00541nas a2200169 4500008004500000245006600045210006600111300001000177490000700187653002100194653000800215653000800223653003000231653001300261100002000274856007700294 2023 Engldsh 00aExpanding the Meaning of Adaptive Testing to Enhance Validity0 aExpanding the Meaning of Adaptive Testing to Enhance Validity a22-310 v1010aAdaptive Testing10aCAT10aCBT10atest-taking disengagement10avalidity1 aWise, Steven, L uhttp://www.iacat.org/expanding-meaning-adaptive-testing-enhance-validity00670nas a2200193 4500008004500000022001400045245007100059210006700130490000700197653002100204653002900225653002500254653003900279653001600318100001400334700002200348700002400370856008200394 2023 Engldsh  a2165-659200aAn Extended Taxonomy of Variants of Computerized Adaptive Testing0 aExtended Taxonomy of Variants of Computerized Adaptive Testing0 v1010aAdaptive Testing10aevidence-centered design10aItem Response Theory10aknowledge-based model construction10amissingness1 aLevy, Roy1 aBehrens, John, T.1 aMislevy, Robert, J. uhttp://www.iacat.org/extended-taxonomy-variants-computerized-adaptive-testing03003nas a2200157 4500008004100000245007600041210006900117260005500186520243700241653002102678653002302699653002002722100001602742700001602758856007102774 2017 eng d00aGenerating Rationales to Support Formative Feedback in Adaptive Testing0 aGenerating Rationales to Support Formative Feedback in Adaptive  aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Computer adaptive testing offers many important benefits to support and promote life-long learning. Computers permit testing on-demand thereby allowing students to take the test at any time during instruction; items on computerized tests are scored immediately thereby providing students with instant feedback; computerized tests permit continuous administration thereby allowing students to have more choice about when they write their exams. But despite these important benefits, the advent of computer adaptive testing has also raised formidable challenges, particularly in the area of item development. Educators must have access to large numbers of diverse, high-quality test items to implement computerize adaptive testing because items are continuously administered to students. Hence, hundreds or even thousands of items are needed to develop the test item banks necessary for computer adaptive testing. Unfortunately, educational test items, as they are currently created, are time consuming and expensive to develop because each individual item is written, initially, by a content specialist and, then, reviewed, edited, and revised by groups of content specialists to ensure the items yield reliable and valid information. Hence, item development is one of the most important problems that must be solved before we can migrate to computer adaptive testing to support life-long learning because large numbers of high-quality, content-specific, test items are required.

One promising item development method that may be used to address this challenge is with automatic item generation. Automatic item generation is a relatively new but rapidly evolving research area where cognitive and psychometric modelling practices are used produce hundreds of new test items with the aid of computer technology. The purpose of our presentation is to describe a new methodology for generating both the items and the rationales required to solve each generated item in order to produce the feedback needed to support life-long learning. Our item generation methodology will first be described. To ensure our description is practical, the method will also be demonstrated using generated items from the health sciences to demonstrate how item generation can promote life-long learning for medical educators and practitioners.

 

Session Video

10aAdaptive Testing10aformative feedback10aItem generation1 aGierl, Mark1 aBulut, Okan uhttps://drive.google.com/open?id=1O5KDFtQlDLvhNoDr7X4JO4arpJkIHKUP01456nas a2200133 4500008004100000245007100041210006900112260005500181520096500236653002101201653000801222100002101230856007101251 2017 eng d00aHow Adaptive is an Adaptive Test: Are all Adaptive Tests Adaptive?0 aHow Adaptive is an Adaptive Test Are all Adaptive Tests Adaptive aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

There are many different kinds of adaptive tests but they all have the characteristic that some feature of the test is customized to the purpose of the test. In the time allotted, it is impossible to consider the adaptation of all of this types so this address will focus on the “classic” adaptive test that matches the difficulty of the test to the capabilities of the person being tested. This address will first present information on the maximum level of adaptation that can occur and then compare the amount of adaptation that typically occurs on an operational adaptive test to the maximum level of adaptation. An index is proposed to summarize the amount of adaptation and it is argued that this type of index should be reported for operational adaptive tests to show the amount of adaptation that typically occurs.

Click for Presentation Video 

10aAdaptive Testing10aCAT1 aReckase, Mark, D uhttps://drive.google.com/open?id=1Nj-zDCKk3DvHA4Jlp1qkb2XovmHeQfxu00723nas a2200205 4500008004500000022001500045245013200060210006900192300000900261490000600270653002100276653003000297653003000327653001600357653002300373100002100396700002000417700002000437856006000457 2016 Engldsh  a2165-6592 00aEffect of Imprecise Parameter Estimation on Ability Estimation in a Multistage Test in an Automatic Item Generation Context 0 aEffect of Imprecise Parameter Estimation on Ability Estimation i a1-180 v410aAdaptive Testing10aautomatic item generation10aerrors in item parameters10aitem clones10amultistage testing1 aColvin, Kimberly1 aKeller, Lisa, A1 aRobin, Frederic uhttp://iacat.org/jcat/index.php/jcat/article/view/59/2701876nas a2200205 4500008003900000245005600039210005600095300001000151490000800161520124900169653002101418653003001439653002501469653001801494653002501512100001301537700001701550700002101567856008201588 2008 d00aComputerized Adaptive Testing of Personality Traits0 aComputerized Adaptive Testing of Personality Traits a12-210 v2163 a

A computerized adaptive testing (CAT) procedure was simulated with ordinal polytomous personality data collected using a
conventional paper-and-pencil testing format. An adapted Dutch version of the dominance scale of Gough and Heilbrun’s Adjective
Check List (ACL) was used. This version contained Likert response scales with five categories. Item parameters were estimated using Samejima’s graded response model from the responses of 1,925 subjects. The CAT procedure was simulated using the responses of 1,517 other subjects. The value of the required standard error in the stopping rule of the CAT was manipulated. The relationship between CAT latent trait estimates and estimates based on all dominance items was studied. Additionally, the pattern of relationships between the CAT latent trait estimates and the other ACL scales was compared to that between latent trait estimates based on the entire item pool and the other ACL scales. The CAT procedure resulted in latent trait estimates qualitatively equivalent to latent trait estimates based on all items, while a substantial reduction of the number of used items could be realized (at the stopping rule of 0.4 about 33% of the 36 items was used).

