A general method is presented for comparing the relative importance of predictors in multiple regression. Dominance analysis (D. V. Budescu, 1993), a procedure that is based on an examination of the R2 values for all possible subset models, is refined and extended by introducing several quantitative measures of dominance that differ in the strictness of the dominance definition. These are shown to be intuitive, meaningful, and informative measures that can address a variety of research questions pertaining to predictor importance. The bootstrap is used to assess the stability of dominance results across repeated sampling, and it is shown that these methods provide the researcher with more insights into the pattern of importance in a set of predictors than were previously available.

Extended the findings from previous meta-analytic work by comparing the effectiveness of behavioral parent-training (BPT) and cognitive-behavioral therapy (CBT) for youth with antisocial behavior problems. Youth demographic variables were also examined as potential moderators of the effectiveness of these 2 types of interventions. Thirty BPT studies and 41 CBT studies met inclusion criteria for this meta-analysis. The weighted mean effect size (ES) for all interventions was 0.40. Youth age was found to moderate the outcome of the 2 interventions, with BPT having a stronger effect for preschool and school-aged youth and CBT having a stronger effect for adolescents. The results also indicate that there may be systematic differences in the outcomes associated with BPT and CBT when the setting of the intervention is considered, suggesting the need to carefully consider the effect of setting in future research. This study also highlights the need for outcome research dealing with more diverse populations and the better classification of research participants on different developmental trajectories of antisocial behavior.

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R 2 analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A simulation study, using both simple random sampling from a known population and bootstrap sampling from a single (parent) random sample, was performed to evaluate the bias, sampling distribution, and confidence intervals of quantitative dominance measures as well as the reproducibility of qualitative dominance measures. Results indicated that the bootstrap procedure is feasible and can be used in applied research to generalize logistic regression dominance analysis results to the population of interest. The procedures for determining and interpreting the general dominance of predictors in a logistic regression context are illustrated with an empirical example.

Differentiation of self involves the capacity to modulate affect, maintain a clear sense of self, and balance intimacy and autonomy in significant relationships. Given the central role of family relationships for individual functioning, the authors tested whether differentiation mediated or moderated relations between college stress and personal adjustment. Differentiation of self partially mediated effects of academic and financial stress and exerted a direct influence on adjustment. Limitations, directions for future research, and implications for counseling are discussed.

Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.