externalizing behaviors as predictors of substance initiation trajectories among rural adolescents

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Original Article

Externalizing behaviors as predictors of substance initiation trajectoriesamong rural adolescents

Catherine J. Lillehoj, Ph.D.*, Linda Trudeau, Ph.D., Richard Spoth, Ph.D.,and Stephanie Madon, Ph.D.

Partnerships in Prevention Science Institute, Iowa State University, Ames, Iowa

Manuscript received February 20, 2004; manuscript accepted September 9, 2004.

bstract Purpose: The purpose of the current study was to investigate the influence of externalizingbehaviors on substance initiation trajectories among rural adolescents over a 42-month period.Methods: Data were obtained from 198 rural adolescents (105 boys, 93 girls) who were partici-pating in a longitudinal study. At the baseline assessment, subjects were on average 12.3 years ofage.Results: Controlling for gender, higher baseline levels of externalizing were associated with agreater number of substances initiated over time. The initiation trajectory was curvilinear. Girls,compared with boys, reported a lower number of substances initiated at baseline, a greater lineargrowth trajectory, and a deceleration of growth over time.Conclusions: The influence of adolescent externalizing behaviors on baseline levels and growthtrajectories of substance initiation and the utility of latent growth curve modeling in the study oflongitudinal change are discussed. © 2005 Society for Adolescent Medicine. All rights reserved.

Journal of Adolescent Health 37 (2005) 493–501

eywords: Adolescence; Externalizing; Gender; Growth curve modeling; Rural; Substance initiation

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A significant body of literature has examined develop-ental changes typical during adolescence [1–3]. A major

heme of this literature concerns developmental influencesn the emergence of substance use behaviors [4–6]. Theurrent study examined the developmental influence of ex-ernalizing behaviors on substance initiation in a sample ofural adolescents.

Over the past 2 decades, national survey data indicateegligible variation in the age of initiation of various sub-tances [7]. For example, retrospective data indicate that1% of sixth graders (i.e., between 11 and 13 years of age)eported tobacco initiation and 26% reported alcohol initi-tion. For marijuana use, the highest initiation rates werevidenced in grades 7 through 11. The data suggest thatubstance initiation among early adolescents remains at an

*Address correspondence to: Catherine J. Lillehoj, Partnerships inrevention Science Institute, ISU Research Park, Building 2, Suite 500,625 North Loop Drive, Iowa State University, Ames, IA 50010-8296,

nE-mail address: [email protected]

054-139X/05/$ – see front matter © 2005 Society for Adolescent Medicine. Alloi:10.1016/j.jadohealth.2004.09.025

larmingly high rate and imply that more serious substance-elated patterns are emerging for middle to late adolescence.

ationale for examining the relationship betweenxternalizing and substance initiation

Variability among adolescents in substance use has beeninked with numerous high-risk behaviors, including exter-alizing behaviors [1–3]. Externalizing behaviors include aost of behaviors that reflect disobedience and misconduct,uch as aggressiveness, defiance, hostility, and general de-tructive acts of delinquency. Externalizing behaviors rep-esent serious problems for early adolescents that might beforerunner of more problematic outcomes in later adoles-

ence and adulthood. Evidence also exists to support atrong link between externalizing behaviors manifested inarly adolescence and later substance use [1,8–9].

Although substance use is one kind of externalizingehavior, it is not by itself representative of the collection ofntisocial behaviors that encompass the construct of exter-
alizing behaviors more generally. As such, substance use is
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494 C. J. Lillehoj et al. / Journal of Adolescent Health 37 (2005) 493–501

ypically conceptualized as distinct from other acts of dis-bedience and misconduct. The distinction between exter-alizing behaviors and substance use is evident in theoret-cal, methodological, and empirical work. Theoreticalnalyses of the cause of substance use explicitly proposehat aggressiveness and other problem behaviors distinctrom substance use increase children’s risk for later sub-tance initiation [10]. Measures of externalizing behaviorslso indicate a conceptual distinction between substance usend the broader class of externalizing behaviors. The Childehavior Checklist—Youth Self-Report (CBCL-YSR)

11,12], for instance, which is one of the most widely usedeasures to assess externalizing behaviors among adoles-

ents, includes 28 items, only 1 of which pertains to sub-tance use. Finally, there is a long line of empirical researchhat shows that general acts of delinquency other than sub-tance use predict later substance use among adolescentamples.

