00576nas a2200109 4500008004200000245016600042210006900208100002400277700002000301700001900321856012600340 In Press d 00aMeasurement efficiency for fixed-precision multidimensional computerized adaptive tests: Comparing health measurement and educational testing using example banks0 aMeasurement efficiency for fixedprecision multidimensional compu1 aPaap, Muirne, C. S.1 aBorn, Sebastian1 aBraeken, Johan uhttp://www.iacat.org/measurement-efficiency-fixed-precision-multidimensional-computerized-adaptive-tests-comparing-health01806nas a2200145 4500008003900000245012400039210006900163300001000232490000700242520130500249100001801554700002401572700001901596856004501615 2020 d00aMultidimensional Test Assembly Using Mixed-Integer Linear Programming: An Application of Kullback–Leibler Information0 aMultidimensional Test Assembly Using MixedInteger Linear Program a17-320 v443 aMany educational testing programs require different test forms with minimal or no item overlap. At the same time, the test forms should be parallel in terms of their statistical and content-related properties. A well-established method to assemble parallel test forms is to apply combinatorial optimization using mixed-integer linear programming (MILP). Using this approach, in the unidimensional case, Fisher information (FI) is commonly used as the statistical target to obtain parallelism. In the multidimensional case, however, FI is a multidimensional matrix, which complicates its use as a statistical target. Previous research addressing this problem focused on item selection criteria for multidimensional computerized adaptive testing (MCAT). Yet these selection criteria are not directly transferable to the assembly of linear parallel test forms. To bridge this gap the authors derive different statistical targets, based on either FI or the Kullback–Leibler (KL) divergence, that can be applied in MILP models to assemble multidimensional parallel test forms. Using simulated item pools and an item pool based on empirical items, the proposed statistical targets are compared and evaluated. Promising results with respect to the KL-based statistical targets are presented and discussed.1 aDebeer, Dries1 avan Rijn, Peter, W.1 aAli, Usama, S. uhttps://doi.org/10.1177/014662161982758601729nas a2200145 4500008003900000245016600039210006900205300001000274490000700284520118400291100002401475700002001499700001901519856004501538 2019 d00aMeasurement Efficiency for Fixed-Precision Multidimensional Computerized Adaptive Tests: Comparing Health Measurement and Educational Testing Using Example Banks0 aMeasurement Efficiency for FixedPrecision Multidimensional Compu a68-830 v433 aIt is currently not entirely clear to what degree the research on multidimensional computerized adaptive testing (CAT) conducted in the field of educational testing can be generalized to fields such as health assessment, where CAT design factors differ considerably from those typically used in educational testing. In this study, the impact of a number of important design factors on CAT performance is systematically evaluated, using realistic example item banks for two main scenarios: health assessment (polytomous items, small to medium item bank sizes, high discrimination parameters) and educational testing (dichotomous items, large item banks, small- to medium-sized discrimination parameters). Measurement efficiency is evaluated for both between-item multidimensional CATs and separate unidimensional CATs for each latent dimension. In this study, we focus on fixed-precision (variable-length) CATs because it is both feasible and desirable in health settings, but so far most research regarding CAT has focused on fixed-length testing. This study shows that the benefits associated with fixed-precision multidimensional CAT hold under a wide variety of circumstances.1 aPaap, Muirne, C. S.1 aBorn, Sebastian1 aBraeken, Johan uhttps://doi.org/10.1177/014662161876571901810nas a2200133 4500008003900000245010200039210006900141300001200210490000700222520136300229100001901592700002001611856004501631 2019 d00aMultidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models0 aMultidimensional Computerized Adaptive Testing Using NonCompensa a464-4800 v433 aCurrent use of multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback–Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback–Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees’ abilities, which shed light on item bank construction for non-compensatory items.1 aHsu, Chia-Ling1 aWang, Wen-Chung uhttps://doi.org/10.1177/014662161880028000564nas a2200145 4500008003900000245008100039210006900120100002400189700002200213700001600235700001900251700002300270700002500293856010000318 2018 d00aMeasuring patient-reported outcomes adaptively: Multidimensionality matters!0 aMeasuring patientreported outcomes adaptively Multidimensionalit1 aPaap, Muirne, C. S.1 aKroeze, Karel, A.1 aGlas, C A W1 aTerwee, C., B.1 avan der Palen, Job1 aVeldkamp, Bernard, P uhttp://www.iacat.org/measuring-patient-reported-outcomes-adaptively-multidimensionality-matters02108nas a2200169 4500008004100000245005200041210005100093260005500144520156900199653002101768653000801789653002301797100001501820700001901835700001301854856007101867 2017 eng d00aMHK-MST Design and the Related Simulation Study0 aMHKMST Design and the Related Simulation Study aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The MHK is a national standardized exam that tests and rates Chinese language proficiency. It assesses non-native Chinese minorities’ abilities in using the Chinese language in their daily, academic and professional lives; Computerized multistage adaptive testing (MST) is a combination of conventional paper-and-pencil (P&P) and item level computerized adaptive test (CAT), it is a kind of test forms based on computerized technology, take the item set as the scoring unit. It can be said that, MST estimate the Ability extreme value more accurate than conventional paper-and-pencil (P&P), also used the CAT auto-adapted characteristic to reduce the examination length and the score time of report. At present, MST has used in some large test, like Uniform CPA Examination and Graduate Record Examination(GRE). Therefore, it is necessary to develop the MST of application in China.

Based on consideration of the MHK characteristics and its future development, the researchers start with design of MHK-MST. This simulation study is conducted to validate the performance of the MHK -MST system. Real difficulty parameters of MHK items and the simulated ability parameters of the candidates are used to generate the original score matrix and the item modules are delivered to the candidates following the adaptive procedures set according to the path rules. This simulation study provides a sound basis for the implementation of MHK-MST.

Session Video

10alanguage testing10aMHK10amultistage testing1 aYuyu, Ling1 aChenglin, Zhou1 aJie, Ren uhttp://www.iacat.org/mhk-mst-design-and-related-simulation-study-003876nas a2200193 4500008004100000245007700041210006900118260005500187520320800242653000803450653002103458653001603479100002003495700001803515700002003533700003803553700002003591856007103611 2017 eng d00aMulti-stage Testing for a Multi-disciplined End-of primary-school Test 0 aMultistage Testing for a Multidisciplined Endof primaryschool Te aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The Dutch secondary education system consists of five levels: basic, lower, and middle vocational education, general secondary education, and pre-academic education. The individual decision for level of secondary education is based on a combination of the teacher’s judgment and an end-of-primaryschool placement test.

This placement test encompasses the measurement of reading, language, mathematics and writing; each skill consisting of one to four subdomains. The Dutch end-of-primaryschool test is currently administered in two linear 200-item paper-based versions. The two versions differ in difficulty so as to motivate both less able and more able students, and measure both groups of students precisely. The primary goal of the test is providing a placement advice for five levels of secondary education. The secondary goal is the assessment of six different fundamental reference levels defined on reading, language, and mathematics. Because of the high stakes advice of the test, the Dutch parliament has instructed to change the format to a multistage test. A major advantage of multistage testing is that the tailoring of the tests is more strongly related to the ability of the students than to the teacher’s judgment. A separate multistage test is under development for each of the three skills measured by the reference levels to increase the classification accuracy for secondary education placement and to optimally measure the performance on the reference-level-related skills.

This symposium consists of three presentations discussing the challenges in transitioning from a linear paper-based test to a computer-based multistage test within an existing curriculum and the specification of the multistage test to meet the measurement purposes. The transitioning to a multistage test has to improve both classification accuracy and measurement precision.

First, we describe the Dutch educational system and the role of the end-of-primary-school placement test within this system. Special attention will be paid to the advantages of multistage testing over both linear testing and computerized adaptive testing, and on practical implications related to the transitioning from a linear to a multistage test.

Second, we discuss routing and reporting on the new multi-stage test. Both topics have a major impact on the quality of the placement advice and the reference mastery decisions. Several methods for routing and reporting are compared.

Third, the linear test contains 200 items to cover a broad range of different skills and to obtain a precise measurement of those skills separately. Multistage testing creates opportunities to reduce the cognitive burden for the students while maintaining the same quality of placement advice and assessment of mastering of reference levels. This presentation focuses on optimal allocation of items to test modules, optimal number of stages and modules per stage and test length reduction.

Session Video 1

Session Video 2

10amst10aMultidisciplined10aproficiency1 aStraat, Hendrik1 aGroen, Maaike1 aZijlstra, Wobbe1 aKeizer-Mittelhaëuser, Marie-Anne1 aLamoré, Michel uhttps://drive.google.com/open?id=1C5ys178p_Wl9eemQuIsI56IxDTck2z8P01950nas a2200121 4500008003900000245011400039210006900153300001200222490000700234520151600241100001801757856005301775 2016 d00aMaximum Likelihood Score Estimation Method With Fences for Short-Length Tests and Computerized Adaptive Tests0 aMaximum Likelihood Score Estimation Method With Fences for Short a289-3010 v403 aA critical shortcoming of the maximum likelihood estimation (MLE) method for test score estimation is that it does not work with certain response patterns, including ones consisting only of all 0s or all 1s. This can be problematic in the early stages of computerized adaptive testing (CAT) administration and for tests short in length. To overcome this challenge, test practitioners often set lower and upper bounds of theta estimation and truncate the score estimation to be one of those bounds when the log likelihood function fails to yield a peak due to responses consisting only of 0s or 1s. Even so, this MLE with truncation (MLET) method still cannot handle response patterns in which all harder items are correct and all easy items are incorrect. Bayesian-based estimation methods such as the modal a posteriori (MAP) method or the expected a posteriori (EAP) method can be viable alternatives to MLE. The MAP or EAP methods, however, are known to result in estimates biased toward the center of a prior distribution, resulting in a shrunken score scale. This study introduces an alternative approach to MLE, called MLE with fences (MLEF). In MLEF, several imaginary “fence” items with fixed responses are introduced to form a workable log likelihood function even with abnormal response patterns. The findings of this study suggest that, unlike MLET, the MLEF can handle any response patterns and, unlike both MAP and EAP, results in score estimates that do not cause shrinkage of the theta scale.1 aHan, Kyung, T uhttp://apm.sagepub.com/content/40/4/289.abstract01299nas a2200145 4500008003900000022001400039245009100053210006900144300001300213490000700226520083800233100002101071700002001092856004101112 2016 d a1745-398400aModeling Student Test-Taking Motivation in the Context of an Adaptive Achievement Test0 aModeling Student TestTaking Motivation in the Context of an Adap a86–1050 v533 aThis study examined the utility of response time-based analyses in understanding the behavior of unmotivated test takers. For the data from an adaptive achievement test, patterns of observed rapid-guessing behavior and item response accuracy were compared to the behavior expected under several types of models that have been proposed to represent unmotivated test taking behavior. Test taker behavior was found to be inconsistent with these models, with the exception of the effort-moderated model. Effort-moderated scoring was found to both yield scores that were more accurate than those found under traditional scoring, and exhibit improved person fit statistics. In addition, an effort-guided adaptive test was proposed and shown by a simulation study to alleviate item difficulty mistargeting caused by unmotivated test taking.1 aWise, Steven, L.1 aKingsbury, Gage uhttp://dx.doi.org/10.1111/jedm.1210201362nas a2200145 4500008003900000022001400039245005800053210005800111300001400169490000700183520095400190100001901144700001201163856004101175 2016 d a1745-398400aMonitoring Items in Real Time to Enhance CAT Security0 aMonitoring Items in Real Time to Enhance CAT Security a131–1510 v533 aAn IRT-based sequential procedure is developed to monitor items for enhancing test security. The procedure uses a series of statistical hypothesis tests to examine whether the statistical characteristics of each item under inspection have changed significantly during CAT administration. This procedure is compared with a previously developed CTT-based procedure through simulation studies. The results show that when the total number of examinees is fixed both procedures can control the rate of type I errors at any reasonable significance level by choosing an appropriate cutoff point and meanwhile maintain a low rate of type II errors. Further, the IRT-based method has a much lower type II error rate or more power than the CTT-based method when the number of compromised items is small (e.g., 5), which can be achieved if the IRT-based procedure can be applied in an active mode in the sense that flagged items can be replaced with new items.1 aZhang, Jinming1 aLi, Jie uhttp://dx.doi.org/10.1111/jedm.1210401549nas a2200145 4500008003900000245010400039210006900143300001200212490000700224520104200231100002601273700002601299700002501325856005301350 2016 d00aMultidimensional Computerized Adaptive Testing for Classifying Examinees With Within-Dimensionality0 aMultidimensional Computerized Adaptive Testing for Classifying E a387-4040 v403 aA classification method is presented for adaptive classification testing with a multidimensional item response theory (IRT) model in which items are intended to measure multiple traits, that is, within-dimensionality. The reference composite is used with the sequential probability ratio test (SPRT) to make decisions and decide whether testing can be stopped before reaching the maximum test length. Item-selection methods are provided that maximize the determinant of the information matrix at the cutoff point or at the projected ability estimate. A simulation study illustrates the efficiency and effectiveness of the classification method. Simulations were run with the new item-selection methods, random item selection, and maximization of the determinant of the information matrix at the ability estimate. The study also showed that the SPRT with multidimensional IRT has the same characteristics as the SPRT with unidimensional IRT and results in more accurate classifications than the latter when used for multidimensional data.1 avan Groen, Maaike, M.1 aEggen, Theo, J. H. M.1 aVeldkamp, Bernard, P uhttp://apm.sagepub.com/content/40/6/387.abstract02104nas a2200133 4500008003900000022001400039245013800053210006900191300001200260490000700272520163500279100001501914856004101929 2014 d a1745-398400aMultidimensional CAT Item Selection Methods for Domain Scores and Composite Scores With Item Exposure Control and Content Constraints0 aMultidimensional CAT Item Selection Methods for Domain Scores an a18–380 v513 a

