Rasch analysis of distractors in multiple-choice items.

In order to apply the Rasch model to multiple-choice items, incorrect responses to distractors are usually aggregated to a single category. In doing so, information of individual distractors disappears. In this paper, a Rasch-type analysis is proposed where one parameter is assigned to each distractor. The information is thus preserved. The proposed distractor model can be applied to investigate the performance of distractors, which is useful for item revision. This model is a necessary condition of the Rasch model, that is, fitting the distractor model will fit the Rasch model, but not vice versa. The results of a small simulation study show that parameter recovery of the distractor model is very satisfactory. A real data set of twenty multiple-choice items was analyzed. Some items were found to fit the Rasch model rather than the distractor model. It is this diagnostic value that makes the distractor model suitable for multiple-choice items.