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Impacts of DIF Item Balance and Effect Size Incorporation With the Rasch Tree.

Nana Amma Berko Asamoah1, Ronna C Turner1, Wen-Juo Lo1

  • 1University of Arkansas, Fayetteville, AR, USA.

Educational and Psychological Measurement
|September 29, 2025
PubMed
Summary
This summary is machine-generated.

The Rasch tree method effectively detects differential item functioning (DIF) with balanced groups and large effect sizes. Its accuracy decreases with unbalanced groups and increased contamination, especially in small samples.

Keywords:
Rasch treecontaminationdifferential item functioningeffect sizeitem balancetest fairness

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Fairness in assessments is crucial, necessitating robust methods for detecting differential item functioning (DIF).
  • The Rasch tree method offers a flexible, model-based approach to DIF detection without pre-specifying groups.
  • Limited research exists on its performance under realistic conditions, particularly with unbalanced DIF.

Purpose of the Study:

  • To evaluate the Rasch tree method's DIF detection performance.
  • To investigate the impact of DIF balance, magnitude, sample size, test length, and contamination.
  • To compare statistical significance with effect size heuristics for DIF detection.

Main Methods:

  • Simulated data were used to assess the Rasch tree method.
  • Key factors manipulated included DIF balance, magnitude, sample size, test length, and contamination.
  • The Educational Testing Service effect size heuristic was incorporated as a performance criterion.

Main Results:

  • The Rasch tree method showed higher true DIF detection rates under balanced DIF conditions and large magnitudes.
  • Accuracy decreased with unbalanced DIF and higher contamination levels.
  • Using effect size reduced the detection of negligible DIF, while smaller samples yielded the lowest detection rates.

Conclusions:

  • The Rasch tree method is sensitive to DIF balance and magnitude.
  • DIF group imbalance and contamination significantly impact detection accuracy.
  • Recommendations are provided for optimizing DIF detection in practical assessment settings, emphasizing caution with small samples.