The intent of this research was to find an item selection procedure in the multidimensional computer adaptive testing (CAT) framework that yielded higher precision for both the domain and composite abilities, had a higher usage of the item pool, and controlled the exposure rate. Five multidimensional CAT item selection procedures (minimum angle; volume; minimum error variance of the linear combination; minimum error variance of the composite score with optimized weight; and Kullback-Leibler information) were studied and compared with two methods for item exposure control (the Sympson-Hetter procedure and the fixed-rate procedure, the latter simply refers to putting a limit on the item exposure rate) using simulated data. The maximum priority index method was used for the content constraints. Results showed that the Sympson-Hetter procedure yielded better precision than the fixed-rate procedure but had much lower item pool usage and took more time. The five item selection procedures performed similarly under Sympson-Hetter. For the fixed-rate procedure, there was a trade-off between the precision of the ability estimates and the item pool usage: the five procedures had different patterns. It was found that (1) Kullback-Leibler had better precision but lower item pool usage; (2) minimum angle and volume had balanced precision and item pool usage; and (3) the two methods minimizing the error variance had the best item pool usage and comparable overall score recovery but less precision for certain domains. The priority index for content constraints and item exposure was implemented successfully.

1 aYao, Lihua uhttp://dx.doi.org/10.1111/jedm.1203201865nas a2200121 4500008003900000245012200039210006900161300001400230490000700244520142300251100001501674856005401689 2013 d00aMutual Information Item Selection Method in Cognitive Diagnostic Computerized Adaptive Testing With Short Test Length0 aMutual Information Item Selection Method in Cognitive Diagnostic a1017-10350 v733 a

Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models aim at classifying examinees into the correct mastery profile group so as to pinpoint the strengths and weakness of each examinee whereas CAT algorithms choose items to determine those strengths and weakness as efficiently as possible. Most of the existing CD-CAT item selection algorithms are evaluated when test length is relatively long whereas several applications of CD-CAT, such as in interim assessment, require an item selection algorithm that is able to accurately recover examinees’ mastery profile with short test length. In this article, we introduce the mutual information item selection method in the context of CD-CAT and then provide a computationally easier formula to make the method more amenable in real time. Mutual information is then evaluated against common item selection methods, such as Kullback–Leibler information, posterior weighted Kullback–Leibler information, and Shannon entropy. Based on our simulations, mutual information consistently results in nearly the highest attribute and pattern recovery rate in more than half of the conditions. We conclude by discussing how the number of attributes, Q-matrix structure, correlations among the attributes, and item quality affect estimation accuracy.

1 aWang, Chun uhttp://epm.sagepub.com/content/73/6/1017.abstract01899nas a2200157 4500008003900000245009300039210007100132300001200203490000700215520139800222100001401620700002101634700001601655700001701671856005301688 2012 d00aA Mixture Rasch Model–Based Computerized Adaptive Test for Latent Class Identification0 aMixture Rasch Model–Based Computerized Adaptive Test for Latent  a469-4930 v363 a

This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback–Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was large, all item selection methods did not differ evidently in terms of accuracy in classifying examinees into different latent classes and estimating latent ability. However, when item separation was small, two methods with class-specific ability estimates performed better than the other two methods based on a single latent ability estimate across all latent classes. The three types of KL information distributions were compared. The KL and the reversed KL information could be the same or different depending on the ability level and the item difficulty difference between latent classes. Although the KL information and the reversed KL information were different at some ability levels and item difficulty difference levels, the use of the KL, the reversed KL, or the adaptive KL information did not affect the results substantially due to the symmetric distribution of item difficulty differences between latent classes in the simulated item pools. Item pool usage and classification convergence points were examined as well.

1 aHong Jiao1 aMacready, George1 aLiu, Junhui1 aCho, Youngmi uhttp://apm.sagepub.com/content/36/6/469.abstract00464nas a2200133 4500008003900000245007200039210006900111300001200180490000700192100002400199700002000223700001900243856006800262 2012 d00aMultistage Computerized Adaptive Testing With Uniform Item Exposure0 aMultistage Computerized Adaptive Testing With Uniform Item Expos a118-1410 v251 aEdwards, Michael, C1 aFlora, David, B1 aThissen, David uhttp://www.tandfonline.com/doi/abs/10.1080/08957347.2012.66036300447nas a2200121 4500008004500000245006900045210006900114300001100183490000600194100001400200700001800214856009300232 2011 Engldsh 00aMeasuring Individual Growth With Conventional and Adaptive Tests0 aMeasuring Individual Growth With Conventional and Adaptive Tests a80-1010 v21 aWeiss, DJ1 aVon Minden, S uhttp://www.iacat.org/content/measuring-individual-growth-conventional-and-adaptive-tests01257nas a2200181 4500008004100000245008300041210006900124260001200193520063300205653000800838653002600846653000900872653003500881653001600916653001400932100002300946856010600969 2011 eng d00aMoving beyond Efficiency to Allow CAT to Provide Better Diagnostic Information0 aMoving beyond Efficiency to Allow CAT to Provide Better Diagnost c10/20113 a
Future CATs will provide better diagnostic information to
–Examinees
–Regulators, Educators, Employers
–Test Developers
This goal will be accomplished by
–Smart CATs which collect additional information during the test
–Psychomagic
The time is now for Reporting
10aCAT10adianostic information10aMIRT10aMultiple unidimensional scales10apsychomagic10asmart CAT1 aBontempo, Brian, D uhttp://www.iacat.org/content/moving-beyond-efficiency-allow-cat-provide-better-diagnostic-information00481nas a2200109 4500008004100000245008100041210006900122260004900191100001400240700001500254856010200269 2010 eng d00aManual for CATSim: Comprehensive simulation of computerized adaptive testing0 aManual for CATSim Comprehensive simulation of computerized adapt aSt. Paul, MNbAssessment Systems Corporation1 aWeiss, DJ1 aGuyer, R D uhttp://www.iacat.org/content/manual-catsim-comprehensive-simulation-computerized-adaptive-testing01561nas a2200157 4500008004100000020004100041245008400082210006900166250001500235300001100250490000700261520099300268100001601261700002301277856010301300 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

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'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.

1 aGlas, C A W1 avan der Linden, WJ uhttp://www.iacat.org/content/marginal-likelihood-inference-model-item-responses-and-response-times00450nas a2200109 4500008004100000245007500041210006900116300001200185100002500197700002400222856009400246 2010 eng d00aMATHCAT: A Flexible Testing System in Mathematics Education for Adults0 aMATHCAT A Flexible Testing System in Mathematics Education for A a137-1501 aVerschoor, Angela, J1 aStraetmans, G J J M uhttp://www.iacat.org/content/mathcat-flexible-testing-system-mathematics-education-adults01871nas a2200157 4500008003900000245008900039210006900128300001200197490000700209520135600216100002501572700001601597700002101613700002601634856005301660 2010 d00aA Method for the Comparison of Item Selection Rules in Computerized Adaptive Testing0 aMethod for the Comparison of Item Selection Rules in Computerize a438-4520 v343 a

In a typical study comparing the relative efficiency of two item selection rules in computerized adaptive testing, the common result is that they simultaneously differ in accuracy and security, making it difficult to reach a conclusion on which is the more appropriate rule. This study proposes a strategy to conduct a global comparison of two or more selection rules. A plot showing the performance of each selection rule for several maximum exposure rates is obtained and the whole plot is compared with other rule plots. The strategy was applied in a simulation study with fixed-length CATs for the comparison of six item selection rules: the point Fisher information, Fisher information weighted by likelihood, Kullback-Leibler weighted by likelihood, maximum information stratification with blocking, progressive and proportional methods. Our results show that there is no optimal rule for any overlap value or root mean square error (RMSE). The fact that a rule, for a given level of overlap, has lower RMSE than another does not imply that this pattern holds for another overlap rate. A fair comparison of the rules requires extensive manipulation of the maximum exposure rates. The best methods were the Kullback-Leibler weighted by likelihood, the proportional method, and the maximum information stratification method with blocking.

