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A multivariate latent class profile analysis for longitudinal data with a latent group variable

  • Lee, Jung Wun (Department of Statistics, University of Connecticut) ;
  • Chung, Hwan (Department of Statistics, Korea University)
  • Received : 2019.05.26
  • Accepted : 2019.09.17
  • Published : 2020.01.31

Abstract

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.

Keywords

References

  1. Bandeen-Roche K, Miglioretti DL, Zeger SL, and Rathouz PJ (1997). Latent variable regression for multiple discrete outcomes, Journal of the American Statistical Association, 92, 1375-1386. https://doi.org/10.1080/01621459.1997.10473658
  2. Bartolucci F, Farcomeni A, and Pennoni F (2010). An overview of latent Markov models for longitudinal categorical data, arXiv preprint arXiv:1003.2804
  3. Chang HC and Chung H (2013). Dealing with multiple local modalities in latent class profile analysis, Computational Statistics & Data Analysis, 68, 296-310. https://doi.org/10.1016/j.csda.2013.07.016
  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. Collins LM and Lanza ST (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences, John Wiley & Sons, New York.
  6. Dempster AP, Laird NM, and Rubin DB (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society: Series B (Methodological), 39, 1-22, https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
  7. Jeon S, Lee J, Anthony JC, and Chung H (2017). Latent class analysis for multiple discrete latent vari-ables: 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
  8. King KA, Vidourek RA, and Merianos AL (2016). Authoritarian parenting and youth depression: Results from a national study, Journal of Prevention & Intervention in the Community, 44, 130-139. https://doi.org/10.1080/10852352.2016.1132870
  9. Lanza ST, Coffman DL, and Xu Shu (2013). Causal inference in latent class analysis, Structural Equation Modeling: A Multidisciplinary Journal, 20, 361-383. https://doi.org/10.1080/10705511.2013.797816
  10. Lee J, Chung H, and Jeon S (2019). Multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use, Journal of the Korean Statistical Society, Manuscript submitted for publication.
  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. Maccoby EE and Martin JA (1983). Socialization in the context of the family: Parent-child interaction. In Paul H. Mussen (Eds), Handbook of Child Psychology: Formerly Carmichael's Manual of Child Psychology, Wiley, New York.