Structure of Problem Behaviors in Preadolescence

Journal of Consulting and Clinical Psychology 1991, Vol. 59, No. 4, 499-506

Copyright 1991 by the American Psychological Association, Inc. 0022-006X/91/S3.00

Structure of Problem Behaviors in Preadolescence Mary Rogers Gillmore, J. David Hawkins, Richard F. Catalano, Jr., L. Edward Day, and Melanie Moore Social Development Research Group University of Washington, School of Social Work

Robert Abbott Educational Psychology, University of Washington Earlier research suggests that diverse adolescent problem behaviors, such as substance use, school problems, early sexual intercourse, and delinquency, reflect a single underlying dimension of behavior. Data from an ongoing longitudinal study were used to examine this issue in a previously unexamined sample (N = 426) of preadolescent sixth-grade youth. Ss included boys and girls from diverse socioeconomic and racial/ethnic backgrounds, whose average ages were 11 and 12. By using confirmatory factor analyses to test competing models, multiple factor structures were detected, suggesting that earlier findings supporting a single factor conceptualization may not be generalizable to this age group. Implications of the finding that problem behaviors may be more differentiated in late childhood than in adolescence are discussed.

Researchers have suggested that diverse adolescent problem behaviors, such as substance use, school problems, delinquency, and early sexuality, reflect a single underlying dimension of behavior (lessor & lessor, 1977), although empirical evidence evaluating this claim is limited. Whether different problem behaviors reflect a single underlying factor or whether they are better conceived as a multidimensional phenomenon has importance for both theory and practice. To the extent that problem behaviors represent a single factor, a general theory of problem behavior need not specify separate causal influences for different behaviors. On the other hand, to the extent that these behaviors are partially independent phenomena, individual theories, multiple factor approaches, or general theoretical frameworks that allow for specific variation become the proper direction for theory development (Osgood, Johnston, O'Malley, & Bachman, 1988) with parallel implications for intervention. The idea that different adolescent problem behaviors have the same underlying cause is evident in several theories of deviance (e.g., Elliott, Huizinga, & Ageton, 1985; Jessor & Jessor, 1977) and is consistent with a growing body of research that documents that positive relationships exist at the bivariate level between deviant behaviors. Positive relationships have been found between drug use and delinquency (Donovan & Jessor, 1985; Elliott, Huizinga, & Menard, 1989; Jessor & Jessor, 1977;

Johnston, O'Malley, & Eveland, 1978; Kaplan, 1985); early sexual intercourse and drug use among adolescents (Bentler & Newcomb, 1986; Donovan & Jessor, 1985; Elliott & Morse, 1987; Zabin, Hardy, Smith, & Hirsch, 1986); delinquency and sexual activity (Elliott and Morse, 1987); dangerous driving, crime, and drug use (Osgood, et al., 1988); and drug use and low educational performance (Bachman, O'Malley, & Johnson, 1978; Jessor, 1987; Smith & Fogg, 1978). Also, Loeber and Schmaling (1985) performed a meta-analysis on factor analyses of ratings of child psychopathology and concluded that various forms of antisocial behavior can be accounted for by a single dimension. Donovan and Jessor (1985) reported the results of a maximum likelihood test for one common factor for problem behaviors in four different samples. They first examined the factor structure of problem behaviors in two cohorts of youths: a high school sample and a college sample. Measures of problem behaviors included alcohol and marijuana use, precocious sexual intercourse, and general deviant behavior. Donovan and Jessor found that with the exception of 3rd-year college men, a onefactor solution provided an adequate fit to the data for both male and female subjects. To test the stability of this finding, they repeated the analysis using data obtained when the subjects were in their 20s. They again reported that one factor adequately accounted for the observed correlations. To examine the generalizability of this finding, they replicated the analysis using data from a national probability sample of 11th- and 12th-grade students. In this analysis they examined both problem behaviors (including cigarettes, alcohol, marijuana, and other illicit drugs and general deviant behavior) and conventional behavior (church attendance and school performance). Again, a single factor adequately accounted for the correlations among the variables for both boys and girls. More recently, Donovan, Jessor, and Costa (1988) replicated the analysis on a new sample of 11th- and 12th-grade students who were

