01989nas a2200133 4500008003900000245006800039210006800107300001000175490000700185520158200192100001701774700001901791856004501810 2019 d00aAdaptive Testing With a Hierarchical Item Response Theory Model0 aAdaptive Testing With a Hierarchical Item Response Theory Model a51-670 v433 aThe hierarchical item response theory (H-IRT) model is very flexible and allows a general factor and subfactors within an overall structure of two or more levels. When an H-IRT model with a large number of dimensions is used for an adaptive test, the computational burden associated with interim scoring and selection of subsequent items is heavy. An alternative approach for any high-dimension adaptive test is to reduce dimensionality for interim scoring and item selection and then revert to full dimensionality for final score reporting, thereby significantly reducing the computational burden. This study compared the accuracy and efficiency of final scoring for multidimensional, local multidimensional, and unidimensional item selection and interim scoring methods, using both simulated and real item pools. The simulation study was conducted under 10 conditions (i.e., five test lengths and two H-IRT models) with a simulated sample of 10,000 students. The study with the real item pool was conducted using item parameters from an actual 45-item adaptive test with a simulated sample of 10,000 students. Results indicate that the theta estimations provided by the local multidimensional and unidimensional item selection and interim scoring methods were relatively as accurate as the theta estimation provided by the multidimensional item selection and interim scoring method, especially during the real item pool study. In addition, the multidimensional method required the longest computation time and the unidimensional method required the shortest computation time.1 aWang, Wenhao1 aKingston, Neal uhttps://doi.org/10.1177/014662161876571401626nas a2200145 4500008003900000245008600039210006900125300001200194490000700206520117400213100001701387700001801404700001301422856004501435 2019 d00aApplication of Dimension Reduction to CAT Item Selection Under the Bifactor Model0 aApplication of Dimension Reduction to CAT Item Selection Under t a419-4340 v433 aMultidimensional computerized adaptive testing (MCAT) based on the bifactor model is suitable for tests with multidimensional bifactor measurement structures. Several item selection methods that proved to be more advantageous than the maximum Fisher information method are not practical for bifactor MCAT due to time-consuming computations resulting from high dimensionality. To make them applicable in bifactor MCAT, dimension reduction is applied to four item selection methods, which are the posterior-weighted Fisher D-optimality (PDO) and three non-Fisher information-based methods—posterior expected Kullback–Leibler information (PKL), continuous entropy (CE), and mutual information (MI). They were compared with the Bayesian D-optimality (BDO) method in terms of estimation precision. When both the general and group factors are the measurement objectives, BDO, PDO, CE, and MI perform equally well and better than PKL. When the group factors represent nuisance dimensions, MI and CE perform the best in estimating the general factor, followed by the BDO, PDO, and PKL. How the bifactor pattern and test length affect estimation accuracy was also discussed.1 aMao, Xiuzhen1 aZhang, Jiahui1 aXin, Tao uhttps://doi.org/10.1177/014662161881308600419nas a2200133 4500008004500000245005400045210005400099300001000153490000600163100001800169700001700187700001700204856006400221 2018 Engldsh 00aAdaptive Item Selection Under Matroid Constraints0 aAdaptive Item Selection Under Matroid Constraints a15-360 v61 aBengs, Daniel1 aBrefeld, Ulf1 aKröhne, Ulf uhttp://www.iacat.org/jcat/index.php/jcat/article/view/64/3201970nas a2200145 4500008004100000245008700041210006900128260005500197520137700252653003201629653001801661653002401679100001701703856010401720 2017 eng d00aAdapting Linear Models for Optimal Test Design to More Complex Test Specifications0 aAdapting Linear Models for Optimal Test Design to More Complex T aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Combinatorial optimization (CO) has proven to be a very helpful approach for addressing test assembly issues and for providing solutions. Furthermore, CO has been applied for several test designs, including: (1) for the development of linear test forms; (2) for computerized adaptive testing and; (3) for multistage testing. In his seminal work, van der Linden (2006) laid out the basis for using linear models for simultaneously assembling exams and item pools in a variety of conditions: (1) for single tests and multiple tests; (2) with item sets, etc. However, for some testing programs, the number and complexity of test specifications can grow rapidly. Consequently, the mathematical representation of the test assembly problem goes beyond most approaches reported either in van der Linden’s book or in the majority of other publications related to test assembly. In this presentation, we extend van der Linden’s framework by including the concept of blocks for test specifications. We modify the usual mathematical notation of a test assembly problem by including this concept and we show how it can be applied to various test designs. Finally, we will demonstrate an implementation of this approach in a stand-alone software, called the ATASolver.

Session Video

10aComplex Test Specifications10aLinear Models10aOptimal Test Design1 aMorin, Maxim uhttp://mail.iacat.org/adapting-linear-models-optimal-test-design-more-complex-test-specifications-005519nas a2200193 4500008004100000245009000041210006900131260005500200520480200255653002305057653001905080653002105099653002605120100002205146700002005168700001605188700001905204856010205223 2017 eng d00aAdaptive Item and Feedback Selection in Personalized Learning with a Network Approach0 aAdaptive Item and Feedback Selection in Personalized Learning wi aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Personalized learning is a term used to describe educational systems that adapt student-specific curriculum sequencing, pacing, and presentation based on their unique backgrounds, knowledge, preferences, interests, and learning goals. (Chen, 2008; Netcoh, 2016). The technological approach to personalized learning provides data-driven models to incorporate these adaptations automatically. Examples of applications include online learning systems, educational games, and revision-aid systems. In this study we introduce Bayesian networks as a methodology to implement an adaptive framework within a personalized learning environment. Existing ideas from Computerized Adaptive Testing (CAT) with Item Response Theory (IRT), where choices about content provision are based on maximizing information, are related to the goals of personalized learning environments. Personalized learning entails other goals besides efficient ability estimation by maximizing information, such as an adaptive configuration of preferences and feedback to the student. These considerations will be discussed and their application in networks will be illustrated.

Adaptivity in Personalized Learning.In standard CAT’s there is a focus on selecting items that provide maximum information about the ability of an individual at a certain point in time (Van der Linden & Glas, 2000). When learning is the main goal of testing, alternative adaptive item selection methods were explored by Eggen (2012). The adaptive choices made in personalized learning applications require additional adaptivity with respect to the following aspects; the moment of feedback, the kind of feedback, and the possibility for students to actively influence the learning process.

Bayesian Networks and Personalized Learning.Personalized learning aims at constructing a framework to incorporate all the aspects mentioned above. Therefore, the goal of this framework is not only to focus on retrieving ability estimates by choosing items on maximum information, but also to construct a framework that allows for these other factors to play a role. Plajner and Vomlel (2016) have already applied Bayesian Networks to adaptive testing, selecting items with help of entropy reduction. Almond et al. (2015) provide a reference work on Bayesian Networks in Educational Assessment. Both acknowledge the potential of the method in terms of features such as modularity options to build finer-grained models. IRT does not allow to model sub-skills very easily and to gather information on fine-grained level, due to its dependency on the assumption of generally one underlying trait. The local independence assumption in IRT implies being interested in mainly the student’s overall ability on the subject of interest. When the goal is to improve student’s learning, we are not just interested in efficiently coming to their test score on a global subject. One wants a model that is able to map educational problems and talents in detail over the whole educational program, while allowing for dependency between items. The moment in time can influence topics to be better mastered than others, and this is exactly what we can to get out of a model. The possibility to model flexible structures, estimate abilities on a very detailed level for sub-skills and to easily incorporate other variables such as feedback in Bayesian Networks makes it a very promising method for making adaptive choices in personalized learning. It is shown in this research how item and feedback selection can be performed with help of the promising Bayesian Networks. A student involvement possibility is also introduced and evaluated.

References

Almond, R. G., Mislevy, R. J., Steinberg, L. S., Yan, D., & Williamson, D. M. (2015). Bayesian Networks in Educational Assessment. Test. New York: Springer Science+Business Media. http://doi.org/10.1007/978-0-387-98138-3

Eggen, T.J.H.M. (2012) Computerized Adaptive Testing Item Selection in Computerized Adaptive Learning Systems. In: Eggen. TJHM & Veldkamp, BP.. (Eds). Psychometrics in Practice at RCEC. Enschede: RCEC

Netcoh, S. (2016, March). “What Do You Mean by ‘Personalized Learning?’. Croscutting Conversations in Education – Research, Reflections & Practice. Blogpost.

Plajner, M., & Vomlel, J. (2016). Student Skill Models in Adaptive Testing. In Proceedings of the Eighth International Conference on Probabilistic Graphical Models (pp. 403-414).

Van der Linden, W. J., & Glas, C. A. (2000). Computerized adaptive testing: Theory and practice. Dordrecht: Kluwer Academic Publishers.

Session Video

10afeedback selection10aitem selection10anetwork approach10apersonalized learning1 avan Buuren, Nikky1 aStraat, Hendrik1 aEggen, Theo1 aFox, Jean-Paul uhttp://mail.iacat.org/adaptive-item-and-feedback-selection-personalized-learning-network-approach01676nas a2200145 4500008004100000245004800041210004800089260005500137520120400192653000801396653002101404653001401425100002401439856006701463 2017 eng d00aAdaptivity in a Diagnostic Educational Test0 aAdaptivity in a Diagnostic Educational Test aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

During the past five years a diagnostic educational test for three subjects (writing Dutch, writing English and math) has been developed in the Netherlands. The test informs students and their teachers about the students’ strengths and weaknesses in such a manner that the learning process can be adjusted to their personal needs. It is a computer-based assessment for students in five different educational tracks midway secondary education that can yield diagnoses of many sub-skills. One of the main challenges at the outset of the development was to devise a way to deliver many diagnoses within a reasonably testing time. The answer to this challenge was to make the DET adaptive.

In this presentation we will discuss first how the adaptivity is shaped towards the purpose of the Diagnostic Educational Test. The adaptive design, particularly working with item blocks, will be discussed as well as the implemented adaptive rules. We will also show a simulation of different adaptive paths of students and some empirical information on the paths students took through the test

Session Video

10aCAT10aDiagnostic tests10aEducation1 aSchouwstra, Sanneke uhttp://mail.iacat.org/adaptivity-diagnostic-educational-test-002709nas a2200157 4500008004100000245007100041210006900112260005500181520213800236653000802374653002002382653001402402100002002416700002602436856008902462 2017 eng d00aAnalysis of CAT Precision Depending on Parameters of the Item Pool0 aAnalysis of CAT Precision Depending on Parameters of the Item Po aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The purpose of this research project is to analyze the measurement precision of a latent variable depending on parameters of the item pool. The influence of the following factors is analyzed:

Factor A – range of variation of items in the pool. This factor varies on three levels with the following ranges in logits: a1 – [-3.0; +3.0], a2 - [-4.0; +4.0], a3 - [-5.0; +5.0].

Factor B – number of items in the pool. The factor varies on six levels with the following number of items for every factor: b1 - 128, b2 - 256, b3 – 512, b4 - 1024, b5 – 2048, b6 – 4096. The items are evenly distributed in each of the variation ranges.

Factor C – examinees’ proficiency varies at 30 levels (c1, c2, …, c30), which are evenly distributed in the range [-3.0; +3.0] logit.

The investigation was based on a simulation experiment within the framework of the theory of latent variables.

Response Y is the precision of measurement of examinees’ proficiency, which is calculated as the difference between the true levels of examinees’ proficiency and estimates obtained by means of adaptive testing. Three factor ANOVA was used for data processing.

The following results were obtained:

1. Factor A is significant. Ceteris paribus, the greater the range of variation of items in the pool, the higher the estimation precision is.

2. Factor B is significant. Ceteris paribus, the greater the number of items in the pool, the higher the estimation precision is.

3. Factor C is statistically insignificant at level α = .05. It means that the precision of estimation of examinees’ proficiency is the same within the range of their variation.

4. The only significant interaction among all interactions is AB. The significance of this interaction is explained by the fact that increasing the number of items in the pool decreases the effect of the range of variation of items in the pool. 

Session Video

10aCAT10aItem parameters10aPrecision1 aMaslak, Anatoly1 aPozdniakov, Stanislav uhttps://drive.google.com/file/d/1Bwe58kOQRgCSbB8x6OdZTDK4OIm3LQI3/view?usp=drive_web01999nas a2200133 4500008003900000245011600039210006900155300001200224490000700236520154300243100001901786700001501805856004501820 2017 d00aApplication of Binary Searching for Item Exposure Control in Cognitive Diagnostic Computerized Adaptive Testing0 aApplication of Binary Searching for Item Exposure Control in Cog a561-5760 v413 aCognitive diagnosis has emerged as a new generation of testing theory for educational assessment after the item response theory (IRT). One distinct feature of cognitive diagnostic models (CDMs) is that they assume the latent trait to be discrete instead of continuous as in IRT. From this perspective, cognitive diagnosis bears a close resemblance to searching problems in computer science and, similarly, item selection problem in cognitive diagnostic computerized adaptive testing (CD-CAT) can be considered as a dynamic searching problem. Previously, item selection algorithms in CD-CAT were developed from information indices in information science and attempted to achieve a balance among several objectives by assigning different weights. As a result, they suffered from low efficiency from a tug-of-war competition among multiple goals in item selection and, at the same time, put an undue responsibility of assigning the weights for these goals by trial and error on users. Based on the searching problem perspective on CD-CAT, this article adapts the binary searching algorithm, one of the most well-known searching algorithms in searching problems, to item selection in CD-CAT. The two new methods, the stratified dynamic binary searching (SDBS) algorithm for fixed-length CD-CAT and the dynamic binary searching (DBS) algorithm for variable-length CD-CAT, can achieve multiple goals without any of the aforementioned issues. The simulation studies indicate their performances are comparable or superior to the previous methods.1 aZheng, Chanjin1 aWang, Chun uhttps://doi.org/10.1177/014662161770750901537nas a2200181 4500008003900000245006700039210006500106300001200171490000700183520099700190100002001187700001801207700002101225700002201246700002201268700002001290856004501310 2017 d00aATS-PD: An Adaptive Testing System for Psychological Disorders0 aATSPD An Adaptive Testing System for Psychological Disorders a792-8150 v773 aThe clinical assessment of mental disorders can be a time-consuming and error-prone procedure, consisting of a sequence of diagnostic hypothesis formulation and testing aimed at restricting the set of plausible diagnoses for the patient. In this article, we propose a novel computerized system for the adaptive testing of psychological disorders. The proposed system combines a mathematical representation of psychological disorders, known as the “formal psychological assessment,” with an algorithm designed for the adaptive assessment of an individual’s knowledge. The assessment algorithm is extended and adapted to the new application domain. Testing the system on a real sample of 4,324 healthy individuals, screened for obsessive-compulsive disorder, we demonstrate the system’s ability to support clinical testing, both by identifying the correct critical areas for each individual and by reducing the number of posed questions with respect to a standard written questionnaire.1 aDonadello, Ivan1 aSpoto, Andrea1 aSambo, Francesco1 aBadaloni, Silvana1 aGranziol, Umberto1 aVidotto, Giulio uhttps://doi.org/10.1177/001316441665218801559nas a2200169 4500008003900000022001400039245007700053210006900130300001300199490000700212520105800219100002101277700001401298700001901312700001701331856004101348 2015 d a1745-398400aAssessing Individual-Level Impact of Interruptions During Online Testing0 aAssessing IndividualLevel Impact of Interruptions During Online  a80–1050 v523 aWith an increase in the number of online tests, the number of interruptions during testing due to unexpected technical issues seems to be on the rise. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees' scores. Researchers such as Hill and Sinharay et al. examined the impact of interruptions at an aggregate level. However, there is a lack of research on the assessment of impact of interruptions at an individual level. We attempt to fill that void. We suggest four methodological approaches, primarily based on statistical hypothesis testing, linear regression, and item response theory, which can provide evidence on the individual-level impact of interruptions. We perform a realistic simulation study to compare the Type I error rate and power of the suggested approaches. We then apply the approaches to data from the 2013 Indiana Statewide Testing for Educational Progress-Plus (ISTEP+) test that experienced interruptions.1 aSinharay, Sandip1 aWan, Ping1 aChoi, Seung, W1 aKim, Dong-In uhttp://dx.doi.org/10.1111/jedm.1206401780nas a2200145 4500008003900000245008400039210006900123300001200192490000700204520131700211100001601528700002301544700001401567856005301581 2015 d00aa-Stratified Computerized Adaptive Testing in the Presence of Calibration Error0 aaStratified Computerized Adaptive Testing in the Presence of Cal a260-2830 v753 aa-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated item parameter estimates as if they are the true population parameter values. Consequently, capitalization on chance may occur. In this article, we examined the performance of the AST method under more realistic conditions where item parameter estimates instead of true parameter values are used in the CAT. Its performance was compared against that of the MFI method when the latter is used in conjunction with Sympson–Hetter or randomesque exposure control. Results indicate that the MFI method, even when combined with exposure control, is susceptible to capitalization on chance. This is particularly true when the calibration sample size is small. On the other hand, AST is more robust to capitalization on chance. Consistent with previous investigations using true item parameter values, AST yields much more balanced item pool usage, with a small loss in the precision of latent trait estimates. The loss is negligible when the test is as long as 40 items.1 aCheng, Ying1 aPatton, Jeffrey, M1 aShao, Can uhttp://epm.sagepub.com/content/75/2/260.abstract00493nas a2200121 4500008003900000245013500039210006900174300001200243490000700255100002100262700002000283856006800303 2013 d00aThe Applicability of Multidimensional Computerized Adaptive Testing for Cognitive Ability Measurement in Organizational Assessment0 aApplicability of Multidimensional Computerized Adaptive Testing  a123-1390 v131 aMakransky, Guido1 aGlas, Cees, A W uhttp://www.tandfonline.com/doi/abs/10.1080/15305058.2012.67235200573nas a2200133 4500008004500000022001400045245013400059210006900193300001200262490000700274100001700281700001600298856012500314 2013 Engldsh  a1530-505800aThe applicability of multidimensional computerized adaptive testing to cognitive ability measurement in organizational assessment0 aapplicability of multidimensional computerized adaptive testing  a123-1390 v131 aMakransky, G1 aGlas, C A W uhttp://mail.iacat.org/content/applicability-multidimensional-computerized-adaptive-testing-cognitive-ability-measurement01583nas a2200133 4500008003900000245012700039210006900166300001200235490000700247520111200254100001701366700001301383856005301396 2013 d00aThe Application of the Monte Carlo Approach to Cognitive Diagnostic Computerized Adaptive Testing With Content Constraints0 aApplication of the Monte Carlo Approach to Cognitive Diagnostic  a482-4960 v373 a

The Monte Carlo approach which has previously been implemented in traditional computerized adaptive testing (CAT) is applied here to cognitive diagnostic CAT to test the ability of this approach to address multiple content constraints. The performance of the Monte Carlo approach is compared with the performance of the modified maximum global discrimination index (MMGDI) method on simulations in which the only content constraint is on the number of items that measure each attribute. The results of the two simulation experiments show that (a) the Monte Carlo method fulfills all the test requirements and produces satisfactory measurement precision and item exposure results and (b) the Monte Carlo method outperforms the MMGDI method when the Monte Carlo method applies either the posterior-weighted Kullback–Leibler algorithm or the hybrid Kullback–Leibler information as the item selection index. Overall, the recovery rate of the knowledge states, the distribution of the item exposure, and the utilization rate of the item bank are improved when the Monte Carlo method is used.

