# A validation study of the Misbehaviors Questionnaire

Racheal Taki Reavy

University of Rhode Island

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## Recommended Citation

Reavy, Racheal Taki, "A validation study of the Misbehaviors Questionnaire" (2009). Open Access Dissertations. Paper 2311.

https://digitalcommons.uri.edu/oa_diss/2311
A VALIDATION STUDY OF THE
MISBEHAVIORS QUESTIONNAIRE

BY
RACHEAL TAKI REAVY

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

IN
PSYCHOLOGY

UNIVERSITY OF RHODE ISLAND

2009
UMI Number: 3378090

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This study examined the validity of the Misbehaviors Questionnaire (MBQ), a
revision of the Self Report Delinquency scale, which aims to diagnose conduct
disorder (CD) and to serve as a valid measure for assessing misbehaviors. The MBQ
further assesses whether each misbehavior involved the use of alcohol or marijuana,
allowing for comparisons of substance-related clusters of misbehaviors.

Participants were 190 incarcerated adolescents (85.8% male; average age 17
years) at a juvenile correctional facility in the Northeast. While incarcerated, they
were asked about substance use and misbehaviors in the three months prior to
incarceration, as well as parental, peer, and demographic information. They were
tracked at three-months post-release, given the MBQ, and assessed for post-release
substance use.

CD severity (symptom count) was significantly associated with two peer
factors: friend substance use and friend prior arrests, with medium to high effect sizes.
Fewer parental influences were significant, with small to medium effect sizes. Age of
CD onset was not strongly related to parental factors, but earlier onset was associated
with earlier marijuana use. Evidence supporting the utility of the MBQ as a measure of
CD diagnosis, including limited concurrent validity, was obtained, more so for alcohol
than for marijuana use.

Three new scales of the MBQ designed to assess general, alcohol-involved,
and marijuana-involved misbehaviors were empirically derived using Principal
Components Analyses. Support was found for concurrent and predictive incremental
validities of these scales in predicting substance use, with stronger findings for the
general and the alcohol-involved scales. Scales related to stealing showed lower validity than those related to more aggressive behaviors.

These analyses suggest that empirically derived scales can offer researchers and clinicians a new psychometrically sound approach to assessing adolescent misbehaviors. The MBQ is a flexible instrument that generally maintains SRD constructs and scales and provides diagnostic information regarding CD and ASPD. Furthermore, the MBQ provides empirically derived general, alcohol-, and marijuana-related scales which extend research and clinical utility.
Acknowledgements

The successful completion of this project would not have been possible
without the assistance and support of several individuals. I would first like to thank
Dr. Lyn Stein for making her data available, and for her support and guidance
throughout. I appreciate her willingness to help shape and direct the project toward a
useful and meaningful end.

My sincere gratitude also goes out to my committee members, who provided
invaluable feedback and assistance at each juncture. Dr. John Boulmetis has been a
consistent source of support and I thank him for sharing his practical advice, which
was necessary to both get my thoughts organized and get this project off on the right
foot. I am extremely grateful to Dr. Andrea Paiva, who tirelessly worked with me and
shared her knowledge of data and statistics. Her help was invaluable and I give to her
my most sincere thanks. My major professor, Dr. Kathryn Quina, is without question a
truly remarkable mentor and amazing influence. Her ability to keep me focused, keep
me writing, and keep me laughing, not only helped bring the project to completion, but
helped me appreciate the balance between having a life and finishing a dissertation. I
thank her for sharing her knowledge of data analysis and writing skills, for being an
admirable leader and friend, and for always going that extra mile.

Finally, I thank my family and friends for their understanding while my
attention was focused elsewhere. I thank everyone, especially those who helped me
with continued inspiration, motivation, and patience after I moved across the country.
Of my tremendous support staff, I want to thank my husband, Brad, for his endless
supply of optimism and encouragement. He was always available and ready to hear
my frustrations and joys. He was completely devoted to our family, which was the
most imperative kind of support for enabling me to continue working, and ultimately
finish, this project. Without him, I do not believe this could have been possible. I also
want to thank my sweet little 3-year old son, Noah, for always greeting me with open
arms and a big kiss, even after long periods apart. Watching him grow and play helped
motivate me with renewed energy and spirit. My dissertation is certainly a better
product because of the amazing people by whom I was surrounded.
Table of Contents

<table><tr><td>Abstract</td><td>ii</td></tr><tr><td>Acknowledgements</td><td>iv</td></tr><tr><td>Table of Contents</td><td>vi</td></tr><tr><td>List of Tables</td><td>viii</td></tr><tr><td>Introduction</td><td>1</td></tr><tr><td>Statement of the Problem</td><td>1</td></tr><tr><td>Etiology and Correlates of Conduct Disorder</td><td>3</td></tr><tr><td>Environmental Factors</td><td>3</td></tr><tr><td>Age of Onset</td><td>4</td></tr><tr><td>Substance Use</td><td>5</td></tr><tr><td>Overview and Hypotheses</td><td>7</td></tr><tr><td>Examining Reliability and Validity</td><td>8</td></tr><tr><td>Study Hypotheses</td><td>10</td></tr><tr><td>Method</td><td>15</td></tr><tr><td>Participants</td><td>15</td></tr><tr><td>Procedure</td><td>15</td></tr><tr><td>Measures</td><td>16</td></tr><tr><td>Results</td><td>20</td></tr><tr><td>Severity of Conduct Disorder</td><td>21</td></tr><tr><td>Age of Onset for Conduct Disorder</td><td>22</td></tr><tr><td>Concurrent Incremental Validity: Alcohol</td><td>24</td></tr><tr><td>Concurrent Incremental Validity: Marijuana</td><td>25</td></tr></table>
<table><tr><td>Predictive Incremental Validity: Alcohol</td><td>26</td></tr><tr><td>Predictive Incremental Validity: Marijuana</td><td>27</td></tr><tr><td>Internal Reliability and Stability of the CD Symptom Clusters and CD Diagnosis</td><td>28</td></tr><tr><td>Creation of Empirically Derived MBQ Scales</td><td>29</td></tr><tr><td>Concurrent Incremental Validity: Empirical Scales and Alcohol Use</td><td>30</td></tr><tr><td>Concurrent Incremental Validity: Empirical Scales and Marijuana Use</td><td>31</td></tr><tr><td>Predictive Incremental Validity: Empirical Scales and Alcohol Use</td><td>32</td></tr><tr><td>Predictive Incremental Validity: Empirical Scales and Marijuana Use</td><td>33</td></tr><tr><td>Discussion</td><td>35</td></tr><tr><td>MBQ as a Diagnostic Tool for CD</td><td>35</td></tr><tr><td>Empirically Derived Scales</td><td>39</td></tr><tr><td>Limitations and Future Directions</td><td>41</td></tr><tr><td>Conclusions</td><td>44</td></tr><tr><td>Appendix</td><td>61</td></tr><tr><td>Bibliography</td><td>63</td></tr></table>
List of Tables

<table><tr><td>Table 1: Items from the SRD</td><td>46</td></tr><tr><td>Table 2: Participant Characteristics</td><td>47</td></tr><tr><td>Table 3: CD Severity, Hypothesis 1</td><td>48</td></tr><tr><td>Table 4: Age of CD Onset, Hypothesis 2</td><td>49</td></tr><tr><td>Table 5: Concurrent Incremental Validity: CD Diagnosis and Alcohol Use</td><td>50</td></tr><tr><td>Table 6: Concurrent Incremental Validity: CD Diagnosis and Marijuana Use</td><td>51</td></tr><tr><td>Table 7: Predictive Incremental Validity: CD Diagnosis and Alcohol Use</td><td>52</td></tr><tr><td>Table 8: Predictive Incremental Validity: CD Diagnosis and Marijuana Use</td><td>53</td></tr><tr><td>Table 9: Empirically Derived MBQ-General</td><td>54</td></tr><tr><td>Table 10: Empirically Derived MBQ-Alcohol</td><td>55</td></tr><tr><td>Table 11: Empirically Derived MBQ-Marijuana</td><td>56</td></tr><tr><td>Table 12: Concurrent Incremental Validity: Empirical Scales and Alcohol Use</td><td>57</td></tr><tr><td>Table 13: Concurrent Incremental Validity: Empirical Scales and Marijuana Use</td><td>58</td></tr><tr><td>Table 14: Predictive Incremental Validity: Empirical Scales and Alcohol Use</td><td>59</td></tr><tr><td>Table 15: Predictive Incremental Validity: Empirical Scales and Marijuana Use</td><td>60</td></tr></table>
# A Validation Study for the Misbehaviors Questionnaire

## *Statement of the Problem*

Conduct Disorder (CD) is a pattern of antisocial behaviors that can include physical aggression, deception, and person and property crimes. CD occurs in boys and girls, children and adolescents, and across racial and ethnic groups. It is generally reported that incarcerated juveniles have higher rates of CD (86.2%; Stein, 2004) than referrals in clinic samples (30-50%; Kazdin, 1985); boys are more frequently diagnosed (1.8%-16%) than girls (0.8%-9.2%, Cohen, Cohen, & Brook, 1993; Furgusson, Horwood, & Lynskey, 1993; Kashani, Daniel, Sulzberger, Rosenberg, & Reid, 1987); and adolescents in the general population appear to have slightly higher rates of CD (7.1%; Cohen et al., 1993) as compared to younger children (5.4%; Loeber, Farrington, Stouthamer-Loeber, & Van Kammen, 1998).

Adolescents with conduct problems and antisocial behaviors often fail to conform to social norms in school, work, family, and other interpersonal contexts. There are several questionnaires available to assess Disruptive Behavior Disorder (DBD) and other problematic behaviors in children and adolescents. For example, the Child Behavior Checklist (CBCL; Achenbach, 1993) is frequently administered to parents for children ages 2-18. The Self-Report Delinquency scale (SRD; Elliott, Ageton, Huizinga, Knowles, & Canter, 1983) is one of the most common measures, focusing on delinquent acts that often coincide with DBD. A well-established self-report measure for 11-19 year olds, the SRD covers index offenses (e.g., stole a motor vehicle), delinquent behaviors (e.g., hit a parent), and drug use, which are rated for frequency of occurrence over the last year. It has been used in epidemiological,
community-based, and treatment outcome research (Stein, Hesselbrock, & Bukstein, 2008).
# Etiology and Correlates of Conduct Disorder

Following is a review of factors that are both related to and thought to maintain CD. Although a number of important correlates and causative factors exist and are noted among researchers (e.g., verbal intelligence, genetics, pubertal status, etc.), this project focuses on a subset of factors whose significance has been well established in the research literature.

## Environmental Factors

**Family environment.** Several aspects of child-rearing practices may contribute to the development of CD. Specific parenting practices, such as poor monitoring, permissive styles, lack of communication, and lack of parental warmth or support have been associated with negative externalizing behaviors (Barnes, Farrell, & Dintcheff, 1997; Keenan, Loeber, & Green, 1999). Loeber and Stouthamer-Loeber (1986) proposed four paradigms that may influence the development of conduct problems in children: neglect, conflict, deviant attitudes, and disruptions in parenting, with conflict being particularly relevant for the etiology of aggression and neglect for the etiology of covert conduct problems.

**Peers.** Developmental researchers posit that children's main source of influence is their parents, but later in development the main influence typically shifts to peers. Blackson, Butler, Belsky, Ammerman, Shaw, and Tarter (1999) state that externalizing behavior problems are precursors to children's breaking away from their parents' sphere of influence and transitioning to deviant peer affiliations, where substance use initiation and subsequent development of CD occur. The effect, then, of conduct problems may occur by the attraction of aggressive children to each other
(Boivin & Vitaro, 1995), because deviant behaviors are reinforced within the peer group (Kiesner, Dishion, & Poulin, 2001), or as a consequence of rejection by non-deviant peers (Patterson, Kapaldi, & Bank, 1991).

## Age of Onset

Keenan et al. (1999) report that researchers and clinicians alike indicate an early age of onset is prognostic of chronic deviance, while a later onset of disruptive behavior tends to be transitory and less serious. Lahey et al. (1998) also note that several researchers suggest youth who engage in the most persistent, serious, and aggressive antisocial behavior initiate this behavior during childhood, rather than adolescence. They suggest that those who do not engage in antisocial behavior until adolescence tend to exhibit antisocial behavior that is less aggressive, less serious, and less likely to continue into adulthood; the probability of a child with CD meeting diagnostic criteria for Antisocial Personality Disorder (ASPD) in adulthood is twice as high if the antisocial behaviors began in childhood than if they began in adolescence. However, it must be noted that these conclusions are based primarily on research with boys; much less is known about CD and girls. Although they may not ultimately be diagnosed with CD girls typically begin to express negative behaviors in the elementary school years, with a sharp increase in the prevalence of psychopathology during adolescence (Keenan, Stouthamer-Loeber, & Loeber, 2005). Keenan et al. (2005) point out that adolescent-onset CD does not appear to be more common in either girls or boys, but once CD has emerged, it seems quite stable for girls.

Lahey et al. (1998) conducted a study validating the subtypes of CD based on age of onset. In addition to early onset having a more negative prognosis, it seems that
children diagnosed with younger age of onset exhibited markedly more aggressive
behavior than those who met criteria for CD with later age of onset. They also
discovered a curvilinear relationship between age of onset of CD and number of
aggressive behaviors, suggesting that a "natural dichotomy" exists between the
childhood- and adolescent-onset subtypes, with obvious changes in aggression
occurring at ages of onset around 10 years of age. It must be noted that there are
common problems with CD studies that examine age of onset (Lahey et al., 1998). The
measures assess CD behaviors present only in the past 12 months, and age of onset is
often asked only for symptoms that were present during this time. Therefore, it is
possible that children could have had an earlier onset than reported if they had
previously exhibited behaviors that were not present during the time covered by the
assessment. This would tend to make the reported comparisons more conservative,
however, as it would tend to misclassify some youths with childhood onset in the
adolescent-onset group and reduce differences between the groups. Additionally, data
from prospective studies (e.g., Lahey et al., 1995) show that youths who exhibit CD
show fluctuating numbers of symptoms over time, meeting criteria for CD in some
years and not in others.

