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Using a Multidimensional IRT Framework to Better Understand Differential Item Functioning (DIF): A Tale of Three DIF

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  • 1University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

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|May 26, 2018
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Differential item functioning (DIF) occurs in multidimensional data when groups differ on secondary abilities. This study shows even small ability differences trigger DIF detection, impacting test fairness.

Keywords:
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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Item Response Theory

Background:

  • Differential item functioning (DIF) is theoretically linked to multidimensionality in assessment data.
  • Existing research suggests DIF detection thresholds may be influenced by secondary ability distributions.

Purpose of the Study:

  • To quantify the necessary difference in secondary ability distributions for DIF detection.
  • To investigate the impact of varying secondary dimension mean differences and inter-dimensional correlations on DIF detection.

Main Methods:

  • Simulated two-dimensional binary data using a compensatory multidimensional item response theory (MIRT) model.
  • Systematically varied mean differences on the second dimension between reference and focal groups.
  • Employed SIBTEST, Mantel-Haenszel, and logistic regression for DIF detection.

Main Results:

  • DIF was detected even with minimal mean differences on the secondary dimension, smaller than previously considered.
  • Small mean differences (0.25) on the secondary dimension consistently led to statistically significant subtest score differences between groups.

Conclusions:

  • The sensitivity of DIF detection procedures is high, identifying bias even with subtle group differences in secondary abilities.
  • Assessment of secondary dimensions is crucial for ensuring test fairness and accurate interpretation of examinee performance.