Flexible Bayesian modelling in dichotomous item response theory using mixtures of skewed item curves

TL;DR

This paper introduces a flexible Bayesian IRT model that accommodates both symmetric and asymmetric item response curves using mixtures of skewed distributions, improving analysis of diverse test items.

Contribution

It proposes a novel Bayesian IRT framework with mixture priors for skewness, enabling flexible modeling of symmetric and asymmetric items without prior item classification.

Findings

Effective in simulated data for identifying item symmetry.

Applied successfully to Brazilian educational exam data.

Provides a computationally efficient MCMC inference method.

Abstract

Most Item Response Theory (IRT) models for dichotomous responses are based on probit or logit link functions which assume a symmetric relationship between the probability of a correct response and the latent traits of individuals submitted to a test. This assumption restricts the use of those models to the case in which all items have a symmetric behaviour. On the other hand, asymmetric models proposed in the literature impose that all the items in a test have an asymmetric behaviour. This assumption is inappropriate for great part of the tests which are, in general, composed by both symmetric and asymmetric items. Furthermore, a straightforward extension of the existing models in the literature would require a prior selection of the items' symmetry/asymmetry status. This paper proposes a Bayesian IRT model that accounts for symmetric and asymmetric items in a flexible though parsimonious way. That is achieved by assigning a finite mixture prior to the skewness parameter, with one of the mixture components being a point-mass at zero. This allows for analyses under both model selection and model averaging approaches. Asymmetric item curves are designed through the centred skew normal distribution, which has a particularly appealing parametrisation in terms of parameter interpretation and computational efficiency. An efficient MCMC algorithm is proposed to perform Bayesian inference and its performance is investigated in some simulated examples. Finally, the proposed methodology is applied to a data set from a large scale educational exam in Brazil.