00933nas a2200133   4500008004100000245006700041210006500108260004700173490000700220520046100227100001800688700001400706856007900720       2011                        eng d00aA framework for the development of computerized adaptive tests0 aframework for the development of computerized adaptive tests  bPractical Assessment Research & Evaluation0 v163 aA substantial amount of research has been conducted over the past 40 years on technical aspects of computerized adaptive testing (CAT), such as item selection algorithms, item exposure controls, and termination criteria. However, there is little literature providing practical guidance on the development of a CAT. This paper seeks to collate some of the available research methodologies into a general framework for the development of any CAT assessment. 1 aThompson, N A1 aWeiss, DJ  uhttp://iacat.org/content/framework-development-computerized-adaptive-tests00387nas a2200109   4500008003900000245006500039210006400104260001200168490000700180100001800187856007200205       2011                          d00aJATT Special Issue on Adaptive Testing: Welcome and Overview0 aJATT Special Issue on Adaptive Testing Welcome and Overview  c05/20110 v121 aThompson, N A  uhttp://www.testpublishers.org/journal-of-applied-testing-technology01418nas a2200133   4500008004100000020001400041245005800055210005800113300001200171490000700183520099600190100001801186856008001204       2009                        eng d  a0013-164400aItem selection in computerized classification testing0 aItem selection in computerized classification testing  a778-7930 v693 aSeveral alternatives for item selection algorithms based on item response theory in computerized classification testing (CCT) have been suggested, with no conclusive evidence on the substantial superiority of a single method. It is argued that the lack of sizable effect is because some of the methods actually assess items very similarly through different calculations and will usually select the same item. Consideration of methods that assess information across a wider range is often unnecessary under realistic conditions, although it might be advantageous to utilize them only early in a test. In addition, the efficiency of item selection approaches depend on the termination criteria that are used, which is demonstrated through didactic example and Monte Carlo simulation. Item selection at the cut score, which seems conceptually appropriate for CCT, is not always the most efficient option. A broad framework for item selection in CCT is presented that incorporates these points. 1 aThompson, N A  uhttp://iacat.org/content/item-selection-computerized-classification-testing01062nas a2200109   4500008004100000245010200041210006900143260009700212520051300309100001800822856011200840       2009                        eng d00aThe MEDPRO project: An SBIR project for a comprehensive IRT and CAT software system: CAT software0 aMEDPRO project An SBIR project for a comprehensive IRT and CAT s  aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.3 aDevelopment of computerized adaptive tests (CAT) requires a number of appropriate software tools.  This paper describes the development of two new CAT software programs.  CATSIM has been designed specifically to conduct several different kinds of simulation studies, which are necessary for planning purposes as well as properly designing live CATs.  FastCAT is a software system for banking items and publishing CAT tests as standalone files, to be administered anywhere.  Both are available for public use.1 aThompson, N A  uhttp://iacat.org/content/medpro-project-sbir-project-comprehensive-irt-and-cat-software-system-cat-software02017nas a2200109   4500008004100000245007400041210006900115260009700184520151800281100001801799856009001817       2009                        eng d00aUtilizing the generalized likelihood ratio as a termination criterion0 aUtilizing the generalized likelihood ratio as a termination crit  aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.3 aComputer-based testing can be used to classify examinees into mutually exclusive groups.  Currently, the predominant psychometric algorithm for designing computerized classification tests (CCTs) is the sequential probability ratio test (SPRT; Reckase, 1983) based on item response theory (IRT).  The SPRT has been shown to be more efficient than confidence intervals around θ estimates as a method for CCT delivery (Spray & Reckase, 1996; Rudner, 2002).  More recently, it was demonstrated that the SPRT, which only uses fixed values, is less efficient than a generalized form which tests whether a given examinee’s θ is below θ1or above θ2 (Thompson, 2007).  This formulation allows the indifference region to vary based on observed data.  Moreover, this composite hypothesis formulation better represents the conceptual purpose of the test, which is to test whether θ is above or below the cutscore.  