Given the pervasiveness of externalizing behaviors dur-ng early adolescence, it is important to understand its as-ociation with the emergence of substance initiation andetermine those factors that might forecast the initiation ofubstance use, as well as those factors that might be asso-iated with its prevention. Because externalizing is distinctrom, and believed to precede initiation, the current studyxamined the relationship of externalizing behaviors andoth initial levels and growth trajectories of substance ini-iation among rural adolescents.

One type of evidence used to assess the causal relation-hip between externalizing and substance use is the relativeiming of the behaviors (i.e., their sequence during adoles-ence). This approach follows from the basic scientific prin-iple that an event can only cause an outcome that it pre-edes [13]. Although the literature suggests that severalypes of problem behaviors are likely to precede the devel-pment of substance use, rigorous research on the linketween externalizing behaviors during early adolescencend substance initiation during early to middle adolescenceas reported variable results. For example, research byuang et al. [14] examined the developmental associationetween alcohol use and interpersonal aggression in a sam-le of urban adolescents. The direction of effect from in-erpersonal aggression to alcohol use was always positive,lthough the effect of alcohol use to interpersonal aggres-ion was negative in early adolescence. Many longitudinaltudies that have been conducted have often not examinedtability and change over time in substance initiation. In onef the few longitudinal studies to explore the cross-timehange in substance use, White et al [9] used a growth curveodel to measure psychopathology (e.g., conduct disorder)

mong early adolescent boys as a predictor of the change inlcohol and marijuana use during adolescence. Psychopa-hology was found to be related to the initial level of alcoholnd marijuana use; however, conduct disorder was nega-
ively related to an increase in alcohol use. The lack of c
onsistency in the empirical record highlights the need forurther research to examine the relationship between exter-alizing behaviors and the initiation of substance use, espe-ially research that focuses on the stability and change ofhese variables over time. A primary goal of the currentesearch, therefore, was to examine the developmental in-uence of externalizing behaviors on the number of sub-tances initiated during early to middle adolescence.

A vast body of research has accumulated over the pasteveral decades that has attempted to determine the factorshat predict adolescent substance use. Though such studiesave produced a wide array of findings, a broad overviewould suggest that adolescents display varying levels of

ubstance use at any given point in time and that the influ-nce of intrapersonal characteristics differs across time.hat patterns of substance use differ has led researchers toxplore the initial levels and growth trajectories of theehavior to better understand differences between adoles-ents (e.g., gender, intrapersonal characteristics) [8,15].ven though a significant body of research has investigated

he predictors of substance use, the systematic examinationf the pathways from externalizing to initial levels androwth trajectories of initiation among rural adolescents haseen minimal. The authors could find no studies focusedpecifically on externalizing behaviors and substance initi-tion among rural adolescents, even though research [16]as demonstrated that diverse geographic settings are char-cterized by individual characteristics (e.g., risk factors) thatverlap only partially.

ender differences in substance initiationnd externalizing

A considerable number of research studies have exam-ned factors identified in the development of substance useehaviors, including gender. For the most part, epidemio-ogic data have found substantial gender differences in sub-tance use prevalence rates, with boys generally reportingigher rates than girls [17,18]. However, this literature hasrimarily focused on the frequency and prevalence rates ofubstance use and has failed to examine the emergence ornitiation of substance behaviors, particularly with ruralamples. Initiation estimates concern the number of newsers of tobacco, alcohol, and marijuana, and prevalencestimates describe the extent of substance use over someime [19].

