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Adolescent problem behavior: the influence of parents and peers
BEHAVIOUR RESEARCH AND THERAPY
PERGAMON
Behaviour Research and Therapy 37 (1999) 217±230
Adolescent problem behavior: the in¯uence of parents and peers Dennis V. Ary *, Terry E. Duncan, Susan C. Duncan, Hyman Hops Oregon Research Institute, 1715 Franklin Blvd., Eugene, Oregon, 97403, USA
Abstract This paper presents evidence that the Patterson et al. (1992) model of development of antisocial behavior in children generalizes to the development of a wide array of problem behaviors during later adolescence and that youth antisocial behavior, high-risk sexual behavior, academic failure and substance use form a single problem behavior construct. Structural equation modeling methods were applied to 24-month longitudinal data from 204 adolescents and parents. The model ®t the data well, accounting for 52% of the variance in adolescent problem behavior. Speci®cally, families experiencing high levels of con¯ict were more likely to have low levels of parent±child involvement. These family conditions were related to poor parental monitoring and association with deviant peers one year later. Poor parental monitoring and associations with deviant peers were strong proximal predictors of engagement in an array of problem behaviors at two-year follow-up. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Problem behavior; Adolescence; Social context; Missingness; Structural equation methodology
1. A model of adolescent problem behavior development This study investigates the extent to which the model of family and peer in¯uences on the development of antisocial behavior proposed by Patterson, Reid, and Dishion (1992) applies to (a) a much older adolescent sample, and (b) the development of a diverse range of adolescent problem behaviors, not just antisocial behavior. Findings in support of this model would suggest that interventions designed to modify these behavior patterns among families with adolescents might eectively and eciently ameliorate a wide array of adolescent problem behaviors. In addition, this study seeks to determine if youth antisocial behavior, high-risk * Corresponding author. 0005-7967/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 0 0 5 - 7 9 6 7 ( 9 8 ) 0 0 1 3 3 - 8
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sexual behavior, academic failure, and substance use form a single ``problem behavior'' construct. 1.1. Development model of antisocial behavior The development model of antisocial behavior advanced by Patterson and colleagues (Dishion, Patterson, and Kavanagh, 1992; Dishion, Patterson, Stoolmiller, and Skinner, 1991; Patterson and Bank, 1989; Patterson, Capaldi, and Bank, 1991; Patterson, DeBaryshe, and Ramsey, 1989; Patterson, Reid, and Dishion, 1992) accounts for delinquent and antisocial behavior in adolescents primarily through proximal peer in¯uence, but suggests that poor family management practices during childhood, especially coercive interactions and poor parental monitoring, explain the child's association with deviant peers. That is, aggressive and oppositional behaviors in the young child can place the child at high risk for a series of negative outcomes that ultimately result in antisocial behavior as adolescents. Speci®cally, this model suggests that harsh and inconsistent parental discipline of early oppositional behavior shapes further aggressive behavior through a process involving progressively more coercive interactions between the parents and the young child. In order to avoid these aversive disciplinary interactions, parents often become increasingly inconsistent in their discipline and monitoring. Consequently, the child's aggressive behavior becomes more established. This pattern of noncompliant and aggressive behavior is later extended to the school environment. Soon, the child is on a trajectory that includes rejection by normal peers and academic failure in the classroom. By early adolescence the child has established a peer group of other rejected and aggressive adolescents, who quickly shape and reinforce additional antisocial behavior. At this point, the child is at high risk for developing a consistent pattern of antisocial and delinquent behavior, as well as drug use. The current study seeks to determine if this model of the development of antisocial behavior generalizes to a broad array of problem behaviors among adolescents. There is evidence that Patterson and colleagues' model of the development of antisocial behavior is applicable to problem behavior generally (Metzler, Biglan, Ary, Noell, and Smolkowski, 1993), as well as to adolescent drug abuse (Dishion, Patterson, and Reid, 1988; Dishion and Ray, 1991). 1.2. Problem behavior syndrome One of the goals of the present study is to examine the extent to which several dierent types of adolescent behaviors are intercorrelated and representative of a general problem behavior syndrome. There is a signi®cant empirical research base supporting such a class of behaviors that includes antisocial behavior, substance use, academic failure, and precocious and risky sexual behavior (e.g., Allen, Leadbeater, and Aber, 1994; Barone et al., 1995; Donovan and Jessor, 1985; Donovan, Jessor, and Costa, 1988; Farrell, Danish, and Howard, 1992; Jessor and Jessor, 1977; McGee and Newcomb, 1992; Metzler, Noell, Biglan, Ary and Smolkowski, 1994; Osgood, Johnston, O'Malley, and Bachman, 1988). Further, there is evidence that the problem behavior construct is descriptive for both male and female adolescents (Donovan and Jessor, 1985; Farrell et al., 1992) and over several development stages (early adolescence to adulthood) (McGee and Newcomb, 1992).
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While the studies cited above suggest that a general problem behavior model provides a reasonable description of the problem behavior data, the model does not account for all the variance in the data. Osgood et al., (1988) found that a single common factor accounted for approximately half the variance (47%) in the variables measured. They concluded that a general factor did not fully explain the relationships between the individual behaviors, and that each behavior had a signi®cant unique component, as well. In another study, Gillmore et al. (1991) found that a single problem behavior factor did not provide an adequate account of cross-sectional data gathered from preadolescent sixth-grade youth. In a study of 15-year-olds in New Zealand, Fergusson, Horwood, and Lynsky (1994) reported substantial comorbidities between dierent types of adolescent problem behaviors, but also found evidence of distinct problem behavior groupings of adolescents. They suggest that these qualitatively dierent subpopulations explain the comorbidity among problem behaviors rather than a single underlying problem behavior dimension. In a cross-sectional study using data from 14±18-year-olds in the U.S., Tildesley, Hops, Ary and Andrews (1995) found that a two-factor solution provided a somewhat better ®t than a single problem behavior factor, although the single problem behavior factor accounted for a large proportion of the variance (66%). Taken together, these studies provide evidence that (a) the general problem behavior factor does not account for all the variance in the individual behaviors, (b) the problem behavior syndrome may not apply to preadolescents, and (c) adolescents may cluster into certain problem behavior combinations. The current study will examine the extent to which the general problem behavior factor ®ts longitudinal data from U.S. adolescents. 1.3. Testing the model Based on the Patterson et al. (1992) model of the development of antisocial behavior, we hypothesize that families with low levels of con¯ict at Time 1, the initial assessment, will have high levels of concurrent, positive family relations. Similarly, low levels of family con¯ict and high levels of positive family relations are expected to be related to high parental monitoring 12 months later at Time 2, which will, in turn, be associated with low levels of deviant peer contact. Finally, it is expected that good parental monitoring and low association with deviant peers at Time 2 would be associated with low levels of engagement in problem behavior 12 months later at Time 3.
