Effects of estimation bias on multiple-category classification with an IRT-based adaptive classification procedure

The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following results were found. (a) The Bayesian estimators were more likely to misclassify examinees into an inward category because of their inward biases, when a fixed start value of zero was assigned to every examinee. (b) When moderately accurate start values were available, however, Bayesian estimators produced classifications that were slightly more accurate than was the maximum likelihood estimator or weighted likelihood estimator. Expected a posteriori was the procedure that produced the most accurate results among the three Bayesian methods. (c) All five estimators produced equivalent efficiencies in terms of number of items required, which was 50 or more items except for abilities that were less than -2.00 or greater than 2.00.