A number of studies have shown gender differences inther adolescent behavioral problems [4,20,21]. Those stud-es typically have found that adolescent boys have higherevels of externalizing behaviors than do girls [22,23]. In aeview of 74 studies examining the association betweenonduct problems and delinquency, however, only 8 (11%)ere found that included both boys and girls [24]. Although

esearch on externalizing behaviors including girls has in-
reased [25], these studies remain relatively fewer than

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495C. J. Lillehoj et al. / Journal of Adolescent Health 37 (2005) 493–501

tudies that have focused exclusively on boys. Even lessesearch has investigated longitudinal relationships amongxternalizing, gender, and substance initiation. There are ateast 2 reasons for the lack of research related to this topic:he limited availability of relevant data and the lack ofheoretical models related to growth trajectories of the num-er of substances initiated. To address this gap in the liter-ture, the current study investigated the association betweenender and externalizing behaviors, as well as the influencef both gender and externalizing on the baseline and growthrajectories of the number of substances initiated.

There are 2 related models of developmental differenceshat provide the necessary framework for evaluating genderariations. The sex-invariant model proposes that the ten-ency to become involved in specific behaviors, such asubstance use, results from early life experiences and influ-nces. According to this model, therefore, differences be-ween boys and girls that are established in childhood re-ain relatively constant over the course of adolescence

26]. In other words, although early adolescent initiationevels could vary between boys and girls, the pattern ofhange over time would not vary by gender from early toiddle adolescence. The sex-variant model, in contrast,

roposes that the factors that cause boys and girls to par-icipate in initiation behaviors change over time; thus, theex-variant model would suggest there might be differencesetween boys and girls in substance initiation over time27]. Because few longitudinal studies have examined eitherodel, little is known about the influence of gender on the

umber of substances initiated over the course of adoles-ence. Therefore a second goal of the current research waso examine whether the initiation process varies betweenoys and girls at different stages of adolescent development.

urpose of current study

The primary purpose of the current study was to examinehe relationship between externalizing behaviors and bothaseline substance initiation and growth trajectories of ini-iation, using 5 waves of data. A secondary purpose of thetudy was to examine the influence of gender on both initialevels and change over time in the number of substancesnitiated. Thus, this study explored simultaneously the influ-nce of both gender and externalizing on substance initiationmong rural adolescents. To that end, 4 specific research ob-ectives were addressed. Because findings discussed earlierave suggested an association between baseline substancenitiation and growth trajectories [17–19], the first studybjective was to examine that association. Second, sub-tance initiation trend patterns established previously haveuggested that the behavior might be greater among boys7]; thus, the study examined the influence of gender onaseline initiation, plus change over time. Because previousesearch related to the link between gender and externaliz-
ng has not examined the behavior exclusively among rural a
dolescents, the third study objective was to assess thatssociation. Specifically, the study evaluated whether ruraloys, compared with girls, displayed an association withaseline externalizing behaviors. Finally, the study exam-ned the pathways from externalizing to baseline initiationnd the growth trajectory of initiation. Because previousesearch has found that the initiation of substances use isonlinear [5], we evaluated the growth trajectories to deter-ine whether a linear or a nonlinear pattern provided a

etter fit with the data.

ethods

articipants

Participants in the study were seventh grade studentsnrolled in 36 rural schools in 22 contiguous counties in aidwestern state who participated in the control condition

f a larger intervention prevention study (for a more thor-ugh description of the study, see Spoth et al [28]). Schoolsncluded in the study were selected on the basis of schoolunch program eligibility (approximately 20% or more ofouseholds within 185% of the federal poverty level in thechool districts), school district size (enrollment of 1,200 orewer), and all middle school grades taught in 1 locationnly.