2. Method 2.1. Subjects Subjects in the present study were involved in a longitudinal investigation of family factors in¯uencing substance use among adolescents (Andrews, Hops, Ary, Lichtenstein, and Tildesley, 1991; Hops, Tildesley, Lichtenstein, Ary, and Sherman, 1990). Participants were from two northwestern urban areas of the United States, with populations of approximately 50,000 and 120,000. Recruitment was through advertisements in local newspapers and ¯iers posted in community centers and prominent street locations. Families contained at least one adolescent
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between the ages of 11 and 15 who was designated as the target.1 Attrition across the ®rst seven years of the study was variable, averaging 6.7% between assessments. The highest attrition rates occurred in the early years of the study. While subjects who dropped out of the study tended to be older, of lower SES, and higher users of substances compared to those who remained, these ®ndings are consistent with attrition eects noted in other longitudinal studies (e.g., Kandel, 1985; Newcomb and Bentler, 1988b). For the purposes of the present study the current sample comprised target youth living at home, and their parents, and utilized data from three consecutive yearly assessments (T5 to T7 of the project; 1990±1993). The T5 to T7 assessments were selected for this analysis because they were the only assessments that included all the measures necessary to evaluate the hypothesized model. This sample included 196 families; 45.13% single-parent, the remainder either married or living in a committed relationship. Target youth included 100 males (51.0%). The majority of the participants were white (91.8%) and had a mean age of 15.98 years (SD = 0.54) at the ®rst assessment. This sample's substance use is representative of the state of Oregon, estimated from the Oregon Employment Division Research and Statistics report on drug use among eighth and eleventh graders in Oregon (Ciranny, 1989). In comparison to national norms, this sample's substance use tends to be somewhat higher than that estimated by the National Institute on Drug Abuse National Household Survey on Drug Abuse (NIDA, 1989). 2.2. Procedures At all assessments, target adolescents and their parents separately completed a series of selfreport questionnaires in our laboratory. Questions assessed substance use, other problem behaviors, and selected psychosocial characteristics. Each participating family member was paid $25 at each assessment, and annual lotteries were held for both targets and parents. To increase the likelihood of valid reporting of behaviors such as substance use, con®dentiality was stressed at each assessment and a certi®cate of con®dentiality was obtained from the National Institute on Drug Abuse that precluded the subpoena of subjects' data. 2.3. Measures The variables used in the present study combine the use of adolescent and parent reports. Where possible, constructs included multiple items and/or multi-agent responses. Table 1 1
To address the possibility that the voluntary nature of this study could aect the generalizability of results, comparisons were made between this study's recruited sample and normative data from other studies on several indicators of pathology. For example, mean scores on depressive symptoms derived from parent reports on the Child Behavior Checklist (CBCL; Achenbach and Edelbrock, 1983) as well as those based on self-reports by the adolescents on the Center for Epidemiologic Studies Depression Scale (CES-D; Radlo, 1977) were found to be within the normal range when compared to the CBCL normative data, and to data from an epidemiological study of adolescent depression (Lewinsohn, Hops, Roberts, Seeley, and Andrews, 1993) and familial characteristics (Hops and Seeley, 1992). Furthermore, cross-sectional analysis by age was also consistent with epidemiologic studies (Hops, 1996). Taken together, these data suggest that our recruited sample was not unique on these indicators compared to both regional and national norms.
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Table 1 Summary of construct items utilized in the structural equation models Construct indicator questionaire item Family con¯ict V1 Family members have big arguments over little things V2 Family members get angry with each other daily V3 Family members get angry with each other three times a week Positive family relations V4 Family members support one another V5 There are feelings of togetherness in our house V6 Family members get along well Inadequate parental monitoring a V7 Parents let child go places without asking V8 Before going out, child tells parents when will be back V9 Child does things without telling parents Peer Deviance V10 Child's friends get into ®ghts Child's friends get into trouble a lot V11 Child's friends would dare child to use drugs Number of times child's friends had problems with drugs How many of child's ®ve closest friends use cigarettes How many of child's ®ve closest friends use alcohol How many of child's ®ve closest friends use marijuana Problem behaviors b Academic failure V12 Child's GPA in school Number hours child spent on homework each night Antisocial behavior c V13 Last 6 months: child damaged others property Last 6 months: child damaged school property Last 6 months: child hit/threatened others Last 6 months: child stole >$50 item Last 6 months: child stole from a store Number of times child arrested in last year Substance use V14 Child's monthly alcohol rate Child's monthly cigarette rate Child's monthly marijuana rate Risky sexual behavior V15 Number of opposite sex partners child had in past year Child has had sex with partners having sex with others
Scaling T/F T/F T/F T/F T/F T/F 1±5 1±5 1±5 T/F T/F T/F Frequent 0±5 0±5 0±5
Minutes 1±6 1±6 1±6 1±6 1±6 Frequent Frequent Frequent Frequent Frequent Frequent
a
Monitoring items scored 1 to 5: 1 = almost never; 5 = almost always.bAdolescent self-report only.cAntisocial behavior items scored 1 to 6: 1 = never; 6 = 10+ times in last year
provides a summary of the items utilised in the structural equation models. Most measures were based on previously validated items; family con¯ict items were taken from the appraisal of mother/father subscales of the Con¯ict Behavior Questionnaire (CBQ; Prinz, Foster, Kent, and O'Leary, 1979); positive family relations items were from the cohesion subscale of the
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Fig. 1. Developmental model of adolescent problem behavior.
Family Environment Scale (FES; Moos and Moos, 1975), parental monitoring questions and peer deviance items were based on prior work by Capaldi and Patterson (1989). The constructs of family con¯ict, positive family relations, parental monitoring, and peer deviance, all include adolescent reports and mother and/or father reports. The problem behavior items all consist of adolescent reports only, and include monthly rate measures for substance use (e.g., alcohol, cigarettes, and marijuana), antisocial behavior items (e.g., vandalism, physical assault, theft) (Jessor and Jessor, 1977), risky sexual behavior items (e.g., number of opposite sex partners) (Metzler et al., 1994), and academic failure (i.e., GPA, time spent on homework each night). 3. Results 3.1. Analytic strategy for missing data The models were tested using EQS, Bentler's (1989) structural equation modeling program, and applied to a situation in which data were missing on speci®c variables. Although standard analyses of incomplete data, generally involving the use of listwise deletion, take advantage of the complete nature of the resulting data set to simplify computations, these procedures provide estimates that are generally inecient, discarding a substantial amount of potentially useful data (MutheÂn, Kaplan, and Hollis, 1987). MutheÂn et al. (1987) have demonstrated that in many applications, model estimation with distinct missing data patterns can be carried out utilizing existing structural equation modeling software that allow for the simultaneous analysis of multiple groups. Brie¯y, the strategy for handling missing data consists of expanding the usual structural equation model to include means, or regression intercepts, and partitioning the sample into subgroups with distinct patterns of missing data. Equality constraints across the various groups
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representing distinct patterns of missingness are used in a multi-sample analysis to obtain unbiased and consistent estimates. It should be emphasized that these equality constraints across the missing data subsamples are not of substantive interest, but, because of the assumption that the hypothesized model is invariant across groups, function solely to insure correct estimation of model parameters.2 3.2. Problem behavior construct A measurement model of the general problem behavior construct examined the extent to which antisocial behavior, high-risk sex, academic failure and substance use were inter-related. The problem behavior measurement model indicated that a general problem behavior construct ®t these data very well, accounting for approximately 67% of the variance in the various problem behaviors (w 2(2, n = 141) = 0.764, P < 0.997, and ®t index CFI = 1.00). 3.3. Development model of problem behavior Fig. 1 presents a model of the peer and parental in¯uences of adolescent problem behavior. Family con¯ict and positive family relations were assessed at Time 1, inadequate monitoring and association with deviant peers at Time 2, and problem behaviors at Time 3. Using the missing data procedures described above, data from four subsets of cases (two subsets for each gender) which represent various stages of ``completeness'' were used. Compared with the original sample of 204, the data in group 1 (n = 141; [72 males and 69 females]) represent the ``complete'' data available for analysis. Utilizing only these data would ignore the possibility of severe bias due to selective subject attrition and nonresponse. The data available for the subsequent groups (n = 55; [28 males and 27 females]) increased the total number of subjects to 196 cases which represents approximately 96% of the original sample. To determine if the model ®ts equally well for males and females, a multiple-sample model was ®t simultaneously to the four subsamples, constraining corresponding parameters to be equal across subsamples.3 Table 2 presents descriptive statistics for the structural equation variables including means, standard deviations, and values of skewness and kurtosis for the combined sample. Values of skewness and kurtosis were, in most cases, minimal. Therefore, 2
A full exposition of this approach to structural equation modeling is beyond the scope of this paper. Readers are referred to Allison and Hauser (1991), MutheÂn et al., (1987), and Rubin (1976) for a complete discussion and references on the issues surrounding the model-based approach to the analysis of missing data. Examples containing EQS program information for the model-based approach can be found in Duncan and Duncan (1994) and Duncan, Oman, and Duncan (1994). 3 To permit an appropriate large sample chi-square test of model ®t, one need only obtain the likelihood value for both restricted H0 and unrestricted H1 model hypotheses. Tests of the H1 hypothesis (involving the test of equality of the moment matrices) and the restricted H0 model (the hypothesized model of interest) involves imposing equality restrictions across the groups representing various missing data patterns for common parameters. The dierence in chi-square values and degrees of freedom for the two analyses, H0 against H1, gives a correct test of the hypothesized model. This is the same degrees of freedom that would be used in corresponding ``complete-case'' analyses which specify no missing data.
