A Tutorial on Bayesian Latent Class Analysis Using JAGS

Authors

DOI:

https://doi.org/10.35566/jbds/v2n2/qiu

Keywords:

Latent class analysis, Mixture models, Bayesian analysis

Abstract

This tutorial introduces readers to latent class analysis (LCA) as a model-based approach to understand the unobserved heterogeneity in a population. Given the growing popularity of LCA, we aim to equip readers with theoretical fundamentals as well as computational tools. We outline some potential pitfalls of LCA and suggest related solutions. Moreover, we demonstrate how to conduct frequentist and Bayesian LCA in R with real and simulated data. To ease learning, the analysis is broken down into a series of simple steps. Beyond the simple LCA, two extensions including mixed-model LCA and growth curve LCA are provided to aid readers’ transition to more advanced models. The complete R code and data set are provided.

 

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Published

2022-12-04

How to Cite

A Tutorial on Bayesian Latent Class Analysis Using JAGS. (2022). Journal of Behavioral Data Science, 2(2), 127-155. https://doi.org/10.35566/jbds/v2n2/qiu