Controlling the False Discovery Rate in DIF Detection With e-Values: Evidence From Multidimensional and Testlet Simulations

-value methods exhibited inflation of Type I error as N increased, whereas e-BH preserved stable error control due to its model-agnostic calibration. An empirical application using Progress in International Reading Literacy Study (PIRLS) data further demonstrated that e-BH produces a more defensible and operationally sustainable set of DIF flags than traditional approaches. Together, these results establish e-values as a powerful and robust evidential tool for DIF detection in modern assessment contexts.