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Table 2 Descriptive statistics for the combined samples Variable Family con¯ict V1 V2 V3 Positive family relations V4 V5 V6 Monitoring V7 V8 V9 Peer deviance V10 V11 Problem behavior V12ÐAcademic V13ÐAntisocial V14ÐSubstances V15ÐRisky sex
Mean
SD
Kurtosis
Skewness
n
1.31 1.20 1.45
0.33 0.28 0.36
ÿ 0.74 0.79 ÿ 1.18
0.62 1.29 0.22
196 196 196
1.79 1.74 1.76
0.31 0.36 0.35
0.67 ÿ 0.22 0.06
ÿ1.32 ÿ1.08 ÿ1.18
196 196 196
3.48 3.57 4.01
1.26 1.17 1.09
ÿ 0.57 ÿ 0.64 0.54
ÿ0.74 ÿ0.58 ÿ1.17
196 196 196
ÿ 0.07 ÿ 0.04
0.55 0.67
5.52 10.81
2.06 2.93
141 141
ÿ 0.14 ÿ 0.16 ÿ 0.25 ÿ 0.16
0.79 0.37 0.72 0.16
0.92 ÿ 0.49 0.49 ÿ 1.33
ÿ0.28 0.92 1.09 0.19
141 141 141 141
the assumption of approximate normality of the observed variables appears tenable. Approximate normality justi®es the use of normal theory maximum likelihood estimation techniques found in structural equation programs such as EQS (Bentler, 1989). Fitting the hypothesized model to the multiple-sample data resulted in an overall model ®t of w 2(204, n = 196) = 286.107, P < 0-.001, and ®t index CFI = 0.907.4 While the CFI ®t index does not indicate an exceptional model ®t, it does re¯ect an adequate and acceptable ®t greater than 0.90 (Bentler, 1989; Bentler and Bonett, 1980). In examining whether the same model described a process which ®t equally well for both males and females, Lagrange multipliers associated with the cross-gender constraints, suggested that only two parameters, the error variances for the antisocial behavior construct and the substance use construct, if relaxed, 4
Under the unrestricted H1 hypothesis, a chi-square test statistic value of w 2(108, n = 196) = 166.793, P < 0.001, was obtained. The test of the unrestricted H1 model is also a test of whether the data are ``missing completely at random'' (MCAR). If the pattern of missingness is completely random, then the subsamples representing missingness can be regarded as distinct random samples from the same population. The model ®t indicates that MCAR should be rejected for these subsamples. If the data are not MCAR, and the model is rejected, it may be preferable to proceed with the test of the hypothesized model under a much less restrictive assumption that the missing data mechanism is simply ``ignorable'' (Rubin, 1976), and the missing data are simply missing at random (MAR). It should be emphasized that when the assumptions of MCAR or MAR are tenable, the maximum-likelihood estimations generated from the structural equation analyses will exhibit no large sample bias. The test of the hypothesized H0 structural model provided a chi-square test statistic value ofw 2(312n = 196) = 452.910 P < 0.001.
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Table 3 Parameter estimates for the hypothesized structural model Coecient Family con¯ict V1, F1 V2, F1 V3, F1 Positive family relations V4, F2 V5, F2 V6, F2 Inadequate parental monitoring V7, F3 V8, F3 V9, F3 Peer deviance V10, F4 V11, F4 Problem behavior V12, F5 V13, F5 V14, F5 V15, F5 Structural coecients F2, F1 F3, F2 F4, F3 F5, F3 F5, F4
Eect
t-Value
0.687 0.724 0.801
7.945 Ð 8.291
0.690 0.846 0.911
Ð 10.364 10.364
0.622 0.771 0.674
Ð 6.547 6.774
0.678 0.783
Ð 5.426
0.352 0.577 0.667 0.657
3.390 5.109 5.563 Ð
ÿ 0.431 ÿ 0.232 0.383 0.173 0.656
4.610 2.552 3.179 1.433 4.052
Note.1 t-Values greater than 1.96 in magnitude indicate a parameter estimate which is signi®cantly dierent from zero
would result in a signi®cant improvement in model ®t. Separate parameter estimates for the error variances for the target adolescents' antisocial behavior and substance use, respectively, indicated that signi®cantly more variance existed for boys' self-reported antisocial behavior and substance use than was reported by females. Despite these dierences, the same structural model appears to ®t equally well for both males and females. The model accounted for approximately 52% of the variance in problem behavior. Associations with deviant peers had a signi®cant direct eect, b = 0.656, t(196) = 4.052, P < 0.05, on adolescent problem behavior. In addition, the indirect eect of monitoring on later problem behavior mediated through deviant peers was signi®cant: b = 0.251, t(196) = 1.990, P < 0.05. The direct eect of monitoring on later problem behavior approached signi®cance: b = 0.173, t(196) = 1.433, P < 0.08 (one-tailed). The structural coecients from positive family relations to parental monitoring were signi®cant: b = -0.232, t(196) = 2.552, P < 0.05 and b = ÿ0.431, t(196) = 4.610, P < 0.05, respectively. Table 3 presents the standardized solution for the hypothesized structural model.
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4. Discussion The goals of this study were twofold: (a) to examine the extent to which the Patterson and colleagues' model of the development of antisocial behavior in children (Patterson and Bank, 1989; Patterson et al., 1991, 1989, 1992) is applicable to problem behavior in late-adolescence, and (b) to determine if youth antisocial behavior, high-risk sexual behavior, academic failure and substance use form a single problem behavior construct. 4.1. Support for the model of the development of adolescent problem behavior The present ®ndings provide signi®cant support for this developmental extension. The model ®t the data well and accounted for 52% of the variance in adolescent problem behavior. The study suggests that a substantial amount of the variance in a range of adolescent problem behaviors can be explained by the inter-relationships among the four elementary constructs of this developmental model. Brie¯y stated, families in which there were high levels of con¯ict and low positive family relations were more likely to develop a social context that includes inadequate parental monitoring and associations with deviant peers. Poor parental monitoring and associations with deviant peers were associated with subsequent engagement in problem behavior. The ®ndings suggest that these processes, which have been shown to relate to the development of antisocial behavior during childhood and early adolescence (e.g., Patterson et al., 1992), are also relevant to the development of a more general problem behavior syndrome during mid-to-late adolescence. These ®ndings are consistent with Patterson and Yoerger (in press) who reported that a late-onset group of adolescent oenders had parents with similar de®cits in parenting practices. The results also indicate that, despite evidence of increasing peer in¯uence among adolescents (Collins, 1990; Montemayor, 1982), parental in¯uence can continue to be a moderating or augmenting source of in¯uence throughout adolescence (Brook, Whiteman, Gordon, and Brook, 1985; Krosnick and Judd, 1982). This ®nding is of interest because it suggests that if interventions are designed to in¯uence the constructs delineated in this developmental model and they are implemented during adolescence, problem behavior among youth in this age group may be eectively ameliorated. 4.2. Problem behavior syndrome The results of this study also provide support for the validity of a single problem behavior construct, accounting for 67% of the variance in problem behavior. Speci®cally, the study presents evidence that youth anti-social behavior, high-risk sexual behavior, academic failure, and substance use form a single problem behavior construct. While this ®nding is consistent with a number of other studies (Donovan and Jessor, 1985; Donovan et al., 1988; Jessor and Jessor, 1977; McGee and Newcomb, 1992; Metzler et al., 1994) and 67% of the variance accounted for is fairly impressive given the problems inherent in conducting behavioral science research with adolescents, not all the variance is explained within the single construct model. Part of the unexplained variance may be due to speci®c subpopulations of youth that exhibit dierent con®gurations or combinations of problem behaviors, rather than all problem behaviors simultaneously (Fergusson et al., 1994; Loeber, 1988). Additional research may be