10aAdaptive Testing10acmoputer-assisted testing10aItem Response Theory10aLikert scales10aPersonality Measures1 aHol, A M1 aVorst, H C M1 aMellenbergh, G J uhttp://www.iacat.org/content/computerized-adaptive-testing-personality-traits01948nas a2200301 4500008004500000020001400045245012900059210006900188300001200257490000700269520093500276653002501211653002101236653002501257653003001282653003001312653001001342653001501352653002601367653002501393653002401418653001501442653001501457100001301472700001701485700002101502856012301523 2007 Engldsh  a0146-621600aComputerized adaptive testing for polytomous motivation items: Administration mode effects and a comparison with short forms0 aComputerized adaptive testing for polytomous motivation items Ad a412-4290 v313 aIn a randomized experiment (n=515), a computerized and a computerized adaptive test (CAT) are compared. The item pool consists of 24 polytomous motivation items. Although items are carefully selected, calibration data show that Samejima's graded response model did not fit the data optimally. A simulation study is done to assess possible consequences of model misfit. CAT efficiency was studied by a systematic comparison of the CAT with two types of conventional fixed length short forms, which are created to be good CAT competitors. Results showed no essential administration mode effects. Efficiency analyses show that CAT outperformed the short forms in almost all aspects when results are aggregated along the latent trait scale. The real and the simulated data results are very similar, which indicate that the real data results are not affected by model misfit. (PsycINFO Database Record (c) 2007 APA ) (journal abstract)10a2220 Tests & Testing10aAdaptive Testing10aAttitude Measurement10acomputer adaptive testing10aComputer Assisted Testing10aitems10aMotivation10apolytomous motivation10aStatistical Validity10aTest Administration10aTest Forms10aTest Items1 aHol, A M1 aVorst, H C M1 aMellenbergh, G J uhttp://www.iacat.org/content/computerized-adaptive-testing-polytomous-motivation-items-administration-mode-effects-and01373nas a2200193 4500008004100000245005000041210004700091300001000138490000600148520081200154653001700966653002100983653002601004653002501030100001501055700001401070700002301084856007201107 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://www.iacat.org/content/authoring-environment-adaptive-testing02152nas a2200229 4500008004100000020002200041245008700063210006900150260004100219300001200260490000700272520135500279653002101634653001501655653002401670653002601694653002501720653002101745653003101766100002301797856010201820 2005 eng d a0022-0655 (Print)00aA comparison of item-selection methods for adaptive tests with content constraints0 acomparison of itemselection methods for adaptive tests with cont bBlackwell Publishing: United Kingdom a283-3020 v423 aIn test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee's ability estimate, such as the test information function in computerized adaptive testing. But it leads to the non-trivial problem of how to realize a set of content constraints on the test—a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of the future consequences of selecting an item. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test and the latter a projection of the test itself. The pros and cons of these methods are analyzed. An empirical comparison between the WDM and STA was conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM.10aAdaptive Testing10aAlgorithms10acontent constraints10aitem selection method10ashadow test approach10aspiraling method10aweighted deviations method1 avan der Linden, WJ uhttp://www.iacat.org/content/comparison-item-selection-methods-adaptive-tests-content-constraints01559nas a2200169 4500008004100000245008000041210006900121300001200190490000700202520094200209653002101151653003001172653005401202100001401256700001301270856010601283 2005 eng d00aControlling item exposure and test overlap in computerized adaptive testing0 aControlling item exposure and test overlap in computerized adapt a204-2170 v293 aThis article proposes an item exposure control method, which is the extension of the Sympson and Hetter procedure and can provide item exposure control at both the item and test levels. Item exposure rate and test overlap rate are two indices commonly used to track item exposure in computerized adaptive tests. By considering both indices, item exposure can be monitored at both the item and test levels. To control the item exposure rate and test overlap rate simultaneously, the modified procedure attempted to control not only the maximum value but also the variance of item exposure rates. Results indicated that the item exposure rate and test overlap rate could be controlled simultaneously by implementing the modified procedure. Item exposure control was improved and precision of trait estimation decreased when a prespecified maximum test overlap rate was stringent. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Content (Test) computerized adaptive testing1 aChen, S-Y1 aLei, P-W uhttp://www.iacat.org/content/controlling-item-exposure-and-test-overlap-computerized-adaptive-testing01476nas a2200193 4500008004100000245020200041210006900243300001200312490000700324520066400331653002100995653003001016653005501046653001101101653001801112100001401130700001701144856012101161 2005 eng d00aSomministrazione di test computerizzati di tipo adattivo: Un' applicazione del modello di misurazione di Rasch [Administration of computerized and adaptive tests: An application of the Rasch Model]0 aSomministrazione di test computerizzati di tipo adattivo Un appl a131-1490 v123 aThe aim of the present study is to describe the characteristics of a procedure for administering computerized and adaptive tests (Computer Adaptive Testing or CAT). Items to be asked to the individuals are interactively chosen and are selected from a "bank" in which they were previously calibrated and recorded on the basis of their difficulty level. The selection of items is performed by increasingly more accurate estimates of the examinees' ability. The building of an item-bank on Psychometrics and the implementation of this procedure allow a first validation through Monte Carlo simulations. (PsycINFO Database Record (c) 2006 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Response Theory computerized adaptive testing10aModels10aPsychometrics1 aMiceli, R1 aMolinengo, G uhttp://www.iacat.org/content/somministrazione-di-test-computerizzati-di-tipo-adattivo-un-applicazione-del-modello-di01668nas a2200229 4500008004100000245005500041210005300096300000800149490000700157520102900164653002101193653001201214653003001226653001801256653000901274100001701283700001201300700001501312700001601327700001401343856008101357 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://www.iacat.org/content/assisted-self-adapted-testing-comparative-study02472nas a2200193 4500008004100000245010700041210007100148300001200219490000700231520172200238653002101960653003401981653001602015653003002031653002302061653004502084100001502129856013402144 2004 eng d00aÉvaluation et multimédia dans l'apprentissage d'une L2 [Assessment and multimedia in learning an L2]0 aÉvaluation et multimédia dans lapprentissage dune L2 Assessment  a475-4870 v163 aIn the first part of this paper different areas where technology may be used for second language assessment are described. First, item banking operations, which are generally based on item Response Theory but not necessarily restricted to dichotomously scored items, facilitate assessment task organization and require technological support. Second, technology may help to design more authentic assessment tasks or may be needed in some direct testing situations. Third, the assessment environment may be more adapted and more stimulating when technology is used to give the student more control. The second part of the paper presents different functions of assessment. The monitoring function (often called formative assessment) aims at adapting the classroom activities to students and to provide continuous feedback. Technology may be used to train the teachers in monitoring techniques, to organize data or to produce diagnostic information; electronic portfolios or quizzes that are built in some educational software may also be used for monitoring. The placement function is probably the one in which the application of computer adaptive testing procedures (e.g. French CAPT) is the most appropriate. Automatic scoring devices may also be used for placement purposes. Finally the certification function requires more valid and more reliable tools. Technology may be used to enhance the testing situation (to make it more authentic) or to facilitate data processing during the construction of a test. Almond et al. (2002) propose a four component model (Selection, Presentation, Scoring and Response) for designing assessment systems. Each component must be planned taking into account the assessment function. 