Bayesian IRT Guessing Models for Partial Guessing Behaviors

According to the recent Nation’s Report Card, 12th-graders failed to produce gains on the 2005 National Assessment of Educational Progress (NAEP) despite earning better grades on average. One possible explanation is that 12th-graders were not motivated taking the NAEP, which is a low-stakes test. We develop three Bayesian IRT mixture models to describe the results from a group of examinees including both nonguessers and partial guessers. The first assumes that the guesser answers questions based on his or her knowledge up to a certain test item, and guesses thereafter. The second model assumes that the guesser answers relatively easy questions based on his or her knowledge and guesses randomly on the remaining items. The third is constructed to describe more general low-motivation behavior. It assumes that the guesser gives less and less effort as he or she proceeds through the test. The models can provide not only consistent estimates of IRT parameters but also estimates of each examinee’s nonguesser/guesser status and degree of guessing behavior. We show results of a simulation study comparing the performance of the three guessing models to the 2PL-IRT model. Finally, an analysis of real data from a low-stakes test administered to university students is presented.