levity, humor, rigor, and
intellect. You took me into your lab with open arms and made me a part of the team from the
very beginning. More than anything, you cared about me as a person just as much as you cared
about me as a student and academic. For all the things I noticed you doing for me and all the
things I was too stressed to notice, thank you.
To my three brothers (Rusheen, Armin, and Shervin), sister-in-laws (Natalie, Tahsin, and
Kristy), and friends who have supported me in all my academic endeavors, thank you for sharing
meals and laughs with me so I could talk about something else besides statistics, therapy, classes,
etc. Thank you for asking “How much longer are you going to be in school?” every time, so I
could remember that there is an endpoint to all of this. You won’t have to ask much longer, but
you will hopefully have to call me “Dr. Khoddam.”
To my parents, I know how hard you worked to create the life you did for my three older
brothers and me. I am acutely aware of the privilege I have with you as parents and my going
into Clinical Psychology is a way for me to pay it forward. People dream to have parents like
you both and it would be selfish for me not to use the life you gave me to leverage it in service of
others. I hope I have made you proud.
Last but certainly not least, thank you to Hannah Lyden (soon to be Hannah Khoddam
with a Ph.D.). If it were not for this program and our mutual love for the field, we would not
have met. For the past two and a half years, you have inspired me to be better in every way
imaginable. Our mutual experience of the program allowed me to think about my research from a
difference perspective. You had a first row seat to my experiences, my stress, my successes, and
6
my failures, and you helped me navigate all of them. Being with you has given me more
perspective than I could have ever asked for. Thank you does not do justice for what you have
done for me.

7
General Introduction
Tobacco, alcohol, and marijuana are the most commonly used drugs in the United States
besides caffeine (NSDUH, 2012; Shi, 2014) and are associated with considerable economic,
social, and psychological consequences that pose a great public health burden (King et al., 2000).
Mid-adolescence (age 14–16) is a high-risk period for the onset and escalation of use of these
substances and is closely linked to the development of substance use disorders (Johnston et al.,
2011; Grant & Dawson, 1997; SAMHSA, 2009). Early age of initiation, in particular, is a
significant risk factor for the progression and outcome for tobacco, alcohol, and marijuana use
(Chassin et al., 1990; Brook et al., 2002; Ellickson et al., 2004). It is important to understand
factors that lead to adolescent substance use uptake to inform etiological models of addiction risk
and preventive interventions that thwart the development of severe addiction trajectories.
Conduct Problems as it Relates to the Conduct Spectrum
The broad construct of conduct problems (CPs) is related to a range of behaviors on the
Conduct Disorder (CD) spectrum and is particularly important to consider with regard to
substance use. CPs have been defined by Jessor and Jessor (1975) as a category of socially
defined actions that significantly depart from regulatory norms to result in or elicit some sort of
social-control response. CPs have been defined to include promiscuity, lying, cheating,
aggression, delinquency, and substance use itself. It is important to consider a broad spectrum of
CPs, including more common symptoms (e.g., skipping school), to capture subclinical levels of
CD-spectrum behaviors that reflect early manifestations of CPs that may eventually develop into
more severe behaviors. The present study aims to examine a broad array of adolescent CPs and
assess the frequency in which adolescents engage in these behaviors, particularly at subclinical
8
levels, which may be expressed in a sizeable proportion of the general population (Loeber,
1990).
Conduct Problems and Adolescent Substance Use
CPs have consistently been associated with adolescent alcohol and drug use (Brown et
al., 1996; Buu et al., 2012; Couwenbergh et al.,2006; Mayzer et al., 2009). These behaviors are
particularly problematic when combined with a comorbid substance use disorder diagnosis, as
individuals with this dual-diagnosis have been found to be the most referred group to the
criminal justice system and social services (Crowley et al., 1998).
Trajectories of CPs and substance use differ across adolescence. With regards to CPs, CP
levels tend to remain relatively stable across time (Deković et al., 2004; LeBlanc et al., 1991).
However, significant individual differences have been found in level/frequency of CPs. Although
gender predicts baseline levels of CPs, the rate of change in CPs across adolescence is similar for
males and females, suggesting demographic factors may operate differently in influencing the
onset of CPs. With regards to substance use, it has been shown to steadily increase throughout
adolescence (Duncan et al., 2006; Johnston et al., 2011; Johnston et al., 2014). Age of initiation,
in particular, is a significant risk factor in determining the progression and outcome for tobacco,
alcohol, and marijuana use (Chassin et al., 1990; Brook et al., 2002; Ellickson et al., 2004; Flory
et al., 2004). Adolescents who report early exposure to alcohol have rates of alcohol
consumption and alcohol-related problems 1.9-2.4 times higher than those who report later
introduction (Fergusson et al., 1994).
Furthermore, possible unique associations of CPs with alcohol, tobacco and marijuana
use as separate substance risk pathways have not been examined. Prior literature has
incorporated the use of several substances as part of a general CP category that includes a
9
combination of illicit drug use and deviant behavior. This is based on an etiological model that
suggests a general underlying association between all CPs and substance use (Jessor & Jessor,
1975; Kendler et al., 2003). Although an underlying vulnerability may exist for CPs and
substance use, the aforementioned studies are limited by the lack of research on the temporal
relationship between CPs and substance use as well as the use of short intervals between
measures. Such research is particularly important given substance-specific differences have been
found with internalizing symptoms (Bonn-Miller et al., 2010), but little research has been done
with respect to externalizing symptoms. Additionally, there is little research on the mediating
mechanisms linking CPs and substance use. If malleable behavioral processes that channel the
risk of substance use due to CPs (and vice versa) are found, developmental psychopathology
theory will be enhanced as will potential targets for offsetting CP-substance use comorbidity via
prevention.
Behavioral Economic Mechanisms Implicated in Substance Use
Behavioral economic theories recognize that one’s preference for substance use arises
within a broader context that involves the availability or utilization of: (1) alternative competing
substance-free reinforcers, such as joining school clubs, volunteering, or other hobbies, which
may deter substance use; and (2) complementary reinforcing activities that occur in conjunction
with substance use (e.g. stimulants with sports), which may increase substance use engagement
(Audrain-McGovern et al., 2004).
It has been suggested that alcohol consumption tends to increase when access to
alternative reinforcers is constrained (Vuchinich & Tucker, 1996). Young adults assigned to
engage in more alternatively reinforcing activities in prevention efforts decreased the frequency
and quantity of alcohol consumed (Correia et al., 2005). Furthermore, engaging in alternative
10
reinforcers reduces the odds of smoking progression almost two-fold (Audrain-McGovern et al.,
2004). Research using alternative reinforcers in a mediational pathway for internalizing
symptoms and smoking has suggested that depression leads to reduced involvement in
alternative reinforcers which, in turn, increases smoking uptake (Audrain-McGovern et al.,
2010). Despite the negative association between alternative reinforcers and substance use, little
work has been done using alternative reinforcers in a mediational pathway, implicating its role in
externalizing CPs and across multiple substances. It is important for researchers to not only
identify the mechanism by which individuals engage in CPs and substance use but to identify
intermediate processes that can be intervened upon to prevent the risk of polysubstance use and
CPs.
Complementary Reinforcers have been less well-examined in the literature, however,
they are critical to examine, as some activities that are inherently social in nature may enhance
substance-using experiences (e.g., dancing, parties, sports) and increase the odds of adolescent
smoking (Audrain-McGovern et al., 2004). Although research has yet to be done with other
substances, the social facilitative properties of substance use may strengthen the reinforcing
qualities of non-drug related reward (e.g. environmental stimuli; Beck & Treiman, 1996;
Caggiula et al., 2009; MacLatchy-Gaudent & Stewart, 2001; Phillips & Fibiger, 1990; Robbins,
1977; Wall, Hinson, & McKee, 1998). Thus, adolescents may learn to associate certain activities
and environments with substance using experiences, increasing their relative reward. This
association may theoretically increase the overall amount of reinforcement derived from
substances and increase the risk for substance use uptake.

11
Developmental Context of the Proposed Study
Studying dimensions of psychopathology, such as CPs, can have important implications
for youth who are at risk for more severe courses (Drabick, 2009). Examining these behaviors in
high school is particularly important given an earlier age in middle school may fail to capture
many marijuana onsets and a later age may fail to capture a period of high substance use
progression (Eaton et al., 2010; SAMHSA, 2009). The age group in the present study is
hypothesized to capture the largest window of variability in which the majority of the sample
will initiate or change in their CP habits and substance use engagement. This period may not be
the peak age of initiation for some CPs, but among those who are still engaging in CPs and at
higher levels, it may be more indicative of life-course persistent antisocial behaviors as opposed
to adolescent-limited behaviors (Moffitt, 1993).
Factors Potentially Impacting the Association Between Conduct Problems, Alternative
Reinforcers, and Substance Use
Internalizing symptoms (e.g. depression, anxiety) represent a critical set of constructs that
may impact the relationship between CPs and substance use. Internalizing symptoms may impact
the relationship between CPs and substance use, as teens experiencing negative affect may be
more likely to engage in substance use as a way of obtaining pleasure and alleviating unpleasant
emotions. It may also be that teens experiencing negative affect are less likely to engage in
alternative reinforcers, thereby, increasing the likelihood of substance use. Although CPs are
often a more robust predictor of substance use than internalizing measures, some research
suggests that several forms of anxiety disorders, including social anxiety (Buckner et al., 2008;
Chavira & Stein, 2005) and generalized anxiety (Sartor, Lynskey, Heath, Jacob, & True, 2007)
are associated with subsequent substance use in adolescents. However, these findings are not
12
always replicated (Pardini et al., 2007) and do not appear to be as strong as the corresponding
risk of substance use associated with CPs (King et al., 2004; Maslowsky and Schulenberg, 2013).
The inconsistent findings suggest that the interplay between CPs and internalizing measures are
complicated and multifaceted. It is critical to examine how the relationship between CPs and
substance use is impacted in the context of different types of internalizing symptomatology.
Demographic factors, including gender and socioeconomic status, are also critical
indicators of substance use risk. Sex differences may exist in substance use traajectores. Some
studies have suggested that males endorse more CPs than females (Maughan, Rowe, Messer,
Goodman, & Meltzer, 2004) and female CPs tend to be relatively lower risk and limited to
adolescence (Fergusson & Horwood, 2002). Given sex differences in the relationship between
CPs and substance use, it is critical to test how mechanisms (i.e. alternative and complementary
reinforcers) might operate differently by sex. Additionally, socioeconomic disparities exist in the
prevalence of substance use, abuse, and dependence across a wide range of substances and
appear to emerge as early as adolescence (Edwards et al., 2007; Pampel, Krueger, & Denney,
2010; Van Lenthe, Martikainen, & Mackenbach, 2007). Teens from low income neighborhoods
have less access to substance-free pleasant activities due to financial restrictions and experience
neighborhood deprivation (i.e. fewer recreational outlets) that may make it more likely to choose
substances as a means of deriving pleasure (LeVeist & Wallace, 2000; Moore, Roux, Evenson,
McGinn, & Brines, 2008).
Collectively, examining the aforementioned factors will be critical to understanding how
CPs are related to substance as well as the extent to which alternative reinforcers may mediate
the association.

13
Aims of Overall Project and Focus of Individual Manuscripts
The proposed study uses an ideally designed naturalistic longitudinal study to test the
model using multiple waves of data beginning at the start of high school. The Happiness and
Health Study utilizes semi-annual assessments of substance use and mental health information
from 10 high schools across the greater Los Angeles data. Although studies have examined
trajectories of CPs and substance use separately, no prospective study, to our knowledge, has
examined the nuances underlying the association between CPs and substance use. The current
study will not only examine the association between CPs and substance use but also factors that
impact their association. Additionally, the current study will test the theoretical model
implicating behavioral economic mechanisms as mediators that account for the association
between CPs and substance use across the transition into high school. We will examine changes
in the co-occurrence of three distinct substances and CPs across adolescence and test alternative
and complementary reinforcers as mechanisms that may account for substance use uptake. This
is particularly important as the proposed study aims to examine trajectories of tobacco, alcohol,
and marijuana separately using a California-based sample, which is a state that has recently
legalized recreational marijuana use.
Two specific aims guided the inquiry for this project:
Specific Aim 1: To investigate the longitudinal association of CPs and substance
use and the extent to which other mental health symptomatology may be
impacting their association in adolescence.
Specific Aim 2: To evaluate the extent to which alternative and complementary
reinforcement mediate the relationship between CP levels and subsequent
changes in substance use.
14
Manuscript 1: The Risk Carried by Conduct Problems and the Overlap with Internalizing
Symptomatology
The first study, “Internalizing Symptoms and Conduct Problems: Redundant,
Incremental, or Interactive Risk Factors for Adolescent Substance Use During the First Year of
High School?” (published December 2016 in Drug and Alcohol Dependence), examines the risk
carried by CPs towards adolescent substance use. Specifically, this is the first study to test the
nuanced interplay between CPs and multiple forms of internalizing symptomatology towards
adolescent substance use. We tested whether the risk carried by internalizing symptomatology
(e.g. depression, anxiety) was redundant, incremental, or interactive with the risk carried by CPs.
This study extends prior literature that has been previously only examined depressive
symptomatology in relation to CPs. Additionally, this study examined the aforementioned
relationships across multiple substances to test whether there were substance-specific differences
in the way the internalizing-externalizing comorbidity manifested over the transition to high
school.
Manuscript 2: Cross-Sectional Examination of Alternative and Complementary
Reinforcers
The second study, “Alternative and Complementary Reinforcers as Mechanisms Linking
Adolescent Conduct Problems and Substance Use” (published August 2016 in Experimental and
Clinical Psychopharmacology), is the first study to evaluate the utility of behavioral economic
mechanisms (e.g. alternative reinforcers) as a potential point of intervention in adolescent
substance use. This study also evaluates the role of complementary reinforcers as an additional
mechanism implicated in the relationship between CPs and substance use. In addition to
evaluating the role of alternative and complementary reinforcement as mediators between CPs
15
and substance use, this study tested the extent to which sex moderated this mediation and
whether there were differences between alcohol, cigarette, marijuana, and any substance use.
Manuscript 3: Longitudinal Examination of Alternative and Complementary Reinforcers
The third and final study, “Longitudinal Examination of Alternative and Complementary
Reinforcers Linking Conduct Problems and Substance Use across Adolescence”, builds on Study
1’s goals by examining whether alternative and complementary reinforcers mediate the
relationship between CPs and substance use across three annual waves of data as teens transition
through high school. This study assessed whether alternative and complementary reinforcers at
12-month follow-up mediated the association between CPs reported at baseline (i.e. beginning of
high school) and substance use at 24-month follow-up (i.e. beginning of 11
th
grade). The study
employed longitudinal structural equation modeling to examine whether the significant
associations found in Study 1 persisted through multiple waves of data collection. Analytically,
the study tested whether teens engaging in more behavioral problems at the beginning of high
school reported more substance use two years following and whether this association is mediated
by teens engaging in diminished levels of alternative reinforcement and increased levels of
complementary reinforcement.
Collectively, these studies will elucidate potentially important mechanisms for at-risk
teens susceptible to substance use. Results from this study will aid in the development of creating
a risk-focused approach to drug abuse prevention by identifying and possibly mitigating
precursors. Many of the existing substance use prevention programs do not address the high rates
of overlap with externalizing behaviors. If the theoretical model is validated with alternative and
complementary reinforcers being key mediators then behavioral activation interventions that
teach kids to engage in healthy, non-drug activities that are pleasurable may be a useful
16
intervention strategy to prevent future substance use. Additionally, interventions targeted at
disassociating environmental stimuli with substance use may help adolescents learn to
experience the rewarding qualities of natural reinforcers without the addition of substances.

Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE 17

Internalizing Symptoms and Conduct Problems: Redundant, Incremental, or Interactive
Risk Factors for Adolescent Substance Use During the First Year of High School?
Rubin Khoddam, Nicholas J. Jackson, & Adam M. Leventhal

As Published in December 2016 Issue of
Drug and Alcohol Dependence
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

18
Abstract

Aim: The complex interplay of externalizing and internalizing problems in substance use risk is
not well understood. This study tested whether the comorbid relationship of conduct problems
and several internalizing disorders with future substance use is redundant, incremental, or
interactive in adolescents.
Methods: Two semiannual waves of data from the Happiness and Health Study were used,
which included 3,383 adolescents (M age = 14.1 years old; 53% females) in Los Angeles who
were beginning high school at baseline. Logistic regression models tested the likelihood of past
six-month alcohol, tobacco, marijuana, and any substance use at follow-up conditional on
baseline conduct problems, symptoms of one of several internalizing disorders (i.e. Social
Phobia and Major Depressive, Generalized Anxiety, Panic, and Obsessive-Compulsive
Disorder), and their interaction adjusting for baseline use and demographic covariates.
Findings: Conduct problems were a robust and consistent risk factor of each substance use
outcome at follow-up. When adjusting for internalizing-conduct comorbidity, depressive
symptoms were the only internalizing problem whose risk for alcohol, tobacco, and any
substance use was incremental to conduct problems. With the exception of social phobia,
antagonistic interactive relationships between each internalizing disorder and conduct problems
when predicting any substance use were found; internalizing symptoms was a more robust risk
factor for substance use in teens with low (vs. high) conduct problems.
Conclusions: Although internalizing and externalizing problems both generally increase risk of
substance use, a closer look reveals important nuances in these risk pathways, particularly among
teens with comorbid externalizing and internalizing problems.
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

19
Keywords: Conduct Problems; Internalizing Symptomatology; Substance Use; Drugs;
Depression; Anxiety
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

20
1. Introduction
Substance use often co-occurs with a range of externalizing problems throughout
adolescence (Brown et al., 1996; Maslowsky et al., 2013; Merikangas et al., 2010). Although
Conduct Problems (CPs) and other externalizing problems are well-established risk factors for
adolescent substance use (King et al., 2004; Maslowsky et al., 2013), the role of internalizing
symptomatology (IntSx) in teen substance use risk is less clear. Some research shows that
anxiety and depression are associated with subsequent substance use in adolescents (Buckner et
al., 2008; Crum et al., 2008; King et al., 2004; Sartor et al., 2007); however, results are not
always replicated (Pardini et al., 2007) and do not appear to be as strong as the corresponding
risk of substance use associated with CPs (King et al., 2004; Maslowsky and Schulenberg, 2013).
Further complicating matters, there is considerable comorbidity between CPs and IntSx in teens
(Lewinsohn et al., 1993). Hence, the interplay between CP and IntSx in adolescent substance use
risk may be multifaceted.
1.1 The interplay of Conduct Problems and Internalizing Symptomatology in adolescent
substance use
There are several ways in which CPs and IntSx may interplay in teen substance use risk.
If the association of IntSx with substance use risk is mostly explained by the overlap with CPs,
this would suggest that the internalizing pathway to substance use risk is redundant with CPs,
whereby, both CPs and IntSx explain the same variance in substance use risk. If the association
of IntSx with substance use risk occurs over and above overlapping risk accounted for by CPs,
this would suggest that the internalizing pathway to substance use risk is incremental and that
some meaningful degree of risk is conferred by IntSx even in the absence of high levels of CPs.
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

21
These two patterns of results can be discerned by models of substance use risk in which IntSx
and CPs are included as simultaneous predictors to control for their overlapping variance.
A third pattern regarding the interplay between CPs and IntSx in substance use risk can
be determined by models of substance use testing the interaction between IntSx and CPs. This
interactive association could be expressed in one of two ways: (1) a synergistic interaction - the
combination of having both high CPs and high IntSx is associated with a disproportionately
larger increase in risk than what would be expected based on the risk carried by IntSx or CPs
alone; and (2) an antagonistic interaction - the combination of having both high CPs and high
IntSx is associated with a disproportionately smaller risk than what would be expected based on
the risk carried by IntSx or CPs alone.
1.2 Research gaps on the interplay of Conduct Problems and Internalizing Symptomatology in
substance use risk
Thus far, research on the CP-IntSx comorbidity has primarily focused on Conduct
Disorder and Major Depressive Disorder as risk factors for substance use. To date, evidence
indicates that both forms of psychopathology are associated with substance use independent of
one another (Connor et al., 2004; Crum et al., 2008; Ingoldsby et al., 2006; King et al., 2004),
incrementally to one another, (Brook et al., 2015; Ingoldsby et al., 2006; Lansford et al., 2008),
and interactively (e.g., Marmorstein and Iacono, 2001; Maslowsky and Schulenberg, 2013).
These latter studies of adolescents have found that high levels of both CPs and depressive
symptoms are associated with a disproportionately larger increase in risk for substance use than
either disorder independently (i.e. synergistic interaction; Marmorstein and Iacono, 2001;
Maslowsky and Schulenberg, 2013; Miller-Johnson et al., 1998; Pardini et al., 2007). However,
research has yet to examine whether this interactive relationship exists between CPs and other
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

22
forms of IntSx, such as the various type of anxiety disorder symptoms often present in
adolescents (Grant et al., 2004). Understanding how the internalizing-externalizing interplay in
substance use risk presents across multiple forms of IntSx can elucidate whether additional IntSx
beyond depression are needed in risk prediction modeling, developmental psychopathology
theories, and prevention strategies that target affect.
1.3 The present study
The present study examines CPs and IntSx at the beginning of 9
th
grade to predict
transitions in substance use by a six-month follow-up period. This period, which reflects the first
year of high school, is a significant developmental period marked by social transitions, new
academic demands, and access to older peer groups, and therefore is a high-risk period for
substance use onset and escalation (Eaton et al., 2010; SAMHSA, 2009). We first examine
relationships of CPs and multiple forms of IntSx (i.e. Major Depressive Disorder, Generalized
Anxiety Disorder, Social Phobia, Panic Disorder, and Obsessive-Compulsive Disorder) to
adolescent substance use risk in isolation from one another to replicate and extend past findings.
We then address the primary aim of this paper which is to examine whether relations involving
CPs and IntSx to substance use risk are redundant, incremental, or interactive. It is hypothesized
that a synergistic interaction exists between depressive symptomatology and CPs on substance
use given recent findings showing a synergistic association rather than the antagonistic kind
(Maslowsky et al., 2013; Maslowsky and Schulenberg, 2013). No a priori hypotheses were put
forth regarding how the other IntSx symptoms associate with substance use risk in the context of
possible CP comorbidity.

Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

23
2. Methods
2.1 Participants and procedures
The current study utilizes survey data from a cohort of 9
th
grade students enrolled in 10
public high schools in the Greater Los Angeles area assessed at baseline (fall 2013) and again at
a six-month follow up (spring 2014). Participating schools were selected based on their
representation of diverse demographic characteristics. Students who were not enrolled in a
special education program (e.g., severe learning disabilities) or English as a Second Language
Programs were eligible to participate (N=4,100). Among those eligible, 3,874 (94.5%) assented
to participate in the study, of whom 3,383 (82.5%) provided active written parental consent and
enrolled in the study at baseline. The study had a 97% retention rate between baseline and
follow-up with a total of 3,293 teens participating at follow-up. Paper-and-pencil surveys were
administered during in-class 60-minute survey administrations. Researchers communicated to
students that their responses would be strictly confidential and not shared with their teachers,
parents, or school staff. Students were not individually compensated; however, each participating
school was compensated for their general activity fund. Questionnaires were administered in a
random order and some students did not complete the entire survey within the time allotted or
were absent on one of the assessment days. Consequently, participants who did not complete key
IntSx, CPs, and substance use measures used in this report were not included in the final sample
used in analyses. Depending on the particular analyses, sample size ranged from 2,896 to 3,229.
The study was approved by the Institutional Review Board at the University of Southern
California.

