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Differential item functioning detection across multiple groups.

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  • 1Department of Economics and Statistics, University of Udine, Udine, Italy.

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Summary
This summary is machine-generated.

This study introduces a new method for detecting differential item functioning (DIF) and converting scales simultaneously. This approach improves accuracy by accounting for DIF during scale conversion, enhancing psychometric analysis.

Keywords:
DIFequatinginvariancelinkingminimax concave penaltymultiple groupspenalized likelihood

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Item Response Theory (IRT)

Background:

  • Differential item functioning (DIF) analysis requires converting item parameters to a common metric across groups.
  • Simultaneous scale conversion and DIF detection are challenging due to their interdependent nature.

Purpose of the Study:

  • To propose a novel method for simultaneously performing scale conversion and DIF detection.
  • To develop an approach where scale conversion automatically accounts for DIF.

Main Methods:

  • A novel method integrating scale conversion and DIF detection.
  • Penalized likelihood estimation for automatic selection of DIF items.
  • Simultaneous estimation of item parameters and scale conversion factors.

Main Results:

  • The proposed method effectively integrates scale conversion and DIF detection.
  • Automatic selection of DIF items using penalized likelihood estimation.
  • Real-data and simulation studies demonstrate the method's good performance.

Conclusions:

  • The novel simultaneous approach improves the accuracy of DIF detection and scale conversion.
  • This method provides a more robust way to analyze group differences in item parameters.
  • The findings have implications for fair and accurate assessment across diverse populations.