A Bayesian Nonparametric IRT Model

TL;DR

This paper presents a flexible Bayesian nonparametric IRT model capable of handling various response types and dimensions, providing robust outlier detection and accurate parameter estimation via MCMC, demonstrated on real teacher data.

Contribution

Introduces a novel infinite-mixture Bayesian nonparametric IRT model that enhances flexibility and robustness in item response analysis compared to traditional models.

Findings

Model achieved zero outliers in real data analysis

Attained an R-squared of one in the case study

Provides a software tool for practical application

Abstract

This paper introduces a flexible Bayesian nonparametric Item Response Theory (IRT) model, which applies to dichotomous or polytomous item responses, and which can apply to either unidimensional or multidimensional scaling. This is an infinite-mixture IRT model, with person ability and item difficulty parameters, and with a random intercept parameter that is assigned a mixing distribution, with mixing weights a probit function of other person and item parameters. As a result of its flexibility, the Bayesian nonparametric IRT model can provide outlier-robust estimation of the person ability parameters and the item difficulty parameters in the posterior distribution. The estimation of the posterior distribution of the model is undertaken by standard Markov chain Monte Carlo (MCMC) methods based on slice sampling. This mixture IRT model is illustrated through the analysis of real data obtained from a teacher preparation questionnaire, consisting of polytomous items, and consisting of other covariates that describe the examinees (teachers). For these data, the model obtains zero outliers and an R-squared of one. The paper concludes with a short discussion of how to apply the IRT model for the analysis of item response data, using menu-driven software that was developed by the author.