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24
2.2 Measures
2.2.1 Conduct Problems. CPs were assessed at the baseline assessment using an 11-item
measure of past six-month behavior (e.g., stealing, lying to parents, running away; Lloyd-
Richardson et al., 2002; Resnick et al., 1997; Thompson et al., 2007). Six of the 11 items
assessed are behaviors consistent with a Conduct Disorder diagnosis. The frequency of each
behavior was ascertained with six ordinal response options varying from never to 10 or more
times in the past six-months (scored 1 to 6, respectively) and a weighted sum score was
computed across the 11 items. A weighted sum score of CPs was used in the analyses to most
accurately reflect CPs endorsed. A weighted score is optimal given both a mean and a sum score
would include individuals who had missing data on items and thus not accurately reflect a true
endorsement of 11 CP items. The CP scale exhibited good internal consistency in the sample (α
= .79) and exhibited characteristic patterns of association with IntSx symptoms and substance
use, suggesting good evidence of construct validity.
2.2.2 Internalizing Symptomatology. The Revised Children’s Anxiety and Depression
Scale (RCADS) was administered at baseline to assess Major Depressive Disorder, Generalized
Anxiety Disorder, Social Phobia, Panic Disorder, and Obsessive-Compulsive Disorder symptoms
(Chorpita et al., 2000). The Major Depressive scale included 10 items relating to depressive
symptoms (e.g., “I feel sad or empty”). The Generalized Anxiety Disorder scale included six
items relating to worry about the future (e.g., “I worry about things”). The Social Phobia scale
includes nine items relating to fear of being evaluated negatively by others (e.g., I feel worried
when I think someone is angry with me”). The Panic Disorder scale has nine items that assess
bodily symptoms of a panic attack (e.g., “When I have a problem, I get a funny feeling in my
stomach”). The Obsessive-Compulsive Disorder scale has six items relating to obtrusive
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25
thoughts (e.g., I have to think of special thoughts (like numbers or words) to stop bad things from
happening.”). The frequency of each behavior was ascertained with four ordinal response options
ranging from Never to Always. Similar to CPs, a weighted sum score was used for each of the
five internalizing disorders.
The RCADS showed strong internal consistency (see Table 1 for psychometric properties
for each form of IntSx), correspondence with DSM-based diagnoses, and convergent and
discriminant validity in prior samples of teens (Chorpita et al., 2000). Table 2 shows that there
was a moderate correlation between each form of IntSx, indicating discriminant validity between
each construct in the current sample as well.
2.2.3 Substance Use. Adolescent substance use was assessed using standard validated
items used in prior epidemiologic surveys (Johnston et al., 2014). For past six-month use, in
particular, students were asked whether they had used any of the 24 listed substances for
recreational purposes or to get “high”. Adolescents who endorsed use of any substance were
coded as past six-month users (26.2% of the sample). For substance specific analyses, binary
past six-month alcohol, tobacco, and marijuana use variables were used. The binary tobacco use
category variable included those who used a cigarette, smokeless tobacco, big cigars, little cigars
or cigarillos, hookah water pipe, blunts, or other forms of tobacco (19.1%). The combined
marijuana use category variable included those who smoked blunts (9.1%). A composite binary
variable was used for primary analyses based on endorsement of any one of these substances.
2.2.4 Covariates. Demographic factors including gender, parental education (high school
diploma or less vs. some college education or greater), and ethnicity (Hispanic vs. Not Hispanic)
were included as covariates. These factors may be associated with substance use, and therefore,
confound key associations (Anthony et al., 1994; Haberstick et al., 2013; Merikangas and
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

26
McClair, 2012). Baseline past six-month use (yes/no) of each substance was also included as a
covariate in all analyses. Age was not considered due to restricted range (all were in same grade).
There were less than seven teens across the analyses that did not have the demographic
covariates examined in the current set of analyses.
2.3 Analytical Approach
The primary approach involved mixed-effects logistic regression models in which past
six-month use of any substance, alcohol, tobacco, and marijuana at the follow-up served as the
outcomes and CPs and/or IntSx were modeled as continuous predictor variables (both
standardized with a mean of zero and standard deviation of one to facilitate interpretation of
parameter estimates across variables with different scales). Each regression was modeled with a
random intercept for school to account for the clustering of students within schools (Hubbard et
al., 2010). Missing data on covariates were accounted for using dummy variable adjustment,
which creates a dummy variable to code for missingness, to allow inclusion of the entire sample
in analyses. Supplementary Table 1 presents a table of sample size and available data across all
study variables. There were approximately 534 adolescents that had some amount of missing
data across all key measures. These teens tended to be male, non-Hispanic, and slightly less
educated.
All models were tested in four stages: (1) CPs as the sole predictor, (2) a single IntSx
index as the sole predictor, (3) CPs and a single IntSx index as simultaneous predictors (to test
for redundant or incremental relationships), and (4) CPs, the IntSx index, and the interaction
term between CPs and IntSx index as simultaneous predictors (to test for interactive
associations). Each of these models were run twice: (1) an unadjusted model that only included
baseline reports of past six-month substance use for the respective substance under study as the
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27
sole covariate and (2) adjusted model that further included demographic covariates, including
gender, ethnicity, and highest parental education in addition to baseline substance use as a
covariate. Furthermore, this modeling strategy was repeated five times, whereby, we substituted
in one of the five the different RCADS scale as the internalizing symptom predictor. This
approach allowed us to compare each internalizing syndrome as a possible redundant,
incremental, or interactive risk factor to CPs. We did not include multiple internalizing
syndrome scales in the same model because of concerns about partialling out construct-relevant
shared variance among the internalizing pathologies that might be accounted for by a common
underlying internalizing dimension.
All analyses were conducted in Stata version 13.1, Stata Corp LP (College Station, TX).
Results from the mixed-effects logistic regression models are reported as Odds Ratios (OR+95%
CIs) and significance was set to .05 (two-tailed).
3. Results
Table 1 presents descriptive statistics and Table 2 presents correlations among
demographic and key study variables, which showed significant relations between all key study
variables except Social Phobia with all substance use variables.
3.1 Primary analyses of Conduct Problems and Internalizing Symptom Indexes as predictors of
substance use at follow-up
Unadjusted and adjusted ORs for associations of CP and IntSx with substance use
outcomes were similar across analyses. Thus, for parsimony, adjusted ORs are reported in
Figures 1-4.
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28
3.1.1 Independent associations. Figure 1 depicts the independent association of each form
of psychopathology to substance use alone without accounting for the covariance between CP
and the internalizing measures.
For the any substance use status outcome measured at follow-up, baseline CPs had a
strong positive association with substance use with results indicating that for each one standard
deviation increase in CPs, the likelihood of an adolescent reporting substance use at follow-up
was 72% greater after adjusting for baseline use and other covariates. The independent
association of each IntSx measure on any substance use varied by the specific form of IntSx.
Each IntSx domain was significantly associated with risk of any use at follow-up (ORs range =
1.04 – 1.28, p < .05), with the exception of social phobia (OR = 1.04 [0.96, 1.13], p = .35).
For substance-specific analyses, CPs were positively association with alcohol (OR =
1.62[1.46, 1.80], p < .0001), tobacco (OR = 1.72[1.53, 1.92], p < .0001), and marijuana use (OR
= 1.75[1.56, 1.96], p < .0001). Major Depressive Disorder symptoms also had a positive
independent association with each measure of substance use, whereas the association of other
IntSx domains with individual drug outcomes varied across IntSx domains and were not
consistently significant. Social Phobia was only associated with marijuana use (OR = 0.87[0.77,
0.97], p < .05), with the OR < 1 indicating social phobia may be a protective factor against
marijuana use.
In each case, the 95% CI for associations involving CPs and associations involving IntSx
did not overlap with one another, providing evidence that CPs were significantly more robust
risk factors for substance use than IntSx (Figure 1).
3.1.2 Incremental associations. Figure 2 depicts the ORs of CPs on substance use when
accounting for the comorbidity of each type of IntSx. CPs were significantly associated with
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29
substance use over and above each internalizing syndrome (p < .0001) and the inclusion of
internalizing problems in the models did not meaningfully reduce the ORs for risk carried by
CPs.
Figure 3 depicts the ORs of each IntSx on substance use when adjusting for comorbid
CPs. Depressive symptoms (OR = 1.19[1.09, 1.30], p < .001) were the only form of IntSx that
had a significant incremental relationship with increased likelihood of alcohol, tobacco, and any
substance use over and above CPs. Social Phobia was incrementally associated with lower
marijuana use (OR = .84[0.74, 0.94]; p < .01) when accounting for co-occurring CPs.
3.1.3 Interactive associations. Figure 4 depicts the interaction between IntSx and CP
symptoms. There were significant interactions between each internalizing measure (except
Social Phobia) and CPs in predicting past six-month any substance use at follow-up (ORs range
= .87 - .91, p < .05), with the ORs < 1 indicating antagonistic interactions. Adolescents endorsing
less CPs reported higher rates of substance use at follow-up as the number of internalizing
symptoms they endorsed increased, whereas, the likelihood of reporting substance use at follow-
up was less pronounced among those with higher levels of CPs.
Only one substance-specific interactive association was found. There was an antagonistic
interaction between Panic Disorder and CPs on tobacco use (OR = .92[.84, .99]; p < .05).
However, this interaction was no longer significant after adjusting for demographic covariates.
4. Discussion
This study provides novel evidence regarding how several forms of IntSx operate as risk
factors for adolescent substance use in the context of CPs during the noteworthy transitional
period of the first year of high school. First, the robust relationships between CPs and substance
use regardless of IntSx levels point to the importance of drug use prevention efforts that
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30
effectively target high-risk teens with externalizing behaviors. Second, the incremental
relationships between CPs and depressive symptomology highlight the additive risk that both
CPs and depression carry above and beyond the association of each disorder independently.
Lastly, the current study found evidence for several interactive relationships between CPs and
IntSx; however, these associations were not synergistic as expected and displayed antagonistic
interplay of CPs and IntSx in substance use risk.
4.1 Conduct Problems as a robust risk factor for substance use
Prior studies examining CPs independently (Hayatbakhsh et al., 2008; Mayzer et al.,
2009) as well as in relation to IntSx (Ingoldsby et al., 2006; King et al., 2004; Lansford et al.,
2008; Maslowsky and Schulenberg, 2013) have noted the robustness of CP-related risk of drug
use. There are many possible reasons that could be underlying this phenomenon, including social
explanations that suggest delinquent peer groups may encourage substance involvement or may
simply use substances as part of their delinquency (Duncan et al., 2006; Hawkins et al., 1992). It
could also be that substance use and CPs are manifestations of a shared underlying externalizing
construct (Krueger et al., 2002) with common genetic influences (Krueger et al., 2002; Slutske et
al., 1998; Young et al., 2000; Young et al., 2009). Current findings reinforce and extend a solid
evidence base that CPs are a robust risk factors for substance use.
4.2 Incremental relationships between Conduct Problems and Internalizing Symptomatology
Depressive symptomatology was the only IntSx to have an incremental relationship with
substance use, such that, the relation of depressive symptomatology with substance use risk went
above and beyond the risks it shares with CPs. This is consistent with prior developmental
psychopathology literature that has found that the comorbidity between Conduct Disorder and
Major Depressive Disorder is associated with subsequent substance use (Brook et al., 2015;
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31
Lansford et al., 2008; Maslowsky et al., 2013; Pardini et al., 2007). The current study also
extends these prior findings by examining incremental associations with multiple facets of IntSx.
No other significant incremental associations were found with other forms of IntSx. The null
finding raises the possibility that the risk marked by anxiety disorder symptoms may be a weak
proxy for CP-related increases in substance use risk.
Additionally, given that only depressive symptoms, and not anxiety symptomatology,
was significantly and incrementally associated with substance use over and above CPs, it may
suggest that there are unique facets to depressive symptomatology that confer some degree of
risk that do not overlap with anxiety symptoms. Depression’s core elements, like anhedonia,
sadness, psychomotor retardation may play a unique role in substance use, whereas depressive
features shared with anxiety (e.g., agitation, insomnia, concentration problems) may have less
relevance to substance use risk. Some literature provides evidence that melancholic subtypes of
depression, which are characterized by anhedonia and psychomotor changes, may be more
strongly associated with nicotine and drug dependence than non-melancholic subtypes
(Leventhal et al., 2008). Further disentanglement of how the different symptoms of Major
Depressive Disorder are related to subsequent substance use in adolescents may be beneficial.
4.3 Antagonistic interactions between Conduct Problems and Internalizing Symptomatology
The present study found an antagonistic interaction, whereby, the combination of high
IntSx and high CPs was associated with a disproportionately smaller increase in risk than what
would be expected based on additive incremental effects of risk carried by the combination of
IntSx alone and CPs alone. One possible explanation of this relationship is that some IntSx may
offset some risk carried by comorbid CPs. For example, symptoms related to fearfulness and
high arousal may suppress some of the behavioral disinhibition associated with CPs and
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32
substance use (Young et al., 2009). It could also be that the association of CPs and substance use
is so robust that the etiological role of IntSx on substance use may have less of an impact on
substance use. Figure 4 provides some support for this hypothesis given that adolescents at the
highest levels of CPs appear to be less impacted by the presence of IntSx compared to those with
lower levels of IntSx.
4.4 Generalizability of findings to specific substances
Although there were few significant substance-specific findings, results suggest that
Social Phobia may be protective against marijuana use, over and above the negative impact of
CPs. Prior literature has also corroborated this finding in adolescent community samples (Kellam
et al., 1982; Myers et al., 2003; Shedler and Block, 1990). Socially anxious teens may not
surround themselves with many peers, and thus, limit their access to negative modeling
behaviors. Consistent with this theory, research shows that children at age 10 with higher levels
of anxiety symptoms were less likely to associate with deviant peers who use drugs (Fergusson
and Horwood, 1999). The current study extends on this by elucidating the relative impact of
different measures of anxiety. Specifically, social anxiety may be protective against marijuana
use, but other measures, including Generalized Anxiety Disorder symptoms and Panic Disorder
symptoms confer some risk to overall substance use.
4.5 Limitations
First, participants were sampled from a restricted geographic region, which raises
limitations on generalizability. Second, the IntSx and CP measures were based on self-report
data and not structured clinical interviews. Thus, response styles could have impacted results.
However, one would not expect that a reporting bias would confound the extent to which a
measure has an incremental or interactive association with substance use. Third, although the CP
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33
measure examines several types of CPs, it is not a diagnostic tool used to examine individuals at
clinical levels for Conduct Disorder. However, this study may be relevant to many teens who
engage in sub-clinical levels of CPs who further progress to substance use. Fourth, a binary any
substance use outcome variable was used, which has high sensitivity but low specificity for
identifying problematic patterns of use. Finally, the brief follow-up period between waves
makes it difficult to discern the longitudinal impact of the IntSx-CP comorbidity as well as
potential substance-specific differences that may emerge later on in adolescence.
4.6 Conclusions
Current study results highlight the robustness of CPs as a risk factor for substance use as
well as nuanced interplay of internalizing-externalizing problems in the developmental
psychopathology of adolescent drug use vulnerability. Because of the risk associated with each
internalizing disorder individually as well as in combination with CPs, it is critical that school
administrators and mental health professionals assess for symptoms along the internalizing-
externalizing spectrum to identify and prevent future substance use. Furthermore, adolescence
who have high levels of CPs and depressive symptoms may benefit most from early drug use
prevention.

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34
References
American Psychiatric Association., 2013. Diagnostic and statistical manual of mental disorders
(5
th
ed.). Washington, DC: Author.
Anthony, J.C., Warner, L.A., Kessler, R.C., 1994. Comparative epidemiology of
dependence on tobacco, alcohol, controlled substances, and inhalants: basic findings from
the National Comorbidity Survey. Exp. Clin. Psychopharmacology. 2, 244-268.
Brook, J.S., Lee, J.Y., Finch, S.J., & Brook, D.W., 2015. Conjoint trajectories of depressive
symptoms and delinquent behavior predicting substance use disorders. Addict. Behav. 42,
14-19.
Buckner, J.D., Schmidt, N.B., Lang, A.R., Small, J.W., Schlauch, R.C., Lewinsohn, P.M.,
2008. Specificity of social anxiety disorder as a risk factor for alcohol and cannabis
dependence. J. Psychiatr. Res. 42, 230-239.
Chavira, D. A., & Stein, M. B. (2005). Childhood Social Anxiety Disorder: From understanding
to treatment. Child Adolesc. Psychiatr. Clin. N. Am. 14, 797-818.
Couwenbergh, C., van den Brink. W., Zwart, K., Vreugdenhil, C., van Wijngaarden-Cremers,
van der Gaag, R. J., 2006. Comorbid psychopathology in adolescents and young adults
treated for substance use disorders. Eur. Child Adolesc. Psychiatry. 15, 319-328.
Crum, R.M., Green, K.M., Storr, C.L., Chan, Y., Ialongo, N., Stuart, E.A., & Anthony, J.C.,
2008. Depressed mood in childhood and subsequent alcohol use through adolescence and
young adulthood. Arch. Gen. Psychiatry. 65, 702-712.
Cukrowicz, K.C., Schlegel, E.F., Smith, P.N., Jacobs, M.P., Van Orden, K.A., Paukert, A.L.,
... Joiner, T.E., 2011. Suicide ideation among college students evidencing subclinical
depression. J. Am. Coll. Health. 59, 575-581.
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

35
Dierker, L.C., Vesel, F., Sledjeski, E.M., Costello, D., Perrine, N., 2007. Testing the dual
pathway hypothesis to substance use in adolescence and young adulthood. Drug Alcohol
Depend. 87, 83-93.
Duncan, S.C., Duncan, T.E., Strycker, L.A., 2006. Alcohol use from ages 9 to 16: A
cohort-sequential latent growth model. Drug Alcohol Depend. 81, 71-81.
Eaton, D.K., Kann, L., Kinchen, S., Shanklin, S., Ross, J., Hawkins, J.,…Wechsler, H., 2010.
Youth risk behavior surveillance - United States, 2009. MMWR Surveillance Summary.
59, 1-142.
Fergusson, D.M., Horwood, L J., Ridder, E.M., 2007. Conduct and attentional problems in
childhood and adolescence and later substance use, abuse and dependence: results of a
25-year longitudinal study. Drug Alcohol Depend. 88, S14-S26.
Grant, B.F., Stinson, F.S., Dawson, D.A., Chou, S.P., Dufour, M.C., Compton, W., ...
Kaplan, K., 2004. Prevalence and co-occurrence of substance use disorders and
independent mood and anxiety disorders: Results from the national epidemiologic survey
on alcohol and related conditions. Arch. Gen. Psychiatry. 61, 807-816.
Haberstick, B.C., Young, S.E., Zeiger, J.S., Lessem, J.M., Hewitt, J.K., Hopfer, C. J.,
2014. Prevalence and correlates of alcohol and cannabis use disorders in the United
States: Results from the national longitudinal study of adolescent health. Drug Alcohol
Depend. 136, 158-161.
Hawkins, J.D., Catalano, R.F., Miller, J.Y., 1992. Risk and protective factors for alcohol
and other drug problems in adolescence and early adulthood: implications for substance
abuse prevention. Psychol. Bull. 112, 64-105.
Henry, B., Feehan, M., McGee, R., Stanton, W., Moffitt, T.E., & Silva, P., 1993. The
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

36
importance of conduct problems and depressive symptoms in predicting adolescent
substance use. J. Ab. Child Psychol. 21, 469-480.
Hussong, A.M., Jones, D.J., Stein, G.L., Baucom, D.H., & Boeding, S., 2011. An
internalizing pathway to alcohol use and disorder. Psychol. Addict. Behav. 25, 390-404.
Jackson, K.M., Sher, K.J., Cooper, M.L., Wood, P.K., 2002. Adolescent alcohol and tobacco use:
onset, persistence and trajectories of use across two samples. Addiction. 97, 517-531.
Kaplow, J.B., Curran, P.J., Angold, A., Costello, E. J., 2001. The prospective relation between
dimensions of anxiety and the initiation of adolescent alcohol use. J. Clin. Child. Psychol,
30. 316-326.
Kellam, S.G., Stevenson, D.L., Rubin, B.R., 1982. How specific are the early predictors of
teenage drug use? National Institute on Drug Abuse: Research Monograph Series. 43,
329-334.
Kilbey, M.M., Downey, K., Breslau, N., 1998. Predicting the emergence and persistence of
alcohol dependence in young adults: the role of expectancy and other risk factors. Exp.
Clin. Psychopharmacology. 6, 149-156.
King, S.M., Iacono, W.G., & McGue, M., 2004. Childhood externalizing and internalizing
psychopathology in the prediction of early substance use. Addiction, 99, 1548-1559.
Koopmans, J.R., Slutske, W.S., Heath, A.C., Neale, M.C., & Boomsma, D.I., 1999. The
genetics of smoking initiation and quantity smoked in Dutch adolescent and young adult
twins. Behav. Genet. 29, 383-393.
Kovacs, M., Paulauskas, S., Gatsonis, C., & Richards, C., 1988. Depressive disorders in
childhood III. A longitudinal study of comorbidity with and risk for conduct disorders. J.
Affect. Disord. 15, 205-217.
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

37
Krueger, R.F., Hicks, B.M., Patrick, C.J., Carlson, S.R., Iacono, W.G., McGue, M., 2002.
Etiologic connections among substance dependence, antisocial behavior, and personality:
Modeling the externalizing spectrum. J. Ab. Psychol. 111, 411-424.
Lansford, J.E., Erath, S., Yu, T., Pettit, G.S., Dodge, K.A., Bates, J.E., 2008. The developmental
course of illicit substance use from age 12 to 22: Links with depressive, anxiety, and
behavior disorders at age 18. J. Child Psychol. Psychiatry. 49, 877-885.
Lewinsohn, P.M., Hops, H., Roberts, R.E., Seeley, J.R., Andrews, J.A., 1993. Adolescent
psychopathology: I. Prevalence and incidence of depression and other DSM-III—R
disorders in high school students. J. Ab. Psychol. 102, 133-144.
Leventhal, A.M., Kahler, C.W., Ray, L.A., Stone, K., Young, D., Chelminski, I., Zimmerman,
M., 2008. Anhedonia and amotivation in psychiatric outpatients with fully remitted
stimulant use disorder. Am. J. Addict. 17. 218-223.
Leventhal, A.M., Waters, A.J., Kahler, C.W., Ray, L.A., Sussman, S., 2009. Relations between
anhedonia and smoking motivation. Nicotine Tob Res. 11, 1047-1054.
Leventhal, A.M., Brightman, M., Ameringer, K.J., Greenberg, J., Mickens, L., Ray, L.A., ...
Sussman, S., 2010. Anhedonia associated with stimulant use and dependence in a
population-based sample of American adults. Exp. Clin. Psychopharmacology. 18, 562-
569.
Lilienfeld, S. O., 2003. Comorbidity between and within childhood externalizing and
internalizing disorders: Reflections and directions. J. Ab. Child Psychol. 31, 285-291.
Lloyd-Richardson, E.E., Papandonatos, G., Kazura, A., Stanton, C., Niaura, R., 2002.
Differentiating stages of smoking intensity among adolescents: stage-specific
psychological and social influences. J. Consult. Clin. Psychol. 70, 998-1009.
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

38
MacLatchy-Gaudet, H.A., Stewart, S.H., 2001. The context-specific positive alcohol outcome
expectancies of university women. Addict. Behav. 26, 31-49.
Marmorstein, N.R., Iacono, W.G., 2001. An investigation of female adolescent twins with both
major depression and conduct disorder. J. Am. Acad. Child Adolesc. Psychiatry. 40, 299-
306.
Maslowsky, J., Schulenberg, J.E., 2013. Interaction matters: Quantifying conduct problem x
depressive symptoms interaction and its association with adolescent alcohol, cigarette,
and marijuana use in a national sample. Dev. Psychopathol. 25, 1029-1043.
Maslowsky, J., Schulenberg, J.E., O’Malley, P.M., Kloska, D.D., 2013. Depressive symptoms,
conduct problems, and risk for polysubstance use among adolescents: Results form US
national surveys. Ment. Health Subst. Use. 7, 157-169.
Maslowsky, J., Schulenberg, J.E., & Zucker, R.A., 2013. Influence of conduct problems and
depressive symptomatology on adolescent substance use: Developmentally proximal
versus distal effects. Dev. Psychol. 50, 1179-1189.
Masse, L.C., Tremblay, R.E., 1997. Behavior of boys in kindergarten and the onset of
substance use during adolescence. Arch. Gen. Psychiatry, 54, 62-68.
Mayzer, R., Fitzgerald, H.E., Zucker, R.A., 2009. Anticipating problem drinking risk from
preschoolers’ antisocial behavior: Evidence for a common delinquency-related diathesis
model. J. Am. Acad. Child Adolesc. Psychiatry. 48, 820-827.
McGue, M., Iacono, W. G., 2005. The association of early adolescent problem behavior with
adult psychopathology. Am. J. Psychiatry. 162, 1118-1124.
Merikangas, K.R., Mehta, R.L., Molnar, B.E., Walters, E.E., Swendsen, J.D., Aguilar-Gaziola,
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

39
S., ... Kessler, R.C., 1998. Comorbidity of substance use disorders with mood and anxiety
disorders: results of the International Consortium in Psychiatric Epidemiology. Addict.
Behav. 23, 893-907.
Merikangas, K.R., He, J.P., Burstein, M., Swanson, S.A., Avenevoli, S., Cui, L., ... Swendsen, J.,
2010. Lifetime prevalence of mental disorders in US adolescents: results from the
National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). J. Am.
Acad. Child Adolesc. Psychiatry. 49, 980-989.
Merikangas, K.R., McClair, V.L., 2012. Epidemiology of substance use disorders. Human
Genet. 131, 779-789.
Miller-Johnson, S., Lochman, J.E., Coie, J.D., Terry, R., Hyman, C., 1998. Comorbidity of
conduct and depressive problems at sixth grade: Substance use outcomes across
adolescence. J. Ab. Child Psychol. 26I, 221-232.
Myers, M.G., Aarons, G.A., Tomlinson, K., Stein, M. B., 2003. Social anxiety, negative
affectivity, and substance use among high school students. Psychol. Addict. Behav. 17,
277-283.
Pardini, D., Lochman, J., Wells, K., 2004. Negative emotions and alcohol use initiation in
high-risk boys: The moderating effect of good inhibitory control. J. Ab. Child Psychol.
32, 505-518.
Pardini, D., White, H.E., Stouthamer-Loeber, M., 2007. Early adolescent psychopathology
as a predictor alcohol use disorders by young adulthood. Drug Alcohol Depend. 88S,
S38-S49.
Resnick, M.D., Bearman, P.S., Blum, R.W., Bauman, K.E., Harris, K.M., Jones, J., ... Udry, J.R.,
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

40
1997. Protecting adolescents from harm: findings from the National Longitudinal Study
on Adolescent Health. JAMA. 278, 823-832.
Rhee, S.H., Hewitt, J.K., Young, S.E., Corley, R.P., Crowley, T.J., Stallings, M. C., 2003.
Genetic and environmental influences on substance initiation, use, and problem use in
adolescents. Arch. Gen. Psychiatry. 60, 1256-1264.
Sartor, C.E., Lynskey, M.T., Heath, A.C., Jacob, T., True, W., 2007. The role of childhood risk
factors in initiation of alcohol use and progression to alcohol dependence. Addiction.
102, 216-225.
Shedler, J., Block, J., 1990. Adolescent drug use and psychological health: A longitudinal
inquiry. Am. Psychol. 45, 612-630.
Slutske, W.S., Heath, A.C., Dinwiddie, S.H., Madden, P.A., Bucholz, K.K., Dunne, M. P., ...
Martin, N. G., 1998. Common genetic risk factors for conduct disorder and alcohol
dependence. J Ab Psychol. 107, 363-374.
Smith, G.T., Goldman, M.S., Greenbaum, P.E., Christiansen, B. A., 1995. Expectancy for
social facilitation from drinking: the divergent paths of high-expectancy and low-
expectancy adolescents. J. Ab. Psychol. 104, 32-40.
Squeglia, L.M., Jacobus, J., Tapert, S.F., 2009. The influence of substance use on adolescent
brain development. Clin. EEG Neurosci. 40, 31-38.
Substance Abuse and Mental Health Services Administration, 2009. Results from the 2008
National Survey on Drug Use and Health: National Findings (Office of Applied Studies,
NSDUH Series H-36, HHS Publication No. SMA 09-4434). Rockville, MD.
Thompson, M.P., Ho, C.H., Kingree, J.B., 2007. Prospective associations between delinquency
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

41
and suicidal behaviors in a nationally representative sample. J. Adolesc. Health. 40, 232-
237.
Young, S.E., Stallings, M.C., Corley, R.P., Krauter, K.S., Hewitt, J.K., 2000. Genetic and
environmental influences on behavioral disinhibition. Am. J. Med. Genet. 96, 684-695.
Young, S.E., Friedman, N.P., Miyake, A., Willcutt, E.G., Corley, R.P., Haberstick, B. C., Hewitt,
J.K., 2009. Behavioral disinhibition: liability for externalizing spectrum disorders and its
genetic and environmental relation to response inhibition across adolescence. J. Ab.
Psychol. 118, 117-130.
Zinbarg, R.E., Barlow, D.H., 1996. Structure of anxiety and the anxiety disorders: A
hierarchical model. J. Ab. Psychol. 106, 181-193.

Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE 42
Table 1.
Sample characteristics among the overall sample.