Students were recruited for participation in the in-schoolssessment, and approximately 20 families in each schoolere recruited for the in-home assessment. Information wastilized from all control group students included at eachime point if they were present for the baseline in-homessessment and provided data for 3 waves of the in-schoolssessment (N � 198). Among control group families thatompleted the baseline assessment, there were on average.25 children. The majority of families (87%) were dual-arent families, which is representative of families of sev-nth graders in the study region. Of those dual-parent fam-lies, 83% included both biologic parents of the targetdolescent. The mean age of the mothers and fathers was9.0 and 41.4 years, respectively. The majority of mothersnd fathers (97% and 95%, respectively) had completedigh school. In addition, 57% of the mothers and 47% of theathers reported some post–high school education. The me-ian household income in the sample was $42,837. Slightlyore than half of the students were male (51%), and mostere Caucasian (96%). Students were on average 12.3 yearsld at the baseline assessment.

rocedures

n-home data collection. Eligible families were contacted tochedule an in-home recruitment visit from a project staffember. Families who indicated a willingness to participate

n project assessments were scheduled for a visit at a con-enient time. During the initial portion of the in-home visit,

household composition interview was conducted, fol-

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owed by administration of written questionnaires to thearticipating family members, who completed them inde-endently. Family members were assured that their ques-ionnaire responses would be kept confidential.

n-school data collection. The in-school data collection wasonducted in classrooms. A passive consent procedure al-owed parents to decline participation for their adolescenttudent; approximately 90% of eligible students participatedn the baseline assessment. Typically, there were 3 or 4 dataollectors in each classroom to coordinate and proctor as-essment procedures; 40 to 45 minutes was required toomplete the assessment. Students were assured that theirssessment responses would be kept confidential. Twoorms of the assessment were administered in each class-oom to enhance student privacy; identical questions weresked in each form, and only the order of questions wasaried. In addition, each student exhaled into a balloon,hich was connected to a meter to provide a carbon mon-xide reading. Using procedures found to enhance validityn answering the self-reported smoking questions, the pri-ary purpose of this strategy was to serve as a “bogus

ipeline” [29]. The same assessment procedures were usedcross all data collection points.

ttrition analyses. A series of analysis of variance (ANOVA)rocedures examined whether those students who initiatedubstance use at a previous measurement occasion had higherithdrawal rates than those who did not withdraw from the

tudy. Each ANOVA analyzed the substance initiation variablet each measurement occasion, that is, from baseline to time 2,ime 2 to time 3, time 3 to time 4, and time 4 to time 5. Studentsho withdrew between baseline and time 2 (N � 10; F [1,191]5.44; p � .02) had significantly higher scores on the initi-

tion measure. However, students who withdrew between timeand time 3 (N � 14); F [1,1968] � 1.14; p � .29) and

etween time 3 and time 4 (N � 25; F [1,196] � 17.40; p �17) did not have significantly higher scores on the initiation

easure. Finally, those students who withdrew between time 4nd time 5 (N � 46; F [1,196] � 22.12; p � .01) hadignificantly higher scores on the substance initiation measure.hose analyses demonstrated higher attrition rates among stu-ents who reported a greater number of substances initiated athe earlier and later measurement occasions. Because of thettrition rate, the current study is limited somewhat to exam-ning a narrower range of the initiation measure at subsequenteasurement occasions. As explained previously, however,

he current study considered students who completed 3 of themeasurement occasions (N � 198). As in any longitudinal

tudy requiring multiple assessments, those adolescents whore prone to be substance users tend to be underrepresented.

easures

xternalizing behaviors. As part of the in-home assessment,dolescent self-reported ratings from the CBCL-YSR were
btained during the fall semester of seventh grade [11]. Factor “
cores of the measure have been found to discriminate betweendolescents referred and not referred for mental health ser-ices. Convergent validity for the CBCL-YSR has been pro-ided with the Conners scale and the Behavior Problemhecklist [12]. The 27-item externalizing scale of the CBCL-SR is a general measure of disobedience and misconduct,

ncluding items related to aggression (e.g., fighting, swearing)nd hostility.