10aAdaptive Testing10aComputer Assisted Instruction10aEducational10aForeign Language Learning10aProgram Evaluation10aTechnology computerized adaptive testing1 aLaurier, M uhttp://www.iacat.org/content/%C3%A9valuation-et-multim%C3%A9dia-dans-lapprentissage-dune-l2-assessment-and-multimedia-learning-l201773nas a2200193 4500008004100000245023000041210006900271300000900340490000700349520093900356653002101295653003001316653001801346653001601364653004201380100001201422700001901434856012601453 2004 eng d00aKann die Konfundierung von Konzentrationsleistung und Aktivierung durch adaptives Testen mit dern FAKT vermieden werden? [Avoiding the confounding of concentration performance and activation by adaptive testing with the FACT]0 aKann die Konfundierung von Konzentrationsleistung und Aktivierun a1-170 v253 aThe study investigates the effect of computerized adaptive testing strategies on the confounding of concentration performance with activation. A sample of 54 participants was administered 1 out of 3 versions (2 adaptive, 1 non-adaptive) of the computerized Frankfurt Adaptive Concentration Test FACT (Moosbrugger & Heyden, 1997) at three subsequent points in time. During the test administration changes in activation (electrodermal activity) were recorded. The results pinpoint a confounding of concentration performance with activation for the non-adaptive test version, but not for the adaptive test versions (p = .01). Thus, adaptive FACT testing strategies can remove the confounding of concentration performance with activation, thereby increasing the discriminant validity. In conclusion, an attention-focusing-hypothesis is formulated to explain the observed effect. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aConcentration10aPerformance10aTesting computerized adaptive testing1 aFrey, A1 aMoosbrugger, H uhttp://www.iacat.org/content/kann-die-konfundierung-von-konzentrationsleistung-und-aktivierung-durch-adaptives-testen-mit01923nas a2200241 4500008004100000245009400041210006900135300001200204490000700216520110100223653002101324653001301345653003001358653005701388653000901445653003201454653002601486653002001512100001401532700001301546700001501559856010701574 2003 eng d00aA Bayesian method for the detection of item preknowledge in computerized adaptive testing0 aBayesian method for the detection of item preknowledge in comput a121-1370 v273 aWith the increased use of continuous testing in computerized adaptive testing, new concerns about test security have evolved, such as how to ensure that items in an item pool are safeguarded from theft. In this article, procedures to detect test takers using item preknowledge are explored. When test takers use item preknowledge, their item responses deviate from the underlying item response theory (IRT) model, and estimated abilities may be inflated. This deviation may be detected through the use of person-fit indices. A Bayesian posterior log odds ratio index is proposed for detecting the use of item preknowledge. In this approach to person fit, the estimated probability that each test taker has preknowledge of items is updated after each item response. These probabilities are based on the IRT parameters, a model specifying the probability that each item has been memorized, and the test taker's item responses. Simulations based on an operational computerized adaptive test (CAT) pool are used to demonstrate the use of the odds ratio index. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aCheating10aComputer Assisted Testing10aIndividual Differences computerized adaptive testing10aItem10aItem Analysis (Statistical)10aMathematical Modeling10aResponse Theory1 aMcLeod, L1 aLewis, C1 aThissen, D uhttp://www.iacat.org/content/bayesian-method-detection-item-preknowledge-computerized-adaptive-testing01840nas a2200205 4500008004100000245009000041210006900131300001100200490000700211520111400218653002101332653003001353653001601383653003201399653001601431653004501447100001501492700001601507856011101523 2003 eng d00aA comparative study of item exposure control methods in computerized adaptive testing0 acomparative study of item exposure control methods in computeriz a71-1030 v403 aThis study compared the properties of five methods of item exposure control within the purview of estimating examinees' abilities in a computerized adaptive testing (CAT) context. Each exposure control algorithm was incorporated into the item selection procedure and the adaptive testing progressed based on the CAT design established for this study. The merits and shortcomings of these strategies were considered under different item pool sizes and different desired maximum exposure rates and were evaluated in light of the observed maximum exposure rates, the test overlap rates, and the conditional standard errors of measurement. Each method had its advantages and disadvantages, but no one possessed all of the desired characteristics. There was a clear and logical trade-off between item exposure control and measurement precision. The M. L. Stocking and C. Lewis conditional multinomial procedure and, to a slightly lesser extent, the T. Davey and C. G. Parshall method seemed to be the most promising considering all of the factors that this study addressed. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aEducational10aItem Analysis (Statistical)10aMeasurement10aStrategies computerized adaptive testing1 aChang, S-W1 aAnsley, T N uhttp://www.iacat.org/content/comparative-study-item-exposure-control-methods-computerized-adaptive-testing01761nas a2200313 4500008004100000245007700041210006900118300001200187490000700199520083400206653002101040653001501061653001701076653001601093653003001109653001701139653001501156653002501171653002001196653001501216653002501231653001801256653000901274100001801283700001201301700001601313700001601329856010201345 2003 eng d00aComputerized adaptive rating scales for measuring managerial performance0 aComputerized adaptive rating scales for measuring managerial per a237-2460 v113 aComputerized adaptive rating scales (CARS) had been developed to measure contextual or citizenship performance. This rating format used a paired-comparison protocol, presenting pairs of behavioral statements scaled according to effectiveness levels, and an iterative item response theory algorithm to obtain estimates of ratees' citizenship performance (W. C. Borman et al, 2001). In the present research, we developed CARS to measure the entire managerial performance domain, including task and citizenship performance, thus addressing a major limitation of the earlier CARS. The paper describes this development effort, including an adjustment to the algorithm that reduces substantially the number of item pairs required to obtain almost as much precision in the performance estimates. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aAlgorithms10aAssociations10aCitizenship10aComputer Assisted Testing10aConstruction10aContextual10aItem Response Theory10aJob Performance10aManagement10aManagement Personnel10aRating Scales10aTest1 aSchneider, RJ1 aGoff, M1 aAnderson, S1 aBorman, W C uhttp://www.iacat.org/content/computerized-adaptive-rating-scales-measuring-managerial-performance01931nas a2200229 4500008004100000245007200041210006900113300001200182490000700194520116000201653001801361653002101379653003001400653001801430653002501448653002501473653005701498653002201555100001501577700001201592856009701604 2003 eng d00aComputerized adaptive testing using the nearest-neighbors criterion0 aComputerized adaptive testing using the nearestneighbors criteri a204-2160 v273 aItem selection procedures designed for computerized adaptive testing need to accurately estimate every taker's trait level (θ) and, at the same time, effectively use all items in a bank. Empirical studies showed that classical item selection procedures based on maximizing Fisher or other related information yielded highly varied item exposure rates; with these procedures, some items were frequently used whereas others were rarely selected. In the literature, methods have been proposed for controlling exposure rates; they tend to affect the accuracy in θ estimates, however. A modified version of the maximum Fisher information (MFI) criterion, coined the nearest neighbors (NN) criterion, is proposed in this study. The NN procedure improves to a moderate extent the undesirable item exposure rates associated with the MFI criterion and keeps sufficient precision in estimates. The NN criterion will be compared with a few other existing methods in an empirical study using the mean squared errors in θ estimates and plots of item exposure rates associated with different distributions. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aStatistical Analysis10aStatistical Estimation computerized adaptive testing10aStatistical Tests1 aCheng, P E1 aLiou, M uhttp://www.iacat.org/content/computerized-adaptive-testing-using-nearest-neighbors-criterion01747nas a2200217 4500008004100000245008700041210006900128300001200197490000700209520100200216653002101218653003001239653002601269653002501295653002001320653001401340653004901354100001401403700001401417856009801431 2003 eng d00aItem exposure constraints for testlets in the verbal reasoning section of the MCAT0 aItem exposure constraints for testlets in the verbal reasoning s a335-3560 v273 aThe current study examined item exposure control procedures for testlet scored reading passages in the Verbal Reasoning section of the Medical College Admission Test with four computerized adaptive testing (CAT) systems using the partial credit model. The first system used a traditional CAT using maximum information item selection. The second used random item selection to provide a baseline for optimal exposure rates. The third used a variation of Lunz and Stahl's randomization procedure. The fourth used Luecht and Nungester's computerized adaptive sequential testing (CAST) system. A series of simulated fixed-length CATs was run to determine the optimal item length selection procedure. Results indicated that both the randomization procedure and CAST performed well in terms of exposure control and measurement precision, with the CAST system providing the best overall solution when all variables were taken into consideration. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aEntrance Examinations10aItem Response Theory10aRandom Sampling10aReasoning10aVerbal Ability computerized adaptive testing1 aDavis, LL1 aDodd, B G uhttp://www.iacat.org/content/item-exposure-constraints-testlets-verbal-reasoning-section-mcat01436nas a2200205 4500008004100000245008800041210007000129300001200199490000700211520067700218653002100895653003000916653002400946653002500970653002600995653005201021100001901073700002301092856011501115 2003 eng d00aOptimal stratification of item pools in α-stratified computerized adaptive testing0 aOptimal stratification of item pools in αstratified computerized a262-2740 v273 aA method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the θ estimates, and increases test reliability. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Content (Test)10aItem Response Theory10aMathematical Modeling10aTest Construction computerized adaptive testing1 aChang, Hua-Hua1 avan der Linden, WJ uhttp://www.iacat.org/content/optimal-stratification-item-pools-%CE%B1-stratified-computerized-adaptive-testing01801nas a2200241 4500008004100000245009300041210006900134300001200203490000700215520098200222653001801204653002101222653003001243653001901273653004601292653001801338653002501356653001501381100001401396700001901410700001501429856011501444 2003 eng d00aThe relationship between item exposure and test overlap in computerized adaptive testing0 arelationship between item exposure and test overlap in computeri a129-1450 v403 aThe purpose of this article is to present an analytical derivation for the mathematical form of an average between-test overlap index as a function of the item exposure index, for fixed-length computerized adaptive tests (CATs). This algebraic relationship is used to investigate the simultaneous control of item exposure at both the item and test levels. The results indicate that, in fixed-length CATs, control of the average between-test overlap is achieved via the mean and variance of the item exposure rates of the items that constitute the CAT item pool. The mean of the item exposure rates is easily manipulated. Control over the variance of the item exposure rates can be achieved via the maximum item exposure rate (r-sub(max)). Therefore, item exposure control methods which implement a specification of r-sub(max) (e.g., J. B. Sympson and R. D. Hetter, 1985) provide the most direct control at both the item and test levels. (PsycINFO Database Record (c) 2005 APA )10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aHuman Computer10aInteraction computerized adaptive testing10aItem Analysis10aItem Analysis (Test)10aTest Items1 aChen, S-Y1 aAnkenmann, R D1 aSpray, J A uhttp://www.iacat.org/content/relationship-between-item-exposure-and-test-overlap-computerized-adaptive-testing01520nas a2200157 4500008004100000245009500041210006900136300001200205490000700217520090100224653002101125653003001146653004501176100002301221856011801244 2003 eng d00aSome alternatives to Sympson-Hetter item-exposure control in computerized adaptive testing0 aSome alternatives to SympsonHetter itemexposure control in compu a249-2650 v283 aTheHetter and Sympson (1997; 1985) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic set of values for the examinees’ ability parameter. Formal properties of the method are identified that help us explain why this iterative process can be slow and does not guarantee admissibility. In addition, some alternatives to the SH method are introduced. The behavior of these alternatives was estimated for an adaptive test from an item pool from the Law School Admission Test (LSAT). Two of the alternatives showed attractive behavior and converged smoothly to admissibility for all items in a relatively small number of iteration steps. 