1 aMao, Xiuzhen1 aXin, Tao uhttp://apm.sagepub.com/content/37/6/482.abstract00331nas a2200097 4500008004100000245004100041210003800082260003300120100001200153856006800165 2012 eng d00aAdaptives Testen [Adaptive testing].0 aAdaptives Testen Adaptive testing aBerlinbHeidelberg: Springer1 aFrey, A uhttp://mail.iacat.org/content/adaptives-testen-adaptive-testing00628nas a2200169 4500008004100000245008900041210006900130260001200199653000800211653002500219653002700244653002100271653001200292100002300304700001600327856011500343 2011 eng d00aAdaptive Item Calibration and Norming: Unique Considerations of a Global Deployment0 aAdaptive Item Calibration and Norming Unique Considerations of a c10/201110aCAT10acommon item equating10aFigural Reasoning Test10aitem calibration10anorming1 aSchwall, Alexander1 aSinar, Evan uhttp://mail.iacat.org/content/adaptive-item-calibration-and-norming-%0Bunique-considerations-global-deployment01667nas a2200169 4500008004100000020004100041022001300082245008200095210006900177250001500246260001000261520108000271100001301351700001601364700001501380856010201395 2011 Eng d a0165-1781 (Print)0165-1781 (Linking) a2120866000aApplying computerized adaptive testing to the CES-D scale: A simulation study0 aApplying computerized adaptive testing to the CESD scale A simul a2011/01/07 cJan 33 aIn this paper we studied the appropriateness of developing an adaptive version of the Center of Epidemiological Studies-Depression (CES-D, Radloff, 1977) scale. Computerized Adaptive Testing (CAT) involves the computerized administration of a test in which each item is dynamically selected from a pool of items until a pre-specified measurement precision is reached. Two types of analyses were performed using the CES-D responses of a large sample of adolescents (N=1392). First, it was shown that the items met the psychometric requirements needed for CAT. Second, CATs were simulated by using the existing item responses as if they had been collected adaptively. CATs selecting only a small number of items gave results which, in terms of depression measurement and criterion validity, were only marginally different from the results of full CES-D assessment. It was concluded that CAT is a very fruitful way of improving the efficiency of the CES-D questionnaire. The discussion addresses the strengths and limitations of the application of CAT in mental health research.1 aSmits, N1 aCuijpers, P1 aStraten, A uhttp://mail.iacat.org/content/applying-computerized-adaptive-testing-ces-d-scale-simulation-study00494nas a2200133 4500008003900000245008200039210006900121300001400190490000800204100001300212700001600225700001500241856010400256 2011 d00aApplying computerized adaptive testing to the CES-D scale: A simulation study0 aApplying computerized adaptive testing to the CESD scale A simul a147–1550 v1881 aSmits, N1 aCuijpers, P1 aStraten, A uhttp://mail.iacat.org/content/applying-computerized-adaptive-testing-ces-d-scale-simulation-study-000412nas a2200109 4500008004100000245006400041210006400105300001200169100001600181700001300197856009200210 2010 eng d00aAdaptive Mastery Testing Using a Multidimensional IRT Model0 aAdaptive Mastery Testing Using a Multidimensional IRT Model a409-4311 aGlas, C A W1 aVos, H J uhttp://mail.iacat.org/content/adaptive-mastery-testing-using-multidimensional-irt-model00361nas a2200097 4500008004100000245005600041210005600097300001200153100001600165856008200181 2010 eng d00aAdaptive Tests for Measuring Anxiety and Depression0 aAdaptive Tests for Measuring Anxiety and Depression a123-1361 aWalter, O B uhttp://mail.iacat.org/content/adaptive-tests-measuring-anxiety-and-depression00445nas a2200121 4500008004100000245006700041210006700108300001200175100001800187700001300205700001400218856009100232 2010 eng d00aAssembling an Inventory of Multistage Adaptive Testing Systems0 aAssembling an Inventory of Multistage Adaptive Testing Systems a247-2661 aBreithaupt, K1 aAriel, A1 aHare, D R uhttp://mail.iacat.org/content/assembling-inventory-multistage-adaptive-testing-systems00407nas a2200109 4500008004500000245006300045210006000108490000700168100001700175700001600192856008900208 2010 Engldsh 00aAn automatic online calibration design in adaptive testing0 aautomatic online calibration design in adaptive testing0 v111 aMakransky, G1 aGlas, C A W uhttp://mail.iacat.org/content/automatic-online-calibration-design-adaptive-testing-000497nas a2200097 4500008004100000245007500041210006900116260009700185100001500282856010200297 2009 eng d00aAdaptive computer-based tasks under an assessment engineering paradigm0 aAdaptive computerbased tasks under an assessment engineering par aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aLuecht, RM uhttp://mail.iacat.org/content/adaptive-computer-based-tasks-under-assessment-engineering-paradigm01920nas a2200109 4500008004100000245010800041210006900149260009700218520135200315100001901667856012401686 2009 eng d00aAdaptive item calibration: A process for estimating item parameters within a computerized adaptive test0 aAdaptive item calibration A process for estimating item paramete aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.3 aThe characteristics of an adaptive test change the characteristics of the field testing that is necessary to add items to an existing measurement scale. The process used to add field-test items to the adaptive test might lead to scale drift or disrupt the test by administering items of inappropriate difficulty. The current study makes use of the transitivity of examinee and item in item response theory to describe a process for adaptive item calibration. In this process an item is successively administered to examinees whose ability levels match the performance of a given field-test item. By treating the item as if it were taking an adaptive test, examinees can be selected who provide the most information about the item at its momentary difficulty level. This should provide a more efficient procedure for estimating item parameters. The process is described within the context of the one-parameter logistic IRT model. The process is then simulated to identify whether it can be more accurate and efficient than random presentation of field-test items to examinees. Results indicated that adaptive item calibration might provide a viable approach to item calibration within the context of an adaptive test. It might be most useful for expanding item pools in settings with small sample sizes or needs for large numbers of items.1 aKingsbury, G G uhttp://mail.iacat.org/content/adaptive-item-calibration-process-estimating-item-parameters-within-computerized-adaptive02652nas a2200181 4500008004100000020001400041245007900055210006900134300001000203490000700213520201300220653005202233653004002285100001502325700001302340700001502353856010202368 2009 eng d a0360-131500aAn adaptive testing system for supporting versatile educational assessment0 aadaptive testing system for supporting versatile educational ass a53-670 v523 aWith the rapid growth of computer and mobile technology, it is a challenge to integrate computer based test (CBT) with mobile learning (m-learning) especially for formative assessment and self-assessment. In terms of self-assessment, computer adaptive test (CAT) is a proper way to enable students to evaluate themselves. In CAT, students are assessed through a process that uses item response theory (IRT), a well-founded psychometric theory. Furthermore, a large item bank is indispensable to a test, but when a CAT system has a large item bank, the test item selection of IRT becomes more tedious. Besides the large item bank, item exposure mechanism is also essential to a testing system. However, IRT all lack the above-mentioned points. These reasons have motivated the authors to carry out this study. This paper describes a design issue aimed at the development and implementation of an adaptive testing system. The system can support several assessment functions and different devices. Moreover, the researchers apply a novel approach, particle swarm optimization (PSO) to alleviate the computational complexity and resolve the problem of item exposure. Throughout the development of the system, a formative evaluation was embedded into an integral part of the design methodology that was used for improving the system. After the system was formally released onto the web, some questionnaires and experiments were conducted to evaluate the usability, precision, and efficiency of the system. The results of these evaluations indicated that the system provides an adaptive testing for different devices and supports versatile assessment functions. Moreover, the system can estimate students' ability reliably and validly and conduct an adaptive test efficiently. Furthermore, the computational complexity of the system was alleviated by the PSO approach. By the approach, the test item selection procedure becomes efficient and the average best fitness values are very close to the optimal solutions.10aArchitectures for educational technology system10aDistance education and telelearning1 aHuang, Y-M1 aLin, Y-T1 aCheng, S-C uhttp://mail.iacat.org/content/adaptive-testing-system-supporting-versatile-educational-assessment00591nas a2200121 4500008004100000245010000041210006900141260009700210100001600307700001500323700001500338856011600353 2009 eng d00aAdequacy of an item pool measuring proficiency in English language to implement a CAT procedure0 aAdequacy of an item pool measuring proficiency in English langua aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aKarino, C A1 aCosta, D R1 aLaros, J A uhttp://mail.iacat.org/content/adequacy-item-pool-measuring-proficiency-english-language-implement-cat-procedure00637nas a2200145 4500008004100000245010000041210006900141260009700210100001300307700001300320700001300333700001400346700001700360856011400377 2009 eng d00aApplications of CAT in admissions to higher education in Israel: Twenty-two years of experience0 aApplications of CAT in admissions to higher education in Israel  aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aGafni, N1 aCohen, Y1 aRoded, K1 aBaumer, M1 aMoshinsky, A uhttp://mail.iacat.org/content/applications-cat-admissions-higher-education-israel-twenty-two-years-experience00572nas a2200109 4500008004100000245010000041210006900141260009700210100001400307700001700321856012400338 2009 eng d00aAn approach to implementing adaptive testing using item response theory both offline and online0 aapproach to implementing adaptive testing using item response th aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aPadaki, M1 aNatarajan, V uhttp://mail.iacat.org/content/approach-implementing-adaptive-testing-using-item-response-theory-both-offline-and-online00569nas a2200121 4500008004100000245008900041210006900130260009800199100001400297700001300311700001300324856011000337 2009 eng d00aAssessing the equivalence of Internet-based vs. paper-and-pencil psychometric tests.0 aAssessing the equivalence of Internetbased vs paperandpencil psy a D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aBaumer, M1 aRoded, K1 aGafni, N uhttp://mail.iacat.org/content/assessing-equivalence-internet-based-vs-paper-and-pencil-psychometric-tests00463nas a2200097 4500008004100000245006300041210006000104260009700164100001700261856008700278 2009 eng d00aAn automatic online calibration design in adaptive testing0 aautomatic online calibration design in adaptive testing aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aMakransky, G uhttp://mail.iacat.org/content/automatic-online-calibration-design-adaptive-testing00376nas a2200121 4500008004100000245004600041210004600087300001000133490000800143100001600151700001400167856007300181 2008 eng d00aAdaptive measurement of individual change0 aAdaptive measurement of individual change a49-580 v2161 aKim-Kang, G1 aWeiss, DJ uhttp://mail.iacat.org/content/adaptive-measurement-individual-change00358nas a2200109 4500008004100000245004500041210004500086300001100131490001100142100002300153856007200176 2008 eng d00aAdaptive models of psychological testing0 aAdaptive models of psychological testing a3–110 v216(1)1 avan der Linden, WJ uhttp://mail.iacat.org/content/adaptive-models-psychological-testing00352nas a2200109 4500008003900000245004500039210004500084300000800129490000800137100002300145856007400168 2008 d00aAdaptive Models of Psychological Testing0 aAdaptive Models of Psychological Testing a1-20 v2161 avan der Linden, WJ uhttp://mail.iacat.org/content/adaptive-models-psychological-testing-002866nas a2200325 4500008004100000020002700041245007400068210006900142250001500211260000800226300001100234490000700245520188300252653003202135653003202167653002502199653002302224653004802247653001102295653001102306653000902317653001602326653001902342653002702361100001502388700001502403700001002418700001202428856010002440 2008 eng d a1537-7385 (Electronic)00aAdaptive short forms for outpatient rehabilitation outcome assessment0 aAdaptive short forms for outpatient rehabilitation outcome asses a2008/09/23 cOct a842-520 v873 aOBJECTIVE: To develop outpatient Adaptive Short Forms for the Activity Measure for Post-Acute Care item bank for use in outpatient therapy settings. DESIGN: A convenience sample of 11,809 adults with spine, lower limb, upper limb, and miscellaneous orthopedic impairments who received outpatient rehabilitation in 1 of 127 outpatient rehabilitation clinics in the United States. We identified optimal items for use in developing outpatient Adaptive Short Forms based on the Basic Mobility and Daily Activities domains of the Activity Measure for Post-Acute Care item bank. Patient scores were derived from the Activity Measure for Post-Acute Care computerized adaptive testing program. Items were selected for inclusion on the Adaptive Short Forms based on functional content, range of item coverage, measurement precision, item exposure rate, and data collection burden. RESULTS: Two outpatient Adaptive Short Forms were developed: (1) an 18-item Basic Mobility Adaptive Short Form and (2) a 15-item Daily Activities Adaptive Short Form, derived from the same item bank used to develop the Activity Measure for Post-Acute Care computerized adaptive testing program. Both Adaptive Short Forms achieved acceptable psychometric properties. CONCLUSIONS: In outpatient postacute care settings where computerized adaptive testing outcome applications are currently not feasible, item response theory-derived Adaptive Short Forms provide the efficient capability to monitor patients' functional outcomes. The development of Adaptive Short Form functional outcome instruments linked by a common, calibrated item bank has the potential to create a bridge to outcome monitoring across postacute care settings and can facilitate the eventual transformation from Adaptive Short Forms to computerized adaptive testing applications easier and more acceptable to the rehabilitation community.10a*Activities of Daily Living10a*Ambulatory Care Facilities10a*Mobility Limitation10a*Treatment Outcome10aDisabled Persons/psychology/*rehabilitation10aFemale10aHumans10aMale10aMiddle Aged10aQuestionnaires10aRehabilitation Centers1 aJette, A M1 aHaley, S M1 aNi, P1 aMoed, R uhttp://mail.iacat.org/content/adaptive-short-forms-outpatient-rehabilitation-outcome-assessment00718nas a2200229 4500008004100000020004100041245005200082210005100134250001500185260000800200300000800208490000700216653003400223653005000257653001100307653003200318653001300350100001500363700001500378700001800393856007700411 2008 eng d a1075-2730 (Print)1075-2730 (Linking)00aAre we ready for computerized adaptive testing?0 aAre we ready for computerized adaptive testing a2008/04/02 cApr a3690 v5910a*Attitude of Health Personnel10a*Diagnosis, Computer-Assisted/instrumentation10aHumans10aMental Disorders/*diagnosis10aSoftware1 aUnick, G J1 aShumway, M1 aHargreaves, W uhttp://mail.iacat.org/content/are-we-ready-computerized-adaptive-testing03437nas a2200481 4500008004100000020004600041245013800087210006900225250001500294260000800309300001200317490000700329520191400336653002702250653002302277653003102300653001502331653001602346653001002362653002102372653002402393653002302417653003802440653001102478653002202489653001102511653001102522653000902533653003702542653002102579653003102600653002602631653001702657653003202674653001602706653002802722100001602750700001502766700001002781700001502791700002502806856012402831 2008 eng d a1532-821X (Electronic)0003-9993 (Linking)00aAssessing self-care and social function using a computer adaptive testing version of the pediatric evaluation of disability inventory0 aAssessing selfcare and social function using a computer adaptive a2008/04/01 cApr a622-6290 v893 aOBJECTIVE: To examine score agreement, validity, precision, and response burden of a prototype computer adaptive testing (CAT) version of the self-care and social function scales of the Pediatric Evaluation of Disability Inventory compared with the full-length version of these scales. DESIGN: Computer simulation analysis of cross-sectional and longitudinal retrospective data; cross-sectional prospective study. SETTING: Pediatric rehabilitation hospital, including inpatient acute rehabilitation, day school program, outpatient clinics; community-based day care, preschool, and children's homes. PARTICIPANTS: Children with disabilities (n=469) and 412 children with no disabilities (analytic sample); 38 children with disabilities and 35 children without disabilities (cross-validation sample). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Summary scores from prototype CAT applications of each scale using 15-, 10-, and 5-item stopping rules; scores from the full-length self-care and social function scales; time (in seconds) to complete assessments and respondent ratings of burden. RESULTS: Scores from both computer simulations and field administration of the prototype CATs were highly consistent with scores from full-length administration (r range, .94-.99). Using computer simulation of retrospective data, discriminant validity, and sensitivity to change of the CATs closely approximated that of the full-length scales, especially when the 15- and 10-item stopping rules were applied. In the cross-validation study the time to administer both CATs was 4 minutes, compared with over 16 minutes to complete the full-length scales. CONCLUSIONS: Self-care and social function score estimates from CAT administration are highly comparable with those obtained from full-length scale administration, with small losses in validity and precision and substantial decreases in administration time.10a*Disability Evaluation10a*Social Adjustment10aActivities of Daily Living10aAdolescent10aAge Factors10aChild10aChild, Preschool10aComputer Simulation10aCross-Over Studies10aDisabled Children/*rehabilitation10aFemale10aFollow-Up Studies10aHumans10aInfant10aMale10aOutcome Assessment (Health Care)10aReference Values10aReproducibility of Results10aRetrospective Studies10aRisk Factors10aSelf Care/*standards/trends10aSex Factors10aSickness Impact Profile1 aCoster, W J1 aHaley, S M1 aNi, P1 aDumas, H M1 aFragala-Pinkham, M A uhttp://mail.iacat.org/content/assessing-self-care-and-social-function-using-computer-adaptive-testing-version-pediatric01225nas a2200133 4500008003900000245008800039210006900127260000900196300001400205520072100219100002000940700002000960856011100980 2008 d00aAn Automated Decision System for Computer Adaptive Testing Using Genetic Algorithms0 aAutomated Decision System for Computer Adaptive Testing Using Ge bIEEE a655–6603 aThis paper proposes an approach to solve the triangle decision tree problem for computer adaptive testing (CAT) using genetic algorithms (GAs). In this approach, item response theory (IRT) parameters composed of discrimination, difficulty, and guess are firstly obtained and stored in an item bank. Then a fitness function, which is based on IRT parameters, of GAs for obtaining an optimal solution is set up. Finally, the GAs is applied to the parameters of the item bank so that an optimal decision tree is generated. Based on a six-level triangle-decision tree for examination items, the experimental results show that the optimal decision tree can be generated correctly when compared with the standard patterns.1 aPhankokkruad, M1 aWoraratpanya, K uhttp://mail.iacat.org/content/automated-decision-system-computer-adaptive-testing-using-genetic-algorithms00545nas a2200121 4500008004100000245007500041210006900116260009700185100001500282700001500297700001300312856009800325 2007 eng d00aAdaptive estimators of trait level in adaptive testing: Some proposals0 aAdaptive estimators of trait level in adaptive testing Some prop aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aRaîche, G1 aBlais, J G1 aMagis, D uhttp://mail.iacat.org/content/adaptive-estimators-trait-level-adaptive-testing-some-proposals00527nas a2200109 4500008004100000245007700041210006900118260009700187100001300284700002200297856009800319 2007 eng d00aAdaptive testing with the multi-unidimensional pairwise preference model0 aAdaptive testing with the multiunidimensional pairwise preferenc aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aStark, S1 aChernyshenko, O S uhttp://mail.iacat.org/content/adaptive-testing-multi-unidimensional-pairwise-preference-model02338nas a2200145 4500008004100000020002200041245011100063210006900174250001500243300001200258490000700270520177800277100001602055856012102071 2007 eng d a0962-9343 (Print)00aApplying item response theory and computer adaptive testing: The challenges for health outcomes assessment0 aApplying item response theory and computer adaptive testing The  a2007/04/10 a187-1940 v163 aOBJECTIVES: We review the papers presented at the NCI/DIA conference, to identify areas of controversy and uncertainty, and to highlight those aspects of item response theory (IRT) and computer adaptive testing (CAT) that require theoretical or empirical research in order to justify their application to patient reported outcomes (PROs). BACKGROUND: IRT and CAT offer exciting potential for the development of a new generation of PRO instruments. However, most of the research into these techniques has been in non-healthcare settings, notably in education. Educational tests are very different from PRO instruments, and consequently problematic issues arise when adapting IRT and CAT to healthcare research. RESULTS: Clinical scales differ appreciably from educational tests, and symptoms have characteristics distinctly different from examination questions. This affects the transferring of IRT technology. Particular areas of concern when applying IRT to PROs include inadequate software, difficulties in selecting models and communicating results, insufficient testing of local independence and other assumptions, and a need of guidelines for estimating sample size requirements. Similar concerns apply to differential item functioning (DIF), which is an important application of IRT. Multidimensional IRT is likely to be advantageous only for closely related PRO dimensions. CONCLUSIONS: Although IRT and CAT provide appreciable potential benefits, there is a need for circumspection. Not all PRO scales are necessarily appropriate targets for this methodology. Traditional psychometric methods, and especially qualitative methods, continue to have an important role alongside IRT. Research should be funded to address the specific concerns that have been identified.1 aFayers, P M uhttp://mail.iacat.org/content/applying-item-response-theory-and-computer-adaptive-testing-challenges-health-outcomes01430nas a2200133 4500008003900000245009900039210006900138300000900207490000700216520097800223100002301201700002101224856005101245 2007 d00aAutomated Simultaneous Assembly of Multistage Testlets for a High-Stakes Licensing Examination0 aAutomated Simultaneous Assembly of Multistage Testlets for a Hig a5-200 v673 a

