01557nas a2200157   4500008004100000020004100041245008400082210006900166250001500235300001100250490000700261520099300268100001601261700002301277856009901300       2010                        eng d  a0007-1102 (Print)0007-1102 (Linking)00aMarginal likelihood inference for a model for item responses and response times0 aMarginal likelihood inference for a model for item responses and  a2010/01/30  a603-260 v633 a<p>Marginal maximum-likelihood procedures for parameter estimation and testing the fit of a hierarchical model for speed and accuracy on test items are presented. The model is a composition of two first-level models for dichotomous responses and response times along with multivariate normal models for their item and person parameters. It is shown how the item parameters can easily be estimated using Fisher&#39;s identity. To test the fit of the model, Lagrange multiplier tests of the assumptions of subpopulation invariance of the item parameters (i.e., no differential item functioning), the shape of the response functions, and three different types of conditional independence were derived. Simulation studies were used to show the feasibility of the estimation and testing procedures and to estimate the power and Type I error rate of the latter. In addition, the procedures were applied to an empirical data set from a computerized adaptive test of language comprehension.</p>
1 aGlas, C A W1 avan der Linden, WJ  uhttp://iacat.org/content/marginal-likelihood-inference-model-item-responses-and-response-times00474nas a2200109   4500008004100000245008900041210007100130300001100201100001400212700002300226856011500249       2010                        eng d00aMultidimensional Adaptive Testing with Kullback–Leibler Information Item Selection0 aMultidimensional Adaptive Testing with Kullback–Leibler Informat  a77-1021 aMulder, J1 avan der Linden, WJ  uhttp://iacat.org/content/multidimensional-adaptive-testing-kullback%E2%80%93leibler-information-item-selection01899nas a2200169   4500008004100000020004100041245008600082210006900168250001500237260000800252300001200260490000700272520131100279100001401590700002301604856010201627       2009                        Eng d  a0033-3123 (Print)0033-3123 (Linking)00aMultidimensional Adaptive Testing with Optimal Design Criteria for Item Selection0 aMultidimensional Adaptive Testing with Optimal Design Criteria f  a2010/02/02  cJun  a273-2960 v743 aSeveral criteria from the optimal design literature are examined for use with item selection in multidimensional adaptive testing. In particular, it is examined what criteria are appropriate for adaptive testing in which all abilities are intentional, some should be considered as a nuisance, or the interest is in the testing of a composite of the abilities. Both the theoretical analyses and the studies of simulated data in this paper suggest that the criteria of A-optimality and D-optimality lead to the most accurate estimates when all abilities are intentional, with the former slightly outperforming the latter. The criterion of E-optimality showed occasional erratic behavior for this case of adaptive testing, and its use is not recommended. If some of the abilities are nuisances, application of the criterion of A(s)-optimality (or D(s)-optimality), which focuses on the subset of intentional abilities is recommended. For the measurement of a linear combination of abilities, the criterion of c-optimality yielded the best results. The preferences of each of these criteria for items with specific patterns of parameter values was also assessed. It was found that the criteria differed mainly in their preferences of items with different patterns of values for their discrimination parameters.1 aMulder, J1 avan der Linden, WJ  uhttp://iacat.org/content/multidimensional-adaptive-testing-optimal-design-criteria-item-selection01689nas a2200277   4500008004100000020001000041245006500051210006400116260009700180300001100277520074500288653002101033653002201054653002501076653002801101653002501129653001601154653001801170653005501188653001501243653001201258100001801270700002301288700001301311856008701324       2002                        eng d  a02-0900aMathematical-programming approaches to test item pool design0 aMathematicalprogramming approaches to test item pool design  aTwente, The NetherlandsbUniversity of Twente, Faculty of Educational Science and Technology  a93-1083 a(From the chapter) This paper presents an approach to item pool design that has the potential to improve on the quality of current item pools in educational and psychological testing and hence to increase both measurement precision and validity. The approach consists of the application of mathematical programming techniques to calculate optimal blueprints for item pools. These blueprints can be used to guide the item-writing process. Three different types of design problems are discussed, namely for item pools for linear tests, item pools computerized adaptive testing (CAT), and systems of rotating item pools for CAT. The paper concludes with an empirical example of the problem of designing a system of rotating item pools for CAT.10aAdaptive Testing10aComputer Assisted10aComputer Programming10aEducational Measurement10aItem Response Theory10aMathematics10aPsychometrics10aStatistical Rotation computerized adaptive testing10aTest Items10aTesting1 aVeldkamp, B P1 avan der Linden, WJ1 aAriel, A  uhttp://iacat.org/content/mathematical-programming-approaches-test-item-pool-design00495nas a2200097   4500008004100000245010600041210006900147260004100216100002300257856011700280       