Overall Sample
(N = 3,383)
Age, M (SD) 14.1 (0.42)
Gender, %

Female 53.0%
Male 46.2%
Ethnicity, %
American Indian / Alaska Native 0.9%
Asian 15.8%
Black / African American 4.9%
Hispanic or Latino 45.9%
Native Hawaiian / Pacific Islander 3.3%
White 15.3%
Other 5.6%
Multiracial 5.9%
Highest parental education, %
High school graduate or less 25.8%
Some college or more 60.5%
RCADS- MDD, M (SD) / α 7.8 (7.0) / .93
RCADS- GAD Symptoms, M (SD) / α 8.1 (4.7) / .89
RCADS- PD Symptoms, M (SD) / α 4.4 (5.3) / .92
RCADS- SP Symptoms, M (SD) / α 11.9 (7.3) / .90
RCADS- OCD Symptoms, M (SD) / α 4.4 (3.9) / .82
CPs, M (SD) / α 15.8 (5.5) / .79
Substance Use, Past six-month use at Follow-Up / Baseline / Baseline Lifetime, %
Any Substance 37.2% / 26.2% / 40.2%
Alcohol 21.1% / 14.3% / 26.1%
Marijuana 14.5% / 9.1% / 15.3%
Tobacco 28.2% / 19.6% / 29.5%
Note. Data from ninth grade students in Los Angeles, California, USA collected in 2013-2014. CP =
Conduct Problems; RCADS = Revised Children’s Anxiety And Depression Scale; MDD =Major
Depressive Disorder; GAD = Generalized Anxiety Disorder; PD = Panic Disorder; SP = Social Phobia;
OCD = Obsessive-Compulsive Disorder.
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

43
Table 2.
Correlation matrix of key study variables.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 Past 6-mo Any Substance Use at Follow-Up
2 Past 6-mo Alcohol at Follow-Up .67
3 Past 6-mo Marijuana at Follow-Up .53 .48
4 Past 6-mo Tobacco Use at Follow-Up .81 .53 .55
5. CP .32 .30 .36 .34
6. MDD .14 .12 .08 .11 .21
7. GAD .08 .06 .04 .08 .16 .42
8. PD .10 .07 .07 .09 .19 .49 .37
9. SP .03 .00 -.04 -.01 .06 .41 .45 .35
10. OCD .07 .05 .04 .04 .15 .41 .35 .39 .37
11. Gender -.00 -.03 .00 .01 -.02 -.20 -.05 -.12 -.16 -.08
12. Ethnicity .02 -.01 .04 .04 .03 .01 -.01 .00 -.00 -.00 .47
13. Parental Education -.01 -.03 .03 .00 .01 -.01 -.01 -.03 -.02 .00 .18 .14
14 Past 6-mo Any Substance Use at Baseline .48 .42. .41 .45 .39 .13 .13 .13 .01 .06 .02 .01 -.05
15. Past 6-mo Alcohol at Baseline .37 .42 .36 .37 .37 .13 .11 .12 -.00 .06 -.00 .03 .01 .53
16. Past 6-mo Marijuana at Baseline .31 .32 .45 .40 .43 .10 .08 .10 -.01 .06 .00 .03 .01 .53 .44
17. Past 6-mo Tobacco Use at Baseline .37 .34 .40 .46 .42 .11 .10 .12 -.01 .08 -.00 .02 -.01 .67 .47 .64
Note. All coefficients are Pearson correlations except the correlation between Parental Education and all other variables are Spearman correlations, as
Parental Education is an ordinal variable. Shaded cells note correlations that were not statistically significant at p < .05. Ns range from 3,028 to 3,313.
CP = Conduct Problems. MDD = Major Depressive Disorder. GAD = Generalized Anxiety Disorder. PD = Panic Disorder. SP = Social Phobia. Gender
is coded as male = 1 and female = 0. Ethnicity is coded as Hispanic = 1, Non-Hispanic = 0. Parental Education is coded as 1 = High school education or
less, 0 = At least some college education.

Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

44
Figure 1. Independent associations of baseline Conduct Problems and Internalizing Symptom indexes in the prediction of past 6-month substance use
at follow-up.

Note: All models adjusted for past six-month substance use at baseline, highest parental education, ethnicity, and gender.
Conduct Problems
Major Depressive Disorder
Generalized Anxiety Disorder
Panic Disorder
Social Phobia
Obsessive-Compulsive Disorder
0.80 0.90 1.00 1.25 1.50 1.75 2.00
Odds Ratio
Log Scale
Any Use
Alcohol
Tobacco
Marijuana
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

45

Figure 2. Incremental associations of baseline Conduct Problems when comorbid with each Internalizing Symptom index in the
prediction of past 6-month substance use at follow-up.

Note: All models adjusted for past six-month substance use at baseline, highest parental education, ethnicity, and gender.

CPs controlling for Generalized Anxiety Disorder
CPs controlling for Panic Disorder
CPs controlling for Social Phobia
CPs controlling for Obsessive-Compulsive Disorder
0.80 0.90 1.00 1.25 1.50 1.75 2.00
Odds Ratio
Log Scale
Any Use
Alcohol
Tobacco
Marijuana
CPs controlling for Major Depressive Disorder
Running head: CONDUCT AND INTERNALIZING PROBLEMS, SUBSTANCE USE

46
Figure 3. Incremental associations of each Internalizing Symptom Index when comorbid with Conduct Problems in the prediction of
past 6-month substance use at follow-up.

Note: All models adjusted for past six-month substance use at baseline, highest parental education, ethnicity, and gender.
Generalized Anxiety Disorder
Panic Disorder
Social Phobia
Obsessive Compulsive Disorder
0.8 0.9 1.0 1.1 1.2 1.3
Odds Ratio
Log Scale
Any Use
Alcohol
Tobacco
Marijuana
Major Depressive Disorder
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 47

Figure 4. Interaction Betwen Baseline Conduct Problems and Internalizing Symptom Indexes in
the Prediction of Past 6-month Any Substance Use at Follow-Up.

Note: MDD = Major Depressive Disorder. GAD = Generalized Anxiety Disorder. PD = Panic
Disorder. OCD = Obsessive Compulsive Disorder.

0.2
0.3
0.4
0.5
0.6
Pr(AnySubstance at W2)
0 5 10 15 20
GAD
0.2
0.3
0.4
0.5
0.6
Pr(AnySubstance at W2)
0 5 10 15 20 25 30
MDD
0.2
0.3
0.4
0.5
0.6
Pr(AnySubstance at W2)
0 5 10 15 20
OCD
0.2
0.3
0.4
0.5
0.6
Pr(AnySubstance at W2)
0 5 10 15 20 25
PD
-1 SD Mean +1 SD
Conduct Problems:
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 48

Supplemental Table 1.
Sample sizes across each set of analyses
Substance Use Outcomes
Internalizing Alc MJ Tob Any
MDD
Have W1 & W2 Substance Use 3097 3189 3225 3229
+ CP 3041 3131 3165 3168
+ Internalizing 2931 3014 3043 3046
+ Sex, Ethnicity, Education 2925 3007 3036 3039
GAD
Have W1 & W2 Substance Use 3097 3189 3225 3229
+ CP 3041 3131 3165 3168
+ Internalizing 2938 3021 3050 3053
+ Sex, Ethnicity, Education 2932 3014 3043 3046
PD
Have W1 & W2 Substance Use 3097 3189 3225 3229
+ CP 3041 3131 3165 3168
+ Internalizing 2918 2999 3028 3031
+ Sex, Ethnicity, Education 2912 2992 3021 3024
SP
Have W1 & W2 Substance Use 3097 3189 3225 3229
+ CP 3041 3131 3165 3168
+ Internalizing 2914 2997 3026 3029
+ Sex, Ethnicity, Education 2908 2990 3019 3022
OCD
Have W1 & W2 Substance Use 3097 3189 3225 3229
+ CP 3041 3131 3165 3168
+ Internalizing 2902 2986 3014 3017
+ Sex, Ethnicity, Education 2896 2979 3007 3010
Note: CP = Conduct Problem. MDD =Major Depressive Disorder; GAD = Generalized Anxiety
Disorder; PD = Panic Disorder; SP = Social Phobia; OCD = Obsessive-Compulsive Disorder.
Alc = Alcohol. MJ = Marijuana. Tob = Tobacco. Any = Any Substance Use. W1 = Wave 1
Baseline data. W2 = Wave 2 Follow-Up Data.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 49

Alternative and Complementary Reinforcers as Mechanisms Linking Adolescent Conduct
Problems and Substance Use

Rubin Khoddam, M.A. & Adam M. Leventhal, Ph.D.

As Published in December 2016 Issue of
Experimental and Clinical Psychopharmacology
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 50
Abstract
The present study tested the hypothesis that teens who engage in conduct problems are
more likely to use substances because they engage in fewer alternative reinforcing (i.e.,
pleasurable) substance-free activities and more complementary reinforcing substance-associated
activities. In a cross-sectional, correlational design, ninth grade students (N=3,383; mean
age=14.6 years) in Los Angeles, California, USA completed surveys in 2013 measuring conduct
problems (e.g., stealing, lying, getting in fights), alternative and complementary reinforcement,
use of a number of licit, illicit, and prescription drugs, and other co-factors. Conduct problems
were positively associated with past six-month use of any substance (yes/no) among the overall
sample and past 30-day use frequency on a composite index that included six substances among
past six-month users. These associations were statistically mediated by diminished alternative
reinforcement and increased complementary reinforcement when adjusting for relevant
covariates. Conduct problems were associated with lower engagement in alternative reinforcers
and increased engagement in complementary reinforcers, which, in turn, was associated with
greater likelihood and frequency of substance use. Most mediational relations persisted adjusting
for demographic, environmental, and intrapersonal co-factors and generalized to alcohol,
cigarette, and marijuana use; though, complementary reinforcers did not significantly mediate
the relation of CPs with alcohol use frequency. These results point to diminished alternative
reinforcement and increased complementary reinforcement as mechanisms linking conduct
problems and adolescent substance use. Interventions that increase access to and engagement in a
diverse set of alternative substance-free activities and deter activities that complement use may
prevent substance use in adolescents who engage in conduct problems.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 51
Keywords: Conduct Problems; Alternative Reinforcers; Complementary Reinforcers;
Adolescents; Substance Use
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 52
Introduction
Conduct problems (CPs; e.g. stealing, lying, skipping school, fighting) reflect a range of
externalizing behaviors that are strongly associated with adolescent substance use (Brown et al.
1996; Connor, Steingard, Cunningham, Anderson, & Melloni, 2004; Couwenbergh et al., 2006;
King, Iacono, & McGue, 2004; Maslowsky & Schulenberg, 2013). In addition to more severe
behaviors seen only in Conduct Disorder (e.g. mugging, using a weapon), it is important to
consider CPs that are relatively common in the general population of adolescents (e.g., skipping
school). Studying a range of CPs and the relative frequency of each would help us better
understand externalizing comorbidity in substance use, as they may capture subclinical levels of
conduct-associated problems worthy of targeting in broad population-based teen prevention
programs. Additionally, CPs and substance use in non-clinical populations early in adolescence
are risk factors for Substance Use Disorders and adult Antisocial Personality Disorder (Grant &
Dawson, 1998; Howard, Finn, Jose, Gallagher, 2012). Thus, it is important to understand
mechanisms that underlie the relation between adolescent CPs and substance use uptake to
inform etiological models of addiction risk and interventions that thwart the development of
severe addiction trajectories.
Behavioral economics is a useful framework for understanding adolescent substance use,
which recognizes that one’s preference for substances arises within a broader context that
involve the availability or utilization of: (1) alternative competing substance-free reinforcers,
such as joining school clubs, volunteering, or other hobbies, which may deter substance use; and
(2) complementary reinforcing activities that occur in conjunction with substance use (e.g.
stimulants with sports), which may increase substance use engagement (Audrain-McGovern et
al., 2004). Alternative reinforcers is defined as any activity (e.g. school clubs, dating,
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 53
volunteering) that is used as a substitute for substance use (Audrain-McGovern et al., 2004).
Prior research has found that engagement in more alternatively reinforcing activities is associated
with decreased substance use in adolescents and young adults (Audrain-McGovern et al., 2004;
Correia et al., 2005). Alternative reinforcement has also been implicated as a mechanism linking
internalizing symptomatology and substance use, whereby, greater levels of depressive
symptoms are associated with decreased engagement in alternatively reinforcing activities and
decreased alternatively reinforcing activities are associated with increased tobacco use (Audrain-
McGovern, Rodriguez, Rodgers, & Cuevas, 2010). However, alternative reinforcement has yet to
be examined for its relationship among externalizing behaviors that are more robustly associated
with substance use than internalizing symptomatology (King et al., 2004; Maslowsky,
Schulenberg, O’Malley, & Kloska, 2013).
Examining alternative reinforcers as a mechanism underlying the link between CPs and
substance use is particularly important, as behavioral economic interventions that have young
adults engage in healthy prosocial activities effectively reduce drinking (Correia, Benson, &
Carey, 2005; Murphy et al., 2012a; Murphy et al., 2012b). Should alternative reinforcers be a
significant mediator between CPs and substance use, it would warrant further research on
applying behavioral economic interventions to at-risk adolescents who have yet to use
substances. To date, these types of interventions have largely been done in college populations
and have yet to be applied to adolescents with high CPs. Similarly, contingency management
interventions may be another type of treatment that can be applied to this at-risk group. This
intervention regards drug use as a form of operant conditioning and posits that, alternative non-
drug reinforcers should decrease substance use if individuals have access to the reinforcer, it is
reinforced at a schedule incompatible to drug use, and it occurs in contexts connected with
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 54
substance abstinence (Carroll, Lac, & Nygaard, 1989; Higgins, Bickel, & Hughes, 1994; Roll &
Higgins, 2000).
Complementary reinforcement, by contrast, has been less well-examined in the
adolescent substance use literature. However, it is important to understand as some activities that
are inherently social in nature may enhance substance-using experiences (e.g., dancing, parties,
sports) and increase the odds of adolescent smoking (Audrain-McGovern et al., 2004). Given
that teens who endorse higher levels of CPs also endorse higher levels of boredom (Newbury &
Duncan, 2001), it may be that teens with high CPs naturally derive less reinforcement from low-
risk activities that adolescents commonly engage in for fun, and are thus motivated to enhance
their ability to derive reinforcement from such activities with substance use. Some substances
may act as reward enhancers and have social facilitative properties that strengthen the
reinforcing effects of the non-drug related rewards experienced while using substances (Beck &
Treiman, 1996; Caggiula et al., 2009; MacLatchy-Gaudent & Stewart, 2001; Phillips & Fibiger,
1990; Robbins, 1977; Wall, Hinson, & McKee, 1998). For example, drinking or smoking while
engaging in activities, such as dancing, may pharmacologically amplify the reinforcing
properties of the non-drug related activities. This association may be desirable as it would
putatively increase the overall amount of environmental reinforcement for adolescents who
engage in CPs. Thus, substances not only act as a primary reward that cause direct psychoactive
effects, but they also alter the reinforcing effects of rewarding stimuli that are present in the
substance using environment.
When considering the role of these behavioral economic mechanisms to the CP-substance
use connection, several important features of the study design should be considered. First, the
beginning of high school is a salient developmental period in which adolescents enter a new
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 55
social atmosphere and are exposed to older teens who may engage in more delinquent behaviors,
have increased access to substances, and have opportunities to begin engaging in activities that
complement substance use (e.g., alcohol use at high school parties). At the same time,
adolescents entering 9
th
grade are also newly exposed to more organizations and clubs that are
associated with a number of positive outcomes and academic resilience and may serve as
alternative reinforcers (Finn & Rock, 1997; Stewart, 2008). Hence, the outset of 9
th
grade is an
important developmental period to study in terms of CPs, behavioral economic factors, and
substance use. Second, it is of use to examine associations of CPs and behavioral economic
variables across alcohol, tobacco, and marijuana separately as well as a considering influences
involving overall risk of use of any substance, as it is possible that these mechanisms may
generalize across substances, but also may be more relevant to certain substances versus other
(e.g., nicotine has particularly strong reward-enhancing pharmacological properties and therefore
may be tightly linked with complementary reinforcement in youth with higher levels of CPs).
Lastly, it is important to examine possible sex differences in how alternative and complementary
reinforcers may operate differently for males and females in terms of their roles in substance use.
Some studies have suggested that males endorse more CPs than females (Maughan, Rowe,
Messer, Goodman, & Meltzer, 2004) and CPs engaged in by females tend to be relatively lower
risk and limited to adolescence (Fergusson & Horwood, 2002). Thus, how alternative and
complementary reinforcers mediate the association with substance use may differ across sexes.
Taken together, these features (i.e. focusing on a salient developmental time period, multiple
substance use outcomes, and studying sex differences) are important goals for research aimed at
advancing a nuanced understanding of how and why youth with CPs are more likely to engage in
substance use.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 56
The current cross-sectional study of 14-year-old high school students reflects an initial
test of the hypothesis that diminished alternative reinforcement and increased complementary
reinforcement are mechanisms underlying the association between CPs and substance use. To
this end, we examined whether diminished alternative reinforcement mediated the relationship
between CPs and markers of two different points on the substance use uptake continuum: (1)
substance use status (yes/no in the past six-months) in the entire sample; and (2) substance use
frequency among past six-month users. Given that complementary reinforcers may be linked
with progression from use experimentation to more frequent use patterns, we also examined
whether increased complementary reinforcement mediated the relationship between CPs and
substance use frequency among past six-month users. Substance-specific analyses were also
conducted for alcohol, cigarette, and marijuana use separately given they are the most commonly
used drugs in the United States (NSDUH, 2012; Shi, 2014) and may perhaps lend themselves to
substance-specific policy interventions. Anxiety and depression are comorbid with CPs (Chan,
Dennis, & Funk, 2008; King et al., 2004; Maslowsky & Schulenberg, 2013) and substance use
(Audrain-McGovern et al., 2010; Wolitzky-Taylor, Lyuba, Zinbarg, Mineka, & Craske, 2012);
these internalizing symptoms may directly diminish the ability to derive reinforcement from
substance-free activities (Lloyd-Richardson, Papandonatos, Kazura, Stanton, & Niaura, 2002).
Hence, in addition to examining how these relations involving CPs, alternative/complementary
reinforcers, and substance use are incremental to common covariates utilized in adolescent
substance use research (e.g., demographics, peer substance use), we also explore whether the
hypothesized pathways are incremental to internalizing symptomatology.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 57
Methods
Participants and Procedures
This paper describes an analysis of a survey of 9
th
grade students enrolled in ten public
high schools in the Greater Los Angeles metropolitan area. Participating schools were selected
based on their adequate representation of diverse demographic characteristics; the percent of
students eligible for free lunch within each school (i.e., student’s parental income < 185% of the
national poverty level) on average across the ten schools was 31.1% (SD=19.7, range: 8.0% -
68.2%). All students who were not enrolled in special education (e.g., severe learning
disabilities) or English as a Second Language Programs (N=4,100) were eligible. Among those
eligible, 3,874 (94.5%) assented to participate in the study, of whom 3,396 (82.8%) provided
active written parental consent and 3,383 (82.5%) completed the first wave of data collection.
Paper-and-pencil surveys were administered in the fall of 2013 during two separate in-class 60-
minute survey administrations conducted less than two weeks apart. Researchers informed
students that their responses would be confidential and not shared with their teachers, parents, or
school staff. Students were not individually compensated; each participating school was
compensated for their general activity fund. The questionnaires were administered in random
order and some students did not complete the entire survey within the time allotted or were
absent on one of assessment days. Thus, participants who did not complete measures used in this
report were not included in the final sample used for each set of analyses. The study was
approved by the University of Southern California Institutional Review Board.
Measures
Conduct problems. CPs were measured via an 11-item measure of past six-month
behavior (e.g., stealing, stealing an item worth more than $50, destroying property, lying to
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 58
parents, running away, physically fighting, skipping school, being suspended, selling illegal
drugs, driving a car without permission, getting in trouble with police); α in current sample = .79
(Lloyd-Richardson et al., 2002; Resnick et al., 1997; Thompson, Ho, & Kingree, 2007). Six of
the 11 items assessed are behaviors consistent with a Conduct Disorder diagnosis. The frequency
of each behavior was ascertained with 6 ordinal response options varying from never to 10 or
more times in the past six-months (scored 1 to 6).
A weighted sum score of CPs was used in the analyses to most accurately reflect CPs
endorsed. A weighted score is optimal given both a mean and a sum score would include
individuals who had missing data on items and thus not accurately reflect a true endorsement of
11 CP items. Of the total sample, 76 participants did not fill in any of the CPs items and an
additional seven participants did not fill in at least six of the 11 items. Participants needed to
endorse at least six of the 11 items to have a weighted sum score calculated; otherwise the data
was considered missing for that participant due to possible instability of the CP estimate. Among
adolescents who responded to at least 6 of the 11-item measure, the weighted score was created
by calculating the mean frequency rating (1 to 6) for the number of items answered was
calculated and then multiplied by the total number of items, which for this measure, was 11. The
possible range for responses was 11 to 66 where a score of 11 indicates that an adolescent never
engaged in any CPs. Approximately 12% of the overall population reported never engaging in
any CPs.
Past Six-Month and Past 30 Day Substance Use. Substance use was assessed using
standard validated items used in epidemiologic surveys of adolescents (Johnson, O’Malley,
Miech, Bachman, & Schulenberg, 2014). For past six-month use, students were asked whether
they had used any of the substances for recreational purposes or to get “high”: few puffs of a
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 59
cigarette (prevalence of endorsement in overall sample, 3.3%), whole cigarette (1.9%), electronic
cigarettes (12.4%), smokeless tobacco (0.7%), big cigars (1.0%), little cigars or cigarillos (1.8%),
hookah water pipes (9.2%), other forms of tobacco products (1.4%), marijuana (8.7%), blunts
(6.4%), one full drink of alcohol (14.3%), inhalants (2.7%), cocaine (0.5%), methamphetamines
(0.3%), ecstasy (0.8%), LSD/mushrooms/psychedelics (1.0%), salvia (0.5%), heroin (0.3%),
prescription pain killers (1.6%), tranquilizers or sedatives (2.0%), diet pills (1.0%), and
prescription stimulant pills (0.6%). Adolescents who endorsed use of any substance were coded
as past six-month users (26.2% of the sample). For substance specific analyses, a binary past six-
month alcohol, cigarette, and marijuana use variables were used. The binary cigarette use
variable included those who smoked just a few puffs of a cigarette and those who smoked a
whole cigarette (3.5%). The combined marijuana use category variable included those who
smoked blunts (9.1%).
Past 30-day frequency of recreational use was assessed for each of six key substances
(alcohol, cigarettes, marijuana, stimulants, prescription stimulants, and prescription opioid) with
9 ordinal response options coded 0 to 8 (0, 1-2, 3-5, 6-9, 10-14, 15-19, 20-24, 25-29, 30 days).
The substances mentioned were chosen for the survey based on their prevalence in prior work in
a demographically-similar sample collected from the region overlapping with the 10
participating schools in this study (Unger, 2014). A mean past 30-day use frequency score that
used data across the six substances was used for the any substance use frequency analyses. For
alcohol, cigarette, and marijuana use separately, if a participant’s frequency response was greater
than two, participant’s response was changed to two to increase power given that less than 3% of
teens reported use in the higher categories.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 60
Alternative and Complementary Reinforcement. We utilized a modified version of the
Pleasant Events Schedule (PES; MacPhillamy & Lewinsohn, 1982) for youths as in prior work
(Audrain-McGovern et al., 2010). Participants rated 44 different typically pleasant activities
(e.g., going out to eat at a restaurant, playing musical instruments, visiting/hanging out with
friends, participating in clubs or community organizations, playing sports) for both frequency of
engagement (0=Never; 1=1-6 times; 2=7 or more times) and pleasure experienced (0=not
pleasurable; 1=somewhat pleasurable; 2=very pleasurable) in the past 30 days. Additionally,
participants were asked to indicate (yes/no) whether they associated the pleasant activity with
alcohol, smoking, or drug use (Madden, 2000). Consistent with prior methods of measuring
alternative reinforcement, the primary outcome is the sum of each item’s product (engagement
frequency × pleasure) for activities marked as not associated with substance use (Murphy,
Correia, Colby, & Vunchinch, 2005). The complementary reinforcement outcome was calculated
similarly for activities that are marked as being associated with substance use.
To prevent biased underestimated scores for those with missing responses, individual
weighted sum scores were calculated and imputed. This imputation was calculated using a
similar method described in the CP measure section above. In instances of missing data when an
individual did not answer questions related to a particular activity, a weighted sum score was
calculated based on the proportion of complementary and alternative reinforcers in which the
adolescents engaged. For instance, if an adolescent only answered 30 questions on the PES out
of the 44 possible, the proportion of alternative reinforcers to complementary reinforcers was
calculated. If 10 of the 30 questions answered were classified as complementary reinforcers, then
we assumed that 33.3% of the total questions that would have been answered would have been
classified as complementary reinforcers as well. The same computation was done for alternative
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 61
reinforcers, whereby, 66.6% of the total questions on the PES could be assumed to be alternative
reinforcers. Subsequently, the average score was multiplied by the number of items that would
have been endorsed as alternative and complementary reinforcers based on the proportion score
previously calculated, with the exception of cases in which less than 60% of the items were
completed (N = 9). In these instances, the sum score was entered as missing. Additionally, if an
individual had not endorsed any substance use, the sum score for complementary reinforcers was
entered as missing given it was not possible to have activities that complement substance use if
they did not use substances. See Table 1 for available N for both alternative and complementary
reinforcers after eliminating those considered as missing.
Covariates. Three sets of adjusted models were included in the analyses. The first
adjusted model included demographic covariates: sex, parental education (high school diploma
or less vs. some college education or greater), and ethnicity (Hispanic vs. Not Hispanic).
The second adjusted model included a measure of positive urgency and peer substance
use, in addition to the demographic covariates. We used the 26-item Positive Urgency subscale
of the UPPS-P Impulsive Behavior Scale, which measures the tendency towards rash, impulsive
action in response to positive affect and has been implicated in the etiology of substance use and
externalizing behaviors; α in current sample = .95 (Cyders et al., 2007; Whiteside & Lynam,
2001). A composite peer substance use variable was created for alcohol, cigarettes, marijuana,
stimulant, prescription stimulant, and prescription painkillers. For each substance use category,
participants answered how many of their five closest friends have used each substance (each
scored 0 to 5). The mean across each substance was used in this model.