1 aBarrada, Juan Ramón1 aOlea, Julio1 aPonsoda, Vicente1 aAbad, Francisco José uhttp://apm.sagepub.com/content/34/6/438.abstract00561nas a2200145 4500008003900000245015500039210006900194300001200263490000700275100001700282700001400299700001800313700001600331856006800347 2010 d00aA Monte Carlo Simulation Investigating the Validity and Reliability of Ability Estimation in Item Response Theory with Speeded Computer Adaptive Tests0 aMonte Carlo Simulation Investigating the Validity and Reliabilit a230-2610 v101 aSchmitt, T A1 aSass, D A1 aSullivan, J R1 aWalker, C M uhttp://www.tandfonline.com/doi/abs/10.1080/15305058.2010.48809800478nas a2200109 4500008004100000245008900041210007100130300001100201100001400212700002300226856011900249 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://www.iacat.org/content/multidimensional-adaptive-testing-kullback%E2%80%93leibler-information-item-selection00581nas a2200121 4500008004400000245017600044210006900220300001000289490000700299100001200306700001500318856012600333 2010 Germdn 00aMultidimensionale adaptive Kompetenzdiagnostik: Ergebnisse zur Messeffizienz [Multidimensional adaptive testing of competencies: Results regarding measurement efficiency].0 aMultidimensionale adaptive Kompetenzdiagnostik Ergebnisse zur Me a40-510 v561 aFrey, A1 aSeitz, N-N uhttp://www.iacat.org/content/multidimensionale-adaptive-kompetenzdiagnostik-ergebnisse-zur-messeffizienz-multidimensional00410nas a2200121 4500008004100000245005400041210005100095300001200146100001700158700001800175700001500193856008000208 2010 eng d00aMultistage Testing: Issues, Designs, and Research0 aMultistage Testing Issues Designs and Research a355-3721 aZenisky, A L1 aHambleton, RK1 aLuecht, RM uhttp://www.iacat.org/content/multistage-testing-issues-designs-and-research01654nas a2200289 4500008004100000020004100041245011100082210006900193250001500262260000800277300001100285490000700296520053700303653004800840653006200888653005700950653001101007653002701018653002401045653005101069653004701120653003101167653001301198100001301211700001901224856012101243 2009 eng d a0007-1102 (Print)0007-1102 (Linking)00aThe maximum priority index method for severely constrained item selection in computerized adaptive testing0 amaximum priority index method for severely constrained item sele a2008/06/07 cMay a369-830 v623 aThis paper introduces a new heuristic approach, the maximum priority index (MPI) method, for severely constrained item selection in computerized adaptive testing. Our simulation study shows that it is able to accommodate various non-statistical constraints simultaneously, such as content balancing, exposure control, answer key balancing, and so on. Compared with the weighted deviation modelling method, it leads to fewer constraint violations and better exposure control while maintaining the same level of measurement precision.10aAptitude Tests/*statistics & numerical data10aDiagnosis, Computer-Assisted/*statistics & numerical data10aEducational Measurement/*statistics & numerical data10aHumans10aMathematical Computing10aModels, Statistical10aPersonality Tests/*statistics & numerical data10aPsychometrics/*statistics & numerical data10aReproducibility of Results10aSoftware1 aCheng, Y1 aChang, Hua-Hua uhttp://www.iacat.org/content/maximum-priority-index-method-severely-constrained-item-selection-computerized-adaptive03147nas a2200457 4500008004100000020004100041245015500082210006900237250001500306260000800321300001200329490000700341520174100348653002502089653001902114653002502133653003002158653001502188653003602203653001002239653002102249653003302270653001102303653001102314653000902325653001802334653001702352653001902369653001602388100001502404700001002419700001502429700002502444700001802469700001702487700001602504700001602520700001402536700001602550856012302566 2009 eng d a0962-9343 (Print)0962-9343 (Linking)00aMeasuring global physical health in children with cerebral palsy: Illustration of a multidimensional bi-factor model and computerized adaptive testing0 aMeasuring global physical health in children with cerebral palsy a2009/02/18 cApr a359-3700 v183 aPURPOSE: The purposes of this study were to apply a bi-factor model for the determination of test dimensionality and a multidimensional CAT using computer simulations of real data for the assessment of a new global physical health measure for children with cerebral palsy (CP). METHODS: Parent respondents of 306 children with cerebral palsy were recruited from four pediatric rehabilitation hospitals and outpatient clinics. We compared confirmatory factor analysis results across four models: (1) one-factor unidimensional; (2) two-factor multidimensional (MIRT); (3) bi-factor MIRT with fixed slopes; and (4) bi-factor MIRT with varied slopes. We tested whether the general and content (fatigue and pain) person score estimates could discriminate across severity and types of CP, and whether score estimates from a simulated CAT were similar to estimates based on the total item bank, and whether they correlated as expected with external measures. RESULTS: Confirmatory factor analysis suggested separate pain and fatigue sub-factors; all 37 items were retained in the analyses. From the bi-factor MIRT model with fixed slopes, the full item bank scores discriminated across levels of severity and types of CP, and compared favorably to external instruments. CAT scores based on 10- and 15-item versions accurately captured the global physical health scores. CONCLUSIONS: The bi-factor MIRT CAT application, especially the 10- and 15-item versions, yielded accurate global physical health scores that discriminated across known severity groups and types of CP, and correlated as expected with concurrent measures. The CATs have potential for collecting complex data on the physical health of children with CP in an efficient manner.10a*Computer Simulation10a*Health Status10a*Models, Statistical10aAdaptation, Psychological10aAdolescent10aCerebral Palsy/*physiopathology10aChild10aChild, Preschool10aFactor Analysis, Statistical10aFemale10aHumans10aMale10aMassachusetts10aPennsylvania10aQuestionnaires10aYoung Adult1 aHaley, S M1 aNi, P1 aDumas, H M1 aFragala-Pinkham, M A1 aHambleton, RK1 aMontpetit, K1 aBilodeau, N1 aGorton, G E1 aWatson, K1 aTucker, C A uhttp://www.iacat.org/content/measuring-global-physical-health-children-cerebral-palsy-illustration-multidimensional-bi01207nas a2200109 4500008004100000245010200041210006900143260009700212520065700309100001500966856011600981 2009 eng d00aThe MEDPRO project: An SBIR project for a comprehensive IRT and CAT software system: IRT 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 aIRTPRO (Item Response Theory for Patient-Reported Outcomes) is an entirely new application for item calibration and test scoring using IRT. IRTPRO implements algorithms for maximum likelihood estimation of item parameters (item calibration) for several unidimensional and multidimensional item response theory (IRT) models for dichotomous and polytomous item responses. In addition, the software provides computation of goodness-of-fit indices, statistics for the diagnosis of local dependence and for the detection of differential item functioning (DIF), and IRT scaled scores. This paper illustrates the use, and some capabilities, of the software.1 aThissen, D uhttp://www.iacat.org/content/medpro-project-sbir-project-comprehensive-irt-and-cat-software-system-irt-software01066nas a2200109 4500008004100000245010200041210006900143260009700212520051300309100001800822856011600840 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://www.iacat.org/content/medpro-project-sbir-project-comprehensive-irt-and-cat-software-system-cat-software01524nas a2200181 4500008004100000020001400041245007400055210006900129300001200198490000800210520093200218653001401150653002401164653002301188100001501211700001901226856009701245 2009 eng d a0377-221700aA mixed integer programming model for multiple stage adaptive testing0 amixed integer programming model for multiple stage adaptive test a342-3500 v1933 aThe last decade has seen paper-and-pencil (P&P) tests being replaced by computerized adaptive tests (CATs) within many testing programs. A CAT may yield several advantages relative to a conventional P&P test. A CAT can determine the questions or test items to administer, allowing each test form to be tailored to a test taker's skill level. Subsequent items can be chosen to match the capability of the test taker. By adapting to a test taker's ability, a CAT can acquire more information about a test taker while administering fewer items. A Multiple Stage Adaptive test (MST) provides a means to implement a CAT that allows review before the administration. The MST format is a hybrid between the conventional P&P and CAT formats. This paper presents mixed integer programming models for MST assembly problems. Computational results with commercial optimization software will be given and advantages of the models evaluated.10aEducation10aInteger programming10aLinear programming1 aEdmonds, J1 aArmstrong, R D uhttp://www.iacat.org/content/mixed-integer-programming-model-multiple-stage-adaptive-testing01383nas a2200145 4500008004100000020001300041245012000054210006900174300001000243490000700253520082700260100001201087700001501099856012301114 2009 eng d a0191491X00aMultidimensional adaptive testing in educational and psychological measurement: Current state and future challenges0 aMultidimensional adaptive testing in educational and psychologic a89-940 v353 aThe paper gives an overview of multidimensional adaptive testing (MAT) and evaluates its applicability in educational and psychological testing. The approach of Segall (1996) is described as a general framework for MAT. The main advantage of MAT is its capability to increase measurement efficiency. In simulation studies conceptualizing situations typical to large scale assessments, the number of presented items was reduced by MAT by about 30–50% compared to unidimensional adaptive testing and by about 70% compared to fixed item testing holding measurement precision constant. Empirical results underline these findings. Before MAT is used routinely some open questions should be answered first. After that, MAT represents a very promising approach to highly efficient simultaneous testing of multiple competencies.1 aFrey, A1 aSeitz, N-N uhttp://www.iacat.org/content/multidimensional-adaptive-testing-educational-and-psychological-measurement-current-state01903nas a2200169 4500008004100000020004100041245008600082210006900168250001500237260000800252300001200260490000700272520131100279100001401590700002301604856010601627 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://www.iacat.org/content/multidimensional-adaptive-testing-optimal-design-criteria-item-selection01773nas a2200145 4500008003900000245006900039210006900108300001000177490000700187520131500194100002501509700002501534700001601559856005201575 2009 d00aMultiple Maximum Exposure Rates in Computerized Adaptive Testing0 aMultiple Maximum Exposure Rates in Computerized Adaptive Testing a58-730 v333 a

Computerized adaptive testing is subject to security problems, as the item bank content remains operative over long periods and administration time is flexible for examinees. Spreading the content of a part of the item bank could lead to an overestimation of the examinees' trait level. The most common way of reducing this risk is to impose a maximum exposure rate (rmax) that no item should exceed. Several methods have been proposed with this aim. All of these methods establish a single value of rmax throughout the test. This study presents a new method, the multiple-rmax method, that defines as many values of rmax as the number of items presented in the test. In this way, it is possible to impose a high degree of randomness in item selection at the beginning of the test, leaving the administration of items with the best psychometric properties to the moment when the trait level estimation is most accurate. The implementation of the multiple-r max method is described and is tested in simulated item banks and in an operative bank. Compared with a single maximum exposure method, the new method has a more balanced usage of the item bank and delays the possible distortion of trait estimation due to security problems, with either no or only slight decrements of measurement accuracy.

1 aBarrada, Juan Ramón1 aVeldkamp, Bernard, P1 aOlea, Julio uhttp://apm.sagepub.com/content/33/1/58.abstract03428nas a2200385 4500008004100000020004100041245010600082210006900188250001500257260001200272300001000284490000700294520220300301653002702504653001502531653001002546653002102556653002402577653002802601653003802629653001102667653001102678653001102689653003902700653000902739653002402748653003102772653004002803100001802843700001502861700001302876700001702889700001402906856012202920 2008 eng d a0271-6798 (Print)0271-6798 (Linking)00aMeasuring physical functioning in children with spinal impairments with computerized adaptive testing0 aMeasuring physical functioning in children with spinal impairmen a2008/03/26 cApr-May a330-50 v283 aBACKGROUND: The purpose of this study was to assess the utility of measuring current physical functioning status of children with scoliosis and kyphosis by applying computerized adaptive testing (CAT) methods. Computerized adaptive testing uses a computer interface to administer the most optimal items based on previous responses, reducing the number of items needed to obtain a scoring estimate. METHODS: This was a prospective study of 77 subjects (0.6-19.8 years) who were seen by a spine surgeon during a routine clinic visit for progress spine deformity. Using a multidimensional version of the Pediatric Evaluation of Disability Inventory CAT program (PEDI-MCAT), we evaluated content range, accuracy and efficiency, known-group validity, concurrent validity with the Pediatric Outcomes Data Collection Instrument, and test-retest reliability in a subsample (n = 16) within a 2-week interval. RESULTS: We found the PEDI-MCAT to have sufficient item coverage in both self-care and mobility content for this sample, although most patients tended to score at the higher ends of both scales. Both the accuracy of PEDI-MCAT scores as compared with a fixed format of the PEDI (r = 0.98 for both mobility and self-care) and test-retest reliability were very high [self-care: intraclass correlation (3,1) = 0.98, mobility: intraclass correlation (3,1) = 0.99]. The PEDI-MCAT took an average of 2.9 minutes for the parents to complete. The PEDI-MCAT detected expected differences between patient groups, and scores on the PEDI-MCAT correlated in expected directions with scores from the Pediatric Outcomes Data Collection Instrument domains. CONCLUSIONS: Use of the PEDI-MCAT to assess the physical functioning status, as perceived by parents of children with complex spinal impairments, seems to be feasible and achieves accurate and efficient estimates of self-care and mobility function. Additional item development will be needed at the higher functioning end of the scale to avoid ceiling effects for older children. LEVEL OF EVIDENCE: This is a level II prospective study designed to establish the utility of computer adaptive testing as an evaluation method in a busy pediatric spine practice.10a*Disability Evaluation10aAdolescent10aChild10aChild, Preschool10aComputer Simulation10aCross-Sectional Studies10aDisabled Children/*rehabilitation10aFemale10aHumans10aInfant10aKyphosis/*diagnosis/rehabilitation10aMale10aProspective Studies10aReproducibility of Results10aScoliosis/*diagnosis/rehabilitation1 aMulcahey, M J1 aHaley, S M1 aDuffy, T1 aPengsheng, N1 aBetz, R R uhttp://www.iacat.org/content/measuring-physical-functioning-children-spinal-impairments-computerized-adaptive-testing01081nas a2200145 4500008004100000245008400041210006900125300001200194490000700206520056600213100001600779700001700795700001300812856011000825 2008 eng d00aModern sequential analysis and its application to computerized adaptive testing0 aModern sequential analysis and its application to computerized a a473-4860 v733 aAfter a brief review of recent advances in sequential analysis involving sequential generalized likelihood ratio tests, we discuss their use in psychometric testing and extend the asymptotic optimality theory of these sequential tests to the case of sequentially generated experiments, of particular interest in computerized adaptive testing.We then show how these methods can be used to design adaptive mastery tests, which are asymptotically optimal and are also shown to provide substantial improvements over currently used sequential and fixed length tests.1 aBartroff, J1 aFinkelman, M1 aLai, T L uhttp://www.iacat.org/content/modern-sequential-analysis-and-its-application-computerized-adaptive-testing01234nas a2200145 4500008003900000245007300039210006900112300001200181490000700193520076500200100002100965700002500986700002401011856005301035 2008 d00aA Monte Carlo Approach for Adaptive Testing With Content Constraints0 aMonte Carlo Approach for Adaptive Testing With Content Constrain a431-4460 v323 a