10aAdaptive Testing10aComputer Assisted Testing10aTest Items computerized adaptive testing1 avan der Linden, WJ uhttp://www.iacat.org/content/some-alternatives-sympson-hetter-item-exposure-control-computerized-adaptive-testing01549nas a2200193 4500008004100000245026000041210006900301300001000370490000700380520067700387653002101064653002201085653001701107653001501124653004801139100002201187700002201209856012401231 2003 eng d00aTiming behavior in computerized adaptive testing: Response times for correct and incorrect answers are not related to general fluid intelligence/Zum Zeitverhalten beim computergestützten adaptiveb Testen: Antwortlatenzen bei richtigen und falschen Lösun0 aTiming behavior in computerized adaptive testing Response times  a57-630 v243 aExamined the effects of general fluid intelligence on item response times for correct and false responses in computerized adaptive testing. After performing the CFT3 intelligence test, 80 individuals (aged 17-44 yrs) completed perceptual and cognitive discrimination tasks. Results show that response times were related neither to the proficiency dimension reflected by the task nor to the individual level of fluid intelligence. Furthermore, the false > correct-phenomenon as well as substantial positive correlations between item response times for false and correct responses were shown to be independent of intelligence levels. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aCognitive Ability10aIntelligence10aPerception10aReaction Time computerized adaptive testing1 aRammsayer, Thomas1 aBrandler, Susanne uhttp://www.iacat.org/content/timing-behavior-computerized-adaptive-testing-response-times-correct-and-incorrect-answers01516nas a2200229 4500008004100000245008700041210006900128300001200197490000700209520075300216653002100969653001300990653003001003653003401033653001101067653001501078653001501093653001801108100002301126700002701149856011001176 2003 eng d00aUsing response times to detect aberrant responses in computerized adaptive testing0 aUsing response times to detect aberrant responses in computerize a251-2650 v683 aA lognormal model for response times is used to check response times for aberrances in examinee behavior on computerized adaptive tests. Both classical procedures and Bayesian posterior predictive checks are presented. For a fixed examinee, responses and response times are independent; checks based on response times offer thus information independent of the results of checks on response patterns. Empirical examples of the use of classical and Bayesian checks for detecting two different types of aberrances in response times are presented. The detection rates for the Bayesian checks outperformed those for the classical checks, but at the cost of higher false-alarm rates. A guideline for the choice between the two types of checks is offered.10aAdaptive Testing10aBehavior10aComputer Assisted Testing10acomputerized adaptive testing10aModels10aperson Fit10aPrediction10aReaction Time1 avan der Linden, WJ1 aKrimpen-Stoop, E M L A uhttp://www.iacat.org/content/using-response-times-detect-aberrant-responses-computerized-adaptive-testing02085nas a2200229 4500008004100000245012900041210006900170300001200239490000700251520124000258653001801498653002101516653004501537653003001582653001801612653002501630653002601655100001601681700001401697700001901711856012501730 2002 eng d00aA comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model0 acomparison of item selection techniques and exposure control mec a147-1630 v263 aThe use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aAlgorithms computerized adaptive testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aMathematical Modeling1 aPastor, D A1 aDodd, B G1 aChang, Hua-Hua uhttp://www.iacat.org/content/comparison-item-selection-techniques-and-exposure-control-mechanisms-cats-using-generalized01638nas a2200217 4500008004100000245009700041210006900138300001200207490000700219520087500226653002101101653003001122653002501152653002301177653002501200653002801225653002701253100001301280700001501293856011201308 2002 eng d00aAn EM approach to parameter estimation for the Zinnes and Griggs paired comparison IRT model0 aEM approach to parameter estimation for the Zinnes and Griggs pa a208-2270 v263 aBorman et al. recently proposed a computer adaptive performance appraisal system called CARS II that utilizes paired comparison judgments of behavioral stimuli. To implement this approach,the paired comparison ideal point model developed by Zinnes and Griggs was selected. In this article,the authors describe item response and information functions for the Zinnes and Griggs model and present procedures for estimating stimulus and person parameters. Monte carlo simulations were conducted to assess the accuracy of the parameter estimation procedures. The results indicated that at least 400 ratees (i.e.,ratings) are required to obtain reasonably accurate estimates of the stimulus parameters and their standard errors. In addition,latent trait estimation improves as test length increases. The implications of these results for test construction are also discussed. 10aAdaptive Testing10aComputer Assisted Testing10aItem Response Theory10aMaximum Likelihood10aPersonnel Evaluation10aStatistical Correlation10aStatistical Estimation1 aStark, S1 aDrasgow, F uhttp://www.iacat.org/content/em-approach-parameter-estimation-zinnes-and-griggs-paired-comparison-irt-model02013nas a2200205 4500008004100000245008200041210006900123300001200192490000700204520132300211653002101534653001501555653003001570653001101600653000901611653004701620100001901667700001301686856010801699 2002 eng d00aHypergeometric family and item overlap rates in computerized adaptive testing0 aHypergeometric family and item overlap rates in computerized ada a387-3980 v673 aA computerized adaptive test (CAT) is usually administered to small groups of examinees at frequent time intervals. It is often the case that examinees who take the test earlier share information with examinees who will take the test later, thus increasing the risk that many items may become known. Item overlap rate for a group of examinees refers to the number of overlapping items encountered by these examinees divided by the test length. For a specific item pool, different item selection algorithms may yield different item overlap rates. An important issue in designing a good CAT item selection algorithm is to keep item overlap rate below a preset level. In doing so, it is important to investigate what the lowest rate could be for all possible item selection algorithms. In this paper we rigorously prove that if every item had an equal possibility to be selected from the pool in a fixed-length CAT, the number of overlapping item among any α randomly sampled examinees follows the hypergeometric distribution family for α ≥ 1. Thus, the expected values of the number of overlapping items among any randomly sampled α examinee can be calculated precisely. These values may serve as benchmarks in controlling item overlap rates for fixed-length adaptive tests. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aAlgorithms10aComputer Assisted Testing10aTaking10aTest10aTime On Task computerized adaptive testing1 aChang, Hua-Hua1 aZhang, J uhttp://www.iacat.org/content/hypergeometric-family-and-item-overlap-rates-computerized-adaptive-testing01693nas a2200277 4500008004100000020001000041245006500051210006400116260009700180300001100277520074500288653002101033653002201054653002501076653002801101653002501129653001601154653001801170653005501188653001501243653001201258100001801270700002301288700001301311856009101324 2002 eng d a02-0900aMathematical-programming approaches to test item pool design0 aMathematicalprogramming approaches to test item pool design aTwente, The NetherlandsbUniversity of Twente, Faculty of Educational Science and Technology a93-1083 a(From the chapter) This paper presents an approach to item pool design that has the potential to improve on the quality of current item pools in educational and psychological testing and hence to increase both measurement precision and validity. The approach consists of the application of mathematical programming techniques to calculate optimal blueprints for item pools. These blueprints can be used to guide the item-writing