The third adjusted model included internalizing symptom measures in addition to the
covariates tested in the first two adjusted models. The Revised Children’s Anxiety and
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 62
Depression Scale (RCADS) was used to assess Major Depressive Disorder, Generalized Anxiety
Disorder and Panic Disorder symptoms (Chorpita, Yim, Moffitt, Umemoto, & Francis, 2000).
The Major Depressive scale included 10 items relating to depressive symptoms; α in current
sample = .93 (e.g., “I feel sad or empty;” “I worry that something awful will happen;” “I worry
that bad things will happen to me;” “I worry about death;” “I worry about what is going to
happen;” “I worry that something bad will happen to me”). The Generalized Anxiety Disorder
scale included six items relating to worry about the future (e.g., “I worry about things”); α in
current sample = .89. The Panic Disorder scale on the RCADS included nine items that assessed
bodily symptoms of a panic attack; α in current sample = .92 (e.g., “When I have a problem, I
get a funny feeling in my stomach;” “I suddenly feel as if I can’t breathe when there is no reason
for this;” “When I have a problem, I feel shaky”).
Analytical Approach
Primary analyses utilized generalized estimating equations GEEs (Zeger, Liang, &
Albert, 1988) that accounted for clustering of students within schools (Hubbard et al., 2010).
Mediational paths for alternative and complementary reinforcers were tested in three stages: (1)
The relation of CPs on the substance use outcome variable (total effect), (2) The relation of CPs
on the mediator (i.e. alternative or complementary reinforcers; A path), and (3) The relation of
the mediator on the substance use outcome variable when adjusting for CPs (B path). In this last
stage, the association between CPs and the substance use outcome variable when adjusting for
the mediator represents the direct effect in the mediational pathway. The product of the
coefficients from the A path and B path models indicated the strength of the indirect
(“mediated”) effect. Using the PRODCLIN approach, we determined significance via
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 63
asymmetric confidence intervals (CIs) around the mediational effect (MacKinnon, Fritz,
Williams, & Lockwood, 2007).

Each step of the analyses used participants who were not missing any data on the key
study variables (i.e. CPs, alternative or complementary reinforcement, and substance use). Table
1 presents available sample size of each variable. Also, scores were standardized (Mean =0 and
Standard Deviation =1) to generate parameter estimates that could be judged on the same metric
across variables. Sample sizes for each of these analyses are reported in Tables 3-7.
All three of the GEEs mentioned above were tested in four steps: (Model 1) unadjusted,
(Model 2) adjusted for demographic covariates, (Model 3) adjusted for demographic covariates
as well as measures of peer substance use and positive urgency, and (Model 4) adjusted for
depression, generalized anxiety, and panic symptoms as well as all aforementioned covariates.
Analyses were conducted in SAS with PROC GENMOD (SAS Institute, 2003) using an
exchangeable correlation matrix and modeling CPs as a continuous variable and past six-month
substance use as a binary variable. PROC SURVEYLOGISTIC was used in addition to PROC
GENMOD for substance-specific analyses to calculate total effect, direct effect, and the B path
beta estimates. In analyses predicting past 30-day frequency of any substance use, the subsample
who endorsed any past six-month substance use was utilized and Poisson distribution was
specified to account for the skewed count outcome distribution. Complementary reinforcement
was not analyzed as a mediator between CPs and past six-month reports of substance use
because teens who have never used a substance cannot report any activities associated with
substance use, which would have generated criterion contamination between the mediator and
outcome. Missing data on covariates were accounted for using dummy variable adjustment
(Cohen & Cohen, 1985), which creates a dummy variable to code for missingness for each
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 64
covariate with missing data, to allow inclusion of the entire sample in analyses. Results are
reported as parameter estimates (B+95% CIs).
Sex Differences. For each step of the analyses previously mentioned, the main effect of
sex was tested along with an interaction term with the independent variable in the model. Thus,
in the total effect and A path models, an interaction term between sex and CPs was created. In
the B path model, an interaction term between sex and alternative or complementary reinforcers
was created. If the sex interaction term was significant across all three models tested, simple
effect models were run to test the mediational path separately among males and females.
Reverse Mediation. Given the cross-sectional nature of this data, each step of the
mediational analyses was run in the reverse direction in a supplementary analysis. Thus, the three
models tested for reverse mediation were (1) The relation of substance use on CPs (total effect),
(2) The relation of substance use on the mediator (i.e. alternative or complementary reinforcers;
A path), and (3) The relation of the mediator on the CPs when adjusting for substance use (B
path).
Results
Preliminary Analyses
Descriptive statistics for demographics and study variables within past six-month
substance users and the overall sample stratified by males and females are depicted in Table 1.
Correlations between study variables are presented in Table 2. Males and females did not
significantly differ in frequency levels of CPs, alternative and complementary reinforcers, and
substance use, and all other study variables (see non-significant associations between gender and
other variables in Table 2).
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 65
Primary Analyses of Alternative Reinforcers as a Mediator between Conduct Problems
and Any Substance Use
As shown in Table 3, in the first set of unadjusted analyses predicting a binary variable of
past six-month any use among the entire sample, there was a significant total effect (β= .96, p <
.0001), which represents the overall relation of CPs to adolescent substance use (i.e., portion of
the association accounted for by the mediator + portion of the association not accounted for by
the mediator). The positive direction of this finding indicates that higher levels of CPs were
associated with greater reports of substance use. Both the A path (β= -.18, p < .0001) and B path
(β= -.13, p < .01) were also significant in the negative direction, indicating that greater levels of
CPs were associated with decreased levels of alternative reinforcement and decreased alternative
reinforcement was associated with a greater likelihood of substance use after adjusting for CPs,
respectively. Multiplying these two path coefficients together provides the indirect effect (β=
.02, p < .01). Lastly, there was a significant direct effect (β= .95, p < .0001), which represents
the association between CPs and substance use after adjusting for the mediator.
For the second set of analyses, we examined the extent to which complementary
reinforcers mediated the relationship between CPs and substance use frequency among past six-
month users. The positive A path (Unadjusted, β= .41, p < .0001) and B path (Unadjusted, β=
.18, p < .001) coefficients signify that higher levels of CPs were associated with greater
engagement in complementary reinforcers and greater engagement in complementary reinforcers
was associated with more frequent substance use, respectively. There was significant indirect
(Unadjusted, β= .08, p < .001) effect, indicating that complementary reinforcers mediated the
relationship between CPs and substance use frequency.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 66
For the third set of analyses, we examined the extent to which alternative reinforcers
mediated the relationship between CPs and substance use frequency among past six-month
substance users. Higher levels of CPs were associated with decreased levels of alternative
reinforcers (Unadjusted, β= -.28, p < .0001) and decreased levels of alternative reinforcers was
associated with increased substance use frequency after adjusting for CPs (Unadjusted, β= -.46,
p < .0001). The product of these two paths revealed a significant indirect effect (Unadjusted, β=
.13, p < .0001), indicating that diminished alternative reinforcers mediated the relationship
between CPs and substance use frequency.
Each of the abovementioned results remained statistically significant after various levels
of covariate adjustment (see Table 4).
Substance-Specific Analyses
Alcohol. Table 4 presents analyses predicting past six-month alcohol use in the overall
sample and alcohol use frequency among past six-month users, revealing a significant total effect
for the association of CPs to alcohol use status and frequency. Among the overall sample,
alternative reinforcers significantly mediated the relationship between CPs and alcohol use,
whereby, higher levels of CPs were associated with decreased likelihood of alternative
reinforcers, which was in turn, was associated with higher levels of alcohol use (Unadjusted
indirect effect, β = .03, p < .0001). This mediational path was also significant when predicting
alcohol use frequency among past six-month drinkers in the first two adjusted models (see Table
4), but was reduced to a non-significant trend after adjusting for internalizing symptomatology in
the most stringent model including all covariates (Indirect effect, β = .03, p = .06).
Complementary reinforcers did not significantly mediate the relationship between CPs and
alcohol use frequency in either the unadjusted or adjusted models.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 67
Cigarettes. Table 5 presents predicting past six-month cigarette use in the overall sample
and cigarette use frequency among past six-month users, revealing a significant total effect for
the association of CPs to cigarette smoking status and frequency. Among the overall sample,
alternative reinforcers significantly mediated the relationship between CPs and past six-month
cigarette use (Unadjusted indirect effect, β= .07, p < .0001). Alternative reinforcers also
significantly mediated the relationship between CPs and cigarette use frequency among past six-
month users (Unadjusted indirect effect, β= .18, p < .001). As with other substances, CPs had a
significant negative association with alternative reinforcers and alternative reinforcers had a
significant negative association with cigarette use frequency. These results remained robust after
adjusting for all covariates. Complementary reinforcers mediated the relationship between CPs
and cigarette use frequency in the unadjusted model (Indirect effect, β= .12, p < .05) and the
adjusted model (Indirect effect, β= .12, p < .05) that only included demographic covariates. After
additionally adjusting for peer substance use, impulsivity, and internalizing symptomatology,
complementary reinforcers was no longer a significant mediator between CPs and cigarette use
frequency in past six-month users.
Marijuana. Table 6 presents analyses predicting past six-month marijuana use in the
overall sample and marijuana use frequency among past six-month users, revealing a significant
total effect for the association of CPs to marijuana use status and frequency. All marijuana use
outcomes were significant at p < .05. Specifically, alternative reinforcers significantly mediated
the relationship between CPs and past six-month marijuana use (Unadjusted indirect effect, β=
.07, p < .0001) as well as past 30-day marijuana use frequency (Unadjusted indirect effect, β=
.18, p < .0001) among users. Thus, higher levels of CPs were associated with decreased
alternative reinforcers, which was associated with greater levels of marijuana use. Similarly,
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 68
complementary reinforcers significantly mediated the relationship between CPs and marijuana
use frequency (Unadjusted indirect effect, β= .50, p < .01). This relationship remained robust
across all adjusted models.
Sex Differences
Tests of the interaction term between sex and the independent variable in the total effect
model, A path, and B path revealed significant interactions for each of these three associations in
predicting past six-month alcohol use status among the overall sample in the unadjusted model
and adjusted model for demographic covariates. In the unadjusted model, the interaction terms
between the independent variable and sex were significant for the total effect (β = -.42, p <
.0001), A path (β = .07, p = .04), and the B path (β = .29, p < .001). Results were maintained
across the total effect (β = -.43, p < .0001), A path (β = .07, p = .04), and B path (β = .27, p <
.01) even after adjusting for demographic covariates. Given the direction of the interaction terms,
and that male was coded 1 and female was 0, results indicated that girls had stronger associations
of CPs with alternative reinforcers, CPs with alcohol use, and alternative reinforcers with alcohol
use compared to boys. The A path was no longer significant when adjusting for peer substance
use and impulsivity in Model 3 as well as internalizing measures in Model 4. No other
significant sex differences were found.
Table 7 presents simple effect analyses to examine the mediational pathway separately
for males and females. Among males in the unadjusted model, the total effect (β = .72, p <
.0001) and A path (β = -.16, p < .0001) were significant, but the B path (β = -.08, p = .21) and
indirect effect (β = .01, p = .07) were not, indicating that alternative reinforcers did not
significantly mediate the relationship between CPs and alcohol use among males. The B path and
indirect effect were also non-significant in the adjusted model. Among females in both the
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 69
unadjusted and adjusted models, alternative reinforcers significantly mediated the association
between CPs and alcohol use (Indirect effect in unadjusted and adjusted models; β = .06, p <
.0001).
Reverse Mediation
Each stage of the mediational path of the aforementioned analyses was run in the reverse
direction, including the total effect (substance use outcome predicting CPs), A path (Substance
use outcome predicting alternative or complementary reinforcers), and B path (alternative or
complementary reinforcer predicting CPs). Across the any substance and most of the substance-
specific variables, there was evidence of statistically significant reverse mediation whereby
greater reports of substance use (entered as the independent variable) was associated with
diminished levels of alternative reinforcers and increased levels of complementary reinforcers
(entered as the mediator), which was in turn associated with greater levels of CPs (entered as the
dependent variable). Detailed reports parameter estimates from the reverse mediation analyses
are available upon request to the first author (RK).
Discussion
The current study offers cross-sectional evidence implicating behavioral economic
processes as mechanisms underlying the relationship between CPs and adolescent substance use.
As described below, these results largely generalized across alternative and complementary
forms of reinforcement, any substance use outcomes, substance-specific outcomes, and both
males and females. Additionally, results support the utility of behavioral economic frameworks
for informing theoretical models of the etiology of CP-substance use comorbidity and
interventions to prevent substance use among teens with CPs. This study expands on prior
research in several critical ways: (1) the examination of novel mechanisms underlying the
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 70
externalizing-substance use comorbidity in a large, current sample of adolescents and (2) the
addition of complementary reinforcers as a mechanism associated with substance use.
Diminished Alternative Reinforcement as a Mechanism Linking CPs and Substance Use
Prior research suggests that adolescents with a Conduct Disorder diagnosis report lower
levels of arousal and lower autonomic responses to emotionally-valenced stimuli (Herpertz et al.,
2005). The degree of arousal and autonomic reactivity in response to a positively-valenced
emotional stimulus (i.e., reinforcer) putatively reflects of the magnitude of motivational salience
a stimulus is appraised to hold. Thus, teens with higher CPs may find a typical rewarding
stimulus as less salient than teens with fewer CPs and therefore require exposure to more potent
rewarding stimuli in order to derive reinforcement. In other words, teens who engage in CPs
may find most healthy substance-free activities (e.g. volunteering) boring, and therefore, not
benefit from the protective effects of alternative reinforcement on substance use. Consequently,
such adolescents may be motivated to seek alternative methods of obtaining reinforcement with
substance use being a potent reinforcer to the population of teens with these externalizing
tendencies.
It is also possible that adolescents who engage in CPs may be subject to environmental
factors that restrict access to certain alternative reinforcers (e.g., less funds to spend on shopping,
limited after school activities offered in the community), which could explain the relations
demonstrated herein. That is, diminished alternative reinforcement in teens who engage in CPs
may not only reflect a diminished hedonic response when engaging in substance-free activities,
but also limited opportunities for such activities. Although we cannot rule out this explanation,
the current results were robust after statistically adjusting for parental education and other factors
that are likely to pose environmental restrictions on access to alternative reinforcers.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 71
Complementary Reinforcement as a Mechanism Linking CPs and Substance Use
The current study also found evidence that teens with CPs derived more reinforcement
from non-drug rewards if such rewards were experienced concurrently with substance use (i.e.
complementary reinforcers). Some substances have been shown to have reward-enhancing and
social facilitative properties that amplify the reinforcing effects of non-drug rewards experienced
concurrently during substance use (Beck & Treiman, 1996; Caggiula et al., 2009; MacLatchy-
Gaudent & Stewart, 2001; Phillips & Fibiger, 1990; Robbins, 1977; Wall et al., 1998). Thus, in
addition to acting as a primary reward that causes direct psychoactive effects irrespective of
environmental context, some drugs also alter the reinforcing effects of rewarding stimuli that are
present in the environmental context in which substances are consumed. For instance, alcohol
has been shown to be a social lubricant that enhances the degree of social reinforcement derived
from interpersonal activities (MacLatchy-Gaudent & Stewart, 2001; Wall et al., 1998; Read,
Wood, Kahler, Maddock, & Palfai, 2003). Perhaps adolescents whe engage in CPs may be more
apt to benefit from the reward-enhancing effects of substances, given that adolescents who derive
reinforcement from deviant, rule-breaking acts may desire pharmacological enhancement of less
deviant pleasant activities. Accordingly, substance use may be a means for matching the
stimulating properties one derives from their environment when breaking the rules. If
adolescents are able to derive greater reinforcement from their environment when using
substances, and teens who engage in CPs do not experience healthy alternative reinforcers as
rewarding, adolescents may eventually turn to substances as both a primary reward and a reward-
enhancer.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 72
Generalizability of Associations Across Substances
Substance-specific analyses of alcohol, cigarettes, and marijuana mirrored the any
substance use outcomes, whereby, higher levels of CPs were associated with diminished
alternative reinforcement, which was also associated with increased reports of substance use. The
primary exception was that after adjusting for internalizing measures, alternative reinforcers no
longer mediated the relationship between CPs and alcohol use frequency. Thus, it could be that
the etiological role of alternative reinforcers in the relation of alcohol to CPs is impacted by the
presence of comorbid internalizing symptomatology. Certain facets of internalizing
symptomatology (e.g. anhedonia, depressive symptoms) may impair a teen’s ability to engage in
and experience pleasure from healthy activities, which may have impacted this association. At
the same time, the majority of results were robust after adjusting for internalizing symptoms and
various other covariates, suggesting empirical specificity of the pathway involving CPs,
alternative reinforcement, and substance use over and above other known risk pathways.
Substance-specific results did not generalize to mediation analyses involving complementary
reinforcers. Although complementary reinforcers significantly mediated the CP-marijuana use
relationship, there was a non-significant trend in the hypothesized direction for several models
predicting alcohol and cigarette use frequency. Consequently, it is possible that there is some
distinction of the interrelations of CPs, behavioral economic mechanisms, and different
substances. It is also possible that this study lacks sufficient power to test the impact of
complementary reinforcers on specific substances. The largely consistent results in most
statistical models suggest that there may be more consistency across substances than divergence.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 73
Sex Differences in Behavioral Economic Mechanisms Linking CPs and Substance Use
In contrast to previous findings (Maughan et al., 2004; Moffitt, 2001), the current study
did not find significant sex differences in the overall levels and prevalence of CPs and substance
use. The lack of sex differences may be indicative of an emerging trend showing less pronounced
sex differences in adolescent risk behavior (Mahalik et al., 2013). Furthermore, few sex
differences were found in the interrelation of CPs, behavioral economic mechanisms, and
substances. In females only, alternative reinforcers mediated the relationship between CPs and
past six-month reports of alcohol use, raising the possibility that the extent to which alternative
reinforcers serves as a mechanism linking CPs and alcohol use may differ for males and females.
However, the mechanism by which this difference emerges is unclear. It may be that females
engage in activities that offer higher levels of reinforcement or experience activities as more
reinforcing than males. Engagement with these rewarding activities may deter females from
subsequent substance use. However, a more nuanced version of the PES is needed to detect
greater variance in the reward saliency of different activities. Lastly, more sex differences may
emerge later on in development as the rate in which teens engage in other substances besides
alcohol increases. However, more research is needed to understand how sex differences develop
and how increased use may impact sex differences longitudinally.
Limitations, Implications, and Conclusions
The cross-sectional, correlational design precludes definitive inferences regarding
directionality or causality of these findings. In fact, results suggest that reverse mediation was
also significant across a number of analyses, raising the possibility that the causal direction of the
association involves substance use diminishing alternative reinforcement and increaseing
complementary reinforcement, which in turn lead to CPs (Howard et al., 2012; Loeber, Burke,
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 74
Lahey, 2002; Myers, Steward, & Brown, 1998). Repeated substance use has been shown to raise
one’s threshold for reinforcement by diminishing responsiveness of the brain’s reward circuit to
non-drug reinforcers (Hatzgiakoumis, Martinotti, Giannantonio, & Janiri, 2011), which could
result in reduced pleasure experienced from alternative reinforcers (Leventhal et al., 2008).
Moreover, diminished alternative reinforcers may motivate the pursuit of additional non-
substance activities, such as CPs, that provide sufficient thrill to engender reinforcing effects.
Lastly, teens who use substances may have more opportunities for developing new contexts and
activities that may complement substance use. Each of these alternative explanations for the
current findings should be addressed in future prospective longitudinal and experimental
research.
To our knowledge, this is the first investigation of behavioral economic mechanisms
linking CPs and youth substance use. Along with the several study strengths (e.g. adequate
sample size, use of multiple outcomes that reflect different points of the substance use uptake
continuum, demographically diverse sample), limitations must be noted. Participants were
sampled from a restricted geographic region, which raises limitations on generalizability.
Additionally, the CP measure is not a diagnostic tool and does not assess for clinical symptoms
of Conduct Disorder. However, the CP measure utilized assessed for frequency of numerous
clinically significant delinquent behaviors exhibited fairly often in those with and without a
Conduct Disorder diagnosis. Hence, the measure is useful for assessing variation at the low to
moderate end of the externalizing behavior continuum and identifying adolescents engaging in
less deviant behaviors but perhaps at a higher rate than their peers. This is particularly important
as teens in the current study are approximately 14 years old and may not be engaging in some of
the more violent behaviors (e.g. mugging, hurting animals) seen in a Conduct Disorder
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 75
diagnosis. Indeed, Table 1 shows that teens are endorsing relatively low rates of CPs. However,
this is not entirely unexpected given the large sample size and that only 5-10% of teens at this
age will meet criteria for a Conduct Disorder diagnosis (Kessler et al., 2005; Maughan et al.,
2004).
Finally, several limitations regarding the PES should be noted. This measure did not ask
students to report which activities were associated with which particular substance. Thus, a
composite outcome of alternative and complementary reinforcers was used that did not assess for
substance-specific relations. This limited the explanatory specificity of the measure across
specific types of substances, which likely reduced our power to detect results for substance-
specific analysis. Relatedly, an activity from the PES was either categorized as an alternative or
complementary reinforcer and did not capture instances when an activity was only occassionally
associated with substance use. This method created a binary view of the behavioral economic
mechanisms, possibly limiting the generalizability of how these constructs occur in everyday
life. Future research that has adolescents fill out the PES twice, once for substance-related and
once for substance-free associations, may provide a more nuanced understanding of this
mechanism (Murphy et al., 2012a; Murphy et al., 2012b). It is also important to note the greater
magnitude of correlation between complementary reinforcers and substance use compared to
alternative reinforcers and substance use. It is accurate that there is some overlap between
complementary reinforcers and substance use, as a teen must engage in substance use for an
activity to be considered a complementary reinforcer. However, these constructs are not
redundant. Complementary reinforcers refer to activities connected to use, whereas, substance
use refers to the actual use behavior irrespective of the context. This is most likely at least part of
the reason that the correlation between complementary reinforcers and substance use is larger
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 76
than that of alternative reinforcers and substance use.
This study underscores the utility of using behavioral economic perspectives for
understanding the association between CPs and adolescent substance use. These results also
provide important implications for adapting substance use interventions to target adolescents
already engaging in risky CP-related behaviors. Providing teens prone to CPs with interventions
helping them identify and engage in more healthy alternative reinforcers have shown benefits in
substance use prevention and intervention efforts (Murphy et al., 2012a; Murphy et al., 2012b).
Adapting existing interventions for adolescents, such as contingency management (Bigelow &
Silverman, 1999) or Substance Free-Activity Session (Murphy et al., 2012a; Murphy et al.,
2012b) may create a more frequent reinforcing strategy of alternative reinforcers, thereby,
altering the reinforcement schedule and relative reinforcing efficacy of substance use. We
speculate that providing a diverse range of healthy alternative reinforcers that provide a similar
thrill to CPs and substance use (e.g. extreme sports, creative performances in front of large
audiences) may be able to capture adolescents prone to CPs and substance use to prevent
escalation into substance use. Additionally, interventions aimed at teaching adolescents to savor
pleasant experiences may also increase the intensity and duration of subjective reward derived
from the alternative reinforcers (Kahler et al., 2015). These interventions are particularly
important given findings that those who use substances engage in activities that facilitate their
use patterns. Because adolescent-onset substance use often leads to chronic and severe
trajectories of adult addiction with harmful health consequences (Johnston et al., 2014;
Toumbourou et al., 2007), research like this may have broad implications for understanding and
preventing the progression of substance use across adolescence.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 77

