CACE modeling is a mixture modeling that identifies longitudinal trajectories by using model constraints defined by the assumptions described previously. To improve the precision of the mixture, covariates can be used to predict engagement status. As with other mixture modeling techniques, CACE analyses do not provide typical estimates of model fit. The entropy index has been developed to assess the quality of growth mixture models (exploratory procedure). It evaluates the likelihood of membership in the classes for each individual on a scale from 0 to 1.0, with values close to 1.0 indicating better classification of the sample (Muthén & Muthén, 2006). For these data, entropy was acceptable, given the confirmatory nature of the CACE analyses, ranging between .58and .76, with entropy increasing as more lenient engagement criteria were used.