## Substance Use

Substance use is strongly correlated with CD. For adolescent alcohol and drug
abusers, CD appears to be the most common comorbid psychiatric diagnosis (Myers,
Brown, & Mott, 1995). It has been reported that roughly 95% of teens treated for
substance use meet criteria for CD when all behaviors are considered, but only 47%
meet the criteria when only behaviors that occur outside the context of substance use
are considered (Brown, Gleghorn, Schuckit, Myers, & Mott, 1996). Stice, Myers, and Brown (1998) reported delinquency as a predictor of adolescent substance use, both cross-sectionally and longitudinally. In their study, delinquency moderated the relationship between consumption and problem use, with highly delinquent teens showing consistently elevated levels of use-related problems regardless of type of substance used.

Youths with CD typically have a higher rate and earlier onset of substance use (including both alcohol and marijuana). Compared with nonusers, marijuana users report poorer physical and mental health and lower academic achievement or attachment to school, are more involved in other delinquency behaviors, are rebellious and tolerant of deviance, feel alienated, and experience greater anxiety and more problems with their parents (Dembo, Williams, Wothke, & Schmeidler, 1992). White, Xie, Thompson, Loeber, and Stouthamer-Loeber (2001) found that higher levels of CD behaviors predicted higher levels of alcohol use. Among CD youth, Attention Deficit Hyperactivity Disorder (ADHD) youth, and violent offenders, only CD youth showed a pattern of linear growth in alcohol use. These findings are consistent with previous research in which CD has been the most consistent disruptive behavior disorder associated with substance use and abuse, and with studies showing that early delinquent behavior is related to later substance use.
Overview and Hypotheses

There are useful and valid tools for measuring CD and associated symptoms.

These include the CBCL, the Diagnostic Interview for Children and Adolescents (DICA; Reich, 2000), the Schedule of Affective Disorders and Schizophrenia for School-Age Children (K-SADS; Puig-Antich & Chambers, 1978), the Diagnostic Interview Schedule for Children (DISC; Shaffer, Fisher, Dulcan, & Davies, 1996), and the Millon Adolescent Clinical Inventory (MACI; Pinto & Grilo, 2004). Similarly, the SRD is a valid and useful tool to assess misbehaviors often associated with CD.

Originally, the SRD was developed to address major criticisms of prior self-report measures (Elliott, Huizinga, & Ageton, 1985), including unrepresentativeness of items, limitations with normative response sets, overlapping items, and low accuracy of recall. Forty items of delinquency were selected to represent the full range of official acts that could get juveniles arrested; this reduced or eliminated the potential for item overlap. Five items were also included to represent non-delinquent acts. An additional seven questions were included about drug use, both more common (e.g., alcohol) and less common (e.g., heroin) drugs alike. The response set for all items included 9 options, ranging from 1 = Never to 9 = 2-3 times a day. Several versions of the SRD exist, as it has been modified over the years; Table 1 presents a version in recent use by Elliott (2008).

While the SRD is an extremely valuable instrument for studying delinquent behaviors, its utility may be expanded by including items assessing the role of substance use in delinquent behaviors, adding items that reflect the full range of antisocial behaviors listed in the *Diagnostic and Statistical Manual of Mental*
*Disorders* (DSM-IV; American Psychiatric Association, 1994), and creating empirically derived scales. The Misbehaviors Questionnaire (MBQ), a measure largely constructed from the already established SRD, was developed for this purpose.

The MBQ is an assessment tool that evaluates an array of misbehaviors, committed with and without the involvement of alcohol and/or marijuana. Although the MBQ is based on the already established SRD, it incorporates significant additions for clinical assessment and research endeavors. The MBQ includes age of onset for CD, an important concept in the study of the disorder. It also allows for examination of CD symptom count and severity of CD diagnosis. Most importantly, it allows us to examine the role of substance abuse in committing the misbehaviors assessed.

The present study utilizes regressions and Principal Components Analysis (PCA) to: (1) validate the MBQ, as a diagnostic tool for CD; and (2) empirically derive and validate scales based on clusters of behaviors, common to adolescents with CD, including substance related behaviors. It is hoped that such a measure will have appeal to both researchers and clinicians.

## Examining Reliability and Validity

Reliability is in large part the quality of measurement. It is the proportion of the observed score that is due to true score, which necessarily provides some information about the error of the scale (Redding, Maddock, & Rossi, 2006). Because a part of calculating reliability has to do with error, it cannot be calculated precisely, and so there are different kinds of reliability estimators. Among them are internal consistency (which examines the degree of homogeneity of scale items) and test-retest reliability (the correlation between the same measurement at two separate time points).
Whereas reliability is concerned with consistency of measurement, the goal of validity is to ensure the scale is measuring what it purports to measure. There are a number of forms of validity, including content, concurrent, and predictive (see Anastasi & Urbina, 1997). Content validity is the extent to which a measure assesses the intended construct. We can assess how well CD is measured on the MBQ by examining the correlations with related constructs. A concurrently valid measure relates meaningfully and significantly to similar measures collected simultaneously. With predictive validity, we can examine whether theoretical predictions hold up, in this case whether MBQ-based scales predict substance use at the follow-up assessment. By comparing predictive validity of a scale to validity of other measures in a Hierarchical Regression Model, the predictive incremental validity can be established (see, e.g., Sellborn, Graham, & Schenk, 2006; Weed, Butcher, McKenna, & Ben-Porath, 1992).

In addition, new empirical scales will be derived from items on the MBQ using the exploratory approach described by Redding et al. (2006). Exploratory factor analysis is an iterative process. Items are put into a preliminary factor analysis and removed based on low factor loadings (<.40) and/or complex loadings (i.e., loading on more than one factor). Once items are removed based on statistical considerations, each scale is examined for breadth of construct and further item eliminations are then based on the content of the items (e.g., redundancy, lack of theoretical fit). Finally, the remaining items are entered into a PCA to help determine the number of factors or dimensions measured by the scale items. This step follows the recommended methods for determining the number of factors or components to retain (Redding et al., 2006),
using Horn's (1965) parallel analysis criterion and Velicer's (1976) minimum average
partial procedure (MAP). The internal consistency of each factor is then evaluated
using coefficient alpha. Once the new scales demonstrate internal consistency,
construct and criterion validity can be explored with regression analyses.

Study Hypotheses

*Hypothesis 1: Severity of CD*. The MBQ is a valid measure for diagnosing CD in terms of symptom severity. The MBQ incorporates CD symptoms listed in the DSM-IV. CD symptom severity can be calculated by counting the number of symptoms indicated. Since previous literature on CD has suggested family background, peer influence, and substance use are associated with CD, composites of these variables will be examined for association with CD symptom severity.

Generally, it is hypothesized that severity will be greater in teens with more (1a) negative parent influences (ever incarcerated, history of substance abuse, and problems because of substance use with family, their job, or the law); more (1b) negative peer influences (number of friends that use alcohol or drugs at least once per month and number of friends that have been arrested); and earlier age of (1c) first alcohol use and (1d) first marijuana use.

*Hypothesis 2: Age of onset for CD*. The MBQ is a valid measure for diagnosing CD in terms of age of CD onset. The MBQ asks respondents to indicate how old they were when they first committed the acts which correspond to DSM-IV-based CD symptoms. Following the DSM-IV, CD onset is typically divided into child (age 10 or under) or adolescent (age 11 or older) onset. Based on the research literature, it is hypothesized that those diagnosed with CD as children will have more
negative (2a) parent and (2b) peer influences, and earlier ages of (2c) first alcohol use and (2d) first marijuana use.

Parental influences were not expected to be as strong as peer influences for this population (Andrews, Hops, & Duncan, 1997; Barnes & Farrell, 1992; Blackson et al., 1999; Dishion, Capaldi, Spracklen, & Li, 1995), so the parent variables were not incorporated into subsequent hypotheses or analyses.

*Hypothesis 3: Concurrent incremental validity with alcohol use.* The MBQ will provide concurrent incremental validity beyond age, race, peer influences, and age of onset for alcohol use, when entered into a multiple regression with alcohol use as the DV. Specifically, after taking into account demographic variables of age and race, peer influence, and age of first alcohol use (Model 1), the number of heavy drinking days at baseline will be predicted by (Model 2): (3a) baseline severity of CD as defined by symptom count, such that those exhibiting more symptoms will use more alcohol; and separately by (3b) age of onset for CD at baseline, such that those diagnosed at a younger age will use more alcohol.

*Hypothesis 4: Concurrent incremental validity with marijuana use.* The MBQ will provide concurrent incremental validity beyond age, race, peer influences, and age of onset for marijuana use with baseline marijuana use as DV. Applying the same approach as hypothesis 3, it is hypothesized that after taking into account demographic variables of age and race, peer influences, and age of first marijuana use (Model 1), the average number of marijuana joints smoked per week at baseline will be predicted by (Model 2): (4a) baseline severity of CD as defined by symptom count, such that those exhibiting more symptoms will use more marijuana; and separately by (4b) age
of onset for CD at baseline, such that those diagnosed at a younger age will use more marijuana.

*Hypothesis 5. Predictive incremental validity with alcohol use at three-month follow-up.* The MBQ will provide predictive incremental validity beyond age, race, peer influences, and baseline alcohol use, when entered into a multiple regression predicting alcohol use at the three-month follow-up assessment. It is hypothesized that after taking into account demographic variables of age and race, peer influences, and alcohol use at baseline (Model 1), alcohol use at the three-month follow-up will be predicted by (Model 2): (5a) baseline severity of CD as defined by symptom count, such that those expressing more symptoms will use more alcohol; and separately by (5b) age of onset for CD, such that those diagnosed as children will use more alcohol.

*Hypothesis 6. Predictive incremental validity with marijuana use at three-month follow-up.* The MBQ will provide predictive incremental validity beyond age, race, peer influences, and baseline marijuana use when entered into a multiple regression predicting marijuana use at the three-month follow-up assessment. Applying the same approach as Hypothesis 5, it is hypothesized that after taking into account demographic variables of age and race, peer influences, and marijuana use at baseline (Model 1), marijuana use at the three-month follow-up will be predicted by (Model 2): (6a) baseline severity of CD as defined by symptom count, such that those expressing more symptoms will use more marijuana; and separately by (6b) age of onset for CD at baseline, such that those diagnosed at a younger age will use more marijuana.
*Hypothesis 7. Internal reliability and stability of CD diagnosis.* Cronbach alphas for CD symptom clusters and CD diagnosis will be comparable to those found for other instruments that assess CD symptomatology. It is hypothesized that these alphas will not be substantially different from the DICA, DISC, K-SADS, CBCL, or MACI.

*Hypothesis 8: Empirical scale derivation.* It is hypothesized that the MBQ will yield empirically derived and useful scales to assess both general misbehaviors and substance-related misbehaviors. Empirically derived MBQ scales reflecting misbehaviors in general will differ from empirically derived scales reflecting misbehaviors related to alcohol and marijuana use, in terms of item content and clustering. It is hypothesized that a comparison of alpha coefficients and factor loadings will reveal differences among the scales on how items cluster together with respect to general misbehaviors, versus how they cluster with respect to misbehaviors related to alcohol, versus how they cluster with respect to misbehaviors related to marijuana. When clusters have been established, the analyses run in hypotheses 3-6 will again be conducted, replacing theoretically meaningful predictors of CD diagnosis with the new empirically derived scales. All analyses will be conducted for scales of behaviors endorsed in general, then again for scales of alcohol-related behaviors, and again for scales of marijuana-related behaviors.

*Hypothesis 9. Validation of empirically derived scales with baseline alcohol use.* It is hypothesized that the newly derived (9a) MBQ-General scales and (9b) MBQ-Alcohol use scales will each provide concurrent incremental validity, beyond demographics and peer influences and age of onset for alcohol use, when entered into
a multiple regression predicting baseline alcohol use. Independent variables (IVs) will be: age and race, peer influences, and age of first alcohol use (Model 1), and the relevant empirically derived scale (Model 2).

*Hypothesis 10. Validation of empirically derived scales with baseline marijuana use.* Similar to Hypothesis 9, the newly derived (10a) MBQ-General scales and (10b) MBQ-Marijuana use scales will each provide concurrent incremental validity, beyond age, race, peer influences, and age of onset for marijuana use, when entered into a multiple regression predicting baseline marijuana use. IVs will be: age and race, peer influences, and age of first marijuana use (Model 1), and the relevant empirically derived scale (Model 2).

*Hypothesis 11. Validation of the empirically derived scales with three-month follow-up alcohol use.* It is hypothesized that the newly derived (11a) MBQ-General scales and (11b) MBQ-Alcohol use scales will each provide predictive incremental validity, beyond age, race, peer influences, and baseline alcohol use, to predict three-month follow-up alcohol use. IVs will be: age and race, peer influences, and baseline alcohol use (Model 1), and relevant empirically derived scale (Model 2).

*Hypothesis 12. Validation of the empirically derived scales with three-month follow-up marijuana use.* Similarly to Hypothesis 11, the newly derived (12a) MBQ-General scales and (12b) the MBQ-Marijuana use scales will each provide predictive incremental validity, beyond age, race, peer influences, and baseline marijuana use, when entered into a multiple regression predicting three-month follow-up marijuana use. IVs will be: age and race, peer influence, baseline marijuana use (Model 1), and relevant empirically derived scale (Model 2).
## Participants

This is a study utilizing secondary data analysis. Participants (N = 190) were incarcerated adolescents at a facility in the Northeast who were recruited for a treatment study described elsewhere (Stein et al., 2006). At three-month follow-up, 180 participants were retained. The study included adjudicated participants who met any of the following criteria: a) in the year prior to incarceration they used marijuana or alcohol at least monthly, or they binge drank (≥5 drinks for boys, ≥4 drinks for girls) at least once; b) they used marijuana or drank in the four weeks preceding the offense for which they were incarcerated; or c) they used marijuana or drank in the four weeks preceding incarceration. Ns will be smaller for some analyses when participants were missing one or more predictor variables at baseline.

The population at the training school facility is diverse, which is reflected in the study sample (see Table 2). The mean age for the sample was 17.1 years (SD = 1.10), with ages broken down as follows: age 14 (6.3%), age 15 (7.2%), age 16 (27.2%), age 17 (35%), age 18 (22.2%), and age 19 (2.1%). The majority of adolescents were male (86%); there was racial diversity among participants, with 32.8% White, 29.5% Hispanic/Latino, and 27.9% African American.

