title: An assessment of functioning and non-functioning distractors in multiple-choice questions: a descriptive analysis.
abstract: BACKGROUND
  Four- or five-option multiple choice questions (MCQs) are the standard in health-science disciplines, both on certification-level examinations and on in-house developed tests. Previous research has shown, however, that few MCQs have three or four functioning distractors. The purpose of this study was to investigate non-functioning distractors in teacher-developed tests in one nursing program in an English-language university in Hong Kong.
  
  
  METHODS
  Using item-analysis data, we assessed the proportion of non-functioning distractors on a sample of seven test papers administered to undergraduate nursing students. A total of 514 items were reviewed, including 2056 options (1542 distractors and 514 correct responses). Non-functioning options were defined as ones that were chosen by fewer than 5% of examinees and those with a positive option discrimination statistic.
  
  
  RESULTS
  The proportion of items containing 0, 1, 2, and 3 functioning distractors was 12.3%, 34.8%, 39.1%, and 13.8% respectively. Overall, items contained an average of 1.54 (SD = 0.88) functioning distractors. Only 52.2% (n = 805) of all distractors were functioning effectively and 10.2% (n = 158) had a choice frequency of 0. Items with more functioning distractors were more difficult and more discriminating.
  
  
  CONCLUSIONS
  The low frequency of items with three functioning distractors in the four-option items in this study suggests that teachers have difficulty developing plausible distractors for most MCQs. Test items should consist of as many options as is feasible given the item content and the number of plausible distractors; in most cases this would be three. Item analysis results can be used to identify and remove non-functioning distractors from MCQs that have been used in previous tests.
authors: Department of Nursing Studies, Li Ka Shing Faculty of Medicine, 21 Sassoon Road, Hong Kong, PR China. tarrantm@hku.hk
authors: Marie
authors: M
authors: Tarrant
authors: James
authors: J
authors: Ware
authors: Ahmed M
authors: AM
authors: Mohammed
mesh_terms: D000704:Analysis of Variance; D004506:Education, Nursing; D004521:Educational Measurement; D007722:Health Knowledge, Attitudes, Practice; D006723:Hong Kong; D006801:Humans; D017145:Models, Educational; D011594:Psychometrics; D013223:Statistics as Topic; D013663:Teaching / Q000379:methods*