Many challenges exist for high-stakes testing programs offering continuous computerized administration. The automated assembly of test questions to exactly meet content and other requirements, provide uniformity, and control item exposure can be modeled and solved by mixed-integer programming (MIP) methods. A case study of the computerized licensing examination of the American Institute of Certified Public Accountants is offered as one application of MIP techniques for test assembly. The solution illustrates assembly for a computer-adaptive multistage testing design. However, the general form of the constraint-based solution can be modified to generate optimal test designs for paper-based or computerized administrations, regardless of the specific psychometric model. An extension of this methodology allows for long-term planning for the production and use of test content on the basis of an exact psychometric test designs and administration schedules.

1 aBreithaupt, Krista1 aHare, Donovan, R uhttp://epm.sagepub.com/content/67/1/5.abstract00460nas a2200121 4500008003900000245009100039210007600130300001200206490000600218100002800224700002100252856006500273 2007 d00aA “Rearrangement Procedure” For Scoring Adaptive Tests with Review Options0 a“Rearrangement Procedure†For Scoring Adaptive Tests with Rev a387-4070 v71 aPapanastasiou, Elena, C1 aReckase, Mark, D uhttp://www.tandfonline.com/doi/abs/10.1080/1530505070163226202177nas a2200181 4500008004100000020002200041245006200063210006200125260003800187300001200225490000700237520155900244653002901803653003401832653001801866100002201884856008901906 2006 eng d a0033-3018 (Print)00aAdaptive success control in computerized adaptive testing0 aAdaptive success control in computerized adaptive testing bPabst Science Publishers: Germany a436-4500 v483 aIn computerized adaptive testing (CAT) procedures within the framework of probabilistic test theory the difficulty of an item is adjusted to the ability of the respondent, with the aim of maximizing the amount of information generated per item, thereby also increasing test economy and test reasonableness. However, earlier research indicates that respondents might feel over-challenged by a constant success probability of p = 0.5 and therefore cannot come to a sufficiently high answer certainty within a reasonable timeframe. Consequently response time per item increases, which -- depending on the test material -- can outweigh the benefit of administering optimally informative items. Instead of a benefit, the result of using CAT procedures could be a loss of test economy. Based on this problem, an adaptive success control algorithm was designed and tested, adapting the success probability to the working style of the respondent. Persons who need higher answer certainty in order to come to a decision are detected and receive a higher success probability, in order to minimize the test duration (not the number of items as in classical CAT). The method is validated on the re-analysis of data from the Adaptive Matrices Test (AMT, Hornke, Etzel & Rettig, 1999) and by the comparison between an AMT version using classical CAT and an experimental version using Adaptive Success Control. The results are discussed in the light of psychometric and psychological aspects of test quality. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aadaptive success control10acomputerized adaptive testing10aPsychometrics1 aHäusler, Joachim uhttp://mail.iacat.org/content/adaptive-success-control-computerized-adaptive-testing01638nas a2200217 4500008004100000020004600041245008900087210006900176260002500245300000900270490000700279520083400286653001901120653002801139653003001167653003301197653002101230653003501251100001801286856011601304 2006 eng d a0895-7347 (Print); 1532-4818 (Electronic)00aApplying Bayesian item selection approaches to adaptive tests using polytomous items0 aApplying Bayesian item selection approaches to adaptive tests us bLawrence Erlbaum: US a1-200 v193 aThis study applied the maximum expected information (MEI) and the maximum posterior- weighted information (MPI) approaches of computer adaptive testing item selection to the case of a test using polytomous items following the partial credit model. The MEI and MPI approaches are described. A simulation study compared the efficiency of ability estimation using the MEI and MPI approaches to the traditional maximal item information (MII) approach. The results of the simulation study indicated that the MEI and MPI approaches led to a superior efficiency of ability estimation compared with the MII approach. The superiority of the MEI and MPI approaches over the MII approach was greatest when the bank contained items having a relatively peaked information function. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aadaptive tests10aBayesian item selection10acomputer adaptive testing10amaximum expected information10apolytomous items10aposterior weighted information1 aPenfield, R D uhttp://mail.iacat.org/content/applying-bayesian-item-selection-approaches-adaptive-tests-using-polytomous-items01964nas a2200265 4500008004100000020002200041245008200063210006900145260002600214300001000240490000700250520112900257653001501386653003401401653001401435653001701449653002401466653001501490653002201505653001501527100002301542700001301565700001801578856010201596 2006 eng d a1076-9986 (Print)00aAssembling a computerized adaptive testing item pool as a set of linear tests0 aAssembling a computerized adaptive testing item pool as a set of bSage Publications: US a81-990 v313 aTest-item writing efforts typically results in item pools with an undesirable correlational structure between the content attributes of the items and their statistical information. If such pools are used in computerized adaptive testing (CAT), the algorithm may be forced to select items with less than optimal information, that violate the content constraints, and/or have unfavorable exposure rates. Although at first sight somewhat counterintuitive, it is shown that if the CAT pool is assembled as a set of linear test forms, undesirable correlations can be broken down effectively. It is proposed to assemble such pools using a mixed integer programming model with constraints that guarantee that each test meets all content specifications and an objective function that requires them to have maximal information at a well-chosen set of ability values. An empirical example with a previous master pool from the Law School Admission Test (LSAT) yielded a CAT with nearly uniform bias and mean-squared error functions for the ability estimator and item-exposure rates that satisfied the target for all items in the pool. 10aAlgorithms10acomputerized adaptive testing10aitem pool10alinear tests10amathematical models10astatistics10aTest Construction10aTest Items1 avan der Linden, WJ1 aAriel, A1 aVeldkamp, B P uhttp://mail.iacat.org/content/assembling-computerized-adaptive-testing-item-pool-set-linear-tests00374nas a2200121 4500008004100000245004100041210004100082300001200123490001000135100001700145700001900162856007100181 2006 eng d00aAssessing CAT Test Security Severity0 aAssessing CAT Test Security Severity a62–630 v30(1)1 aYi, Zhang, J1 aChang, Hua-Hua uhttp://mail.iacat.org/content/assessing-cat-test-security-severity00545nas a2200097 4500008004100000245006200041210006200103260017400165100001900339856008900358 2005 eng d00aAdaptive orientation methods in computer adaptive testing0 aAdaptive orientation methods in computer adaptive testing aProceedings E-Learn 2005 World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 1290-1295, Vancouver, Canada, AACE, October 2005.1 aEconomides, AA uhttp://mail.iacat.org/content/adaptive-orientation-methods-computer-adaptive-testing00509nam a2200097 4500008004100000245009800041210006900139260006400208100001900272856012000291 2005 eng d00aAdaptive selection of personality items to inform a neural network predicting job performance0 aAdaptive selection of personality items to inform a neural netwo aUnpublished doctoral dissertation, University of Washington1 aThissen-Roe, A uhttp://mail.iacat.org/content/adaptive-selection-personality-items-inform-neural-network-predicting-job-performance01470nas a2200217 4500008004100000245015400041210006900195260004600264300001200310520063100322653003000953653001100983653002500994653001601019653002201035653002301057100001801080700001501098700001401113856012501127 2005 eng d00aApplications of item response theory to improve health outcomes assessment: Developing item banks, linking instruments, and computer-adaptive testing0 aApplications of item response theory to improve health outcomes  aCambridge, UKbCambridge University Press a445-4643 a(From the chapter) The current chapter builds on Reise's introduction to the basic concepts, assumptions, popular models, and important features of IRT and discusses the applications of item response theory (IRT) modeling to health outcomes assessment. In particular, we highlight the critical role of IRT modeling in: developing an instrument to match a study's population; linking two or more instruments measuring similar constructs on a common metric; and creating item banks that provide the foundation for tailored short-form instruments or for computerized adaptive assessments. (PsycINFO Database Record (c) 2005 APA )10aComputer Assisted Testing10aHealth10aItem Response Theory10aMeasurement10aTest Construction10aTreatment Outcomes1 aHambleton, RK1 aGotay, C C1 aSnyder, C uhttp://mail.iacat.org/content/applications-item-response-theory-improve-health-outcomes-assessment-developing-item-banks03124nas a2200385 4500008004100000020002200041245012900063210006900192250001500261260000800276300001000284490000700294520188400301653002502185653002702210653001502237653001002252653002102262653002802283653003802311653001102349653001102360653001102371653000902382653004602391653002702437653003002464653003202494100001502526700001602541700001602557700001502573700002502588856012502613 2005 eng d a0003-9993 (Print)00aAssessing mobility in children using a computer adaptive testing version of the pediatric evaluation of disability inventory0 aAssessing mobility in children using a computer adaptive testing a2005/05/17 cMay a932-90 v863 aOBJECTIVE: To assess score agreement, validity, precision, and response burden of a prototype computerized adaptive testing (CAT) version of the Mobility Functional Skills Scale (Mob-CAT) of the Pediatric Evaluation of Disability Inventory (PEDI) as compared with the full 59-item version (Mob-59). DESIGN: Computer simulation analysis of cross-sectional and longitudinal retrospective data; and cross-sectional prospective study. SETTING: Pediatric rehabilitation hospital, including inpatient acute rehabilitation, day school program, outpatient clinics, community-based day care, preschool, and children's homes. PARTICIPANTS: Four hundred sixty-nine children with disabilities and 412 children with no disabilities (analytic sample); 41 children without disabilities and 39 with disabilities (cross-validation sample). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Summary scores from a prototype Mob-CAT application and versions using 15-, 10-, and 5-item stopping rules; scores from the Mob-59; and number of items and time (in seconds) to administer assessments. RESULTS: Mob-CAT scores from both computer simulations (intraclass correlation coefficient [ICC] range, .94-.99) and field administrations (ICC=.98) were in high agreement with scores from the Mob-59. Using computer simulations of retrospective data, discriminant validity, and sensitivity to change of the Mob-CAT closely approximated that of the Mob-59, especially when using the 15- and 10-item stopping rule versions of the Mob-CAT. The Mob-CAT used no more than 15% of the items for any single administration, and required 20% of the time needed to administer the Mob-59. CONCLUSIONS: Comparable score estimates for the PEDI mobility scale can be obtained from CAT administrations, with losses in validity and precision for shorter forms, but with a considerable reduction in administration time.10a*Computer Simulation10a*Disability Evaluation10aAdolescent10aChild10aChild, Preschool10aCross-Sectional Studies10aDisabled Children/*rehabilitation10aFemale10aHumans10aInfant10aMale10aOutcome Assessment (Health Care)/*methods10aRehabilitation Centers10aRehabilitation/*standards10aSensitivity and Specificity1 aHaley, S M1 aRaczek, A E1 aCoster, W J1 aDumas, H M1 aFragala-Pinkham, M A uhttp://mail.iacat.org/content/assessing-mobility-children-using-computer-adaptive-testing-version-pediatric-evaluation-000640nas a2200169 4500008004100000020001300041245012900054210006900183300001200252490000700264100001300271700001400284700001400298700001300312700002200325856012300347 2005 eng d a0003999300aAssessing Mobility in Children Using a Computer Adaptive Testing Version of the Pediatric Evaluation of Disability Inventory0 aAssessing Mobility in Children Using a Computer Adaptive Testing a932-9390 v861 aHaley, S1 aRaczek, A1 aCoster, W1 aDumas, H1 aFragalapinkham, M uhttp://mail.iacat.org/content/assessing-mobility-children-using-computer-adaptive-testing-version-pediatric-evaluation01374nas a2200193 4500008004100000245005000041210004700091300001000138490000600148520081200154653001700966653002100983653002601004653002501030100001501055700001401070700002301084856007301107 2005 eng d00aAn Authoring Environment for Adaptive Testing0 aAuthoring Environment for Adaptive Testing a66-760 v83 a

SIETTE is a web-based adaptive testing system. It implements Computerized Adaptive Tests. These tests are tailor-made, theory-based tests, where questions shown to students, finalization of the test, and student knowledge estimation is accomplished adaptively. To construct these tests, SIETTE has an authoring environment comprising a suite of tools that helps teachers create questions and tests properly, and analyze students’ performance after taking a test. In this paper, we present this authoring environment in the
framework of adaptive testing. As will be shown, this set of visual tools, that contain some adaptable eatures, can be useful for teachers lacking skills in this kind of testing. Additionally, other systems that implement adaptive testing will be studied. 

