The two correlated-factors model was compared to other solutions. A model with a single common factor provided acceptable fit for the phenotypic (χ2(27)=14967.064, Comparative Fit Index=.978, standardized root mean square residual=.070) and genetic (χ2(27)=350.785, Comparative Fit Index=.949, standardized root mean square residual=.094) factor analyses, but it did not minimize the standardized difference between the observed and predicted correlations as well as the correlated factors model (Table S7). The parallel factor model (i.e., the sum score model) exhibited very poor fit, reflected by the strong, unanimous bias observed in the model implied correlations [phenotypic model: (χ2(34)=43655.530, Comparative Fit Index=.936, standardized root mean square residual=.143), genetic model: (χ2(43)=607.196, Comparative Fit Index=.911, standardized root mean square residual=.470)]. Accordingly, we identified the correlated factors model as the best fitting and most appropriate model for further genetic analyses.
