01306nas a2200169   4500008004100000245009500041210006900136300001200205490000700217520070700224653003400931100001400965700001600979700001600995700001701011856010801028       2002                        eng d00aEvaluation of selection procedures for computerized adaptive testing with polytomous items0 aEvaluation of selection procedures for computerized adaptive tes  a393-4110 v263 aIn the present study, a procedure that has been used to select dichotomous items in computerized adaptive testing was applied to polytomous items. This procedure was designed to select the item with maximum weighted information. In a simulation study, the item information function was integrated over a fixed interval of ability values and the item with the maximum area was selected. This maximum interval information item selection procedure was compared to a maximum point information item selection procedure. Substantial differences between the two item selection procedures were not found when computerized adaptive tests were evaluated on bias and the root mean square of the ability estimate. 10acomputerized adaptive testing1 aRijn, P W1 aEggen, Theo1 aHemker, B T1 aSanders, P F  uhttp://iacat.org/content/evaluation-selection-procedures-computerized-adaptive-testing-polytomous-items01594nas a2200157   4500008004100000245008200041210006900123300001100192490000700203520101400210653003401224653004001258100001601298700002401314856009801338       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://iacat.org/content/computerized-adaptive-testing-classifying-examinees-three-categories