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Detecting item bias with the Rasch model.

Richard M Smith¹

¹Data Recognition Corporation. rsmith.arm@att.net

Journal of Applied Measurement

|October 22, 2004

Summary

This study clarifies item bias definitions in Rasch measurement versus other models. It presents Rasch-based methods for detecting item bias, comparing them with traditional approaches using math test examples.

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Area of Science:

Psychometrics
Educational Measurement
Statistics

Background:

Item bias, or differential item functioning (DIF), is a critical concern in standardized testing.
Existing definitions of item bias vary across different measurement frameworks, leading to potential inconsistencies.
Rasch measurement offers a distinct perspective on item bias compared to true-score models and latent trait models.

Purpose of the Study:

To introduce and differentiate the concept of item bias within the Rasch measurement framework.
To compare the Rasch definition of item bias with those used in non-model-based and latent trait approaches.
To explore and evaluate methods for assessing item bias specifically within the Rasch model.

Main Methods:

Conceptual analysis of item bias definitions across measurement theories.
Description of Rasch measurement-based techniques for item bias detection.
Comparative analysis of Rasch separate calibration t-test and Mantel-Haenszel methods.
Application of methods to item bias assessment in mathematics tests.

Main Results:

The study highlights significant definitional differences in item bias between Rasch measurement and other models.
Rasch-based methods demonstrate power in detecting item bias.
Comparative examples illustrate the practical application and differences between Rasch and non-Rasch bias detection techniques.

Conclusions:

Understanding the nuanced definition of item bias in Rasch measurement is crucial for accurate assessment.
Rasch measurement provides robust tools for identifying item bias.
The comparison of methods underscores the importance of selecting appropriate techniques for bias detection in educational assessments.

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