01802nas a2200241   4500008004100000245009300041210006900134300001200203490000700215520098200222653001801204653002101222653003001243653001901273653004601292653001801338653002501356653001501381100001401396700001901410700001501429856011601444       2003                        eng d00aThe relationship between item exposure and test overlap in computerized adaptive testing0 arelationship between item exposure and test overlap in computeri  a129-1450 v403 aThe purpose of this article is to present an analytical derivation for the mathematical form of an average between-test overlap index as a function of the item exposure index, for fixed-length computerized adaptive tests (CATs). This algebraic relationship is used to investigate the simultaneous control of item exposure at both the item and test levels. The results indicate that, in fixed-length CATs, control of the average between-test overlap is achieved via the mean and variance of the item exposure rates of the items that constitute the CAT item pool. The mean of the item exposure rates is easily manipulated. Control over the variance of the item exposure rates can be achieved via the maximum item exposure rate (r-sub(max)). Therefore, item exposure control methods which implement a specification of r-sub(max) (e.g., J. B. Sympson and R. D. Hetter, 1985) provide the most direct control at both the item and test levels. (PsycINFO Database Record (c) 2005 APA )10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aHuman Computer10aInteraction computerized adaptive testing10aItem Analysis10aItem Analysis (Test)10aTest Items1 aChen, S-Y1 aAnkenmann, R D1 aSpray, J A  uhttp://mail.iacat.org/content/relationship-between-item-exposure-and-test-overlap-computerized-adaptive-testing01685nas 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-test00621nas a2200133   4500008004100000245011200041210006900153260003800222653003400260653003800294100001500332700001700347856012300364       1987                        eng d00aThe effect of item parameter estimation error on decisions made using the sequential probability ratio test0 aeffect of item parameter estimation error on decisions made usin  aIowa City, IA. USAbDTIC Document10acomputerized adaptive testing10aSequential probability ratio test1 aSpray, J A1 aReckase, M D  uhttp://mail.iacat.org/content/effect-item-parameter-estimation-error-decisions-made-using-sequential-probability-ratio