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needed to fully understand the subtleties of the co-occcurence of problem behaviors. Nevertheless, the current study suggests that for a large number of adolescents, multiple problem behaviors are interrelated and their etiology is consistent with the in¯uence of the family and peer factors presented here. 4.3. Implications for interventions to prevent adolescent problem behaviors If the ®ndings of this study are replicated in subsequent investigations, it would indicate that eorts to modify the social conditions depicted in the developmental model may lead to the prevention of a diverse set of youth problem behaviors for a signi®cant number of youths. While it might be dicult to justify the cost of such an intervention eort in order to reduce or eliminate a single problem behavior, it may be cost eective to mount such an intervention if it aects a wide range of problem behaviors. Based on the ®ndings of this study, such an intervention eort might focus on improving parenting practices and increasing parental monitoring. There is research evidence that these goals might be reached through the provision of parent training programs (e.g., Dishion and Andrews, 1995), school-based communications to parents, and targeted media on speci®c parenting practices, such as parental monitoring. Prevention eorts directed early on in the developmental progression of such problem behaviors may be more powerful and less expensive than interventions targeting middle or high school age children. A focus on family factors and on the child's early entry into deviant peer groups may prevent the development of problematic family practices and deviant peer associations. For example, the current study suggests that poor family management practices (i.e., family con¯ict and low positive family relations) lead to poor parental monitoring and associations with deviant peers. Patterson and colleagues found that ineective parenting practices at age 10 places the child on a trajectory that eventually results in antisocial and delinquent behavior. This suggests that parent training for the parents of high-risk children at a much earlier age may be a more desirable approach. Patterson, Chamberlain and Reid (1982) have demonstrated signi®cant reductions in antisocial behavior using parent training programs based on this approach. It may also be advisable to improve the social competence of the child in order to preclude the onset of deviant peer relations. Peer rejection and aggression have been shown to be highly predictive of a variety of problem behaviors (Parker and Asher, 1987; Kellam, Simon, and Esminger, 1983). Thus, an integrated prevention eort that strengthens parenting practices, monitoring norms in a community, and childhood social skills early on in the developmental process may provide a powerful, cost-eective set of prevention procedures. 4.4. Study limitations and future directions This study does address several design concerns through the utilization of both parent and adolescent reports, multiple indicators for each construct, and three-year longitudinal data. However, several limitations remain. First, the data are not multi-method, but questionnaire only. The inclusion of observational data or behavioral records (e.g., arrest records, number of referrals) would strengthen the ®ndings. Second, the generalizability of the ®ndings may be limited by the representativeness of the study sample. The sample is over 90% Caucasian, and while the drug use of the sample is representative of the state of Oregon, the use levels are
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somewhat higher than national norms. Third, while the Patterson et al., model ®ts the data well and provides an interpretable model, it should be noted that other models might also provide plausible interpretations of the relevant processes. Fourth, the study utilized annual assessments which might not be the optimal interval to assess change in the model constructs. For example, it is conceivable that monthly assessments might provide greater sensitivity to change and identify substantively dierent relationships. Lastly, the availability of only three data points precludes the inclusion of constructs involving the measurement of change (e.g., residualized change or simple change) in the testing of the complete Patterson et al., model. Future research might address these limitations through the use of multiple methods of assessment, representative samples, a variety of assessment intervals, and four or more assessment occasions.
Acknowledgements This research was supported by Grant No. DA03706 from the National Institute of Drug Abuse. Preparation of this manuscript was supported in part by Grant Nos. DA03706, DA07389, DA09678 and DA09306 from the National Institute on Drug Abuse and Grant No. CA38273 from the National Cancer Institute.
References Achenbach, T. M., & Edelbrock, C. S. (1983). Manual for the child behavior checklist and revised child behavior pro®le. Burlington, VT: University of Vermont, Department of Psychiatry. Allen, J. P., Leadbeater, B. J., & Aber, J. L. (1994). The development of problem behavior syndromes in at-risk adolescents. Development and Psychopathology, 6, 323±342. Allison, P. D., & Hauser, R. M. (1991). Reducing bias in estimates of linear models by remeasurement of a random subsample. Sociological Methods and Research, 19, 466±492. Andrews, J. A., Hops, H., Ary, D., Lichtenstein, E., & Tildesley, E. (1991). The construction, validation and use of a Guttman scale of adolescent substance use: An investigation of family relationships. Journal of Drug Issues, 21, 557±572. Barone, C., Weissberg, R. P., Kasprow, W. J., Voyce, C. K., Arthur, M. W., & Shriver, T. P. (1995). Involvement in multiple problem behaviors of young urban adolescents. The Journal of Primary Prevention, 15(3), . Bentler, P. M. (1989). EQS structural equations program manual. Los Angeles, CA: BMDP Statistical Software. Bentler, P. M., & Bonett, D. G. (1980). Signi®cance tests and goodness-of-®t in the analysis of covariance structures. Psychological Bulletin, 88, 588±606. Brook, J. S., Whiteman, M., Gordon, A. S., & Brook, D. W. (1985). Father's in¯uence on his daughter's marijuana use viewed in a mother and peer context. Advances in Alcohol and Substances Abuse, 165(190), . Capaldi, D. M., & Patterson, G. R. 1989. Psychometric properties of fourteen latent constructs from the Oregon Youth Study. New York: Springer-Verlag. Ciranny, J. (1989). Is Oregon's future at risk? A pro®le of Oregon's Youth. Salem, OR: Oregon Employment Division Research and Statistics. Collins, W. A. (1990). Parent-child relationships in the transition to adolescence: Continuity and change in interaction, aect, and cognition. In R. Montmayor, G. R. Adams, & P. Gullotta (Ed.), From childhood to adolescence: A transitional period?, (pp. 85±106). Newbury Park, CA: Sage. Dishion, T. J., & Andrews, D. W. (1995). Preventing escalation in problem behaviors with high-risk young adolescents: Immediate and 1-year outcomes. Journal of Consulting an Clinical Psychology 63, 538±548. Dishion, T., Patterson, G. R., & Kavanagh, K. (1992). An experimental test of the coercion model: Linking theory measurement, and intervention. In J. McCord & R. Tremblay (Eds.) The interaction of theory and practice: Experimental studies of intervention (pp. 253±282). New York: Guilford.
