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Abstract

We introduce a restricted latent class exploratory model for longitudinal data with ordinal attributes and respondent-specific covariates. Responses follow a time-inhomogeneous hidden Markov model where the probability of a respondent’s latent state at the current time point is conditional on the respondent’s latent state at the previous time point as well as the respondent’s covariates at the current time point. We prove that the model is identifiable, state a Bayesian formulation, and demonstrate its efficacy in a variety of scenarios through two simulation studies. We apply the model to response data from a mathematics examination, comparing the results to a previously published confirmatory analysis, and also apply it to emotional state response data, which was measured over a several-day period.

Keywords

Bayesian inference hidden Markov model longitudinal multivariate probit restricted latent class model

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A Restricted Latent Class Hidden Markov Model for Polytomous Responses,Polytomous Attributes,and Covariates: Identifiability and Application

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