[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.29220/CSAM.2020.27.2.241

Joint latent class analysis for longitudinal data: an application on adolescent emotional well-being

Kim, Eun Ah (Department of Statistics, Korea University)
Chung, Hwan (Department of Statistics, Korea University)
Jeon, Saebom (Department of Marketing Information Consulting, Mokwon University)

Publication Information

Communications for Statistical Applications and Methods / v.27, no.2, 2020 , pp. 241-254 More about this Journal

Abstract

This study proposes generalized models of joint latent class analysis (JLCA) for longitudinal data in two approaches, a JLCA with latent profile (JLCPA) and a JLCA with latent transition (JLTA). Our models reflect cross-sectional as well as longitudinal dependence among multiple latent classes and track multiple class-sequences over time. For the identifiability and meaningful inference, EM algorithm produces maximum-likelihood estimates under local independence assumptions. As an empirical analysis, we apply our models to track the joint patterns of adolescent depression and anxiety among US adolescents and show that both JLCPA and JLTA identify three adolescent emotional well-being subgroups. In addition, JLCPA classifies two representative profiles for these emotional well-being subgroups across time, and these profiles have different tendencies according to the parent-adolescent-relationship subgroups.

Keywords

joint latent class profile analysis; joint latent transition analysis; EM algorithm; adolescent depression;

Citations & Related Records

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