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Syndrome of problem behavior in adolescence: A replication. Journal of Consulting and Clinical Psychology, 56(5), 762±765. Duncan, S. C., & Duncan, T. E. (1994). Modeling incomplete longitudinal substance use data using latent variable growth curve methodology. Multivariate Behavioral Research, 29(4), 313±338. Duncan, T. E., Oman, R. F., & Duncan, S. C. (1994). Modeling incomplete data in exercise behavior reseach using structural equation methodology. Journal of Sport and Exercise Psychology, 16, 187±203. Farrel, A. D., Danish, S. J., & Howard, C. W. (1992). Relationship between drug abuse and other problem behaviors in urban adolescents. Journal of Consulting and Clinical Psychology, 60(5), 705±712. Fergusson, D. M., Horwood, L. J., & Lynskey, M. T. (1994). The comorbidities of adolescent problem behaviors: A latent class model. Journal of Abnormal Child Psychology, 22(3), . Gillmore, M. R., Hawkins, J. D., Catalano, R. F., Jr., Day, L. E., Moore, M., & Abbott, R. (1991). Structure of problem behaviors in preadolescence. Journal of Consulting and Clinical Psychology, 59(4), 499±506. Hops, H. (1996). Intergenerational transfer of depressive symptoms: Gender and developmental considerations. In C. H. Mundt, M. J. Goldstein, K. Hahlweg, & P. Fiedler (Ed.), Interpersonal Factors in the Origin and Course of Aective Disorders, (pp. 113±129). London: Royal College of Psychiatrists. Hops, H., & Seeley, J. R. (1992). Parent participation in studies of family interaction: Methodological and substantive considerations. Behavioral Assessment, 14, 229±243. Hops, H., Tildesley, E., Lichenstein, E., Ary, D., & Sherman, L. (1990). Parent-adolescent problem-solving interactions and drug use. American Journal of Drug and Alcohol Abuse, 16, 239±258. Jessor, R., & Jessor, S. L. (1977). Problem behavior and psychosocial development. New York: Academic Press. Kandel, D. B. (1985). On processes of peer in¯uences in adolescent drug use: A developmental perspective. 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Kazdin (Ed.), Advances in clinical child psychology (11, pp. 73±124). New York: Plenum. McGee, L., & Newcomb, M. D. (1992). General deviance syndrome: Expanded hierarchical evaluations at four ages from early adolescence to adulthood. Journal of Consulting and Clinical Psychology, 60(5), 766±776. Metzler, C., Biglan, A., Ary, D., Noell, J., & Smolkowski, K. (1993). The relationships among high-risk sexual behavior and other adolescent problem behaviors. Journal of Behavioral Medicine, 17(4), 419±438. Metzler, C. W., Noell, J., Biglan, A., Ary, D., & Smolkowski, K. (1994). The social context for high-risk sexual behavior in adolescents. Journal of Behavioral Medicine, 17(4), 419±443. Montemayor, R. (1982). The relationship between parent-adolescent con¯ict and the amount of time adolescents spend alone and with parents and peers. Child Development, 53, 1512±1519. Moos, R., & Moos, B. (1975). Families. In R. H. Moos (Ed.), Evaluating correctional and community settings. NY, NY: Wiley. MutheÂn, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431±462. National Institute on Drug Abuse (1989). National Household Survey on Drug Abuse: Population Estimates 1988. Rockville, MD: U.S. Department of Health and Human Services, PHS. Newcomb, M. D., & Bentler, P. M. (1988b). Impact of adolescent drug use and social support on problems of young adults: A longitudinal study. Journal of Abnormal Psychology, 97, 64±75. Osgood, D. W., Johnson, L. D., O'Malley, P. M., & Bachman, J. G. (1988). The generality of deviance in late adolescence and early adulthood. American Sociology Review, 53, 81±93.
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Parker, J. G., & Asher, S. R. (1987). Peer relations and later personal adjustment: Are low-accepted children at risk?. Psychological Bulletin Nov., 102(3), 357±389. Patterson, G. R., & Bank, L. (1989). Some amplifying mechanisms for pathologic processes in families. In M. R. Gunnar, & E. Thelan (Ed.), Systems and development: Minnesota symposium on child psychology (22, pp. 167±210). Hillsdale, NJ: Lawrence Erlbaum Associates. Patterson, G. R., Capaldi, D. M., & Bank, L. (1991). An early starter model for predicting delinquency. In D. J. Pepler, & K. H. Rubin (Ed.), The development and treatment of childhood aggression, (pp. 139±168). Hillsdale, NJ: Lawrence Erlbaum Associates. Patterson, G. R., Chamberlain, P., & Reid, J. B. (1982). A comparative evaluation of a parent-training program. Behavior Therapy, 13, 638±650. Patterson, G. R., DeBaryshe, B. D., & Ramsey, E. (1989). A developmental perspective on antisocial behavior. American Psychologist, 44, 329±335. Patterson, G. R., Reid, J. B., & Dishion, T. (1992). Antisocial boys: A social interactional approach (4. Eugene, OR: Castalia Publishing Company. Patterson, G. R., & Yoerger, K. (in press). A developmental model for late-onset delinquency. In D. W. Osgood (Ed.), Motivation and delinquency. Lincoln: University of Nebraska Press. Prinz, R. J., Foster, S., Kent, R. N., & O'Leary, K. D. (1979). Multivariate assessment of con¯ict in distressed and nondistressed mother-adolescent dyads. Journal of Applied Behavior Analysis, 12, 691±700. Radlo, L. S. (1977). The CES-D Scale: A self-reported depressive scale for research in the general population. Applied Psychological Measurement, Sum.1, (3), 385±401. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581±592. Tildesley, L., Hops, H., Ary, D., & Andrews, J. A. (1995). A multitrait-multimethod model for adolescent deviance, drug use, academic, and sexual behaviors. Journal of Psychopathology and Behavioral Assessment, 17(2), 185±215.
PERGAMON
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Adolescent problem behavior: the in¯uence of parents and peers Dennis V. Ary *, Terry E. Duncan, Susan C. Duncan, Hyman Hops Oregon Research Institute, 1715 Franklin Blvd., Eugene, Oregon, 97403, USA
Abstract This paper presents evidence that the Patterson et al. (1992) model of development of antisocial behavior in children generalizes to the development of a wide array of problem behaviors during later adolescence and that youth antisocial behavior, high-risk sexual behavior, academic failure and substance use form a single problem behavior construct. Structural equation modeling methods were applied to 24-month longitudinal data from 204 adolescents and parents. The model ®t the data well, accounting for 52% of the variance in adolescent problem behavior. Speci®cally, families experiencing high levels of con¯ict were more likely to have low levels of parent±child involvement. These family conditions were related to poor parental monitoring and association with deviant peers one year later. Poor parental monitoring and associations with deviant peers were strong proximal predictors of engagement in an array of problem behaviors at two-year follow-up. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Problem behavior; Adolescence; Social context; Missingness; Structural equation methodology
1. A model of adolescent problem behavior development This study investigates the extent to which the model of family and peer in¯uences on the development of antisocial behavior proposed by Patterson, Reid, and Dishion (1992) applies to (a) a much older adolescent sample, and (b) the development of a diverse range of adolescent problem behaviors, not just antisocial behavior. Findings in support of this model would suggest that interventions designed to modify these behavior patterns among families with adolescents might eectively and eciently ameliorate a wide array of adolescent problem behaviors. In addition, this study seeks to determine if youth antisocial behavior, high-risk * Corresponding author. 0005-7967/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 0 0 5 - 7 9 6 7 ( 9 8 ) 0 0 1 3 3 - 8
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sexual behavior, academic failure, and substance use form a single ``problem behavior'' construct. 1.1. Development model of antisocial behavior The development model of antisocial behavior advanced by Patterson and colleagues (Dishion, Patterson, and Kavanagh, 1992; Dishion, Patterson, Stoolmiller, and Skinner, 1991; Patterson and Bank, 1989; Patterson, Capaldi, and Bank, 1991; Patterson, DeBaryshe, and Ramsey, 1989; Patterson, Reid, and Dishion, 1992) accounts for delinquent and antisocial behavior in adolescents primarily through proximal peer in¯uence, but suggests that poor family management practices during childhood, especially coercive interactions and poor parental monitoring, explain the child's association with deviant peers. That is, aggressive and oppositional behaviors in the young child can place the child at high risk for a series of negative outcomes that ultimately result in antisocial behavior as adolescents. Speci®cally, this model suggests that harsh and inconsistent parental discipline of early oppositional behavior shapes further aggressive behavior through a process involving progressively more coercive interactions between the parents and the young child. In order to avoid these aversive disciplinary interactions, parents often become increasingly inconsistent in their discipline and monitoring. Consequently, the child's aggressive behavior becomes more established. This pattern of noncompliant and aggressive behavior is later extended to the school environment. Soon, the child is on a trajectory that includes rejection by normal peers and academic failure in the classroom. By early adolescence the child has established a peer group of other rejected and aggressive adolescents, who quickly shape and reinforce additional antisocial behavior. At this point, the child is at high risk for developing a consistent pattern of antisocial and delinquent behavior, as well as drug use. The current study seeks to determine if this model of the development of antisocial behavior generalizes to a broad array of problem behaviors among adolescents. There is evidence that Patterson and colleagues' model of the development of antisocial behavior is applicable to problem behavior generally (Metzler, Biglan, Ary, Noell, and Smolkowski, 1993), as well as to adolescent drug abuse (Dishion, Patterson, and Reid, 1988; Dishion and Ray, 1991). 1.2. Problem behavior syndrome One of the goals of the present study is to examine the extent to which several dierent types of adolescent behaviors are intercorrelated and representative of a general problem behavior syndrome. There is a signi®cant empirical research base supporting such a class of behaviors that includes antisocial behavior, substance use, academic failure, and precocious and risky sexual behavior (e.g., Allen, Leadbeater, and Aber, 1994; Barone et al., 1995; Donovan and Jessor, 1985; Donovan, Jessor, and Costa, 1988; Farrell, Danish, and Howard, 1992; Jessor and Jessor, 1977; McGee and Newcomb, 1992; Metzler, Noell, Biglan, Ary and Smolkowski, 1994; Osgood, Johnston, O'Malley, and Bachman, 1988). Further, there is evidence that the problem behavior construct is descriptive for both male and female adolescents (Donovan and Jessor, 1985; Farrell et al., 1992) and over several development stages (early adolescence to adulthood) (McGee and Newcomb, 1992).
