01685nas a2200145   4500008004100000245010000041210006900141300001000210490000700220520112900227653003401356100001601390700001501406856011801421       1999                        eng d00aThe effect of model misspecification on classification decisions made using a computerized test0 aeffect of model misspecification on classification decisions mad  a47-590 v363 aMany computerized testing algorithms require the fitting of some item response theory (IRT) model to examinees' responses to facilitate item selection, the determination of test stopping rules, and classification decisions. Some IRT models are thought to be particularly useful for small volume certification programs that wish to make the transition to computerized adaptive testing (CAT). The 1-parameter logistic model (1-PLM) is usually assumed to require a smaller sample size than the 3-parameter logistic model (3-PLM) for item parameter calibrations. This study examined the effects of model misspecification on the precision of the decisions made using the sequential probability ratio test. For this comparison, the 1-PLM was used to estimate item parameters, even though the items' characteristics were represented by a 3-PLM. Results demonstrate that the 1-PLM produced considerably more decision errors under simulation conditions similar to a real testing environment, compared to the true model and to a fixed-form standard reference set of items. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aKalohn, J C1 aSpray, J A  uhttp://mail.iacat.org/content/effect-model-misspecification-classification-decisions-made-using-computerized-test