10aAdaptability10aAdaptive Testing10aAuthoring environment10aItem Response Theory1 aGuzmán, E1 aConejo, R1 aGarcía-Hervás, E uhttp://mail.iacat.org/content/authoring-environment-adaptive-testing00443nas a2200133 4500008003900000245005900039210005900098300001200157490000600169100002300175700002000198700002500218856006600243 2005 d00aAutomated Simultaneous Assembly for Multistage Testing0 aAutomated Simultaneous Assembly for Multistage Testing a319-3300 v51 aBreithaupt, Krista1 aAriel, Adelaide1 aVeldkamp, Bernard, P uhttp://www.tandfonline.com/doi/abs/10.1207/s15327574ijt0503_800431nas a2200097 4500008004100000245004600041210004200087260011900129100001600248856006900264 2004 eng d00aThe ABCs of Computerized Adaptive Testing0 aABCs of Computerized Adaptive Testing aT. M. Wood and W. Zhi (Eds.), Measurement issues and practice in physical activity. Champaign, IL: Human kinetics.1 aGershon, RC uhttp://mail.iacat.org/content/abcs-computerized-adaptive-testing00503nas a2200109 4500008004100000245011600041210006900157260001700226100001400243700001300257856012300270 2004 eng d00aAchieving accuracy of retest calibration for a national CAT placement examination with a restricted test length0 aAchieving accuracy of retest calibration for a national CAT plac aSan Diego CA1 aWang, X B1 aWiley, A uhttp://mail.iacat.org/content/achieving-accuracy-retest-calibration-national-cat-placement-examination-restricted-test03708nas a2200481 4500008004100000245005200041210005200093300001200145490000700157520221100164653001902375653002902394653005802423653001002481653005302491653000902544653001102553653002502564653002602589653003302615653001102648653001002659653000902669653001602678653002402694653007402718653001802792653002902810653005802839653003102897653003202928653003602960653003202996100001503028700001603043700001603059700001603075700001003091700001403101700001803115700001503133856007803148 2004 eng d00aActivity outcome measurement for postacute care0 aActivity outcome measurement for postacute care aI49-1610 v423 aBACKGROUND: Efforts to evaluate the effectiveness of a broad range of postacute care services have been hindered by the lack of conceptually sound and comprehensive measures of outcomes. It is critical to determine a common underlying structure before employing current methods of item equating across outcome instruments for future item banking and computer-adaptive testing applications. OBJECTIVE: To investigate the factor structure, reliability, and scale properties of items underlying the Activity domains of the International Classification of Functioning, Disability and Health (ICF) for use in postacute care outcome measurement. METHODS: We developed a 41-item Activity Measure for Postacute Care (AM-PAC) that assessed an individual's execution of discrete daily tasks in his or her own environment across major content domains as defined by the ICF. We evaluated the reliability and discriminant validity of the prototype AM-PAC in 477 individuals in active rehabilitation programs across 4 rehabilitation settings using factor analyses, tests of item scaling, internal consistency reliability analyses, Rasch item response theory modeling, residual component analysis, and modified parallel analysis. RESULTS: Results from an initial exploratory factor analysis produced 3 distinct, interpretable factors that accounted for 72% of the variance: Applied Cognition (44%), Personal Care & Instrumental Activities (19%), and Physical & Movement Activities (9%); these 3 activity factors were verified by a confirmatory factor analysis. Scaling assumptions were met for each factor in the total sample and across diagnostic groups. Internal consistency reliability was high for the total sample (Cronbach alpha = 0.92 to 0.94), and for specific diagnostic groups (Cronbach alpha = 0.90 to 0.95). Rasch scaling, residual factor, differential item functioning, and modified parallel analyses supported the unidimensionality and goodness of fit of each unique activity domain. CONCLUSIONS: This 3-factor model of the AM-PAC can form the conceptual basis for common-item equating and computer-adaptive applications, leading to a comprehensive system of outcome instruments for postacute care settings.10a*Self Efficacy10a*Sickness Impact Profile10aActivities of Daily Living/*classification/psychology10aAdult10aAftercare/*standards/statistics & numerical data10aAged10aBoston10aCognition/physiology10aDisability Evaluation10aFactor Analysis, Statistical10aFemale10aHuman10aMale10aMiddle Aged10aMovement/physiology10aOutcome Assessment (Health Care)/*methods/statistics & numerical data10aPsychometrics10aQuestionnaires/standards10aRehabilitation/*standards/statistics & numerical data10aReproducibility of Results10aSensitivity and Specificity10aSupport, U.S. Gov't, Non-P.H.S.10aSupport, U.S. Gov't, P.H.S.1 aHaley, S M1 aCoster, W J1 aAndres, P L1 aLudlow, L H1 aNi, P1 aBond, T L1 aSinclair, S J1 aJette, A M uhttp://mail.iacat.org/content/activity-outcome-measurement-postacute-care01885nas a2200241 4500008004100000245006000041210005900101260004800160300001200208520112200220653001501342653003401357653002201391653002101413653001901434653001601453653001701469653001301486653002801499100001301527700001601540856008701556 2004 eng d00aAdaptive computerized educational systems: A case study0 aAdaptive computerized educational systems A case study aSan Diego, CA. USAbElsevier Academic Press a143-1693 a(Created by APA) Adaptive instruction describes adjustments typical of one-on-one tutoring as discussed in the college tutorial scenario. So computerized adaptive instruction refers to the use of computer software--almost always incorporating artificially intelligent services--which has been designed to adjust both the presentation of information and the form of questioning to meet the current needs of an individual learner. This chapter describes a system for Internet-delivered adaptive instruction. The author attempts to demonstrate a sharp difference between the teaching that takes place outside of the classroom in universities and the kind that is at least afforded, if not taken advantage of by many, students in a more personalized educational setting such as those in the small liberal arts colleges. The author describes a computer-based technology that allows that gap to be bridged with the advantage of at least having more highly prepared learners sitting in college classrooms. A limited range of emerging research that supports that proposition is cited. (PsycINFO Database Record (c) 2005 APA )10aArtificial10aComputer Assisted Instruction10aComputer Software10aHigher Education10aIndividualized10aInstruction10aIntelligence10aInternet10aUndergraduate Education1 aRay, R D1 aMalott, R W uhttp://mail.iacat.org/content/adaptive-computerized-educational-systems-case-study00452nas a2200121 4500008004100000245006900041210006900110300001200179490001000191100001700201700001900218856009300237 2004 eng d00aAdaptive exploration of user knowledge in computer based testing0 aAdaptive exploration of user knowledge in computer based testing a322-3270 v3 (1)1 aLamboudis, D1 aEconomides, AA uhttp://mail.iacat.org/content/adaptive-exploration-user-knowledge-computer-based-testing01341nas a2200145 4500008003900000245008200039210006900121300001200190490000700202520087300209100001601082700001901098700002101117856005701138 2004 d00aAdaptive Testing With Regression Trees in the Presence of Multidimensionality0 aAdaptive Testing With Regression Trees in the Presence of Multid a293-3160 v293 a

It is unrealistic to suppose that standard item response theory (IRT) models will be appropriate for all the new and currently considered computer-based tests. In addition to developing new models, we also need to give attention to the possibility of constructing and analyzing new tests without the aid of strong models. Computerized adaptive testing currently relies heavily on IRT. Alternative, empirically based, nonparametric adaptive testing algorithms exist, but their properties are little known. This article introduces a nonparametric, tree-based algorithm for adaptive testing and shows that it may be superior to conventional, IRT-based adaptive testing in cases where the IRT assumptions are not satisfied. In particular, it shows that the tree-based approach clearly outperformed (one-dimensional) IRT when the pool was strongly two-dimensional.

1 aYan, Duanli1 aLewis, Charles1 aStocking, Martha uhttp://jeb.sagepub.com/cgi/content/abstract/29/3/29302777nas a2200421 4500008004100000020004600041245011200087210006900199250001500268260001000283300000700293490000600300520144100306653002701747653003001774653004701804653001001851653000901861653002201870653002801892653001101920653001101931653002001942653000901962653001601971653001601987653001902003653001602022653003502038653002902073653004002102653003002142100001402172700001702186700001702203700001402220856012102234 2004 eng d a1477-7525 (Electronic)1477-7525 (Linking)00aThe AMC Linear Disability Score project in a population requiring residential care: psychometric properties0 aAMC Linear Disability Score project in a population requiring re a2004/08/05 cAug 3 a420 v23 aBACKGROUND: Currently there is a lot of interest in the flexible framework offered by item banks for measuring patient relevant outcomes, including functional status. However, there are few item banks, which have been developed to quantify functional status, as expressed by the ability to perform activities of daily life. METHOD: This paper examines the psychometric properties of the AMC Linear Disability Score (ALDS) project item bank using an item response theory model and full information factor analysis. Data were collected from 555 respondents on a total of 160 items. RESULTS: Following the analysis, 79 items remained in the item bank. The remaining 81 items were excluded because of: difficulties in presentation (1 item); low levels of variation in response pattern (28 items); significant differences in measurement characteristics for males and females or for respondents under or over 85 years old (26 items); or lack of model fit to the data at item level (26 items). CONCLUSIONS: It is conceivable that the item bank will have different measurement characteristics for other patient or demographic populations. However, these results indicate that the ALDS item bank has sound psychometric properties for respondents in residential care settings and could form a stable base for measuring functional status in a range of situations, including the implementation of computerised adaptive testing of functional status.10a*Disability Evaluation10a*Health Status Indicators10aActivities of Daily Living/*classification10aAdult10aAged10aAged, 80 and over10aData Collection/methods10aFemale10aHumans10aLogistic Models10aMale10aMiddle Aged10aNetherlands10aPilot Projects10aProbability10aPsychometrics/*instrumentation10aQuestionnaires/standards10aResidential Facilities/*utilization10aSeverity of Illness Index1 aHolman, R1 aLindeboom, R1 aVermeulen, M1 aHaan, R J uhttp://mail.iacat.org/content/amc-linear-disability-score-project-population-requiring-residential-care-psychometric00522nam a2200097 4500008004100000245010500041210006900146260006900215100001700284856012300301 2004 eng d00aThe application of cognitive diagnosis and computerized adaptive testing to a large-scale assessment0 aapplication of cognitive diagnosis and computerized adaptive tes aUnpublished doctoral dissertation, University of Texas at Austin1 aMcGlohen, MK uhttp://mail.iacat.org/content/application-cognitive-diagnosis-and-computerized-adaptive-testing-large-scale-assessment01669nas a2200229 4500008004100000245005500041210005300096300000800149490000700157520102900164653002101193653001201214653003001226653001801256653000901274100001701283700001201300700001501312700001601327700001401343856008201357 2004 eng d00aAssisted self-adapted testing: A comparative study0 aAssisted selfadapted testing A comparative study a2-90 v203 aA new type of self-adapted test (S-AT), called Assisted Self-Adapted Test (AS-AT), is presented. It differs from an ordinary S-AT in that prior to selecting the difficulty category, the computer advises examinees on their best difficulty category choice, based on their previous performance. Three tests (computerized adaptive test, AS-AT, and S-AT) were compared regarding both their psychometric (precision and efficiency) and psychological (anxiety) characteristics. Tests were applied in an actual assessment situation, in which test scores determined 20% of term grades. A sample of 173 high school students participated. Neither differences in posttest anxiety nor ability were obtained. Concerning precision, AS-AT was as precise as CAT, and both revealed more precision than S-AT. It was concluded that AS-AT acted as a CAT concerning precision. Some hints, but not conclusive support, of the psychological similarity between AS-AT and S-AT was also found. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aAnxiety10aComputer Assisted Testing10aPsychometrics10aTest1 aHontangas, P1 aOlea, J1 aPonsoda, V1 aRevuelta, J1 aWise, S L uhttp://mail.iacat.org/content/assisted-self-adapted-testing-comparative-study00497nas a2200121 4500008004100000245009100041210006900132260001700201100001800218700001300236700001600249856011000265 2004 eng d00aAutomated Simultaneous Assembly of Multi-Stage Testing for the Uniform CPA Examination0 aAutomated Simultaneous Assembly of MultiStage Testing for the Un aSan Diego CA1 aBreithaupt, K1 aAriel, A1 aVeldkamp, B uhttp://mail.iacat.org/content/automated-simultaneous-assembly-multi-stage-testing-uniform-cpa-examination00479nas a2200109 4500008004100000245009600041210006900137260001500206100001200221700001700233856011900250 2003 eng d00aAccuracy of reading and mathematics ability estimates under the shadow-test constraint MCAT0 aAccuracy of reading and mathematics ability estimates under the  aChicago IL1 aLi, Y H1 aSchafer, W D uhttp://mail.iacat.org/content/accuracy-reading-and-mathematics-ability-estimates-under-shadow-test-constraint-mcat01391nas a2200121 4500008004100000245010300041210006900144260011000213300000900323520079300332100001701125856012701142 2003 eng d00aAn adaptation of stochastic curtailment to truncate Wald’s SPRT in computerized adaptive testing0 aadaptation of stochastic curtailment to truncate Wald s SPRT in  aLos AngelesbNational Center for Research on Evaluation, Standards, and Student TestingcSteptember, 2003 a1-263 a

Computerized adaptive testing (CAT) has been shown to increase eÆciency in educational measurement. One common application of CAT is to classify students as either pro cient or not proficient in ability. A truncated form of Wald's sequential probability ratio test (SPRT), in which examination is halted after a prespeci ed number of questions, has been proposed to provide a diagnosis of prociency. This article studies the further truncation provided by stochastic curtailment, where an exam is stopped early if completion of the remaining questions would be unlikely to alter the classi cation of the examinee. In a simulation study presented, the increased truncation is shown to offer substantial improvement in test length with only a slight decrease in accuracy.

