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Summary
This summary is machine-generated.

This study introduces new statistics for detecting and quantifying response bias in test items. These methods offer a flexible and powerful approach for analyzing bias in categorical data.

Keywords:
DBFDIFDTFSIBTESTcrossing-SIBTESTdifferential bundle functioningdifferential item functioningdifferential test functioningeffect sizesitem response theoryresponse bias

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Response bias in standardized testing can impact score validity.
  • Existing methods for detecting bias may lack flexibility or optimal statistical properties.

Purpose of the Study:

  • To propose a novel model-based family of statistics for detecting and quantifying response bias in item bundles.
  • To introduce compensatory (CDRF) and non-compensatory (NCDRF) response bias measures.

Main Methods:

  • Development of model-based statistics linked to item response theory (IRT) estimation.
  • Utilizing multiple-group estimation for sample realizations and variability.
  • Comparison with SIBTEST and likelihood-based methods via Monte Carlo simulations.

Main Results:

  • The proposed CDRF and NCDRF statistics provide more optimal effect size estimates of marginal response bias than SIBTEST.
  • These new statistics are competitive with likelihood-based methods for item-level bias detection.
  • CDRF and NCDRF demonstrate superior performance in detecting differential bundle and test bias.

Conclusions:

  • The proposed model-based statistics offer a powerful and flexible approach to studying response bias in categorical data.
  • These statistics outperform existing methods in specific scenarios, particularly for bundle and test-level bias.
  • The findings contribute to more accurate and reliable measurement in educational and psychological assessments.