Mixed-effects logistic regression models for indirectly observed discrete outcome variables

Jeroen K. Vermunt*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

41 Citations (Scopus)

Abstract

A well-established approach to modeling clustered data introduces random effects in the model of interest. Mixed-effects logistic regression models can be used to predict discrete outcome variables when observations are correlated. An extension of the mixed-effects logistic regression model is presented in which the dependent variable is a latent class variable. This method makes it possible to deal simultaneously with the problems of correlated observations and measurement error in the dependent variable. As is shown, maximum likelihood estimation is feasible by means of an EM algorithm with an E step that makes use of the special structure of the likelihood function. The new model is illustrated with an example from organizational psychology.

Original languageEnglish
Pages (from-to)281-301
JournalMultivariate Behavioral Research
Volume40
Issue number3
DOIs
Publication statusPublished - 2005

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