Exploring the Enumeration Accuracy of Cross-Validation Indices in Latent Class Analysis. Issue 3 (4th May 2021) - Whittaker, Tiffany A.; Miller, J. E.
ABSTRACT: A crucial issue when estimating mixture models is selecting the model with the correct number of latent classes underlying the data, which is commonly referred to as class enumeration. Although cross-validation methods have been suggested with mixture models to help augment the class enumeration process (Masyn, 2013), they have been seldom used. The purpose of this simulation study was to compare the performance of traditionally used single sample enumeration indices with the performance of cross-validation indices when selecting the correct latent class model with binary indicators. Various conditions were manipulated, including the number of indicators, sample size, class separation, mixing proportions, and number of latent classes. The enumeration accuracy of sixteen indices (traditional, cross-validated, and double cross-validated) were documented in the manipulated conditions. The traditional sample-size adjusted BIC index was the most accurate among the indices. The performance of the double cross-validated -2LL was also promising. Recommendations are provided.