First, forward selection was used to select predictors of the intercepts. Second, in a separate model, forward selection was used to select predictors of the slopes. Third, the predictors of the intercepts and slopes were then combined, and forward selection was used to select the set of best predictors. After forward selection of the combined set of predictors, in the final model we included variables that were, in the separate analyses, significant predictors (p < .05) of the intercepts or slopes in the final model. The parameter estimates from the final model are in Table 5. We present the analyses with externalizing problems in their raw metric of POM scores. The POM scores were not very normally distributed, so we tested the model with externalizing scores square-root transformed. The predictors remained essentially the same.