## Procedure

All procedures were approved by the Brown University and the University of Rhode Island Institutional Review Boards, and by the correctional facility's Research Review Committee. Participants were identified through daily contacts with each of the seven units that house adjudicated adolescents. Adolescents who were at least 18
years old at initial contact were asked for their consent. For others, consent was
obtained from guardians and the adolescents themselves gave written assent.

Guardians and teens were presented with a description of the study including
procedures, potential risks and benefits, and confidentiality, before being asked to sign
a consent form. Assessments were conducted in private with trained research
interviewers. Baseline assignment was completed shortly after adjudication. Follow-
up assessment after release was conducted three months after release from
incarceration. The average time between baseline assessment and follow-up was
253.37 days (SD = 92.19 days).

## Measures

*Parent influences.* Negative parental influence was assessed, with all questions
following the response format of: 0 = No; 1 = Yes; or 2 = Unable to determine. Those
responding “unable to determine” were omitted from analyses. First, adolescents were
asked if their biological mother/father was ever incarcerated or put in detention.

Second, adolescents were asked the following three separate questions for each parent:
"Did your mother/father (biological) ever have trouble with the following because of alcohol or drug use? 1) family or friends, 2) their job, or 3) the law." This was followed by a general question of whether or not each biological parent had a history of alcohol/drug abuse, and then whether or not each parent had been to counseling for drugs or alcohol. A total of 12 questions were asked.

*Peer influences.* Peer influences were assessed with open-ended responses. The adolescents were asked, "About how many of your friends use alcohol or drugs at least once per month?" and "About how many of your friends have been arrested before?"
Both forms of peer influences were categorized using quartile split for chi square and ANOVA analyses.

*Age of first substance use.* Adolescents were asked to report on the age at which they first used alcohol, with an option for “have not used”. They were also asked to report on the age at which they first used marijuana, with an option for “have not used”. Responses were categorized by quartile split for chi square and ANOVA analyses.

*Substance use.* At both baseline and at the follow-up assessment, a three-month time line follow-back (TLFB) was administered for both alcohol and marijuana use, similar to recommendations by Sobell and Sobell (1995). The TLFB is a calendar-assisted approach to help participants recall their substance use over a specified period of time. At baseline participants were asked to report alcohol use and marijuana use for each day during the 90 days before incarceration. At the follow-up assessment participants were again asked to complete the TLFB, which covers the three months since release from incarceration. Alcohol use is measured as the number of heavy drinking days (at baseline and follow-up separately), and marijuana use is measured as the average number of joints smoked per week.

*Misbehaviors Questionnaire.* The MBQ is a 37-item instrument used to assess the number of times misbehaviors were committed in the year prior to incarceration, the number of times alcohol and marijuana were involved in each act, and age at which the teen first and last committed the act (see Appendix for abbreviated version). The final item, separate from assessing misbehaviors, focuses on whether or not the
teen has remorse for his/her actions. The MBQ was also administered at follow-up and
covered the three months since release from incarceration.

The MBQ is based on the work of Dembo and colleagues (e.g., Dembo & Schmeidler, 2003; Dembo, Walters, & Meyers, 2005) and on the SRD by Elliott, Huizinga and colleagues (e.g., Elliott et al., 1985; Huizinga, 1991; Huizinga & Esbensen, 1988). Comparing items from the SRD in Table 1 with the MBQ items in the Appendix shows that the items on the MBQ closely resemble those of the SRD. Some SRD items were combined on the MBQ to save time: stealing something worth < $5 and stealing something worth between $5 and $50 were combined into stealing something worth ≤ $50, for example; hitting a parent and hitting a teacher were combined into hitting (or threatening to hit) an adult. Selling hard drugs as shown on the SRD was split into selling cocaine/crack and selling hard drugs such as heroin/LSD. SRD items reflecting drug use and sexual intercourse were removed from MBQ to prevent redundancy with other measures often used with delinquent adolescents. Finally, in order to develop an instrument with the dual purpose of providing a CD diagnosis, and an ASPD diagnosis, items were added from the DSM-IV. Although a focus of this study is on CD, it also important to be able to assess ASPD for the minority of teens older than 18, who may be encountered in juvenile justice settings. Responses are open-ended for the number of times the individual had committed the act in the prior year.

Similar to the SRD and measures before it (Elliott et al., 1985), the baseline
assessment for the MBQ involved a moderate recall period of one year. At baseline,
teens were asked the number of times each crime or misbehavior was committed over
the last 12 months prior to incarceration, and over the previous three months for
follow-up. They were also asked for age the first and last time the act was committed,
and the number of times each act was committed with alcohol or marijuana involved.
“Involved” was defined in the instructions as “you were on alcohol (or drugs) at the
time or you were doing the activity in order to get alcohol (or drugs) to take.”

From this questionnaire, checklists for Conduct Disorder and Antisocial
Personality Disorder (for those ages 18 and older) were developed. Note that for teens
who qualify for ASPD, CD is no longer an appropriate DSM-IV diagnosis. However,
for purposes of this study, each participant's CD symptom count was obtained, as was
age of CD onset (which is relevant for ASPD).

*Age of onset for CD.* The actual age of onset for CD was calculated from the ages at which acts were first committed, as assessed on the MBQ. The DSM-IV classifies onset categorically, as childhood-onset type or adolescent-onset type, depending on whether the diagnosis came prior to 10 years of age. To be consistent, we used this form of classification, along with a third category for participants not diagnosed, for data analyses.

*Severity of CD.* Conduct disorder severity was measured by counting the number of symptoms endorsed during the year before lockup, as assessed on the MBQ. The DSM-IV recommends specifying severity in terms of mild, moderate, or severe. Although the MBQ scoring algorithm provides these categories, they are not used in the analyses because there is insufficient support in the literature for categories such as these (e.g., Kazdin, 2006).
The data were analyzed with the Statistical Package for the Social Sciences (version 16.0). Because the analyses are focused on initial steps of scale development, the decision was made to maintain alpha at the .05 level. Kazdin (2006) has argued for the use of effect size regardless of *p* values, so that alpha levels were maintained at .05 or better. Power issues were also considered. With up to six IVs in the analyses, *n*’s of ~170 and medium effect sizes, the power is .846.

Preliminary procedures for data cleaning were utilized based on the recommendations of Tabachnick and Fidel (2001). Descriptive analyses were computed on all continuous variables to examine any irregularities in the data that may have lead to violations of the assumptions of normality and linearity. Variables that exceeded the acceptable range of skewness and kurtosis of two and four, respectively, were adjusted as recommended. Specifically, scores for the few participants who were considered “far outliers” based on their extreme responses to specific items were recoded to equal one value greater than the next largest outlier. Examination of univariate distributions revealed out-of-range skewness and kurtosis for several variables. In particular, the number of friends that use alcohol or drugs, the number of friends who have been arrested, baseline and follow-up drinking, and baseline and follow-up marijuana use all demonstrated either skewness or kurtosis beyond our *a priori* limits.

Ten participants were missing at three-month follow-up. One withdrew at baseline; three withdrew at follow-up; five were lost to follow-up; and one withdrew
while still detained. No differences on any demographic variable were found between
the 180 completed cases and 10 withdrawn cases.

Severity of Conduct Disorder

Hypothesis 1a predicted that CD symptom severity is associated with more problematic parent influences. Each parental influence (incarceration, history of drug and alcohol abuse, counseling for drug and alcohol abuse, and problems because of substance abuse with friends and family, work, and with the law) was categorized as “yes” or “no”, and one-way ANOVAs were performed on each variable, with number of CD symptoms as the DV (Table 3). Effect size is estimated from partial eta squared (Cohen, 1988), and described as small (<.09), medium (.09-.24), and large (> .25).

Only two of the parent variables were significant: father's problems with work due to substance abuse, F(1,158) = 5.41, p = .021, ηₚ² = .033, and mother's problems with the law due to substance abuse, F(1,184) = 5.53, p = .030, ηₚ² = .029, were significantly related to CD. As anticipated, both significant influences were such that adolescents with more problematic parents expressed more symptoms of CD, with small effect sizes.

Hypothesis 1b predicted that CD severity is associated with more negative peer variables (friend substance use and friend arrest). The number of friends that use alcohol or drugs at least once per month and the number of friends that have been arrested were categorized using a quartile split. ANOVAs revealed both influences were related to CD severity, as expected. Severity was related to friend substance use, F(3,185) = 4.70, *p* = .003, η<sub>p</sub><sup>2</sup> = .071, a small effect size, with the significant difference (*p* = .002) found between the lowest group, with 0-1 friend (M = 5.05, SD =
2.89), and the highest group, with 13+ friends (M = 7.41, SD = 2.94). Severity was also related to number of friends arrested, $F(3,185) = 4.39, p = .005, \eta_p^2 = .066$, also a small effect size, with significant differences between the lowest group (0-1 friend; M = 5.04, SD = 2.84) and all three other groups: 2-4 friends (M = 6.61, SD = 2.77, p = .041), 5-10 friends (M = 6.79, SD = 3.20, p = .022), and 11+ friends (M = 7.08, SD = 2.99, p = .009).

Hypothesis 1c predicted that earlier age of first time alcohol use would be negatively related to CD symptoms. Adolescents were split into four quartiles based on their age of first alcohol use. In contrast to our specific hypothesis, an ANOVA revealed no significant differences in CD severity by age of first alcohol use, $F(3,178) = 2.54, p > .05, \eta_p^2 = .041$. A similar analysis for age of first time marijuana use tested hypothesis 1d, that age would be negatively related to CD symptoms. Again, the ANOVA was not significant, $F(3,185) = 2.24, p > .05, \eta_p^2 = .035$, again with small effect sizes.

## Age of Onset for Conduct Disorder

Hypothesis 2a predicted that adolescents diagnosed with CD as a child would have more problematic parents than adolescents diagnosed as a teen and those who had not been diagnosed with CD. For these analyses, participants were divided into three groups according to age of onset of CD (child, teen, or none), and parent behaviors were categorized as present/absent. Separate chi square analyses were performed on mother's and father's incarceration, history of drug and alcohol abuse, counseling for drug and alcohol use, and problems because of substance abuse with friends and family, work, and with the law (N's varied because of missing data).
values estimated effect sizes, with small (Φ=.1), medium (Φ=.3), and large (Φ=.5) (Cohen, 1988). Table 4 provides detailed results. Two influences were significantly associated with a younger age of diagnosis: Father ever been incarcerated, $\chi^2(\text{df}=2, N = 166) = 8.02, p = .02, \Phi = .220$ and father's history of alcohol and drug abuse, $\chi^2(\text{df}=2, N = 167) = 6.34, p = .04, \Phi = .195$, both small effect sizes. Arcsine transformations were performed on the data, and these differences were examined more closely with Cohen's (1988) h, with effect sizes of small (h = .2), medium (h = .5), and large (h = .8). With father's incarceration, all three age groups were different from each other, with a large effect size for none/child (h = 0.91), a medium effect size for child/teen (h = 0.51), and a small effect size for none/teen (h = 0.40). Child onset had the highest number of fathers who had been incarcerated, followed by teen onset. For father's history of alcohol and drug abuse, the difference was between none/teen (h = 0.39, a small effect size), with participants with teen onset having more fathers with a history of substance abuse.

Hypothesis 2b predicted that adolescents diagnosed with CD as children would report more peers who use alcohol or drugs and more friends who have been arrested. Chi square tests found no significant associations between age of diagnosis and either friend substance use ($\chi^2[\text{df}=6, N=189] = 8.48, p > .05, \Phi = .212$) or friend arrest ($\chi^2[\text{df}=6, N=189] = 11.47, p > .05, \Phi = .246$), although effect sizes were small-to-medium.

Hypothesis 2c predicted that adolescents diagnosed with CD as children would report starting to use alcohol at an earlier age. An ANOVA was conducted using the
three CD onset age groups as the IV and reported age of first use as the DV. The result
was non-significant F(2,187) = 1.70, p = .186, η² = .018, with small effect size.

Hypothesis 2d predicted that participants diagnosed with CD as a child would report starting to use marijuana at an earlier age than the two other groups. The ANOVA was significant, F(2,187) = 3.18, p = .044, η² = .033, a small effect size. A follow-up Tukey test revealed a significant difference between child (M = 10.86, SD = 2.41) and teen onset (M = 12.43, SD = 2.27), indicating that indeed the group diagnosed with childhood onset of CD also started using marijuana at a younger age.

*Concurrent Incremental Validity: Alcohol*

A directed hierarchical multiple regression (HMR) was used to investigate proposed relationships between severity of CD and the number of heavy drinking days at baseline (baseline alcohol use), after accounting for the proportion of variance explained by the initial independent variables. Because parents are not a major source of influence in the lives of these adolescents, with mostly small effect sizes on initial analyses, and in order to preserve parsimony in the regression models, parental influences were not included in any HMR. In order to control for the effects of age, race, peer influences, and age of first substance use, these influences were entered in the first regression model. Entering variables into the HMR in a theory-grounded process allows for us to examine the unique contribution of each variable or group of variables. A second regression analysis allows for an assessment of the importance of newly entered variables through the calculation of F$_{change}$.

Hypothesis 3a predicted that, after controlling for demographic variables, peer influences, and age of first alcohol use, greater severity of CD at baseline would be
significantly related to more days of heavy drinking at baseline. Concurrent
incremental validity is shown in Table 5 for alcohol use. In the first model
demographic variables, peer influences, and age of first alcohol use were not
significant predictors of alcohol use, R² = .05, F(5,174) = 1.81, p > .05. In the next
model, holding all other variables constant, R² = .09, F(6,173) = 2.77, p = .013; β =
.21, t(173) = 2.69, p = .008. Adding CD symptom count into the second model
increased the R² by 3.8%, which was statistically significant (F_change(1,173) = 7.25, p =
.008).

Hypothesis 3b predicted that, after controlling for demographic variables, peer influences, and age of first alcohol use, childhood age of onset for CD would be associated with more days of heavy drinking at baseline. A directed HMR was conducted, similar to the previous hypothesis. The initial model did not contribute uniquely to the variance of baseline alcohol use: R² = .05, F(5,174) = 1.81, p > .05. Additionally, age of onset for CD added in the second model resulted in a non-significant contribution to baseline drinking: R² = .06, F(6,173) = 1.77, p > .05, which resulted in only a 0.1 increase in R².

