A novel nonparametric framework for DIF detection using kernel-smoothed item response curves

TL;DR

This paper presents a new nonparametric method for detecting Differential Item Functioning in binary response data by comparing item response curves directly, offering a flexible alternative to traditional parametric models.

Contribution

It introduces a novel nonparametric framework with a new variance estimator and optimal weight functions, extending existing methods to binary data and complex DIF scenarios.

Findings

Effectively controls Type I error in simulations

Achieves comparable power to logistic regression

Detects subtle DIF patterns missed by parametric models

Abstract

This study introduces a novel nonparametric approach for detecting Differential Item Functioning (DIF) in binary items through direct comparison of Item Response Curves (IRCs). Building on prior work on nonparametric comparison of regression curves, we extend the methodology to accommodate binary response data, which is typical in psychometric applications. The proposed approach includes a new estimator of the asymptotic variance of the test statistic and derives optimal weight functions that maximise local power. Because the asymptotic distribution of the resulting test statistic is unknown, a wild bootstrap procedure is applied for inference. A Monte Carlo simulation study demonstrates that the nonparametric approach effectively controls Type I error and achieves power comparable to the traditional logistic regression method, outperforming it in cases with multiple intersections of the underlying IRCs. The impact of bandwidth and weight specification is explored. Application to a verbal aggression dataset further illustrates the method's ability to detect subtle DIF patterns missed by parametric models. Overall, the proposed nonparametric framework provides a flexible and powerful alternative for detecting DIF, particularly in complex scenarios where traditional model-based assumptions may not be applicable.