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Using multilabel classification neural network to detect intersectional DIF with small sample sizes.

Yale Quan1, Chun Wang1

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Summary
This summary is machine-generated.

InterDIFNet, a new neural network, effectively detects intersectional differential item functioning (DIF) in small samples. It outperforms existing methods in identifying complex DIF across multiple groups, enhancing assessment fairness.

Keywords:
machine learningmeasurement invarianceneural networkspsychometrics

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Artificial Intelligence

Background:

  • Traditional differential item functioning (DIF) methods often require large sample sizes.
  • Marginal DIF approaches may not capture complex effects of intersecting identities.

Purpose of the Study:

  • Introduce InterDIFNet, a novel neural network for detecting intersectional DIF.
  • Address limitations of existing methods in small sample sizes and complex group interactions.

Main Methods:

  • Developed InterDIFNet, a multilabel classification neural network.
  • Employed an optimized thresholding procedure for power and Type 1 error control.
  • Conducted Monte Carlo simulations comparing InterDIFNet with Truncated Lasso Penalty (TLP) and other intersectional DIF methods.

Main Results:

  • InterDIFNet demonstrated higher statistical power than TLP when trained with TLP features.
  • Maintained comparable Type 1 error control, especially with three or more intersectional groups.
  • Empirical application confirmed the practical utility of InterDIFNet in real assessment data.

Conclusions:

  • InterDIFNet offers a scalable, data-driven solution for identifying intersectional DIF.
  • The method is particularly effective for educational and psychological assessments with small sample sizes.
  • Provides a more nuanced approach to fairness in testing by considering intersecting identities.