Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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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
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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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References

  1. Angel R, Burton L, Chase-Lansdale LP, Cherlin A, and Moffitt R (2009). Welfare, Children, and Families: A Three-City Study, ICPSR04701-v7. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.
  2. Center for Behavioral Health Statistics and Quality (2018). Key substance use and mental health indicators in the United States: Results from the 2017 National Survey on Drug Use and Health, Substance Abuse and Mental Health Services Administration, Rockville, MD.
  3. Collins LM and Lanza ST (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences, 718, John Wiley & Sons.
  4. Chung H, Anthony JC, and Schafer JL (2011). Latent class profile analysis: an application to stage sequential processes in early onset drinking behaviours, Journal of the Royal Statistical Society. Series A: Statistics in Society, 174, 689-712. https://doi.org/10.1111/j.1467-985X.2010.00674.x
  5. Chung H, Lanza ST, and Loken E (2008). Latent transition analysis: inference and estimation, Statistics in Medicine, 27, 1834-1854. https://doi.org/10.1002/sim.3130
  6. Garber J and Weersing VR (2010). Comorbidity of anxiety and depression in youth: Implications for treatment and prevention, Clinical Psychology: Science and Practice, 17, 293-306. https://doi.org/10.1111/j.1468-2850.2010.01221.x
  7. Gutman LM, McLoyd VC, and Tokoyawa T (2005). Financial strain, neighborhood stress, parenting behaviors, and adolescent adjustment in urban African American families, Journal of Research on Adolescence, 15, 425-449. https://doi.org/10.1111/j.1532-7795.2005.00106.x
  8. Horn PJ and Wuyek LA (2010). Anxiety disorders as a risk factor for subsequent depression, International Journal of Psychiatry in Clinical Practice, 14, 244-247. https://doi.org/10.3109/13651501.2010.487979
  9. Jeon S, Lee J, Anthony JC, and Chung H (2017). Latent class analysis for multiple discrete latent variables: a study on the association between violent behavior and drug-using behaviors, Structural Equation Modeling: A Multidisciplinary Journal, 24, 911-925. https://doi.org/10.1080/10705511.2017.1340844
  10. Kreuter F, Yan T, and Tourangeau R (2008). Good item or bad - can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions Journal of the Royal Statistical Society: Series A (Statistics in Society), 171, 723-738. https://doi.org/10.1111/j.1467-985X.2007.00530.x
  11. Lee JW and Chung H (2017). Latent class analysis with multiple latent group variables, Communications for Statistical Applications and Methods, 24, 173-191. https://doi.org/10.5351/CSAM.2017.24.2.173
  12. Mooijaart AB and Van der Heijden PG (1992). The EM algorithm for latent class analysis with equality constraints, Psychometrika, 57, 261-269. https://doi.org/10.1007/BF02294508
  13. Nurius PS and Macy RJ (2008). Heterogeneity among violence-exposed women: applying personoriented research methods, Journal of Interpersonal Violence, 23, 389-415. https://doi.org/10.1177/0886260507312297
  14. Schleider JL, Krause ED, and Gillham JE (2014). Sequential comorbidity of anxiety and depression in youth: present knowledge and future directions, Current Psychiatry Reviews, 10, 75-87. https://doi.org/10.2174/1573400509666131217010652
  15. van Lang ND, Ferdinand RF, Ormel J, and Verhulst FC (2006). Latent class analysis of anxiety and depressive symptoms of the youth self-report in a general population sample of young adolescents, Behaviour Research and Therapy, 44, 849-860.
  16. White RMB and Roosa MW (2012). Neighborhood contexts, fathers, and Mexican American young adolescents' internalizing symptoms, Journal of Marriage and Family, 74, 152-166. https://doi.org/10.1111/j.1741-3737.2011.00878.x