1 aFinkelman, M uhttp://mail.iacat.org/content/adaptation-stochastic-curtailment-truncate-wald%E2%80%99s-sprt-computerized-adaptive-testing00551nas a2200121 4500008004100000245006500041210006500106260011000171100001700281700001900298700002000317856009200337 2003 eng d00aAdaptive exploration of assessment results under uncertainty0 aAdaptive exploration of assessment results under uncertainty aProceedings 3rd IEEE ternational Conference on Advanced Learning Technologies, ICALT '03, 460-461, 2003.1 aLamboudis, D1 aEconomides, AA1 aPapastergiou, A uhttp://mail.iacat.org/content/adaptive-exploration-assessment-results-under-uncertainty00478nas a2200097 4500008004100000245012000041210006900161260001500230100001600245856011900261 2003 eng d00aAn adaptive exposure control algorithm for computerized adaptive testing using a sharing item response theory model0 aadaptive exposure control algorithm for computerized adaptive te aChicago IL1 aSegall, D O uhttp://mail.iacat.org/content/adaptive-exposure-control-algorithm-computerized-adaptive-testing-using-sharing-item00475nas a2200121 4500008004100000245008000041210006900121300001200190490000700202100002300209700001900232856010200251 2003 eng d00aAlpha-stratified adaptive testing with large numbers of content constraints0 aAlphastratified adaptive testing with large numbers of content c a107-1200 v271 avan der Linden, WJ1 aChang, Hua-Hua uhttp://mail.iacat.org/content/alpha-stratified-adaptive-testing-large-numbers-content-constraints00353nas a2200109 4500008004100000245004500041210004100086260001500127100001900142700001400161856006800175 2003 eng d00aThe assembly of multiple form structures0 aassembly of multiple form structures aChicago IL1 aArmstrong, R D1 aLittle, J uhttp://mail.iacat.org/content/assembly-multiple-form-structures00464nas a2200109 4500008004100000245007000041210006600111260005300177100001900230700001700249856008800266 2003 eng d00aThe assembly of multiple stage adaptive tests with discrete items0 aassembly of multiple stage adaptive tests with discrete items aNewtown, PA: Law School Admission Council Report1 aArmstrong, R D1 aEdmonds, J J uhttp://mail.iacat.org/content/assembly-multiple-stage-adaptive-tests-discrete-items00407nas a2200109 4500008004100000245006200041210006200103260001500165100001900180700001300199856008500212 2003 eng d00aAssessing CAT security breaches by the item pooling index0 aAssessing CAT security breaches by the item pooling index aChicago IL1 aChang, Hua-Hua1 aZhang, J uhttp://mail.iacat.org/content/assessing-cat-security-breaches-item-pooling-index01228nas a2200193 4500008004100000245002900041210002900070260003200099300001200131520066800143653003000811653003200841653001400873653004500887100001200932700001500944700001600959856005900975 2003 eng d00aAssessing question banks0 aAssessing question banks aLondon, UKbKogan Page Ltd. a171-2303 aIn Chapter 14, Joanna Bull and James Daziel provide a comprehensive treatment of the issues surrounding the use of Question Banks and Computer Assisted Assessment, and provide a number of excellent examples of implementations. In their review of the technologies employed in Computer Assisted Assessment the authors include Computer Adaptive Testing and data generation. The authors reveal significant issues involving the impact of Intellectual Property rights and computer assisted assessment and make important suggestions for strategies to overcome these obstacles. (PsycINFO Database Record (c) 2005 APA )http://www-jime.open.ac.uk/2003/1/ (journal abstract)10aComputer Assisted Testing10aCurriculum Based Assessment10aEducation10aTechnology computerized adaptive testing1 aBull, J1 aDalziel, J1 aVreeland, T uhttp://mail.iacat.org/content/assessing-question-banks00419nas a2200097 4500008004100000245008000041210006900121260001500190100001600205856010000221 2003 eng d00aAssessing the efficiency of item selection in computerized adaptive testing0 aAssessing the efficiency of item selection in computerized adapt aChicago IL1 aWeissman, A uhttp://mail.iacat.org/content/assessing-efficiency-item-selection-computerized-adaptive-testing00412nas a2200121 4500008003900000245006100039210005900100300001400159490000700173100001000180700001600190856008400206 2003 d00aa-Stratified multistage CAT design with content-blocking0 aaStratified multistage CAT design with contentblocking a359–3780 v561 aYi, Q1 aChang, H -H uhttp://mail.iacat.org/content/stratified-multistage-cat-design-content-blocking00512nas a2200109 4500008004100000245012600041210006900167260001900236100001200255700001200267856012300279 2002 eng d00aAccuracy of the ability estimate and the item exposure rate under multidimensional adaptive testing with item constraints0 aAccuracy of the ability estimate and the item exposure rate unde aNew Orleans LA1 aLi, Y H1 aYu, N Y uhttp://mail.iacat.org/content/accuracy-ability-estimate-and-item-exposure-rate-under-multidimensional-adaptive-testing00528nas a2200121 4500008004100000245009600041210006900137260004600206100001100252700001300263700001600276856011400292 2002 eng d00aAdaptive testing without IRT in the presence of multidimensionality (Research Report 02-09)0 aAdaptive testing without IRT in the presence of multidimensional aPrinceton NJ: Educational Testing Service1 aYan, D1 aLewis, C1 aStocking, M uhttp://mail.iacat.org/content/adaptive-testing-without-irt-presence-multidimensionality-research-report-02-0902860nas a2200265 4500008004100000245006600041210006600107260000800173300000900181490000700190520208800197653002102285653002902306653003002335653001202365653001102377653001302388653003102401653001902432100001302451700001502464700001302479700001502492856008702507 2002 eng d00aAdvances in quality of life measurements in oncology patients0 aAdvances in quality of life measurements in oncology patients cJun a60-80 v293 aAccurate assessment of the quality of life (QOL) of patients can provide important clinical information to physicians, especially in the area of oncology. Changes in QOL are important indicators of the impact of a new cytotoxic therapy, can affect a patient's willingness to continue treatment, and may aid in defining response in the absence of quantifiable endpoints such as tumor regression. Because QOL is becoming an increasingly important aspect in the management of patients with malignant disease, it is vital that the instruments used to measure QOL are reliable and accurate. Assessment of QOL involves a multidimensional approach that includes physical, functional, social, and emotional well-being, and the most comprehensive instruments measure at least three of these domains. Instruments to measure QOL can be generic (eg, the Nottingham Health Profile), targeted toward specific illnesses (eg, Functional Assessment of Cancer Therapy - Lung), or be a combination of generic and targeted. Two of the most widely used examples of the combination, or hybrid, instruments are the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 Items and the Functional Assessment of Chronic Illness Therapy. A consequence of the increasing international collaboration in clinical trials has been the growing necessity for instruments that are valid across languages and cultures. To assure the continuing reliability and validity of QOL instruments in this regard, item response theory can be applied. Techniques such as item response theory may be used in the future to construct QOL item banks containing large sets of validated questions that represent various levels of QOL domains. As QOL becomes increasingly important in understanding and approaching the overall management of cancer patients, the tools available to clinicians and researchers to assess QOL will continue to evolve. While the instruments currently available provide reliable and valid measurement, further improvements in precision and application are anticipated.10a*Quality of Life10a*Sickness Impact Profile10aCross-Cultural Comparison10aCulture10aHumans10aLanguage10aNeoplasms/*physiopathology10aQuestionnaires1 aCella, D1 aChang, C-H1 aLai, J S1 aWebster, K uhttp://mail.iacat.org/content/advances-quality-life-measurements-oncology-patients03320nas a2200133 4500008004100000245005900041210005900100300001000159490000700169520289000176100001903066700001603085856008503101 2002 eng d00aApplicable adaptive testing models for school teachers0 aApplicable adaptive testing models for school teachers a55-590 v393 aThe purpose of this study was to investigate the attitudinal effects on SPRT adaptive testing environment for junior high school students. Subjects were 39 eighth graders from a selected junior high school. Major instruments for the study were the Junior High School Natural Sciences Adaptive Testing System driven by the SPRT algorithm, and a self-developed attitudinal questionnaire, factors examined include: test anxiety, examinee preference, adaptability of the test, and acceptance of the test result. The major findings were that overall, junior high school students" attitudes towards computerized adaptive tests were positive, no significant correlations existed between test attitude and the test length. The results indicated that junior high school students generally have positive attitudes towards adaptive testing.Modèles de tests d"adaptation à l"usage des enseignants. L"objectif de cette étude était d"enquêter sur les effets causés par une passation de tests d"adaptation ( selon l"algorithme "Sequential Probability Radio Test " (SPRT) ) dans une classe de trente-neuf élèves de huitième année du secondaire inférieur. Les principaux instruments utilisés ont été ceux du système de tests d"adaptation (avec le SPRT) et destiné aux classes de sciences naturelles du degré secondaire inférieur. Un questionnaire d"attitude, développé par nos soins, a également été utilisé pour examiner les facteurs suivants: test d"anxiété, préférence des candidats, adaptabilité du test et acceptation des résultats. Les principales conclusions ont été que, dans l"ensemble, l"attitude des élèves du secondaire inférieur face aux tests d"adaptation informatisés a été positive, aucune corrélation significative existant entre cette attitude et la longueur des tests. Les résultats démontrent aussi que les élèves du secondaire ont une attitude généralement positive envers les tests d"adaptation.Test Modelle zur Anwendung durch Klassenlehrer Zweck dieser Untersuchung war, die Auswirkungen über die Einstellung von Jun. High School Schülern im Zusammenhang mit dem SPRT Testumfeld zu untersuchen. 39 Achtklässler einer Jun. High School nahmen an dem Test teil. Die Untersuchung stützte sich hauptsächlich auf das Jun. High School Natural. Sciences Adaptive Testing System, das auf dem SPRT Rechnungsverfahren basiert sowie einem selbst erstellten Fragebogen mit folgenden Faktoren: Testängste, Präferenzen der Testperson, Geeignetheit des Tests, Anerkennung des Testergebnisses. Es stellte sich heraus, dass die Einstellung der Studenten zu den Computer adaptierten Tests im allgemeinen positiv waren; es ergaben sich keine bedeutsamen Wechselwirkungen zwischen persönlicher Testeinstellung und Testlänge. Die Ergebnisse belegen, dass Jun. High School Schüler im allgemeinen eine positive Haltung zu adaptierten Tests haben. 1 aChang-Hwa, W A1 aChuang, C-L uhttp://mail.iacat.org/content/applicable-adaptive-testing-models-school-teachers00538nas a2200121 4500008004100000245014000041210006900181300001000250490000700260100001300267700001600280856012000296 2002 eng d00aApplication of an empirical Bayes enhancement of Mantel-Haenszel differential item functioning analysis to a computerized adaptive test0 aApplication of an empirical Bayes enhancement of MantelHaenszel  a57-760 v261 aZwick, R1 aThayer, D T uhttp://mail.iacat.org/content/application-empirical-bayes-enhancement-mantel-haenszel-differential-item-functioning00471nam a2200097 4500008004100000245008000041210006900121260006500190100001600255856010200271 2002 eng d00aAssessing the efficiency of item selection in computerized adaptive testing0 aAssessing the efficiency of item selection in computerized adapt aUnpublished doctoral dissertation, University of Pittsburgh.1 aWeissman, A uhttp://mail.iacat.org/content/assessing-efficiency-item-selection-computerized-adaptive-testing-001983nas a2200289 4500008004100000245007600041210006900117260000800186300001200194490000700206520114900213653002401362653001301386653002101399653002001420653001501440653001001455653001001465653001101475653001101486653000901497653002401506653002601530100001601556700001501572856010601587 2002 eng d00aAssessing tobacco beliefs among youth using item response theory models0 aAssessing tobacco beliefs among youth using item response theory cNov aS21-S390 v683 aSuccessful intervention research programs to prevent adolescent smoking require well-chosen, psychometrically sound instruments for assessing smoking prevalence and attitudes. Twelve thousand eight hundred and ten adolescents were surveyed about their smoking beliefs as part of the Teenage Attitudes and Practices Survey project, a prospective cohort study of predictors of smoking initiation among US adolescents. Item response theory (IRT) methods are used to frame a discussion of questions that a researcher might ask when selecting an optimal item set. IRT methods are especially useful for choosing items during instrument development, trait scoring, evaluating item functioning across groups, and creating optimal item subsets for use in specialized applications such as computerized adaptive testing. Data analytic steps for IRT modeling are reviewed for evaluating item quality and differential item functioning across subgroups of gender, age, and smoking status. Implications and challenges in the use of these methods for tobacco onset research and for assessing the developmental trajectories of smoking among youth are discussed.10a*Attitude to Health10a*Culture10a*Health Behavior10a*Questionnaires10aAdolescent10aAdult10aChild10aFemale10aHumans10aMale10aModels, Statistical10aSmoking/*epidemiology1 aPanter, A T1 aReeve, B B uhttp://mail.iacat.org/content/assessing-tobacco-beliefs-among-youth-using-item-response-theory-models00493nas a2200121 4500008004100000245008700041210006900128260001500197100001400212700001900226700001700245856010900262 2001 eng d00aAdaptation of a-stratified method in variable length computerized adaptive testing0 aAdaptation of astratified method in variable length computerized aSeattle WA1 aWen, J -B1 aChang, Hua-Hua1 aHau, K -T   uhttp://mail.iacat.org/content/adaptation-stratified-method-variable-length-computerized-adaptive-testing00509nas a2200097 4500008004100000245008000041210006900121260010600190100001600296856009900312 2001 eng d00aApplication of data mining to response data in a computerized adaptive test0 aApplication of data mining to response data in a computerized ad aPaper presented at the Annual Meeting of the National Council on Measurement in Education, Seattle WA1 aMendez, F A uhttp://mail.iacat.org/content/application-data-mining-response-data-computerized-adaptive-test00473nas a2200097 4500008004100000245011500041210006900156260001500225100001500240856012000255 2001 eng d00aApplication of score information for CAT pool development and its connection with "likelihood test information0 aApplication of score information for CAT pool development and it aSeattle WA1 aKrass, I A uhttp://mail.iacat.org/content/application-score-information-cat-pool-development-and-its-connection-likelihood-test02225nas a2200145 4500008004100000245011600041210006900157300001100226490000600237520165200243653003401895100001301929700001601942856012101958 2001 eng d00aAssessment in the twenty-first century: A role of computerised adaptive testing in national curriculum subjects0 aAssessment in the twentyfirst century A role of computerised ada a241-570 v53 aWith the investment of large sums of money in new technologies forschools and education authorities and the subsequent training of teachers to integrate Information and Communications Technology (ICT) into their teaching strategies, it is remarkable that the old outdated models of assessment still remain. This article highlights the current problems associated with pen-and paper-testing and offers suggestions for an innovative and new approach to assessment for the twenty-first century. Based on the principle of the 'wise examiner' a computerised adaptive testing system which measures pupils' ability against the levels of the United Kingdom National Curriculum has been developed for use in mathematics. Using constructed response items, pupils are administered a test tailored to their ability with a reliability index of 0.99. Since the software administers maximally informative questions matched to each pupil's current ability estimate, no two pupils will receive the same set of items in the same order therefore removing opportunities for plagarism and teaching to the test. All marking is automated and a journal recording the outcome of the test and highlighting the areas of difficulty for each pupil is available for printing by the teacher. The current prototype of the system can be used on a school's network however the authors envisage a day when Examination Boards or the Qualifications and Assessment Authority (QCA) will administer Government tests from a central server to all United Kingdom schools or testing centres. Results will be issued at the time of testing and opportunities for resits will become more widespr10acomputerized adaptive testing1 aCowan, P1 aMorrison, H uhttp://mail.iacat.org/content/assessment-twenty-first-century-role-computerised-adaptive-testing-national-curriculum00375nas a2200109 4500008004100000245005000041210004800091260002400139100001000163700001900173856007300192 2001 eng d00aa-stratified CAT design with content-blocking0 aastratified CAT design with contentblocking aKing of Prussia, PA1 aYi, Q1 aChang, Hua-Hua uhttp://mail.iacat.org/content/stratified-cat-design-content-blocking00465nas a2200109 4500008004100000245008800041210006900129260001500198100001200213700001900225856011100244 2001 eng d00aa-stratified computerized adaptive testing with unequal item exposure across strata0 aastratified computerized adaptive testing with unequal item expo aSeattle WA1 aDeng, H1 aChang, Hua-Hua uhttp://mail.iacat.org/content/stratified-computerized-adaptive-testing-unequal-item-exposure-across-strata00843nas a2200157 4500008004100000245007400041210006900115300001100184490000700195520030900202653003400511100001900545700001200564700001200576856009700588 2001 eng d00aa-stratified multistage computerized adaptive testing with b blocking0 aastratified multistage computerized adaptive testing with b bloc a333-410 v253 aProposed a refinement, based on the stratification of items developed by D. Weiss (1973), of the computerized adaptive testing item selection procedure of H. Chang and Z. Ying (1999). Simulation studies using an item bank from the Graduate Record Examination show the benefits of the new procedure. (SLD)10acomputerized adaptive testing1 aChang, Hua-Hua1 aQian, J1 aYang, Z uhttp://mail.iacat.org/content/stratified-multistage-computerized-adaptive-testing-b-blocking01099nas a2200157 4500008004100000020001400041245007400055210006900129300001200198490000700210520058200217100001900799700001200818700001200830856009900842 2001 eng d a0146-621600aa-Stratified multistage computerized adaptive testing with b blocking0 aaStratified multistage computerized adaptive testing with b bloc a333-3410 v253 aChang & Ying’s (1999) computerized adaptive testing item-selection procedure stratifies the item bank according to a parameter values and requires b parameter values to be evenly distributed across all strata. Thus, a and b parameter values must be incorporated into how strata are formed. A refinement is proposed, based on Weiss’ (1973) stratification of items according to b values. Simulation studies using a retired item bank of a Graduate Record Examination test indicate that the new approach improved control of item exposure rates and reduced mean squared errors. 