The relative fit of models was assessed using a multimethod approach to evaluating latent class models (Bauer and Curran, 2003; Muthén and Muthén, 2000). Models with differing numbers of classes were compared using the model log-likelihood and sample sized adjusted Bayesian Information Criterion (aBIC), which is a criterion for assessing relative model fit based on the log-likelihood and the number of parameters (Henson et al., 2007). A lower aBIC indicates a better fitting model, in comparison with models with a relatively higher aBIC. The bootstrapped likelihood ratio test (BLRT) was used to test the fit of k-1 classes against k classes, with a significant p value indicating that the null hypothesis of k-1 classes should be rejected in favor of a model with at least k classes (Lo et al., 2001; Nylund et al., 2007).
