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Author: Lee, Jung Wun
Resulting in 2 citations.
1. Lee, Jung Wun
Chung, Hwan
A Multivariate Latent Class Profile Analysis for Longitudinal Data with a Latent Group Variable
Communications for Statistical Applications and Methods 27 (2020): 15-35.
Also: http://www.csam.or.kr/journal/view.html?doi=10.29220/CSAM.2020.27.1.015
Cohort(s): NLSY97
Publisher: Korean Statistical Society
Keyword(s): Depression (see also CESD); Modeling, Latent Class Analysis/Latent Transition Analysis; Parenting Skills/Styles; Statistical Analysis; Substance Use

Permission to reprint the abstract has not been received from the publisher.

In research on behavioral studies, significant attention has been paid to the stage-sequential process for multiple latent class variables. We now explore the stage-sequential process of multiple latent class variables using the multivariate latent class profile analysis (MLCPA). A latent profile variable, representing the stage-sequential process in MLCPA, is formed by a set of repeatedly measured categorical response variables. This paper proposes the extended MLCPA in order to explain an association between the latent profile variable and the latent group variable as a form of a two-dimensional contingency table. We applied the extended MLCPA to the National Longitudinal Survey on Youth 1997 (NLSY97) data to investigate the association between of developmental progression of depression and substance use behaviors among adolescents who experienced Authoritarian parental styles in their youth.
Bibliography Citation
Lee, Jung Wun and Hwan Chung. "A Multivariate Latent Class Profile Analysis for Longitudinal Data with a Latent Group Variable." Communications for Statistical Applications and Methods 27 (2020): 15-35.
2. Lee, Jung Wun
Chung, Hwan
Jeon, Saebom
Bayesian Multivariate Latent Class Profile Analysis: Exploring the Developmental Progression of Youth Depression and Substance Use
Computational Statistics and Data Analysis 161 (September 2021): 107261.
Also: https://www.sciencedirect.com/science/article/pii/S0167947321000955
Cohort(s): NLSY97
Publisher: Elsevier
Keyword(s): Bayesian; Depression (see also CESD); Modeling, Latent Class Analysis/Latent Transition Analysis; Monte Carlo; Statistical Analysis; Substance Use

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.
Bibliography Citation
Lee, Jung Wun, Hwan Chung and Saebom Jeon. "Bayesian Multivariate Latent Class Profile Analysis: Exploring the Developmental Progression of Youth Depression and Substance Use." Computational Statistics and Data Analysis 161 (September 2021): 107261.