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A Multivariate Latent Class Profile Analysis with Latent Group

EasyChair Preprint no. 874

38 pagesDate: April 4, 2019


This paper suggests a new type of latent variable model which discovers the association between several categorical latent variables. A set of repeatedly measured categorical response variables forms a latent profile variable, while the other set of item variables identifies a latent group variable. Latent class profile analysis with group variable (GLCPA) explains an association between these two categorical latent variables as a form of two-dimensional contingency table. We applied GLCPA model to the NLSY 97 data to investigate the association between of depression process and the longitudinal behaviors of substance use development among adolescents who experienced an Authoritarian parental styles in their youth.

Keyphrases: latent class analysis, logistic regression, longitudinal data, recursive EM algorithm

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jung Wun Lee},
  title = {A Multivariate Latent Class Profile Analysis with Latent Group},
  howpublished = {EasyChair Preprint no. 874},

  year = {EasyChair, 2019}}
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