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

This study introduces InterDIFNet, a multilabel classification neural network for detecting intersectional differential item functioning (DIF) in educational and psychological assessments, with a focus on small sample sizes. Unlike traditional marginal DIF methods, which often fail to capture the effects of intersecting identities and require large samples, InterDIFNet models uniform and non-uniform DIF across multiple intersectional groups simultaneously and utilizes an optimized thresholding procedure to balance power and Type 1 error control. A Monte Carlo simulation compared InterDIFNet to the Truncated Lasso Penalty (TLP) test and other intersectional DIF methods across varying sample sizes, numbers of groups and DIF prevalence rates. Results show that when trained using TLP features, InterDIFNet consistently achieved higher power than TLP while maintaining comparable Type 1 error control, particularly in scenarios with three or more intersectional groups. An empirical application to real assessment data further demonstrated the method's practical utility. InterDIFNet provides a scalable, data-driven solution for identifying intersectional DIF across multiple small sample groups.