Students responded to the following item stem: “Below is aist of behaviors that describe some teenagers. How true is eachf these for you now or within the past 6 months?” All itemsere scored on a 3-point scale ranging from “Not true” to

Very true or often true.” Individual items were averaged toorm an overall rating of externalizing behaviors; the scalesed in the current study does not contain the item related toubstance use. The Cronbach � reliability for the self-reportedxternalizing scale at baseline was .88.

umber of substances initiated. As part of the in-schoolssessment during the fall semester of seventh grade, ado-escent self-reported ratings were obtained for 3 dichoto-ous items regarding the lifetime use of cigarettes, alcohol,

nd marijuana. The scale was constructed by combining theitems. At each wave of the in-school assessment, studentsere asked if they had ever “Smoked a cigarette,” “Had arink of alcohol,” or “Smoked marijuana.” Responses wereoded 0 � No and 1 � Yes, and were summed to computehe scale. Thus, the scale was coded 0 � No, for all sub-tance use, 1 � Yes, for use of 2 substance, 2 � Yes, for usef 2 substances, and 3 � Yes, for use of 3 substances. Theronbach � reliability for the baseline scale was .40; test-

etest reliability between baseline and time 2 was r � .79.he composite index of initiation used in the current studyore completely and reliably represents the general ten-

ency for substance initiation than does a single behaviori.e., smoke a cigarette, drink alcohol, smoke marijuana).

ender. Adolescent boys were coded as 1, and girls wereoded as 0.

reliminary analyses

lustering. Multivariate analysis of variance was used to eval-ate the unit of assignment (i.e., school) to assess the need formultilevel analysis. Analyses of the school intraclass corre-

ations for the study variables found that there were school-evel effects; the intraclass correlations ranged from � � .02 to30. Thus, an analysis was necessary to control for the multi-evel school effects. Study objectives were examined withatent growth curve modeling using MPLUS 3.0 [30], in whichstructural equation modeling program was used to control for

he clustered sample (i.e., students were clustered withinchools). Using a maximum likelihood fitting function withobust standard errors and a mean-adjusted �2 test statistic,stimation of the initiation growth trajectories over time werevaluated. Analyses were conducted using both “complex” and
complex mixture” analytic procedures to adjust the standard

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rrors to account for intraunit dependency (students nestedithin schools). The complex procedure was used to assess thet of the models, and the complex mixture procedure was used

o provide accurate estimates of standard errors and t testalues (Muthén B. Personal communication, October 31,001). Overall model fit was assessed using the �2 value and 2t indices: the likelihood ratio �2 and the standardized rootean square residual. Please see the Appendix for a descrip-

ion of latent growth curve modeling.

ormality. The outcome variables were examined for evi-ence of normality. Each measure of initiation was evalu-ted for skewness and kurtosis. The measure of initiationemonstrated a range of skewness (.46 to 1.26) and kurtosis�.21 to .98) over the waves of data collection. As a roughuide, a skewness and kurtosis value greater than 3 standardeviations is taken to indicate a departure from symmetry.urther, Bollen [31] states that the consistency of the esti-ators from the maximum likelihood method is relatively

obust to the violation of the multinormality of the factors.or those reasons, it was not considered necessary to trans-orm the initiation measure.

esults

escriptive statistics

Means, standard deviations, and correlations of theodel variables are presented in Table 1. Values for girls

re presented below the diagonal, and values for boys arebove the diagonal. Correlations of the model variablesere all in the expected direction. Externalizing behaviorsere associated significantly with the number of substances

nitiated at each measurement occasion for boys with thexception of time 3, and for girls with the exception of timeand time 5. Across time, scores for initiation were higher

able 1eans, standard deviations, and correlations for model variables

ariable Substance initiation

Baseline Time 2 Time 3

ubstance initiationBaseline 1.00 .84 .73Time 2 .79 1.00 .83Time 3 .59 .74 1.00Time 4 .50 .67 .87Time 5 .54 .68 .82

xternalizing*† .30 .29 .28ender*† .19 .13 .13ean (girls) .48 .72 1.08

tandard deviation .73 .77 .98

Correlation coefficients for boys (n � 105) above diagonal, and for girls.84 [p � .01].* Baseline assessment.† Both boys and girls.

