Analyses began with point-biserial and Pearson Product-Moment correlations among the manifest (e.g., LR) and latent variables used in the SEM. Subsequently, structural equation modeling was carried out based on both AMOS version 18, as well as Mplus Version 5.1, using the maximum likelihood estimation for analysis of the variance/covariance matrix (Arbuckle, 2009; Muthén and Muthén, 2007). Any violations of distributional assumptions for the model and parameter estimations were evaluated within AMOS using bootstrapping procedures which were repeated 1,000 times. Within the model, when needed, exponential and square root transformations were carried out, depending on the topography of the data (i.e., exponential transformation for EXPECT, two indicators of COPE, and for alcohol problems, while square root transformations were used for one COPE indicator and for the alcohol outcome indicator of maximum drinks lifetime). In our approach, for clarity of interpretation, the measurement model was first determined using a confirmatory factor analysis specifying correlations among the latent variables, after which the results were incorporated into the full SEM.
