Many studies on differential item functioning (DIF) detection rely on single detection methods (SDMs), each of which necessitates specific assumptions that may not always be validated. Using an inappropriate SDM can lead to diminished accuracy in DIF detection. To address this limitation, a novel multi-detector combination (MDC) approach is proposed. Unlike SDMs, MDC effectively evaluates the relevance of different SDMs under various test conditions and integrates them using supervised learning, thereby mitigating the risk associated with selecting a suboptimal SDM for DIF detection. This study aimed to validate the accuracy of the MDC approach by applying five types of SDMs and four distinct supervised learning methods in MDC modeling. Model performance was assessed using the area under the curve (AUC), which provided a comprehensive measure of the ability of the model to distinguish between classes across all threshold levels, with higher AUC values indicating higher accuracy. The MDC methods consistently achieved higher average AUC values compared to SDMs in both matched test sets (where test conditions align with the training set) and unmatched test sets. Furthermore, MDC outperformed all SDMs under each test condition. These findings indicated that MDC is highly accurate and robust across diverse test conditions, establishing it as a viable method for practical DIF detection.
Keywords: area under the curve; differential item functioning; multi-detector combination; supervised learning; test fairness.
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