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Relationship Between Item Difficulty and Discrimination Indices in True/False-Type Multiple Choice Questions of a Para-clinical Multidisciplinary Paper
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Introduction: This paper reports the relationship between the difficulty level and the discrimination power of true/false-type multiple-choice questions (MCQs) in a multidisciplinary paper for the para-clinical year of an undergraduate medical programme. Materials and Methods: MCQ items in papers taken from Year II Parts A, B and C examinations for Sessions 2001/02, and Part B examinations for 2002/03 and 2003/04, were analysed to obtain their difficulty indices and discrimination indices. Each paper consisted of 250 true/false items (50 questions of 5 items each) on topics drawn from different disciplines. The questions were first constructed and vetted by the individual departments before being submitted to a central committee, where the final selection of the MCQs was made, based purely on the academic judgement of the committee. Results: There was a wide distribution of item difficulty indices in all the MCQ papers analysed. Furthermore, the relationship between the difficulty index (P) and discrimination index (D) of the MCQ items in a paper was not linear, but more dome-shaped. Maximal discrimination (D = 51% to 71%) occurred with moderately easy/difficult items (P = 40% to 74%). On average, about 38% of the MCQ items in each paper were “very easy” (P≥75%), while about 9% were “very difficult” (P <25%). About two-thirds of these very easy/difficult items had “very poor” or even negative discrimination (D ≤20%). Conclusions: MCQ items that demonstrate good discriminating potential tend to be moderately difficult items, and the moderately-to-very difficult items are more likely to show negative discrimination. There is a need to evaluate the effectiveness of our MCQ items. Key words: Assessment methods, Educational measurement, Item analysis, Year II medical test
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“Other authors have reported a significant linear and dome-shaped relationship between difficulty and the discrimination index [6, 56].”
From AI in medical education: uses of AI in construction type A MCQs (2024) · cites this paper as [56]
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Additional file 1 of Large language models for generating medical examinations: systematic review
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Additional file 3 of Large language models for generating medical examinations: systematic review
Artsi, Y., Sorin, V., Konen, E., Glicksberg, B. S., Nadkarni, G., & Klang, E. (2024) · Figshare · doi:10.6084/m9.figshare.26699645
Additional file 2 of Large language models for generating medical examinations: systematic review
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Metadata
- Authors
- Sim, S.-M., & Rasiah, R. I.
- Affiliations
- University of Malaya (MY)
- ORCID
- Sim, Si-Mui
- Venue
- Annals of the Academy of Medicine, Singapore
- Publisher
- Academy of Medicine, Singapore
- Published
- 2006-01-15
- Volume
- 35
- Issue
- 2
- Pages
- 67-71
- ISSN
- 0304-4602 (ISSN-L)
- OpenAlex
- W2141521353
- Cited by
- 196
- References
- 10
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 10 · 2023: 10 · 2022: 14 · 2021: 25 · 2020: 19 · 2019: 12 · 2018: 13 · 2017: 18 · 2016: 12 · 2015: 10 · 2014: 8 · 2013: 5 · 2012: 5
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.um.edu.my
- Locations
- Annals of the Academy of Medicine Singapore (publishedVersion)
- PubMed (publishedVersion)
- The University of Malaya Research Repository (University of Malaya) (submittedVersion)
- Topics
- Social Sciences › Education › Innovations in Medical Education
- Social Sciences › Education › Educational Assessment and Pedagogy
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- item difficulty, item discrimination, item analysis, negative discrimination, undergraduate medical education
- MeSH terms
- Adult, Choice Behavior, Education, Medical, Undergraduate, Educational Measurement, Humans
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