Evaluation of exam questions using bootstrapping: Practical applications in R and SPSS with a case study.

Abstract

This article presents a step-by-step guide to using R and SPSS to bootstrap exam questions. Bootstrapping, a versatile nonparametric analytical technique, can help to improve the psychometric qualities of exam questions in the process of quality assurance. Bootstrapping is particularly useful in disciplines such as medical education, where student cohorts are normally too small to reliably use parametric analysis to evaluate the quality of exam questions. Traditional parametric approaches need large samples; otherwise, they can yield unreliable estimates of metrics such as item difficulty and point-biserial correlations with small cohorts, potentially misleading the evaluation of exam questions and consequently leading to flawed assessments. By employing bootstrapping, educators can resample data to obtain robust confidence intervals for key metrics. This allows for a more accurate evaluation of question quality. This guide provides a step-by-step approach using R and SPSS, along with explaining the necessary code to bootstrap exam question means, standard deviations, item difficulty, and point-biserial correlations. In addition, the code includes automated visualizations and the capability to export results in reader-friendly tables, enhancing time efficiency and streamlining both data analysis and presentation processes. Furthermore, this article includes a case study in which the code is applied and the results are discussed to showcase how bootstrapping can inform decisions regarding exam question revisions.