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Examining Differential Item Functioning from a Multidimensional IRT Perspective

Published online by Cambridge University Press:  01 January 2025

Terry A. Ackerman Open the ORCID record for Terry A. Ackerman [Opens in a new window]  and
Ye Ma Open the ORCID record for Ye Ma [Opens in a new window]
Terry A. Ackerman*
Affiliation:
The University of Iowa
Ye Ma
Affiliation:
Amazon Web Services
*
Correspondence should be made to Terry A. Ackerman, The University of Iowa, 8 North Shore Drive, Edwardsville, IL62025, USA. [email protected]
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Abstract

Differential item functioning (DIF) is a standard analysis for every testing company. Research has demonstrated that DIF can result when test items measure different ability composites, and the groups being examined for DIF exhibit distinct underlying ability distributions on those composite abilities. In this article, we examine DIF from a two-dimensional multidimensional item response theory (MIRT) perspective. We begin by delving into the compensatory MIRT model, illustrating and how items and the composites they measure can be graphically represented. Additionally, we discuss how estimated item parameters can vary based on the underlying latent ability distributions of the examinees. Analytical research highlighting the consequences of ignoring dimensionally and applying unidimensional IRT models, where the two-dimensional latent space is mapped onto a unidimensional, is reviewed. Next, we investigate three different approaches to understanding DIF from a MIRT standpoint: 1. Analytically Uniform and Nonuniform DIF: When two groups of interest have different two-dimensional ability distributions, a unidimensional model is estimated. 2. Accounting for complete latent ability space: We emphasize the importance of considering the entire latent ability space when using DIF conditional approaches, which leads to the mitigation of DIF effects. 3. Scenario-Based DIF: Even when underlying two-dimensional distributions are identical for two groups, differing problem-solving approaches can still lead to DIF. Modern software programs facilitate routine DIF procedures for comparing response data from two identified groups of interest. The real challenge is to identify why DIF could occur with flagged items. Thus, as a closing challenge, we present four items (Appendix A) from a standardized test and invite readers to identify which group was favored by a DIF analysis.

Keywords

multidimensional IRTdifferential item functioningcompensatory and noncompensatory MIRT models
Type
Theory & Methods
Information
Psychometrika , Volume 89 , Issue 1 , March 2024 , pp. 4 - 41
Copyright
Copyright © 2024 The Author(s), under exclusive licence to The Psychometric Society

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