Work

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

Year: 2026

Type: article

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.

Source: Journal of Educational and Behavioral Statistics

DOI: 10.3102/10769986251415569

Authors: Eric Alan Wayman, Steven Andrew Culpepper, Jeff Douglas, Jesse Bowers

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: Psychometric Methodologies and Testing

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: National Science Foundation, Division of Social and Economic Sciences

Awards: Identification and Estimation of Dynamic Restricted Latent Class Models for Cognitive Diagnosis, G4639534388, G5707664468, Bayesian Estimation of Restricted Latent Class Models for Ordinal and Nominal Response Data