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While the studies cited above suggest that a general problem behavior model provides a reasonable description of the problem behavior data, the model does not account for all the variance in the data. Osgood et al., (1988) found that a single common factor accounted for approximately half the variance (47%) in the variables measured. They concluded that a general factor did not fully explain the relationships between the individual behaviors, and that each behavior had a signi®cant unique component, as well. In another study, Gillmore et al. (1991) found that a single problem behavior factor did not provide an adequate account of cross-sectional data gathered from preadolescent sixth-grade youth. In a study of 15-year-olds in New Zealand, Fergusson, Horwood, and Lynsky (1994) reported substantial comorbidities between dierent types of adolescent problem behaviors, but also found evidence of distinct problem behavior groupings of adolescents. They suggest that these qualitatively dierent subpopulations explain the comorbidity among problem behaviors rather than a single underlying problem behavior dimension. In a cross-sectional study using data from 14±18-year-olds in the U.S., Tildesley, Hops, Ary and Andrews (1995) found that a two-factor solution provided a somewhat better ®t than a single problem behavior factor, although the single problem behavior factor accounted for a large proportion of the variance (66%). Taken together, these studies provide evidence that (a) the general problem behavior factor does not account for all the variance in the individual behaviors, (b) the problem behavior syndrome may not apply to preadolescents, and (c) adolescents may cluster into certain problem behavior combinations. The current study will examine the extent to which the general problem behavior factor ®ts longitudinal data from U.S. adolescents. 1.3. Testing the model Based on the Patterson et al. (1992) model of the development of antisocial behavior, we hypothesize that families with low levels of con¯ict at Time 1, the initial assessment, will have high levels of concurrent, positive family relations. Similarly, low levels of family con¯ict and high levels of positive family relations are expected to be related to high parental monitoring 12 months later at Time 2, which will, in turn, be associated with low levels of deviant peer contact. Finally, it is expected that good parental monitoring and low association with deviant peers at Time 2 would be associated with low levels of engagement in problem behavior 12 months later at Time 3.
2. Method 2.1. Subjects Subjects in the present study were involved in a longitudinal investigation of family factors in¯uencing substance use among adolescents (Andrews, Hops, Ary, Lichtenstein, and Tildesley, 1991; Hops, Tildesley, Lichtenstein, Ary, and Sherman, 1990). Participants were from two northwestern urban areas of the United States, with populations of approximately 50,000 and 120,000. Recruitment was through advertisements in local newspapers and ¯iers posted in community centers and prominent street locations. Families contained at least one adolescent
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between the ages of 11 and 15 who was designated as the target.1 Attrition across the ®rst seven years of the study was variable, averaging 6.7% between assessments. The highest attrition rates occurred in the early years of the study. While subjects who dropped out of the study tended to be older, of lower SES, and higher users of substances compared to those who remained, these ®ndings are consistent with attrition eects noted in other longitudinal studies (e.g., Kandel, 1985; Newcomb and Bentler, 1988b). For the purposes of the present study the current sample comprised target youth living at home, and their parents, and utilized data from three consecutive yearly assessments (T5 to T7 of the project; 1990±1993). The T5 to T7 assessments were selected for this analysis because they were the only assessments that included all the measures necessary to evaluate the hypothesized model. This sample included 196 families; 45.13% single-parent, the remainder either married or living in a committed relationship. Target youth included 100 males (51.0%). The majority of the participants were white (91.8%) and had a mean age of 15.98 years (SD = 0.54) at the ®rst assessment. This sample's substance use is representative of the state of Oregon, estimated from the Oregon Employment Division Research and Statistics report on drug use among eighth and eleventh graders in Oregon (Ciranny, 1989). In comparison to national norms, this sample's substance use tends to be somewhat higher than that estimated by the National Institute on Drug Abuse National Household Survey on Drug Abuse (NIDA, 1989). 2.2. Procedures At all assessments, target adolescents and their parents separately completed a series of selfreport questionnaires in our laboratory. Questions assessed substance use, other problem behaviors, and selected psychosocial characteristics. Each participating family member was paid $25 at each assessment, and annual lotteries were held for both targets and parents. To increase the likelihood of valid reporting of behaviors such as substance use, con®dentiality was stressed at each assessment and a certi®cate of con®dentiality was obtained from the National Institute on Drug Abuse that precluded the subpoena of subjects' data. 2.3. Measures The variables used in the present study combine the use of adolescent and parent reports. Where possible, constructs included multiple items and/or multi-agent responses. Table 1 1
To address the possibility that the voluntary nature of this study could aect the generalizability of results, comparisons were made between this study's recruited sample and normative data from other studies on several indicators of pathology. For example, mean scores on depressive symptoms derived from parent reports on the Child Behavior Checklist (CBCL; Achenbach and Edelbrock, 1983) as well as those based on self-reports by the adolescents on the Center for Epidemiologic Studies Depression Scale (CES-D; Radlo, 1977) were found to be within the normal range when compared to the CBCL normative data, and to data from an epidemiological study of adolescent depression (Lewinsohn, Hops, Roberts, Seeley, and Andrews, 1993) and familial characteristics (Hops and Seeley, 1992). Furthermore, cross-sectional analysis by age was also consistent with epidemiologic studies (Hops, 1996). Taken together, these data suggest that our recruited sample was not unique on these indicators compared to both regional and national norms.
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Table 1 Summary of construct items utilized in the structural equation models Construct indicator questionaire item Family con¯ict V1 Family members have big arguments over little things V2 Family members get angry with each other daily V3 Family members get angry with each other three times a week Positive family relations V4 Family members support one another V5 There are feelings of togetherness in our house V6 Family members get along well Inadequate parental monitoring a V7 Parents let child go places without asking V8 Before going out, child tells parents when will be back V9 Child does things without telling parents Peer Deviance V10 Child's friends get into ®ghts Child's friends get into trouble a lot V11 Child's friends would dare child to use drugs Number of times child's friends had problems with drugs How many of child's ®ve closest friends use cigarettes How many of child's ®ve closest friends use alcohol How many of child's ®ve closest friends use marijuana Problem behaviors b Academic failure V12 Child's GPA in school Number hours child spent on homework each night Antisocial behavior c V13 Last 6 months: child damaged others property Last 6 months: child damaged school property Last 6 months: child hit/threatened others Last 6 months: child stole >$50 item Last 6 months: child stole from a store Number of times child arrested in last year Substance use V14 Child's monthly alcohol rate Child's monthly cigarette rate Child's monthly marijuana rate Risky sexual behavior V15 Number of opposite sex partners child had in past year Child has had sex with partners having sex with others
Scaling T/F T/F T/F T/F T/F T/F 1±5 1±5 1±5 T/F T/F T/F Frequent 0±5 0±5 0±5
Minutes 1±6 1±6 1±6 1±6 1±6 Frequent Frequent Frequent Frequent Frequent Frequent
a
Monitoring items scored 1 to 5: 1 = almost never; 5 = almost always.bAdolescent self-report only.cAntisocial behavior items scored 1 to 6: 1 = never; 6 = 10+ times in last year
provides a summary of the items utilised in the structural equation models. Most measures were based on previously validated items; family con¯ict items were taken from the appraisal of mother/father subscales of the Con¯ict Behavior Questionnaire (CBQ; Prinz, Foster, Kent, and O'Leary, 1979); positive family relations items were from the cohesion subscale of the
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Fig. 1. Developmental model of adolescent problem behavior.
