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A Latent Transition Model With Logistic Regression

Published online by Cambridge University Press:  01 January 2025

Hwan Chung ,
Theodore A. Walls  and
Yousung Park
Hwan Chung*
Affiliation:
Michigan State University
Theodore A. Walls
Affiliation:
University of Rhode Island
Yousung Park
Affiliation:
Korea University
*
Requests for reprints should be sent to Hwan Chung, Assistant Professor, Department of Epidemiology, Michigan State University, B 601 West Fee Hall, East Lansing, MI 48824, USA. E-mail: walls@uri.edu.; or to Theodore Walls, Assistant Professor, Department of Psychology, University of Rhode Island, 10 Chafee Road, Suite 15W, Kingston, RI 02881, USA. E-mail: walls@uri.edu.
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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.

Keywords

latent transitionlogistic regressionMCMCacademic achievement

Information

Type
Original Paper
Information
Psychometrika , Volume 72 , Issue 3 , September 2007 , pp. 413 - 435
Copyright
Copyright © 2007 The Psychometric Society

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Footnotes

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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