2002                        eng d00aModifications of the Sympson-Hetter method for item-exposure control in computerized adaptive testing0 aModifications of the SympsonHetter method for itemexposure contr  aManuscript submitted for publication1 avan der Linden, WJ  uhttp://iacat.org/content/modifications-sympson-hetter-method-item-exposure-control-computerized-adaptive-testing01155nas a2200133   4500008004100000245007100041210006900112300001200181490000700193520069200200100001800892700002300910856008800933       2002                        eng d00aMultidimensional adaptive testing with constraints on test content0 aMultidimensional adaptive testing with constraints on test conte  a575-5880 v673 aThe case of adaptive testing under a multidimensional response model with large numbers of constraints on the content of the test is addressed. The items in the test are selected using a shadow test approach. The 0–1 linear programming model that assembles the shadow tests maximizes posterior expected Kullback-Leibler information in the test. The procedure is illustrated for five different cases of multidimensionality. These cases differ in (a) the numbers of ability dimensions that are intentional or should be considered as ldquonuisance dimensionsrdquo and (b) whether the test should or should not display a simple structure with respect to the intentional ability dimensions.1 aVeldkamp, B P1 avan der Linden, WJ  uhttp://iacat.org/content/multidimensional-adaptive-testing-constraints-test-content00377nas a2200109   4500008004100000245005100041210005100092260001500143100001600158700002300174856007000197       2001                        eng d00aModeling variability in item parameters in CAT0 aModeling variability in item parameters in CAT  aSeattle WA1 aGlas, C A W1 avan der Linden, WJ  uhttp://iacat.org/content/modeling-variability-item-parameters-cat00610nas a2200109   4500008004100000245009500041210006900136260014400205100001800349700002300367856011000390       2000                        eng d00aMultidimensional adaptive testing with constraints on test content (Research Report 00-11)0 aMultidimensional adaptive testing with constraints on test conte  aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aVeldkamp, B P1 avan der Linden, WJ  uhttp://iacat.org/content/multidimensional-adaptive-testing-constraints-test-content-research-report-00-1101086nas a2200133   4500008004100000245007800041210006900119300001200188490000700200520059200207653003400799100002300833856009600856       1999                        eng d00aMultidimensional adaptive testing with a minimum error-variance criterion0 aMultidimensional adaptive testing with a minimum errorvariance c  a398-4120 v243 aAdaptive testing under a multidimensional logistic response model is addressed. An algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to evaluate. In addition, it is shown how the algorithm can be modified if the interest is in a test with a "simple ability structure". The statistical properties of the adaptive ML estimator are demonstrated for a two-dimensional item pool with several linear combinations of the abilities. 10acomputerized adaptive testing1 avan der Linden, WJ  uhttp://iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion01435nas a2200145   4500008004100000245005300041210005100094300001200145490000700157520098100164653003401145100002301179700001501202856007201217       1998                        eng d00aA model for optimal constrained adaptive testing0 amodel for optimal constrained adaptive testing  a259-2700 v223 aA model for constrained computerized adaptive testing is proposed in which the information in the test at the trait level (0) estimate is maximized subject to a number of possible constraints on the content of the test. At each item-selection step, a full test is assembled to have maximum information at the current 0 estimate, fixing the items already administered. Then the item with maximum in-formation is selected. All test assembly is optimal because a linear programming (LP) model is used that automatically updates to allow for the attributes of the items already administered and the new value of the 0 estimator. The LP model also guarantees that each adaptive test always meets the entire set of constraints. A simulation study using a bank of 753 items from the Law School Admission Test showed that the 0 estimator for adaptive tests of realistic lengths did not suffer any loss of efficiency from the presence of 433 constraints on the item selection process. 10acomputerized adaptive testing1 avan der Linden, WJ1 aReese, L M  uhttp://iacat.org/content/model-optimal-constrained-adaptive-testing00560nas a2200097   4500008004100000245010200041210006900143260010900212100002300321856011800344       1997                        eng d00aMultidimensional adaptive testing with a minimum error-variance criterion (Research Report 97-03)0 aMultidimensional adaptive testing with a minimum errorvariance c  aEnschede, The Netherlands: University of Twente, Department of Educational Measurement and Data Analysis1 avan der Linden, WJ  uhttp://iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion-research-report-97-0300419nas a2200097   4500008004100000245007800041210006900119260001200188100002300200856009800223       1997                        eng d00aMultidimensional adaptive testing with a minimum error-variance criterion0 aMultidimensional adaptive testing with a minimum errorvariance c  aChicago1 avan der Linden, WJ  uhttp://iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion-0