Latent Impact and Differential Item Functioning Analysis for Asymmetric IRT Models

TL;DR

This paper introduces a flexible framework for analyzing impact and differential item functioning in asymmetric IRT models, handling unobserved groups and unknown anchor items with regularized estimation.

Contribution

It proposes a joint analysis method for impact and DIF using latent classes and regularization, without needing observed group labels or pre-specified anchor items.

Findings

The method accurately recovers impact, item parameters, and DIF effects in simulations.

Applied to educational testing data, it identified impact and DIF in one dataset and impact only in another.

Abstract

Differential item functioning (DIF) arises alongside latent population heterogeneity in many applications, and both must be accounted for when assessing measurement invariance. In many practical settings, however, the comparison groups are unobserved and anchor items are unknown. A further challenge is that item response theory models traditionally assume symmetric link functions, yet empirical response processes may exhibit substantial asymmetry. This paper proposes a general framework for jointly analysing impact and DIF under asymmetric item response models. Unobserved group differences are represented by latent classes within a mixture item response model, while item-specific shifts capture DIF effects. Assuming the number of DIF items is relatively small, an $\ell_1$-regularised estimator is used to simultaneously identify the latent classes and select DIF items without requiring observed group labels or pre-specified anchor items. A simulation study evaluates recovery of impact, item parameters, and DIF effects across a range of configurations. The method is illustrated using two empirical applications from educational testing. In one dataset, the selected model reveals both impact and item-level DIF, whereas in the other, the results indicate substantial impact but little evidence of item-level DIF.