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Detecting DIF with the Multi-Unidimensional Pairwise Preference Model: Lord's Chi-square and IPR-NCDIF Methods.

Lavanya S Kumar1, Naidan Tu2, Sean Joo3

  • 1Department of Psychology, University of South Florida, Tampa, FL, USA.

Applied Psychological Measurement
|July 4, 2025
PubMed
Summary
This summary is machine-generated.

Differential item functioning (DIF) detection methods were adapted for multidimensional forced choice (MFC) measures. Established methods like Lord's chi-square and item parameter replication (IPR) effectively detect DIF in MFC tests, offering reliable insights for noncognitive assessment.

Keywords:
differential item functioningitem response theorylinkingmeasurement invariancemulti-unidimensional pairwise preference modelmultidimensional forced choice

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Noncognitive Assessment

Background:

  • Multidimensional forced choice (MFC) measures are increasingly utilized in noncognitive assessment.
  • Limited research exists on detecting differential item functioning (DIF) within these MFC models.

Purpose of the Study:

  • To extend and evaluate two established DIF detection methods for MFC measures.
  • To investigate the performance of Lord's chi-square and item parameter replication (IPR) methods within the Multi-Unidimensional Pairwise Preference (MUPP) model.

Main Methods:

  • A Monte Carlo simulation was employed to examine Type I error rates and statistical power.
  • Key variables manipulated included sample size, impact, DIF source (discrimination, threshold, location), and DIF magnitude.

Main Results:

  • Both Lord's chi-square and IPR methods demonstrated consistent statistical power and controlled Type I error rates effectively across various conditions.
  • Lord's chi-square showed superior performance when DIF originated from statement discrimination, while IPR was better for statement threshold DIF.
  • Both methods performed comparably with better power when DIF originated from statement location.

Conclusions:

  • Established DIF detection methods are suitable for use with the MUPP model in MFC tests.
  • The choice between Lord's chi-square and IPR may depend on the specific source of DIF.
  • Recommendations for practical application and limitations of DIF detection in MFC measures are provided.