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Examining Differential Item Functioning from a Multidimensional IRT Perspective.

Terry A Ackerman1, Ye Ma2

  • 1The University of Iowa, 8 North Shore Drive, Edwardsville, IL, 62025, USA. tackerman@uiowa.edu.

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|April 4, 2024
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Summary
This summary is machine-generated.

Differential item functioning (DIF) occurs when test items measure different abilities. This study uses two-dimensional multidimensional item response theory (MIRT) to analyze DIF, considering complex ability distributions and problem-solving approaches.

Keywords:
compensatory and noncompensatory MIRT modelsdifferential item functioningmultidimensional IRT

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistics

Background:

  • Differential item functioning (DIF) is a critical analysis in test development, identifying biased items across different groups.
  • Existing research indicates DIF can arise when items tap into distinct ability composites, particularly with varied group ability distributions.
  • Standard DIF analyses often overlook the multidimensional nature of latent abilities, potentially leading to misinterpretations.

Purpose of the Study:

  • To examine differential item functioning (DIF) through the lens of two-dimensional multidimensional item response theory (MIRT).
  • To illustrate the compensatory MIRT model, item-ability composite representation, and the impact of latent ability distributions on item parameters.
  • To review the consequences of ignoring dimensionality and applying unidimensional item response theory (IRT) models.

Main Methods:

  • Investigated DIF using a two-dimensional MIRT framework, focusing on the compensatory MIRT model.
  • Analyzed three approaches to understanding DIF from a MIRT perspective: uniform/nonuniform DIF with differing distributions, accounting for the full latent space, and scenario-based DIF.
  • Reviewed analytical research on the effects of unidimensional IRT models on two-dimensional latent spaces.

Main Results:

  • Estimated item parameters can vary based on examinee latent ability distributions.
  • Ignoring dimensionality in IRT models can lead to inaccurate DIF assessments when latent abilities are multidimensional.
  • Considering the complete latent ability space is crucial for mitigating DIF effects in conditional approaches.

Conclusions:

  • A two-dimensional MIRT perspective provides a more nuanced understanding of DIF, accounting for complex ability structures.
  • Differing ability distributions and even distinct problem-solving approaches can induce DIF, even with identical latent distributions.
  • While software facilitates DIF detection, identifying the root causes of DIF requires deeper analytical insight beyond statistical flagging.