Item and test analysis to identify quality multiple choice questions (MCQS) from an assessment of medical students of Ahmedabad, Gujarat
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BACKGROUND: Multiple choice questions (MCQs) are frequently used to assess students in different educational streams for their objectivity and wide reach of coverage in less time. However, the MCQs to be used must be of quality which depends upon its difficulty index (DIF I), discrimination index (DI) and distracter efficiency (DE). OBJECTIVE: To evaluate MCQs or items and develop a pool of valid items by assessing with DIF I, DI and DE and also to revise/ store or discard items based on obtained results. SETTINGS: Study was conducted in a medical school of Ahmedabad. MATERIALS AND METHODS: An internal examination in Community Medicine was conducted after 40 hours teaching during 1(st) MBBS which was attended by 148 out of 150 students. Total 50 MCQs or items and 150 distractors were analyzed. STATISTICAL ANALYSIS: Data was entered and analyzed in MS Excel 2007 and simple proportions, mean, standard deviations, coefficient of variation were calculated and unpaired t test was applied. RESULTS: Out of 50 items, 24 had "good to excellent" DIF I (31 - 60%) and 15 had "good to excellent" DI (> 0.25). Mean DE was 88.6% considered as ideal/ acceptable and non functional distractors (NFD) were only 11.4%. Mean DI was 0.14. Poor DI (< 0.15) with negative DI in 10 items indicates poor preparedness of students and some issues with framing of at least some of the MCQs. Increased proportion of NFDs (incorrect alternatives selected by < 5% students) in an item decrease DE and makes it easier. There were 15 items with 17 NFDs, while rest items did not have any NFD with mean DE of 100%. CONCLUSION: Study emphasizes the selection of quality MCQs which truly assess the knowledge and are able to differentiate the students of different abilities in correct manner.
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Such findings were reported in anatomy exam [48], community Medicine [49], and multidisciplinary summative exams [50, 51] that were constructed traditionally.
“Such findings were reported in anatomy exam [48], community Medicine [49], and multidisciplinary summative exams [50, 51] that were constructed traditionally.”
From AI in medical education: uses of AI in construction type A MCQs (2024) · cites this paper as [49]
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Mustaffer, N. H., Ramli, N. S., Tongkhao, K., Vangnai, K., Rosni, N. K., & Rukayadi, Y. (2021) · Food Research · doi:10.26656/fr.2017.5(4).668 · Cited by 1 · see citing works
- Authors
- Kumar, P., Gajjar, S., Sharma, R., & Rana, M.
- Corresponding author
- Kumar, Pradeep
- Venue
- Indian Journal of Community Medicine
- Published
- 2014
- Volume
- 39
- Issue
- 1
- Pages
- 17
- ISSN
- 0970-0218 (ISSN-L)
- PMC
- PMC3968575
- OpenAlex
- W2044844953
- Cited by
- 140
- References
- 7
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 10 · 2023: 13 · 2022: 16 · 2021: 15 · 2020: 13 · 2019: 8 · 2018: 15 · 2017: 18 · 2016: 11 · 2015: 2 · 2014: 3
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- Indian Journal of Community Medicine (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › 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
- multiple-choice questions, item analysis, test analysis, item difficulty index, discrimination index, distractor efficiency, medical student assessment, item quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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