2022/03/01 by Kuan‐Yu Jin, Wai‐Lok Siu, Xiaoting Huang · 1 citation
Computer Science · Decision Sciences · Social Sciences · #Advanced Statistical Modeling Techniques #Psychometric Methodologies and Testing #Student Assessment and Feedback
paper · pdf · doi:10.1111/jedm.12310
crossref issued 2022/03/01 · crossref published 2022/03/01 · crossref published-print 2022/03/01 · openalex publication_date 2022/03/01 · crossref published-online 2022/03/09 · crossref created 2022/03/10 · crossref deposited 2023/11/18 · openalex created_date 2025/10/10 · crossref indexed 2026/07/25 · openalex updated_date 2026/07/25
Abstract Multiple‐choice (MC) items are widely used in educational tests. Distractor analysis, an important procedure for checking the utility of response options within an MC item, can be readily implemented in the framework of item response theory (IRT). Although random guessing is a popular behavior of test‐takers when answering MC items, none of the existing IRT models for distractor analysis have considered the influence of random guessing in this process. In this article, we propose a new IRT model to distinguish the influence of random guessing from response option functioning. A brief simulation study was conducted to examine the parameter recovery of the proposed model. To demonstrate its effectiveness, the new model was applied to the mathematics tests of the Hong Kong Diploma of Secondary Education Examination (HKDSE) from 2015 to 2019. The results of empirical analyses suggest that the complexity of item contents is a key factor in inducing students’ random guessing. The implications and applications of the new model to other testing situations are also discussed.