1 aChang, Hua-Hua1 aQian, J1 aYing, Z uhttp://mail.iacat.org/content/stratified-multistage-computerized-adaptive-testing-b-blocking-000646nas a2200109 4500008004100000245012800041210006900169260014400238100001600382700001300398856012500411 2000 eng d00aAdaptive mastery testing using a multidimensional IRT model and Bayesian sequential decision theory (Research Report 00-06)0 aAdaptive mastery testing using a multidimensional IRT model and  aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aGlas, C A W1 aVos, H J uhttp://mail.iacat.org/content/adaptive-mastery-testing-using-multidimensional-irt-model-and-bayesian-sequential-decision02443nas a2200169 4500008004100000245012400041210007300165300001000238490000700248520174900255653003402004100002002038700001802058700002202076700002202098856015302120 2000 eng d00aAlgoritmo mixto mínima entropía-máxima información para la selección de ítems en un test adaptativo informatizado0 aAlgoritmo mixto mínima entropíamáxima información para la selecc a12-140 v123 aEl objetivo del estudio que presentamos es comparar la eficacia como estrat egia de selección de ítems de tres algo ritmos dife rentes: a) basado en máxima info rmación; b) basado en mínima entropía; y c) mixto mínima entropía en los ítems iniciales y máxima info rmación en el resto; bajo la hipótesis de que el algo ritmo mixto, puede dotar al TAI de mayor eficacia. Las simulaciones de procesos TAI se re a l i z a ron sobre un banco de 28 ítems de respuesta graduada calibrado según el modelo de Samejima, tomando como respuesta al TAI la respuesta ori ginal de los sujetos que fueron utilizados para la c a l i b ración. Los resultados iniciales mu e s t ran cómo el cri t e rio mixto es más eficaz que cualquiera de los otros dos tomados indep e n d i e n t e m e n t e. Dicha eficacia se maximiza cuando el algo ritmo de mínima entropía se re s t ri n ge a la selección de los pri m e ros ítems del TAI, ya que con las respuestas a estos pri m e ros ítems la estimación de q comienza a ser re l evante y el algo ritmo de máxima informaciónse optimiza.Item selection algo rithms in computeri zed adap t ive testing. The aim of this paper is to compare the efficacy of three different item selection algo rithms in computeri zed adap t ive testing (CAT). These algorithms are based as follows: the first one is based on Item Info rm ation, the second one on Entropy, and the last algo rithm is a mixture of the two previous ones. The CAT process was simulated using an emotional adjustment item bank. This item bank contains 28 graded items in six categories , calibrated using Samejima (1969) Graded Response Model. The initial results show that the mixed criterium algorithm performs better than the other ones.10acomputerized adaptive testing1 aDorronsoro, J R1 aSanta-Cruz, C1 aRubio Franco, V J1 aAguado García, D uhttp://mail.iacat.org/content/algoritmo-mixto-m%C3%ADnima-entrop%C3%ADa-m%C3%A1xima-informaci%C3%B3n-para-la-selecci%C3%B3n-de-%C3%ADtems-en-un-test00449nas a2200109 4500008004100000245007300041210006900114260002700183100001800210700001300228856009800241 2000 eng d00aApplying specific information item selection to a passage-based test0 aApplying specific information item selection to a passagebased t aNew Orleans, LA, April1 aThompson, T D1 aDavey, T uhttp://mail.iacat.org/content/applying-specific-information-item-selection-passage-based-test00471nas a2200133 4500008004100000245006900041210006900110260001600179100001200195700001100207700001300218700001100231856009500242 2000 eng d00aAssembling parallel item pools for computerized adaptive testing0 aAssembling parallel item pools for computerized adaptive testing aNew Orleans1 aWang, T1 aFan, Q1 aBan, J C1 aZhu, D uhttp://mail.iacat.org/content/assembling-parallel-item-pools-computerized-adaptive-testing00560nas a2200097 4500008004100000245008100041210006900122260014400191100002300335856010400358 1999 eng d00aAdaptive testing with equated number-correct scoring (Research Report 99-02)0 aAdaptive testing with equated numbercorrect scoring Research Rep aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 avan der Linden, WJ uhttp://mail.iacat.org/content/adaptive-testing-equated-number-correct-scoring-research-report-99-0200442nas a2200109 4500008004100000245007500041210006900116260002100185100001100206700001300217856010200230 1999 eng d00aAdjusting computer adaptive test starting points to conserve item pool0 aAdjusting computer adaptive test starting points to conserve ite aMontreal, Canada1 aZhu, D1 aM., Fan. uhttp://mail.iacat.org/content/adjusting-computer-adaptive-test-starting-points-conserve-item-pool00507nas a2200145 4500008004100000245007100041210006900112260002100181100001200202700001000214700001300224700001600237700001600253856009200269 1999 eng d00aAdjusting "scores" from a CAT following successful item challenges0 aAdjusting scores from a CAT following successful item challenges aMontreal, Canada1 aWang, T1 aYi, Q1 aBan, J C1 aHarris, D J1 aHanson, B A uhttp://mail.iacat.org/content/adjusting-scores-cat-following-successful-item-challenges00474nas a2200097 4500008004100000245011800041210006900159260001100228100001300239856012400252 1999 eng d00aAlternative item selection strategies for improving test security and pool usage in computerized adaptive testing0 aAlternative item selection strategies for improving test securit aCanada1 aRobin, F uhttp://mail.iacat.org/content/alternative-item-selection-strategies-improving-test-security-and-pool-usage-computerized00513nas a2200121 4500008004100000245009600041210006900137300000900206490000700215653003400222100002300256856011200279 1999 eng d00aAlternative methods for the detection of item preknowledge in computerized adaptive testing0 aAlternative methods for the detection of item preknowledge in co a37650 v5910acomputerized adaptive testing1 aMcLeod, Lori Davis uhttp://mail.iacat.org/content/alternative-methods-detection-item-preknowledge-computerized-adaptive-testing00416nas a2200121 4500008004100000245005800041210005700099300001200156490000700168100001900175700001200194856008800206 1999 eng d00aa-stratified multistage computerized adaptive testing0 aastratified multistage computerized adaptive testing a211-2220 v231 aChang, Hua-Hua1 aYing, Z uhttp://mail.iacat.org/content/stratified-multistage-computerized-adaptive-testing-100418nas a2200121 4500008004100000245005800041210005700099300001400156490000700170100001900177700001200196856008800208 1999 eng d00aa-stratified multistage computerized adaptive testing0 aastratified multistage computerized adaptive testing a211–2220 v231 aChang, Hua-Hua1 aYing, Z uhttp://mail.iacat.org/content/stratified-multistage-computerized-adaptive-testing-001735nas a2200145 4500008004100000245005800041210005700099300001200156490000700168520126300175653003401438100001901472700001201491856008601503 1999 eng d00aa-stratified multistage computerized adaptive testing0 aastratified multistage computerized adaptive testing a211-2220 v233 aFor computerized adaptive tests (CAT) based on the three-parameter logistic mode it was found that administering items with low discrimination parameter (a) values early in the test and administering those with high a values later was advantageous; the skewness of item exposure distributions was reduced while efficiency was maintain in trait level estimation. Thus, a new multistage adaptive testing approach is proposed that factors a into the item selection process. In this approach, the items in the item bank are stratified into a number of levels based on their a values. The early stages of a test use items with lower as and later stages use items with higher as. At each stage, items are selected according to an optimization criterion from the corresponding level. Simulation studies were performed to compare a-stratified CATs with CATs based on the Sympson-Hetter method for controlling item exposure. Results indicated that this new strategy led to tests that were well-balanced, with respect to item exposure, and efficient. The a-stratified CATs achieved a lower average exposure rate than CATs based on Bayesian or information-based item selection and the Sympson-Hetter method. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aChang, Hua-Hua1 aYing, Z uhttp://mail.iacat.org/content/stratified-multistage-computerized-adaptive-testing00490nas a2200109 4500008004100000245009800041210006900139260002100208100001500229700001900244856011700263 1999 eng d00aAutomated flawed item detection and graphical item used in on-line calibration of CAT-ASVAB. 0 aAutomated flawed item detection and graphical item used in onlin aMontreal, Canada1 aKrass, I A1 aThomasson, G L uhttp://mail.iacat.org/content/automated-flawed-item-detection-and-graphical-item-used-line-calibration-cat-asvab00634nas a2200109 4500008004100000245011500041210006900156260014400225100001600369700001300385856012600398 1998 eng d00aAdaptive mastery testing using the Rasch model and Bayesian sequential decision theory (Research Report 98-15)0 aAdaptive mastery testing using the Rasch model and Bayesian sequ aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aGlas, C A W1 aVos, H J uhttp://mail.iacat.org/content/adaptive-mastery-testing-using-rasch-model-and-bayesian-sequential-decision-theory-research00349nas a2200121 4500008004100000245003300041210003300074260001700107100001100124700001300135700001600148856006300164 1998 eng d00aAdaptive testing without IRT0 aAdaptive testing without IRT aSan Diego CA1 aYan, D1 aLewis, C1 aStocking, M uhttp://mail.iacat.org/content/adaptive-testing-without-irt00405nas a2200109 4500008004100000245005700041210005700098260002000155100001400175700002100189856008500210 1998 eng d00aAlternatives for scoring computerized adaptive tests0 aAlternatives for scoring computerized adaptive tests aPhiladelphia PA1 aDodd, B G1 aFitzpatrick, S J uhttp://mail.iacat.org/content/alternatives-scoring-computerized-adaptive-tests-000494nas a2200133 4500008004100000245005700041210005700098260005700155100001400212700002100226700001600247700001400263856008300277 1998 eng d00aAlternatives for scoring computerized adaptive tests0 aAlternatives for scoring computerized adaptive tests aMahwah, N.J., USAbLawrence Erlbaum Associates, Inc.1 aDodd, B G1 aFitzpatrick, S J1 aFremer, J J1 aWard, W C uhttp://mail.iacat.org/content/alternatives-scoring-computerized-adaptive-tests00470nas a2200109 4500008004100000245009500041210006900136260001400205100001300219700001500232856011300247 1998 eng d00aApplication of an IRT ideal point model to computer adaptive assessment of job performance0 aApplication of an IRT ideal point model to computer adaptive ass aDallas TX1 aStark, S1 aDrasgow, F uhttp://mail.iacat.org/content/application-irt-ideal-point-model-computer-adaptive-assessment-job-performance00449nas a2200097 4500008004100000245009600041210006900137260001700206100001500223856011300238 1998 eng d00aApplication of direct optimization for on-line calibration in computerized adaptive testing0 aApplication of direct optimization for online calibration in com aSan Diego CA1 aKrass, I A uhttp://mail.iacat.org/content/application-direct-optimization-line-calibration-computerized-adaptive-testing00487nam a2200097 4500008004100000245006700041210006700108260010400175100001700279856009300296 1998 eng d00aApplications of network flows to computerized adaptive testing0 aApplications of network flows to computerized adaptive testing aDissertation, Rutgers Center for Operations Research (RUTCOR), Rutgers University, New Brunswick NJ1 aCordova, M J uhttp://mail.iacat.org/content/applications-network-flows-computerized-adaptive-testing-001457nas a2200133 4500008004100000245006700041210006700108300000900175490000700184520098800191653003401179100001901213856009101232 1998 eng d00aApplications of network flows to computerized adaptive testing0 aApplications of network flows to computerized adaptive testing a08550 v593 aRecently, the concept of Computerized Adaptive Testing (CAT) has been receiving ever growing attention from the academic community. This is so because of both practical and theoretical considerations. Its practical importance lies in the advantages of CAT over the traditional (perhaps outdated) paper-and-pencil test in terms of time, accuracy, and money. The theoretical interest is sparked by its natural relationship to Item Response Theory (IRT). This dissertation offers a mathematical programming approach which creates a model that generates a CAT that takes care of many questions concerning the test, such as feasibility, accuracy and time of testing, as well as item pool security. The CAT generated is designed to obtain the most information about a single test taker. Several methods for eatimating the examinee's ability, based on the (dichotomous) responses to the items in the test, are also offered here. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aClaudio, M J C uhttp://mail.iacat.org/content/applications-network-flows-computerized-adaptive-testing00585nas a2200145 4500008004100000245011400041210006900155260001200224100001400236700001700250700001600267700001600283700001900299856012100318 1997 eng d00aThe accuracy of examinee judgments of relative item difficulty: Implication for computerized adaptive testing0 aaccuracy of examinee judgments of relative item difficulty Impli aChicago1 aWise, S L1 aFreeman, S A1 aFinney, S J1 aEnders, C K1 aSeverance, D D uhttp://mail.iacat.org/content/accuracy-examinee-judgments-relative-item-difficulty-implication-computerized-adaptive00396nas a2200145 4500008004100000245003300041210003300074300001200107490000700119100001700126700001500143700001600158700001600174856006000190 1997 eng d00aAdapting to adaptive testing0 aAdapting to adaptive testing a171-1850 v501 aOverton, R C1 aHarms, H J1 aTaylor, L R1 aZickar, M J uhttp://mail.iacat.org/content/adapting-adaptive-testing00384nas a2200097 4500008004100000245006400041210006400105260001500169100001200184856009000196 1997 eng d00aAdministering and scoring the computerized adaptive testing0 aAdministering and scoring the computerized adaptive testing aChicago IL1 aZara, A uhttp://mail.iacat.org/content/administering-and-scoring-computerized-adaptive-testing00383nas a2200097 4500008004100000245006300041210006200104260001500166100001400181856009000195 1997 eng d00aAlternate methods of scoring computer-based adaptive tests0 aAlternate methods of scoring computerbased adaptive tests aChicago IL1 aGreen, BF uhttp://mail.iacat.org/content/alternate-methods-scoring-computer-based-adaptive-tests00369nas a2200109 4500008004100000245005300041210005000094300001200144490000700156100001800163856007800181 1997 eng d00aAn alternative method for scoring adaptive tests0 aalternative method for scoring adaptive tests a365-3890 v211 aStocking, M L uhttp://mail.iacat.org/content/alternative-method-scoring-adaptive-tests-000526nas a2200097 4500008004100000245009900041210006900140260008500209100001300294856012100307 1997 eng d00aApplications of Bayesian decision theory to sequential mastery testing (Research Report 97-06)0 aApplications of Bayesian decision theory to sequential mastery t aTwente, The Netherlands: Department of Educational Measurement and Data Analysis1 aVos, H J uhttp://mail.iacat.org/content/applications-bayesian-decision-theory-sequential-mastery-testing-research-report-97-0600365nas a2200097 4500008004100000245005400041210005400095260002100149100001600170856008100186 1997 eng d00aApplications of multidimensional adaptive testing0 aApplications of multidimensional adaptive testing aMontreal, Canada1 aSegall, D O uhttp://mail.iacat.org/content/applications-multidimensional-adaptive-testing00458nas a2200121 4500008004100000245006900041210006700110260001500177100001600192700001600208700001600224856009600240 1997 eng d00aAssessing speededness in variable-length computer-adaptive tests0 aAssessing speededness in variablelength computeradaptive tests aChicago IL1 aBontempo, B1 aJulian, E R1 aGorham, J L uhttp://mail.iacat.org/content/assessing-speededness-variable-length-computer-adaptive-tests00718nas a2200121 4500008004100000245008100041210006900122260025000191100001700441700001800458700001600476856010400492 1996 eng d00aAdaptive assessment and training using the neighbourhood of knowledge states0 aAdaptive assessment and training using the neighbourhood of know aFrasson, C. and Gauthier, G. and Lesgold, A. (eds.) Intelligent Tutoring Systems, Third International Conference, ITS'96, Montral, Canada, June 1996 Proceedings. Lecture Notes in Computer Science 1086. Berlin Heidelberg: Springer-Verlag 578-587.1 aDowling, C E1 aHockemeyer, C1 aLudwig, A H uhttp://mail.iacat.org/content/adaptive-assessment-and-training-using-neighbourhood-knowledge-states00705nas a2200121 4500008004100000245007200041210006900113260025200182100001700434700001500451700001500466856010200481 1996 eng d00aAdaptive assessment using granularity hierarchies and Bayesian nets0 aAdaptive assessment using granularity hierarchies and Bayesian n aFrasson, C. and Gauthier, G. and Lesgold, A. (Eds.) Intelligent Tutoring Systems, Third International Conference, ITS'96, Montréal, Canada, June 1996 Proceedings. Lecture Notes in Computer Science 1086. Berlin Heidelberg: Springer-Verlag 569-577.1 aCollins, J A1 aGreer, J E1 aHuang, S X uhttp://mail.iacat.org/content/adaptive-assessment-using-granularity-hierarchies-and-bayesian-nets00374nam a2200097 4500008004100000245003800041210003800079260007900117100001700196856006300213 1996 eng d00aAdaptive testing with granularity0 aAdaptive testing with granularity aMasters thesis, University of Saskatchewan, Department of Computer Science1 aCollins, J A uhttp://mail.iacat.org/content/adaptive-testing-granularity00367nas a2200109 4500008004100000245005300041210005000094300001200144490000700156100001800163856007600181 1996 eng d00aAn alternative method for scoring adaptive tests0 aalternative method for scoring adaptive tests a365-3890 v211 aStocking, M L uhttp://mail.iacat.org/content/alternative-method-scoring-adaptive-tests02610nas a2200133 4500008004100000245012000041210006900161300000900230490000700239520205500246653003402301100001602335856012502351 1995 eng d00aAssessment of scaled score consistency in adaptive testing from a multidimensional item response theory perspective0 aAssessment of scaled score consistency in adaptive testing from  a55980 v553 aThe purpose of this study was twofold: (a) to examine whether the unidimensional adaptive testing estimates are comparable for different ability levels of examinees when the true examinee-item interaction is correctly modeled using a compensatory multidimensional item response theory (MIRT) model; and (b) to investigate the effects of adaptive testing estimation when the procedure of item selection of computerized adaptive testing (CAT) is controlled by either content-balancing or selecting the most informative item in a user specified direction at the current estimate of unidimensional ability. A series of Monte Carlo simulations were conducted in this study. Deviation from the reference composite angle was used as an index of the theta1,theta2-composite consistency across the different levels of unidimensional CAT estimates. In addition, the effect of the content-balancing item selection procedure and the fixed-direction item selection procedure were compared across the different ability