ABSTRACT: An investigation into the optimal number of distractors in single-best answer exams
In UK medical schools, five-option single-best answer (SBA) questions are the most widely accepted format of summative knowledge assessment. However, writing SBA questions with four effective incorrect options is difficult and time consuming, and consequently, many SBAs contain a high frequency of implausible distractors. Previous research has suggested that fewer than five-options could hence be used for assessment, without deterioration in quality. Despite an existing body of empirical research in this area however, evidence from undergraduate medical education is sparse. The study investigated the frequency of non-functioning distractors in a sample of 480 summative SBA questions at Cardiff University. Distractor functionality was analysed, and then various question models were tested to investigate the impact of reducing the number of distractors per question on examination difficulty, reliability, discrimination and pass rates. A survey questionnaire was additionally administered to 108 students (33 % response rate) to gain insight into their perceptions of these models. The simulation of various exam models revealed that, for four and three-option SBA models, pass rates, reliability, and mean item discrimination remained relatively constant. The average percentage mark however consistently increased by 1-3 % with the four and three-option models, respectively. The questionnaire survey revealed that the student body had mixed views towards the proposed format change. This study is one of the first to comprehensively investigate distractor performance in SBA examinations in undergraduate medical education. It provides evidence to suggest that using three-option SBA questions would maximise efficiency whilst maintaining, or possibly improving, psychometric quality, through allowing a greater number of questions per exam paper.
via An investigation into the optimal number of distractors in single-best answer exams. – PubMed – NCBI.