References
Audrain-McGovern, J., Rodriguez, D., Rodgers, K., & Cuevas, J. (2010). Declining alternative
reinforcers link depression to young adult smoking. Addiction, 106, 178-187. doi:
10.1111/j.1360-0443.2010.03113.x
Audrain-McGovern, J., Rodriguez, D., Tercyak, K. P., Epstein, L. H., Goldman, P., & Wileyto,
E. P. (2004). Applying a behavioral economic framework to understanding adolescent
smoking. Psychology of Addictive Behaviors, 18, 64-73. doi: 10.1037/0893-164x.18.1.64
Beck, K. H., & Treiman, K. A. (1996). The relationship of social context of drinking, perceived
social norms, and parental influence to various drinking patterns of adolescents. Addictive
Behavirs, 21, 633-644.
Bigelow, G. E., & Silverman, K. (1999). Theoretical and empirical foundations of contingency
management treatments for drug abuse.
Brady, K. T., & Randall, C. L. (1999). Gender differences in substance use disorders. Psychiatric
Clinics of North America, 22, 241-252.
Brady, K. T., & Sinha R. (2014). Co-occuring mental and substance use disorders: The
neurobiological effects of chronic stress. American Journal of Psychiatry, 162, 1483-
1493.
Brown, S. A., Gleghorn, A., Schuckit, M. A., Myers, M. G., & Mott, M. A. (1996). Conduct
disorder among adolescent alcohol and drug abusers. Journal of Studies on Alcohol, 57,
314-324.
Caggiula, A. R., Donny, E. C., Palmatier, M. I., Liu, X., Chaudhri, N., & Sved, A. F. (2009).
Chapter 6: The Role of Nicotine in Smoking: A Dual-Reinforcement Model. Nebraska
Symposium on Motivation. Nebraska Symposium on Motivation, 55, 91-109.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 78
Chan, Y., Dennis, M. L., Funk, R. R. (2008). Prevalence and comorbidity of major
internalizing and externalizing problems among adolescents and adults presenting to
substance abuse treatment. Journal of Substance Abuse Treatmeant, 34, 14-24.
Chorpita, B.F., Yim, L., Moffitt, C., Umemoto, L. A., & Francis, S. E. (2000). Assessment of
symptoms of DSM-IV anxiety and depression in children: a revised child anxiety and
depression scale. Behaviour Research and Therapy, 38, 835-855.
Cohen J., & Cohen P. (1985). Applied Multiple Regression and Correlation Analysis for the
Behavioral Sciences. Vol 2. Mahwah, NJ: Lawrence Erlbaum Associates.
Connor, D. F., Steingard, R. J., Cunningham, J. A., Anderson, J. J., & Melloni Jr., R. H. (2004).
Proactive and reactive aggression in referred children and adolescents. American Journal
of Orthopsychiatry, 74, 129-136.
Correia, C. J., Benson, T. A., & Carey, K. B. (2005). Decreased substance use following
increased in alternative behaviors: A preliminary investigation. Addictive Behaviors, 30,
19-27.
Couwenbergh, C., van den Brink. W., Zwart, K., Vreugdenhil, C., van Wijngaarden-Cremers, P.,
& van der Gaag, R. J. (2006). Comorbid psychopathology in adolescents and young
adults treated for substance use disorders. European Child & Adolescent Psychiatry, 15,
319-328.
Crowley, T. J., Mikulich, S. K., MacDonald, M., Young, S. E, & Zerbe, G. O. (1998).
Substance-dependent, conduct-disordered adolescent males: severity of diagnosis predicts
2-year outcome. Drug and Alcohol Dependence, 49, 225-237.
Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C. (2007).
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 79
Integration of impulsivity and positive mood to predict risky behavior: development and
validation of a measure of positive urgency. Psychological Assessment, 19, 107.
Fergusson, D. M., Horwood, L. J., & Lynskey, M. T. (1993). Prevalence and comorbidity of
DSM-III-R diagnoses in a birth cohort of 15 year olds. Journal of the American Academy
of Child & Adolescent Psychiatry, 32, 1127-1134.
Fergusson, D. M., & Horwood, L. J. (2002). Male and female offending trajectories.
Development and Psychopathology, 14, 159-177.
Finn, J. D., & Rock, D. A. (1997). Academic success among students at risk for school failure.
Journal of Applied Psychology, 82, 221.
Grant, B. F., & Dawson, D. A. (1998). Age of onset of drug use and its association with DSM-IV
drug abuse and dependence: results from the National Longitudinal Alcohol
Epidemiologic Survey. Journal of Substance Abuse, 10, 163-173.
Hatzgiakoumis, D. S., Martinotti, G., Giannantonio, M. D., & Janiri, L. (2011). Anhedonia and
substance dependence: Clinical correlates and treatment options. Frontiers in Psychiatry,
2, 1-12.
Herpertz, S. C., Mueller, B., Qunaibi, M., Lichterfeld, C., Konrad, K., & Herpertz-Dahlmann, B.
(2005). Response to emotional stimuli in boys with conduct disorder. American Journal
of Psychiatry, 162, 1100-1107.
Higgins, S. T., Bickel, W. K., & Hughes, J. R. (1994). Influence of an alternative reinforcer on
human cocaine self-administration. Life Sciences, 55, 179-187.
Howard, R., Finn, P., Jose, P., & Gallagher, J. (2012). Adolescent-onset alcohol abuse
exacerbates the infuence of childhood conduct disorder on late adolescent and early adult
antisocial behaviour. Journal of Forensic Psychiatry & Psychology, 23, 7-22.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 80
Hubbard, A. E., Ahern, J., Fleischer, N. L., Van der Laan, M., Lippman, S. A., Jewell, N., … &
Satariano, W. A. (2010). To GEE or not to GEE: comparing population average and
mixed models for estimating the associations between neighborhood risk factors and
health. Epidemiology, 21, 467-474.
Johnston, L. D., O'Malley, P. M., Miech, R. A., Bachman, J. G., Schulenberg, J. E. (2014).
Monitoring the Future national survey results on drug use: 1975-2013: Overview, key
findings on adolescent drug use. Ann Arbor: Institute for Social Research, The University
of Michigan, 84pp.
Kahler, C. W., Spillane, N. S., Day, A. M., Cioe, P. A., Parks, A., Leventhal, A. M., & Brown,
R. A. (2015). Positive Psychotherapy for Smoking Cessation: A Pilot Randomized
Controlled Trial. Nicotine & Tobacco Research, ntv011.
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005).
Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National
Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593-602.
King, S. M., Iacono, W. G., & McGue, M. (2004). Childhood externalizing and internalizing
psychopathology in the prediction of early substance use. Addiction, 99, 1548-1559.
Krueger R. F., Hicks B. M., Patrick C. J., Carlson S. R., Iacono W. G., & McGue M. (2002).
Etiologic connections among substance dependence, antisocial behavior, and personality:
Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411-424.
Leventhal, A. M., Kahler, C. W., Ray, L. A., Stone, K., Young, D., Chelminski, I., &
Zimmerman, M. (2008). Anhedonia and amotivation in psychiatric outpatients with fully
remitted stimulant use disorder. American Journal on Addictions, 17, 218-223.
Lloyd-Richardson, E. E., Papandonatos, G., Kazura, A., Stanton, C., & Niaura, R. (2002).
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 81
Differentiating stages of smoking intensity among adolescents: stage-specific
psychological and social influences. Journal of Consulting and Clinical Psychology, 70,
998-1009.
Loeber, R., Burke, J. D., & Lahey, B. B. (2002). What are adolescent antecedents to antisocial
personality disorder? Criminal Behaviour and Mental Health, 12, 24-36.
MacKinnon, D. P., Fritz, M. S., Williams, J., & Lockwood, C. M. (2007). Distribution of the
product confidence limits for the indirect effect: Program PRODCLIN. Behavior
Research Methods, 39, 384–389.
MacLatchy-Gaudent H. A., & Stewart S. H. (2001). The context-specific alcohol outcome
expectancies of university women. Addictive Behaviors, 26, 31-49.
MacPhillamy D. J., Lewinsohn P. M. (1976). Manual for the pleasant events schedule: DJ
MacPhillamy & PM Lewinsohn.
Madden, G. J. (2000). A behavioral-economics primer. In. W. K. Bickel & R. Vuchinich (Eds.),
Reframing Health Behavior Change with Behavioral Economics (pp. 3-26). Mahwah, NJ:
Lawrence Erlbaum & Associates.
Mahalik, J. R., Levine Coley, R., McPherran Lombardi, C., Doyle Lynch, A., Markowitz, A. J.,
& Jaffee, S. R. (2013). Changes in health risk behaviors for males and females from early
adolescence through early adulthood. Health psychology, 32(6), 685.
Maslowsky J., & Schulenberg J. E. (2013). Interaction matters: Quantifying conduct problem x
Depressive syptoms interactino and its association with adolescent alcohol, cigarette, and
marijunaa use ine in a national sample. Developmental Psychopathology, 25, 1029-1043.
Maslowsky, J., Schulenberg, J. E., O’Malley, P. M., & Kloska, D. D. (2013). Depressive
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 82
symptoms, conduct problems, and risk for polysubstance use among adolescents: Results
form US national surveys. Mental Health and Substance Use, 7, 157-169.
Maughan, B., Rowe, R., Messer, J., Goodman, R., & Meltzer, H. (2004). Conduct disorder and
oppositional defiant disorder in a national sample: developmental epidemiology. Journal
of Child Psychology and Psychiatry, 45, 609-621.
Moffitt, T. E. (2001). Sex differences in antisocial behaviour: Conduct disorder, delinquency,
and violence in the Dunedin Longitudinal Study. Cambridge University Press.
Murphy, J. G., Correia, C. J., Colby, S. M., & Vuchinich, R. E. (2005). Using behavioral theories
of choice to predict drinking outcomes following a brief intervention. Experimental and
Clinical Psychopharmacology, 13, 93-101.
Murphy J. G., Dennhardt A.A., Skidmore J. R., Borsari B., Barnett N. P., Colby S. M., &
Martins, M. P. (2012a). A randomized controlled trial of a behavioral economic
supplement to brief motivational interventions for college drinking. Journal of
Consulting and Clinical Psychology, 80, 876-886.
Murphy J. G., Skidmore J. R., Dennhardt A. A., Martens M. P., Borsari B, Barnett, N. P., &
Colby, S. M. (2012b). A behavioral economic supplement to brief motivational
interventions for college drinking. Addiction Research & Theory, 20, 456-465.
Myers M. G., Steward D. G., & Brown S. A. (1998). Progression from Conduct Disorder to
Antisocial Peronality Disorder following treatment for adolescent sbustance abuse.
American Journal of Psychiatry, 155, 479-485.
Newberry, A. L., & Duncan, R. D. (2001). Roles of Boredom and Life Goals in Juvenile
Delinquency1. Journal of Applied Social Psychology, 31(3), 527-541.
Pardini D., White H. R., Stouthamer-Loeber M. Early adolescent psychopathology as a predictor
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 83
of alcohol use disorders by young adulthood. Drug Alcohol Depend 2007; 885: 538-549.
Phillips A. G., & Fibiger H. C. (1990). Role of reward and enhancement of conditioned reward
in persistence of responding for cocaine. Behavioural Pharmacology, 1, 269-282.
Prescott, C. A., & Kendler, K. S. (1999). Age at first drink and risk for alcoholism: A noncausal
association. Alcoholism: Clinical and Experimental Research, 23, 101-107.
Read J. P., Wood M. D., Kahler C. W., Maddock J. E., & Palfai T. P. (2003). Examining the
role of drinking motives in college student alcohol use and problems. Psychology of
Addictive Behaviors, 17, 13-23.
Resnick M. D., Bearman P. S., Blum R. W., Bauman K. E., Harris K. M., Jones J, ... & Udry, J.
R. (1997). Protecting adolescents from harm. Findings from the National Longitudinal
Study on Adolescent Health. JAMA, 278, 823-832.
Robbins, T. W. (1977). Reward enhancement by psychomotor stimulant drugs [proceedings].
Neuropharmacology, 16, 529-530.
SAS Institute Inc. (2003). The SAS System for Windows (Version 8.2). Cary, NC: SAS Institute
Inc.
Stewart, E. B. (2008). School structural characteristics, student effort, peer associations, and
parental involvement the influence of school-and individual-level factors on academic
Achievement. Education and Urban Society, 40, 179-204.
Thompson, M. P., Ho, C. H., & Kingree, J. B. (2007). Prospective associations between
delinquency and suicidal behaviors in a nationally representative sample. Journal of
Adolescent Health, 40, 232-237.
Toumbourou, J. W., Stockwell, T., Neighbors, C., Marlatt, G. A., Sturge, J., & Rehm, J. (2007).
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 84
Interventions to reduce harm associated with adolescent substance use. The Lancet, 369,
1391-1401.
Unger, J. B. (2014). Cultural Influences on Substance Use Among Hispanic Adolescents and
Young Adults: Findings From Project RED. Child Development Perspectives, 8, 48-53.
Wall A. M., Hinson R. E., & McKee S. A. (1998). Alcohol outcome expectancies, attitudes
toward drinking, and the theory of planned behavior. Journal of Studies on Alcohol, 59,
409-419.
Whiteside S. P., & Lynam D. R. (2001). The five factor model and impulsivity: Using a
structural model of personality to understand impulsivity. Personality and Individual
Differences, 30, 669-689.
Windle, M. (1990). A longitudinal study of antisocial behaviors in early adolescence as
predictors of late adolescent substance use: gender and ethnic group differences. Journal
of Abnormal Psychology, 99, 86.
Wolitzky-Taylor K., Lyuba B., Zinbarg R. E., Mineka S., & Craske M. G. (2012). Longitudinal
investigation of the impact of anxiety and mood disroders in adolescence on subsequent
substance use disorder onset and vice versa. Addictive Behaviors, 37, 982-985.
Zeger, S. L., Liang, K.-Y., & Albert, P. S. (1988). Models for Longitudinal Data: A Generalized
Estimating Equation Approach. Biometrics, 44, 1049–1060. doi:10.2307/2531734

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 85
Table 1. Sample Characteristics among the overall sample and past six-month substance users stratified by sex.
Note. Data from ninth grade students in Los Angeles, California, USA collected in 2013. Columns on male and female substance users
stratify data by those who used any substance in the last six months.
a
The overall sample between males and females (1,568+1,801 =
3,369) does not add up to the total available N for the overall sample because 27 teens did not report their gender.
b
Sample size is low
Overall Sample Males Females

Available N
(N = 3,383)
a

Overall Sample
(N = 1,568)
Substance Users
(N = 393)
Overall Sample
(N = 1,801)
Substance Users
(N = 493)
Age, M (SD) 3,360 14.6 (0.4) 14.7 (0.5) 14.5 (0.4) 14.6 (0.4)
Ethnicity, % 3,311
Hispanic or Latino -- 43.1% 47.1% 48.8% 56.0%
Not Hispanic/Latino -- 54.5% 53.7% 49.8% 42.2%
Highest parental education, % 2,931
High school graduate or less -- 25.6% 32.6% 26.3% 35.9%
Some college or more -- 60.0% 56.2% 61.7% 54.6%
RCADS- MDD, M (SD) 3,208 6.2 (6.2) 7.3 (6.7) 9.2 (7.4) 11.3 (7.9)
RCADS- GAD Symptoms, M (SD) 3,217 7.1 (4.5) 8.2 (4.7) 9.0 (4.6) 10.0 (4.7)
RCADS- PD Symptoms, M (SD) 3,192 3.4 (4.6) 4.3 (5.5) 5.3 (5.7) 6.7 (6.4)
UPPS-P- Positive Urgency, M (SD) 3,203 3.4 (0.6) 3.2 (0.6) 3.4 (0.6) 3.1 (0.7)
Peer Substance Use, M (SD) 3,329 14.4 (117.5) 5.9 (71.1) 14.9 (119.1) 19.3 (133.7)
Alternative Reinforcers, M (SD) 3,279 69.3 (29.2) 65.9 (31.4) 73.1 (27.5) 65.2 (28.7)
Complementary Reinforcers, M (SD) 520
b
-- 22.2 (27.5) -- 13.9 (18.9)
CPs, M (SD) 3,313 16.2 (6.0) 20.1 (8.3) 15.6 (5.0) 19.0 (7.1)
Past Six-Month Substance Use, %
Any Substance 3,366 25.1% 100% 27.4% 100%
Alcohol 3,255 11.9% 47.6% 16.5% 60.2%
Cigarette 3,353 3.3% 13.0% 3.7% 13.6%
Marijuana 3,336 8.9% 35.6% 9.3% 33.9%
Past 30-Day Substance Use Frequency, M
(SD)

Any Substance 502 -- 0.33 (0.7) -- 0.37 (0.7)
Alcohol 390 -- 0.65 (1.3) -- 0.86 (1.4)
Cigarette 83 -- 0.18 (0.8) -- 0.22 (0.9)
Marijuana 263 -- 0.97 (2.0) -- 0.83 (1.8)
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 86
because an individual had to endorse substance use to endorse at least one complementary reinforcer. CPs = Conduct Problems; CESD
= Center for Epidemiologic Studies of Depression Scale; RCADS = Revised Children’s Anxiety And Depression Scale; MDD =
Major Depressive Disorder; GAD = Generalized Anxiety Disorder; PD = Panic Disorder; UPPS-P = Urgency, Premeditation,
Perseverance, Sensation Seeking, and Positive Urgency. Data on complementary reinforcers and substance use frequency is not given
in the overall sample because it is not possible to engage in activities that complement substance use if the adolescent does not use
substances. Thus, numbers are the same across both columns given.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 87
Table 2. Correlation matrix of all study variables.

1 2 3 4 5 6 7 8 9 10 11 12 13
14 15 16 17 18 19
1. CP 1.00 -.27 .41 N/A .16 .27 .36 .55 .39 .31 .49 -.02 .00 .00 -.02 .06 .15 .10 .16
2. Alternative Reinforcers -.19 1.00 -.45 N/A -.11 -.18 -.28 -.36 -.24 -.22 -.37 -.02 -.02 .02 -.01 -.05 -.09 -.03 -.09
3. Complementary Reinforcers .42 -.45 1.00 N/A .13 .25 .35 .41 .21 .19 .52 -.02 .00 .03 -.06 .04 .06 .03 .08
4. Past 6-month Any Substance Use .39 -.13 .24 1.00 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
5. Past 6-month Alcohol Use .37 -.13 .26 .69 1.00 .10 .13 .28 .40 .13 .16 -.04 -.06 -.05 .04 -.01 .08 .06 .08
6. Past 6-month Cigarette Use .31 -.13 .29 .32 .29 1.00 .24 .34 .22 .45 .24 .02 .01 .05 .02 .02 .10 .06 .10
7. Past 6-month Marijuana Use .43 -.20 .40 .53 .44 .36 1.00 .45 .26 .21 .60 -.01 .05 -.03 -.06 .06 .05 .02 .07
8. Past 30 Day Any Use Frequency .52 -.32 .41 .25 .37 .37 .49 1.00 .76 .67 .76 -.03 -.02 .00 -.02 .01 .16 .06 .11
9. Past 30 Day Alcohol Frequency
.43 -.18 .28 .41 .56 .32 .42 .78 1.00 .38 .45 -.04 -.04
.02 -.01 .04 .14 .03 .10
10. Past 30 Day Cigarette Frequency
.30 -.14 .21 .19 .22 .48 .38 .66 .41 1.00 .31 -.02 -.03
.01 -.01 -.01 .16 .10 .16
11. Past 30 Day Marijuana Frequency .48 -.24 .53 .36 .36 .33 .66 .77 .53 .35 1.00 -.03 .01 .02 -.03 .03 .05 .02 .01
12. Sex -.02 .02 -.01 .02 -.02 .02 .00 -.02 -.01 -.01 -.01 1.00 .22 .05 -.01 .04 -.26 -.05 -.16
13. Ethnicity .03 -.01 .03 .01 -.03 .01 .03 -.03 -.02 -.02 .01 .47 1.00 .22 -.02 .07 -.02 -.02 -.04
14. Parental Education -.08 .01 -.05 -.11 -.10 -.01 -.07 .02 -.07 .02 -.05 .04 .23 1.00 .01 .05 -.08 -.08 -.08
15. Peer Substance Use -.01 -.01 -.02 -.01 -.03 .01 -.03 -.03 .00 -.01 -.02 .44 .25 -.09 1.00 .00 .00 .02 -.03
16. Impulsivity .05 -.03 .07 .06 .03 .03 .06 .03 .05 .01 .05 .19 .15 .05 .13 1.00 -.02 .02 -.02
17. MDD .21 -.10 .08 .13 .15 .10 .10 .15 .14 .12 .07 -.20 .01 -.04 -.03 .00 1.00 .43 .51
18. GAD .16 -.01 .08 .13 .07 .08 .08 .07 .07 .08 .05 -.05 -.01 -.04 -.01 -.02 .42 1.00 .37
19. PD
.19 -.07 .09 .13 .11 .09 .10 .11 .11 .12 .06 -.12 .00 -.05 -.01 -.01 .49 .37
1.00
Note. All coefficients are Pearson correlations except the correlation between Parental Education and all other variables are Spearman correlations,
as Parental Education is an ordinal variable. Values above the diagonal represent correlations between study variables among those who reported past
six-month any substance use (N = 890). Values below the diagonal represent correlations between study variables among the overall sample (N =
3,383). Shaded cells note correlations that were not statistically significant at p < .05. MDD = Major Depressive Disorder; GAD = Generalized
Anxiety Disorder; PD = Panic Disorder. N/A = Not Applicable.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 88

Table 3. Association of Conduct Problems to Substance Use-Related Outcomes and Mediation by Alternative and Complementary Reinforcement
Total Effect Component Paths Mediation:
CP à Mediator à Outcome
CP à Outcome CP à Mediator Mediator à
Outcome
Controlling for CP
Indirect effect Direct effect
Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI)
Outcome: Past Six-Month Use Status; Mediator: Alternative Reinforcers (Overall Sample; N = 3,202)
Model 1 (Unadjusted) .96 (.81, 1.10)† -.18 (-.21, -.15)† -.13 (-.22, -.03)** .02 (.001, .05)** .95 (.81, 1.09)†
Model 2 (Adjusted)
a
.96 (.82, 1.11)† -.17 (-.21, -.14)† -.14 (-.24, -.04)** .02 (.002, .05)** .95 (.82, 1.09)†
Model 3 (Adjusted)
b
.69 (.58, .80)† -.14 (-.17, -.11)† -.11 (-.21, -.01)* .02 (.002, .03)* .68 (.57, .79)†
Model 4 (Adjusted)
c
.68 (.56, .80)† -.13 (-.16, -.09)† -.12 (-.23, -.02)* .02 (.002, .03)* .67 (.55, .78)†
Outcome: Frequency Use; Mediator: Complementary Reinforcers (Past Six-Month Substance Users; N = 340)
Model 1 (Unadjusted) .44 (.34, .53)† .41 (.21, .60)† .18 (.08, .28)*** .08 (.002, .17)*** .36 (.23, .48)†
Model 2 (Adjusted)
a
.44 (.35, .53)† .42 (.22, .61)† .20 (.09, .30)*** .08 (.002, .17)*** .36 (.25, .47)†
Model 3 (Adjusted)
b
.34 (.24, .43)† .37 (.17, .56)† .18 (.08, .29)*** .07 (.01, .15)** .27 (.14, .41)†
Model 4 (Adjusted)
c
.34 (.26, .42)† .37 (.16, .59)† .19 (.14, .24)† .07 (.003, .15)*** .27 (.19, .34)†
Outcome: Frequency Use; Mediator: Alternative Reinforcers (Past Six-Month Substance Users; N = 833)
Model 1 (Unadjusted) .52 (.46, .58)† -.28 (-.32, -.23)† -.46 (-.60, -.31)† .13 (.05, .22)† .39 (.33, .46)†
Model 2 (Adjusted)
a
.53 (.46, .60)† -.25 (-.32, -.19)† -.44 (-.59, -.29)† .11 (.03, .22)† .41 (.33, .48)†
Model 3 (Adjusted)
b
.38 (.31, .46)† -.19 (-.28, -.11)† -.38 (-.54, -.24)† .07 (.01, .18)† .30 (.23, .38)†
Model 4 (Adjusted)
c
.41 (.35, .46)† -.18 (-.26, -.10)† -.35 (-.49, -.22)† .06 (.004, .16)† .33 (.26, .40)†
Note. B (95%CI) = Parameter estimate for predictor from Generalized Estimating Equation with 95% confidence interval. CP = Conduct
Problem.
a
Adjusted model is adjusted for demographic covariates, including ethnicity, sex, and parental education level.
b
Adjusted

model is
adjusted for peer substance use and positive urgency in addition to demographic covariates tested in
a
Adjusted.
c
Adjusted

model is adjusted
for depression, anxiety, and panic symptoms in addition to covariates tested in
a
Adjusted

and
b
Adjusted. * p < .05, ** p < .01, *** p < .001,
† p < .0001.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 89

Table 4. Association of Conduct Problems to Alcohol Use-Related Outcomes and Mediation by Alternative and Complementary
Reinforcement
Total Effect Component Paths Mediation:
CP à Mediator à Outcome
CP à Outcome CP à Mediator Mediator à
Outcome
Controlling for CP
Indirect effect Direct effect
Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI)
Outcome: Past Six-Month Use Status; Mediator: Alternative Reinforcers (Overall Sample; N = 3,104)
Model 1 (Unadjusted) .80 (.68, .91)† -.18 (-.21, -.15)† -.19 (-.25, -.13)† .03 (.01, .06)† .77 (.65, .89)†
Model 2 (Adjusted)
a
.84 (.74, .94)† -.17 (-.20, -.14)† -.22 (-.30, -.14)† .04 (.01, .07)† .81 (.71, .91)†
Model 3 (Adjusted)
b
.56 (.46, .65)† -.14 (-.17, -.11)† -.18 (-.29, -.08)*** .03 (.01, .05)** .53 (.43, .63)†
Model 4 (Adjusted)
c
.54 (.43, .65)† -.12 (-.15, -.09)† -.19 (-.30, -.08)*** .02 (.001, .05)*** .52 (.40, .64)†
Outcome: Alcohol Frequency Use; Mediator: Complementary Reinforcers (Past Six-Month Alcohol Substance Users; N = 338)
Model 1 (Unadjusted) .53 (.28, .88)† .41 (.21, .60)† .19 (-.08, .46) .08 (-.03, .21) .45 (.19, .72)***
Model 2 (Adjusted)
a
.61 (.34, .88)† .40 (.21, .60)† .24 (-.05, .52) .10 (-.02, .24) .51 (.23, .80)***
Model 3 (Adjusted)
b
.41 (.14, .68)** .34 (.15, .53)*** .15 (-.12, .43) .05 (-.04, .17) .36 (.08, .65)*
Model 4 (Adjusted)
c
.44 (.18, .71)** .33 (.14, .55)** .16 (-.11, .42) .05 (-.04, .17) .39 (.11, .67)**
Outcome: Alcohol Frequency Use; Mediator: Alternative Reinforcers (Past Six-Month Alcohol Substance Users; N = 826)
Model 1 (Unadjusted) .73 (.56, .90)† -.27 (-.31, -.23)† -.24 (-.39, -.08)** .06 (.01, .12)** .66 (.48, .84)†
Model 2 (Adjusted)
a
.77 (.59, .96)† -.25 (-.31, -.19)† -.28 (.42, -.14)** .06 (.01, .12)** .72 (.56, .88)†
Model 3 (Adjusted)
b
.55 (.36, .74)† -.19 (-.28, -.11)† -.16 (-.32, -.001)* .03 (.000, .07)* .51 (.31, .71)†
Model 4 (Adjusted)
c
.56 (.38, .74)† -.18 (-.26, -.09)† -.16 (-.32, .01) .03 (-.001, .06) .53 (.34, .72)†
Note. B (95%CI) = Parameter estimate for predictor from Generalized Estimating Equation with 95% confidence interval. CP = Conduct
Problem.
a
Adjusted model is adjusted for demographic covariates, including ethnicity, sex, and parental education level.
b
Adjusted

model is
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 90
adjusted for peer substance use and positive urgency in addition to demographic covariates tested in
a
Adjusted.
c
Adjusted

model is adjusted for
depression, anxiety, and panic symptoms in addition to covariates tested in
a
Adjusted

and
b
Adjusted. * p < .05, ** p < .01, *** p < .001, † p < .0001.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 91

Table 5. Association of Conduct Problems to Cigarette Use-Related Outcomes and Mediation by Alternative and Complementary
Reinforcement
Total Effect Component Paths Mediation:
CP à Mediator à Outcome
CP à Outcome CP à Mediator Mediator à
Outcome
Controlling for CP
Indirect effect Direct effect
Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI)
Outcome: Past Six-Month Use Status; Mediator: Alternative Reinforcers (Overall Sample; N = 3,193)
Model 1 (Unadjusted) .74 (.57, .91)† -.18 (-.21, -.15)† -.42 (-.57, -.26)† .07 (.02, .14)† .66 (.50, .82)†
Model 2 (Adjusted)
a
.75 (.58, .92)† -.17 (-.20, -.14)† -.42 (-.57, -.26)† .07 (.02, 149)† .68 (.51, .84)†
Model 3 (Adjusted)
b
.46 (.29, .64)† -.13 (-.16, -.10)† -.36 (-.52, -.21)† .05 (.01, .10)† .40 (.23, .57)†
Model 4 (Adjusted)
c
.44 (.26, .61)† -.11 (-.15, -.08)† -.36 (-.52, -.19)† .04 (.003, .10)† .38 (.21, .55)†
Outcome: Cigarette Frequency Use; Mediator Complementary Reinforcers (Past Six-Month Cigarette Substance Users; N = 337)
Model 1 (Unadjusted) .54 (.30, .79)† .41 (.21, .61)† .29 (.05, .54)* .12 (.02, .25)* .42 (.14, .70)**
Model 2 (Adjusted)
a
.58 (.32, .84)† .41 (.21, .60)† .30 (.05, .55)* .12 (.02, .26)* .45 (.17, .74)**
Model 3 (Adjusted)
b
.35 (.04, .66)* .34 (.16, .53)*** .26 (-.02, .54) .09 (-.01, .22) .25 (-.11, .60)
Model 4 (Adjusted)
c
.36 (.04, .68)* .35 (.14, .55)*** .24 (-.08, .56)* .08 (-.03, .23) .26 (-.11, .63)
Outcome: Cigarette Frequency Use; Mediator Alternative Reinforcers (Past Six-Month Cigarette Substance Users; N = 827)
Model 1 (Unadjusted) .65 (.44, .82)† -.25 (-.32, -.19)† -.64 (-.98, -.30)*** .18 (.02, .35)*** .48 (.26, .72)†
Model 2 (Adjusted)
a
.65 (.45, .84)† -.25 (-.32, -.19)† -.65 (-1.00, -.30)*** .16 (.05, .31)** .48 (.26, .70)†
Model 3 (Adjusted)
b
.36 (.12, .60)** -.19 (-.28, -.11)† -.54 (-.87, -.21)** .11 (.02, .23)** .25 (.01, .49)*
Model 4 (Adjusted)
c
.37 (.13, .61)** -.18 (-.26, -.09)† -.54 (-.87, -.20)** .09 (.01, .21)** .26 (.02, .50)*
Note. B (95%CI) = Parameter estimate for predictor from Generalized Estimating Equation with 95% confidence interval. CP = Conduct
Problem.
a
Adjusted model is adjusted for demographic covariates, including ethnicity, sex, and parental education level.
b
Adjusted

model is
adjusted for peer substance use and positive urgency in addition to demographic covariates tested in
a
Adjusted.
c
Adjusted

model is adjusted
for depression, anxiety, and panic symptoms in addition to covariates tested in
a
Adjusted

and
b
Adjusted. * p < .05, ** p < .01, *** p < .001,
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 92