The purpose of this study was to explore the specifications of the new generalized likelihood ratio (GLR; Huang, 2004).  As with the SPRT, the efficiency of the procedure depends on the nominal error rates and the distance between θ1 and θ2 (Eggen, 1999).  This study utilized a monte-carlo approach, with 10,000 examinees simulated under each condition, to evaluate differences in efficiency and accuracy due to hypothesis structure, nominal error rate, and indifference region size.  The GLR was always at least as efficient as the fixed-point SPRT while maintaining equivalent levels of accuracy.  1 aThompson, N A  uhttp://iacat.org/content/utilizing-generalized-likelihood-ratio-termination-criterion00512nam a2200097   4500008004100000245010500041210006900146260006300215100001800278856011800296       2007                        eng d00aA comparison of two methods of polytomous computerized classification testing for multiple cutscores0 acomparison of two methods of polytomous computerized classificat  aUnpublished doctoral dissertation, University of Minnesota1 aThompson, N A  uhttp://iacat.org/content/comparison-two-methods-polytomous-computerized-classification-testing-multiple-cutscores00500nas a2200121   4500008004100000245006600041210006600107260005700173653003400230100001800264700001000282856008600292       2007                        eng d00aComputerized classification testing with composite hypotheses0 aComputerized classification testing with composite hypotheses  aSt. Paul, MNbGraduate Management Admissions Council10acomputerized adaptive testing1 aThompson, N A1 aRo, S  uhttp://iacat.org/content/computerized-classification-testing-composite-hypotheses00496nas a2200109   4500008004100000245006600041210006600107260009700173100001800270700001000288856008800298       2007                        eng d00aComputerized classification testing with composite hypotheses0 aComputerized classification testing with composite hypotheses  aD. J. Weiss (Ed.). Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aThompson, N A1 aRo, S  uhttp://iacat.org/content/computerized-classification-testing-composite-hypotheses-000465nas a2200109   4500008004100000245008900041210006900130260001700199100001000216700001800226856011100244       2007                        eng d00aCutscore location and classification accuracy in computerized classification testing0 aCutscore location and classification accuracy in computerized cl  aTokyo, Japan1 aRo, S1 aThompson, N A  uhttp://iacat.org/content/cutscore-location-and-classification-accuracy-computerized-classification-testing00382nas a2200097   4500008004100000245005800041210005800099260002700157100001800184856008200202       2007                        eng d00aItem selection in computerized classification testing0 aItem selection in computerized classification testing  aUniversity of Nebraska1 aThompson, N A  uhttp://iacat.org/content/item-selection-computerized-classification-testing-000449nas a2200109   4500008003900000245008500039210006900124300000900193490000700202100001800209856011200227       2007                          d00aA Practitioner’s Guide for Variable-length Computerized Classification Testing0 aPractitioner s Guide for Variablelength Computerized Classificat  a1-130 v121 aThompson, N A  uhttp://iacat.org/content/practitioner%E2%80%99s-guide-variable-length-computerized-classification-testing-101453nas a2200181   4500008004100000245008200041210006900123260001300192490000800205520080800213653000801021653001901029653003001048653003401078653004001112100001801152856010101170       2007                        eng d00aA practitioner's guide to variable-length computerized classification testing0 apractitioners guide to variablelength computerized classificatio  c7/1/20090 v12 3 aVariable-length computerized classification tests, CCTs, (Lin & Spray, 2000; Thompson, 2006) are a powerful and efficient approach to testing for the purpose of classifying examinees into groups. CCTs are designed by the specification of at least five technical components: psychometric model, calibrated item bank, starting point, item selection algorithm, and termination criterion. Several options exist for each of these CCT components, creating a myriad of possible designs. Confusion among designs is exacerbated by the lack of a standardized nomenclature. This article outlines the components of a CCT, common options for each component, and the interaction of options for different components, so that practitioners may more efficiently design CCTs. It also offers a suggestion of nomenclature. 10aCAT10aclassification10acomputer adaptive testing10acomputerized adaptive testing10aComputerized classification testing1 aThompson, N A  uhttp://iacat.org/content/practitioners-guide-variable-length-computerized-classification-testing