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)
  • Received : 2019.11.07
  • Accepted : 2020.01.12
  • Published : 2020.03.31


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.


Supported by : National Research Foundation of Korea (NRF)


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