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Factorial modeling of differential distractor functioning in multiple-choice items.

W C Wang1

  • 1Department of Psychology, National Chung Cheng University, Chia-Yi 621, Taiwan. psywcw@ccunix.ccu.edu.tw

Journal of Applied Measurement
|May 25, 2002
PubMed
Summary

This study introduces a new method using general linear models to detect differential distractor functioning in multiple-choice tests. The approach effectively identifies item parameter variations across different student groups.

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistical Modeling

Background:

  • Differential item functioning (DIF) analysis is crucial for test fairness.
  • Existing methods may not fully capture nuanced distractor performance variations.
  • Investigating how distractors function differently across examinee groups is essential.

Purpose of the Study:

  • To propose a novel factorial procedure for analyzing differential distractor functioning (DDF).
  • To model distractor performance using general linear models within a DIF framework.
  • To assess the effectiveness of the proposed method in identifying DDF.

Main Methods:

  • A factorial procedure based on general linear models was developed.
  • Grouping factors were treated as independent variables, and item parameters as dependent variables.

Related Experiment Videos

  • Distractibility parameters for each distractor were modeled, including main and interaction effects.

    • Main Results:

    • Simulation studies demonstrated excellent recovery of the proposed model parameters.
    • The factorial procedure successfully partitioned distractibility parameters across groups.
    • Analysis of a real test dataset (1997 Taiwan Joint College Entrance Examination) validated the method.

    Conclusions:

    • The proposed factorial procedure offers a robust framework for investigating DDF.
    • This method enhances the understanding of item performance variations across examinee subgroups.
    • The findings have implications for improving the quality and fairness of multiple-choice assessments.