Bayesian Latent Class Analysis: Sample Size, Model Size, and Classification Precision

The current literature includes limited information on the classification precision of Bayes estimation for latent class analysis (BLCA). (1) Objectives: present study compared BLCA with robust maximum likelihood (MLR) procedure, which is default procedure Mplus 8.0 software. (2) Method: Markov chain Monte Carlo simulations were used to estimate two-, three-, and four-class models measured by four binary observed indicators samples 1000, 750, 500, 250, 100, 75 observations, respectively. With each sample, number replications was entropy average probabilities most likely membership recorded. (3) Results: values more stable ranged between 0.644 1. Bayes’ 0.528 MLR 0.552 0.958. 0.539 0.993. two-class model, outperformed all sample sizes. three-class had higher 75-sample size, whereas performed slightly better 750- 1000-sample 4-class underperformed an increased unsuccessful computations, particularly smaller samples.