or boys than for girls except for the time 4 measurement. n

oth for boys and for girls, levels of initiation increasedteadily with age. Baseline externalizing scores for boys andor girls were similar (.30 and .27, respectively).

ongitudinal model

The longitudinal model evaluated concurrently the initi-tion growth trajectory with the individual-level predictorsf externalizing and gender. The final model is presented inigure 1. Standardized values are presented for the coeffi-ients. The loadings for the 5 initiation measures on thentercept were set at 1; the linear growth factor loadingsere set to correspond to the time between assessments (i.e.,, 1, 3, 5, and 7), and the quadratic factor loadings were seto correspond to the square of the linear factor loadings (i.e.,, 1, 9, 25, and 49). Because of the nature of the dependentariable (i.e., “count” of number of substances initiated,hich could only increase or remain the same), the residualsetween adjacent measurement occasions for initiation wereorrelated and constrained to equality. In addition, the ex-genous predictors (i.e., externalizing and gender) werellowed to correlate, as were the residuals of the growthurve factors. Analysis of the self-reported data found thatmodel of quadratic growth in initiation provided the bestt to the data (�2

(9) � 15.12; p � .09). Because the fit of theodel improved when the quadratic factor was added (�

2(6) � 125.43; p � .01), that model was used to evaluate

he study variables. Overall, the study variables contributedo 16% of the variance in the initiation intercept, 5% of theariance in the initiation linear growth trajectory, and 4% ofhe variance in the initiation quadratic.

The intercept factor mean (.25) was significant, indicat-ng that the baseline level of initiation was appreciablyifferent from zero. Furthermore, the residual variance (.84)as significant, demonstrating individual differences ataseline. The linear slope factor mean (1.02) was also sig-

Externalizing Mean(boys)

Standarddeviation

4 Time 5

.68 .35 .69 .80

.67 .30 .93 .84

.72 .25 1.18 .85

.76 .27 1.22 .871.00 .38 1.50 .88

.18 1.00 .30 .26

.08 .091.46 .27.95 .23

3) below diagonal (9 missing). Correlations � .26 � .28 [p � .05] � .33

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ion; in addition, the residual variance (.95) was significant,ointing to substantial individual variation in linear changever time. Finally, the quadratic factor mean (�.63) wasignificant, as well as the residual variance (.96); somendividuals demonstrated acceleration or deceleration ofhange over time in initiation. An examination of the tra-ectories by gender (Figure 2) graphically illustrates nonlin-arity and the influence of gender on the residual slopeariance.

The first study objective was to examine the associationetween baseline initiation and the linear and quadraticrowth factors. The associations between baseline initiationnd the linear growth factor, as well as with the quadratic,ere nonsignificant. The association between the linearrowth factor and the quadratic growth factor (i.e., acceler-tion or deceleration) was significant and inverse; adoles-ents with slower linear growth in initiation exhibitedigher rates of acceleration in initiation.

Next, the influence of gender was assessed. Resultsound that gender was significantly predictive of baselinenitiation (� � .11; p � .05); adolescent boys had higheraseline initiation levels. Findings also demonstrated that

Fig. 1. Change over time in number of substances initiated from la

he path coefficient from gender to the linear growth trajec- s

ory was inverse and significant (� � �.16; p � .01);dolescent girls had a faster rate of linear growth. Theathway from gender to the initiation quadratic was signif-cant (� � .15; p � .01); adolescent boys demonstrated aaster rate of acceleration in the number of substances ini-iated across time (Figure 2).

wth curve analyses; standardized coefficient values are presented.

ig. 2. Change over time in number of substances initiated for gender
ubgroups.