Table 6. Association of Conduct Problems to Marijuana Use-Related Outcomes and Mediation by Alternative and Complementary
Reinforcement
Total Effect Component Paths Mediation:
CP à Mediator à Outcome
CP à Outcome CP à Mediator Mediator à
Outcome
Controlling for CP
Indirect effect Direct effect
Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI)
Outcome: Past Six-Month Use Status; Mediator: Alternative Reinforcers (Overall Sample; N = 3,179)
Model 1 (Unadjusted) 1.00 (.85, 1.14)† -.18 (-.21, -.15)† -.41 (-.49, -.33)† .07 (.04, .11)† .86 (.76, .97)†
Model 2 (Adjusted)
a
.99 (.84, 1.13)† -.17 (-.20, -.14)† -.43 (-.51, -.35)† .07 (.04, .12)† .88 (.78, .98)†
Model 3 (Adjusted)
b
.68 (.53, .84)† -.13 (-.17, -.10)† -.40 (-.50, -.30)† .05 (.02, .10)† .61 (.48, .74)†
Model 4 (Adjusted)
c
.67 (.52, .83)† -.12 (-.15, -.09)† -.41 (-.52, -.30)† .05 (.02, .09)† .60 (.47, .73)†
Outcome: Marijuana Frequency Use; Mediator Complementary Reinforcers (Past Six-Month Marijuana Substance Users; N = 335)
Model 1 (Unadjusted) .80 (.47, 1.13)† .41 (.21, .61)† 1.22 (.40, 2.04)** .50 (.05, 1.17)** .48 (.15, .80)**
Model 2 (Adjusted)
a
.77 (.44, 1.10)† .41 (.21, .61)† 1.28 (.42, 2.13)** .52 (.09, 1.21)** .44 (.11, .77)**
Model 3 (Adjusted)
b
.54 (.22, .87)** .34 (.23, .45)† 1.15 (.27, 2.03)* .39 (.09, .71)* .30 (-.03, .64)
Model 4 (Adjusted)
c
.62 (.29, .94)*** .34 (.13, .55)** 1.21 (.24, 2.17)* .41 (.06, .90)* .37 (.05, .69)*
Outcome: Marijuana Frequency Use; Mediator Alternative Reinforcers (Past Six-Month Marijuana Substance Users; N = 825)
Model 1 (Unadjusted) .97 (.75, 1.20)† -.28 (-.32, -.23)† -.63 (-.82, -.44)† .18 (.07, .31)† .84 (.60, 1.07)†
Model 2 (Adjusted)
a
.94 (.71, 1.17)† -.25 (-.32, -.19)† -.61 (-.80, -.42)† .16 (.05, .31)† .83 (.66, 1.07)†
Model 3 (Adjusted)
b
.68 (.45, .92)† -.19 (-.27, -.12)† -.55 (-.85, -.35)† .11 (.02, .24)† .59 (.35, .84)†
Model 4 (Adjusted)
c
.72 (.49, .95)† -.18 (-.26, -.09)† -.56 (-.86, -.36)† .10 (.01, .25)† .64 (.40, .88)†
Note. B (95%CI) = Parameter estimate for predictor from Generalized Estimating Equation with 95% confidence interval. CP = Conduct
Problem.
a
Adjusted model is adjusted for demographic covariates, including ethnicity, sex, and parental education level.
b
Adjusted

model is
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 93

adjusted for peer substance use and positive urgency in addition to demographic covariates tested in
a
Adjusted.
c
Adjusted

model is adjusted
for depression, anxiety, and panic symptoms in addition to covariates tested in
a
Adjusted

and
b
Adjusted. * p < .05, ** p < .01, *** p < .001,
† p < .0001.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 94
Table 7. Association of Conduct Problems to Past Six-Month Alcohol Use and Mediation by Alternative Reinforcement among Males
and Females
Total Effect Component Paths Mediation:
Substance Use à Mediator à Outcome
Substance Use à
Outcome
Substance Use à
Mediator
Mediator à
Outcome
Controlling for
Substance Use
Indirect effect Direct effect
Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI) Β (95% CI)
Outcome: Past Six-Month Use Status among Males; Mediator: Alternative Reinforcers (Overall Sample; N = 1,415)
Model 1 (Unadjusted) .72 (.57, .87)† -.16 (-.20, -.11)† -.08 (-.20, .04) .01 (-.01, .03) .71 (.55, .86)†
Model 2 (Adjusted)
a
.71 (.57, .86)† -.15 (-.20, -.10)† -.09 (-.22, .04) .01 (-.01, .04) .70 (.55, .85)†
Outcome: Past Six-Month Use Status among Females; Mediator: Alternative Reinforcers (Overall Sample; N = 1,682)
Model 1 (Unadjusted) 1.00 (.92, 1.09)† -.20 (-.24, -.16)† -.30 (-.41, -.19)† .06 (.02, .12)† .95 (.86, 1.04)†
Model 2 (Adjusted)
a
1.02 (.93, 1.11)† -.19 (-.23, -.16)† -.31 (-.43, -.19)† .06 (.01, .12)† .98 (.88, 1.07)†
Note. B (95%CI) = Parameter estimate for predictor from Generalized Estimating Equation with 95% confidence interval. CP = Conduct
Problem.
a
Adjusted model is adjusted for demographic covariates, including ethnicity, sex, and parental education level. * p < .05, ** p <
.01, *** p < .001, † p < .0001.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 95

Longitudinal Examination of Alternative and Complementary Reinforcers Linking
Conduct Problems and Substance Use across Adolescence

Rubin Khoddam, M.A., Junhan Cho, Ph.D.

, & Adam M. Leventhal, Ph.D.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 96
Abstract
The current study hypothesized that teens who report more conduct problems at Wave 1
would engage in fewer alternatively reinforcing activities (i.e. substance-free activities) and
increased complementary reinforcing activities (i.e. substance-associated activities) at Wave 2,
which in turn, would be associated with increases in substance use at Wave 3. To examine this
mediational hypothesis, 3,396 high school students in Los Angeles, CA were administered three
annual surveys. A binary past six-month use and an ordinal past 30-day use variable were used
for each substance use outcome (i.e. alcohol, marijuana, cigarettes, and a composite any
substance use variable). Results indicated that conduct problems at Wave 1 were significantly
associated with past six-months (β=.23, p < .0001) and past 30-days (β=.28, p < .0001) any
substance use outcomes at Wave 3 after adjusting for relevant covariates. The association of
conduct problems with any substance use was significantly mediated by alternative
reinforcement at Wave 2 for only past six-month use (indirect effect, β= .01, p < .01). When
examining substance-specific associations, the indirect association of conduct problems through
alternative reinforcement was significant only for marijuana use in past six-months (β=.02, p <
.0001) and past 30-days (β=.02, p < .0001). Complementary reinforcers significantly mediated
the association between conduct problems and alcohol use (β=.02, p < .05), but no other
outcome. Overall, tailoring prevention efforts that provide greater access to alternative activities
and teach teens to derive greater levels of pleasure from such activities may thwart the
development and progression of substance use later in adolescence.
Keywords: Conduct Problems; Alternative Reinforcers; Behavioral Economics;
Adolescents; Substance Use
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 97
Introduction
Behavioral economic theory provides a useful framework for understanding the etiology
and progression of adolescent substance use progression. It recognizes that one’s preference for
substance use arises within a larger context that involves the availability or utilization of (1)
alternative substance-free reinforcers (e.g. school organizations, dating) and (2) complementary
reinforcing activities that occur in conjunction with substance use (e.g. stimulants with sports). In
particular, alternative reinforcement is one commonly examined construct used to identify the
extent to which teens engage in and derive pleasure out of substance-free activities. Specifically,
an alternative reinforcer is any activity (e.g. volunteering, working on art projects) used as a
substitute for substance use (Audrain-McGovern et al., 2004). A growing body of literature has
examined how a lack of alternative reinforcers is associated with increased substance use across
adolescence (Audrain-McGovern et al., 2004; Audrain-McGovern, Rodriguez, Rodgers, &
Cuevas, 2010; Correia, Benson, & Carey, 2005; Khoddam & Leventhal, 2016). Comparatively,
complementary reinforcers tends to be less well-examined. Activities, such as sports, parties, and
dancing, are risky group activities that can involve older peers with access to substances. Such
activities may then become associated with substance use after repeated use. However, research
is fairly limited in its understanding of how these behavioral economic mechanisms may act as a
mediator between early substance use risk factors and substance use itself.
One particularly salient substance use risk factor is conduct problems (CPs; e.g. lying,
stealing, getting into fights). CPs reflect a range of behaviors that have been consistently
associated with substance use (Brown et al. 1996; Connor, Steingard, Cunningham, Anderson, &
Melloni, 2004; Couwenbergh et al., 2006; Khoddam, Jackson, & Leventhal, 2016; King, Iacono,
& McGue, 2004; Maslowsky & Schulenberg, 2013). Assessing a range of CPs and frequency of
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 98
such behaviors provides greater variability and a more nuanced understanding of how these
behaviors are related to subsequent substance use. Given that the occurrence of CPs and
substance use in non-clinical populations early in adolescence are risk factors for substance use
disorders and adult antisocial personality disorder (Grant & Dawson, 1998; Howard, Finn, Jose,
Gallagher, 2012), it is critical to test mechanisms that underlie the relation between adolescent
CPs and substance use uptake. This temporal relationship is particularly important given that
research has found that CPs tend to precede substance use initiation (Fergusson & Horwood,
1998; Fergusson, Horwood, & Ridder, 2007). Examining mechanisms (i.e. alternative and
complementary reinforcers) linking CPs and substance use will inform etiological models of
addiction and perhaps provide new pathways to prevent and intervene on early risk factors.
Mediational models have shown that alternative and complementary reinforcers link CPs
with substance use using cross-sectional data (Khoddam & Leventhal, 2016). To date, only one
prior study has examined this relationship. Results supported the hypothezsized model with
adolescents who reported higher levels of CPs engaging in fewer alternatively reinforcing
activities and higher levels of complementary reinforcing activities and these, in turn, being
associated with higher levels of reported substance use. However, this study was limited in its
ability to examine the longitudinal nature of the association between these important variables
and thus directionality of the association as well as how the relationship may change over
development remains unclear.
Longitudinal studies are especially important when examining statistical mediation
during the transition to high school for several reasons. First, this is a salient developmental
period in which teens enter a new social atmosphere and are exposed to older teens. These older
peers may engage in more delinquent behaviors that are consistent with life-course persistent
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 99
antisocial behavior and report higher levels of substance use (Moffitt, 1993). Second, students
begin to have access to more organizations and clubs when entering high school. These
organizations are predictive of academic resilience and may serve as alternative reinforcers (Finn
& Rock, 1997; Stewart, 2008). Third, the mediational role of alternative and complementary
reinforcers may change across high school when polysubstance use is common (Moss, Chen, Yi,
2014). Hence, examining the longitudinal nature of how alternative and complementary
reinforcement link CPs and substance use across high school is crucial given the changing nature
of adolescent behaviors and the risk associated with early substance use in high school (Grant et
al., 2005; Pedersen & Skrondal, 1998).
Recent research has also emphasized the importance of examining sex differences in the
association between CPs, behavioral economic mechanisms, and substance use. Although few
sex differences emerged in the aforementioned cross-sectional study (Khoddam & Leventhal,
2016), it may be that differences emerge as physical, emotional, and neurological maturation
occurs throughout high school. Examining manifestations of such differences early in
development is essential, as males are more likely to be characterized as heavy drinkers (Grant,
1998; Johnston, O’Malley, Bachman, & Schulenberg, 2011; McMahon et al., 1994) and tend to
have an earlier age of onset (Faden, 2006). Aggressive males, in particular, use alcohol and other
substances more frequently (Kellam, Brown, & Fleming, 1982). Given sex differences in CPs
and substance use, research should test for possible sex differences relating to how behavioral
economic mechanisms link CPs and substance use.
Examining whether students who report more behavioral problems also engage in fewer
alternative activities and students who engage in fewer alternative activities report more
substance use is critical for targeting prevention and intervention efforts. Behavioral economic
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 100
interventions that have young adults engage in healthy prosocial activities have been shown to be
effective at reducing drinking (Correia et al., 2005; Murphy et al., 2012a; Murphy et al., 2012b).
Thus, it may be possible to utilize these existing interventions and adapt them for adolescents in
order to (1) create greater access to alternative activities and (2) help them derive more pleasure
from such activities. If alternative reinforcement significantly mediates the association between
CPs and substance use, it would indicate that such interventions can potentially be applied to at-
risk adolescents who exhibit high levels of behavioral problems but who have yet to engage in
high levels of substance use. By providing teens with multiple pathways towards reinforcement
outside substance use behaviors, it may thwart the development of substance using behaviors and
prevent uptake.
The current study is the first study to test the role of alternative and complementary
reinforcers longitudinally as a critical risk factor linking CPs and adolescent substance use across
three waves of data. The study is unique in several ways. Specifically, alternative and
complementary reinforcers have yet to be examined (1) longitudinally during the transition to
high school (2) while also simultaneously examining a variety of substance use outcomes (i.e.
alcohol, marijuana, cigarettes) and (3) with other forms of psychopathology across multiple
waves of data (i.e. conduct problems). We hypothesize that teens who report more CPs at Wave
1 (baseline) will report fewer alternatively reinforcing activities and more complementary
reinforcing activities at Wave 2 (i.e. 12-month follow-up) and these will, in turn, be associated
with greater reports of substance use at Wave 3 (i.e. 24-month follow-up). We will examine this
relationship across four primary outcomes: alcohol, marijuana, cigarette, and a composite any
substance use variable. Additionally, we will examine whether the mediational relationships
differ across sex.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 101
Methods
Participants and Procedures
Data from the Happiness and Health study was utilized. The Happiness and Health study
is a longitudinal survey of substance use and mental health among students from 10 public high
schools in the Greater Los Angeles area. Schools were selected based on their adequate
representation of demographic characteristics; the percent of students eligible for free lunch (i.e.,
student’s parental income < 185% of the national poverty level) across the participating schools
was 31.1% (SD=19.7, range 8.0% - 68.2%). Students who were not enrolled in special education
(e.g. severe learning disabilities) or English as a Second Language Programs (N=4,100) were
eligible. Of the 4,100 eligible students, 3,874 (94.5%) assented to participate in the study, of
whom 3,396 (82.8%) provided active written parental consent. Data collection involved 3 annual
assessments: Wave 1 (baseline; 9
th
grade, fall 2013, N = 3,383), Wave 2 (12-month follow-up;
10
th
grade, fall 2014, number of students surveyed = 3,283), and Wave 3 (24-month follow-up;
11
th
grade, fall 2015, number of students surveyed = 3,235). Paper-and-pencil surveys were
administered at each wave in the students’ classrooms. Students were offered small incentives
(e.g. key chains, pens) on these days for participating in the study. Students who were absent the
day of data collection completed telephone, postal mail, or online surveys. These students were
offered $10 gift cards to two retailers for completing the survey outside of school. Each
participating school was compensated for their general activity fund. The University of Southern
California Institutional Review Board approved this study.
Measures
Conduct problems. An 11-item conduct problems (CP) measure was used at baseline to
assess past six-month behavior (e.g., stealing, stealing an item worth more than $50, destroying
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 102
property, lying to parents, running away, physically fighting, skipping school, being suspended,
selling illegal drugs, driving a car without permission, getting in trouble with police; Lloyd-
Richardson et al., 2002; Resnick et al., 1997; Thompson, Ho, & Kingree, 2007). The Cronbach α
was .79. The frequency of each behavior was assessed using six ordinal response options varying
from 1 (never) to 6 (10 or more times in the past six-months) and a weighted sum score was
computed across the 11 items. A weighted sum score is ideal given both a mean and a traditional
sum score would include individuals with missing data on items. If a participant responded to at
least six of the 11-item measure, a weighted score was created by calculating the mean frequency
rating (1 to 6) for the number of items answered and then multiplied by the total number of items
(i.e. 11 items). The weighted sum score was then log transformed to account for the skewed
distribution.
Past Six-Month and Past 30 Day Substance Use. Wave 1 and Wave 3 substance use
variables were assessed using standard validated items used in epidemiologic surveys of
adolescents (Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2014). For past six-month
use (yes/no) at baseline and follow-up, students were asked whether they had used any of the
substances: few puffs of a cigarette, whole cigarette, marijuana, blunts, one full drink of alcohol,
stimulants, prescription pain killers, and prescription stimulant pills.
For substance specific analyses on each substance use outcome, a binary past six-month
cigarette, alcohol, and marijuana use variable was used (0 = No, 1 = Yes). The cigarette use
variable included those who smoked just a few puffs of a cigarette and those who smoked a
whole cigarette. The alcohol use variable included those who reported consuming one full drink
of alcohol. The combined marijuana use category variable included those who smoked blunts in
addition to marijuana. Also, a latent construct of multi-substance use variables was created using
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 103
past six-month use of alcohol, marijuana, cigarette, stimulants, prescription stimulants, and
prescription painkillers. Confirmatory factor analysis of the six indicators of multi-substance use
indicated a single factor with loadings ranging from .738 to .946.
At Wave 1 and Wave 3, adolescents reported past 30-day frequency of six substances
(i.e. alcohol, cigarettes, marijuana, stimulants, prescription stimulants, and prescription
painkillers). The response set ranged from 0 (0 days) to 8 (All 30 days). To assess multi-
substance use in the past 30 days, a latent construct was created using these six substance uses.
Confirmatory factor analysis of the six substance use indicators yielded a single factor with
loadings ranging from .534 to .985.
Alternative and Complementary Reinforcement. At Waves 1 and 2, we utilized a
modified version of the Pleasant Events Schedule (PES; MacPhillamy & Lewinsohn, 1976) for
youths as in prior work (Audrain-McGovern et al., 2010). Participants rated 44 different typically
pleasant activities (e.g., going out to eat at a restaurant, playing musical instruments,
visiting/hanging out with friends, participating in clubs or community organizations, playing
sports) for both frequency of engagement (0=Never; 1=1-6 times; 2=7 or more times) and
pleasure experienced (0=not pleasurable; 1=somewhat pleasurable; 2=very pleasurable) in the
past 30 days. Additionally, participants were asked to indicate (yes/no) whether they associated
the pleasant activity with alcohol, smoking, or drug use (Madden, 2000). Consistent with prior
methods of measuring alternative reinforcement, the primary outcome is the sum of each item’s
product (engagement frequency × pleasure) for activities marked as not associated with
substance use (Murphy, Correia, Colby, & Vunchinch, 2005). The complementary reinforcement
outcome is the same product for activities that are marked as being associated with substance
use.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 104
A sum score using the product of alternative and complementary reinforcers was used in
the analyses. To prevent biased underestimated scores for those with missing responses,
individual weighted sum scores were calculated and imputed. If questions related to a particular
activity were not answered, a weighted sum score was calculated based on the proportion of
complementary and alternative reinforcers the adolescent reported engaging in. This imputation
was calculated using a similar method described in the CP measure section above and in a prior
paper (Khoddam & Leventhal, 2016). Once the weighted sum score was calculated, the number
was log transformed.
Covariates. Demographic factors (i.e. gender, parental education, and ethnicity),
positive urgency, peer substance use, and internalizing symptoms were included as covariates. In
addition to the above covariates, baseline levels of substance use and alternative reinforcers were
also included in each model.
Gender was coded dichotomously (0 = Female, 1 = Male). Parental education was coded
as 0 (parents who have at least some college education) versus 1 (those whose parents had a high
school degree or less). The highest education level across the two parents was used in analyses.
The 26-item Positive Urgency subscale of the UPPS-P Impulsive Behavior Scale was also used
as a covariate. This subscale measures the tendency towards rash, impulsive action in response to
positive affect and has been implicated in the etiology of substance use and externalizing
behaviors; α in current sample = .95 (Cyders et al., 2007; Whiteside & Lynam, 2001).
Additionally, a composite peer substance use variable was created for alcohol, cigarettes,
marijuana, stimulant, prescription stimulant, and prescription painkillers. Participants answered
how many of their five closest friends have used each substance (each scored 0 to 5). The mean
across each substance was used in this model.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 105
Internalizing symptoms were measured using the Revised Children’s Anxiety and
Depression Scale (RCADS; Chorpita, Yim, Moffitt, Umemoto, & Francis, 2000). Specifically,
we utilized the Major Depressive Disorder, Generalized Anxiety Disorder, and Panic Disorder
scales. The Major Depressive scale included 10 items relating to depressive symptoms; α in
current sample = .93 (e.g., “I feel sad or empty;” “I worry that something awful will happen;” “I
worry that bad things will happen to me;” “I worry about death;” “I worry about what is going to
happen;” “I worry that something bad will happen to me”). The Generalized Anxiety Disorder
scale included six items relating to worry about the future (e.g., “I worry about things”); α in
current sample = .89. The Panic Disorder scale on the RCADS has nine items that assess bodily
symptoms of a panic attack; α in current sample = .92 (e.g., “When I have a problem, I get a
funny feeling in my stomach;” “I suddenly feel as if I can’t breathe when there is no reason for
this;” “When I have a problem, I feel shaky”).
Analysis Plan
The hypothesized conceptual model (see Figure 1) was tested using structural equation
modeling (SEM) in Mplus (Muthén & Muthén, 2010). All paths of the mediation analyses were
estimated in a model that included (1) CPs at baseline statistically predicting alternative
reinforcers at Wave 2 (A path) after adjusting for baseline level of alternative/complementary
reinforcers and (2) alternative/complementary reinforcers at Wave 2 statistically predicting
substance use at Wave 3 (B path) after adjusting for baseline levels of substance use. In the latter
analyses, baseline levels of CPs statistically predicting substance use at Wave 3 represents the
direct effect of the mediational analyses as it adjusts for the level of alternative/complementary
reinforcement at Wave 2. The covariance between each of these variables was also estimated in
the model.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 106
Each SEM model was tested in two steps: (1) unadjusted and (2) adjusted for covariates.
Unadjusted and adjusted SEM models were separately tested for primary analyses using a latent
construct outcome of multiple substances as well as substance-specific analyses focusing on
alcohol, marijuana, and cigarette use separately. Scores were standardized (Mean =0 and
Standard Deviation =1) to generate parameter estimates that could be judged on the same metric
across variables.
Complementary reinforcers were only analyzed among people who reported lifetime use
of each substance-specific outcome. For the composite multi-substance use variable, lifetime use
of any of the six substances (i.e. alcohol, marijuana, cigarettes, stimulants, prescription
stimulants, prescription painkillers) analyzed was reported.
Moderational hypotheses were also tested using multigroup structural equation models
comparing males and females. The loglikelihood from a model with the 3 key parameters (i.e.
CPs to substance use, CPs to alternative/complementary reinforcers, and
alternative/complementary reinforcers to substance use) set to be equal between males and
females was compared to the loglikelihood from a model that allowed these 3 key parameters to
be unequal across males and females. All other parameters in the model were free to vary. The
loglikelihoods were compared using the Satorra-Bentler Scaled Chi-Square test (Satorra, 2000).
Results
Preliminary Analyses
Table 1 presents descriptive statistics. Approximately 17.6% of students reported past
six-month alcohol use at baseline. The prevalence of use increased to 27.3% at Wave 2 and
27.6% at Wave 3. Similar trends were found for marijuana and cigarette use whereby use tended
to increase between baseline and the first follow-up time point, but remained relatively stable
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 107
between the second and third time point. Table 2 presents correlations among study variables.
CPs, alternative reinforcers, and each measure of substance use were significantly associated
with each other.
Analyses of Multi-Substance Use Composite Variable and Alternative Reinforcers
Unadjusted and adjusted estimates for mediational analyses were similar across analyses,
and thus, only adjusted estimates are reported. Table 3 presents adjusted analyses predicting a
binary past six-month variable and ordinal past 30-day variable.
Multi-Substance Use Composite Variable. There was a significant total effect of Wave 1
CPs to increases in past six-month any substance use at Wave 3 (β = .23 [.20, .26], p < .0001),
indicating that teens who engage in more CPs at baseline tend to engage in more substance use at
Wave 3. The total effect of baseline CPs with the multi-substance use variable in the past six-
months was mediated by alternative reinforcers. The A path (β = -.11 [-.14, -.08], p < .0001) of
CPs to alternative reinforcers as well as the B path (β = -.07 [-.12, -.03], p < .01) of alternative
reinforcers to any substance use was significant for past six-month. There was a significant
indirect effect for past six-months (β = .01 [.003, .01], p < .01).
Although there was a significant total effect (β = .28 [.25, .31], p < .0001) of CPs at
Wave 1 to past 30-day any substance use at Wave 3, this relationship was not significantly
mediated by alternative reinforcers at Wave 2 (indirect effect, β = .001 [-.002, .003], p = .46).
Substance-Specific Analyses and Mediating Role of Alternative Reinforcers
Marijuana Use. Table 3 also indicates that there was a significant total effect of CPs on
past six-month (β = .24 [.21, .27], p < .0001) and past-30 day marijuana use (β = .27 [.23, .32], p
< .0001). The A path and B path were also significant in a similar direction as the any substance
use outcomes for both past six-month and past-30 day use (p < .0001). There were significant
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 108
indirect effects as well, indicating that alternative reinforcement significantly mediated the
relationship between baseline CPs and marijuana use at Wave 3.
Cigarette Use. Baseline CPs were significantly associated with cigarette use (i.e. total
effect) and alternative reinforcement (i.e. A path) at their respective follow-ups. However,
alternative reinforcement at Wave 2 was not significantly associated with past six-month (β = -
.06 [-.15, .02], p = .14) and past 30-day cigarette use (β = -.04 [-.14, .08], p = .54) at Wave 3 (i.e.
B path). Additionally, no significant results were found for mediation across past six-months (β
= .01 [-.003, .02], p = .14) and past-30 day (β = .004 [-.01, .02], p = .54) outcomes.
Alcohol Use. Although baseline CPs were significantly associated with alcohol use (i.e.
total effect) and alternative reinforcement (i.e. A path) at their respective follow-up time points,
alternative reinforcement was not significantly associated with alcohol use within the past six-
months (β = -.003 [-.06, .05], p = .92) or past 30-days (β = -.03 [-.08, .02], p = .20). Thus,
alternative reinforcement did not significantly mediate the relationship between CPs and past six-
month (β = .000 [-.01, .01], p = .92) or past-30 day alcohol use (β = -.003 [-.002, .01], p = .17).
Analyses of All Substance Use Outcomes and Complementary Reinforcers
Complementary reinforcers only significantly mediated the association between CPs and
alcohol use, but not any substance, cigarette or marijuana use outcomes. The A path examining
the association between CPs and complementary reinforcers (β = .09 [.02, .16], p < .05) as well
as the B path from complementary reinforcers to alcohol use (β = .17 [.10, .25], p < .0001) were
significant. The resulting indirect effect was also significant for alcohol use (β = .02 [.001, .03],
p < .05).

