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A Latent Transition Model With Logistic Regression

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Abstract

Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian approach via Markov chain Monte Carlo (MCMC) is employed in order to achieve more robust estimates. A case example illustrating the model is provided using data on academic beliefs and achievement in a low-income sample of adolescents in the United States.

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Authors and Affiliations

  1. Department of Epidemiology, Michigan State University, B 601 West Fee Hall, East Lansing, MI, 48824, USA

    Hwan Chung (Assistant Professor)

  2. Department of Psychology, University of Rhode Island, 10 Chafee Road, Suite 15W, Kingston, RI, 02881, USA

    Theodore A. Walls (Assistant Professor)

  3. Korea University, Korea

    Yousung Park

Authors
  1. Hwan Chung
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  2. Theodore A. Walls
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  3. Yousung Park
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Correspondence to Hwan Chung.

Additional information

This research was partially supported by the National Institute on Drug Abuse Grant 1-R03-DA021639. This research was partially supported by the National Institute on Drug Abuse Grant 1-P50-DA10075, The Methodology Center, The Pennsylvania State University. This research was partially supported by the National Institute of Mental Health funds as part of the Studying Diverse Lives research support program at the Henry A. Murray Research Archive, Institute for Quantitative Science, Harvard University.

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Chung, H., Walls, T.A. & Park, Y. A Latent Transition Model With Logistic Regression. Psychometrika 72, 413–435 (2007). https://doi.org/10.1007/s11336-005-1382-y

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