Concurrent Incremental Validity: Marijuana

Hypothesis 4a predicted that, after controlling for demographic variables, peer influences, and age of first marijuana use, greater severity of CD at baseline would be significantly associated with a higher average number of marijuana joints smoked per week. The initial model revealed a significant relationship with baseline marijuana use: R² = .07, F(5,174) = 2.53, p = .031. Age of first marijuana use was significantly and negatively related to baseline use, such that younger ages of onset were related to
more frequent use at baseline: $β = -.24, t(174) = -3.17, p = .002$. Additionally, the second model with baseline CD severity was significant: $R^2 = .07, F(6,173) = 2.16, p = .049$; however, the 0.2% change in $R^2$ due to CD severity was non-significant ($F_{change}(1,173) = 0.33, p > .05$) (see Table 6).

Hypothesis 4b predicted that, after controlling for demographic variables, peer influences, and age of first marijuana use, age of onset for CD would be significantly associated with a higher average number of joints smoked per week at baseline. The initial model revealed a significant relationship with baseline marijuana use: $R^2 = .07, F(5,174) = 2.53, p = .031$. Unlike with severity, the second model with the addition of CD onset was not significant: $R^2 = .07, F(6,173) = 2.06, p > .05$.

### Predictive Incremental Validity: Alcohol

Predictive incremental validity is shown in Tables 7 (for alcohol) and 8 (for marijuana). Hypothesis 5a predicted that, after taking into account demographic variables, peer influences, and the number of heavy drinking days at baseline, greater severity of CD at baseline would be significantly related to more heavy drinking days at three month follow-up. In the initial model, demographic variables, peer influences, and the number of heavy drinking days at baseline were significantly associated with use at three month follow-up: $R^2 = .28, F(5,155) = 11.73, p < .001$; of these influences in the initial model, only baseline drinking was a unique contributor: $β = .48, t(155) = 6.82, p < .001$. The second model was significant after adding CD severity ($R^2 = .28, F(6,154) = 10.00, p < .001$); however, the increase was only 0.6%, not a significant change in $R^2$ ($F_{change}(1,154)= 1.22, p > .05$).
Hypothesis 5b predicted that, after taking into account demographic variables, peer influences, and the number of heavy drinking days at baseline, childhood onset for CD would be significantly associated with greater alcohol use at three-month follow-up. The baseline model resulted in a significant overall relationship: $R^2 = .28$, $F(5,155) = 11.73, p < .001$. The addition of age of onset for CD, again, did not increase $R^2$: $F_{change}(1,154) = 0.19, p > .05$.

*Predictive Incremental Validity: Marijuana*

Hypothesis 6a predicted that, after controlling for demographic variables, peer influences, and average number of joints smoked per week at baseline, greater severity of CD at baseline would predict more joints smoked per week at three-month follow-up. The baseline variables (demographics, peer influences, and baseline marijuana use) accounted for 19.1% of the variability in follow-up marijuana use: $F(5, 155) = 7.33, p < .001$. Specifically, baseline use was a positive and significant influence: $\beta = .42, t(155) = 5.75, p < .001$. CD symptom count, added in the second model, did not contribute to the overall variance accounted for: $F_{change}(1,154) = 0.50, p > .05$.

Hypothesis 6b predicted that, after controlling for demographic variables, peer influences, and average number of joints smoked per week at baseline, childhood onset of CD would predict greater marijuana use at follow-up. Both models were significant overall: $F(5,155) = 7.33, p < .001$ and $F(6,154) = 6.12, p < .001$ for Models 1 and 2, respectively. Contrary to the specific hypothesis, age of onset for CD did not contribute to follow-up marijuana use over and above the variables in the initial model: $F_{change}(1,154) = 0.26, p > .05$.
*Internal reliability and stability of the CD symptom clusters and CD diagnosis.*

It was hypothesized (Hypothesis 7) that Cronbach's alpha would be comparable to previous measures that have examined CD behaviors. Clusters of misbehaviors were created based on DSM-IV criteria for CD. Items were coded to reflect the following constructs: Aggression to people and animals, destruction of property, deceitfulness or theft, and serious violations of the rules. The CD scale as a whole had moderate loadings (α = .745), as did items for aggression to people and animals (α = .690), and the deceitfulness or theft cluster (α = .563). However the destruction of property (α = .151) and serious violations of the rules (α = .376) both had low loadings.

It was also hypothesized that stability between baseline and follow-up for CD and its symptom clusters would be comparable to other measures. Test-retest reliabilities were calculated between baseline and follow-up assessments, and are presented with internal consistencies for each of the four CD symptom clusters. All alphas were lower at 3-month follow-up. Large differences were found within deceitfulness or theft and destruction of property. Reliabilities and follow-up alphas are as follows: aggression to people and animals: r = .155, p = .037, α = .672; deceitfulness or theft: r = .025, p = .737, α = -.007; destruction of property: r = .082, p = .272, α = .000; and serious violations of the rules: r = -.070, p = .347, α = .210. The CD scale as a whole showed a followup alpha of .708, but the reliability was not significant: r = .099, p = .176.
## *Creation of Empirically Derived MBQ Scales*

Prior to examination of Hypothesis 8, several steps were taken to evaluate items for inclusion in scales and then to identify factors that emerged, using the exploratory approach, described earlier, recommended by Redding et al. (2006). The 36 items (the remorse item was removed because it used a different response format) of the MBQ were first dichotomized to indicate if each behavior was exhibited (yes=1) or not (no=0). Item analyses were conducted separately for the general, alcohol-related, and marijuana-related MBQ items, using MAP and parallel analysis as indicators of the number of factors to retain and factor loadings per item. Clear scales, in terms of content and factor loadings and respectable internal reliabilities, emerged for all three measures, MBQ-General, MBQ-Alcohol, and MBQ-Marijuana (see Tables 9-11, respectively).

It was hypothesized that the three measures would differ from each other in terms of item content and clustering. Support was largely found. The MBQ-General scales consisted of two factors, labeled “Predatory Aggression” (e.g., used a weapon, attacked someone with the idea of seriously hurting or killing, used force to get money or things from other teens or adults) with $α = .799$, and “Stealing and Generalized Delinquency” (e.g., stolen [or tried to steal] a motor vehicle, stolen [or tried to steal] something worth more than $50, ran away from home overnight), $α = .685$, together accounting for 33.33% of the variance (Table 9). Two factors formed the MBQ-Alcohol scales (Stealing and Impulsive Delinquency and Predatory Aggression), with alphas of .834 and .821, respectively, accounting for 35.62% of the variance (Table 10). Two factors formed the MBQ-Marijuana scales (Stealing and Generalized
Delinquency, and Drug Dealing and Aggressive Behavior), with alphas of .781 and
.653, respectively, accounting for 40.93% of the variance.

Internal reliabilities for the scales were also calculated at followup. For the
three aggression-related measures, alphas were similar to those assessed at baseline:
.526, .843, and .676 for General Predatory Aggression, Alcohol Predatory Aggression,
and Marijuana Drug Dealing and Aggressive Behavior, respectively. However, alphas
were lower for the three stealing and delinquency scales: .026, .422, and .587 for
General Stealing and Generalized Delinquency, Alcohol Stealing and Impulsive
Delinquency, and Marijuana Stealing and Generalized Delinquency. The low alpha
observed for the General Stealing and Generalized Delinquency measure appears to be
caused by one item, "Stayed out at night despite house rules not to." Removing this
item increases the alpha for the scale to .577.

Stability of the empirical scales from baseline to three-month followup was
examined with test-retest correlations. Scores on all six scales were significantly
correlated at the two times: General Predatory Aggression *r* =.348, *p*<.001; General
Stealing and Generalized Delinquency *r* =.204, *p* =.006; Alcohol Stealing and
Impulsive Delinquency *r* =.293, *p* <.001; Alcohol Predatory Aggression *r* =.377, *p*
<.001; Marijuana Stealing and Generalized Delinquency *r* =.279, *p* < .001; Marijuana
Drug Dealing and Aggressive Behavior *r* =.237, *p* = .001.

*Concurrent Incremental Validity: Empirical Scales and Alcohol Use*

Concurrent incremental validity results for the empirical scales are shown in
Tables 12 (alcohol use) and 13 (marijuana use). Relationships between the empirically
derived MBQ scales and the number of heavy drinking days at baseline, after
controlling for demographic variables, peer influences, and age of first alcohol use, were investigated with the use of directed HMRs. Hypothesis 9a tested the concurrent incremental validity of the MBQ-General scales. In the first model, an omnibus F test revealed a non-significant relationship between the first model and the number of heavy drinking days at baseline, contributing 5.0% of the variance, F(5,174) = 1.81, *p* = .113. With the MBQ-General scales entered in the second model, a significant omnibus F test was produced (R² = .10, F[7,172] = 2.76, *p* = .010). The MBQ-General scales were significant predictors, holding the initial variables constant (Fchange [2,172] = 4.93, *p* = .008). Only the General Predatory Aggression subscale was a significant predictor, β= .161, t(172) = 2.06, *p* = .041.

Support for Hypothesis 9b, which examined concurrent incremental validity of the MBQ-Alcohol scales, was found in the HMR models. Model 1 was significantly related to the number of heavy drinking days at baseline, F(5,174) = 1.81, *p* = .113, R² = .05. With the addition of the MBQ-Alcohol scales in the second model, F(7,172) = 14.06, *p* < .001, R² increased to .36 (a 31.4% increase), which was a significant addition holding all other variables constant: Fchange (2,172) = 42.51, *p* < .001. Both alcohol-involved scales were significant predictors: Alcohol-Involved Stealing and Impulsive Delinquency (β= .355, t(172) = 4.60, *p* < .001) and Alcohol-Involved Predatory Aggression (β= .299, t(172) = 3.85, *p* < .001).

*Concurrent Incremental Validity: Empirical Scales and Marijuana Use*

Relationships between newly derived scales and baseline marijuana use were also examined, again after taking into account demographic variable, peer influences, and age of first marijuana use. Hypothesis 10a predicted a significant relationship
between MBQ-General scales and baseline use of marijuana, defined as the average number of joints smoked per week. Demographic variables, peer influences, and age of first use were significant when entered in the first model; age of first marijuana use was the only significant predictor, with a negative relationship: R² = .07, F (5,174) = 2.78, *p* = .019; β = -.26, *t*(174) = -3.36, *p* = .001. In the second model, entering the MBQ-General scales provided a 1.4% increase in variance explained, which was not significant, Fchange (2,172) = 1.36, *p* = .260. As this was a non-significant test, there is not a significant contribution of either scale.

Hypothesis 10b investigated the influence of MBQ-Marijuana in the second model to determine if this scale added unique variance to baseline marijuana use. As with the previous analysis, demographic variables, peer influences, and age of first use were again entered in the first model, with only age of first use a significant negative predictor (R² = .07, F[5,174] = 2.78, *p* = .019). The MBQ-Marijuana scales were then entered, and for Model 2, R² = .18, F(7,172) = 5.24, *p* < .001. The 10.2% increase in variance explained by the MBQ-Marijuana scales was significant: Fchange(2,172) = 10.61, *p* < .001. Marijuana-Involved Drug Dealing and Aggressive Behavior was the only significant scale (β = .32, *t*(172) = 4.12, *p* < .001).

*Predictive Incremental Validity: Empirical Scales and Alcohol Use*

Hypothesis 11a, regarding the predictive incremental validity of the empirically derived MBQ-General scales with follow-up alcohol use, was examined using the same approach as described above. The omnibus F test between the first model and follow-up use of alcohol was significant: R² = .28, F(5,155) = 11.73, *p* < .001. Baseline use regressed onto follow-up use: β = .48, *t*(155) = 6.82, *p* < .001. The
second model was overall significantly related to follow-up alcohol use ($R^2 = .31$, $F[7,153] = 9.79, p < .001$), with a significant 3.5% change in $R^2$ ($F_{change}[2,153] = 3.85, p = .023$) for the MBQ-General scales. MBQ-General Predatory Aggression was the only significant scale ($\beta = .20, t(153) = 2.76, p = .006$). Tables 14 and 15 show the predictive incremental validities for the empirically derived scales.

Similarly, analyses for hypothesis 11b, regarding MBQ-Alcohol scales, produced a significant omnibus F test between the initial model and follow-up use of alcohol: $R^2 = .28$, $F(5,155) = 11.73, p < .001$. In Model 2, the MBQ-Alcohol scales were added: $R^2 = .35$, $F(7,153) = 11.86, p < .001$; $F_{change}(2,153) = 9.11, p < .001$, with MBQ-Alcohol Predatory Aggression the only significant scale ($\beta = .345, t(153) = 3.76, p < .001$).

## Predictive Incremental Validity: Empirical Scales and Marijuana Use

Similar to many previous models, the HMR for assessing Hypothesis 12a, examining the association between MBQ-General scales and follow-up marijuana use, had significant omnibus models: $R^2 = .19$, $F(5,155) = 7.33, p < .001$ and $R^2 = .23$, $F(7,153) = 6.54, p < .001$ for Models 1 and 2, respectively. Baseline marijuana use contributed significantly on its own to followup marijuana use, $\beta = .42, t(155) = 5.75, p < .001$. Additionally, the inclusion of the general scales did contribute significantly over what was explained in the initial model: $F_{change}(2,153) = 3.87, p = .023$. The MBQ-General Predatory Aggression scale was again the only significant scale ($\beta = .197, t(155) = 2.57, p = .011$).

Hypothesis 12b, assessing the MBQ-Marijuana scales, produced different results. There was overall significance with Model 1 ($R^2 = .19$, $F[5,155] = 7.33, p < .001$).
.001) and Model 2 (R² = .21, F[7,153] = 5.63, p < .001), but the 1.4% change in R² was not significant: Fchange (2,153) = 1.30, p > .05.
MBQ as a Diagnostic Tool for CD

The MBQ was assessed as a measure for diagnosing CD in terms of severity, measured by symptom count. Constructs noted to have important associations with the disorder were included in the analyses. CD severity was associated with more problematic parental influences, as anticipated. However, fewer specific influences were significantly related than hypothesized. Father's problems with work due to substance use and mother's problems with the law due to substance use related significantly to increased severity of CD, with small effect sizes. Peer influences were a significant influence: CD severity was related to friend substance use and friend arrest. The main sources of peer influences were found when adolescents had a large social environment of substance using peers (i.e., 13+ friends), and when they had more than one friend who had been arrested. Surprisingly, severity of CD was not related to age of first use of either alcohol or marijuana.

