A latent transition model with logistic regression
Document Type
Article
Date of Original Version
9-1-2007
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. © 2007 The Psychometric Society.
Publication Title, e.g., Journal
Psychometrika
Volume
72
Issue
3
Citation/Publisher Attribution
Chung, Hwan, Theodore A. Walls, and Yousung Park. "A latent transition model with logistic regression." Psychometrika 72, 3 (2007): 413-435. doi: 10.1007/s11336-005-1382-y.