which individuals are grouped on the basis of their highest probability of membership), but probability of class membership is the dependent variable modeled in analyses. Missing data are accommodated in Mplus via robust maximum likelihood estimation, which takes advantage of all available data rather than deleting cases with partially missing data in a listwise manner. A set of standard indices was used to assess relative model fit, quality of classification, and the following direct comparisons between models: Bayesian information criterion,48,49 Akaike information criterion,50 entropy coefficients,51 and the Lo-Mendel-Rubin likelihood ratio test.52