The research reported here and the preparation of this article were supported by Grant DA-03721 from the National Institute on Drug Abuse. We gratefully acknowledge the assistance of William Goldsmith and Marilyn Hoppe in data collection and management, and we appreciate the helpful comments of anonymous reviewers on drafts of this article. Correspondence concerning this article should be addressed to J. David Hawkins, Social Development Research Group, 146 North Canal Street, Suite 211, University of Washington, XD-50, Seattle, Washington 98103. 499

500

GILLMORE ET AL.

surveyed in 1985. They again reported that a single factor accounted for the correlations among alcohol use, marijuana use, sexual intercourse, and general deviant behavior. Osgood et al. (1988) used LISREEs linear structural equation modeling approach to examine the fit of a factor model in which both shared, and unique components of deviant behaviors were examined. For this analysis, they used longitudinal data from a sample of White high school seniors for whom follow-up data were available. Measures of deviant behavior included illegal behavior directed at victims, heavy alcohol use, marijuana use, use of other illicit drugs, and dangerous driving. They found that when longitudinal relationships were included, a model in which both general and specific deviance factors were specified provided a better fit to the data than did a model in which only a general deviance factor was included. They concluded that each deviant behavior is in part not only a function of a general deviance factor, as suggested by Donovan and Jessor (1985), but also a function of factors unique to the particular behavior in question. Our research provides additional empirical evidence on the question of whether different problem behaviors reflect a single underlying factor or are better conceived as multidimensional phenomena. These analyses (a) expand on earlier work, (b) examine the structure of problem behavior in a multiethnic sample of preadolescents in sixth grade, and (c) use confirmatory factor analysis to explicitly test competing models of the factor structure underlying the measured variables. This approach is consistent with the suggestions of Bentler (1980) and Maruyama and McGarvey (1980) that absolute fitting of a model is less meaningful than comparing the relative fit of competing models. Four alternative factor models were tested (Figure 1). Model 1 represents a single-factor model in which three types of problem behaviors, school problems, delinquent acts, and substance use, were hypothesized to reflect a single underlying factor. This model is consistent with Jessor and colleagues' theory and empirical findings. Model 3 is a three-factor model in which these problem behaviors are specified as separate but correlated dimensions of deviance. Model 2a specifies delinquency and problem behavior at school as a single factor and substance use as a separate factor. This model is consistent with empirical findings that school problems (Jessor & Jessor, 1977; Kandel, Kessler, & Margulies, 1978) and delinquency often precede drug use (Elliott et al., 1989). Finally, because there is a strong positive association between delinquency and drug use in these data, as well as in data from older adolescent samples (e.g., Donovan & Jessor, 1985; Elliott et al., 1989; Johnston et al., 1978), we also estimated Model 2b, in which delinquency and drug use are viewed as one factor, and problem behavior at school represents a separate factor.

Method Study Participants Data were collected as part of an ongoing longitudinal study seeking to identify childhood risk factors for adolescent drug use and delinquency and to test the effects of preventive interventions (Catalano & Hawkins, 1986). Data collection began in 1981 with a panel of 568 first-grade students in 8 elementary schools in the Seattle school dis-