Family Environment Scale (FES; Moos and Moos, 1975), parental monitoring questions and peer deviance items were based on prior work by Capaldi and Patterson (1989). The constructs of family con¯ict, positive family relations, parental monitoring, and peer deviance, all include adolescent reports and mother and/or father reports. The problem behavior items all consist of adolescent reports only, and include monthly rate measures for substance use (e.g., alcohol, cigarettes, and marijuana), antisocial behavior items (e.g., vandalism, physical assault, theft) (Jessor and Jessor, 1977), risky sexual behavior items (e.g., number of opposite sex partners) (Metzler et al., 1994), and academic failure (i.e., GPA, time spent on homework each night). 3. Results 3.1. Analytic strategy for missing data The models were tested using EQS, Bentler's (1989) structural equation modeling program, and applied to a situation in which data were missing on speci®c variables. Although standard analyses of incomplete data, generally involving the use of listwise deletion, take advantage of the complete nature of the resulting data set to simplify computations, these procedures provide estimates that are generally inecient, discarding a substantial amount of potentially useful data (MutheÂn, Kaplan, and Hollis, 1987). MutheÂn et al. (1987) have demonstrated that in many applications, model estimation with distinct missing data patterns can be carried out utilizing existing structural equation modeling software that allow for the simultaneous analysis of multiple groups. Brie¯y, the strategy for handling missing data consists of expanding the usual structural equation model to include means, or regression intercepts, and partitioning the sample into subgroups with distinct patterns of missing data. Equality constraints across the various groups
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representing distinct patterns of missingness are used in a multi-sample analysis to obtain unbiased and consistent estimates. It should be emphasized that these equality constraints across the missing data subsamples are not of substantive interest, but, because of the assumption that the hypothesized model is invariant across groups, function solely to insure correct estimation of model parameters.2 3.2. Problem behavior construct A measurement model of the general problem behavior construct examined the extent to which antisocial behavior, high-risk sex, academic failure and substance use were inter-related. The problem behavior measurement model indicated that a general problem behavior construct ®t these data very well, accounting for approximately 67% of the variance in the various problem behaviors (w 2(2, n = 141) = 0.764, P < 0.997, and ®t index CFI = 1.00). 3.3. Development model of problem behavior Fig. 1 presents a model of the peer and parental in¯uences of adolescent problem behavior. Family con¯ict and positive family relations were assessed at Time 1, inadequate monitoring and association with deviant peers at Time 2, and problem behaviors at Time 3. Using the missing data procedures described above, data from four subsets of cases (two subsets for each gender) which represent various stages of ``completeness'' were used. Compared with the original sample of 204, the data in group 1 (n = 141; [72 males and 69 females]) represent the ``complete'' data available for analysis. Utilizing only these data would ignore the possibility of severe bias due to selective subject attrition and nonresponse. The data available for the subsequent groups (n = 55; [28 males and 27 females]) increased the total number of subjects to 196 cases which represents approximately 96% of the original sample. To determine if the model ®ts equally well for males and females, a multiple-sample model was ®t simultaneously to the four subsamples, constraining corresponding parameters to be equal across subsamples.3 Table 2 presents descriptive statistics for the structural equation variables including means, standard deviations, and values of skewness and kurtosis for the combined sample. Values of skewness and kurtosis were, in most cases, minimal. Therefore, 2
A full exposition of this approach to structural equation modeling is beyond the scope of this paper. Readers are referred to Allison and Hauser (1991), MutheÂn et al., (1987), and Rubin (1976) for a complete discussion and references on the issues surrounding the model-based approach to the analysis of missing data. Examples containing EQS program information for the model-based approach can be found in Duncan and Duncan (1994) and Duncan, Oman, and Duncan (1994). 3 To permit an appropriate large sample chi-square test of model ®t, one need only obtain the likelihood value for both restricted H0 and unrestricted H1 model hypotheses. Tests of the H1 hypothesis (involving the test of equality of the moment matrices) and the restricted H0 model (the hypothesized model of interest) involves imposing equality restrictions across the groups representing various missing data patterns for common parameters. The dierence in chi-square values and degrees of freedom for the two analyses, H0 against H1, gives a correct test of the hypothesized model. This is the same degrees of freedom that would be used in corresponding ``complete-case'' analyses which specify no missing data.
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Table 2 Descriptive statistics for the combined samples Variable Family con¯ict V1 V2 V3 Positive family relations V4 V5 V6 Monitoring V7 V8 V9 Peer deviance V10 V11 Problem behavior V12ÐAcademic V13ÐAntisocial V14ÐSubstances V15ÐRisky sex
Mean
SD
Kurtosis
Skewness
n
1.31 1.20 1.45
0.33 0.28 0.36
ÿ 0.74 0.79 ÿ 1.18
0.62 1.29 0.22
196 196 196
1.79 1.74 1.76
0.31 0.36 0.35
0.67 ÿ 0.22 0.06
ÿ1.32 ÿ1.08 ÿ1.18
196 196 196
3.48 3.57 4.01
1.26 1.17 1.09
ÿ 0.57 ÿ 0.64 0.54
ÿ0.74 ÿ0.58 ÿ1.17
196 196 196
ÿ 0.07 ÿ 0.04
0.55 0.67
5.52 10.81
2.06 2.93
141 141
ÿ 0.14 ÿ 0.16 ÿ 0.25 ÿ 0.16
0.79 0.37 0.72 0.16
0.92 ÿ 0.49 0.49 ÿ 1.33
ÿ0.28 0.92 1.09 0.19
141 141 141 141
the assumption of approximate normality of the observed variables appears tenable. Approximate normality justi®es the use of normal theory maximum likelihood estimation techniques found in structural equation programs such as EQS (Bentler, 1989). Fitting the hypothesized model to the multiple-sample data resulted in an overall model ®t of w 2(204, n = 196) = 286.107, P < 0-.001, and ®t index CFI = 0.907.4 While the CFI ®t index does not indicate an exceptional model ®t, it does re¯ect an adequate and acceptable ®t greater than 0.90 (Bentler, 1989; Bentler and Bonett, 1980). In examining whether the same model described a process which ®t equally well for both males and females, Lagrange multipliers associated with the cross-gender constraints, suggested that only two parameters, the error variances for the antisocial behavior construct and the substance use construct, if relaxed, 4
Under the unrestricted H1 hypothesis, a chi-square test statistic value of w 2(108, n = 196) = 166.793, P < 0.001, was obtained. The test of the unrestricted H1 model is also a test of whether the data are ``missing completely at random'' (MCAR). If the pattern of missingness is completely random, then the subsamples representing missingness can be regarded as distinct random samples from the same population. The model ®t indicates that MCAR should be rejected for these subsamples. If the data are not MCAR, and the model is rejected, it may be preferable to proceed with the test of the hypothesized model under a much less restrictive assumption that the missing data mechanism is simply ``ignorable'' (Rubin, 1976), and the missing data are simply missing at random (MAR). It should be emphasized that when the assumptions of MCAR or MAR are tenable, the maximum-likelihood estimations generated from the structural equation analyses will exhibit no large sample bias. The test of the hypothesized H0 structural model provided a chi-square test statistic value ofw 2(312n = 196) = 452.910 P < 0.001.
