Person Proficiency Estimates in the Dichotomous Rasch Model When Random Guessing Is Removed From Difficulty Estimates of Multiple Choice Items

Abstract

Andrich, Marais, and Humphry showed formally that Waller’s procedure that removes responses to multiple choice (MC) items that are likely to be guessed eliminates the bias in the Rasch model (RM) estimates of difficult items and makes them more difficult. The former did not study any consequences on the person proficiency estimates. This article shows that when the procedure is applied, the more proficient persons who are least likely to guess benefit by a greater amount than the less proficient, who are most likely to guess. This surprising result is explained by appreciating that the more proficient persons answer difficult items correctly at a greater rate than do the less proficient, even when the latter guess some items correctly. As a consequence, increasing the difficulty of the difficult items benefits them more than the less proficient persons. Analyses of a simulated and real example are shown illustratively. To not disadvantage the more proficient persons, it is suggested that Waller’s procedure be used when the RM is used to analyze MC items.