trict. In 1985, when subjects entered fifth grade, the panel was expanded to include all fifth-grade students in 18 elementary schools (N = 1,053). Of this eligible population of fifth-grade students, 919 (87%) completed the fifth-grade fall survey, and 608 (66%) of these students completed surveys in the spring of their sixth-grade year. Donovan et al. (1988) and Donovan and Jessor (1985) reported much lower initial response rates (typically approximately 53%) and comparable retention rates (65-73%) in their studies. Questionnaires were administered in classrooms by project personnel who read aloud each question and its associated response categories to students. Students had copies of the survey on which they indicated their responses to each question. At the conclusion of the sixthgrade school year, teachers were asked to fill out the teacher's form of the Achenbach and Edelbrock (1983) Child Behavior Checklist (CBCL) for each student in their classroom. Data from the 426 subjects for whom usable data were provided on both the sixth-grade student survey and the teacher CBCL were used in the analyses reported here. The sample of 426 subjects was 49.1% White, 21.3% Black, 19.7% Asian-American, and 9.9% other racial or ethnic groups. About half (52%) were male and half female (48%). According to official school district records, 33.3% qualified for the federally funded free or reduced fee lunch program. Most of the subjects (89%) were 11 or 12 years of age by the time of the survey. These demographic characteristics were virtually identical to those in the larger sixth-grade sample of 608 students. In addition, comparisons at fifth grade indicated that the sample used in the present analysis did not differ significantly from the fifth-grade panel on most sociodemographic and antisocial behavior variables including sex, racial composition, eligibility for free or reduced price lunches, substance use, delinquent behaviors, and school problems. Students in the present sample were slightly more likely to come from two-parent families and were rated as somewhat less aggressive by their teachers, however. Measures Our definition of problem behaviors is consistent with that of Donovan and Jessor (1985), Jessor and Jessor (1977), and Osgood et al. (1988), who viewed problem behaviors as behaviors that are socially defined as undesirable and typically evoke efforts at social control. We included multiple measures of three different kinds of behaviors that are proscribed in this age group: serious school misbehaviors, delinquent behaviors, and substance use. We included behavior problems at school because they have been shown to be related to both drug use and delinquency (e.g., Hawkins & Lishner, 1987; Hawkins, Lishner, Catalano, & Howard, 1986) and because early aggressiveness at school is predictive of later problem behaviors such as adolescent substance use and delinquency in adolescent males (e.g., Ensminger, Kellam, & Rubin, 1983). Certain problem behaviors included in previous studies, such as dangerous driving (Osgood et al, 1988) and sexual intercourse (Donovan & Jessor, 1985), were not included in the present analyses because they are extremely rare at ages 11-12. Although our measures did not exhaust the problem behaviors possible in this age group, they were chosen to represent a broad range of problem behaviors and are similar to those included in earlier studies. School Problems Three indicators of school problems were created including students' self-reported problems at school and two measures of teacherrated behavior problems at school. School trouble is an index composed of four student self-report items including (a) the extent to which the student got in trouble in school in the past year scored on a 4-point scale including NO.'(I), No (2), Yes (3), and YES!(4), indicating degree of endorsement with the statement; (b) the number of times in the past year the student was sent out of class for misbehavior (0-4); (c) the number of times the student was suspended or expelled in the past year (0-4); and (d) whether the student has ever hit a teacher (No/Yes).

PREADOLESCENT PROBLEM BEHAVIORS

501

Delinquency School Troubles F1 School Troubles Delinquency Substance Use F1

Model 1 One factor

Model 2a Two factors

Delinquency Substance Use F2

Model 2b Alternative Two factors

Model 3 Three factors

Figure 1. Four alternative models of the factor structure underlying problem behaviors. (F = factor; VI = school problems; V2 = aggression; V3 = acting out; V4 = delinquency—breaking and entering, picked fights or assaulted others; V5 = delinquency—throwing objects at people or cars and shoplifting; V6 = delinquency—stealing and vandalism; V7 = tobacco use; V8 = alcohol use; V9 = illicit drugs.)

Higher scores on the items indicate more school problems. Eighty percent of the sample reported one or more of these school problems. Because the response formats differ, the items were first transformed to z scores, then averaged to form the index of school trouble. This transformation did not change the pattern of correlations among the variables. Factor analysis of the ratings on the teacher CBCL items found that 10 items loaded on the same factor and formed a conceptually coherent scale that could be labeled aggressiveness. Because multiple indicators have the dual advantage of providing more information and greater degrees of freedom, two indicators of teacher-rated aggressive behaviors were constructed from these 10 CBCL items. We would expect these indicators to be highly correlated, and indeed they are. However, this should not affect the comparisons of the models, and the advantage of having multiple indicators outweighs the disadvantages in view of the fact that the indicators are not perfectly correlated. Each item ranged from not true of the student (0) to very true of the

student (2). The first scale (Aggression) consisted of the average score on five items including gets in many fights, explosive or unpredictable, bullying or meanness, defiant or talks back, and swears or uses obscene language. The second scale (acting out) was the average score on five items including threatens others, argues a lot, doesn't get along with others, physically attacks others, and temper tantrums or hot temper. Twenty-five to 30% of the sample were rated by teachers as having engaged in at least one or more of the behaviors on each scale.

