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Summary
This summary is machine-generated.

Differential item functioning (DIF) compromises test validity. This study introduces generalized logistic regression (GLR) methods to detect DIF in multiple groups within cognitive diagnostic assessment, outperforming traditional methods.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Cognitive Diagnostic Assessment (CDA)

Background:

  • Differential item functioning (DIF) can undermine the validity and fairness of assessments.
  • Existing DIF detection methods primarily focus on two-group comparisons, limiting their application in complex, multi-group scenarios.
  • Detecting DIF in cognitive diagnostic assessment (CDA) across multiple groups remains an under-researched area.

Purpose of the Study:

  • To investigate the efficacy of generalized logistic regression (GLR) methods for detecting DIF in multi-group CDA settings.
  • To compare the performance of GLR-based Wald test (GLR-Wald) and GLR-based likelihood ratio test (GLR-LRT) against the ordinary Wald test.
  • To evaluate the impact of using estimated attribute profiles as matching criteria in GLR-based DIF detection.

Main Methods:

  • Employed generalized logistic regression (GLR) to detect DIF items.
  • Utilized estimated attribute profiles as matching criteria within the GLR framework.
  • Conducted a simulation study comparing GLR-Wald, GLR-LRT, and the ordinary Wald test, supplemented by a real data analysis.

Main Results:

  • Both GLR-Wald and GLR-LRT demonstrated superior control of Type I error rates compared to the ordinary Wald test across most simulated conditions.
  • The GLR methods yielded higher empirical rejection rates, indicating greater power in detecting DIF items than the ordinary Wald test.
  • Employing estimated attribute profiles as matching criteria resulted in comparable Type I error rates and empirical rejection rates for GLR-Wald and GLR-LRT.

Conclusions:

  • The GLR method, particularly with estimated attribute profiles, offers a robust approach for detecting DIF in multi-group CDA.
  • GLR-based tests (Wald and LRT) provide improved accuracy and power for DIF detection compared to the ordinary Wald test in multi-group contexts.
  • These findings support the application of GLR methods for enhancing the validity and fairness of cognitive diagnostic assessments across diverse groups.