A Generalized Multi-Detector Combination Approach for Differential Item Functioning Detection.

ABSTRACT: 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 t

SUBMITTER: Huang S 

PROVIDER: S-EPMC11660104 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature