Bivariate correlations were computed using Spearman’s rho statistic, a rank-order statistic robust to departures from bivariate normality (18). Confirmatory factor analysis (19) was used to model the pattern of correlations among substance use disorders over time. For each assessment age, a single factor was fit to account for correlations among symptom counts of alcohol, nicotine, and marijuana dependence. Due to prohibitively low variance, symptoms at age 11 were not included in the model. This resulted in a model with five general factors, one for each age of assessment, and each representing the covariance among substance use disorders at each age. All factors were allowed to correlate, and all same-drug residuals were allowed to correlate across ages to account for the within-person correlated nature of the longitudinal data. Loadings were standardized. As such, the variance of the symptom count variables can be modeled as a function of the general and residual (substance specific) factors: Var(SymptomCount)=(FactorLoading)2×Var(GeneralFactor)+(Residual)2, where “Var” denotes variance. Since the variance of the general factor was set to 1, the variance in a standardized symptom count accounted for by the
