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arXiv · 2408.13143

A Restricted Latent Class Model with Polytomous Attributes and Respondent-Level Covariates

Eric Alan Wayman·Steven Andrew Culpepper·Jeff Douglas·Jesse Bowers
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Abstract

We present an exploratory restricted latent class model where response data is for a single time point, polytomous, and differing across items, and where latent classes reflect a multi-attribute state where each attribute is ordinal. Our model extends previous work to allow for correlation of the attributes through a multivariate probit specification and to allow for respondent-specific covariates. We demonstrate that the model recovers parameters well in a variety of realistic scenarios, and apply the model to the analysis of a particular dataset designed to diagnose depression. The application demonstrates the utility of the model in identifying the latent structure of depression beyond single-factor approaches which have been used in the past.

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Eric Alan Wayman, Steven Andrew Culpepper, Jeff Douglas, Jesse Bowers. 2024-08-23. A Restricted Latent Class Model with Polytomous Attributes and Respondent-Level Covariates. https://doi.org/10.1007/s41237-025-00271-8

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