Separating Latent Classes by Information Criteria

This study evaluates performance of information criteria used to separate latent classes. In the evaluations, various numbers of latent classes, sample sizes, parameter structures and latent-class complexities were designed to simulate datasets. The average accuracy rates of information criteria in selecting the designed numbers of latent classes were the core results in this experiment. The study revealed that widely used information criteria, e.g., AIC, BIC, CAIC, could perform poorly under some circumstances. By including a sample size adjustment (Rissanen, 1978), the unsatis-factory performances could be improved considerably. The sample size adjustment provides a plausible solution for separating latent classes. Guidelines are provided to help achieve optimum use of the model fit indices.

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