Delinquent Acts Delinquent acts were measured by six items from the student survey asking how often in the past year the subject had stolen things, vandalized property, thrown objects at cars or people, shoplifted, broken and entered, or picked fights with others. These behaviors represent the more common forms of index offenses reported in this age group. Scores on the items range from never in the past year (1) to more than 4

502

GILLMORE ET AL.

times in the past year (4). To create multiple indicators for the delinquency factor, three 2-item indexes were formed from the six items by averaging responses on the relevant items. Because all items were positively intercorrelated, considerations of distribution largely dictated which two items to combine. The first indicator, labeled delinquency-1, consisted of breaking and entering and picked fights or assaulted others, and the second, labeled delinquency-2, included throwing objects at people or cars and shoplifting. Stealing and vandalism constituted the third indicator, labeled delinquency-3. Thirty-seven percent reported engaging in at least one of the behaviors constituting the first indicator, 33% reported engaging at least one of the behaviors in the second indicator, and 23% reported engaging in one or more behaviors in the third.

For all models, paths that were not hypothesized were set to zero. It is necessary either to fix the regression weight of one indicator for each factor, or, alternatively, to fix the variance of each factor in order to identify each factor. Because, for our purposes, the estimates of factor loadings were of more substantive interest than were estimates of factor variances, we set the factor variances to 1.0 to define the scales. For all models that specified more than one latent factor (i.e., Models 2a, 2b, 3), the factors were allowed to correlate. Consistent with the work of Donovan and Jessor (1985), we analyzed the data separately for boys and girls. The raw data from each subsample were used as input for the analyses.

Substance Use Three indicators of substance use were created: use of tobacco, use of alcohol, and use of other illicit drugs. Cigarette use ranging from never (1) to more than four times in the last month (4) and use of chewing tobacco (ever/never used) were combined into one measure of tobacco use. This variable was scored 0 if neither cigarettes nor chewing tobacco had ever been used, 1 if either chewing tobacco had been used or if cigarettes had been used but not in the past month, 2 if cigarettes had been used once or twice in the past month, 3 if used 3 or 4 times, and 4 if used more than 4 times. Alcohol use was also scored on a 4-point scale: 0 if alcohol had never been used, 1 if it had been used but not in the past month, 2 if it had been used 1 or 2 times in the past month, and 3 if it had been used more than 2 times in the past month. For the measure of illicit drug use, the drug with the highest frequency (using the scoring method described for alcohol) represented the measure. These drugs included marijuana, cocaine, psychedelics, sniffing glue or other inhalants, amphetamines, tranquilizers, and sedatives not prescribed by a physician. Although initiation of alcohol is not rare in this sample—42% have used alcohol—the measure is somewhat skewed as would be expected in this age group. Tobacco use is more skewed, but even so, over a quarter (26%) of the sample had tried cigarettes or chewing tobacco by spring of sixth grade. As would be expected in this age group, use of illicit drugs is the most skewed; only 14% of the sample had ever tried any of these drugs. In the interest of space, we labeled the nine indicators of problem behavior VI-V9, respectively, in figures and tables.

Results Model Estimation The models were estimated with the Bentler-Weeks model for confirmatory factor analysis (CFA), as implemented in the EQS computer program (Bentler, 1986). Maximum likelihood estimation was used although our indicators are somewhat skewed, because (a) this procedure has been shown to be robust with respect to violations of the normality assumption (see Huba & Harlow, 1986; Muthen & Kaplan, 1985; Tanaka & Bentler, 1985); (b) our sample size is reasonably large; and (c) for CFA models the distributions of the variables have little effect on the chi-square statistic or the standard errors of the factor loadings (Amemiya & Anderson, 1985; Anderson & Amemiya, 1985). Consistent with Osgood et al. (1988) and Wheaton (1988), we compared the fit of the alternative factor structure models using the Bentler-Bonett normed fit index (Bentler & Bonett, 1980), which is relatively independent of sample size and little affected by the distribution of variables, as well as using a chi-squared difference test.

