01598nas a2200157   4500008004100000245008200041210006900123300001100192490000700203520101400210653003401224653004001258100001601298700002401314856010201338       2000                        eng d00aComputerized adaptive testing for classifying examinees into three categories0 aComputerized adaptive testing for classifying examinees into thr  a713-340 v603 aThe objective of this study was to explore the possibilities for using computerized adaptive testing in situations in which examinees are to be classified into one of three categories.Testing algorithms with two different statistical computation procedures are described and evaluated. The first computation procedure is based on statistical testing and the other on statistical estimation. Item selection methods based on maximum information (MI) considering content and exposure control are considered. The measurement quality of the proposed testing algorithms is reported. The results of the study are that a reduction of at least 22% in the mean number of items can be expected in a computerized adaptive test (CAT) compared to an existing paper-and-pencil placement test. Furthermore, statistical testing is a promising alternative to statistical estimation. Finally, it is concluded that imposing constraints on the MI selection strategy does not negatively affect the quality of the testing algorithms10acomputerized adaptive testing10aComputerized classification testing1 aEggen, Theo1 aStraetmans, G J J M  uhttp://www.iacat.org/content/computerized-adaptive-testing-classifying-examinees-three-categories