Estimation of trait level in computerized adaptive testing

In computerized adaptive testing (CAT), an examinee's trait level (theta) must be estimated with reasonable accuracy based on a small number of item responses. A successful implementation of CAT depends on (1) the accuracy of statistical methods used far estimating theta and (2) the efficiency of the item-selection criterion. Methods of estimating theta suitable far CAT are reviewed, and the differences between Fisher and Kullback-Leibler information criteria for selecting items are discussed. The accuracy of different CAT algorithms was examined in an empirical study. The results showed that correcting a estimates for bias was necessary at earlier stages of CAT, but most CAT algorithms performed equally well for tests of 10 or more items.