Findings The zero-order correlations of the measured variables for boys are presented below the diagonal in Table 1. All correlations were positive, and most were significant. All subsequent analyses were based on the variance/covariance matrix. The standardized parameter estimates, corresponding z statistics, and overall model fit indices for the one-, two-, and three-factor models for boys are presented in Table 2. The decreases in chi-square for the two-factor models relative to the one-factor model were significant: x2(l, N= 222) = 16.20, p < .001, for Model 2a versus Model 1; x20, N= 222) = 283.68, p < .001, for Model 2b versus Model 1. Inspection of both the Bentler-Bonett normed and non-normed fit indices suggested that a two-factor model, which distinguishes school trouble from delinquency and drug use (Figure 2b), fitted the data better than the alternative two-factor model (Model 2a). There was slight improvement in fit by adding a third factor. The decrease in chi-square for Model 3 versus Model 2b, although significant, X2(2, N = 222) = 8.72, p < .05, was not very large, and the Bentler-Bonett normed fit index hardly changed at all (.86 to .87). In fact, the non-normed fit indices were virtually identical (.83, .83). As can be seen in Table 2, the correlations among the factors were positive and significant. Additionally, the normed fit indices for both Model 2b and Model 3 approached the .90 criterion proposed by Bentler and Bonett (1980) as representing a good fit in an absolute sense. Because our primary interest was in the relative fit of alternative models, we did not conduct specification searches for clues to improving absolute fit. A multifactor outcome also was found for girls, but the results are slightly different. The zero-order correlations of the measured variables for girls are presented above the diagonal in Table 1. As was true in the case of the boys, all relationships among measured variables were in the expected direction, and most were significantly different from zero. The standardized parameter estimates, corresponding z statistics, and overall model-fit measures for the one-, two-, and three-factor models for girls are presented in Table 3. Similar to the findings for boys, both two-factor models represented a significant improvement in fit over a single-factor model: x2(l, AT = 204) = 81.28, p < .001, for Model 2a versus Model 1; x20, N= 204) = 228.55, p < .001, for Model 2b versus Model 1. Judging by the Bentler-Bonett fit indices, Model 2b appears superior to Model 2a, just as for boys. Model 3 produced a significant improvement in fit relative to Model 2b as judged by

PREADOLESCENT PROBLEM BEHAVIORS

503

Table 1 Intercorrelations, Means, and Standard Deviations for Measured Indicators of Problem Behaviors Variable VI. School problems V2. Aggression V3. Acting out V4. Delinquency- 1 V5. Delinquency-2 V6. Delinquency-3 V7. Tobacco use V8. Alcohol use V9. Illicit drugs Boys M SD Girls M SD

VI

V2

V3

V4

V5

V6

V7

V8

V9

_ .45* .49* .47* .43* .53* .31* .20* .12

.44* — .90* .31* .31* .39* .28* .11 .28*

.55* .84* — .25* .21* .33* .18* .04 .15*

.52* .26* .33* — .50* .54* .41* .34* .45*

.49* .20* .20* .51* — .66* .38* .41* .36*

.39* .04 .08 .31* .51* — .41*

.38* .20* .17* .29* .53* .44* — .39* .37*

.28* .04 .07 .23* .41* .39* .61*

0.15 0.75

0.19 0.39

0.22 0.41

1.41 0.60

1.38 0.65

1.31 0.68

0.39 0.72

0.73 0.94

.37* .05 .04 .28* .44* .40* .60* .53* — 0.22 0.56

-0.19 0.61

0.07 0.23

0.11 0.27

1.22 0.44

1.22 0.50

1.14 0.39

0.37 0.75

0.56 0.82

0.21 0.57

.36* .36*

.27*

Note. Coefficients above the diagonal are for girls (n = 204), below the diagonal are for boys (n = 222). *p