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 109
Multigroup Analyses
Across each substance use outcome, multigroup analyses were conducted to test for
differences between males and females in the mediating processes of alternative and
complementary reinforcers. After examining model fit in the overall SEM between a model that
equated the three key pathways (i.e. CPs to substance use, CPs to alternative reinforcers, and
alternative reinforcers to substance use) between sexes and one that did not, results indicated that
there were no significant group differences in the overall mediating process of either alternative
or complementary reinforcers across any of the substance use outcomes. Despite no differences
in the mediational process of alternative and complementary reinforcers across outcomes, there
was one significant sex difference in the B path of complementary reinforcers to marijuana use.
Specifically, the B path from complementary reinforcers to marijuana use was significant for
females (β = .64 [.33, .94], p < .0001) but not for males (β = .18 [-.13, .49], p = .25).
Sensitivity Analyses
Among study enrollees, 3,383 (99.6%) provided baseline data. Models were tested
separately for the total sample of participants as well as only those who completed all waves (N
= 3,163, 93.5%). No meaningful differences were found between these results and those
presented in the primary analyses. Results are available upon request to the first author (RK).
Discussion
The present study builds on prior cross-sectional work examining the longitudinal
association of alternative and complementary reinforcers as potential mechanisms underlying the
risk associated with CPs and substance use. Findings support the role of alternative
reinforcement as a critical risk factor for marijuana and any substance use uptake among teens
with behavioral problems; however, complementary reinforcement did not appear to play a
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 110
significant role in this process. The longitudinal nature of this design supports directionally-
specific inferences regarding the mediational process of alternative reinforcers that was not
possible with cross-sectional data. Specifically, results indicated that teens who engaged in more
behavioral problems at baseline tended to engage in fewer alternatively reinforcing activities at
Wave 2 and engaging in fewer activities was associated with higher levels of any substance use
and marijuana use at Wave 3. Given these results and prior research implicating alternative
reinforcers as a mechanism in substance use prevention (Audrain-McGovern et al., 2004;
Audrain-McGovern et al., 2010; Khoddam & Leventhal, 2016), it is critical for researchers to
examine how providing greater access to alternative reinforcers and means of obtaining greater
pleasure in these activities may slow the progression of substance use. This is particularly
important for teens with behavioral problems who are at greater risk for substance use (King et
al., 2004; Maslowsky, Schulenberg, O’Malley, & Kloska, 2013).
Prior research among college students suggests that interventions targeting alternative
reinforcement have been effective in reducing substance use (Murphy et al., 2012a; Murphy et
al., 2012b). Such interventions encourage participation in healthy activities. One particular study
assigned participants to either increase their physical and creative activities by 50% or to reduce
their substance use by 50% over a month long period (Correia et al., 2005). Both groups showed
reductions in substance use, indicating that it is possible to intervene on substance use through
indirect measures that increase alternative activities rather than explicitly seeking to decrease
substance use. The current study adds to the theoretical foundation of interventions like these and
Substance-Free Activity Sessions (Daughters et al., 2008; Daughters, Magidson, Lejuez, &
Chen, 2016) that emphasize the importance of increasing the amount of alternative reinforcers.
However, such interventions have yet to target adolescents - a salient developmental period (i.e.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 111
9
th
grade) when adolescents are exposed to greater numbers of organizations and clubs that may
serve as alternative reinforcers (Finn & Rock, 1997; Stewart, 2008).
With regards to substance-specific analyses, results from the current study indicated that
the mediational role of alternative reinforcers was consistently significant for marijuana use, but
not alcohol or cigarette use. Additionally, alternative reinforcers significantly mediated the
relationship between CPs and past six-month any substance use, but not past 30-day use. There
are many possible reasons why results did not consistently generalize across each set of
substances. First, it may be that more variance in substance use involvement over the course of
high school will increase statistical power and prove these findings to be more robust,
particularly given significant findings across the any substance use outcomes. Second, it may be
that certain alternative reinforcers are more protective against use of certain substances either
through physiological (e.g. arousal level) or psychosocial (e.g. access to reinforcers)
mechanisms. It has been theorized that addictions involve an action system that involves high
salience and low variety, meaning that certain drugs provide higher levels of salience (e.g.
arousal) and restrict the variety of alternative behaviors engaged in (Loonis, Apter, & Sztulman,
2000). Thus, if particular drugs have higher levels of salience, it may require there to be either
greater salience from alternative reinforcers (e.g. more pleasure) or a greater variety of behaviors
that increase the frequency of engagement. Future research examining differences in the relative
salience (e.g. rewarding properties) of different substances and alternative reinforcers may help
illuminate substance-specific differences.
The current study found that complementary reinforcers mediated the relationship
between CPs and alcohol use but not any other substance use outcomes. Although results did not
generalize across substances, the significant finding for alcohol use was consistent with our
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 112
hypothesis. Research has suggested that substances can have reward-enhancing and social-
facilitative properties that can amplify the reinforcing effects of non-drug rewards experienced
concurrently during substance use (Beck & Treiman, 1996; Caggiula et al., 2009; MacLatchy-
Gaudent & Stewart, 2001; Phillips & Fibiger, 1990; Robbins, 1977; Wall et al., 1998). Thus,
alcohol may act as a social lubricant that enhances the degree of reinforcement from
interpersonal activities associated with alcohol use uptake. It is critical for future studies to
examine the association between complementary reinforcers and various substance use
outcomes, as it may be that the reinforcing properties of certain activities change over time.
Furthermore, it will be important to examine how the level of reinforcement relates to different
substances as teens gain greater access to older peers and different substances later in
development.
Multigroup analyses indicated that results largely generalized across males and females
with the exception of the path between complementary reinforcers and past 30-day marijuana
use. Despite differences in this particular path, the indirect effect of complementary reinforcers
was still non-significant across both sexes. Additionally, no overall differences were found in the
mediational process of alternative or complementary reinforcers across any substance use
outcomes. These results are largely similar to the cross-sectional analysis of these key study
variables showing few sex differences (Khoddam & Leventhal, 2016). Although some studies
have noted sex differences in the association between CPs and substance use (Fergusson &
Horwood, 2002; Maughan, Rowe, Messer, Goodman, & Meltzer, 2004; Windle, 1990), the
present study did not find any evidence for this effect. The current study may be limited in its
ability to examine a greater breadth of CPs. For example, a large study of adolescents previously
found that property offenses (e.g. vandalism) tended to be more strongly associated with alcohol
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 113
use for females than males (Windle, 1990). Thus, future studies that use a broader measure of
CPs with a larger number of rule-breaking and aggressive acts may be able to provide a more
nuanced understanding of possible sex differences.
Although the current study has many strengths that build on previous literature (e.g.
longitudinal design, large sample), this study is not without its limitations. First, the PES did not
ask students to report which specific activities were associated with which specific substance. It
is important for future research to examine these differences, as it may allow researchers to better
understand the differential association of certain types of activities on different types of
substances. Second, the study is limited in its ability to assess for substance use problems. Future
studies that follow students over longer periods of time and incorporate more in-depth measures
of substance use disorders may be able to further clarify the role of alternative reinforcers along
the addiction trajectory. Third, the CP measure was not a diagnostic tool and does not assess
whether individuals meet criteria for Conduct Disorder. Despite this limitation, the CP measure
used allowed us to obtain more variability in behavior as it assessed frequency of each behavior
as opposed to dichotomously asking participants whether they had ever engaged in a certain
activity.
This is the first study to longitudinally examine how diminished alternative reinforcers is
a critical mechanism underlying why adolescents with higher behavioral problems may use
substances. Results provide important implications for creating prevention and intervention
programs that aim to increase access to alternative reinforcers (e.g. extracurricular activities) as
well as means of obtaining pleasure from these activities. It is possible that providing a range of
activities that reflect the interests of male and female adolescents may be able to move
adolescents into these activities that are pro-social in nature, rather than activities that facilitate
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 114
delinquent behaviors and peer groups that use substances. Tailoring interventions like Substance-
Free Activity Sessions (Daughters et al., 2011) and others aiming to increase engagement in
alternative activities (Correia et al., 2005; Murphy et al., 2012a; Murphy et al., 2012b) may
prove to be fruitful in decreasing substance use in adolescents.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 115

References
Audrain-McGovern, J., Rodriguez, D., Rodgers, K., & Cuevas, J. (2010). Declining alternative
reinforcers link depression to young adult smoking. Addiction, 106, 178-187. doi:
10.1111/j.1360-0443.2010.03113.x
Audrain-McGovern, J., Rodriguez, D., Tercyak, K. P., Epstein, L. H., Goldman, P., & Wileyto,
E. P. (2004). Applying a behavioral economic framework to understanding adolescent
smoking. Psychology of Addictive Behaviors, 18, 64-73. doi: 10.1037/0893-164x.18.1.64
Beck, K. H., & Treiman, K. A. (1996). The relationship of social context of drinking, perceived
social norms, and parental influence to various drinking patterns of adolescents. Addictive
Behavirs, 21, 633-644.
Bigelow, G. E., & Silverman, K. (1999). Theoretical and empirical foundations of contingency
management treatments for drug abuse.
Brady, K. T., & Randall, C. L. (1999). Gender differences in substance use disorders. Psychiatric
Clinics of North America, 22, 241-252.
Brady, K. T., & Sinha R. (2014). Co-occuring mental and substance use disorders: The
neurobiological effects of chronic stress. American Journal of Psychiatry, 162, 1483-
1493.
Brown, S. A., Gleghorn, A., Schuckit, M. A., Myers, M. G., & Mott, M. A. (1996). Conduct
disorder among adolescent alcohol and drug abusers. Journal of Studies on Alcohol, 57,
314-324.
Caggiula, A. R., Donny, E. C., Palmatier, M. I., Liu, X., Chaudhri, N., & Sved, A. F. (2009).
Chapter 6: The Role of Nicotine in Smoking: A Dual-Reinforcement Model. Nebraska
Symposium on Motivation. Nebraska Symposium on Motivation, 55, 91-109.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 116
Chan, Y., Dennis, M. L., Funk, R. R. (2008). Prevalence and comorbidity of major
internalizing and externalizing problems among adolescents and adults presenting to
substance abuse treatment. Journal of Substance Abuse Treatmeant, 34, 14-24.
Chorpita, B.F., Yim, L., Moffitt, C., Umemoto, L. A., & Francis, S. E. (2000). Assessment of
symptoms of DSM-IV anxiety and depression in children: a revised child anxiety and
depression scale. Behaviour Research and Therapy, 38, 835-855.
Cohen J., & Cohen P. (1985). Applied Multiple Regression and Correlation Analysis for the
Behavioral Sciences. Vol 2. Mahwah, NJ: Lawrence Erlbaum Associates.
Connor, D. F., Steingard, R. J., Cunningham, J. A., Anderson, J. J., & Melloni Jr., R. H. (2004).
Proactive and reactive aggression in referred children and adolescents. American Journal
of Orthopsychiatry, 74, 129-136.
Correia, C. J., Benson, T. A., & Carey, K. B. (2005). Decreased substance use following
increased in alternative behaviors: A preliminary investigation. Addictive Behaviors, 30,
19-27.
Couwenbergh, C., van den Brink. W., Zwart, K., Vreugdenhil, C., van Wijngaarden-Cremers, P.,
& van der Gaag, R. J. (2006). Comorbid psychopathology in adolescents and young
adults treated for substance use disorders. European Child & Adolescent Psychiatry, 15,
319-328.
Crowley, T. J., Mikulich, S. K., MacDonald, M., Young, S. E, & Zerbe, G. O. (1998).
Substance-dependent, conduct-disordered adolescent males: severity of diagnosis predicts
2-year outcome. Drug and Alcohol Dependence, 49, 225-237.
Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C. (2007).
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 117
Integration of impulsivity and positive mood to predict risky behavior: development and
validation of a measure of positive urgency. Psychological Assessment, 19, 107.
Daughters, S. B., Magidson, J. F., Lejuez, C. W., & Chen, Y. (2016). LETS ACT: a behavioral
activation treatment for substance use and depression. Advances in Dual Diagnosis, 9,
74-84.
Daughters, S. B., Braun, A. R., Sargeant, M. N., Reynolds, E. K., Hopko, D. R., Blanco, C., &
Lejuez, C. W. (2008). Effectiveness of a brief behavioral treatment for inner-city illicit
drug users with elevated depressive symptoms: the life enhancement treatment for
substance use (LETS Act!). Journal of Clinical Psychiatry, 69, 122-129.
Fergusson, D. M., Horwood, L. J., & Lynskey, M. T. (1993). Prevalence and comorbidity of
DSM-III-R diagnoses in a birth cohort of 15 year olds. Journal of the American Academy
of Child & Adolescent Psychiatry, 32, 1127-1134.
Fergusson, D. M., & Horwood, L. J. (1998). Early conduct problems and later life opportunities.
Journal of Child Psychology and Psychiatry, 39, 1097-1108.
Fergusson, D. M., & Horwood, L. J. (2002). Male and female offending trajectories.
Development and Psychopathology, 14, 159-177.
Fergusson, D. M., Horwood, L. J., & Ridder, E. M. (2007). Conduct and attentional problems in
childhood and adolescence and later substance use, abuse and dependence: results of a
25-year longitudinal study. Drug and Alcohol Dependence, 88, S14-S26.
Finn, J. D., & Rock, D. A. (1997). Academic success among students at risk for school failure.
Journal of Applied Psychology, 82, 221.
Grant, B. F., & Dawson, D. A. (1998). Age of onset of drug use and its association with DSM-IV
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 118
drug abuse and dependence: results from the National Longitudinal Alcohol
Epidemiologic Survey. Journal of Substance Abuse, 10, 163-173.
Hatzgiakoumis, D. S., Martinotti, G., Giannantonio, M. D., & Janiri, L. (2011). Anhedonia and
substance dependence: Clinical correlates and treatment options. Frontiers in Psychiatry,
2, 1-12.
Herpertz, S. C., Mueller, B., Qunaibi, M., Lichterfeld, C., Konrad, K., & Herpertz-Dahlmann, B.
(2005). Response to emotional stimuli in boys with conduct disorder. American Journal
of Psychiatry, 162, 1100-1107.
Higgins, S. T., Bickel, W. K., & Hughes, J. R. (1994). Influence of an alternative reinforcer on
human cocaine self-administration. Life Sciences, 55, 179-187.
Howard, R., Finn, P., Jose, P., & Gallagher, J. (2012). Adolescent-onset alcohol abuse
exacerbates the infuence of childhood conduct disorder on late adolescent and early adult
antisocial behaviour. Journal of Forensic Psychiatry & Psychology, 23, 7-22.
Hubbard, A. E., Ahern, J., Fleischer, N. L., Van der Laan, M., Lippman, S. A., Jewell, N., … &
Satariano, W. A. (2010). To GEE or not to GEE: comparing population average and
mixed models for estimating the associations between neighborhood risk factors and
health. Epidemiology, 21, 467-474.
Johnston, L. D., O'Malley, P. M., Miech, R. A., Bachman, J. G., Schulenberg, J. E. (2014).
Monitoring the Future national survey results on drug use: 1975-2013: Overview, key
findings on adolescent drug use. Ann Arbor: Institute for Social Research, The University
of Michigan, 84pp.
Kahler, C. W., Spillane, N. S., Day, A. M., Cioe, P. A., Parks, A., Leventhal, A. M., & Brown,
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 119
R. A. (2015). Positive Psychotherapy for Smoking Cessation: A Pilot Randomized
Controlled Trial. Nicotine & Tobacco Research, ntv011.
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005).
Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National
Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593-602.
King, S. M., Iacono, W. G., & McGue, M. (2004). Childhood externalizing and internalizing
psychopathology in the prediction of early substance use. Addiction, 99, 1548-1559.
Krueger R. F., Hicks B. M., Patrick C. J., Carlson S. R., Iacono W. G., & McGue M. (2002).
Etiologic connections among substance dependence, antisocial behavior, and personality:
Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411-424.
Leventhal, A. M., Kahler, C. W., Ray, L. A., Stone, K., Young, D., Chelminski, I., &
Zimmerman, M. (2008). Anhedonia and amotivation in psychiatric outpatients with fully
remitted stimulant use disorder. American Journal on Addictions, 17, 218-223.
Lloyd-Richardson, E. E., Papandonatos, G., Kazura, A., Stanton, C., & Niaura, R. (2002).
Differentiating stages of smoking intensity among adolescents: stage-specific
psychological and social influences. Journal of Consulting and Clinical Psychology, 70,
998-1009.
Loeber, R., Burke, J. D., & Lahey, B. B. (2002). What are adolescent antecedents to antisocial
personality disorder? Criminal Behaviour and Mental Health, 12, 24-36.
MacKinnon, D. P., Fritz, M. S., Williams, J., & Lockwood, C. M. (2007). Distribution of the
product confidence limits for the indirect effect: Program PRODCLIN. Behavior
Research Methods, 39, 384–389.
MacLatchy-Gaudent H. A., & Stewart S. H. (2001). The context-specific alcohol outcome
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 120
expectancies of university women. Addictive Behaviors, 26, 31-49.
MacPhillamy D. J., Lewinsohn P. M. (1976). Manual for the pleasant events schedule: DJ
MacPhillamy & PM Lewinsohn.
Madden, G. J. (2000). A behavioral-economics primer. In. W. K. Bickel & R. Vuchinich (Eds.),
Reframing Health Behavior Change with Behavioral Economics (pp. 3-26). Mahwah, NJ:
Lawrence Erlbaum & Associates.
Mahalik, J. R., Levine Coley, R., McPherran Lombardi, C., Doyle Lynch, A., Markowitz, A. J.,
& Jaffee, S. R. (2013). Changes in health risk behaviors for males and females from early
adolescence through early adulthood. Health psychology, 32(6), 685.
Maslowsky J., & Schulenberg J. E. (2013). Interaction matters: Quantifying conduct problem x
Depressive syptoms interactino and its association with adolescent alcohol, cigarette, and
marijunaa use ine in a national sample. Developmental Psychopathology, 25, 1029-1043.
Maslowsky, J., Schulenberg, J. E., O’Malley, P. M., & Kloska, D. D. (2013). Depressive
symptoms, conduct problems, and risk for polysubstance use among adolescents: Results
form US national surveys. Mental Health and Substance Use, 7, 157-169.
Maughan, B., Rowe, R., Messer, J., Goodman, R., & Meltzer, H. (2004). Conduct disorder and
oppositional defiant disorder in a national sample: developmental epidemiology. Journal
of Child Psychology and Psychiatry, 45, 609-621.
Moffitt, T. E. (2001). Sex differences in antisocial behaviour: Conduct disorder, delinquency,
and violence in the Dunedin Longitudinal Study. Cambridge University Press.
Murphy, J. G., Correia, C. J., Colby, S. M., & Vuchinich, R. E. (2005). Using behavioral theories
of choice to predict drinking outcomes following a brief intervention. Experimental and
Clinical Psychopharmacology, 13, 93-101.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 121
Murphy J. G., Dennhardt A.A., Skidmore J. R., Borsari B., Barnett N. P., Colby S. M., &
Martins, M. P. (2012a). A randomized controlled trial of a behavioral economic
supplement to brief motivational interventions for college drinking. Journal of
Consulting and Clinical Psychology, 80, 876-886.
Murphy J. G., Skidmore J. R., Dennhardt A. A., Martens M. P., Borsari B, Barnett, N. P., &
Colby, S. M. (2012b). A behavioral economic supplement to brief motivational
interventions for college drinking. Addiction Research & Theory, 20, 456-465.
Myers M. G., Steward D. G., & Brown S. A. (1998). Progression from Conduct Disorder to
Antisocial Peronality Disorder following treatment for adolescent sbustance abuse.
American Journal of Psychiatry, 155, 479-485.
Newberry, A. L., & Duncan, R. D. (2001). Roles of Boredom and Life Goals in Juvenile
Delinquency1. Journal of Applied Social Psychology, 31(3), 527-541.
Pardini D., White H. R., Stouthamer-Loeber M. Early adolescent psychopathology as a predictor
of alcohol use disorders by young adulthood. Drug Alcohol Depend 2007; 885: 538-549.
Phillips A. G., & Fibiger H. C. (1990). Role of reward and enhancement of conditioned reward
in persistence of responding for cocaine. Behavioural Pharmacology, 1, 269-282.
Prescott, C. A., & Kendler, K. S. (1999). Age at first drink and risk for alcoholism: A noncausal
association. Alcoholism: Clinical and Experimental Research, 23, 101-107.
Read J. P., Wood M. D., Kahler C. W., Maddock J. E., & Palfai T. P. (2003). Examining the
role of drinking motives in college student alcohol use and problems. Psychology of
Addictive Behaviors, 17, 13-23.
Resnick M. D., Bearman P. S., Blum R. W., Bauman K. E., Harris K. M., Jones J, ... & Udry, J.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 122
R. (1997). Protecting adolescents from harm. Findings from the National Longitudinal
Study on Adolescent Health. JAMA, 278, 823-832.
Robbins, T. W. (1977). Reward enhancement by psychomotor stimulant drugs [proceedings].
Neuropharmacology, 16, 529-530.
Satorra, A. (2000). Scaled and adjusted restricted tests in multi-sample analysis of moment
structures. In Innovations in multivariate statistical analysis (pp. 233-247). Springer US.
SAS Institute Inc. (2003). The SAS System for Windows (Version 8.2). Cary, NC: SAS Institute
Inc.
Stewart, E. B. (2008). School structural characteristics, student effort, peer associations, and
parental involvement the influence of school-and individual-level factors on academic
Achievement. Education and Urban Society, 40, 179-204.
Thompson, M. P., Ho, C. H., & Kingree, J. B. (2007). Prospective associations between
delinquency and suicidal behaviors in a nationally representative sample. Journal of
Adolescent Health, 40, 232-237.
Toumbourou, J. W., Stockwell, T., Neighbors, C., Marlatt, G. A., Sturge, J., & Rehm, J. (2007).
Interventions to reduce harm associated with adolescent substance use. The Lancet, 369,
1391-1401.
Unger, J. B. (2014). Cultural Influences on Substance Use Among Hispanic Adolescents and
Young Adults: Findings From Project RED. Child Development Perspectives, 8, 48-53.
Wall A. M., Hinson R. E., & McKee S. A. (1998). Alcohol outcome expectancies, attitudes
toward drinking, and the theory of planned behavior. Journal of Studies on Alcohol, 59,
409-419.
Whiteside S. P., & Lynam D. R. (2001). The five factor model and impulsivity: Using a
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 123
structural model of personality to understand impulsivity. Personality and Individual
Differences, 30, 669-689.
Windle, M. (1990). A longitudinal study of antisocial behaviors in early adolescence as
predictors of late adolescent substance use: gender and ethnic group differences. Journal
of Abnormal Psychology, 99, 86.
Wolitzky-Taylor K., Lyuba B., Zinbarg R. E., Mineka S., & Craske M. G. (2012). Longitudinal
investigation of the impact of anxiety and mood disroders in adolescence on subsequent
substance use disorder onset and vice versa. Addictive Behaviors, 37, 982-985.
Zeger, S. L., Liang, K.-Y., & Albert, P. S. (1988). Models for Longitudinal Data: A Generalized
Estimating Equation Approach. Biometrics, 44, 1049–1060. doi:10.2307/2531734

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 124
Table 1. Sample Characteristics among the overall sample.

Overall Sample
(N = 3,383)
Age, M (SD) 14.1 (0.42)
Gender, N=3,369 (%)
Female 53.0%
Male 46.2%
Ethnicity, N=3,311 (%)
American Indian / Alaska Native 0.9%
Asian 15.8%
Black / African American 4.9%
Hispanic or Latino 45.9%
Native Hawaiian / Pacific Islander 3.3%
White 15.3%
Other 5.6%
Multiracial 5.9%
Highest parental education, N=2,931 (%)
High school graduate or less 25.8%
Some college or more 60.5%
CPs, M (SD) / α 15.8 (5.5) / .79
Substance Use, Past six-month use at Baseline / 12-Month Follow-Up / 24-Month Follow-Up
Alcohol 17.6% / 27.3% / 27.6%
Marijuana 10.5% / 16.3% / 17.5%
Cigarette 4.3% / 7.7% / 7.3%
Stimulants 2.4% / 4.9% / 4.7%
Prescription Stimulants 1.7% / 3.8% / 3.3%
Prescription Painkillers 2.8% / 5.2% / 4.6%
Substance Use, Past 30-day use at Baseline / 12-Month Follow-Up / 24-Month Follow-Up, M (SD)
Alcohol 0.23 (.80) / .38 (.98) / .39 (.98)
Marijuana 0.25 (1.05) / .37 (1.28) / .40 (1.34)
Cigarette 0.06 (.45) / .09 (.59) / .11 (.70)
Stimulants 0.01 (.26) / .02 (.33) / .03 (.37)
Prescription Stimulants 0.03 (.36) / .06 (.53) / .05 (.47)
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 125
Prescription Painkillers 0.06 (.50) / .10 (.66) / .08 (.59)
Note. Data from ninth grade students in Los Angeles, California, USA collected in 2013-2015. CPs = Conduct Problems. RCADS =
Revised Children’s Anxiety And Depression Scale; MDD = Major Depressive Disorder; GAD = Generalized Anxiety Disorder; PD =
Panic Disorder; UPPS-P = Urgency, Premeditation, Perseverance, Sensation Seeking, and Positive Urgency
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 126

Table 2.
Correlation matrix of key study variables.