We next examined the same background influences on age of onset for CD diagnosis. Peer influences were not related to age of onset, and the only parent variables directly related was father ever having been incarcerated and father's history of alcohol or drug abuse. However, adolescents diagnosed with CD as children (rather than as teens) reported using marijuana at younger ages. There was a stronger association between age of onset for CD and first marijuana use when the first use was at age 10 or younger.

Taken together, these two sets of results suggest that on the one hand, peers may greatly influence the amount of CD symptoms a teen is exhibiting, but that on the
other hand, current peers have no direct relation to CD age of onset within this sample.

Whereas first substance use was unrelated to severity, it was significantly related to age of onset for CD diagnosis, with earlier initiation of marijuana use being significantly associated with earlier CD diagnosis. These data indicate that parent-related constructs are associated with age of onset of CD, and to a lesser extent with severity, but with very few significant associations it is difficult to understand the nature or extent of parent influences. On the other hand, while problematic peers are unrelated to age of CD onset, the significant relationship between more problematic peers and greater CD severity may reflect an adolescent's peer choices. These relationships provide preliminary evidence for the content validity of the MBQ to provide diagnostic information on CD.

The CD diagnosis evidenced some incremental concurrent validity. After holding demographics, peer influences, and age of first substance use constant, severity of CD was significantly related to number of heavy drinking days at baseline but not to the average number of joints smoked per week at baseline. Similar analyses were then run with focus on age of onset for CD rather than severity, with results indicating no incremental concurrent validity for either number of heavy drinking days at baseline or the average number of joints smoked per week. Additional analyses indicate neither CD severity nor age of onset for CD provided incremental predictive validity. We know of no other instruments assessing CD diagnosis that have been put to the rigorous test of incremental concurrent or predictive validity. Although analyses did not produce overwhelming evidence for incremental validity, the finding that some
limited incremental concurrent validity was found for CD severity's association with
number of heavy drinking days is noteworthy.

Test-retest reliability is difficult to compare to other scales, since other studies
have based their psychometrics on relatively short intervals (4 to 21 days). Edelbrock,
Costello, Dulcan, Kalas, and Conover (1985) found moderate test-retest reliability
over an average of 9 days (1-3 weeks) for the children's version of DISC, the DISC-C,
(average r = .71 for ages 14-18), with particularly high reliabilities for the Conduct
Disorder subscales (r = .77). They further noted that reliability for adolescents was
greater than that for young children (ages 6-9, r = .43), which was much lower than
older children (r = .60). Noting cautions of Rogers (2001) regarding clinical research,
Chambers et al. (1985) obtained test-retest reliabilities of .72 over a 72-hour test-retest
period for the K-SADS summary scales. Achenbach and Rescorla (2001) report that 8-
day test-retest reliabilities for the CBCL ranged from .80 to .94. Subscales for Rule-
Breaking, Aggressive Behavior and DSM-IV-Oriented Conduct Problems had better
reliability than most other scales (r= .90 to .93). Reliabilities for the present DSM-IV-
based CD scale overall, as well as subscales, were much smaller, ranging from -.07 to
.155. Only the aggression scale reached statistical significance (p = .037). This lower
association may be due to the greater elapsed time in the present study, as well as the
interventions received by adolescents between testings. It would be useful to examine
reliabilities with these scales utilizing a shorter test-retest interval.

Internal consistencies of the DSM-IV-based CD clusters were mixed. As a
whole, the CD scale appears reliable, with moderate loadings (α = .745). However,
results varied for subscales. For aggression to people/animals and deceitfulness/theft,
factor loadings for the MBQ items assessed in general were within an acceptable range, with α's of .690 and .563, respectively. While Cronbach's alpha for the aggression scale was α = .672 at follow-up and demonstrated stability across time, follow-up alpha for deceitfulness was α = -.007 (r = .025, p = .737), much lower than the baseline assessment. Furthermore, for destruction of property and serious violations of the rules, alpha levels at baseline did not exceed .376, considered unacceptable for a scale, and were low at follow-up as well.

We could find no diagnostic tools presenting internal consistencies with data for CD and its symptom clusters. However, published data on comparable psychometrics for other assessments of youth misbehavior show similarly mixed patterns of internal consistency. Pinto and Grilo (2004) reported internal consistencies for the MACI subscales ranging from .73 to .91. Achenbach and Rescorla (2001) reported internal consistencies ranging from .63 to .94 on the CBCL. As with the present data, they found particularly high internal consistency for Aggressive Behavior and DSM-IV-Oriented Conduct Problems (α = .94 and .91, respectively). It may be that internal consistencies are somewhat more attenuated for the MBQ CD and clusters because of fewer items per CD symptom cluster as compared to the MACI and CBCL scales.

Elliott et al. (1985) discuss a preference to exclude internal reliabilities with clinical measures, particularly those like the SRD which list a number of possibly unrelated criteria. Huizinga and Elliott (1986) note that the SRD index is multidimensional with heterogeneous items. As such, they argue that with this and other self-reported delinquency measures, the use of split-half or other internal
measures of consistency is inappropriate. The same may be argued regarding use of
the MBQ to provide a CD diagnosis; the items, which are selected to represent DSM-
IV criteria, are not empirically based nor necessarily psychometrically related, since
they are intended to aid in a clinical diagnosis. While the internal consistencies for
three of the subscales were poor, it should be noted that there were a small number of
items per subscale, so that item heterogeneity had an even greater impact on their
psychometric properties. Furthermore, the items ask about frequencies of relatively
low base rate, opportunistic behaviors, which would be expected to vary over time and
circumstances. In spite of these cautions, the MBQ CD scale and the subscales for
aggression and deceitfulness are still valid for assessing clinically relevant behaviors
at a given time.

*Empirically Derived Scales*

The second major aim of this study was to explore the MBQ with the goal of producing empirically derived scales. It was hypothesized that acts committed in general would form different scales as compared to acts committed with the involvement of alcohol or marijuana. We employed PCAs for each of the general, alcohol-, and marijuana-involved behaviors, utilizing the MAP criterion and parallel analysis. Several iterations generated clear scales within the three areas (general, alcohol, and marijuana). Factors formed for behaviors assessed generally were: Predatory Aggression, and Stealing and Generalized Delinquency. Factors for behaviors assessed with alcohol were Stealing and Impulsive Delinquency and Predatory Aggression. For behaviors endorsed involving marijuana, we found two
factors: Stealing and Generalized Delinquency, and Drug Dealing and Aggressive Behavior.

Internal consistencies for the empirical scales were .653 to .834 at baseline, but were somewhat lower when calculated on followup data. This could be due to the intervening treatment adolescents had received; the data tended to be more bimodal (either no/one occurrence, or multiple incidents). While significant, test-retest correlations were also below adequate levels of .70 (Tabachnick & Fidell, 2001). This could be explained by the long test-retest interval (8 months), the intervening treatment, and the fact that the scales were developed to measure behavioral patterns at a given time, not personality traits.

Assessments of validity of the MBQ empirically derived scales demonstrated good concurrent and predictive incremental validity for the general, alcohol-involved, and marijuana-involved empirical scales. The alcohol-involved scales were significantly associated with the number of heavy drinking days at baseline over and above other predictors (demographics, peer influences, and age of onset for substance use), and one of the marijuana-involved scales (Drug Dealing and Aggressive Behavior) was significantly associated with the average number of joints smoked per week at baseline, over and above other predictors. The MBQ-General Predatory Aggression scale was significantly related to baseline alcohol, but not marijuana, use.

The empirically derived MBQ-General and Alcohol-involved scales were significantly associated with follow-up alcohol use, adding a significant increase in variance explained (in both cases, the Predatory Aggression scale was the contributing factor). However, although the MBQ-General Predatory Aggression scale was
significantly associated with follow-up marijuana use, the Marijuana-involved scales did not significantly contribute to variance explained over and above the initial model variables. Clearly, the measures of marijuana use need to be examined further and perhaps refined.

In summary, the MBQ-Alcohol empirical scales have good concurrent and predictive validity overall, as does the MBQ-General Predatory Aggression scale. MBQ-Marijuana empirical scales are somewhat more limited, with only the Drug Dealing and Aggressive Behavior scale having concurrent incremental validity. Across the MBQ-General, Alcohol-, and Marijuana-related empirical scales, scales relating to stealing did not appear to have as much incremental validity as those related to more aggressive behaviors.

## Limitations and Future Directions

While there are several exciting results from this study, there are some general limitations to acknowledge. The sample size is small, relative to the types of analyses that were conducted. Important similarities or group differences may have been missed due to small cell sizes. For example, non-significant relationships between the various influences surrounding CD (e.g. parental substance abuse) and both CD severity and age of onset for CD may in fact be significant, but are undetected because of lopsided group distributions. In the empirical scales, content items that fell out due to lower loadings may be very important, but not strong enough to remain in the analyses. Future studies should seek a larger sample, with greater female representation. In addition, it would be interesting to extend the sample to examine adults diagnosed with ASPD as well as youth. Assessing the MBQ across settings
would help determine reliability and validity as a more generally useful assessment instrument. With increased numbers of diverse participants, the items could be examined for racial or ethnic bias. Finally, it would be valuable to determine the applicability of the MBQ to the measurement of treatment effectiveness.

In an effort to be thorough in the psychometric evaluation of CD diagnostic information, internal consistency and test-retest stability were provided. However, as was already noted, the test-retest period was lengthy (about 8 months). In addition, adolescents received treatment between testings, and the TLFB period covered was 3 months. Future studies may wish to assess CD for a six-month period and use a two-week test-retest period.

This study relied on the use of self-report data, which is limited to the honesty and memory of adolescents. It is a consideration for much research examining substance use and in clinical populations. Several attempts were made to enhance the credibility of self-reports in this study. Privacy was enforced at all times. Participants were aware that their responses would not gain them any form of reward during their incarceration, nor would they receive any punishment, and they were also aware that responses would be checked against records and biological markers. Dennis, Funk, Godley, Godley, and Waldron (2004) assessed the reliability and predictive validity of the self-reported Form 90 Timeline Followback method, and concluded it was a useful method of measuring substance use, as it related to other indicators of abuse-related problems.

An important next step is to replicate the results. The MBQ may be of interest to clinicians and researchers as a diagnostic tool for CD, and with the use of empirical
scales, it may afford users a closer look at clusters of misbehaviors. The MBQ can
provide diagnostic information on CD mapped onto DSM-IV, including age of onset
for CD and symptom severity. Preliminary evidence for the MBQ as a tool to provide
diagnostic information on CD was presented. Generally, severity of CD symptom
count and age of onset for CD were significantly associated with the social influences
and substance use items that were examined, and in the anticipated directions.

Although very limited evidence emerged for the incremental validity of the CD
diagnostic information, we know of no other studies offering such rigorous validity
testing of other tools' CD diagnostic information. Persons interested in the SPSS
program for obtaining CD diagnostic information may contact the author.
Conclusions

The MBQ is a flexible instrument that generally maintains SRD constructs and scales and provides diagnostic information regarding CD and ASPD. Furthermore, the MBQ provides empirically derived General, Alcohol-, and Marijuana-related scales, which extend research and clinical utility. Across MBQ-General, Alcohol-, and Marijuana-related behaviors, two factors emerged that involved stealing and aggression. However, Alcohol-related Stealing also included a somewhat more impulsive flavor, whereas Marijuana-related Aggression included an element of drug dealing. Of interest is whether results replicate in other samples, and if so why such nuanced differences emerge, as well as how such information may assist with assessing and treating incarcerated adolescents. Also of interest is how well the empirically derived scales maintain their strong psychometric properties, especially with respect to their incremental validity.

These empirically derived scales can be helpful in determining an adolescent's relative involvement in different types of misbehavior, by calculating the proportion of misbehaviors in each category. At this time we recommend first converting all items to 0 (not done) or 1 (did once or more). Within a domain (general, alcohol, or marijuana), calculate the average number of misbehaviors per factor, by adding up the total number of misbehaviors and dividing by the number of items within that factor. Next, divide the sum for each factor by the domain total and multiply by 100 to obtain a percent. For example, within the MBQ-General domain, there are 20 items, seven Stealing and Generalized Delinquency and 13 Predatory Aggression items. A particular teen scores 5 on Stealing and Generalized Delinquency and 4 on Predatory
Aggression, for proportional scores of .714 (5/7) and .308 (4/13), respectively. Adding .714 and .308 provides a total of 1.022. Divide .714 by 1.022 and multiply by 100; divide .308 by 1.022 and multiply by 100. This yields 69.86% and 30.14%, respectively. Relatively speaking, this adolescent is more involved in Stealing and Generalized Delinquency than Predatory Aggression-type misbehaviors.

