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

Authors
Kim, Eun AhChung, HwanJeon, Saebom
Issue Date
3월-2020
Publisher
KOREAN STATISTICAL SOC
Keywords
joint latent class profile analysis; joint latent transition analysis; EM algorithm; adolescent depression
Citation
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.27, no.2, pp.241 - 254
Indexed
SCOPUS
KCI
Journal Title
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
Volume
27
Number
2
Start Page
241
End Page
254
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/57471
DOI
10.29220/CSAM.2020.27.2.241
ISSN
2287-7843
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.
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