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Applying Logistic Regression to Detect Differential Item Functioning in Multidimensional Data.

Hui-Fang Chen1, Kuan-Yu Jin2

  • 1Social and Behavioral Sciences, City University of Hong Kong, Kowloon, Hong Kong.

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|August 14, 2018
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Summary
This summary is machine-generated.

This study found that using multiple subscores in logistic regression (LR) for differential item functioning (DIF) detection is superior for multidimensional assessments. It offers better control of false positive rates and higher true positive rates compared to total scores or single subscores.

Keywords:
differential item functioninggroup impactlogistic regressionmatching variablesmultidimensionality

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Assessment

Background:

  • Conventional differential item functioning (DIF) methods often assume unidimensionality and use total scores for matching, which is problematic for multidimensional assessments.
  • Multidimensional assessments are common in education and psychology, necessitating robust DIF detection methods that account for test structure.

Purpose of the Study:

  • To propose and evaluate a novel logistic regression (LR) approach for DIF detection using all subscores of a scale.
  • To compare the performance of the proposed method against traditional total score and single subscore matching methods.

Main Methods:

  • Simulated data with varying test structures, cross-loaded items, latent ability differences, DIF magnitudes, and sample sizes.
  • Focused on uniform DIF detection for a single studied item within a 21-item, two-dimensional scale.
  • Compared logistic regression (LR) using total scores, individual subscores, and all subscores as matching variables.

Main Results:

  • Conventional LR with total scores showed inflated false positive rates (FPRs) when groups differed in latent abilities, especially with contrasting abilities across dimensions.
  • LR with a single subscore performed adequately when group differences were minimal or uni-dimensional but yielded inflated FPRs with bi-dimensional ability differences.
  • The proposed LR using two subscores demonstrated well-controlled FPRs and the highest true positive rates (TPRs) across all simulated conditions.
  • For items measuring two domains, total score and two subscores approaches showed good FPR control, while single subscore led to inflated FPRs.

Conclusions:

  • The use of multiple subscores in logistic regression is recommended for differential item functioning detection in multidimensional data.
  • This approach provides a more accurate and reliable method for identifying DIF compared to traditional total score or single subscore matching.
  • Accurate DIF detection is crucial for maintaining the fairness and validity of educational and psychological assessments.