Enhancing Exam Question Quality in Medical Education Through Bootstrapping

Document Type

Article

Publication Date

8-1-2025

Publication Title

Anat Sci Educ

Abstract

Medical schools are required to assess and evaluate their curricula and to develop exam questions with strong reliability and validity evidence, often based on data derived from statistically small samples of medical students. Achieving a large enough sample to reliably and validly evaluate courses, assessments, and exam questions would require extensive data collection over many years, which is inefficient, especially in the fast-changing educational environment of medical schools. This article demonstrates how advanced quantitative methods, such as bootstrapping, can provide reliable data by resampling a single dataset to create many simulated samples. This economic approach, among others, allows for the creation of confidence intervals and, consequently, the accurate evaluation of exam questions as well as broader course and curriculum assessments. Bootstrapping offers a robust alternative to traditional methods, improving the psychometric quality of exam questions, and contributing to fair and valid assessments in medical education.

Volume

18

Issue

8

First Page

852

Last Page

857

DOI

10.1002/ase.2522

ISSN

1935-9780

PubMed ID

39425243

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