LCA models incorporating the design of the complex datasets including weighting, clustering, and stratification, were carried out using the Latent Gold 4.5 software package [25]. To account for the complex sampling design of the NCS and NCS-R datasets, models were estimated using weighted data, while controlling for stratification and clustering. Participants within each sample were assigned varying weights to adjust for survey nonresponding, variation in probability of selection within and between households, differences across successive phases of surveying, and to approximate the distribution of major demographic variables in the US population. This weighting procedure resulted in adjusted sample sizes (i.e., ‘weighted Ns’; see below) for each sample. Models specifying from 2 to 10 classes were compared. Model fit was assessed using the Bayesian Information Criterion (BIC) [26] and the Akaike Information Criterion (AIC) [27]. Both AIC and BIC are model selection indices that balance model fit and parsimony, but penalize model complexity to different degrees. When comparing models, generally, lowest values of both, or a scree-plot like test (i.e., where AIC and BIC values begin to level off) may be
