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

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  • Lee, Jung Wun
  • Chung, Hwan
  • Jeon, Saebom

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.

Suggested Citation

  • Lee, Jung Wun & Chung, Hwan & Jeon, Saebom, 2021. "Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:csdana:v:161:y:2021:i:c:s0167947321000955
    DOI: 10.1016/j.csda.2021.107261
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    References listed on IDEAS

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