Improving Measures via Examining the Behavior of Distractors in Multiple-Choice Tests

Abstract

The purpose of the present article was to illustrate, using an example from a national assessment, the value from analyzing the behavior of distractors in measures that engage the multiple-choice format. A secondary purpose of the present article was to illustrate four remedial actions that can potentially improve the measurement of the construct(s) under study. Participants were 2,248 individuals who took a national examination of chemistry. The behavior of the distractors was analyzed by modeling their behavior within the Rasch model. Potentially informative distractors were (a) further modeled using the partial credit model, (b) split onto separate items and retested for model fit and parsimony, (c) combined to form a “super” item or testlet, and (d) reexamined after deleting low-ability individuals who likely guessed on those informative, albeit erroneous, distractors. Results indicated that all but the item split strategies were associated with better model fit compared with the original model. The best fitted model, however, involved modeling and crediting informative distractors via the partial credit model or eliminating the responses of low-ability individuals who likely guessed on informative distractors. The implications, advantages, and disadvantages of modeling informative distractors for measurement purposes are discussed.