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Related Experiment Videos

Modeling effects of differential item functioning in polytomous 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 23, 2002
PubMed
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This study introduces factorial differential item functioning (DIF) analysis for polytomous items, extending traditional methods. The new approach effectively identifies bias across multiple groups and factors in educational and psychological assessments.

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistics

Background:

  • Differential item functioning (DIF) analysis is crucial for detecting item bias in assessments.
  • Traditional DIF methods primarily focus on dichotomous items and single grouping factors.
  • Analyzing polytomous items with multiple grouping factors requires advanced statistical approaches.

Purpose of the Study:

  • To extend conventional two-group DIF analysis to factorial DIF analysis for polytomous items.
  • To jointly analyze multiple grouping factors, each with multiple groups.
  • To provide a robust framework for detecting complex sources of item bias.

Main Methods:

  • Utilized a general linear model (GLM) framework.
  • Treated item parameters as dependent variables and grouping factors as independent variables.

Related Experiment Videos

  • Reparameterized item parameters into grand item parameters and DIF parameters (main and interaction effects).

    • Main Results:

    • Simulation studies demonstrated satisfactory recovery of proposed model parameters.
    • The factorial DIF model was successfully applied to a real dataset of 10 polytomous items and 1924 subjects.
    • The method effectively identified main and interaction effects of grouping factors on item parameters.

    Conclusions:

    • The proposed factorial DIF analysis is a viable extension for polytomous items with multiple grouping factors.
    • This methodology enhances the ability to detect nuanced forms of differential item functioning.
    • The findings have significant implications for test fairness and accurate measurement in diverse populations.