levels. The characteristics of item selection, test information and the relationship between unidimensional and multidimensional models were also investigated. In addition to employing statistical analysis to examine the robustness of the CAT procedure violations of unidimensionality, this research also included graphical analyses to present the results. The results were summarized as follows: (a) the reference angles for the no-control-item-selection method were disparate across the unidimensional ability groups; (b) the unidimensional CAT estimates from the content-balancing item selection method did not offer much improvement; (c) the fixed-direction-item selection method did provide greater consistency for the unidimensional CAT estimates across the different levels of ability; (d) and, increasing the CAT test length did not provide greater score scale consistency. Based on the results of this study, the following conclusions were drawn: (a) without any controlling (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aFan, Miechu uhttp://mail.iacat.org/content/assessment-scaled-score-consistency-adaptive-testing-multidimensional-item-response-theory00485nas a2200133 4500008004100000245008100041210006900122300001200191490000700203100001500210700000900225700001600234856010100250 1994 eng d00aADTEST: A computer-adaptive tests based on the maximum information principle0 aADTEST A computeradaptive tests based on the maximum information a680-6860 v541 aPonsoda, V1 aOlea1 aRevuelta, J uhttp://mail.iacat.org/content/adtest-computer-adaptive-tests-based-maximum-information-principle00469nas a2200133 4500008004100000245007100041210006700112300001200179490000700191100001800198700001500216700001600231856008800247 1993 eng d00aThe application of an automated item selection method to real data0 aapplication of an automated item selection method to real data a167-1760 v171 aStocking, M L1 aSwanson, L1 aPearlman, M uhttp://mail.iacat.org/content/application-automated-item-selection-method-real-data00502nas a2200121 4500008004100000245009700041210006900138300001400207490000700221653003400228100001200262856010600274 1993 eng d00aAn application of Computerized Adaptive Testing to the Test of English as a Foreign Language0 aapplication of Computerized Adaptive Testing to the Test of Engl a4257-42580 v5310acomputerized adaptive testing1 aMoon, O uhttp://mail.iacat.org/content/application-computerized-adaptive-testing-test-english-foreign-language00514nas a2200133 4500008004100000245008100041210006900122300001000191490000700201653003400208100001900242700001600261856010300277 1993 eng d00aAssessing the utility of item response models: computerized adaptive testing0 aAssessing the utility of item response models computerized adapt a21-270 v1210acomputerized adaptive testing1 aKingsbury, G G1 aHouser, R L uhttp://mail.iacat.org/content/assessing-utility-item-response-models-computerized-adaptive-testing00469nas a2200097 4500008003900000245010200039210006900141260001800210100001700228856012600245 1992 d00aAbility measure equivalence of computer adaptive and paper and pencil tests: A research synthesis0 aAbility measure equivalence of computer adaptive and paper and p aSan Francisco1 aBergstrom, B uhttp://mail.iacat.org/content/ability-measure-equivalence-computer-adaptive-and-paper-and-pencil-tests-research-synthesis01221nas a2200157 4500008004100000245006600041210006600107300001200173490000600185520069800191653003400889100002400923700001400947700001600961856008600977 1992 eng d00aAltering the level of difficulty in computer adaptive testing0 aAltering the level of difficulty in computer adaptive testing a137-1490 v53 aExamines the effect of altering test difficulty on examinee ability measures and test length in a computer adaptive test. The 225 Ss were randomly assigned to 3 test difficulty conditions and given a variable length computer adaptive test. Examinees in the hard, medium, and easy test condition took a test targeted at the 50%, 60%, or 70% probability of correct response. The results show that altering the probability of a correct response does not affect estimation of examinee ability and that taking an easier computer adaptive test only slightly increases the number of items necessary to reach specified levels of precision. (PsycINFO Database Record (c) 2002 APA, all rights reserved).10acomputerized adaptive testing1 aBergstrom, Betty, A1 aLunz, M E1 aGershon, RC uhttp://mail.iacat.org/content/altering-level-difficulty-computer-adaptive-testing00419nas a2200121 4500008004100000245006300041210005900104300001000163490000700173100001800180700001600198856008300214 1992 eng d00aThe application of latent class models in adaptive testing0 aapplication of latent class models in adaptive testing a71-880 v571 aMacready, G B1 aDayton, C M uhttp://mail.iacat.org/content/application-latent-class-models-adaptive-testing00411nas a2200097 4500008004100000245006900041210006900110100002400179700001500203856009500218 1992 eng d00aAssessing existing item bank depth for computer adaptive testing0 aAssessing existing item bank depth for computer adaptive testing1 aBergstrom, Betty, A1 aStahl, J A uhttp://mail.iacat.org/content/assessing-existing-item-bank-depth-computer-adaptive-testing00440nas a2200097 4500008004100000245007900041210006900120260004500189100001500234856009300249 1991 eng d00aAn analysis of CAT-ASVAB scores in the Marine Corps JPM data (CRM- 91-161)0 aanalysis of CATASVAB scores in the Marine Corps JPM data CRM 911 aAlexandria VA: Center for Naval Analysis1 aDivgi, D R uhttp://mail.iacat.org/content/analysis-cat-asvab-scores-marine-corps-jpm-data-crm-91-16100417nas a2200121 4500008004100000245005800041210005700099260001500156100001300171700001400184700001500198856008200213 1991 eng d00aApplications of computer-adaptive testing in Maryland0 aApplications of computeradaptive testing in Maryland aChicago IL1 aBaghi, H1 aGabrys, R1 aFerrara, S uhttp://mail.iacat.org/content/applications-computer-adaptive-testing-maryland00548nas a2200121 4500008004100000245010100041210006900142260004600211100001800257700001500275700001600290856012000306 1991 eng d00aAutomatic item selection (AIS) methods in the ETS testing environment (Research Memorandum 91-5)0 aAutomatic item selection AIS methods in the ETS testing environm aPrinceton NJ: Educational Testing Service1 aStocking, M L1 aSwanson, L1 aPearlman, M uhttp://mail.iacat.org/content/automatic-item-selection-ais-methods-ets-testing-environment-research-memorandum-91-500462nas a2200109 4500008004100000245009200041210006900133300000800202490001100210100001900221856011200240 1990 eng d00aAdapting adaptive testing: Using the MicroCAT Testing System in a local school district0 aAdapting adaptive testing Using the MicroCAT Testing System in a a3-60 v29 (2)1 aKingsbury, G G uhttp://mail.iacat.org/content/adapting-adaptive-testing-using-microcat-testing-system-local-school-district00556nas a2200133 4500008004100000245010600041210006900147260003100216100001100247700001300258700001100271700001600282856012400298 1990 eng d00aAn adaptive algebra test: A testlet-based, hierarchically structured test with validity-based scoring0 aadaptive algebra test A testletbased hierarchically structured t aETS Technical Report 90-921 aWainer1 aLewis, C1 aKaplan1 aBraswell, J uhttp://mail.iacat.org/content/adaptive-algebra-test-testlet-based-hierarchically-structured-test-validity-based-scoring00508nas a2200133 4500008004100000245009100041210006900132300001200201490000700213100001300220700001600233700001600249856010900265 1990 eng d00aAdaptive designs for Likert-type data: An approach for implementing marketing research0 aAdaptive designs for Likerttype data An approach for implementin a304-3210 v271 aSingh, J1 aHowell, R D1 aRhoads, G K uhttp://mail.iacat.org/content/adaptive-designs-likert-type-data-approach-implementing-marketing-research00373nas a2200109 4500008004100000245005400041210005400095300001100149490000700160100001500167856008100182 1990 eng d00aApplying computerized adaptive testing in schools0 aApplying computerized adaptive testing in schools a311-380 v231 aOlson, J B uhttp://mail.iacat.org/content/applying-computerized-adaptive-testing-schools00455nas a2200109 4500008004100000245008100041210006900122260001400191100001900205700001600224856010500240 1990 eng d00aAssessing the utility of item response models: Computerized adaptive testing0 aAssessing the utility of item response models Computerized adapt aBoston MA1 aKingsbury, G G1 aHouser, R L uhttp://mail.iacat.org/content/assessing-utility-item-response-models-computerized-adaptive-testing-000548nas a2200145 4500008004500000245009400045210006900139300001200208490000700220100001500227700001500242700001700257700001400274856011400288 1989 Engldsh 00aAdaptive and Conventional Versions of the DAT: The First Complete Test Battery Comparison0 aAdaptive and Conventional Versions of the DAT The First Complete a363-3710 v131 aHenly, S J1 aKlebe, K J1 aMcBride, J R1 aCudeck, R uhttp://mail.iacat.org/content/adaptive-and-conventional-versions-dat-first-complete-test-battery-comparison-000542nas a2200145 4500008004100000245009400041210006900135300001200204490000700216100001500223700001500238700001700253700001400270856011200284 1989 eng d00aAdaptive and conventional versions of the DAT: The first complete test battery comparison0 aAdaptive and conventional versions of the DAT The first complete a363-3710 v131 aHenly, S J1 aKlebe, K J1 aMcBride, J R1 aCudeck, R uhttp://mail.iacat.org/content/adaptive-and-conventional-versions-dat-first-complete-test-battery-comparison00467nas a2200121 4500008004500000245008100045210006900126300001200195490000700207100001400214700001400228856010300242 1989 Engldsh 00aAdaptive Estimation When the Unidimensionality Assumption of IRT is Violated0 aAdaptive Estimation When the Unidimensionality Assumption of IRT a373-3900 v131 aFolk, V G1 aGreen, BF uhttp://mail.iacat.org/content/adaptive-estimation-when-unidimensionality-assumption-irt-violated-000456nas a2200121 4500008004100000245008100041210006900122300001200191490000700203100000900210700001400219856010100233 1989 eng d00aAdaptive estimation when the unidimensionality assumption of IRT is violated0 aAdaptive estimation when the unidimensionality assumption of IRT a373-3890 v131 aFolk1 aGreen, BF uhttp://mail.iacat.org/content/adaptive-estimation-when-unidimensionality-assumption-irt-violated00428nas a2200133 4500008004100000020001400041245005100055210005000106300001000156490000600166653003400172100001700206856007100223 1989 eng d a1745-399200aAdaptive testing: The evolution of a good idea0 aAdaptive testing The evolution of a good idea a11-150 v810acomputerized adaptive testing1 aReckase, M D uhttp://mail.iacat.org/content/adaptive-testing-evolution-good-idea00506nas a2200121 4500008004100000245009800041210006900139300000900208490000700217653003400224100001400258856011200272 1989 eng d00aApplication of computerized adaptive testing to the University Entrance Exam of Taiwan, R.O.C0 aApplication of computerized adaptive testing to the University E a36620 v4910acomputerized adaptive testing1 aHung, P-H uhttp://mail.iacat.org/content/application-computerized-adaptive-testing-university-entrance-exam-taiwan-roc00394nam a2200097 4500008004100000245005400041210005100095260005300146100001700199856008000216 1989 eng d00aAn applied study on computerized adaptive testing0 aapplied study on computerized adaptive testing aAmsterdam, The Netherlands: Swets and Zeitlinger1 aSchoonman, W uhttp://mail.iacat.org/content/applied-study-computerized-adaptive-testing-001764nas a2200133 4500008004100000245005400041210005100095260005500146300000800201520129200209653003401501100001701535856007801552 1989 eng d00aAn applied study on computerized adaptive testing0 aapplied study on computerized adaptive testing aGroningen, The NetherlandsbUniversity of Groingen a1853 a(from the cover) The rapid development and falling prices of powerful personal computers, in combination with new test theories, will have a large impact on psychological testing. One of the new possibilities is computerized adaptive testing. During the test administration each item is chosen to be appropriate for the person being tested. The test becomes tailor-made, resolving some of the problems with classical paper-and-pencil tests. In this way individual differences can be measured with higher efficiency and reliability. Scores on other meaningful variables, such as response time, can be obtained easily using computers. /// In this book a study on computerized adaptive testing is described. The study took place at Dutch Railways in an applied setting and served practical goals. Topics discussed include the construction of computerized tests, the use of response time, the choice of algorithms and the implications of using a latent trait model. After running a number of simulations and calibrating the item banks, an experiment was carried out. In the experiment a pretest was administered to a sample of over 300 applicants, followed by an adaptive test. In addition, a survey concerning the attitudes of testees towards computerized testing formed part of the design.10acomputerized adaptive testing1 aSchoonman, W uhttp://mail.iacat.org/content/applied-study-computerized-adaptive-testing00521nas a2200109 4500008004100000245013200041210006900173260001800242100001900260700001600279856011600295 1989 eng d00aAssessing the impact of using item parameter estimates obtained from paper-and-pencil testing for computerized adaptive testing0 aAssessing the impact of using item parameter estimates obtained  aSan Francisco1 aKingsbury, G G1 aHouser, R L uhttp://mail.iacat.org/content/assessing-impact-using-item-parameter-estimates-obtained-paper-and-pencil-testing00492nam a2200097 4500008004100000245009300041210006900134260006200203100001700265856011200282 1988 eng d00aApplication of appropriateness measurement to a problem in computerized adaptive testing0 aApplication of appropriateness measurement to a problem in compu aUnpublished doctoral dissertation, University of Illinois1 aCandell, G L uhttp://mail.iacat.org/content/application-appropriateness-measurement-problem-computerized-adaptive-testing00487nas a2200121 4500008004100000245009400041210006900135300001000204490000700214100001700221700001400238856011300252 1988 eng d00aAssessment of academic skills of learning disabled students with classroom microcomputers0 aAssessment of academic skills of learning disabled students with a81-880 v171 aWatkins, M W1 aKush, J C uhttp://mail.iacat.org/content/assessment-academic-skills-learning-disabled-students-classroom-microcomputers00562nam a2200097 4500008004100000245011600041210006900157260009800226100001700324856012300341 1987 eng d00aAn adaptive test of musical memory: An application of item response theory to the assessment of musical ability0 aadaptive test of musical memory An application of item response  aDoctoral dissertation, University of Illinois. Dissertation Abstracts International, 49, 79A.1 aVispoel, W P uhttp://mail.iacat.org/content/adaptive-test-musical-memory-application-item-response-theory-assessment-musical-ability00303nas a2200121 4500008004100000245002100041210002100062300001200083490000700095100001400102700001400116856005100130 1987 eng d00aAdaptive testing0 aAdaptive testing a249-2620 v361 aWeiss, DJ1 aVale, C D uhttp://mail.iacat.org/content/adaptive-testing00458nas a2200097 4500008003900000245006400039210006200103260009200165100001500257856008800272 1987 d00aAdaptive testing, information, and the partial credit model0 aAdaptive testing information and the partial credit model aMelbourne, Australia: University of Melbourne, Center for the Study of Higher Education1 aAdams, R J uhttp://mail.iacat.org/content/adaptive-testing-information-and-partial-credit-model01525nas a2200157 4500008004100000020001400041245008900055210006900144300001000213490000700223520095700230653003401187100001801221700002001239856010801259 1986 eng d a0013-164400aAn application of computer adaptive testing with communication handicapped examinees0 aapplication of computer adaptive testing with communication hand a23-350 v463 aThis study was conducted to evaluate a computerized adaptive testing procedure for the measurement of mathematical skills of entry level deaf college students. The theoretical basis of the study was the Rasch model for person measurement. Sixty persons were tested using an Apple II Plus microcomputer. Ability estimates provided by the computerized procedure were compared for stability with those obtained six to eight weeks earlier from conventional (written) testing of the same subject matter. Students' attitudes toward their testing experiences also were measured. Substantial increases in measurement efficiency (by reducing test length) were realized through the adaptive testing procedure. Because the item pool used was not specifically designed for adaptive testing purposes, the psychometric quality of measurements resulting from the different testing methods was approximately equal. Attitudes toward computerized testing were favorable.10acomputerized adaptive testing1 aGarrison, W M1 aBaumgarten, B S uhttp://mail.iacat.org/content/application-computer-adaptive-testing-communication-handicapped-examinees00650nas a2200121 4500008004100000245022600041210006900267300000900336490000700345653003400352100001900386856012300405 1985 eng d00aAdaptive self-referenced testing as a procedure for the measurement of individual change due to instruction: A comparison of the reliabilities of change estimates obtained from conventional and adaptive testing procedures0 aAdaptive selfreferenced testing as a procedure for the measureme a30570 v4510acomputerized adaptive testing1 aKingsbury, G G uhttp://mail.iacat.org/content/adaptive-self-referenced-testing-procedure-measurement-individual-change-due-instruction00310nas a2200109 4500008004100000245003300041210003300074300001200107490000700119100001400126856006000140 1985 eng d00aAdaptive testing by computer0 aAdaptive testing by computer a774-7890 v531 aWeiss, DJ uhttp://mail.iacat.org/content/adaptive-testing-computer00447nas a2200121 4500008004100000245007200041210006900113300000800182490000600190100002000196700001400216856009500230 1985 eng d00aALPHATAB: A lookup table for Bayesian computerized adaptive testing0 aALPHATAB A lookup table for Bayesian computerized adaptive testi a3260 v91 ade Ayala, R. J.1 aKoch, W R uhttp://mail.iacat.org/content/alphatab-lookup-table-bayesian-computerized-adaptive-testing00624nas a2200133 4500008004100000245012600041210006900167260006700236100001900303700001400322700001600336700001500352856012300367 1985 eng d00aArmed Services Vocational Aptitude Battery: Development of an adaptive item pool (AFHLR-TR-85-19; Technical Rep No 85-19)0 aArmed Services Vocational Aptitude Battery Development of an ada aBrooks Air Force Base TX: Air Force Human Resources Laboratory1 aPrestwood, J S1 aVale, C D1 aMassey, R H1 aWelsh, J R uhttp://mail.iacat.org/content/armed-services-vocational-aptitude-battery-development-adaptive-item-pool-afhlr-tr-85-1900643nam a2200097 4500008004100000245022200041210006900263260007500332100001900407856011900426 1984 eng d00aAdaptive self-referenced testing as a procedure for the measurement of individual change in instruction: A comparison of the reliabilities of change estimates obtained from conventional and adaptive testing procedures0 aAdaptive selfreferenced testing as a procedure for the measureme aUnpublished doctoral dissertation, Univerity of Minnesota, Minneapolis1 aKingsbury, G G uhttp://mail.iacat.org/content/adaptive-self-referenced-testing-procedure-measurement-individual-change-instruction00423nas a2200097 