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Published on: October 11, 2018
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Using multilabel classification neural network to detect intersectional DIF with small sample sizes
Summary
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.
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.
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