Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use

Jung Wun Lee, Hwan Chung and Saebom Jeon

Computational Statistics & Data Analysis, 2021, vol. 161, issue C

Abstract: Multivariate latent class profile analysis (MLCPA) is a useful tool for exploring the stage-sequential process of multiple latent class variables, but the inference can be challenging due to the high-dimensional latent structure of the model. In this paper, a Bayesian approach via Markov chain Monte Carlo (MCMC) is proposed for MLCPA as an alternative to the maximum-likelihood (ML) method. Compared to the ML solution, Bayesian estimates are less sensitive to the set of initial values as well as easier to obtain standard errors. We also address issues in MCMC such as label-switching problem with a dynamic data-dependent prior and computational complexity with a recursive formula. Simulation studies revealed the validity and efficiency of the proposed algorithm. An empirical analysis of MLCPA using the National Longitudinal Survey of Youth 97 (NLSY97) identified a small number of representative developmental progressions of adolescent depression and substance use.

Keywords: Adolescent depression; Substance use; Label switching; Latent class analysis; Longitudinal data; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:161:y:2021:i:c:s0167947321000955

DOI: 10.1016/j.csda.2021.107261

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