Data on Variable Collected at Baseline
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. CPs (baseline) 1.00 -.14 .38 .43 .30 .39 .43 .24
2. Alternative Reinforcers (Wave 2) -.19 1.00 -.14 -.19 -.12 -.15 -.24 -.13 .01 -.02 .00 -.05 -.01 -.11 .00 -.03
3. Past 6-mo Alcohol Use (Wave 3) .25 -.07 1.00 .47 .31 .63 .38 .22 .07 .18 -.10 .19 .02 .16 .14 .15
4. Past 6-mo Marijuana (Wave 3) .30 -.13 .47 1.00 .39 .45 .68 .27 .01 .25 -.08 .15 -.03 .12 .08 .11
5. Past 6-mo Cigarette Use (Wave 3) .21 -.09 .31 .37 1.00 .35 .36 .58 .02 .28 -.02 .12 .00 .13 .07 .11
6. Past 30-day Alcohol Use (Wave 3) .23 -.08 .62 .40 .33 1.00 .53 .43 -.01 .15 -.07 .16 .00 .16 .09 .12
7. Past 30-day Marijuana (Wave 3) .28 -.17 .33 .63 .36 .47 1.00 .37 -.01 .15 -.06 .12 -.02 .09 .05 .06
8. Past 30-day Cigarette Use (Wave 3) .15 -.04 .21 .28 .56 .45 .43 1.00 -.01 .17 -.03 .13 -.01 .14 .07 .13
9. Sex -.02 -.01 .08 .02 .03 .04 .04 .06 1.00
10. Ethnicity .14 .12 .10 .14 .06 .08 .07 .05 .34 1.00
11. Parental Education -.08 .01 -.06 -.03 -.02 -.04 -.03 -.01 .04 .01 1.00
12. Peer Substance Use .33 -.10 .15 .15 .10 .12 .09 .06 .00 .01 -.03 1.00
13. Impulsivity .98 -.00 .03 .01 .00 .01 .02 .04 .44 .25 .11 .00 1.00
14. MDD .28 -.13 .12 .14 .11 .07 .07 .09 .00 .00 -.01 .44 -.01 1.00
15. GAD .21 -.06 .13 .12 .07 .08 .05 .06 -.02 -.02 -.03 .40 -.01 .61 1.00
16. PD .23 -.08 10 .11 .10 .07 .06 .07 .01 .00 -.03 .42 -.02 .63 .52 1.00
Note. CPs = Conduct Problems. Wave 2 refers to 12-month follow-up. Wave 3 refers to 24-month follow-up. All substance use correlations reported above the diagonal use
baseline substance use. All coefficients are Pearson correlations except the following: The correlation between Parental Education and all other variables are Spearman
correlations, as Parental Education is an ordinal variable; The association between binary variables (e.g. Gender and Past 6-month drug use) were calculated using the Phi
Coefficient; The association between Ethnicity and substance use variables were calculated using Cramer’s V. The association between ethnicity and CPs as well as ethnicity and
alternative reinforcers was tested using an ANOVA and the square-root of R-squared was taken and reported in the table. Ethnicity was coded as a nominal variable. Empty cells
signify duplicate correlations from those below the diagonal. MDD = Major Depressive Disorder; GAD = Generalized Anxiety Disorder; PD = Panic Disorder.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 127

Table 3. Association of Conduct Problems to Substance Use-Related Outcomes and Mediation by Alternative and Complementary Reinforcement
Component Paths Mediation
Outcome
Total Effect
CP à Outcome
CP à Mediator
[95% CI]
Mediator à Outcome
[95% CI]
Direct Effect
CP à Outcome
Adjusting for
Mediator
a

[95% CI]

Indirect Effect
[95% CI]

Outcome: Any Substance Use
Past 6-month (yes/no) 0.23 (0.20, 0.26)† -0.11 (-0.14, -0.08)† -0.07 (-0.12, -0.03)*** 0.22 (0.19, 0.26)† 0.01 (0.003, 0.01)**
Past-30 Day Frequency (AR) 0.28 (0.25, 0.31)† -0.01 (-0.04, 0.02) -0.10 (-0.14, -0.06)† 0.28 (0.25, 0.31)† 0.001 (-0.002, 0.003)
Past-30 Day Frequency (CR) 0.19 (0.16, 0.21)† 0.04 (-0.03, 0.12) 0.24 (0.20, 0.28)† 0.18 (0.14, 0.21)† 0.01 (-0.01, 0.03)

Outcome: Marijuana Use
Past 6-month (yes/no) 0.24 (0.21, 0.27)† -0.14 (-0.17, -0.11)† -0.11 (-0.16, -0.05)† 0.23 (0.20, 0.26)† 0.02 (0.01, 0.02)†
Past-30 Day Frequency (AR) 0.27 (0.23, 0.32)† -0.15 (-0.18, -0.12)† -0.13 (-0.19, -0.08)† 0.25 (0.21, 0.29)† 0.02 (0.01, 0.03)†
Past-30 Day Frequency (CR) 0.08 (-0.003, 0.16)* 0.02 (-0.04, 0.09) 0.29 (0.20, 0.37)† 0.07 (-0.01, 0.15) 0.01 (-0.01, 0.02)

Outcome: Cigarette Use
Past 6-month (yes/no) 0.25 (0.19 0.32)† -0.13 (-0.17, -0.10)† -0.06 (-0.15, 0.02) 0.25 (0.17, 0.32)† 0.01 (-0.003, 0.02)
Past-30 Day Frequency (AR) 0.26 (0.20, 0.32)† -0.10 (-0.14, -0.07)† -0.04 (-0.14, 0.08) 0.25 (0.19, 0.32)† 0.004 (-0.01, 0.02)
Past-30 Day Frequency (CR) 0.06 (-0.04, 0.15) 0.004 (-0.09, 0.10) 0.25 (0.12, 0.38)† 0.06 (-0.03, 0.14) 0.001 (-0.02, 0.03)

Outcome: Alcohol Use
Past 6-month (yes/no) 0.19 (0.14, 0.24)† -0.13 (-0.16, -0.10)† -0.003 (-0.06, 0.05) 0.19 (0.15, 0.24)† 0.000 (-0.01, 0.01)
Past-30 Day Frequency (AR) 0.20 (0.17, 0.23)† -0.12 (-0.15, -0.09)† -0.03 (-0.08, 0.02) 0.20 (0.17, 0.23)† -0.003 (-0.002, 0.01)
Past-30 Day Frequency (CR) 0.08 (0.05, 0.11)† 0.09 (0.02, 0.16)* 0.17 (0.10, 0.25)† 0.06 (0.03, 0.10)** 0.02 (0.001, 0.03)*
Note. B (95%CI) = Parameter estimate with 95% confidence interval. CP = Conduct Problem. AR = Alternative Reinforcers as a mediator. CR =
Complementary Reinforcers as a mediator. The model is adjusted for highest parental education, ethnicity, gender, peer substance use, positive
urgency, depression, anxiety, and panic symptoms.
a
This model also represents the Direct Effect in traditional path analysis. Past six-month use was
not examined for CRs given that only those who report “yes” to past six-month use can report any CRs.
*
p < .05,
**
p < .01,
***
p < .001, † p < .0001.

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128
128
Figure 1. The conceptual framework of alternative reinforcement mediation between conduct problems and substance use.

Running head: CONDUCT PROBLEMS AND SUBSTANCE USE 129

General Discussion
The current set of studies provides novel evidence for the longitudinal association of CPs
and substance use as well as the mediating role of alternative and complementary reinforcers
across the transition of high school. Despite a robust literature implicating CPs as a risk factor
for substance use, there is limited literature examining factors mediating and moderating this
association. To this end, the current set of studies aimed to (1) characterize the association of
CPs and substance use longitudinally and (2) examine the extent to which alternative and
complementary reinforcers mediated the association between CPs and substance use.
Study 1 tested the first aim of this paper as well as how the risk carried by internalizing
symptomology is redundant, incremental, or interactive with CPs. Specifically, Study 1 indicated
that for every one standard deviation increase on the CP measure, one’s risk for subsequent any
substance use increased by 72%. When adjusting for the internalizing-conduct comorbidity,
depressive symptoms were the only internalizing problem whose risk for alcohol, tobacco, and
any substance use was incremental to CPs. Moreover, an antagonistic interactive relationship
existed between each internalizing disorder and CPs when predicting any substance use,
whereby, internalizing symptoms was a more robust risk factor for substance use in teens with
low (vs. high) CPs. Findings from the current set of studies are consistent with prior literature
implicating CPs as a significant risk factor for substance use across adolescence (Brown et al.
1996; Connor, Steingard, Cunningham, Anderson, & Melloni, 2004; Couwenbergh et al., 2006;
King, Iacono, & McGue, 2004; Maslowsky & Schulenberg, 2013). Such findings add to a
growing body of literature reiterating the critical risk early behavioral problems have on
subsequent substance use.

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130
Given the robust nature of the relationship between CPs and substance use, Specific Aim
2 sought to test the extent to which alternative and complementary reinforcers mediated this
relationship. Study 2 was a preliminary study to examine this association using cross-sectional
data. This study found that alternative and complementary reinforcers were significant mediators
between the relationship between CP’s and substance use even after adjusting for numerous
demographic and psychosocial covariates. Study 3 sought to test these associations
longitudinally by using three waves of data as adolescents transition from 9
th
grade to 11
th
grade.
Results indicated that alternative reinforcers significantly mediated the relationship between CPs
and any substance use and marijuana use, but not alcohol or cigarette use. Complementary
reinforcers significantly mediated the relationship between CPs and alcohol use, but no other
substance-specific outcome. Collectively, these studies point to (1) the significant risk carried by
CPs for future substance use, (2) alternative and complementary reinforcers as critical
mechanisms, and (3) the significance of such findings even in the context of comorbid
internalizing symptomatology.
A goal of the current set of studies was to examine the extent to which findings would
generalize across multiple substance use outcomes. With regards to Specific Aim 1, findings
from all three studies indicated that the risk of CPs generalized to all forms of substance use (i.e.
alcohol, marijuana, cigarette, and any substance use). However, with regards to Specific Aim 2,
results were more variable when examining the role of alternative and complementary
reinforcers. Although alternative reinforcers significantly mediated the association between CPs
and all forms of substance use in cross-sectional analyses, longitudinal analyses indicated that
only any substance use and marijuana use outcomes were significant. There are many possible
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

131
reasons underlying the inconsistent substance-specific relationships that can be addressed with
additional research.
The future research directions that follow are based on a set of possible mechanisms that
might help researchers better understand ways in which alternative and complementary
reinforcers may operate. First, it may be that adolescents high in CPs find healthy alternative
non-drug reinforcers as less stimulating. This is based on research suggesting that adolescents
high in CPs are less physiologically responsive to affective stimuli compared to those without
CPs (Herpertz et al., 2005). Second, it may be that adolescents high in CPs are more immune to
the punishing aspects of deviant behaviors. Thus, these adolescents may only be experiencing the
physiological arousal associated with deviant behaviors and drug use rather than the associated
social consequences. Third, it may be that adolescents high CPs socially isolate themselves from
peers involved in prosocial activities, and thus, self-select themselves into problem behavior
trajectories. Lastly, although the current set of studies adjusted for socioeconomic status, it is
important to further examine how socioeconomic status limits access to alternative reinforcers,
as prior studies show that adolescents from lower income families tend to engage in less healthy
alternatively reinforcing activities (Leventhal et al., 2015; Andrabi, Khoddam, & Leventhal,
2017).
Future Research Directions
The current set of studies focused on the prospective association between CPs and
substance use and how alternative reinforcers may mediate the association. Moreover, the
overlap between CPs and internalizing symptomatology was also examined, as the interaction
between the two have been found to disproportionally increase the risk for substance use.
Despite the many innovative findings from the current set of studies, future research is required
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

132
to fill in additional research gaps. Specifically, longitudinal research that follows adolescents
later into high school and college will be required to examine the role of alternative and
complementary reinforcers as substance use uptake increases through late adolescence and
young adulthood. The quantity and quality of the reinforcers may change throughout
development, particularly as social context changes and peer groups may change. Thus,
measures of alternative and complementary reinforcers need to be incorporated into future
longitudinal studies to provide a more nuanced understanding of how teens engagement of
behaviors change across time and how such changes relate to substance use.
Additionally, creating a more nuanced measure of alternative and complementary
reinforcers that is able to distinguish between substance-specific reinforcers would help create
more tailored prevention strategies. It may be that certain reinforcers are more protective against
certain types of substances based on the level and salience of that reinforcement. It may be that
specific activities (e.g. interpersonal activities vs. solitary activities) are more closely associated
with specific substances. More research is needed to tease apart the specific association between
activities and substances to further target prevention efforts.
In our studies, internalizing symptomatology was significantly associated with many of
the key variables examined, including CPs. Although prior studies found internalizing
symptomatology to disproportionately increase the risk for substance use among those with high
CPs as well (Maslowsky and Schulenberg, 2013), Study 1 found that internalizing
symptomatology only appeared to impact substance use in the context of those with low levels of
CPs. Future studies are needed to further clarify the nature of this relationship across adolescent
development and how comorbid externalizing and internalizing problems may interact in one’s
risk for substance use.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

133
Lastly, more research is needed to further clarify how alternative and complementary
reinforcers specifically protect against substance use. It may be that adolescents engaging in
more behavioral problems do not obtain as much physiological arousal from healthy pro-social
activities and that the physiological salience of substances perhaps mimics the rewarding
qualities that accompanies breaking rules. It may also be that adolescents who engage in more
CPs are more immune to the punishing qualities of breaking rules or getting into fights.
However, more psychophysiological studies that examine the rewarding qualities of both deviant
and pro-social behavior are needed to clarify how alternative and complementary reinforcers are
associated with CPs and substance use.
Conclusions
Adolescence is a particularly salient developmental time period in which individuals
experience a wide range of psychosocial, physiological, and neurological changes (Blakemore,
2008; Spear, 2000). These changes may exacerbate genetic and environmental vulnerabilities or
may protect against potentially dangerous developmental trajectories (e.g. drug use,
incarceration). Thus, given the changes occurring this period, it is critical to examine factors that
can prevent teens from such dangerous paths, particularly those with multiple risk factors (e.g.
conduct problems, low socioeconomic status). The research described in this dissertation
portfolio has sought to examine the risk associated with CPs (e.g. lying, stealing) as well as a
critical set of behavioral economic mechanisms that can potentially thwart its progression to
more deviant behavior problems (e.g. substance use). Findings elucidate the role of alternative
and complementary reinforcers in the developmental process of CPs to substance use as well as
how such findings fit into broader prevention and intervention strategies.

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134
References
For General Introduction & Discussion
Andrabi, N., Khoddam, R., & Leventhal, A. M. (2017). Socioeconomic disparities in adolescent
substance use: Role of enjoyable alternative substance-free activities. Social Science &
Medicine, 176, 175-182.
Audrain-McGovern, J., Rodriguez, D., Tercyak, K. P., Epstein, L. H., Goldman, P., & Wileyto, E. P.
(2004). Applying a behavioral economic framework to understanding adolescent smoking.
Psychology of Addictive Behaviors, 18, 64-73.
Audrain-McGovern, J., Rodriguez, D., Rodgers, K., & Curevas, J. (2010). Declining alternative
reinforcers link depression to young adult smoking. Addiction, 106, 178-187.
Blakemore, S. J. (2008). The social brain in adolescence. Nature Reviews Neuroscience, 9(4),
267-277.
Bonn-Miller, M. O., Zvolensky, M. J., & Johnson, K. A. (2010). Uni-morbid and co-occurring
marijuana and tobacco use: Examination of concurrent associations with negative mood
states. Journal of Addictive Diseases, 29, 68-77.
Brook, J. S., Adams, R. E., Balka, E. B., & Johnson, E. (2002). Early adolescent marijuana use:
Risks for the transition to young adulthood. Psychological Medicine, 32, 79-91.
Brown, S. A., Gleghorn, A., Schuckit, M. A., Myers, M. G., & Mott, M. A. (1996). Conduct disorder
among adolescent alcohol and drug abusers. Journal of Studies on Alcohol, 57, 314-324.
Buu, A., Wang, W., Schroder, S. A., Kalaida, N. L., Puttler, L. I., & Zucker, R. A. (2012).
Developmental emergence of alcohol use disorder symptoms and their potential as early
indicators for progression to alcohol dependence in a high risk sample: A longitudinal study
from childhood to early adulthood. Journal of Abnormal Psychology, 121, 897-908.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

135
Chassin, L., Presson, C. C., Sherman, S. J., & Edwards, D. A. (1990). The natural history of
cigarette smoking: Predicting young-adult smoking outcomes from adolescent smoking
patterns. Health Psychology, 9, 701-716.
Clapper, R. L., Buka, S. L., Goldfield, E. C., Lipsitt, L. P., & Tsuant, M. T. (1995). Adolescent
problem behaviors as predictors of adult alcohol diagnoses. The International Journal of the
Addictions, 30, 507-523.
Connor, D. F., Steingard, R. J., Cunningham, J. A., Anderson, J. J., & Melloni Jr., R. H. (2004).
Proactive and reactive aggression in referred children and adolescents. American Journal of
Orthopsychiatry, 74, 129-136.
Correia, C. J., Benson, T. A., & Carey, K. B. (2005). Decreased substance use following increased in
alternative behaviors: A preliminary investigation. Addictive Behaviors, 30, 19-27.
Couwenbergh, C., van den Brink. W., Zwart, K., Vreugdenhil, C., van Wijngaarden-Cremers, & van
der Gaag, R. J. (2006). Comorbid psychopathology in adolescents and young adults treated
for substance use disorders. European Child & Adolescent Psychiatry, 15, 319-328.
Crowley, T. J., Mikulich, S. K., MacDonald, M., Young, S. E, & Zerbe, G. O. (1998). Substance-
dependent, conduct-disordered adolescent males: severity of diagnosis predicts 2-year
outcome. Drug and Alcohol Dependence, 49, 225-237.
Deković, M., Buist, K. L., & Reitz, E. (2004). Stability and changes in problem behavior during
adolescence: Latent growth analysis. Journal of Youth and Adolescence, 33, 1-12.
Dodge, K. A., & Pettit, G. S. (2003). A biopsychosocial model of the development of chronic
conduct problems in adolescence. Developmental Psychology, 39, 349-371.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

136
Drabick, D. A. (2009). Can a developmental psychopathology perspective facilitate a paradigm shift
toward a mixed categorical-dimensional classification system? Clinical Psychology: Science
and Practice, 16, 41–49.
Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Ross, J., Hawkins, J.,…Wechsler, H. (2010).
Youth risk behavior surveillance - United States, 2009. MMWR Surveillance Summary, 59, 1-
142.
Ellickson, P. L., Tucker, J. S., Klein, D. J., & Saner, H. (2004). Antecedents and outcomes of
marijuana use initiation during adolescence. Preventive Medicine, 39, 976-984.
Ferrer E., & McArdle J. J. (2004). An experimental analysis of dynamic hypotheses about cognitive
abilities and achievement from childhood to early adulthood. Developmental Psychology, 40,
935–952.
Ghisletta P., & Lindenberger, U. (2003). Age-based structural dynamics between perceptual speed
and knowledge in the Berlin Aging Study: Direct evidence for ability dedifferentiation in old
age. Psychological Aging, 18, 696–713.
Ghisletta P., & Lindenberger, U. 2005. Exploring the structural dynamics of the link between
sensory and cognitive functioning in old age: Longitudinal evidence from the Berlin Aging
Study. Intelligence, 33, 555–587.
Grant, B. F., & Dawson, D. A. (1997). Age at onset of alcohol use and its association with DSM-IV
alcohol abuse and dependence: Results form the National Longitudinal Alcohol
Epidemiologic Survey. Journal of Substance Abuse, 9, 103-110.
Griffiths, R. R., Bigelow, G. E., & Henningfield, J. E. (1980). Similarities in animal and human
drug-taking behavior. In N. K. Mello (Ed.), Advances in substance abuse: Behavioral and
biological research, vol. 1 (pp. 1–90). Greenwich, CT: JAI Press.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

137
Hamagami, F, McArdle J. J., Cohen, P. (2000). Bivariate dynamic systems analyses based on a
latent difference score approach for personality disorder ratings. In Temperament and
Personality Development Across the Life Span, ed. VJ Molfese, DL Molfese, pp. 253–80.
Mahwah, NJ: Erlbaum.
Herpertz, S. C., Mueller, B., Qunaibi, M., Lichterfeld, C., Konrad, K., & Herpertz-Dahlmann, B.
(2005). Response to emotional stimuli in boys with conduct disorder. American Journal of
Psychiatry, 162, 1100-1107.
Jessor, R., & Jessor, S. L. (1975). Adolescent development and the onset of drinking. Journal of
Studies on Alcohol, 36, 27-51.
Johnston, L.D., O’Malley, P.M., Bachman, J.G., & Schulenberg, J.E. (2011). Monitoring the future
national survey results on adolescent drug use: Overview of key findings, 2010. Bethesda,
MD: National Institute on Drug Abuse.
Kendler, K. S., Jacobson, K. C., Prescott, C. A., & Neale, M. C. (2003). Specificity of genetic and
environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens,
sedatives, stimulants, and opiates in male twins. American Journal of Psychiatry, 160, 687-
695.
Khoddam, R., & Doran, N. (2013). Family history moderates the effects of expectancies on smoking
initiation in college students. Addictive Behaviors, 38, 2384-2387.
King, R. D., Gaines, L. S., Lambert, E. W., Summerfelt, W. T., & Bickman, L. (2000). The co-
occurrence of psychiatric and substance use diagnoses in adolescents in difference service
systems: Frequency, recognition, cost, and outcomes. The Journal of Behavioral Health
Services & Research, 27, 417-430.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

138
Krueger, R. F., Hicks, B. M., Patrick, C. J., Carlson, S. R., Iacono, W. G., & McGue, M. (2002).
Etiologic connections among substance dependence, antisocial behavior, and personality:
Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411-424.
Leblanc, M., Côté, G., & Loeber, R. (1991). Temporal paths in delinquency: Stability, regression,
and progression analyzed with panel data from an adolescent and a delinquent male sample.
Canadian Journal of Criminology, 33, 23-44.
Leventhal, A. M., A. J. Waters, E.T. Moolchan, S. J. Heishman, C. Lerman, & W. B. Pickworth.
Phenomonology of the Acute Tobacco Withdrawal Syndrome. in American Psychological
Association Annual Convention. 2008. Boston, MA.
Leventhal, A. M., Bello, M. S., Unger, J. B., Strong, D. R., Kirkpatrick, M. G., & Audrain-
McGovern, J. (2015). Diminished alternative reinforcement as a mechanism underlying
socioeconomic disparities in adolescent substance use. Preventive Medicine, 80, 75-81.
Loeber, R. (1990). Development and risk factors of juvenile antisocial behavior and delinquency.
Clincial Psychology Review, 10, 1-41.
Martell, C. R., Addis, M. E., & Jacobson, N. S. (2001). Depression in context: Strategies for guided
action. New York: W.W. Norton.
Maser, J., Norman, S., Zisook, S., Everall, I., Stein, M., Schettler, P., … Judd, L.L. (2009).
Psychiatric nosology is ready for a paradigm shift in DSM-V. Clinical Psychology: Science
and Practice, 16, 24–40.
Mayzer, R., Fitzgerald, H. E., & Zucker, R. A. (2009). Anticipating problem drinking risk from
preschoolers’ antisocial behavior: Evidence for a common delinquency-related diathesis
model. Journal of the American Academy of Child & Adolescent Psychiatry, 48, 820-827.
Running head: CONDUCT PROBLEMS AND SUBSTANCE USE

139
McGue, M. & Iacono, W. G. (2005). The association of early adolescent problem behavior with
adult psychopathology. American Journal of Psychiatry, 162, 1118-1124.
Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A
developmental taxonomy. Psychological Review, 100, 674-701.
NSDUH (2012).National Survey on Drug Use and Health, 2011. ICPSR34481-v1. In (Vol.
2013).Ann Arbor, MI: Inter-university Consortium for Political and Social Research: United
States Department of Health and Human Services. Substance Abuse and Mental Health
Services Administration (Center for Behavioral Health Statistics and Quality)
Resnick, M.D., Bearman, P.S. , Blum, R.W., Bauman, K.E. , Harris, K.M., Jones, J., … Udry, J.R.
(1997). Protecting adolescents from harm. Findings from the National Longitudinal Study on
Adolescent Health. Journal of American Medical Association, 278, 823-832.
Shi, Y. (2014). At high risk and want to quit: Marijuana use among adults with depression or serious
psychological distress. Addictive behaviors, 39, 761-767.
Spear, L. P. (2000). The adolescent brain and age-related behavioral manifestations.
Neuroscience & Biobehavioral Reviews, 24(4), 417-463.
Substance Abuse and Mental Health Services Administration, 2009. Results from the 2008 National
Survey on Drug Use and Health: National Findings (Office of Applied Studies, NSDUH
Series H-36, HHS Publication No. SMA 09-4434). Rockville, MD.
Vuchinich, R. E., & Tucker, J. A. (1988). Contributions from behavioral theories of choice to an
analysis of alcohol abuse. Journal of Abnormal Psychology, 97, 181–195.
White, H. R., & Labouvie, E.W. (1989). Towards the assessment of adolescent problem drinking.
Journal of Studies on Alcohol, 50, 30-37.

Abstract (if available)

Abstract The proposed study uses an ideally designed naturalistic longitudinal study to test the model using multiple waves of data beginning at the start of high school. The Happiness and Health Study utilizes semi-annual assessments of substance use and mental health information from 10 high schools across the greater Los Angeles data. Although studies have examined trajectories of CPs and substance use separately, no prospective study, to our knowledge, has examined the nuances underlying the association between CPs and substance use. The current study will not only examine the association between CPs and substance use but also factors that impact their association. Additionally, the current study will test the theoretical model implicating behavioral economic mechanisms as mediators that account for the association between CPs and substance use across the transition into high school. We will examine changes in the co-occurrence of three distinct substances and CPs across adolescence and test alternative and complementary reinforcers as mechanisms that may account for substance use uptake. This is particularly important as the proposed study aims to examine trajectories of tobacco, alcohol, and marijuana separately using a California-based sample, which is a state that has recently legalized recreational marijuana use.

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Asset Metadata

Creator Khoddam, Rubin (author)

Core Title Adolescent conduct problems and substance use: an examination of the risk pathway across the transition to high school

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 07/11/2017

Defense Date 06/07/2017

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

Tag alternative reinforcement,anxiety,behavioral economics,complementary reinforcement,conduct problems,Depression,Drugs,internalizing symptomatology,OAI-PMH Harvest,substance use

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Leventhal, Adam Matthew (committee chair), Margolin, Gayla (committee chair), Monterosso, John (committee member), Sussman, Steven Yale (committee member)

Creator Email khoddam@usc.edu,rubin.khoddam@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-397139

Unique identifier UC11264491

Identifier etd-KhoddamRub-5499.pdf (filename),usctheses-c40-397139 (legacy record id)

Legacy Identifier etd-KhoddamRub-5499.pdf

Dmrecord 397139

Document Type Dissertation

Rights Khoddam, Rubin

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