Joint trajectories for social and physical aggression as predictors of adolescent maladjustment: internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features

Abstract

This investigation examined the relation between developmental trajectories jointly estimated for social and physical aggression and adjustment problems at age 14. Teachers provided ratings of children's social and physical aggression in Grades 3, 4, 5, 6, and 7 for a sample of 255 children (131 girls, 21% African American, 52% European American, 21% Mexican American). Participants, parents, and teachers completed measures of the adolescent's adjustment to assess internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features. Results showed that membership in a high and rising trajectory group predicted rule-breaking behaviors and borderline personality features. Membership in a high desister group predicted internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features. The findings suggest that although low levels of social and physical aggression may not bode poorly for adjustment, individuals engaging in high levels of social and physical aggression in middle childhood may be at greatest risk for adolescent psychopathology, whether they increase or desist in their aggression through early adolescence.

Figure 1

The change in (a) social aggression for joint aggression trajectory groups and b) physical aggression for joint trajectory groups, from Underwood et al. (2009)

Figure 2

The conceptual model of the relationship between (a) aggression trajectories and internalizing problems and (b) aggression trajectories and rule breaking. Traditional fit indices (e.g., comparative fit index, root mean square error of approximation) were not available for this analysis because of the use of numerical integration in the estimation of the categorical indicators. In this situation, means, variances, and covariances are not sufficient statistics for model estimation. Therefore, chi-square and related fit measures are not available (Muthén, 2006).