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The third objective of the study was to assess the asso-iation between gender and externalizing. The associationetween gender and externalizing behaviors was not signif-cant (r � .04). We next examined the effect of externaliz-ng on initiation. Findings demonstrated that the path coef-cient from externalizing to baseline initiation was positivend significant (� � .27; p � .01); adolescents with higherxternalizing behaviors reported a greater number of sub-tances initiated at baseline. Findings also demonstrated thathe path coefficient from externalizing to the initiation linearrowth trajectory and the quadratic were both nonsignifi-ant; adolescents with both higher and lower rates of exter-alizing demonstrated a similar growth pattern.

iscussion

The current study evaluated the influence of externaliz-ng behaviors on initial levels and change over time in theumber of substances initiated. In addition, the study exam-ned the influence of gender on initial levels and changever time in initiation, as well as the association with ex-ernalizing behaviors, in a sample of rural adolescents. Thetudy extends prior research exploring the relationship be-ween externalizing and the initiation process with a growthurve model to examine gender differences in change overime. The growth curve analysis found a curvilinear trajec-ory; adolescent boys, compared with girls, reported areater number of substances initiated at baseline, a slowerate of linear increase, and an accelerating trend of increasever time. As noted earlier, studies have suggested genderifferences in the initial levels of substance use. However,revious research findings related specifically to genderifferences in the initiation process have been inconclusive.he current study would support a sex-variant model; that

s, the pattern of initiation over time differed between boysnd girls. The previously mixed findings might be a result ofiffering study methods, including specific differences inhe measurement of substance initiation. Previous studieseporting higher levels of initiation among adolescent boys32] have not explicitly examined a composite index.learly, more research is needed to explore baseline andhange over time in the number of substances initiated foriffering population subgroups (e.g., gender).

A further study objective was to evaluate the associationetween gender and externalizing. The finding that a base-ine association between gender and externalizing was notvident is inconsistent with previous research that indicatesoys exhibit more externalizing behaviors than girls. How-ver, it is important to note that only 1 gender differenceas found in the study by Achenbach et al [33], which was

arge enough to qualify as a medium effect [34]. Althoughrevious findings have documented that the percentage ofariance accounted for by gender is typically small (i.e., 1%o 5%), studies have consistently documented that boys
xhibit more externalizing behaviors than do girls [33]. r
lthough a greater prevalence rate of externalizing behav-ors among boys has generally been found, the difference ismall among relatively average youth when compared withouth referred for mental health services [33]. Further, re-ults from previous research might differ from findings inhe current report, because studies have compared adoles-ents who lived in varying geographic settings. Related tohis point, Rutter [16] concluded that problem behaviorates were higher for urban youth. Subsequent researchupported Rutter’s findings of higher prevalence rates forehavioral disorders among urban youth compared withural youth [35].

Study findings related to the final objective concerningifferences in the relationship between externalizing withhe number of substances initiated led to 2 general conclu-ions. First, higher baseline levels of externalizing wereound to significantly influence the baseline number of sub-tances initiated. Analyses in the current study lend supporto previous research documenting evidence related to thenfluence of externalizing behaviors on initial levels of sub-tance use [36]. This suggests that initiation might result asconsequence of the occurrence of externalizing behaviors.owever, the specific mechanism leading to this pattern ofndings points to the need to investigate the factors thatause such covariation. The psychologic processes that con-ributed to the vulnerability of the sample (i.e., rural mid-estern adolescents) to the influence of externalizing on theumber of substances initiated have not been examinedully [37]. Consequently, further research is required toonfirm the association between externalizing and initialevels of initiation, as well as to explore the relationshipith other predictors that might mediate or moderate that

ssociation (e.g., peer group norms).Second, the current study did not find conclusive evi-

ence that adolescents with higher levels of externalizingad slower rates of linear increase in the number of sub-tances initiated and a faster rate of acceleration over time.his finding does not support evidence documented byhite et al [9] that conduct disorder among early adolescent

