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Using Odds Ratios to Detect Differential Item Functioning.

Kuan-Yu Jin1, Hui-Fang Chen2, Wen-Chung Wang3

  • 1The University of Hong Kong, Pokfulam, Hong Kong.

Applied Psychological Measurement
|December 19, 2018
PubMed
Summary
This summary is machine-generated.

A new odds ratio (OR) method effectively detects differential item functioning (DIF), outperforming logistic regression (LR) and Mantel-Haenszel (MH) methods, especially with high DIF percentages and missing data.

Keywords:
Mantel–Haenszeldifferential item functioninglogistic regressionmissing dataodds ratioscale purification

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Differential item functioning (DIF) compromises test score comparability and validity.
  • Conventional DIF detection methods (logistic regression, Mantel-Haenszel) are susceptible to high DIF item percentages and missing data.
  • Existing methods may introduce bias through total score reliance.

Purpose of the Study:

  • To introduce and evaluate a novel odds ratio (OR) method for DIF detection.
  • To compare the performance of the OR method against logistic regression (LR) and Mantel-Haenszel (MH) methods.
  • To assess the OR method's robustness under various conditions, including high DIF item percentages and missing data.

Main Methods:

  • Developed an odds ratio (OR) method utilizing all examinee response data.
  • Conducted extensive simulation studies manipulating DIF patterns, sample sizes, purification procedures, DIF item percentages, and missing data proportions.
  • Compared the OR method's performance (false positive and true positive rates) against LR and MH methods.

Main Results:

  • The OR method, particularly without purification, demonstrated superior control of false positive rates and high true positive rates when a high percentage of DIF items favored the same group.
  • The OR method proved feasible for item matrix sampling designs, a limitation for LR and MH.
  • Simulation results were further supported by an empirical example.

Conclusions:

  • The odds ratio (OR) method offers a robust and effective alternative for detecting differential item functioning (DIF).
  • It excels in challenging scenarios, including high DIF item prevalence and missing data, and is suitable for item matrix sampling.
  • This method enhances the validity and comparability of test scores in psychometric and educational assessments.