Probit regression was then used to test the hypothesis that each TF component would uniquely and independently discriminate between those subjects with an externalizing disorder and those with no disorder, above and beyond P3 amplitude’s ability to do so. Probit regression is analogous to logistic regression (Amemiya, 1981) and thus is suitable for dichotomous outcome variables. We used a robust weighted least squares estimator in Mplus (ver. 4.2; Muthén & Muthén, 2007), which facilitated accounting for the non-independence of the twin-pairs’ observations in our sample, using Mplus’s method for deriving standard errors that are appropriately adjusted when data are nested in groups as with twin pairs. Two approaches were employed. First, univariate regression models, in which P3 peak amplitude and each TF component’s peak energy amplitude were entered into separate models, were used to determine the components’ individual relationships to each externalizing disorder. Second, bivariate regression models, in which P3 and each TF component were entered into the model together (i.e. a model including P3 and TF component 1, a model including P3 and TF component 2, etc.), were used