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the item pool, and (c) more robust ability estimates. Computer simulations with Law School Admission Test items demonstrated that the new algorithm (a) produces similar ability estimates as shadow CAT but with half the maximum item exposure rate and 100% pool utilization and (b) produces more robust estimates when a high- (or low-) ability examinee performs poorly (or well) at the beginning of the test.

1 aBelov, Dmitry, I1 aArmstrong, Ronald, D1 aWeissman, Alexander uhttp://apm.sagepub.com/content/32/6/431.abstract00475nas a2200133 4500008003900000245007300039210006900112300001200181490000700193100001500200700001900215700001600234856009100250 2008 d00aA monte carlo approach for adaptive testing with content constraints0 amonte carlo approach for adaptive testing with content constrain a431-4460 v321 aBelov, D I1 aArmstrong, R D1 aWeissman, A uhttp://www.iacat.org/content/monte-carlo-approach-adaptive-testing-content-constraints01426nas a2200133 4500008003900000245009600039210006900135300001200204490000700216520097000223100002101193700002501214856005301239 2008 d00aA Monte Carlo Approach to the Design, Assembly, and Evaluation of Multistage Adaptive Tests0 aMonte Carlo Approach to the Design Assembly and Evaluation of Mu a119-1370 v323 a

This article presents an application of Monte Carlo methods for developing and assembling multistage adaptive tests (MSTs). A major advantage of the Monte Carlo assembly over other approaches (e.g., integer programming or enumerative heuristics) is that it provides a uniform sampling from all MSTs (or MST paths) available from a given item pool. The uniform sampling allows a statistically valid analysis for MST design and evaluation. Given an item pool, MST model, and content constraints for test assembly, three problems are addressed in this study. They are (a) the construction of item response theory (IRT) targets for each MST path, (b) the assembly of an MST such that each path satisfies content constraints and IRT constraints, and (c) an analysis of the pool and constraints to increase the number of nonoverlapping MSTs that can be assembled from the pool. The primary intent is to produce reliable measurements and enhance pool utilization.

1 aBelov, Dmitry, I1 aArmstrong, Ronald, D uhttp://apm.sagepub.com/content/32/2/119.abstract00472nas a2200109 4500008003900000245009600039210006900135300001400204490000700218100002600225856011100251 2008 d00aA Monte Carlo approach to the design, assembly, and evaluation of multistage adaptive tests0 aMonte Carlo approach to the design assembly and evaluation of mu a119–1370 v321 aBelov, Armstrong, R D uhttp://www.iacat.org/content/monte-carlo-approach-design-assembly-and-evaluation-multistage-adaptive-tests01119nas a2200169 4500008004100000020002300041245008300064210006900147300001400216490000700230520054200237100001500779700001500794700001700809700001500826856010800841 2007 eng d a0962-93431573-264900aMethodological issues for building item banks and computerized adaptive scales0 aMethodological issues for building item banks and computerized a a109-119, 0 v163 aAbstract This paper reviews important methodological considerations for developing item banks and computerized adaptive scales (commonly called computerized adaptive tests in the educational measurement literature, yielding the acronym CAT), including issues of the reference population, dimensionality, dichotomous versus polytomous response scales, differential item functioning (DIF) and conditional scoring, mode effects, the impact of local dependence, and innovative approaches to assessment using CATs in health outcomes research.1 aThissen, D1 aReeve, B B1 aBjorner, J B1 aChang, C-H uhttp://www.iacat.org/content/methodological-issues-building-item-banks-and-computerized-adaptive-scales02212nas a2200229 4500008004100000020004600041245008500087210006900172260004500241300001000286490000600296520140300302653003401705653002301739653002601762653001701788653002601805100001701831700001201848700001501860856010701875 2007 eng d a1614-1881 (Print); 1614-2241 (Electronic)00aMethods for restricting maximum exposure rate in computerized adaptative testing0 aMethods for restricting maximum exposure rate in computerized ad bHogrefe & Huber Publishers GmbH: Germany a14-230 v33 aThe Sympson-Hetter (1985) method provides a means of controlling maximum exposure rate of items in Computerized Adaptive Testing. Through a series of simulations, control parameters are set that mark the probability of administration of an item on being selected. This method presents two main problems: it requires a long computation time for calculating the parameters and the maximum exposure rate is slightly above the fixed limit. Van der Linden (2003) presented two alternatives which appear to solve both of the problems. The impact of these methods in the measurement accuracy has not been tested yet. We show how these methods over-restrict the exposure of some highly discriminating items and, thus, the accuracy is decreased. It also shown that, when the desired maximum exposure rate is near the minimum possible value, these methods offer an empirical maximum exposure rate clearly above the goal. A new method, based on the initial estimation of the probability of administration and the probability of selection of the items with the restricted method (Revuelta & Ponsoda, 1998), is presented in this paper. It can be used with the Sympson-Hetter method and with the two van der Linden's methods. This option, when used with Sympson-Hetter, speeds the convergence of the control parameters without decreasing the accuracy. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive testing10aitem bank security10aitem exposure control10aoverlap rate10aSympson-Hetter method1 aBarrada, J R1 aOlea, J1 aPonsoda, V uhttp://www.iacat.org/content/methods-restricting-maximum-exposure-rate-computerized-adaptative-testing00588nas a2200109 4500008004100000245011500041210006900156260010200225100001300327700001900340856011900359 2007 eng d00aThe modified maximum global discrimination index method for cognitive diagnostic computerized adaptive testing0 amodified maximum global discrimination index method for cognitiv a  D. J. Weiss (Ed.).  Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aCheng, Y1 aChang, Hua-Hua uhttp://www.iacat.org/content/modified-maximum-global-discrimination-index-method-cognitive-diagnostic-computerized00559nas a2200121 4500008004100000245011100041210006900152260003400221100001800255700002500273700001600298856012300314 2007 eng d00aA multiple objective test assembly approach for exposure control problems in computerized adaptive testing0 amultiple objective test assembly approach for exposure control p aArnhem, The NetherlandsbCito1 aVeldkamp, B P1 aVerschoor, Angela, J1 aEggen, Theo uhttp://www.iacat.org/content/multiple-objective-test-assembly-approach-exposure-control-problems-computerized-adaptive01310nas a2200121 4500008003900000245007300039210006900112300001000181490000700191520091400198100002401112856005201136 2007 d00aMutual Information Item Selection in Adaptive Classification Testing0 aMutual Information Item Selection in Adaptive Classification Tes a41-580 v673 a

A general approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback-Leibler distance, or relative entropy. MI works efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local- and global-information measures in the multiple-category classification setting. Results from simulation studies using three item selection methods, Fisher information (FI), posterior-weighted FI (FIP), and MI, are provided for an adaptive four-category classification test. Both across and within the four classification categories, it is shown that in general, MI item selection classifies the highest proportion of examinees correctly and yields the shortest test lengths. The next best performance is observed for FIP item selection, followed by FI.

