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Evaluation of MIMIC-Model Methods for DIF Testing With Comparison to Two-Group Analysis.

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Differential item functioning (DIF) detection is crucial for test fairness. Multiple-indicator multiple-cause (MIMIC) models offer a more accurate approach than traditional 2-group item response theory (IRT) methods, especially with small focal groups.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Differential item functioning (DIF) is critical for ensuring test fairness across diverse groups.
  • Existing DIF detection methods, like 2-group item response theory (IRT), have limitations, particularly with small focal groups.
  • The accuracy and sample size requirements for multiple-indicator multiple-cause (MIMIC) models in DIF testing are not well-established.

Purpose of the Study:

  • To examine the accuracy of MIMIC structural equation models for DIF testing when the focal group sample size is small.
  • To compare the performance of MIMIC models against traditional 2-group IRT methods for DIF detection.
  • To provide recommendations for applying MIMIC methods in DIF testing scenarios with limited focal group data.

Main Methods:

  • Utilized multiple-indicator multiple-cause (MIMIC) structural equation models, parameterized as item response models.
  • Employed simulation studies to assess the accuracy of MIMIC methods under small focal group conditions.
  • Compared MIMIC model results with those from 2-group item response theory (IRT) analyses.

Main Results:

  • MIMIC models demonstrated superior accuracy in detecting uniform DIF compared to 2-group IRT, particularly with small focal-group samples.
  • The accuracy of MIMIC methods was validated for both binary and 5-category ordinal response data.
  • Results support the utility and robustness of the MIMIC approach for DIF testing in challenging sample size situations.

Conclusions:

  • The MIMIC modeling approach is a valuable and accurate tool for differential item functioning (DIF) testing, especially when dealing with small focal groups.
  • Findings suggest that MIMIC models provide more reliable DIF detection than 2-group IRT under these conditions.
  • The study offers practical guidance for researchers and test developers on implementing MIMIC methods for enhanced measurement fairness.