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Table 3 Parameter estimates for the hypothesized structural model Coecient Family con¯ict V1, F1 V2, F1 V3, F1 Positive family relations V4, F2 V5, F2 V6, F2 Inadequate parental monitoring V7, F3 V8, F3 V9, F3 Peer deviance V10, F4 V11, F4 Problem behavior V12, F5 V13, F5 V14, F5 V15, F5 Structural coecients F2, F1 F3, F2 F4, F3 F5, F3 F5, F4
Eect
t-Value
0.687 0.724 0.801
7.945 Ð 8.291
0.690 0.846 0.911
Ð 10.364 10.364
0.622 0.771 0.674
Ð 6.547 6.774
0.678 0.783
Ð 5.426
0.352 0.577 0.667 0.657
3.390 5.109 5.563 Ð
ÿ 0.431 ÿ 0.232 0.383 0.173 0.656
4.610 2.552 3.179 1.433 4.052
Note.1 t-Values greater than 1.96 in magnitude indicate a parameter estimate which is signi®cantly dierent from zero
would result in a signi®cant improvement in model ®t. Separate parameter estimates for the error variances for the target adolescents' antisocial behavior and substance use, respectively, indicated that signi®cantly more variance existed for boys' self-reported antisocial behavior and substance use than was reported by females. Despite these dierences, the same structural model appears to ®t equally well for both males and females. The model accounted for approximately 52% of the variance in problem behavior. Associations with deviant peers had a signi®cant direct eect, b = 0.656, t(196) = 4.052, P < 0.05, on adolescent problem behavior. In addition, the indirect eect of monitoring on later problem behavior mediated through deviant peers was signi®cant: b = 0.251, t(196) = 1.990, P < 0.05. The direct eect of monitoring on later problem behavior approached signi®cance: b = 0.173, t(196) = 1.433, P < 0.08 (one-tailed). The structural coecients from positive family relations to parental monitoring were signi®cant: b = -0.232, t(196) = 2.552, P < 0.05 and b = ÿ0.431, t(196) = 4.610, P < 0.05, respectively. Table 3 presents the standardized solution for the hypothesized structural model.
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4. Discussion The goals of this study were twofold: (a) to examine the extent to which the Patterson and colleagues' model of the development of antisocial behavior in children (Patterson and Bank, 1989; Patterson et al., 1991, 1989, 1992) is applicable to problem behavior in late-adolescence, and (b) to determine if youth antisocial behavior, high-risk sexual behavior, academic failure and substance use form a single problem behavior construct. 4.1. Support for the model of the development of adolescent problem behavior The present ®ndings provide signi®cant support for this developmental extension. The model ®t the data well and accounted for 52% of the variance in adolescent problem behavior. The study suggests that a substantial amount of the variance in a range of adolescent problem behaviors can be explained by the inter-relationships among the four elementary constructs of this developmental model. Brie¯y stated, families in which there were high levels of con¯ict and low positive family relations were more likely to develop a social context that includes inadequate parental monitoring and associations with deviant peers. Poor parental monitoring and associations with deviant peers were associated with subsequent engagement in problem behavior. The ®ndings suggest that these processes, which have been shown to relate to the development of antisocial behavior during childhood and early adolescence (e.g., Patterson et al., 1992), are also relevant to the development of a more general problem behavior syndrome during mid-to-late adolescence. These ®ndings are consistent with Patterson and Yoerger (in press) who reported that a late-onset group of adolescent oenders had parents with similar de®cits in parenting practices. The results also indicate that, despite evidence of increasing peer in¯uence among adolescents (Collins, 1990; Montemayor, 1982), parental in¯uence can continue to be a moderating or augmenting source of in¯uence throughout adolescence (Brook, Whiteman, Gordon, and Brook, 1985; Krosnick and Judd, 1982). This ®nding is of interest because it suggests that if interventions are designed to in¯uence the constructs delineated in this developmental model and they are implemented during adolescence, problem behavior among youth in this age group may be eectively ameliorated. 4.2. Problem behavior syndrome The results of this study also provide support for the validity of a single problem behavior construct, accounting for 67% of the variance in problem behavior. Speci®cally, the study presents evidence that youth anti-social behavior, high-risk sexual behavior, academic failure, and substance use form a single problem behavior construct. While this ®nding is consistent with a number of other studies (Donovan and Jessor, 1985; Donovan et al., 1988; Jessor and Jessor, 1977; McGee and Newcomb, 1992; Metzler et al., 1994) and 67% of the variance accounted for is fairly impressive given the problems inherent in conducting behavioral science research with adolescents, not all the variance is explained within the single construct model. Part of the unexplained variance may be due to speci®c subpopulations of youth that exhibit dierent con®gurations or combinations of problem behaviors, rather than all problem behaviors simultaneously (Fergusson et al., 1994; Loeber, 1988). Additional research may be
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needed to fully understand the subtleties of the co-occcurence of problem behaviors. Nevertheless, the current study suggests that for a large number of adolescents, multiple problem behaviors are interrelated and their etiology is consistent with the in¯uence of the family and peer factors presented here. 4.3. Implications for interventions to prevent adolescent problem behaviors If the ®ndings of this study are replicated in subsequent investigations, it would indicate that eorts to modify the social conditions depicted in the developmental model may lead to the prevention of a diverse set of youth problem behaviors for a signi®cant number of youths. While it might be dicult to justify the cost of such an intervention eort in order to reduce or eliminate a single problem behavior, it may be cost eective to mount such an intervention if it aects a wide range of problem behaviors. Based on the ®ndings of this study, such an intervention eort might focus on improving parenting practices and increasing parental monitoring. There is research evidence that these goals might be reached through the provision of parent training programs (e.g., Dishion and Andrews, 1995), school-based communications to parents, and targeted media on speci®c parenting practices, such as parental monitoring. Prevention eorts directed early on in the developmental progression of such problem behaviors may be more powerful and less expensive than interventions targeting middle or high school age children. A focus on family factors and on the child's early entry into deviant peer groups may prevent the development of problematic family practices and deviant peer associations. For example, the current study suggests that poor family management practices (i.e., family con¯ict and low positive family relations) lead to poor parental monitoring and associations with deviant peers. Patterson and colleagues found that ineective parenting practices at age 10 places the child on a trajectory that eventually results in antisocial and delinquent behavior. This suggests that parent training for the parents of high-risk children at a much earlier age may be a more desirable approach. Patterson, Chamberlain and Reid (1982) have demonstrated signi®cant reductions in antisocial behavior using parent training programs based on this approach. It may also be advisable to improve the social competence of the child in order to preclude the onset of deviant peer relations. Peer rejection and aggression have been shown to be highly predictive of a variety of problem behaviors (Parker and Asher, 1987; Kellam, Simon, and Esminger, 1983). Thus, an integrated prevention eort that strengthens parenting practices, monitoring norms in a community, and childhood social skills early on in the developmental process may provide a powerful, cost-eective set of prevention procedures. 4.4. Study limitations and future directions This study does address several design concerns through the utilization of both parent and adolescent reports, multiple indicators for each construct, and three-year longitudinal data. However, several limitations remain. First, the data are not multi-method, but questionnaire only. The inclusion of observational data or behavioral records (e.g., arrest records, number of referrals) would strengthen the ®ndings. Second, the generalizability of the ®ndings may be limited by the representativeness of the study sample. The sample is over 90% Caucasian, and while the drug use of the sample is representative of the state of Oregon, the use levels are
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somewhat higher than national norms. Third, while the Patterson et al., model ®ts the data well and provides an interpretable model, it should be noted that other models might also provide plausible interpretations of the relevant processes. Fourth, the study utilized annual assessments which might not be the optimal interval to assess change in the model constructs. For example, it is conceivable that monthly assessments might provide greater sensitivity to change and identify substantively dierent relationships. Lastly, the availability of only three data points precludes the inclusion of constructs involving the measurement of change (e.g., residualized change or simple change) in the testing of the complete Patterson et al., model. Future research might address these limitations through the use of multiple methods of assessment, representative samples, a variety of assessment intervals, and four or more assessment occasions.
Acknowledgements This research was supported by Grant No. DA03706 from the National Institute of Drug Abuse. Preparation of this manuscript was supported in part by Grant Nos. DA03706, DA07389, DA09678 and DA09306 from the National Institute on Drug Abuse and Grant No. CA38273 from the National Cancer Institute.
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