For computational reasons, we were limited to fitting nine variables at a time in our structural models. We therefore chose, for both the externalizing and internalizing spectra, nine a priori variables that reflected a broad range of potentially relevant disorders and traits. Once this first model was fitted, we then picked one variable with a relatively weak loading on the common factor and substituted for that variable, one at a time, other additional variables until we had exhausted the set of variables we wanted to examine. In this way, we were able to obtain results that should closely resemble those that we would find if we could fit simultaneously a single model encompassing all the variables of interest. Finally, we chose four high-loading variables on the genetic common factor for the internalizing and externalizing spectra, and fit a model examining the genetic and individual-specific environmental correlations between them.