Of course, we are not recommending the MBQ as a substitute over the SRD, which remains a standard in the field. However, the results of this study suggest the MBQ offers improvements in several ways: 1) the MBQ incorporates age of onset for each misbehavior, addressing the concerns of Lahey et al. (1995) that were mentioned earlier; and 2) it provides both standard conduct disorder and empirically derived scales that are valid and reliable.
Table 1

Items from the SRDª

Delinquent

1) Damaged property (family)

2) Damaged property (school)

3) Damaged property (other)

4) Stole motor vehicle

5) Stole something > $50

6) Bought, sold, held stolen goods

7) Thrown objects at cars or people

8) Ran away from home

9) Lied about age

10) Carried weapon

11) Stole something < $5

12) Attacked someone

13) Paid for having sex

14) Had sex other than spouse

15) Been involved in gang fights

16) Sold marijuana

17) Cheated on school tests

18) Hitchhiked illegally

19) Stole money (family)

20) Hit (teacher)

21) Hit (parent)

22) Hit (student)

23) Disorderly conduct

24) Sold hard drugs

25) Taken vehicle for ride

26) Gave liquor to minor

27) Had sex w/someone against will

28) Strong-arm (student)

29) Strong-arm (teacher)

30) Strong-arm (other)

31) Avoided paying for things

32) Been drunk in public

33) Stole something between $5 - $50

34) Stole something at school

35) Broke into something to look around

36) Begged from strangers

37) Skipped class

38) Did not return extra change to cashier

39) Suspended from school

40) Made obscene phone calls

Non-Delinquent

1) Found something, returned to owner

2) Helped someone who was badly hurt

3) Gave to someone in need

4) Refused to cheat w/another student

5) Talked friends out of something illegal

Drug Use

1) Alcohol

2) Marijuana

3) Hallucinogens

4) Amphetamine

5) Barbiturates

6) Heroin

7) Cocaine

<footnote>ªItems were assessed for frequency during the previous year</footnote>
<caption>Table 2</caption>

*Participant Characteristics*

<table><thead><tr><th></th><th>Percentage</th><th>n</th></tr></thead><tbody><tr><td>Gender</td><td></td><td></td></tr><tr><td>Female</td><td>14.2</td><td>27</td></tr><tr><td>Male</td><td>85.8</td><td>163</td></tr><tr><td>Race</td><td></td><td></td></tr><tr><td>African American</td><td>27.9</td><td>53</td></tr><tr><td>Asian American</td><td>3.2</td><td>6</td></tr><tr><td>Native American</td><td>3.7</td><td>7</td></tr><tr><td>Caucasian</td><td>32.6</td><td>62</td></tr><tr><td>Hispanic/Latino</td><td>29.4</td><td>56</td></tr><tr><td>Other</td><td>3.2</td><td>6</td></tr><tr><td>Age of Onset for CD</td><td></td><td></td></tr><tr><td>None</td><td>22.1</td><td>42</td></tr><tr><td>Child</td><td>7.4</td><td>14</td></tr><tr><td>Teen</td><td>70.5</td><td>134</td></tr><tr><td>CD Severity (# of symptoms endorsed)</td><td></td><td></td></tr><tr><td>1.0</td><td>3.7</td><td>7</td></tr><tr><td>2.0</td><td>7.9</td><td>15</td></tr><tr><td>3.0</td><td>10.5</td><td>20</td></tr><tr><td>4.0</td><td>6.8</td><td>13</td></tr><tr><td>5.0</td><td>11.1</td><td>21</td></tr><tr><td>6.0</td><td>12.6</td><td>24</td></tr><tr><td>7.0</td><td>11.1</td><td>21</td></tr><tr><td>8.0</td><td>12.6</td><td>24</td></tr><tr><td>9.0</td><td>4.8</td><td>9</td></tr><tr><td>10.0</td><td>10.5</td><td>20</td></tr><tr><td>11.0</td><td>2.6</td><td>5</td></tr><tr><td>12.0</td><td>4.8</td><td>9</td></tr><tr><td>13.0</td><td>0.5</td><td>1</td></tr><tr><td>14.0</td><td>0.5</td><td>1</td></tr></tbody></table>
<caption>Table 3</caption>

CD Severity, Hypothesis 1

<table><thead><tr><th>Item</th><th>df between</th><th>df within</th><th>F</th><th>p</th><th>partial η²</th></tr></thead><tbody><tr><td>Father ever incarcerated</td><td>1</td><td>164</td><td>1.44</td><td>.23</td><td>.009</td></tr><tr><td>Mother ever incarcerated</td><td>1</td><td>184</td><td>2.33</td><td>.13</td><td>.012</td></tr><tr><td>Father history of alcohol/drug abuse</td><td>1</td><td>165</td><td>2.65</td><td>.11</td><td>.016</td></tr><tr><td>Mother history of alcohol/drug abuse</td><td>1</td><td>186</td><td>1.83</td><td>.18</td><td>.010</td></tr><tr><td>Father counseling for alcohol/drug abuse</td><td>1</td><td>188</td><td>1.38</td><td>.24</td><td>.007</td></tr><tr><td>Mother counseling for alcohol/drug abuse</td><td>1</td><td>188</td><td>0.95</td><td>.76</td><td>.001</td></tr><tr><td>Father problem with family &amp; friends</td><td>1</td><td>164</td><td>3.64</td><td>.06</td><td>.022</td></tr><tr><td>Mother problem with family &amp; friends</td><td>1</td><td>184</td><td>1.55</td><td>.22</td><td>.008</td></tr><tr><td>Father problem with work</td><td>1</td><td>158</td><td>5.41</td><td>.02*</td><td>.033</td></tr><tr><td>Mother problem with work</td><td>1</td><td>184</td><td>1.47</td><td>.23</td><td>.008</td></tr><tr><td>Father problem with law</td><td>1</td><td>159</td><td>1.94</td><td>.17</td><td>.012</td></tr><tr><td>Mother problem with law</td><td>1</td><td>184</td><td>5.53</td><td>.03*</td><td>.029</td></tr><tr><td>Friend use</td><td>3</td><td>185</td><td>4.70</td><td>.003**</td><td>.071</td></tr><tr><td>Friend arrested</td><td>3</td><td>185</td><td>4.39</td><td>.005**</td><td>.066</td></tr><tr><td>Age 1st alcohol use</td><td>3</td><td>178</td><td>2.54</td><td>.06</td><td>.041</td></tr><tr><td>Age 1st marijuana use</td><td>3</td><td>185</td><td>2.24</td><td>.09</td><td>.035</td></tr></tbody></table>

Note: * p < .05, ** p < .01
<caption>Table 4</caption>

Age of CD Onset, Hypothesis 2

<table><thead><tr><th>Item</th><th>N</th><th>df</th><th>χ<sup>2</sup></th><th>p =</th><th>phi</th></tr></thead><tbody><tr><td>Father ever incarcerated</td><td>166</td><td>2</td><td>8.02</td><td>.02*</td><td>.220</td></tr><tr><td>Mother ever incarcerated</td><td>186</td><td>2</td><td>2.72</td><td>.26</td><td>.121</td></tr><tr><td>Father history of alcohol/drug abuse</td><td>167</td><td>2</td><td>6.34</td><td>.04*</td><td>.195</td></tr><tr><td>Mother history of alcohol/drug abuse</td><td>188</td><td>2</td><td>1.30</td><td>.52</td><td>.083</td></tr><tr><td>Father counseling for alcohol/drug abuse</td><td>190</td><td>2</td><td>0.78</td><td>.68</td><td>.064</td></tr><tr><td>Mother counseling for alcohol/drug abuse</td><td>190</td><td>2</td><td>0.39</td><td>.14</td><td>.144</td></tr><tr><td>Father problem with family &amp; friends</td><td>166</td><td>2</td><td>1.97</td><td>.37</td><td>.109</td></tr><tr><td>Mother problem with family &amp; friends</td><td>186</td><td>2</td><td>3.46</td><td>.18</td><td>.136</td></tr><tr><td>Father problem with work</td><td>160</td><td>2</td><td>0.63</td><td>.73</td><td>.063</td></tr><tr><td>Mother problem with work</td><td>186</td><td>2</td><td>5.47</td><td>.07</td><td>.172</td></tr><tr><td>Father problem with law</td><td>161</td><td>2</td><td>3.78</td><td>.15</td><td>.153</td></tr><tr><td>Mother problem with law</td><td>186</td><td>2</td><td>2.98</td><td>.23</td><td>.127</td></tr><tr><td>Friend use</td><td>189</td><td>6</td><td>8.48</td><td>.21</td><td>.212</td></tr><tr><td>Friend arrested</td><td>189</td><td>6</td><td>11.47</td><td>.08</td><td>.246</td></tr></tbody></table><table><thead><tr><th>Item</th><th>N</th><th>df</th><th>F</th><th>p</th><th>partial<br>η<sup>2</sup></th></tr></thead><tbody><tr><td>Age 1<sup>st</sup> alcohol use</td><td>188</td><td>2,187</td><td>1.70</td><td>.186</td><td>.018</td></tr><tr><td>Age 1<sup>st</sup> marijuana use</td><td>188</td><td>2,187</td><td>3.18</td><td>.044*</td><td>.033</td></tr></tbody></table>

Note: * p < .05
<caption>Table 5</caption>

Concurrent Incremental Validity: CD Diagnosis and Alcohol Use

<table><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>&Delta; R<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p</th></tr></thead><tbody><tr><td colspan="7"><u>Alcohol Use, 3 Months before Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Age of 1<sup>st</sup><br>Alcohol Use</td><td>.050</td><td>.022</td><td>.050</td><td>F = 1.81</td><td>5, 174</td><td>.113</td></tr><tr><td>2) CD Symptom<br>Count</td><td>.088</td><td>.056</td><td>.038</td><td>F = 2.77<br>F<sub>ch</sub> = 7.25</td><td>6, 173<br>1, 173</td><td>.013<br>.008**</td></tr><tr><td colspan="7"><u>Alcohol Use, 3 Months before Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Age of 1<sup>st</sup><br>Alcohol Use</td><td>.050</td><td>.022</td><td>.050</td><td>F = 1.81</td><td>5, 174</td><td>.113</td></tr><tr><td>2) Age of Onset<br>for CD</td><td>.058</td><td>.025</td><td>.008</td><td>F = 1.77<br>F<sub>ch</sub> = 1.55</td><td>6, 173<br>1, 173</td><td>.107<br>.216</td></tr></tbody></table>

<footnote><sup>a</sup>N = 180, due to participants missing one or more predictor variables at baseline.</footnote>

<footnote>Note: ** p < .01</footnote>
<caption>Table 6</caption>

Concurrent Incremental Validity: CD Diagnosis and Marijuana Use

<table><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>ΔR<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p</th></tr></thead><tbody><tr><td colspan="7"><u>Marijuana Use, 3 Months before Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Age of 1<sup>st</sup><br>Marijuana Use</td><td>.068</td><td>.041</td><td>.068</td><td>F = 2.53</td><td>5, 174</td><td>.031*</td></tr><tr><td>2) CD Symptom<br>Count</td><td>.070</td><td>.037</td><td>.002</td><td>F = 2.16<br>F<sub>ch</sub> = 0.33</td><td>6, 173<br>1,173</td><td>.049*<br>.568</td></tr><tr><td colspan="7"><u>Marijuana Use, 3 Months before Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Age of 1<sup>st</sup><br>Marijuana Use</td><td>.068</td><td>.041</td><td>.068</td><td>F = 2.53</td><td>5, 174</td><td>.031*</td></tr><tr><td>2) Age of Onset<br>for CD</td><td>.068</td><td>.036</td><td>.000</td><td>F = 2.06<br>F<sub>ch</sub> = 0.03</td><td>6, 173<br>1, 173</td><td>.054<br>.746</td></tr></tbody></table>

<footnote><sup>a</sup>N = 180, due to participants missing one or more predictor variables at baseline.</footnote>

<footnote>Note: * p < .05</footnote>
<caption>Table 7</caption>

Predictive Incremental Validity: CD Diagnosis and Alcohol Use

<table><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>ΔR<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p</th></tr></thead><tbody><tr><td colspan="7"><u>Alcohol Use, 3 Months after Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Baseline<br>Alcohol Use</td><td>.275</td><td>.251</td><td>.275</td><td>F = 11.73</td><td>5, 155</td><td>.000***</td></tr><tr><td>2) CD Symptom<br>Count</td><td>.280</td><td>.252</td><td>.006</td><td>F = 10.00<br>F<sub>ch</sub> = 1.22</td><td>6, 154<br>1, 154</td><td>.000***<br>.271</td></tr><tr><td colspan="7"><u>Alcohol Use, 3 Months after Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Baseline<br>Alcohol Use</td><td>.275</td><td>.251</td><td>.275</td><td>F = 11.73</td><td>5, 155</td><td>.000***</td></tr><tr><td>2) Age of Onset<br>for CD</td><td>.275</td><td>.247</td><td>.001</td><td>F = 9.76<br>F<sub>ch</sub> = 0.19</td><td>6, 154<br>1, 154</td><td>.000***<br>.666</td></tr></tbody></table>

<footnote><sup>a</sup>N = 161, due to availability of data on alcohol use 3 mo. after incarceration; n= 19 were in a controlled environment again soon after release.</footnote>

<footnote>Note: *** p < .001</footnote>
<caption>Table 8</caption>

Predictive Incremental Validity: CD Diagnosis and Marijuana Use

<table><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>&Delta;R<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p</th></tr></thead><tbody><tr><td colspan="7"><u>Marijuana Use, 3 Months after Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Baseline<br>Marijuana Use</td><td>.191</td><td>.165</td><td>.191</td><td>F = 7.33</td><td>5, 155</td><td>.000***</td></tr><tr><td>2) CD Symptom<br>Count</td><td>.194</td><td>.163</td><td>.003</td><td>F = 6.18<br>F<sub>ch</sub> = 0.50</td><td>6, 154<br>1, 154</td><td>.000***<br>.481</td></tr><tr><td colspan="7"><u>Marijuana Use, 3 Months after Incarceration</u></td></tr><tr><td>1) Age, Race,<br>Friend Use,<br>Friend Arrested,<br>Baseline<br>Marijuana Use</td><td>.191</td><td>.165</td><td>.191</td><td>F = 7.33</td><td>5, 155</td><td>.000***</td></tr><tr><td>4) Age of Onset<br>for CD</td><td>.193</td><td>.161</td><td>.001</td><td>F = 6.12<br>F<sub>ch</sub> = 0.26</td><td>6, 154<br>1, 154</td><td>.000***<br>.629</td></tr></tbody></table>

<footnote><sup>a</sup>N = 161, due to availability of data on marijuana use 3 mo. after incarceration; n= 19 were in a controlled environment again soon after release.</footnote>

<footnote>Note: *** p < .001</footnote>
<caption>Table 9</caption>

Empirically Derived MBQ-General

<table><thead><tr><th>Factor</th><th>Label</th><th>Question</th><th>Factor Loading</th></tr></thead><tbody><tr><td rowspan="2">1</td><td rowspan="2"></td><td>Used a weapon</td><td>.756</td></tr><tr><td>Attacked someone with the idea of seriously hurting or killing</td><td>.613</td></tr><tr><td rowspan="10">α = .799</td><td rowspan="10">Predatory Aggression</td><td>Initiated physical fights</td><td>.573</td></tr><tr><td>Used force to get money or things from other teens</td><td>.564</td></tr><tr><td>Used force to get money or things from an adult</td><td>.553</td></tr><tr><td>Carried a hidden weapon other than a plain pocketknife</td><td>.549</td></tr><tr><td>Hit (or threatened to hit) an adult</td><td>.535</td></tr><tr><td>Bullied, threatened, or intimidated others</td><td>.514</td></tr><tr><td>Sold cocaine or crack</td><td>.479</td></tr><tr><td>Hit (or threatened to hit) other teens</td><td>.462</td></tr><tr><td>Been physically cruel to people</td><td>.459</td></tr><tr><td>Been involved in gang fights</td><td>.437</td></tr><tr><td></td><td>Been loud, rowdy, or unruly in public (disorderly conduct)</td><td>.419</td></tr><tr><td rowspan="7">α = .685</td><td rowspan="7">Stealing and Generalized Delinquency</td><td>Taken a vehicle for a ride without the owner's permission</td><td>.624</td></tr><tr><td>Stolen (or tried to steal) a motor vehicle</td><td>.616</td></tr><tr><td>Stolen (or tried to steal) something worth more than $50</td><td>.589</td></tr><tr><td>Broke into a building or vehicle (or tired to break in) to steal something or just look around</td><td>.582</td></tr><tr><td>Stolen (or tried to steal) something worth less than $50</td><td>.524</td></tr><tr><td>Stayed out at night despite house rules not to</td><td>.488</td></tr><tr><td>Ran away from home overnight</td><td>.464</td></tr></tbody></table>

The 2 factors account for 33.33% of the variance.

