01931nas a2200229   4500008004100000245007200041210006900113300001200182490000700194520116000201653001801361653002101379653003001400653001801430653002501448653002501473653005701498653002201555100001501577700001201592856009701604       2003                        eng d00aComputerized adaptive testing using the nearest-neighbors criterion0 aComputerized adaptive testing using the nearestneighbors criteri  a204-2160 v273 aItem selection procedures designed for computerized adaptive testing need to accurately estimate every taker's trait level (θ) and, at the same time, effectively use all items in a bank. Empirical studies showed that classical item selection procedures based on maximizing Fisher or other related information yielded highly varied item exposure rates; with these procedures, some items were frequently used whereas others were rarely selected. In the literature, methods have been proposed for controlling exposure rates; they tend to affect the accuracy in θ estimates, however. A modified version of the maximum Fisher information (MFI) criterion, coined the nearest neighbors (NN) criterion, is proposed in this study. The NN procedure improves to a moderate extent the undesirable item exposure rates associated with the MFI criterion and keeps sufficient precision in estimates. The NN criterion will be compared with a few other existing methods in an empirical study using the mean squared errors in θ estimates and plots of item exposure rates associated with different distributions. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aStatistical Analysis10aStatistical Estimation computerized adaptive testing10aStatistical Tests1 aCheng, P E1 aLiou, M  uhttp://www.iacat.org/content/computerized-adaptive-testing-using-nearest-neighbors-criterion01801nas a2200241   4500008004100000245009300041210006900134300001200203490000700215520098200222653001801204653002101222653003001243653001901273653004601292653001801338653002501356653001501381100001401396700001901410700001501429856011501444       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://www.iacat.org/content/relationship-between-item-exposure-and-test-overlap-computerized-adaptive-testing02085nas a2200229   4500008004100000245012900041210006900170300001200239490000700251520124000258653001801498653002101516653004501537653003001582653001801612653002501630653002601655100001601681700001401697700001901711856012501730       2002                        eng d00aA comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model0 acomparison of item selection techniques and exposure control mec  a147-1630 v263 aThe use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aAlgorithms computerized adaptive testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aMathematical Modeling1 aPastor, D A1 aDodd, B G1 aChang, Hua-Hua  uhttp://www.iacat.org/content/comparison-item-selection-techniques-and-exposure-control-mechanisms-cats-using-generalized01432nas a2200193   4500008004100000245006300041210006300104300001200167490000700179520079500186653001800981653002100999653003001020653001801050653005701068100001501125700001201140856008601152       2000                        eng d00aEstimation of trait level in computerized adaptive testing0 aEstimation of trait level in computerized adaptive testing  a257-2650 v243 aNotes that in computerized adaptive testing (CAT), a examinee's trait level (θ) 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 for estimating θ and (2) the efficiency of the item-selection criterion. Methods of estimating θ suitable for 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 show that correcting θ estimates for bias was necessary at earlier stages of CAT, but most CAT algorithms performed equally well for tests of 10 or more items. (PsycINFO Database Record (c) 2005 APA )10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis10aStatistical Estimation computerized adaptive testing1 aCheng, P E1 aLiou, M  uhttp://www.iacat.org/content/estimation-trait-level-computerized-adaptive-testing