factors (A1, A2, and A3). For each of these submodels, the AIC increased and the chi-squared change was significant for the change in degrees of freedom, indicating that these models provided a worse fit to the data. The best-fitting model (Model VII; shown in Fig. 3), obtained by systematically dropping parameters based on the order of magnitude until no further pathways could be dropped without causing a significant decrease in fit, allowed for 4 latent genetic factors. Additionally, this model dropped the individual pathway from the third latent genetic factor (A5 in Fig. 1) loading onto the RAPI. This model indicates that genetic variance across the measures of AC and problems is accounted for by multiple latent genetic factors. The genetic correlations, computed for each pair of variables as the covariance of the 2 measures divided by the square root of the product of the variances of each of the measures, are shown in Table 4. They range from 0.45 (frequency of alcohol use with max drinks) to 0.99 (frequency of heavy drinking and frequency of intoxication).