Factor 1 accounts for: 19.80% variance

Factor 2 accounts for: 13.53% variance
<caption>Table 10</caption>

Empirically Derived MBQ-Alcohol

<table><thead><tr><th>Factor</th><th>Label</th><th>Question</th><th>Factor Loading</th></tr></thead><tbody><tr><td rowspan="8">α = .834</td><td rowspan="8">Stealing and Impulsive Delinquency</td><td>Engaging in activities that could be dangerous to yourself or others</td><td>.644</td></tr><tr><td>Stolen (or tried to steal) something worth &lt; $50</td><td>.625</td></tr><tr><td>Broke into a building or vehicle (or tired to break in) to steal something or just look around</td><td>.598</td></tr><tr><td>Stayed out at night despite house rules not to</td><td>.574</td></tr><tr><td>Stolen (or tried to steal) something worth &gt; $50</td><td>.574</td></tr><tr><td>Knowingly bought, stole, or held goods (or tried to do this)</td><td>.556</td></tr><tr><td>Stolen (or tried to steal) a motor vehicle</td><td>.540</td></tr><tr><td>Been loud, rowdy, or unruly in public (disorderly conduct)</td><td>.521</td></tr><tr><td rowspan="6"></td><td rowspan="6"></td><td>Lied to obtain goods or favors or to avoid obligations</td><td>.509</td></tr><tr><td>Was incarcerated</td><td>.504</td></tr><tr><td>Ran away from home overnight</td><td>.503</td></tr><tr><td>Taken a vehicle for a ride without the owner's permission</td><td>.491</td></tr><tr><td>Done something before thinking of what might happen if you did it</td><td>.461</td></tr></tbody></table>

<table><tbody><tr><td rowspan="10">α = .821</td><td rowspan="10">Predatory Aggression</td><td>Used a weapon</td><td>.712</td></tr><tr><td>Attacked someone with the idea of seriously hurting or killing</td><td>.588</td></tr><tr><td>Hit (or threatened to hit) other teens</td><td>.573</td></tr><tr><td>Bullied, threatened, or intimidated others</td><td>.562</td></tr><tr><td>Sold hard drugs such as heroin or LSD</td><td>.541</td></tr><tr><td>Hit (or threatened to hit) an adult</td><td>.527</td></tr><tr><td>Sold cocaine or crack</td><td>.523</td></tr><tr><td>Carried a hidden weapon other than a plain pocketknife</td><td>.513</td></tr><tr><td>Used force to get money or things from other teens</td><td>.483</td></tr><tr><td>Been involved in gang fights</td><td>.473</td></tr><tr><td>Used force to get money or things from an adult</td><td>.468</td></tr><tr><td>Been physically cruel to people</td><td>.458</td></tr></tbody></table>

The 2 factors account for 35.62% of the variance.

Factor 1 accounts for: 19.07% variance

Factor 2 accounts for: 16.55% variance
<caption>Table 11</caption>

Empirically Derived MBQ-Marijuana

<table><thead><tr><th>Factor</th><th>Label</th><th>Question</th><th>Factor Loading</th></tr></thead><tbody><tr><td rowspan="8">α = .781</td><td rowspan="8">Stealing and Generalized Delinquency</td><td>Broke into a building or vehicle (or tried to break in) to steal something or just look around</td><td>.704</td></tr><tr><td>Stolen (or tried to steal) something worth more than $50</td><td>.683</td></tr><tr><td>Taken a vehicle for a ride without the owner's permission</td><td>.653</td></tr><tr><td>Stolen (or tried to steal) a motor vehicle</td><td>.622</td></tr><tr><td>Knowingly bought, stole, or held stolen goods (or tried to do this)</td><td>.567</td></tr><tr><td>Stolen (or tried to steal) something worth less than $50</td><td>.563</td></tr><tr><td>Been loud, rowdy or unruly in public (disorderly conduct)</td><td>.488</td></tr><tr><td>Stayed out at night despite house rules not to</td><td>.490</td></tr><tr><td rowspan="5">α = .653</td><td rowspan="5">Drug Dealing and Aggressive Behavior</td><td> Destroyed others' property on purpose (not by fire setting)</td><td>.464</td></tr><tr><td>Sold cocaine or crack</td><td>.769</td></tr><tr><td>Sold hard drugs such as heroin or LSD</td><td>.658</td></tr><tr><td>Carried a hidden weapon other than a plain pocketknife</td><td>.644</td></tr><tr><td>Hit (or threatened to hit) other teens</td><td>.562</td></tr><tr><td></td><td></td><td>Attacked someone with the idea of seriously hurting or killing</td><td>.525</td></tr></tbody></table>

The 2 factors account for 40.93% of the variance.

Factor 1 accounts for: 23.87% variance

Factor 2 accounts for: 17.06% variance
<caption>Table 12</caption>

Concurrent Incremental Validity: Empirical Scales and Alcohol Use

<table><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>&Delta; R<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p =</th></tr></thead><tbody><tr><td colspan="7"><u>Alcohol Use, 3 Months before Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race, Friend</td><td>.050</td><td>.022</td><td>.050</td><td>F = 1.81</td><td>5, 174</td><td>.113</td></tr><tr><td>Use, Friend Arrested,<br>Age of 1<sup>st</sup> Alcohol Use</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2) MBQ-General:</td><td>.101</td><td>.064</td><td>.052</td><td>F = 2.76</td><td>7, 172</td><td>.01**</td></tr><tr><td>Stealing/Generalized<br>Delinquency,<br>Predatory Aggression</td><td></td><td></td><td></td><td>F<sub>ch</sub> = 4.93</td><td>2, 172</td><td>.008*</td></tr><tr><td colspan="7"><u>Alcohol Use, 3 Months before Incarceration</u></td></tr><tr><td>1) Age, Race, Friend</td><td>.050</td><td>.022</td><td>.050</td><td>F = 1.81</td><td>5, 174</td><td>.113</td></tr><tr><td>Use, Friend Arrested,<br>Age of 1<sup>st</sup> Alcohol Use</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2) MBQ-Alcohol:</td><td>.364</td><td>.338</td><td>.314</td><td>F = 14.06</td><td>7, 172</td><td>.000***</td></tr><tr><td>Stealing/Impulsive<br>Delinquency,<br>Predatory Aggression</td><td></td><td></td><td></td><td>F<sub>ch</sub> = 42.51</td><td>2, 172</td><td>.000***</td></tr></tbody></table>

<sup>a</sup>N = 180, due to participants missing one or more predictor variables at baseline.

Note: * p < .05, Note: ** p < .01, Note: *** p < .001
<caption>Table 13</caption>

Concurrent Incremental Validity: Empirical Scales and Marijuana Use

<table><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>&Delta; R<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p =</th></tr></thead><tbody><tr><td colspan="7"><u>Marijuana Use, 3 Months before Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race, Friend</td><td>.074</td><td>.047</td><td>.074</td><td>F = 2.78</td><td>5, 174</td><td>.019**</td></tr><tr><td>Use, Friend Arrested, Age of 1<sup>st</sup> Marijuana Use</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2) MBQ-General:</td><td>.088</td><td>.051</td><td>.014</td><td>F = 2.38</td><td>7, 172</td><td>.024*</td></tr><tr><td>Stealing/Generalized</td><td></td><td></td><td></td><td>F<sub>ch</sub> = 1.36</td><td>2, 172</td><td>.260</td></tr><tr><td>Delinquency, Predatory Aggression</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="7"><u>Marijuana Use, 3 Months before Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race, Friend</td><td>.074</td><td>.047</td><td>.074</td><td>F = 2.78</td><td>5, 174</td><td>.019**</td></tr><tr><td>Use, Friend Arrested, Age of 1<sup>st</sup> Marijuana Use</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2) MBQ-Marijuana:</td><td>.176</td><td>.142</td><td>.102</td><td>F = 5.24</td><td>7, 172</td><td>.000***</td></tr><tr><td>Stealing/Generalized</td><td></td><td></td><td></td><td>F<sub>ch</sub> = 10.61</td><td>2, 172</td><td>.000***</td></tr><tr><td>Delinquency, Drug Dealing &amp; Aggressive Behavior</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<sup>a</sup>N = 180, due to participants missing one or more predictor variables at baseline.

Note: * p < .05, Note: ** p < .01, Note: *** p < .001
<caption>Table 14</caption>

Predictive Incremental Validity: Empirical Scales and Alcohol Use

<table><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>ΔR<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p =</th></tr></thead><tbody><tr><td colspan="7"><u>Alcohol Use, 3 Months after Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race, Friend<br>Use, Friend Arrested,<br>Baseline Alcohol Use</td><td>.275</td><td>.251</td><td>.275</td><td>F = 11.73</td><td>5, 155</td><td>.000***</td></tr><tr><td>2) MBQ-General:<br>Stealing/Generalized<br>Delinquency,<br>Predatory Aggression</td><td>.309</td><td>.278</td><td>.035</td><td>F = 9.79<br>F<sub>ch</sub> = 3.85</td><td>7, 153<br>2, 153</td><td>.000***<br>.023*</td></tr><tr><td colspan="7"><u>Alcohol Use, 3 Months after Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race, Friend<br>Use, Friend Arrested,<br>Baseline Alcohol Use</td><td>.275</td><td>.251</td><td>.275</td><td>F = 11.73</td><td>5, 155</td><td>.000***</td></tr><tr><td>2) MBQ-Alcohol:<br>Stealing/Impulsive<br>Delinquency,<br>Predatory Aggression</td><td>.352</td><td>.322</td><td>.077</td><td>F = 11.86<br>F<sub>ch</sub> = 9.11</td><td>7, 153<br>2, 153</td><td>.000***<br>.000***</td></tr></tbody></table>

<footnote><sup>a</sup>N = 161, due to availability of data on alcohol use 3 mo. after incarceration; n= 19 were in a controlled environment again soon after release.</footnote>

<footnote>Note: * p < .05, Note: *** p < .001</footnote>
<table><caption>Table 15
Predictive Incremental Validity: Empirical Scales and Marijuana Use</caption><thead><tr><th>Model</th><th>R<sup>2</sup></th><th>R<sup>2</sup><sub>adj</sub></th><th>&Delta;R<sup>2</sup></th><th>ANOVA</th><th>df</th><th>p =</th></tr></thead><tbody><tr><td colspan="7"><u>Marijuana Use, 3 Months after Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race, Friend</td><td>.191</td><td>.165</td><td>.191</td><td>F = 7.33</td><td>5, 155</td><td>.000***</td></tr><tr><td>Use, Friend Arrested,<br>Baseline Marijuana Use</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2) MBQ-General:</td><td>.230</td><td>.195</td><td>.039</td><td>F = 6.54</td><td>7, 153</td><td>.000***</td></tr><tr><td>Stealing/Generalized<br>Delinquency,<br>Predatory Aggression</td><td></td><td></td><td></td><td>F<sub>ch</sub> = 3.87</td><td>2, 153</td><td>.023*</td></tr><tr><td colspan="7"><u>Marijuana Use, 3 Months after Incarceration<sup>a</sup></u></td></tr><tr><td>1) Age, Race, Friend</td><td>.191</td><td>.165</td><td>.191</td><td>F = 7.33</td><td>5, 155</td><td>.000***</td></tr><tr><td>Use, Friend Arrested,<br>Baseline Marijuana Use</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2) MBQ-Marijuana:</td><td>.205</td><td>.168</td><td>.014</td><td>F = 5.63</td><td>7, 153</td><td>.000***</td></tr><tr><td>Stealing/Generalized<br>Delinquency, Drug<br>Dealing &amp; Aggressive<br>Behavior</td><td></td><td></td><td></td><td>F<sub>ch</sub> = 1.30</td><td>2, 153</td><td>.276</td></tr></tbody></table>

<footnote><sup>a</sup>N = 161, due to availability of data on marijuana use 3 mo. after incarceration; n= 19 were in a controlled environment again soon after release.</footnote>

<footnote>Note: * p < .05, Note: *** p < .001</footnote>
Appendix
Misbehaviors Questionnaire

For each item, youth were asked to indicate each of the following:

Age when the act was first committed

Age when the act was last committed

In the year before lock-up, no. of times happened

In the year before lock-up, no. of times happened with alcohol involved

In the year before lock-up, no. of times happened with marijuana involved

They were instructed that "with alcohol involved" and "with marijuana involved"
"...means you were on alcohol or drugs at the time you were doing the activity or you
were doing the activity in order to get alcohol or drugs to take."