1 aWeissman, Alexander uhttp://epm.sagepub.com/content/67/1/41.abstract03119nas a2200277 4500008004100000020002200041245010900063210006900172250001500241260000800256300001200264490000700276520221700283653002902500653002002529653002502549653002102574653001502595653002802610653001102638653002502649100001702674700001502691700001202706856012302718 2006 eng d a0214-9915 (Print)00aMaximum information stratification method for controlling item exposure in computerized adaptive testing0 aMaximum information stratification method for controlling item e a2007/02/14 cFeb a156-1590 v183 aThe proposal for increasing the security in Computerized Adaptive Tests that has received most attention in recent years is the a-stratified method (AS - Chang and Ying, 1999): at the beginning of the test only items with low discrimination parameters (a) can be administered, with the values of the a parameters increasing as the test goes on. With this method, distribution of the exposure rates of the items is less skewed, while efficiency is maintained in trait-level estimation. The pseudo-guessing parameter (c), present in the three-parameter logistic model, is considered irrelevant, and is not used in the AS method. The Maximum Information Stratified (MIS) model incorporates the c parameter in the stratification of the bank and in the item-selection rule, improving accuracy by comparison with the AS, for item banks with a and b parameters correlated and uncorrelated. For both kinds of banks, the blocking b methods (Chang, Qian and Ying, 2001) improve the security of the item bank.Método de estratificación por máxima información para el control de la exposición en tests adaptativos informatizados. La propuesta para aumentar la seguridad en los tests adaptativos informatizados que ha recibido más atención en los últimos años ha sido el método a-estratificado (AE - Chang y Ying, 1999): en los momentos iniciales del test sólo pueden administrarse ítems con bajos parámetros de discriminación (a), incrementándose los valores del parámetro a admisibles según avanza el test. Con este método la distribución de las tasas de exposición de los ítems es más equilibrada, manteniendo una adecuada precisión en la medida. El parámetro de pseudoadivinación (c), presente en el modelo logístico de tres parámetros, se supone irrelevante y no se incorpora en el AE. El método de Estratificación por Máxima Información (EMI) incorpora el parámetro c a la estratificación del banco y a la regla de selección de ítems, mejorando la precisión en comparación con AE, tanto para bancos donde los parámetros a y b correlacionan como para bancos donde no. Para ambos tipos de bancos, los métodos de bloqueo de b (Chang, Qian y Ying, 2001) mejoran la seguridad del banco.10a*Artificial Intelligence10a*Microcomputers10a*Psychological Tests10a*Software Design10aAlgorithms10aChi-Square Distribution10aHumans10aLikelihood Functions1 aBarrada, J R1 aMazuela, P1 aOlea, J uhttp://www.iacat.org/content/maximum-information-stratification-method-controlling-item-exposure-computerized-adaptive02567nas a2200349 4500008004100000020002200041245016600063210006900229250001500298260000800313300001100321490000700332520142900339653002701768653001601795653001501811653001001826653002101836653001401857653005201871653001501923653001101938653001101949653003701960653001801997653001402015100001502029700001002044700001602054700002502070856012202095 2006 eng d a0003-9993 (Print)00aMeasurement precision and efficiency of multidimensional computer adaptive testing of physical functioning using the pediatric evaluation of disability inventory0 aMeasurement precision and efficiency of multidimensional compute a2006/08/29 cSep a1223-90 v873 aOBJECTIVE: To compare the measurement efficiency and precision of a multidimensional computer adaptive testing (M-CAT) application to a unidimensional CAT (U-CAT) comparison using item bank data from 2 of the functional skills scales of the Pediatric Evaluation of Disability Inventory (PEDI). DESIGN: Using existing PEDI mobility and self-care item banks, we compared the stability of item calibrations and model fit between unidimensional and multidimensional Rasch models and compared the efficiency and precision of the U-CAT- and M-CAT-simulated assessments to a random draw of items. SETTING: Pediatric rehabilitation hospital and clinics. PARTICIPANTS: Clinical and normative samples. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Not applicable. RESULTS: The M-CAT had greater levels of precision and efficiency than the separate mobility and self-care U-CAT versions when using a similar number of items for each PEDI subdomain. Equivalent estimation of mobility and self-care scores can be achieved with a 25% to 40% item reduction with the M-CAT compared with the U-CAT. CONCLUSIONS: M-CAT applications appear to have both precision and efficiency advantages compared with separate U-CAT assessments when content subdomains have a high correlation. Practitioners may also realize interpretive advantages of reporting test score information for each subdomain when separate clinical inferences are desired.10a*Disability Evaluation10a*Pediatrics10aAdolescent10aChild10aChild, Preschool10aComputers10aDisabled Persons/*classification/rehabilitation10aEfficiency10aHumans10aInfant10aOutcome Assessment (Health Care)10aPsychometrics10aSelf Care1 aHaley, S M1 aNi, P1 aLudlow, L H1 aFragala-Pinkham, M A uhttp://www.iacat.org/content/measurement-precision-and-efficiency-multidimensional-computer-adaptive-testing-physical02405nas a2200337 4500008004100000020002200041245010800063210006900171250001500240260000800255300001100263490000700274520139300281653002101674653002101695653001001716653001101726653001801737653001101755653000901766653001601775653003001791653002801821100001801849700001701867700001801884700001401902700001701916700001701933856011701950 2006 eng d a0962-9343 (Print)00aMultidimensional computerized adaptive testing of the EORTC QLQ-C30: basic developments and evaluations0 aMultidimensional computerized adaptive testing of the EORTC QLQC a2006/03/21 cApr a315-290 v153 aOBJECTIVE: Self-report questionnaires are widely used to measure health-related quality of life (HRQOL). Ideally, such questionnaires should be adapted to the individual patient and at the same time scores should be directly comparable across patients. This may be achieved using computerized adaptive testing (CAT). Usually, CAT is carried out for a single domain at a time. However, many HRQOL domains are highly correlated. Multidimensional CAT may utilize these correlations to improve measurement efficiency. We investigated the possible advantages and difficulties of multidimensional CAT. STUDY DESIGN AND SETTING: We evaluated multidimensional CAT of three scales from the EORTC QLQ-C30: the physical functioning, emotional functioning, and fatigue scales. Analyses utilised a database with 2958 European cancer patients. RESULTS: It was possible to obtain scores for the three domains with five to seven items administered using multidimensional CAT that were very close to the scores obtained using all 12 items and with no or little loss of measurement precision. CONCLUSION: The findings suggest that multidimensional CAT may significantly improve measurement precision and efficiency and encourage further research into multidimensional CAT. Particularly, the estimation of the model underlying the multidimensional CAT and the conceptual aspects need further investigations.10a*Quality of Life10a*Self Disclosure10aAdult10aFemale10aHealth Status10aHumans10aMale10aMiddle Aged10aQuestionnaires/*standards10aUser-Computer Interface1 aPetersen, M A1 aGroenvold, M1 aAaronson, N K1 aFayers, P1 aSprangers, M1 aBjorner, J B uhttp://www.iacat.org/content/multidimensional-computerized-adaptive-testing-eortc-qlq-c30-basic-developments-and00465nas a2200121 4500008004100000245006900041210006900110260002200179100001700201700001800218700001200236856009500248 2006 eng d00aMultiple maximum exposure rates in computerized adaptive testing0 aMultiple maximum exposure rates in computerized adaptive testing aBudapest, Hungary1 aBarrada, J R1 aVeldkamp, B P1 aOlea, J uhttp://www.iacat.org/content/multiple-maximum-exposure-rates-computerized-adaptive-testing00404nas a2200121 4500008003900000245005500039210005300094300001200147490000700159100001900166700003100185856006600216 2006 d00aMultistage Testing: Widely or Narrowly Applicable?0 aMultistage Testing Widely or Narrowly Applicable a257-2600 v191 aStark, Stephen1 aChernyshenko, Oleksandr, S uhttp://www.tandfonline.com/doi/abs/10.1207/s15324818ame1903_602902nas a2200409 4500008004100000245012300041210006900164260000800233300001000241490000700251520159700258653004701855653001001902653000901912653001901921653003101940653002601971653001101997653002902008653001102037653000902048653001602057653003902073653001402112653002502126653002702151653003002178653003202208653002802240653002202268100001502290700001602305700001702321700001602338700001502354856012302369 2005 eng d00aMeasuring physical function in patients with complex medical and postsurgical conditions: a computer adaptive approach0 aMeasuring physical function in patients with complex medical and cOct a741-80 v843 aOBJECTIVE: To examine whether the range of disability in the medically complex and postsurgical populations receiving rehabilitation is adequately sampled by the new Activity Measure--Post-Acute Care (AM-PAC), and to assess whether computer adaptive testing (CAT) can derive valid patient scores using fewer questions. DESIGN: Observational study of 158 subjects (mean age 67.2 yrs) receiving skilled rehabilitation services in inpatient (acute rehabilitation hospitals, skilled nursing facility units) and community (home health services, outpatient departments) settings for recent-onset or worsening disability from medical (excluding neurological) and surgical (excluding orthopedic) conditions. Measures were interviewer-administered activity questions (all patients) and physical functioning portion of the SF-36 (outpatients) and standardized chart items (11 Functional Independence Measure (FIM), 19 Standardized Outcome and Assessment Information Set (OASIS) items, and 22 Minimum Data Set (MDS) items). Rasch modeling analyzed all data and the relationship between person ability estimates and average item difficulty. CAT assessed the ability to derive accurate patient scores using a sample of questions. RESULTS: The 163-item activity item pool covered the range of physical movement and personal and instrumental activities. CAT analysis showed comparable scores between estimates using 10 items or the total item pool. CONCLUSION: The AM-PAC can assess a broad range of function in patients with complex medical illness. CAT achieves valid patient scores using fewer questions.10aActivities of Daily Living/*classification10aAdult10aAged10aCohort Studies10aContinuity of Patient Care10aDisability Evaluation10aFemale10aHealth Services Research10aHumans10aMale10aMiddle Aged10aPostoperative Care/*rehabilitation10aPrognosis10aRecovery of Function10aRehabilitation Centers10aRehabilitation/*standards10aSensitivity and Specificity10aSickness Impact Profile10aTreatment Outcome1 aSiebens, H1 aAndres, P L1 aPengsheng, N1 aCoster, W J1 aHaley, S M uhttp://www.iacat.org/content/measuring-physical-function-patients-complex-medical-and-postsurgical-conditions-computer01198nas a2200133 4500008003900000245006700039210006700106300001200173490000700185520077300192100002100965700002500986856005301011 2005 d00aMonte Carlo Test Assembly for Item Pool Analysis and Extension0 aMonte Carlo Test Assembly for Item Pool Analysis and Extension a239-2610 v293 a

A new test assembly algorithm based on a Monte Carlo random search is presented in this article. A major advantage of the Monte Carlo test assembly over other approaches (integer programming or enumerative heuristics) is that it performs a uniform sampling from the item pool, which provides every feasible item combination (test) with an equal chance of being built during an assembly. This allows the authors to address the following issues of pool analysis and extension: compare the strengths and weaknesses of different pools, identify the most restrictive constraint(s) for test assembly, and identify properties of the items that should be added to a pool to achieve greater usability of the pool. Computer experiments with operational pools are given.

1 aBelov, Dmitry, I1 aArmstrong, Ronald, D uhttp://apm.sagepub.com/content/29/4/239.abstract01441nas a2200157 4500008003900000245006700039210006700106300001200173490000700185520094700192100002901139700002301168700001901191700002001210856005301230 2004 d00aMokken Scale Analysis Using Hierarchical Clustering Procedures0 aMokken Scale Analysis Using Hierarchical Clustering Procedures a332-3540 v283 a

Mokken scale analysis (MSA) can be used to assess and build unidimensional scales from an item pool that is sensitive to multiple dimensions. These scales satisfy a set of scaling conditions, one of which follows from the model of monotone homogeneity. An important drawback of the MSA program is that the sequential item selection and scale construction procedure may not find the dominant underlying dimensionality of the responses to a set of items. The authors investigated alternative hierarchical item selection procedures and compared the performance of four hierarchical methods and the sequential clustering method in the MSA context. The results showed that hierarchical clustering methods can improve the search process of the dominant dimensionality of a data matrix. In particular, the complete linkage and scale linkage methods were promising in finding the dimensionality of the item response data from a set of items.

