A class of Multidimensional Latent Class IRT models for ordinal polytomous item responses

TL;DR

This paper introduces a flexible class of multidimensional latent class IRT models for ordinal polytomous responses, allowing for various parameterizations and latent class structures, with applications to psychological assessment data.

Contribution

It extends existing multidimensional IRT models to ordinal responses with discrete latent traits and provides estimation and model selection strategies.

Findings

Successful application to anxiety and depression data

Flexible modeling of ordinal polytomous responses

Effective maximum likelihood estimation via EM algorithm

Abstract

We propose a class of Item Response Theory models for items with ordinal polytomous responses, which extends an existing class of multidimensional models for dichotomously-scored items measuring more than one latent trait. In the proposed approach, the random vector used to represent the latent traits is assumed to have a discrete distribution with support points corresponding to different latent classes in the population. We also allow for different parameterizations for the conditional distribution of the response variables given the latent traits - such as those adopted in the Graded Response model, in the Partial Credit model, and in the Rating Scale model - depending on both the type of link function and the constraints imposed on the item parameters. For the proposed models we outline how to perform maximum likelihood estimation via the Expectation-Maximization algorithm. Moreover, we suggest a strategy for model selection which is based on a series of steps consisting of selecting specific features, such as the number of latent dimensions, the number of latent classes, and the specific parametrization. In order to illustrate the proposed approach, we analyze data deriving from a study on anxiety and depression as perceived by oncological patients.