1. Stolen (or tried to steal) a motor vehicle, such as a car or motorcycle.

2. Stolen (or tried to steal) something worth more than $50.

3. Knowingly bought, stole, or held stolen goods (or tried to do any of these things).

4. Carried a hidden weapon other than a plain pocketknife.

5. Stolen (or tried to steal) something worth $50 or less.

6. Attacked someone with the idea of seriously hurting or killing him/her.

7. Been paid for having sexual relations with someone.

8. Been involved in gang fights.

9. Sold marijuana or hashish (pot, grass, hash).

10. Hit (or threatened to hit) an adult.

11. Hit (or threatened to hit) other teens.

12. Been loud, rowdy, or unruly in a public place (disorderly conduct).

13. Sold cocaine or crack.

14. Sold hard drugs such as heroin or LSD.

15. Taken a vehicle for a ride (drive) without the owner's permission.

16. Had (or tried to have) sexual relations with someone against their will.

17. Used force (strong-arm methods) to get money or things from other teens.

18. Used force (strong-arm methods) to get money or things from an adult.

19. Broke into a building or vehicle (or tried to break in) to steal something or just look around.

20. Begged for money or things from strangers.

21. Was incarcerated.

22. Bullied, threatened, or intimidated others.

23. Initiated physical fights.

24. Used a weapon (bat, brick, broken bottle, knife, gun).

25. Been physically cruel to animals.

26. Been physically cruel to people.

27. Set fires with the intention of causing serious damage.

28. Destroyed others' property on purpose (not by fire setting).

29. Lied to obtain goods or favors or to avoid obligations (cons others).

30. Stayed out at night despite house rules not to.

31. Ran away from home overnight.
32. Skipped school.

33. Could not pay bills (loans, child support, etc.).

34. Done something (left school, a job, etc.) before thinking of what might happen if you did it (had no other plans).

35. Got in trouble at work, was late for work, or missed work.

36. Engaging in activities that could be dangerous to yourself or others (speeding, etc.).
Bibliography

Achenbach, T. M. (1993). Implications of multiaxial empirically based assessment for
behavior therapy with children. *Behavior Therapy, 24*, 91-116.

Achenbach, T. M., & Rescorla, L. A. (2001). *Manual for ASEBA School-Age Forms & Profiles*. Burlington, VT: University of Vermont, Research Center for Children, Youth, & Families.

American Psychiatric Association. (1994). *Diagnostic and statistical manual of mental disorders* (4th ed.). Washington, DC: Author.

Andrews, J. A., Hops, H., & Duncan, S. C. (1997). Adolescent modeling of parent substance use: The moderating effect of the relationship with the parent.
*Journal of Family Psychology, 11*(3), 259-270.

Anastasi, A., & Urbina, S. (1997). *Psychological testing* (7th ed.). New York: Prentice Hall.

Apter, A., Orvaschel, H., Laseg, M., Moses, R., & Tyano, S. (1989). Psychometric properties of the K-SADS-P in an Israeli adolescent inpatient population.
*Journal of the American Academy of Child and Adolescent Psychiatry, 28*, 61-65.

Barnes, G. M., & Farrell, M. P. (1992). Parental support and control as predictors of adolescent drinking, delinquency, and related problem behaviors. *Journal of Marriage and the Family, 54*, 763-776.

Barnes, G. M., Farrell, M. P., & Dintcheff, B. A. (1997). Family socialization effects on alcohol abuse and related problem behaviors among female and male adolescents. In S. Wilsnack & R. Wilsnack (Eds.), *Gender and alcohol* (pp. 156-175). Piscataway, NJ: Rutgers Center of Alcohol Studies.
Blackson, T. C., Butler, T., Belsky, J., Ammerman, R. T., Shaw, D. S., & Tarter, R. E. (1999). Individual traits and family contexts predict sons' externalizing behavior and preliminary relative risk ratios for conduct disorder and substance use disorder outcomes. *Drug and Alcohol Dependence, 56*(2), 115-131.

Boivin, N., & Vitaro, F. (1995). The impact of peer relationships on aggression in childhood: Inhibition through coercion or promotion through peer support. In J. McCord (Ed.), *Coercion and punishment in long-term perspectives* (pp. 183-197). Cambridge: Cambridge University Press.

Brown, R. A. (1999). Assessing attitudes and behaviors of high-risk adolescents: An evaluation of the self-report method. *Adolescence, 34*, 25-32.

Brown, S. A., Gleghorn, A., Schuckit, M., Myers, M. G., & Mott, M. A. (1996). Conduct disorder among adolescent alcohol and drug abusers. *Journal of Studies on Alcohol, 57*, 314-324.

Chambers, W. J., Puig-Antich, J., Hirsch, M., Paez, P., Ambrosini, P. J., Tabrizi, M. A., et al. (1985). The assessment of affective disorders in children and adolescents by semistructured interview: Test-retest reliability of the Schedule for Affective Disorders and Schizophrenia for School-Age Children, present episode version. *Archives of General Psychiatry, 42*, 696-702.

Cohen, J. (1988). *Statistical power analyses for the behavioral sciences* (2nd ed.). Hillside, NJ: Lawrence Erlbaum.

Cohen, P., Cohen, J., & Brook, J. (1993). An epidemiological study of disorder in late childhood and adolescence: II. Persistence of disorders. *Journal of Child Psychology and Psychiatry, 34*, 869-877.
Dembo, R., & Schmeidler, J. (2003). A classification of high-risk use. *Crime and Delinquency, 49*, 201-230.

Dembo, R., Walters, W., & Meyers, K. (2005). A practice/research collaborative: An innovative approach to identifying and responding to psychosocial functioning problems and recidivism risk among juvenile arrestees. *Journal of Offender Rehabilitation, 41*, 39-66.

Dembo, R., Williams, L., Wothke, W., & Schmeidler, J. (1992). Examining a structural model of the relationships among alcohol use, marijuana/hashish use, their effects, and emotional and psychological problems over time in a cohort of high-risk youths. *Deviant Behavior: An Interdisciplinary Journal, 13*, 185-215.

Dennis, M. L., Funk, R., Godley, S. H., Godley, M. D., & Waldron, H. (2004). Cross-validation of the alcohol and cannabis use measures in the Global Appraisal of Individual Needs (GAIN) and Timeline Followback (TLFB; Form 90) among adolescents in substance abuse treatment. *Addiction, 99*(Suppl. 2), 120-128.

Dishion, T. J., Capaldi, D., Spracklen, K. M., & Li, F. (1995). Peer ecology of male adolescent drug use. *Development and Psychopathology, 7*, 803-824.

Edelbrock, C., Costello, A. J., Dulcan, M. K., Kalas, R., & Conover, N. C. (1985). Age differences in the reliability of the psychiatric interview of the child. *Child Development, 56*, 265-275.

Elliott, D. (2008). *National Youth Survey [United States]: Wave V, 1980*. ICPSR09112-v2. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.
Elliott, D. S., & Ageton, S. S. (1980). Reconciling race and class differences in self-reported and official estimates of delinquency. *American Sociological Review*, 45, 95-110.

Elliott, D. S., Ageton, S. S., Huizinga, D., Knowles, B. A., & Canter, R. J. (1983). *The prevalence and incidence of delinquent behavior: 1976-1980* (*National Youth Survey Report No. 26*). Boulder, CO: Behavioral Research Institute.

Elliott, D. S., Huizinga, D., & Ageton, S. (1985). *Explaining Delinquency and Drug Use*. Beverly Hills: Sage Publications.

Furgusson, 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 and Adolescent Psychiatry*, 32, 1127-1134.

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. *Psychometrika*, 30, 179-185.

Huizinga, D. (1991). Assessing violent behavior with self-reports. In J. S. Milner (Ed.), *Neuropsychology of Aggression*, (pp. 44-76). Boston: Kluwer Academic.

Huizinga, D., & Elliott, D. S. (1986). Reassessing the reliability and validity of self-report delinquency measures. *Quantitative Criminology*, 2, 293-327.

Huizinga, D., & Esbensen, F. (1988) *Common measures of the program of research on the causes and correlates of delinquency*. [Report to the Office of Juvenile Justice and Delinquency Prevention]. Washington DC: Huizinga.

Kashini, J. H., Daniel, A. E., Sulzberger, L. A., Rosenberg, T. K., & Reid, J. C. (1987). Conduct disordered adolescents from a community sample. *Canadian Journal of Psychiatry*, 32, 756-760.
Kazdin, A. E. (1985). *Treatment of antisocial behavior in children and adolescents*.
Homewood, IL: Dorsey.

Kazdin, A. E. (2006). Arbitrary metrics: Implications for identifying evidence-based treatments. *American Psychologist*, *61*(1), 42-49.

Keenan, K., Loeber, R., & Green, S. (1999). Conduct disorder in girls: A review of the literature. *Clinical Child and Family Psychology Review*, *2*(1), 3-19.

Keenan, K., Stouthamer-Loeber, M., & Loeber, R. (2005). Developmental approaches to studying conduct problems in girls. In D. J. Pepler, K. C. Madsen, C. Webster, & K. S. Levene (Eds.), *The development and treatment of girlhood aggression* (pp. 29-46). Mahwah, NJ: Lawrence Erlbaum Associates.

Kiesner, J., Dishion, T. J., & Poulin, F. (2001). A reinforcement model of conduct problems in children and adolescence: Advances in theory and intervention. In J. Hill & B. Maughan (Eds.), *Conduct disorders in childhood and adolescence* (pp. 264-291). Cambridge: Cambridge University Press.

Lahey, B. B., Loeber, R., Hart, E. L., Frick, P. J., Applegate, B., Zhang, Q., et al. (1995). Four-year longitudinal study of conduct disorder in boys: Patterns and predictors of persistence. *Journal of Abnormal Psychology*, *104*(1), 83-93.

Lahey, B. B., Loeber, R., Quay, H. C., Applegate, B., Shaffer, D., Waldman, I., et al. (1998). Validity of DSM-IV subtypes of conduct disorder based on age of onset. *Journal of the American Academy of Child and Adolescent Psychiatry*, *37*(4), 435-442.
Loeber, R., Farrington, D. P., Stouthamer-Loeber, M., & Van Kammen, W. B. (1998).
*Antisocial behavior and mental health problems: Explanatory factors in
childhood and adolescence*. Mahwah, NJ: Erlbaum.

Loeber, R., & Stouthamer-Loeber, M. (1986). Family factors as correlates and predictors of juvenile conduct problems and delinquency. In M. Tonry & N. Morris (Eds.), *Crime and justice* (Vol.7, pp 29-149). Chicago: University of Chicago Press.

Meyers, K., Hagan, T. A., Zanis, D., Webb, A., Frantz, J., Ring-Kurtz, S. et al. (1999).
*Critical issues in adolescent substance use assessment.* *Drug and Alcohol Dependence*, *55*, 235-246.

Myers, M. G., Brown, S. A., & Mott, M. A. (1995). Preadolescent conduct disorder behaviors predict relapse and progression of addiction for adolescent alcohol and drug abusers. *Alcoholism: Clinical and Experimental Research, 19*, 1528-1536.

Patterson, G. R., Kapaldi, D. M., & Bank, L. (1991). An early starter model predicting delinquency. In D. J. Pepler & K. A. Rubin (Eds.), *The development and treatment of childhood aggression* (pp. 139-168). Hillsdale, NJ: Lawrence Erlbaum Associates.

Pinto, M., & Grilo, C. (2004.) Reliability, diagnostic efficiency, and validity of the Millon Adolescent Clinical Inventory: Examination of selected scales in psychiatrically hospitalized adolescents. *Behaviour Research and Therapy*, *42*(12), 1505-1519.
Puig-Antich, J., & Chambers, W. J. (1978). *Schedule for Affective Disorders and Schizophrenia for School-Age Children: Kiddie SADS (K-SADS)*. New York: Department of Child and Adolescent Psychiatry, New York State Psychiatric Institute.

Redding, C., Maddock, J., & Rossi, J. (2006). The sequential approach to measurement of health behavior constructs: Issues in selecting and developing measures. *California Journal of Health Promotion, 4*, 83-101.

Reich, W. (2000). Diagnostic Interview for Children and Adolescents (DICA). *Journal of the American Academy of Child and Adolescent Psychiatry, 39*, 59-66.

Rogers, R. (2001). *Handbook of diagnostic and structured interviewing*. New York: The Guilford Press.

Sellborn, M., Graham, J. R., & Schenk, P. W. (2006). Incremental validity of the MMPI-2 Restructured Clinical (RC) scales in a private practice sample. *Journal of Personality Assessment, 86*(2), 196-205.

Shaffer, D., Fisher, P., Dulcan, M. K., & Davies, M. (1996). The NIMH Diagnostic Interview Schedule for Children version 2.3 (DISC-2.3): Description, acceptability, prevalence rates, and performance in the MECA study. *Journal of the American Academy of Child and Adolescent Psychiatry, 35*, 865-877.

Sobell, L. C., & Sobell, M. B. (1995). Alcohol timeline followback users' manual. *Addiction Research Foundation, Toronto, Canada*.
Stein, L. A. R. (2004, December). *Preliminary Findings of a Randomized Clinical Trial in a Juvenile Correctional Setting*. Paper present at NIDA conference, Rockville, MD.

Stein, L. A. R., Colby, S. M., Barnett, N. P., Monti, P. M., Golembeske, C., Lebeau-Craven, R., & Miranda, R. (2006). Enhancing substance abuse treatment engagement in incarcerated adolescents. *Psychological Services*, 3(1), 25-34.

Stein, L. A. R., Hesselbrock, V., & Bukstein, O. (2008). Disruptive behavior disorders (conduct disorder and oppositional defiant disorder) and adolescent substance abuse. In Y. Kaminer & O. G. Bukstein (Eds.), *Adolescent substance abuse: Psychiatric comorbidity and high risk behaviors* (pp. 163-194). Binghamton, NY: Haworth Press.

Stice, E., Myers, M. G., & Brown, S. A. (1998). Relations of delinquency to adolescent substance use and problem use: A prospective study. *Psychology of Addictive Behaviors*, 12(2), 136-146.

Tabachnick, B. G., & Fidell, L. S. (2001). *Using multivariate statistics* (4th Ed.). Boston, MA: Allyn and Bacon.

Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. *Psychometrika*, 41, 321-327.

Violence Institute of America. (nd). *Searchable Inventory of Instruments Assessing Violent Behavior and Related Constructs in Children and Adolescents: Child Behavior Checklist*. Retrieved 4/28/08 from http://vinst.umdnj.edu/VAID/TestReport.asp?Code=CBCA.
Weed, N. C., Butcher, J. N., McKenna, T., & Ben-Porath, Y. S. (1992). New measures for assessing alcohol and drug abuse with the MMPI-2: The APS and AAS.

*Journal of Personality Assessment*, 58(2), 389-405.

White, H. R., Xie, M., Thompson, W., Loeber, R., & Stouthamer-Loeber, M. (2001).

Psychopathology as a predictor of adolescent drug use trajectories. *Psychology of Addictive Behaviors*, 15(3), 210-218.