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

TL;DR

This paper introduces a flexible latent class model that captures multi-attribute, ordinal responses with respondent covariates, improving the analysis of complex psychological data such as depression diagnostics.

Contribution

The model extends existing latent class frameworks by incorporating attribute correlation and respondent covariates, enabling more nuanced psychological assessments.

Findings

Model accurately recovers parameters in simulated scenarios

Effectively identifies latent depression structures

Demonstrates utility in real-world dataset analysis

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.