4500008004100000245005500041210005100096260007800147100001700225856008300242 1984 eng d00aAdaptive testing (Final Report Contract OPM-29-80)0 aAdaptive testing Final Report Contract OPM2980 aUrbana-Champaign IL: University of Illinois, Aviation Research Laboratory1 aTrollip, S R uhttp://mail.iacat.org/content/adaptive-testing-final-report-contract-opm-29-8000407nas a2200109 4500008003900000245004400039210004400083260006900127100001600196700001400212856007100226 1984 d00aAnalysis of experimental CAT ASVAB data0 aAnalysis of experimental CAT ASVAB data aBaltimore MD: Johns Hopkins University, Department of Psychology1 aAllred, L A1 aGreen, BF uhttp://mail.iacat.org/content/analysis-experimental-cat-asvab-data00456nas a2200109 4500008004100000245006300041210006300104260006900167100001100236700001400247856008500261 1984 eng d00aAnalysis of speeded test data from experimental CAT system0 aAnalysis of speeded test data from experimental CAT system aBaltimore MD: Johns Hopkins University, Department of Psychology1 aGreaud1 aGreen, BF uhttp://mail.iacat.org/content/analysis-speeded-test-data-experimental-cat-system00550nas a2200121 4500008004100000245008200041210006900123260008300192100001800275700001800293700001500311856010200326 1984 eng d00aApplication of adaptive testing to a fraction test (Research Report 84-3-NIE)0 aApplication of adaptive testing to a fraction test Research Repo aUrbana IL: Univerity of Illinois, Computer-Based Education Research Laboratory1 aTatsuoka, K K1 aTatsuoka, M M1 aBaillie, R uhttp://mail.iacat.org/content/application-adaptive-testing-fraction-test-research-report-84-3-nie00446nas a2200097 4500008004100000245003300041210003300074260016500107100001400272856006200286 1983 eng d00aAdaptive testing by computer0 aAdaptive testing by computer aR. B. Ekstrom (ed.), Measurement, technology, and individuality in education. New directions for testing and measurement, Number 17. San Francisco: Jossey-Bass.1 aGreen, BF uhttp://mail.iacat.org/content/adaptive-testing-computer-000626nas a2200121 4500008004100000245011400041210006900155260011200224100001500336700001200351700001400363856012700377 1983 eng d00aAlternate forms reliability and concurrent validity of adaptive and conventional tests with military recruits0 aAlternate forms reliability and concurrent validity of adaptive  aMinneapolis MN: University of Minnesota, Department of Psychology, Computerized Adaptive Testing Laboratory1 aKiely, G L1 aZara, A1 aWeiss, DJ uhttp://mail.iacat.org/content/alternate-forms-reliability-and-concurrent-validity-adaptive-and-conventional-tests-military00452nas a2200121 4500008004100000245007800041210006900119300001000188490000700198100001500205700001400220856009600234 1983 eng d00aAn application of computerized adaptive testing in U. S. Army recruiting.0 aapplication of computerized adaptive testing in U S Army recruit a87-890 v101 aSands, W A1 aGade, P A uhttp://mail.iacat.org/content/application-computerized-adaptive-testing-u-s-army-recruiting00572nas a2200121 4500008004100000245015600041210006900197300000900266490000700275653003400282100001500316856011900331 1982 eng d00aAbility measurement, test bias reduction, and psychological reactions to testing as a function of computer adaptive testing versus conventional testing0 aAbility measurement test bias reduction and psychological reacti a42330 v4210acomputerized adaptive testing1 aOrban, J A uhttp://mail.iacat.org/content/ability-measurement-test-bias-reduction-and-psychological-reactions-testing-function00438nas a2200121 4500008004100000245007000041210006900111300001200180490000600192100000900198700001700207856009200224 1982 eng d00aAdaptive EAP estimation of ability in a microcomputer environment0 aAdaptive EAP estimation of ability in a microcomputer environmen a431-4440 v61 aBock1 aMislevy, R J uhttp://mail.iacat.org/content/adaptive-eap-estimation-ability-microcomputer-environment00405nas a2200109 4500008004100000245004900041210004600090260004800136100001700184700001800201856007600219 1982 eng d00aAn adaptive Private Pilot Certification Exam0 aadaptive Private Pilot Certification Exam aAviation, Space, and Environmental Medicine1 aTrollip, S R1 aAnderson, R I uhttp://mail.iacat.org/content/adaptive-private-pilot-certification-exam00434nas a2200109 4500008004100000245007000041210006900111260001300180100001800193700002000211856009300231 1982 eng d00aAssessing mathematics achievement with a tailored testing program0 aAssessing mathematics achievement with a tailored testing progra aNew York1 aGarrison, W M1 aBaumgarten, B S uhttp://mail.iacat.org/content/assessing-mathematics-achievement-tailored-testing-program00521nas a2200133 4500008004100000245009100041210006900132300001200201490000700213100001000220700001800230700001600248856012300264 1982 eng d00aAutomated tailored testing using Raven’s Matrices and the Mill Hill vocabulary tests0 aAutomated tailored testing using Raven s Matrices and the Mill H a331-3440 v171 aWatts1 aBaddeley, A D1 aWilliams, M uhttp://mail.iacat.org/content/automated-tailored-testing-using-raven%E2%80%99s-matrices-and-mill-hill-vocabulary-tests00646nam a2200097 4500008004100000245014800041210006900189260015400258100001500412856012100427 1981 eng d00aAbility measurement, test bias reduction, and psychological reactions to testing as a function of computer adaptive testing versus conventional0 aAbility measurement test bias reduction and psychological reacti aUnpublished doctoral dissertation, Virginia Polytechnic Institute and State University. Dissertational Abstracts International, 1982, 42,(10-B), 42331 aOrban, J A uhttp://mail.iacat.org/content/ability-measurement-test-bias-reduction-and-psychological-reactions-testing-function-000469nas a2200121 4500008004100000245004000041210004000081260011500121100001600236700001400252700001300266856006800279 1981 eng d00aAdaptive testing without a computer0 aAdaptive testing without a computer aCatalog of Selected Documents in Psychology, Nov 1981, 11, 74-75 (Ms. No. 2350). AFHRL Technical Report 80-66.1 aFriedman, D1 aSteinberg1 aRee, M J uhttp://mail.iacat.org/content/adaptive-testing-without-computer00594nas a2200097 4500008004100000245005800041210005800099260023900157100001700396856008300413 1980 eng d00aAdaptive verbal ability testing in a military setting0 aAdaptive verbal ability testing in a military setting aD. J. Weiss (Ed.), Proceedings of the 1979 Computerized Adaptive Testing Conference (pp. 4-15). Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory.1 aMcBride, J R uhttp://mail.iacat.org/content/adaptive-verbal-ability-testing-military-setting00631nas a2200109 4500008004100000245014500041210006900186260011400255100001900369700001400388856011900402 1980 eng d00aAn alternate-forms reliability and concurrent validity comparison of Bayesian adaptive and conventional ability tests (Research Report 80-5)0 aalternateforms reliability and concurrent validity comparison of aMinneapolis, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory1 aKingsbury, G G1 aWeiss, DJ uhttp://mail.iacat.org/content/alternate-forms-reliability-and-concurrent-validity-comparison-bayesian-adaptive-and00477nas a2200097 4500008004100000245007300041210006900114260008800183100001700271856009100288 1979 eng d00aAdaptive mental testing: The state of the art (Technical Report 423)0 aAdaptive mental testing The state of the art Technical Report 42 aAlexandria VA: U.S. Army Research Institute for the Behavioral and Social Sciences.1 aMcBride, J R uhttp://mail.iacat.org/content/adaptive-mental-testing-state-art-technical-report-423-000527nas a2200109 4500008004100000245007800041210006900119260009700188100001900285700001400304856009900318 1979 eng d00aAn adaptive testing strategy for mastery decisions (Research Report 79-5)0 aadaptive testing strategy for mastery decisions Research Report  aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aKingsbury, G G1 aWeiss, DJ uhttp://mail.iacat.org/content/adaptive-testing-strategy-mastery-decisions-research-report-79-500552nas a2200097 4500008004100000245006400041210006300105260018000168100001700348856008900365 1979 eng d00aAdaptive tests' usefulness for military personnel screening0 aAdaptive tests usefulness for military personnel screening aIn M. Wiskoff, Chair, Military Applications of Computerized Adaptive Testing. Symposium presented at the Annual Convention of the American Psychological Association, New York.1 aMcBride, J R uhttp://mail.iacat.org/content/adaptive-tests-usefulness-military-personnel-screening00373nas a2200097 4500008004100000245005900041210005400100260002200154100001700176856008200193 1978 eng d00aAn adaptive test designed for paper-and-pencil testing0 aadaptive test designed for paperandpencil testing aSan Francisco, CA1 aMcBride, J R uhttp://mail.iacat.org/content/adaptive-test-designed-paper-and-pencil-testing00523nas a2200097 4500008004100000245007200041210006900113260013000182100001700312856009600329 1978 eng d00aApplications of latent trait theory to criterion-referenced testing0 aApplications of latent trait theory to criterionreferenced testi aD.J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis, MN: University of Minnesota.1 aMcBride, J R uhttp://mail.iacat.org/content/applications-latent-trait-theory-criterion-referenced-testing00610nas a2200109 4500008004100000245007300041210006900114260018600183100001700369700001500386856009900401 1978 eng d00aApplications of sequential testing procedures to performance testing0 aApplications of sequential testing procedures to performance tes aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aEpstein, K I1 aKnerr, C S uhttp://mail.iacat.org/content/applications-sequential-testing-procedures-performance-testing-000568nas a2200109 4500008003900000245005900039210005800098260018500156100001700341700001600358856008400374 1977 d00aAdaptive Branching in a Multi-Content Achievement Test0 aAdaptive Branching in a MultiContent Achievement Test aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aPennell, R J1 aHarris, D A uhttp://mail.iacat.org/content/adaptive-branching-multi-content-achievement-test00408nas a2200097 4500008004100000245004500041210004200086260009600128100001700224856006900241 1977 eng d00aAn adaptive test of arithmetic reasoning0 aadaptive test of arithmetic reasoning athe Proceedings of the Nineteenth Military Testing Association conference, San Antonio, TX.1 aMcBride, J R uhttp://mail.iacat.org/content/adaptive-test-arithmetic-reasoning00528nas a2200097 4500008004100000245005500041210005500096260018700151100001400338856007800352 1977 eng d00aAdaptive testing and the problem of classification0 aAdaptive testing and the problem of classification aD. Weiss (Ed.), Applications of computerized adaptive testing (Research Report 77-1). Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aVale, C D uhttp://mail.iacat.org/content/adaptive-testing-and-problem-classification00632nas a2200109 4500008003900000245008400039210006900123260018500192100001800377700001600395856011100411 1977 d00aAdaptive Testing Applied to Hierarchically Structured Objectives-Based Programs0 aAdaptive Testing Applied to Hierarchically Structured Objectives aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aHambleton, RK1 aEignor, D R uhttp://mail.iacat.org/content/adaptive-testing-applied-hierarchically-structured-objectives-based-programs00539nas a2200109 4500008003900000245008700039210006900126260009700195100001500292700001400307856010800321 1977 d00aAn adaptive testing strategy for achievement test batteries (Research Rep No 77-6)0 aadaptive testing strategy for achievement test batteries Researc aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aBrown, J M1 aWeiss, DJ uhttp://mail.iacat.org/content/adaptive-testing-strategy-achievement-test-batteries-research-rep-no-77-600458nas a2200133 4500008004100000245006300041210006300104300001200167490000600179100001700185700001700202700001800219856008700237 1977 eng d00aApplication of tailored testing to achievement measurement0 aApplication of tailored testing to achievement measurement a158-1610 v91 aEnglish, R A1 aReckase, M D1 aPatience, W M uhttp://mail.iacat.org/content/application-tailored-testing-achievement-measurement00534nam a2200097 4500008004100000245013000041210006900171260005700240100001700297856012200314 1977 eng d00aAn application of the Rasch one-parameter logistic model to individual intelligence testing in a tailored testing environment0 aapplication of the Rasch oneparameter logistic model to individu aDissertation Abstracts International, 37 (9-A), 57661 aIreland, C M uhttp://mail.iacat.org/content/application-rasch-one-parameter-logistic-model-individual-intelligence-testing-tailored00594nas a2200097 4500008004100000245007800041210006900119260019100188100001500379856010200394 1977 eng d00aApplications of adaptive testing in measuring achievement and performance0 aApplications of adaptive testing in measuring achievement and pe aD. J. Weiss (Ed.), Applications of computerized adaptive testing (Research Report 77-1). Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program. 1 aBejar, I I uhttp://mail.iacat.org/content/applications-adaptive-testing-measuring-achievement-and-performance00535nas a2200097 4500008004100000245007300041210006900114260014200183100001400325856009800339 1977 eng d00aApplications of computerized adaptive testing (Research Report 77-1)0 aApplications of computerized adaptive testing Research Report 77 aMinneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory1 aWeiss, DJ uhttp://mail.iacat.org/content/applications-computerized-adaptive-testing-research-report-77-100472nas a2200109 4500008004100000245007300041210006900114260005000183100001700233700001500250856009700265 1977 eng d00aApplications of sequential testing procedures to performance testing0 aApplications of sequential testing procedures to performance tes aMinneapolis, MN. USAbUniversity of Minnesota1 aEpstein, K I1 aKnerr, C S uhttp://mail.iacat.org/content/applications-sequential-testing-procedures-performance-testing00475nas a2200097 4500008004100000245007300041210006900114260008800183100001700271856008900288 1976 eng d00aAdaptive mental testing: The state of the art (Technical Report 423)0 aAdaptive mental testing The state of the art Technical Report 42 aWashington DC: U.S. Army Research Institute for the Social and Behavioral Sciences.1 aMcBride, J R uhttp://mail.iacat.org/content/adaptive-mental-testing-state-art-technical-report-42300433nas a2200109 4500008004100000245008400041210006900125300000700194490001000201100001200211856010000223 1976 eng d00aAdaptive testing: A Bayesian procedure for the efficient measurement of ability0 aAdaptive testing A Bayesian procedure for the efficient measurem a360 v13(2)1 aWood, R uhttp://mail.iacat.org/content/adaptive-testing-bayesian-procedure-efficient-measurement-ability00590nas a2200097 4500008004100000245009200041210006900133260016000202100001400362856011600376 1976 eng d00aAdaptive testing research at Minnesota: Overview, recent results, and future directions0 aAdaptive testing research at Minnesota Overview recent results a aC. L. Clark (Ed.), Proceedings of the First Conference on Computerized Adaptive Testing (pp. 24-35). Washington DC: United States Civil Service Commission.1 aWeiss, DJ uhttp://mail.iacat.org/content/adaptive-testing-research-minnesota-overview-recent-results-and-future-directions00618nas a2200097 4500008004100000245011800041210006900159260015300228100001700381856012200398 1976 eng d00aAdaptive testing research at Minnesota: Some properties of a Bayesian sequential adaptive mental testing strategy0 aAdaptive testing research at Minnesota Some properties of a Baye aC. K. Clark (Ed.), Proceedings of the First Conference on Computerized Adaptive Testing (pp. 36-53). Washington DC: U.S. Government Printing Office.1 aMcBride, J R uhttp://mail.iacat.org/content/adaptive-testing-research-minnesota-some-properties-bayesian-sequential-adaptive-mental00368nas a2200109 4500008004100000245005400041210005100095300001000146490000700156100001700163856007800180 1974 eng d00aAn application of latent trait mental test theory0 aapplication of latent trait mental test theory a29-480 v271 aJensema, C J uhttp://mail.iacat.org/content/application-latent-trait-mental-test-theory00407nas a2200097 4500008004100000245007400041210006900115260001700184100001700201856009100218 1974 eng d00aAn application of the Rasch simple logistic model to tailored testing0 aapplication of the Rasch simple logistic model to tailored testi aSt. Loius MO1 aReckase, M D uhttp://mail.iacat.org/content/application-rasch-simple-logistic-model-tailored-testing00519nas a2200109 4500008004100000245007400041210006900115260009700184100001400281700001400295856010000309 1973 eng d00aAbility measurement: Conventional or adaptive? (Research Report 73-1)0 aAbility measurement Conventional or adaptive Research Report 731 aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aWeiss, DJ1 aBetz, N E uhttp://mail.iacat.org/content/ability-measurement-conventional-or-adaptive-research-report-73-100506nas a2200097 4500008004100000245010000041210006900141260006400210100001700274856011700291 1972 eng d00aAn application of latent trait mental test theory to the Washington Pre-College Testing Battery0 aapplication of latent trait mental test theory to the Washington aUnpublished doctoral dissertation, University of Washington1 aJensema, C J uhttp://mail.iacat.org/content/application-latent-trait-mental-test-theory-washington-pre-college-testing-battery00567nas a2200109 4500008004100000245011200041210006900153260008500222100001800307700001100325856012100336 1971 eng d00aThe application of item generators for individualizing mathematics testing and instruction (Report 1971/14)0 aapplication of item generators for individualizing mathematics t aPittsburgh PA: University of Pittsburgh Learning Research and Development Center1 aFerguson, R L1 aHsu, T uhttp://mail.iacat.org/content/application-item-generators-individualizing-mathematics-testing-and-instruction-report00343nas a2200109 4500008004100000245004100041210004100082300001200123490001500135100001500150856006800165 1970 eng d00aAdaptive testing of cognitive skills0 aAdaptive testing of cognitive skills a167-1680 v5 (part 1)1 aWargo, M J uhttp://mail.iacat.org/content/adaptive-testing-cognitive-skills00333nas a2200109 4500008004100000245004000041210004000081300001200121490000700133100001600140856006700156 1969 eng d00aAutomation of psychological testing0 aAutomation of psychological testing a287-2890 v241 aElwood, D L uhttp://mail.iacat.org/content/automation-psychological-testing00371nas a2200121 4500008004100000245004400041210004400085300001200129490000700141100001900148700001400167856006800181 1965 eng d00aAdaptive testing in an older population0 aAdaptive testing in an older population a193-1980 v601 aGreenwood, D I1 aTaylor, C uhttp://mail.iacat.org/content/adaptive-testing-older-population00495nam a2200097 4500008004100000245010000041210006900141260006100210100001900271856010700290 1961 eng d00aAn analysis of the application of utility theory to the development of two-stage testing models0 aanalysis of the application of utility theory to the development aUnpublished doctoral dissertation, University of Buffalo1 aRosenbach, J H uhttp://mail.iacat.org/content/analysis-application-utility-theory-development-two-stage-testing-models00390nas a2200109 4500008004100000245006200041210005900103300001200162490000700174100001600181856008300197 1946 eng d00aAn application of sequential sampling to testing students0 aapplication of sequential sampling to testing students a547-5560 v411 aCowden, D J uhttp://mail.iacat.org/content/application-sequential-sampling-testing-students