1 aAbswoude, Alexandra, A H1 aVermunt, Jeroen, K1 aHemker, Bas, T1 aArk, Andries, L uhttp://apm.sagepub.com/content/28/5/332.abstract00423nas a2200097 4500008004100000245007800041210006900119260001700188100001600205856010400221 2004 eng d00aMutual information item selection in multiple-category classification CAT0 aMutual information item selection in multiplecategory classifica aSan Diego CA1 aWeissman, A uhttp://www.iacat.org/content/mutual-information-item-selection-multiple-category-classification-cat00452nas a2200145 4500008004100000245005100041210005100092260001500143100001500158700001400173700001200187700001500199700001500214856007700229 2003 eng d00aMaintaining scale in computer adaptive testing0 aMaintaining scale in computer adaptive testing aChicago IL1 aSmith, R L1 aRizavi, S1 aPaez, R1 aDamiano, M1 aHerbert, E uhttp://www.iacat.org/content/maintaining-scale-computer-adaptive-testing00424nas a2200109 4500008004100000245006500041210006200106260002700168100001900195700001500214856008500229 2003 eng d00aA method to determine targets for multi-stage adaptive tests0 amethod to determine targets for multistage adaptive tests aUnpublished manuscript1 aArmstrong, R D1 aRoussos, L uhttp://www.iacat.org/content/method-determine-targets-multi-stage-adaptive-tests00377nas a2200109 4500008004100000245005500041210005500096260001500151100001000166700001400176856007700190 2003 eng d00aMethods for item set selection in adaptive testing0 aMethods for item set selection in adaptive testing aChicago IL1 aLu, Y1 aRizavi, S uhttp://www.iacat.org/content/methods-item-set-selection-adaptive-testing00489nas a2200109 4500008004100000245009900041210006900140260001600209100001200225700001700237856012500254 2003 eng d00aMultidimensional computerized adaptive testing in recovering reading and mathematics abilities0 aMultidimensional computerized adaptive testing in recovering rea aChicago, IL1 aLi, Y H1 aSchafer, W D uhttp://www.iacat.org/content/multidimensional-computerized-adaptive-testing-recovering-reading-and-mathematics-abilities00521nas a2200097 4500008004100000245008500041210006900126260010600195100001400301856010800315 2003 eng d00aA multidimensional IRT mechanism for better understanding adaptive test behavior0 amultidimensional IRT mechanism for better understanding adaptive aPaper presented at the annual meeting of the National Council on Measurement in Education, Chicago IL1 aJodoin, M uhttp://www.iacat.org/content/multidimensional-irt-mechanism-better-understanding-adaptive-test-behavior00409nas a2200097 4500008004100000245007500041210006900116100001700185700001700202856009200219 2002 eng d00aMapping the Development of Pre-reading Skills with STAR Early Literacy0 aMapping the Development of Prereading Skills with STAR Early Lit1 aMcBride, J R1 aTardrew, S P uhttp://www.iacat.org/content/mapping-development-pre-reading-skills-star-early-literacy01693nas a2200277 4500008004100000020001000041245006500051210006400116260009700180300001100277520074500288653002101033653002201054653002501076653002801101653002501129653001601154653001801170653005501188653001501243653001201258100001801270700002301288700001301311856009101324 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://www.iacat.org/content/mathematical-programming-approaches-test-item-pool-design02415nas a2200277 4500008004100000245012200041210006900163260000800232300001000240490000700250520148700257653002101744653002101765653002001786653001001806653002201816653002901838653001101867653001801878653001901896653004101915653003201956100001301988700001802001856011802019 2002 eng d00aMeasuring quality of life in chronic illness: the functional assessment of chronic illness therapy measurement system0 aMeasuring quality of life in chronic illness the functional asse cDec aS10-70 v833 aWe focus on quality of life (QOL) measurement as applied to chronic illness. There are 2 major types of health-related quality of life (HRQOL) instruments-generic health status and targeted. Generic instruments offer the opportunity to compare results across patient and population cohorts, and some can provide normative or benchmark data from which to interpret results. Targeted instruments ask questions that focus more on the specific condition or treatment under study and, as a result, tend to be more responsive to clinically important changes than generic instruments. Each type of instrument has a place in the assessment of HRQOL in chronic illness, and consideration of the relative advantages and disadvantages of the 2 options best drives choice of instrument. The Functional Assessment of Chronic Illness Therapy (FACIT) system of HRQOL measurement is a hybrid of the 2 approaches. The FACIT system combines a core general measure with supplemental measures targeted toward specific diseases, conditions, or treatments. Thus, it capitalizes on the strengths of each type of measure. Recently, FACIT questionnaires were administered to a representative sample of the general population with results used to derive FACIT norms. These normative data can be used for benchmarking and to better understand changes in HRQOL that are often seen in clinical trials. Future directions in HRQOL assessment include test equating, item banking, and computerized adaptive testing.10a*Chronic Disease10a*Quality of Life10a*Rehabilitation10aAdult10aComparative Study10aHealth Status Indicators10aHumans10aPsychometrics10aQuestionnaires10aResearch Support, U.S. Gov't, P.H.S.10aSensitivity and Specificity1 aCella, D1 aNowinski, C J uhttp://www.iacat.org/content/measuring-quality-life-chronic-illness-functional-assessment-chronic-illness-therapy00302nas a2200097 4500008004100000245003200041210003000073260003000103100001200133856005900145 2002 eng d00aMIRTCAT [computer software]0 aMIRTCAT computer software aUpper Marlboro MD: Author1 aLi, Y H uhttp://www.iacat.org/content/mirtcat-computer-software00499nas a2200097 4500008004100000245010600041210006900147260004100216100002300257856012100280 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://www.iacat.org/content/modifications-sympson-hetter-method-item-exposure-control-computerized-adaptive-testing03062nas a2200325 4500008004100000020004100041245008100082210006900163250001500232260000800247300001100255490000700266520201300273653001502286653001002301653004002311653005702351653003302408653001102441653001102452653001802463653000902481653002802490653001202518653005502530100001502585700001802600700001502618856010302633 2002 eng d a0025-7079 (Print)0025-7079 (Linking)00aMultidimensional adaptive testing for mental health problems in primary care0 aMultidimensional adaptive testing for mental health problems in  a2002/09/10 cSep a812-230 v403 aOBJECTIVES: Efficient and accurate instruments for assessing child psychopathology are increasingly important in clinical practice and research. For example, screening in primary care settings can identify children and adolescents with disorders that may otherwise go undetected. However, primary care offices are notorious for the brevity of visits and screening must not burden patients or staff with long questionnaires. One solution is to shorten assessment instruments, but dropping questions typically makes an instrument less accurate. An alternative is adaptive testing, in which a computer selects the items to be asked of a patient based on the patient's previous responses. This research used a simulation to test a child mental health screen based on this technology. RESEARCH DESIGN: Using half of a large sample of data, a computerized version was developed of the Pediatric Symptom Checklist (PSC), a parental-report psychosocial problem screen. With the unused data, a simulation was conducted to determine whether the Adaptive PSC can reproduce the results of the full PSC with greater efficiency. SUBJECTS: PSCs were completed by parents on 21,150 children seen in a national sample of primary care practices. RESULTS: Four latent psychosocial problem dimensions were identified through factor analysis: internalizing problems, externalizing problems, attention problems, and school problems. A simulated adaptive test measuring these traits asked an average of 11.6 questions per patient, and asked five or fewer questions for 49% of the sample. There was high agreement between the adaptive test and the full (35-item) PSC: only 1.3% of screening decisions were discordant (kappa = 0.93). This agreement was higher than that obtained using a comparable length (12-item) short-form PSC (3.2% of decisions discordant; kappa = 0.84). CONCLUSIONS: Multidimensional adaptive testing may be an accurate and efficient technology for screening for mental health problems in primary care settings.10aAdolescent10aChild10aChild Behavior Disorders/*diagnosis10aChild Health Services/*organization & administration10aFactor Analysis, Statistical10aFemale10aHumans10aLinear Models10aMale10aMass Screening/*methods10aParents10aPrimary Health Care/*organization & administration1 aGardner, W1 aKelleher, K J1 aPajer, K A uhttp://www.iacat.org/content/multidimensional-adaptive-testing-mental-health-problems-primary-care01159nas a2200133 4500008004100000245007100041210006900112300001200181490000700193520069200200100001800892700002300910856009200933 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://www.iacat.org/content/multidimensional-adaptive-testing-constraints-test-content00497nas a2200145 4500008004100000245007200041210006900113300001000182490000700192100001400199700001400213700001900227700001200246856009300258 2001 eng d00aOn maximizing item information and matching difficulty with ability0 amaximizing item information and matching difficulty with ability a69-770 v661 aBickel, P1 aBuyske, S1 aChang, Hua-Hua1 aYing, Z uhttp://www.iacat.org/content/maximizing-item-information-and-matching-difficulty-ability00450nas a2200109 4500008004100000245007700041210006900118260002100187100001400208700001500222856010300237 2001 eng d00aMeasurement efficiency of multidimensional computerized adaptive testing0 aMeasurement efficiency of multidimensional computerized adaptive aSan Francisco CA1 aWang, W-C1 aChen, B -H uhttp://www.iacat.org/content/measurement-efficiency-multidimensional-computerized-adaptive-testing00489nas a2200097 4500008004100000245013100041210006900172260001500241100001600256856011900272 2001 eng d00aMeasuring test compromise in high-stakes computerized adaptive testing: A Bayesian Strategy for surrogate test-taker detection0 aMeasuring test compromise in highstakes computerized adaptive te aSeattle WA1 aSegall, D O uhttp://www.iacat.org/content/measuring-test-compromise-high-stakes-computerized-adaptive-testing-bayesian-strategy00576nas a2200121 4500008004100000245012500041210006900166260004600235100001800281700001500299700001600314856012400330 2001 eng d00aA method for building a realistic model of test taker behavior for computerized adaptive testing (Research Report 01-22)0 amethod for building a realistic model of test taker behavior for aPrinceton NJ: Educational Testing Service1 aStocking, M L1 aSteffen, M1 aEignor, D R uhttp://www.iacat.org/content/method-building-realistic-model-test-taker-behavior-computerized-adaptive-testing-research00401nas a2200097 4500008004100000245007100041210006900112260001500181100001600196856009100212 2001 eng d00aMethods to test invariant ability across subgroups of items in CAT0 aMethods to test invariant ability across subgroups of items in C aSeattle WA1 aMeijer, R R uhttp://www.iacat.org/content/methods-test-invariant-ability-across-subgroups-items-cat00448nas a2200109 4500008004100000245009100041210006900132300001200201490000700213100001300220856010500233 2001 eng d00aA minimax procedure in the context of sequential testing problems in psychodiagnostics0 aminimax procedure in the context of sequential testing problems  a139-1590 v541 aVos, H J uhttp://www.iacat.org/content/minimax-procedure-context-sequential-testing-problems-psychodiagnostics00381nas a2200109 4500008004100000245005100041210005100092260001500143100001600158700002300174856007400197 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://www.iacat.org/content/modeling-variability-item-parameters-cat00503nas a2200133 4500008004100000245007900041210006900120260002400189100001500213700001400228700001700242700001200259856009800271 2001 eng d00aMonitoring items for changes in performance in computerized adaptive tests0 aMonitoring items for changes in performance in computerized adap aSeattle, Washington1 aSmith, R L1 aWang, M M1 aWingersky, M1 aZhao, C uhttp://www.iacat.org/content/monitoring-items-changes-performance-computerized-adaptive-tests00435nas a2200121 4500008004100000245006800041210006400109260001500173100001600188700001500204700001600219856007800235 2001 eng d00aA monte carlo study of the feasibility of on-the-fly assessment0 amonte carlo study of the feasibility of onthefly assessment aSeattle WA1 aRevuelta, J1 aBejar, I I1 aStocking, M uhttp://www.iacat.org/content/monte-carlo-study-feasibility-fly-assessment03152nas a2200133 4500008004100000245007900041210006900120300000900189490000700198520266000205653003402865100001502899856010402914 2001 eng d00aMultidimensional adaptive testing using the weighted likelihood estimation0 aMultidimensional adaptive testing using the weighted likelihood  a47460 v613 aThis study extended Warm's (1989) weighted likelihood estimation (WLE) to a multidimensional computerized adaptive test (MCAT) setting. WLE was compared with the maximum likelihood estimation (MLE), expected a posteriori (EAP), and maximum a posteriori (MAP) using a three-dimensional 3PL IRT model under a variety of computerized adaptive testing conditions. The dependent variables included bias, standard error of ability estimates (SE), square root of mean square error (RMSE), and test information. The independent variables were ability estimation methods, intercorrelation levels between dimensions, multidimensional structures, and ability combinations. Simulation results were presented in terms of descriptive statistics, such as figures and tables. In addition, inferential procedures were used to analyze bias by conceptualizing this Monte Carlo study as a statistical sampling experiment. The results of this study indicate that WLE and the other three estimation methods yield significantly more accurate ability estimates under an approximate simple test structure with one dominant dimension and several secondary dimensions. All four estimation methods, especially WLE, yield very large SEs when a three equally dominant multidimensional structure was employed. Consistent with previous findings based on unidimensional IRT model, MLE and WLE are less biased in the extreme of the ability scale; MLE and WLE yield larger SEs than the Bayesian methods; test information-based SEs underestimate actual SEs for both MLE and WLE in MCAT situations, especially at shorter test lengths; WLE reduced the bias of MLE under the approximate simple structure; test information-based SEs underestimates the actual SEs of MLE and WLE estimators in the MCAT conditions, similar to the findings of Warm (1989) in the unidimensional case. The results from the MCAT simulations did show some advantages of WLE in reducing the bias of MLE under the approximate simple structure with a fixed test length of 50 items, which was consistent with the previous research findings based on different unidimensional models. It is clear from the current results that all four methods perform very poorly when the multidimensional structures with multiple dominant factors were employed. More research efforts are urged to investigate systematically how different multidimensional structures affect the accuracy and reliability of ability estimation. Based on the simulated results in this study, there is no significant effect found on the ability estimation from the intercorrelation between dimensions. