Two-Level Latent Class Analysis with Bayesian Inference

Abstract

The purpose of this paper is to propose an extension model of the existing Latent Class Analysis (LCA). This model assumes multi-level latent classes so that the interpretation of the latent classes should be made clearer. The inference about the parameters involved in the model was made numerically using Gibbs Sampler. This inference procedure was applied to real data, and yielded the meaningful results.