oys was negatively related to an increase in alcohol use.revious research findings [25] that have documented evi-ence of a positive linear increase have examined the rela-ionship between delinquency and substance use. Con-ersely, the current study focused on externalizingehaviors rather than the more serious problem of delin-uency. An additional explanation for the lack of a signif-cant finding might be related to the measurement of initi-tion. Findings from earlier research [38] have been basedrimarily on specific substance prevalence rates; the presenttudy combined 3 substances to represent a composite mea-ure. As noted earlier, differences might exist among thearious substances. However, given the strong associationsmong tobacco, alcohol, and marijuana use in adolescence,composite measure was considered to more completely

epresent the general initiation tendency. Overall, results

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rom the current study lend support to previous researchndings related to externalizing and baseline initiation fordolescent boys and girls. From the viewpoint of substancese etiology, gender differences in the process of initiationver time are important to consider. Specifically, adolescentoys reported a higher number of substances initiated ataseline. Although gender was not associated with exter-alizing behaviors, that variable was an important predictorf baseline initiation for both gender groups. The mecha-ism leading to differences in the initiation process is un-nown, and points to the need to investigate the factors thatause such variation. The psychological processes thatake adolescent boys and girls uniquely vulnerable to sub-

tance initiation are not well understood. Consequently,ndings from the current study warrant further investigationf the influence of gender on the relationship between ex-ernalizing and change over time in the initiation process.

tudy limitations

The present study has several limitations. First, the studyample consisted entirely of rural midwestern students inarly to middle adolescence. Although the sample was pre-ominately Caucasian, it is representative of the midwesternegion in which the study was conducted. However, thisimits generalizability of findings to other groups, such asrban minority youth. Second, the measures presented in thetudy were based on adolescent self-reported information,nd might be somewhat limited due to the perspective of aingle informant. In that regard, a supplemental analysisas conducted to examine the study model using external-

zing behaviors as reported by the parents of their adoles-ent child. That analysis found the same pattern of results asresented in the study. The final limitation concerns studyttrition. Specifically, adolescents prone to initiate sub-tance use were underrepresented in the study sample.

In summary, the current study used latent growth curveodeling to extend the analyses of adolescent substance

nitiation and strengthened accumulating evidence that ex-ernalizing behaviors influence initiation among rural ado-escents. Latent growth curve modeling afforded the oppor-unity to examine the complex predictors of change overime in the initiation process. Despite evidence of thoseivariate relationships, longitudinal studies of the relation-hips specified in the model clearly are needed. Furtheresearch efforts should include additional predictor vari-bles (e.g., peer group norms) to explore longitudinalrowth models of initiation growth trajectories.

cknowledgments

This research was supported by grants DA 070 29-01 and
A 10815-01 from the National Institute on Drug Abuse.
ppendix

atent Growth Curve Modeling

Although the analytic technique of latent growth curveodeling has been gaining popularity in some disciplines,

ypically it has not been used to evaluate longitudinal stud-es of substance initiation. Growth curve estimation begany describing change over time for each adolescent in thetudy [39]. Conceptually, this was done by fitting a regres-ion line (i.e., growth curve) linking the outcome variablei.e., consecutive waves of substance initiation) to time forach adolescent. Individual regression prediction equationsere then summarized to obtain an average intercept and an

verage slope for all adolescents, each with a variance. Theoal was then to explain why some adolescents had highernitial levels (i.e., intercepts) than others and why somedolescents had steeper slopes (i.e., rates of change).

Although each individual growth trajectory varied inevel and rate of change, they were aggregated for the entireample, so that there was an average (i.e., mean) level withvariance and an average (i.e., mean) rate of change, alsoith a variance. The mean and variance of the individual-

evel parameters identified the overall average of the indi-idual levels and variability of levels (i.e., dispersion)cross individuals. The mean for the rate of change de-cribed the average overall change in behaviors of individ-als over time, while the population variance for the changearameter reflected differences in the rates of change acrossndividuals. Significant variance in the growth parametersemonstrated the heterogeneity of the individual-specificrajectory population. This variance is the fluctuation pre-ictors of change explain in the analysis of change.

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externalizing behaviors as predictors of substance initiation trajectories among rural adolescents

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