A Monte Carlo investigation of factors influencing latent class analysis: an application to eating disorder research.

OBJECTIVE: Latent class analysis (LCA) has frequently been used to identify qualitatively distinct phenotypes of disordered eating. However, little consideration has been given to methodological factors that may influence the accuracy of these results.
METHOD: Monte Carlo simulations were used to evaluate methodological factors that may influence the accuracy of LCA under scenarios similar to those seen in previous eating disorder research.
RESULTS: Under these scenarios, the aBIC provided the best overall performance as an information criterion, requiring sample sizes of 300 in both balanced and unbalanced structures to achieve accuracy proportions of at least 80%. The BIC and cAIC required larger samples to achieve comparable performance, while the AIC performed poorly universally in comparison. Accuracy generally was lower with unbalanced classes, fewer indicators, greater or nonrandom missing data, conditional independence assumption violations, and lower base rates of indicator endorsement. DISCUSSION: These results provide critical information for interpreting previous LCA research and designing future classification studies.