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aTseng, F-L uhttp://www.iacat.org/content/multidimensional-adaptive-testing-using-weighted-likelihood-estimation00502nas a2200109 4500008004100000245011100041210006900152260001500221100001600236700001400252856012600266 2001 eng d00aMultidimensional adaptive testing using weighted likelihood estimation: A comparison of estimation methods0 aMultidimensional adaptive testing using weighted likelihood esti aSeattle WA1 aTseng, F -E1 aHsu, T -C uhttp://www.iacat.org/content/multidimensional-adaptive-testing-using-weighted-likelihood-estimation-comparison-estimation00424nas a2200109 4500008004100000245006700041210006600108260001500174100001300189700001600202856009600218 2001 eng d00aMultidimensional IRT-based adaptive sequential mastery testing0 aMultidimensional IRTbased adaptive sequential mastery testing aSeattle WA1 aVos, H J1 aGlas, C E W uhttp://www.iacat.org/content/multidimensional-irt-based-adaptive-sequential-mastery-testing00503nas a2200109 4500008004100000245005600041210005600097260013700153100001800290700001300308856007200321 2000 eng d00aMethods of controlling the exposure of items in CAT0 aMethods of controlling the exposure of items in CAT aW. J. van der Linden and C. A. W. Glas (eds.), Computerized adaptive testing: Theory and practice (pp. 163-182). Norwell MA: Kluwer.1 aStocking, M L1 aLewis, C uhttp://www.iacat.org/content/methods-controlling-exposure-items-cat00518nas a2200097 4500008004100000245006200041210006000103260015900163100001300322856008500335 2000 eng d00aA minimax solution for sequential classification problems0 aminimax solution for sequential classification problems aH. A. L. Kiers, J.-P.Rasson, P. J. F. Groenen, and M. Schader (Eds.), Data analysis, classification, and related methods (pp. 121-126). Berlin: Springer. 1 aVos, H J uhttp://www.iacat.org/content/minimax-solution-sequential-classification-problems00498nas a2200133 4500008004100000245006100041210006000102260005900162300001200221100001600233700001100249700001700260856008700277 2000 eng d00aMML and EAP estimation in testlet-based adaptive testing0 aMML and EAP estimation in testletbased adaptive testing aDordrecht, The NetherlandsbKluwer Academic Publishers a271-2871 aGlas, C A W1 aWainer1 aBradlow, E T uhttp://www.iacat.org/content/mml-and-eap-estimation-testlet-based-adaptive-testing00618nas a2200097 4500008004100000245012400041210006900165260014400234100001800378856012400396 2000 eng d00aModifications of the branch-and-bound algorithm for application in constrained adaptive testing (Research Report 00-05)0 aModifications of the branchandbound algorithm for application in aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aVeldkamp, B P uhttp://www.iacat.org/content/modifications-branch-and-bound-algorithm-application-constrained-adaptive-testing-research00614nas a2200109 4500008004100000245009500041210006900136260014400205100001800349700002300367856011400390 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://www.iacat.org/content/multidimensional-adaptive-testing-constraints-test-content-research-report-00-1100414nas a2200109 4500008004100000245006100041210006100102260002700163100001000190700001900200856008500219 2000 eng d00aMultiple stratification CAT designs with content control0 aMultiple stratification CAT designs with content control aUnpublished manuscript1 aYi, Q1 aChang, Hua-Hua uhttp://www.iacat.org/content/multiple-stratification-cat-designs-content-control00373nas a2200097 4500008004100000245006100041210006100102260002100163100001100184856008000195 1999 eng d00aManaging CAT item development in the face of uncertainty0 aManaging CAT item development in the face of uncertainty aMontreal, Canada1 aGuo, F uhttp://www.iacat.org/content/managing-cat-item-development-face-uncertainty00566nas a2200097 4500008004100000245009300041210006900134260014400203100001300347856010800360 1999 eng d00aA minimax procedure in the context of sequential mastery testing (Research Report 99-04)0 aminimax procedure in the context of sequential mastery testing R aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aVos, H J uhttp://www.iacat.org/content/minimax-procedure-context-sequential-mastery-testing-research-report-99-0400351nas a2200109 4500008004100000245004300041210004300084260002100127100001300148700001100161856006900172 1999 eng d00aMore efficient use of item inventories0 aMore efficient use of item inventories aMontreal, Canada1 aSmith, R1 aZhu, R uhttp://www.iacat.org/content/more-efficient-use-item-inventories01090nas a2200133 4500008004100000245007800041210006900119300001200188490000700200520059200207653003400799100002300833856010000856 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://www.iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion01240nas a2200157 4500008004100000245006600041210006600107300001000173490000600183520071600189653003400905100001500939700001700954700001900971856009200990 1998 eng d00aMaintaining content validity in computerized adaptive testing0 aMaintaining content validity in computerized adaptive testing a29-410 v33 aThe authors empirically demonstrate some of the trade-offs which can occur when content balancing is imposed in computerized adaptive testing (CAT) forms or conversely, when it is ignored. The authors contend that the content validity of a CAT form can actually change across a score scale when content balancing is ignored. However they caution that, efficiency and score precision can be severely reduced by over specifying content restrictions in a CAT form. The results from 2 simulation studies are presented as a means of highlighting some of the trade-offs that could occur between content and statistical considerations in CAT form assembly. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aLuecht, RM1 aChamplain, A1 aNungester, R J uhttp://www.iacat.org/content/maintaining-content-validity-computerized-adaptive-testing00395nas a2200121 4500008004100000245005100041210005100092300001200143490000700155100001100162700002000173856008000193 1998 eng d00aMeasuring change conventionally and adaptively0 aMeasuring change conventionally and adaptively a882-8970 v581 aMay, K1 aNicewander, W A uhttp://www.iacat.org/content/measuring-change-conventionally-and-adaptively01439nas a2200145 4500008004100000245005300041210005100094300001200145490000700157520098100164653003401145100002301179700001501202856007601217 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://www.iacat.org/content/model-optimal-constrained-adaptive-testing00412nas a2200121 4500008004100000245005400041210005400095260001500149100001600164700001600180700001800196856007600214 1997 eng d00aMaintaining a CAT item pool with operational data0 aMaintaining a CAT item pool with operational data aChicago IL1 aLevine, M L1 aSegall, D O1 aWilliams, B A uhttp://www.iacat.org/content/maintaining-cat-item-pool-operational-data00449nas a2200109 4500008004100000245008000041210006900121260001600190100001700206700001500223856010100238 1997 eng d00aMaintaining item and test security in a CAT environment: A simulation study0 aMaintaining item and test security in a CAT environment A simula aChicago IL)1 aPatsula, L N1 aSteffen, M uhttp://www.iacat.org/content/maintaining-item-and-test-security-cat-environment-simulation-study00410nas a2200097 4500008004100000245007300041210006900114260001500183100001500198856009900213 1997 eng d00aMathematical programming approaches to computerized adaptive testing0 aMathematical programming approaches to computerized adaptive tes aChicago IL1 aJones, D H uhttp://www.iacat.org/content/mathematical-programming-approaches-computerized-adaptive-testing00545nas a2200097 4500008004100000245011500041210006900156260008500225100001300310856012400323 1997 eng d00aA minimax sequential procedure in the context of computerized adaptive mastery testing (Research Report 97-07)0 aminimax sequential procedure in the context of computerized adap aTwente, The Netherlands: Department of Educational Measurement and Data Analysis1 aVos, H J uhttp://www.iacat.org/content/minimax-sequential-procedure-context-computerized-adaptive-mastery-testing-research-report00630nas a2200109 4500008004100000245011500041210006900156260013700225100001700362700001600379856012500395 1997 eng d00aModification of the Computerized Adaptive Screening Test (CAST) for use by recruiters in all military services0 aModification of the Computerized Adaptive Screening Test CAST fo aFinal Technical Report FR-WATSD-97-24, Contract No. MDA903-93-D-0032, DO 0054. Alexandria VA: Human Resources Research Organization.1 aMcBride, J R1 aCooper, R R uhttp://www.iacat.org/content/modification-computerized-adaptive-screening-test-cast-use-recruiters-all-military-services00564nas a2200097 4500008004100000245010200041210006900143260010900212100002300321856012200344 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://www.iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion-research-report-97-0300423nas a2200097 4500008004100000245007800041210006900119260001200188100002300200856010200223 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://www.iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion-000322nas a2200097 4500008004100000245004300041210004200084100001900126700001200145856006700157 1997 eng d00aMulti-stage CAT with stratified design0 aMultistage CAT with stratified design1 aChang, Hua-Hua1 aYing, Z uhttp://www.iacat.org/content/multi-stage-cat-stratified-design01528nas a2200229 4500008004100000020004100041245010500082210006900187250001500256260001200271300000900283490000700292520069800299653002500997653002501022653004301047653003701090653001101127100001601138700001801154856012601172 1996 eng d a0363-3624 (Print)0363-3624 (Linking)00aMethodologic trends in the healthcare professions: computer adaptive and computer simulation testing0 aMethodologic trends in the healthcare professions computer adapt a1996/07/01 cJul-Aug a13-40 v213 aAssessing knowledge and performance on computer is rapidly becoming a common phenomenon in testing and measurement. Computer adaptive testing presents an individualized test format in accordance with the examinee's ability level. The efficiency of the testing process enables a more precise estimate of performance, often with fewer items than traditional paper-and-pencil testing methodologies. Computer simulation testing involves performance-based, or authentic, assessment of the examinee's clinical decision-making abilities. The authors discuss the trends in assessing performance through computerized means and the application of these methodologies to community-based nursing practice.10a*Clinical Competence10a*Computer Simulation10aComputer-Assisted Instruction/*methods10aEducational Measurement/*methods10aHumans1 aForker, J E1 aMcDonald, M E uhttp://www.iacat.org/content/methodologic-trends-healthcare-professions-computer-adaptive-and-computer-simulation-testing00553nas a2200121 4500008004500000245015200045210006900197300001200266490000700278100001600285700001500301856011500316 1996 Spandsh 00aMetodos sencillos para el control de las tasas de exposicion en tests adaptativos informatizados [Simple methods for item exposure control in CATs]0 aMetodos sencillos para el control de las tasas de exposicion en  a161-1720 v171 aRevuelta, J1 aPonsoda, V uhttp://www.iacat.org/content/metodos-sencillos-para-el-control-de-las-tasas-de-exposicion-en-tests-adaptativos00550nas a2200109 4500008004100000245013000041210006900171260004600240100001700286700001300303856012400316 1996 eng d00aMissing responses and IRT ability estimation: Omits, choice, time limits, and adaptive testing (Research Report RR-96-30-ONR)0 aMissing responses and IRT ability estimation Omits choice time l aPrinceton NJ: Educational Testing Service1 aMislevy, R J1 aWu, P -K uhttp://www.iacat.org/content/missing-responses-and-irt-ability-estimation-omits-choice-time-limits-and-adaptive-testing00436nas a2200097 4500008004100000245008600041210006900127260001600196100001900212856010700231 1996 eng d00aA model for score maximization within a computerized adaptive testing environment0 amodel for score maximization within a computerized adaptive