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Differential Item Functioning via Robust Scaling.

Peter F Halpin¹

¹University of North Carolina at Chapel Hill, 100 E Cameron Ave, Office 1070G, Chapel Hill, NC, 27514, USA. peter.halpin@unc.edu.

|May 4, 2024

Summary

This summary is machine-generated.

This study introduces a novel method for detecting differential item functioning (DIF) in item response theory (IRT) models without needing predefined anchor items. The approach reframes DIF as outlier detection, offering a robust alternative for psychometric analysis.

Keywords:

differential item functioning item response theory robust statistics test scaling and equating

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Area of Science:

Psychometrics
Educational Measurement
Statistics

Background:

Differential Item Functioning (DIF) is crucial for unbiased testing.
Current DIF detection methods often rely on pre-specified anchor items, limiting their applicability.
Item Response Theory (IRT) provides a framework for analyzing item and person characteristics.

Purpose of the Study:

To propose a novel method for assessing DIF in IRT models.
To develop a DIF detection technique that does not require anchor item pre-specification.
To enhance the robustness and efficiency of DIF analysis.

Main Methods:

Re-formulating DIF detection as an outlier detection problem within IRT scaling.
Utilizing robust statistics, specifically a redescending M-estimator, for IRT parameter estimation.
Tuning the estimator to control the asymptotic type I error rate for DIF detection.

Main Results:

Theoretical analysis demonstrates the estimator's efficiency without DIF and robustness with DIF.
Simulation studies indicate the proposed method outperforms existing DIF detection approaches.
A real data example showcases the method's practical utility in scenarios lacking anchor items.

Conclusions:

The proposed robust statistics-based method offers a viable alternative for DIF detection without anchor items.
This approach enhances the reliability of psychometric assessments, particularly in complex research settings.
The method is primarily focused on the two-parameter logistic model but has potential for broader applications.