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Examining Differential Item Functioning from a Multidimensional IRT Perspective.

Terry A Ackerman¹, Ye Ma²

¹The University of Iowa.

|February 25, 2026

Summary

This summary is machine-generated.

Differential item functioning (DIF) occurs when test items measure different abilities. This study examines DIF using two-dimensional multidimensional item response theory (MIRT), offering insights into its causes and mitigation strategies.

Keywords:

compensatory and noncompensatory MIRT models differential item functioning multidimensional IRT

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Area of Science:

Psychometrics
Educational Measurement
Item Response Theory

Background:

Differential item functioning (DIF) is a standard analysis in testing.
DIF can arise when items measure different ability composites and groups have distinct ability distributions.
Existing research highlights consequences of ignoring dimensionality in DIF analysis.

Purpose of the Study:

To examine DIF from a two-dimensional multidimensional item response theory (MIRT) perspective.
To illustrate the compensatory MIRT model and graphical representation of items and composites.
To investigate three MIRT-based approaches for understanding DIF.

Main Methods:

Utilizing a two-dimensional multidimensional item response theory (MIRT) framework.
Reviewing analytical research on the consequences of unidimensional IRT models for two-dimensional data.
Investigating uniform/nonuniform DIF with differing ability distributions, accounting for the full latent ability space, and scenario-based DIF.

Main Results:

Item parameters can vary based on latent ability distributions.
Ignoring dimensionality in IRT models can lead to inaccurate DIF detection.
Considering the complete latent ability space helps mitigate DIF effects.
Differing problem-solving approaches can cause DIF even with identical distributions.

Conclusions:

A two-dimensional MIRT perspective provides a robust framework for understanding DIF.
Accurate DIF analysis requires considering the full latent ability space and potential scenario-based causes.
Identifying the root causes of DIF in flagged items remains a critical challenge.