test aNew York NY1 aChang, Hua-Hua uhttp://www.iacat.org/content/model-score-maximization-within-computerized-adaptive-testing-environment00465nas a2200121 4500008004100000245008000041210006900121260001300190100001300203700001200216700001300228856010200241 1996 eng d00aModifying the NCLEXTM CAT item selection algorithm to improve item exposure0 aModifying the NCLEXTM CAT item selection algorithm to improve it aNew York1 aWay, W D1 aZara, A1 aLeahy, J uhttp://www.iacat.org/content/modifying-nclextm-cat-item-selection-algorithm-improve-item-exposure00331nas a2200109 4500008004100000245003800041210003800079300001200117490000700129100001600136856006900152 1996 eng d00aMultidimensional adaptive testing0 aMultidimensional adaptive testing a331-3540 v611 aSegall, D O uhttp://www.iacat.org/content/multidimensional-adaptive-testing-000978nas a2200121 4500008004100000245003800041210003800079300001200117490000700129520063700136100001600773856006700789 1996 eng d00aMultidimensional adaptive testing0 aMultidimensional adaptive testing a331-3540 v613 aMaximum likelihood and Bayesian procedures for item selection and scoring of multidimensional adaptive tests are presented. A demonstration using simulated response data illustrates that multidimensional adaptive testing (MAT) can provide equal or higher reliabilities with about one-third fewer items than are required by one-dimensional adaptive testing (OAT). Furthermore, holding test-length constant across the MAT and OAT approaches, substantial improvements in reliability can be obtained from multidimensional assessment. A number of issues relating to the operational use of multidimensional adaptive testing are discussed.1 aSegall, D O uhttp://www.iacat.org/content/multidimensional-adaptive-testing00359nas a2200109 4500008004100000245004700041210004700088260001600135100001100151700001100162856007600173 1996 eng d00aMultidimensional computer adaptive testing0 aMultidimensional computer adaptive testing aNew York NY1 aFan, M1 aHsu, Y uhttp://www.iacat.org/content/multidimensional-computer-adaptive-testing01610nas a2200133 4500008004100000245009100041210006900132300001200201490000700213520109200220653003401312100001501346856011501361 1996 eng d00aMultidimensional computerized adaptive testing in a certification or licensure context0 aMultidimensional computerized adaptive testing in a certificatio a389-4040 v203 a(from the journal abstract) Multidimensional item response theory (MIRT) computerized adaptive testing, building on a recent work by D. O. Segall (1996), is applied in a licensing/certification context. An example of a medical licensure test is used to demonstrate situations in which complex, integrated content must be balanced at the total test level for validity reasons, but items assigned to reportable subscore categories may be used under a MIRT adaptive paradigm to improve the reliability of the subscores. A heuristic optimization framework is outlined that generalizes to both univariate and multivariate statistical objective functions, with additional systems of constraints included to manage the content balancing or other test specifications on adaptively constructed test forms. Simulation results suggested that a multivariate treatment of the problem, although complicating somewhat the objective function used and the estimation of traits, nonetheless produces advantages from a psychometric perspective. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aLuecht, RM uhttp://www.iacat.org/content/multidimensional-computerized-adaptive-testing-certification-or-licensure-context00466nas a2200109 4500008004500000245009100045210006900136300001200205490000700217100001500224856011700239 1996 Engldsh 00aMultidimensional Computerized Adaptive Testing in a Certification or Licensure Context0 aMultidimensional Computerized Adaptive Testing in a Certificatio a389-4040 v201 aLuecht, RM uhttp://www.iacat.org/content/multidimensional-computerized-adaptive-testing-certification-or-licensure-context-000507nas a2200121 4500008004100000245009300041210006900134300000900203490000700212653003400219100001300253856011900266 1994 eng d00aMonte Carlo simulation comparison of two-stage testing and computerized adaptive testing0 aMonte Carlo simulation comparison of twostage testing and comput a25480 v5410acomputerized adaptive testing1 aKim, H-O uhttp://www.iacat.org/content/monte-carlo-simulation-comparison-two-stage-testing-and-computerized-adaptive-testing00456nas a2200121 4500008004500000245007300045210006900118300001200187490000700199100001800206700001500224856009500239 1993 Engldsh 00aA Method for Severely Constrained Item Selection in Adaptive Testing0 aMethod for Severely Constrained Item Selection in Adaptive Testi a277-2920 v171 aStocking, M L1 aSwanson, L uhttp://www.iacat.org/content/method-severely-constrained-item-selection-adaptive-testing-100445nas a2200121 4500008004100000245007300041210006900114300001200183490000700195100001300202700001500215856009300230 1993 eng d00aA method for severely constrained item selection in adaptive testing0 amethod for severely constrained item selection in adaptive testi a277-2920 v171 aStocking1 aSwanson, L uhttp://www.iacat.org/content/method-severely-constrained-item-selection-adaptive-testing00448nas a2200121 4500008004100000245007300041210006900114300001200183490000700195100001300202700001500215856009600230 1993 eng d00aA model and heuristic for solving very large item selection problems0 amodel and heuristic for solving very large item selection proble a151-1660 v171 aStocking1 aSwanson, L uhttp://www.iacat.org/content/model-and-heuristic-solving-very-large-item-selection-problems00326nas a2200097 4500008004100000245004100041210004100082260001700123100001800140856007000158 1993 eng d00aModern computerized adaptive testing0 aModern computerized adaptive testing aPrinceton NJ1 aStocking, M L uhttp://www.iacat.org/content/modern-computerized-adaptive-testing00476nas a2200109 4500008004100000245009300041210006900134260001600203100001100219700001500230856012100245 1993 eng d00aMonte Carlo simulation comparison of two-stage testing and computerized adaptive testing0 aMonte Carlo simulation comparison of twostage testing and comput aAtlanta, GA1 aKim, H1 aPlake, B S uhttp://www.iacat.org/content/monte-carlo-simulation-comparison-two-stage-testing-and-computerized-adaptive-testing-000757nas a2200241 4500008004100000020002200041245006700063210006400130250001500194260000800209300001100217490000700228653001500235653002600250653003700276653002300313653001800336100002100354700001400375700002400389700001400413856008800427 1993 eng d a0744-6314 (Print)00aMoving in a new direction: Computerized adaptive testing (CAT)0 aMoving in a new direction Computerized adaptive testing CAT a1993/01/01 cJan a80, 820 v2410a*Computers10aAccreditation/methods10aEducational Measurement/*methods10aLicensure, Nursing10aUnited States1 aJones-Dickson, C1 aDorsey, D1 aCampbell-Warnock, J1 aFields, F uhttp://www.iacat.org/content/moving-new-direction-computerized-adaptive-testing-cat00491nas a2200097 4500008004100000245010200041210006900143260004100212100001500253856012500268 1993 eng d00aMultiple-category classification using a sequential probability ratio test (Research report 93-7)0 aMultiplecategory classification using a sequential probability r aIowa City: American College Testing.1 aSpray, J A uhttp://www.iacat.org/content/multiple-category-classification-using-sequential-probability-ratio-test-research-report-9300499nam a2200109 4500008004100000245008800041210006900129260004600198100001800244700002000262856010700282 1992 eng d00aManual for the General Scholastic Aptitude Test (Senior) Computerized adaptive test0 aManual for the General Scholastic Aptitude Test Senior Computeri aPretoria: Human Sciences Research Council1 aVon Tonder, M1 aClaasswn, N C W uhttp://www.iacat.org/content/manual-general-scholastic-aptitude-test-senior-computerized-adaptive-test00494nas a2200109 4500008004100000245007300041210006900114260007300183100001800256700001500274856009500289 1992 eng d00aA method for severely constrained item selection in adaptive testing0 amethod for severely constrained item selection in adaptive testi aEducational Testing Service Research Report (RR-92-37): Princeton NJ1 aStocking, M L1 aSwanson, L uhttp://www.iacat.org/content/method-severely-constrained-item-selection-adaptive-testing-000297nas a2200085 4500008004100000245004200041210004200083100001500125856007100140 1992 eng d00aMultidimensional CAT simulation study0 aMultidimensional CAT simulation study1 aLuecht, RM uhttp://www.iacat.org/content/multidimensional-cat-simulation-study00452nas a2200121 4500008004100000245006800041210006700109260001700176100001700193700001500210700001500225856009000240 1990 eng d00aMusicCAT: An adaptive testing program to assess musical ability0 aMusicCAT An adaptive testing program to assess musical ability aSan Diego CA1 aVispoel, W P1 aCoffman, D1 aScriven, D uhttp://www.iacat.org/content/musiccat-adaptive-testing-program-assess-musical-ability00520nas a2200109 4500008004100000245012700041210006900168260002100237100001600258700001400274856012200288 1987 eng d00aMultidimensional adaptive testing: A procedure for sequential estimation of the posterior centroid and dispersion of theta0 aMultidimensional adaptive testing A procedure for sequential est aMontreal, Canada1 aBloxom, B M1 aVale, C D uhttp://www.iacat.org/content/multidimensional-adaptive-testing-procedure-sequential-estimation-posterior-centroid-and00483nas a2200097 4500008004100000245011800041210006900159260002000228100001500248856012200263 1986 eng d00aMeasuring up in an individualized way with CAT-ASVAB: Considerations in the development of adaptive testing pools0 aMeasuring up in an individualized way with CATASVAB Consideratio aSan Franciso CA1 aSchartz, M uhttp://www.iacat.org/content/measuring-individualized-way-cat-asvab-considerations-development-adaptive-testing-pools00387nas a2200097 4500008004100000245006200041210006200103260002900165100001000194856008500204 1985 eng d00aMethods of selecting successive items in adaptive testing0 aMethods of selecting successive items in adaptive testing aUniversity of Pittsburgh1 aYu, L uhttp://www.iacat.org/content/methods-selecting-successive-items-adaptive-testing00440nas a2200109 4500008004100000245008400041210006900125300000900194490000700203100001800210856010200228 1985 eng d00aMonitoring item calibrations from data yielded by an adaptive testing procedure0 aMonitoring item calibrations from data yielded by an adaptive te a9-120 v101 aGarrison, W M uhttp://www.iacat.org/content/monitoring-item-calibrations-data-yielded-adaptive-testing-procedure00540nas a2200133 4500008004100000245007700041210006900118260006500187100001200252700001400264700001500278700001500293856009800308 1984 eng d00aMicrocomputer network for computerized adaptive testing (CAT) (TR-84-33)0 aMicrocomputer network for computerized adaptive testing CAT TR84 aSan Diego CA: Navy Personnel Research and Development Center1 aQuan, B1 aPark, T A1 aSandahl, G1 aWolfe, J H uhttp://www.iacat.org/content/microcomputer-network-computerized-adaptive-testing-cat-tr-84-3300651nas a2200097 4500008004100000245008200041210006900123260024200192100001700434856010200451 1980 eng d00aA model for computerized adaptive testing related to instructional situations0 amodel for computerized adaptive testing related to instructional aD. J. Weiss (Ed.). Proceedings of the 1979 Computerized Adaptive Testing Conference (pp. 101-119). Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory.1 aKalisch, S J uhttp://www.iacat.org/content/model-computerized-adaptive-testing-related-instructional-situations00481nas a2200133 4500008004500000245007700045210006900122300001000191490000600201100001400207700001700221700001300238856009600251 1979 Engldsh 00aMonte Carlo Evaluation of Implied Orders As a Basis for Tailored Testing0 aMonte Carlo Evaluation of Implied Orders As a Basis for Tailored a65-740 v31 aCudeck, R1 aMcCormick, D1 aCliff, N uhttp://www.iacat.org/content/monte-carlo-evaluation-implied-orders-basis-tailored-testing-000479nas a2200133 4500008004100000245007700041210006900118300001000187490000600197100001400203700001900217700001500236856009400251 1979 eng d00aMonte carlo evaluation of implied orders as a basis for tailored testing0 aMonte carlo evaluation of implied orders as a basis for tailored a65-740 v31 aCudeck, R1 aMcCormick, D J1 aCliff, N A uhttp://www.iacat.org/content/monte-carlo-evaluation-implied-orders-basis-tailored-testing00466nas a2200109 4500008004100000245005200041210005000093260011600143300001000259100001700269856007000286 1978 eng d00aA model for testing with multidimensional items0 amodel for testing with multidimensional items aMinneapolis, MN. USAbUniversity of Minnesota, Department of Psychology, Psychometrics Methods Programc06/1978 a82-981 aSympson, J B uhttp://www.iacat.org/content/model-testing-multidimensional-items00516nas a2200097 4500008004100000245005200041210005000093260018600143100001700329856007200346 1977 eng d00aA model for testing with multidimensional items0 amodel for testing with multidimensional items aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aSympson, J B uhttp://www.iacat.org/content/model-testing-multidimensional-items-000551nas a2200109 4500008003900000245005400039210005300093260018600146100001400332700001500346856008000361 1977 d00aMulti-Content Adaptive Measurement of Achievement0 aMultiContent Adaptive Measurement of Achievement aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aWeiss, DJ1 aBrown, J M uhttp://www.iacat.org/content/multi-content-adaptive-measurement-achievement00616nas a2200097 4500008003900000245009400039210006900133260018600202100001400388856011600402 1977 d00aA Multivariate Model Sampling Procedure and a Method of Multidimensional Tailored Testing0 aMultivariate Model Sampling Procedure and a Method of Multidimen aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. 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