A maximum likelihood estimator was used for all models. The Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the sample size-adjusted Bayesian Information Criterion (ABIC), and likelihood ratio tests (LRT) were used to choose the best model. AIC, BIC and ABIC are global fit indices which combine goodness of fit (log-likelihood value) and parsimony. A smaller value indicates better model fit. The bootstrap likelihood ratio test (BLRT) and the Lo Mendell Rubin likelihood ratio test (LMR-LRT) provide a p value which indicates whether the k-1 class model is rejected in favour of the k model [28]. Greater importance was given to the BIC and BLRT because they are typically more reliable than the AIC, and to a lesser extent, the ABIC and the Lo Mendell Rubin likelihood ratio test (LMR-LRT) [28]. Although classification quality is not a useful means of identifying the best fitting model, classification quality for the LCA and FMM was evaluated using entropy. Entropy is a summary indicator of classification quality and ranges from 0 to 1, with figures closer to 1 indicating higher classification quality [45].