process. Three different types of design problems are discussed, namely for item pools for linear tests, item pools computerized adaptive testing (CAT), and systems of rotating item pools for CAT. The paper concludes with an empirical example of the problem of designing a system of rotating item pools for CAT.10aAdaptive Testing10aComputer Assisted10aComputer Programming10aEducational Measurement10aItem Response Theory10aMathematics10aPsychometrics10aStatistical Rotation computerized adaptive testing10aTest Items10aTesting1 aVeldkamp, B P1 avan der Linden, WJ1 aAriel, A uhttp://www.iacat.org/content/mathematical-programming-approaches-test-item-pool-design01631nas a2200241 4500008004100000245005900041210005800100300001200158490000700170520087100177653002101048653003401069653002801103653002001131653003201151653002501183653001501208653002701223653002201250653001601272100001601288856008501304 2002 eng d00aOutlier detection in high-stakes certification testing0 aOutlier detection in highstakes certification testing a219-2330 v393 aDiscusses recent developments of person-fit analysis in computerized adaptive testing (CAT). Methods from statistical process control are presented that have been proposed to classify an item score pattern as fitting or misfitting the underlying item response theory model in CAT Most person-fit research in CAT is restricted to simulated data. In this study, empirical data from a certification test were used. Alternatives are discussed to generate norms so that bounds can be determined to classify an item score pattern as fitting or misfitting. Using bounds determined from a sample of a high-stakes certification test, the empirical analysis showed that different types of misfit can be distinguished Further applications using statistical process control methods to detect misfitting item score patterns are discussed. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10acomputerized adaptive testing10aEducational Measurement10aGoodness of Fit10aItem Analysis (Statistical)10aItem Response Theory10aperson Fit10aStatistical Estimation10aStatistical Power10aTest Scores1 aMeijer, R R uhttp://www.iacat.org/content/outlier-detection-high-stakes-certification-testing02034nas a2200253 4500008004100000245010900041210006900150300000900219490000600228520114100234653002101375653001501396653003901411653002201450653002501472653001801497653002201515653005501537653001501592653001201607100001701619700002501636856011901661 2002 eng d00aA structure-based approach to psychological measurement: Matching measurement models to latent structure0 astructurebased approach to psychological measurement Matching me a4-160 v93 aThe present article sets forth the argument that psychological assessment should be based on a construct's latent structure. The authors differentiate dimensional (continuous) and taxonic (categorical) structures at the latent and manifest levels and describe the advantages of matching the assessment approach to the latent structure of a construct. A proper match will decrease measurement error, increase statistical power, clarify statistical relationships, and facilitate the location of an efficient cutting score when applicable. Thus, individuals will be placed along a continuum or assigned to classes more accurately. The authors briefly review the methods by which latent structure can be determined and outline a structure-based approach to assessment that builds on dimensional scaling models, such as item response theory, while incorporating classification methods as appropriate. Finally, the authors empirically demonstrate the utility of their approach and discuss its compatibility with traditional assessment methods and with computerized adaptive testing. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aAssessment10aClassification (Cognitive Process)10aComputer Assisted10aItem Response Theory10aPsychological10aScaling (Testing)10aStatistical Analysis computerized adaptive testing10aTaxonomies10aTesting1 aRuscio, John1 aRuscio, Ayelet Meron uhttp://www.iacat.org/content/structure-based-approach-psychological-measurement-matching-measurement-models-latent01149nas a2200205 4500008004100000245008200041210006900123260005600192520043100248653002100679653003000700653001600730653001600746653001800762100001500780700001700795700001700812700001400829856010000843 2002 eng d00aThe work ahead: A psychometric infrastructure for computerized adaptive tests0 awork ahead A psychometric infrastructure for computerized adapti aMahwah, N.J. USAbLawrence Erlbaum Associates, Inc.3 a(From the chapter) Considers the past and future of computerized adaptive tests and computer-based tests and looks at issues and challenges confronting a testing program as it implements and operates a computer-based test. Recommendations for testing programs from The National Council of Measurement in Education Ad Hoc Committee on Computerized Adaptive Test Disclosure are appended. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aEducational10aMeasurement10aPsychometrics1 aDrasgow, F1 aPotenza, M P1 aFreemer, J J1 aWard, W C uhttp://www.iacat.org/content/work-ahead-psychometric-infrastructure-computerized-adaptive-tests01617nas a2200193 4500008004100000245008600041210006900127300001200196490000700208520094500215653002101160653003001181653004101211653000901252653001701261100001601278700001701294856011201311 2001 eng d00aDifferences between self-adapted and computerized adaptive tests: A meta-analysis0 aDifferences between selfadapted and computerized adaptive tests  a235-2470 v383 aSelf-adapted testing has been described as a variation of computerized adaptive testing that reduces test anxiety and thereby enhances test performance. The purpose of this study was to gain a better understanding of these proposed effects of self-adapted tests (SATs); meta-analysis procedures were used to estimate differences between SATs and computerized adaptive tests (CATs) in proficiency estimates and post-test anxiety levels across studies in which these two types of tests have been compared. After controlling for measurement error the results showed that SATs yielded proficiency estimates that were 0.12 standard deviation units higher and post-test anxiety levels that were 0.19 standard deviation units lower than those yielded by CATs. The authors speculate about possible reasons for these differences and discuss advantages and disadvantages of using SATs in operational settings. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aScores computerized adaptive testing10aTest10aTest Anxiety1 aPitkin, A K1 aVispoel, W P uhttp://www.iacat.org/content/differences-between-self-adapted-and-computerized-adaptive-tests-meta-analysis01840nas a2200229 4500008004100000245007400041210006900115300001000184490000700194520110700201653002101308653000901329653004801338653001801386653002801404653002401432653001501456100001601471700001701487700001601504856009001520 2001 eng d00aEvaluation of an MMPI-A short form: Implications for adaptive testing0 aEvaluation of an MMPIA short form Implications for adaptive test a76-890 v763 aReports some psychometric properties of an MMPI-Adolescent version (MMPI-A; J. N. Butcher et al, 1992) short form based on administration of the 1st 150 items of this test instrument. The authors report results for both the MMPI-A normative sample of 1,620 adolescents (aged 14-18 yrs) and a clinical sample of 565 adolescents (mean age 15.2 yrs) in a variety of treatment settings. The authors summarize results for the MMPI-A basic scales in terms of Pearson product-moment correlations generated between full administration and short-form administration formats and mean T score elevations for the basic scales generated by each approach. In this investigation, the authors also examine single-scale and 2-point congruences found for the MMPI-A basic clinical scales as derived from standard and short-form administrations. The authors present the relative strengths and weaknesses of the MMPI-A short form and discuss the findings in terms of implications for attempts to shorten the item pool through the use of computerized adaptive assessment approaches. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aMean10aMinnesota Multiphasic Personality Inventory10aPsychometrics10aStatistical Correlation10aStatistical Samples10aTest Forms1 aArcher, R P1 aTirrell, C A1 aElkins, D E uhttp://www.iacat.org/content/evaluation-mmpi-short-form-implications-adaptive-testing01465nas a2200253 4500008004100000245013900041210006900180260005000249300001200299520053700311653002100848653002500869653001200894653002900906653002200935653002700957653002600984653001501010653001601025100001501041700001601056700001701072856012201089 2001 eng d00aItem response theory applied to combinations of multiple-choice and constructed-response items--approximation methods for scale scores0 aItem response theory applied to combinations of multiplechoice a aMahwah, N.J. USAbLawrence Erlbaum Associates a289-3153 a(From the chapter) The authors develop approximate methods that replace the scoring tables with weighted linear combinations of the component scores. Topics discussed include: a linear approximation for the extension to combinations of scores; the generalization of two or more scores; potential applications of linear approximations to item response theory in computerized adaptive tests; and evaluation of the pattern-of-summed-scores, and Gaussian approximation, estimates of proficiency. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aItem Response Theory10aMethod)10aMultiple Choice (Testing10aScoring (Testing)10aStatistical Estimation10aStatistical Weighting10aTest Items10aTest Scores1 aThissen, D1 aNelson, L A1 aSwygert, K A uhttp://www.iacat.org/content/item-response-theory-applied-combinations-multiple-choice-and-constructed-response-items01985nas a2200205 4500008004100000245010100041210006900142300001200211490000700223520124900230653001201479653002101491653003001512653001501542653001601557653004501573100001701618700001901635856012501654 2001 eng d00aItem selection in computerized adaptive testing: Should more discriminating items be used first?0 aItem selection in computerized adaptive testing Should more disc a249-2660 v383 aDuring computerized adaptive testing (CAT), items are selected continuously according to the test-taker's estimated ability. Test security has become a problem because high-discrimination items are more likely to be selected and become overexposed. So, there seems to be a tradeoff between high efficiency in ability estimations and balanced usage of items. This series of four studies addressed the dilemma by focusing on the notion of whether more or less discriminating items should be used first in CAT. The first study demonstrated that the common maximum information method with J. B. Sympson and R. D. Hetter (1985) control resulted in the use of more discriminating items first. The remaining studies showed that using items in the reverse order, as described in H. Chang and Z. Yings (1999) stratified method had potential advantages: (a) a more balanced item usage and (b) a relatively stable resultant item pool structure with easy and inexpensive management. This stratified method may have ability-estimation efficiency better than or close to that of other methods. It is argued that the judicious selection of items, as in the stratified method, is a more active control of item exposure. (PsycINFO Database Record (c) 2005 APA )10aability10aAdaptive Testing10aComputer Assisted Testing10aEstimation10aStatistical10aTest Items computerized adaptive testing1 aHau, Kit-Tai1 aChang, Hua-Hua uhttp://www.iacat.org/content/item-selection-computerized-adaptive-testing-should-more-discriminating-items-be-used-first01859nas a2200193 4500008004100000245012400041210007100165300001200236490000700248520110700255653002101362653002601383653002201409653001401431653005901445100001601504700001701520856012801537 2001 eng d00aNouveaux développements dans le domaine du testing informatisé [New developments in the area of computerized testing]0 aNouveaux développements dans le domaine du testing informatisé N a221-2300 v463 aL'usage de l'évaluation assistée par ordinateur s'est fortement développé depuis la première formulation de ses principes de base dans les années soixante et soixante-dix. Cet article offre une introduction aux derniers développements dans le domaine de l'évaluation assistée par ordinateur, en particulier celui du testing adaptative informatisée (TAI). L'estimation de l'aptitude, la sélection des items et le développement d'une base d'items dans le cas du TAI sont discutés. De plus, des exemples d'utilisations innovantes de l'ordinateur dans des systèmes intégrés de testing et de testing via Internet sont présentés. L'article se termine par quelques illustrations de nouvelles applications du testing informatisé et des suggestions pour des recherches futures.Discusses the latest developments in computerized psychological assessment, with emphasis on computerized adaptive testing (CAT). Ability estimation, item selection, and item pool development in CAT are described. Examples of some innovative approaches to CAT are presented. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Applications10aComputer Assisted10aDiagnosis10aPsychological Assessment computerized adaptive testing1 aMeijer, R R1 aGrégoire, J uhttp://www.iacat.org/content/nouveaux-d%C3%A9veloppements-dans-le-domaine-du-testing-informatis%C3%A9-new-developments-area01705nas a2200181 4500008004100000245007300041210006900114300001100183490000700194520110100201653002101302653003001323653002501353653001501378100001701393700001501410856009801425 2001 eng d00aOutlier measures and norming methods for computerized adaptive tests0 aOutlier measures and norming methods for computerized adaptive t a85-1040 v263 aNotes that the problem of identifying outliers has 2 important aspects: the choice of outlier measures and the method to assess the degree of outlyingness (norming) of those measures. Several classes of measures for identifying outliers in Computerized Adaptive Tests (CATs) are introduced. Some of these measures are constructed to take advantage of CATs' sequential choice of items; other measures are taken directly from paper and pencil (P&P) tests and are used for baseline comparisons. Assessing the degree of outlyingness of CAT responses, however, can not be applied directly from P&P tests because stopping rules associated with CATs yield examinee responses of varying lengths. Standard outlier measures are highly correlated with the varying lengths which makes comparison across examinees impossible. Therefore, 4 methods are presented and compared which map outlier statistics to a familiar probability scale (a p value). The methods are explored in the context of CAT data from a 1995 Nationally Administered Computerized Examination (NACE). (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aStatistical Analysis10aTest Norms1 aBradlow, E T1 aWeiss, R E uhttp://www.iacat.org/content/outlier-measures-and-norming-methods-computerized-adaptive-tests01348nas a2200181 4500008004100000245007300041210006900114260005600183300001200239520069300251653002100944653003000965653002200995653002001017100001601037700001701053856009601070 2001 eng d00aPractical issues in setting standards on computerized adaptive tests0 aPractical issues in setting standards on computerized adaptive t aMahwah, N.J. USAbLawrence Erlbaum Associates, Inc. a355-3693 a(From the chapter) Examples of setting standards on computerized adaptive tests (CATs) are hard to find. Some examples of CATs involving performance standards include the registered nurse exam and the Novell systems engineer exam. Although CATs do not require separate standard setting-methods, there are special issues to be addressed by test specialist who set performance standards on CATs. Setting standards on a CAT will typical require modifications on the procedures used with more traditional, fixed-form, paper-and -pencil examinations. The purpose of this chapter is to illustrate why CATs pose special challenges to the standard setter. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aPerformance Tests10aTesting Methods1 aSireci, S G1 aClauser, B E uhttp://www.iacat.org/content/practical-issues-setting-standards-computerized-adaptive-tests01591nas a2200205 4500008004100000245016500041210006900206300001200275490000700287520076900294653002101063653002601084653003001110653002501140653005101165100001301216700001701229700002101246856011801267 2001 eng d00aToepassing van een computergestuurde adaptieve testprocedure op persoonlijkheidsdata [Application of a computerised adaptive test procedure on personality data]0 aToepassing van een computergestuurde adaptieve testprocedure op  a119-1330 v563 aStudied the applicability of a computerized adaptive testing procedure to an existing personality questionnaire within the framework of item response theory. The procedure was applied to the scores of 1,143 male and female university students (mean age 21.8 yrs) in the Netherlands on the Neuroticism scale of the Amsterdam Biographical Questionnaire (G. J. Wilde, 1963). The graded response model (F. Samejima, 1969) was used. The quality of the adaptive test scores was measured based on their correlation with test scores for the entire item bank and on their correlation with scores on other scales from the personality test. The results indicate that computerized adaptive testing can be applied to personality scales. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Applications10aComputer Assisted Testing10aPersonality Measures10aTest Reliability computerized adaptive testing1 aHol, A M1 aVorst, H C M1 aMellenbergh, G J uhttp://www.iacat.org/content/toepassing-van-een-computergestuurde-adaptieve-testprocedure-op-persoonlijkheidsdata01389nas a2200193 4500008004100000245009400041210006900135300001200204490000700216520067900223653002100902653003000923653002500953653005700978100001401035700001901049700001901068856010801087 2000 eng d00aA comparison of item selection rules at the early stages of computerized adaptive testing0 acomparison of item selection rules at the early stages of comput a241-2550 v243 aThe effects of 5 item selection rules--Fisher information (FI), Fisher interval information (FII), Fisher information with a posterior distribution (FIP), Kullback-Leibler information (KL), and Kullback-Leibler information with a posterior distribution (KLP)--were compared with respect to the efficiency and precision of trait (θ) estimation at the early stages of computerized adaptive testing (CAT). FII, FIP, KL, and KLP performed marginally better than FI at the early stages of CAT for θ=-3 and -2. For tests longer than 10 items, there appeared to be no precision advantage for any of the selection rules. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis (Test)10aStatistical Estimation computerized adaptive testing1 aChen, S-Y1 aAnkenmann, R D1 aChang, Hua-Hua uhttp://www.iacat.org/content/comparison-item-selection-rules-early-stages-computerized-adaptive-testing03274nas a2200217 4500008004100000245020800041210007000249300001000319490000700329520241300336653002102749653002402770653003202794653001302826100001902839700001802858700001702876700001802893700001702911856012802928 2000 eng d00aDiagnostische programme in der Demenzfrüherkennung: Der Adaptive Figurenfolgen-Lerntest (ADAFI) [Diagnostic programs in the early detection of dementia: The Adaptive Figure Series Learning Test (ADAFI)]0 aDiagnostische programme in der Demenzfrüherkennung Der Adaptive  a16-290 v133 aZusammenfassung: Untersucht wurde die Eignung des computergestützten Adaptiven Figurenfolgen-Lerntests (ADAFI), zwischen gesunden älteren Menschen und älteren Menschen mit erhöhtem Demenzrisiko zu differenzieren. Der im ADAFI vorgelegte Aufgabentyp der fluiden Intelligenzdimension (logisches Auffüllen von Figurenfolgen) hat sich in mehreren Studien zur Erfassung des intellektuellen Leistungspotentials (kognitive Plastizität) älterer Menschen als günstig für die genannte Differenzierung erwiesen. Aufgrund seiner Konzeption als Diagnostisches Programm fängt der ADAFI allerdings einige Kritikpunkte an Vorgehensweisen in diesen bisherigen Arbeiten auf. Es konnte gezeigt werden, a) daß mit dem ADAFI deutliche Lokationsunterschiede zwischen den beiden Gruppen darstellbar sind, b) daß mit diesem Verfahren eine gute Vorhersage des mentalen Gesundheitsstatus der Probanden auf Einzelfallebene gelingt (Sensitivität: 80 %, Spezifität: 90 %), und c) daß die Vorhersageleistung statusdiagnostischer Tests zur Informationsverarbeitungsgeschwindigkeit und zum Arbeitsgedächtnis geringer ist. Die Ergebnisse weisen darauf hin, daß die plastizitätsorientierte Leistungserfassung mit dem ADAFI vielversprechend für die Frühdiagnostik dementieller Prozesse sein könnte.The aim of this study was to examine the ability of the computerized Adaptive Figure Series Learning Test (ADAFI) to differentiate among old subjects at risk for dementia and old healthy controls. Several studies on the subject of measuring the intellectual potential (cognitive plasticity) of old subjects have shown the usefulness of the fluid intelligence type of task used in the ADAFI (completion of figure series) for this differentiation. Because the ADAFI has been developed as a Diagnostic Program it is able to counter some critical issues in those studies. It was shown a) that distinct differences between both groups are revealed by the ADAFI, b) that the prediction of the cognitive health status of individual subjects is quite good (sensitivity: 80 %, specifity: 90 %), and c) that the prediction of the cognitive health status with tests of processing speed and working memory is worse than with the ADAFI. The results indicate that the ADAFI might be a promising plasticity-oriented tool for the measurement of cognitive decline in the elderly, and thus might be useful for the early detection of dementia.10aAdaptive Testing10aAt Risk Populations10aComputer Assisted Diagnosis10aDementia1 aSchreiber, M D1 aSchneider, RJ1 aSchweizer, A1 aBeckmann, J F1 aBaltissen, R uhttp://www.iacat.org/content/diagnostische-programme-der-demenzfr%C3%BCherkennung-der-adaptive-figurenfolgen-lerntest-adafi01432nas a2200193 4500008004100000245006300041210006300104300001200167490000700179520079500186653001800981653002100999653003001020653001801050653005701068100001501125700001201140856008601152 2000 eng d00aEstimation of trait level in computerized adaptive testing0 aEstimation of trait level in computerized adaptive testing a257-2650 v243 aNotes that in computerized adaptive testing (CAT), a examinee's trait level (θ) must be estimated with reasonable accuracy based on a small number of item responses. A successful implementation of CAT depends on (1) the accuracy of statistical methods used for estimating θ and (2) the efficiency of the item-selection criterion. Methods of estimating θ suitable for CAT are reviewed, and the differences between Fisher and Kullback-Leibler information criteria for selecting items are discussed. The accuracy of different CAT algorithms was examined in an empirical study. The results show that correcting θ estimates for bias was necessary at earlier stages of CAT, but most CAT algorithms performed equally well for tests of 10 or more items. (PsycINFO Database Record (c) 2005 APA )10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis10aStatistical Estimation computerized adaptive testing1 aCheng, P E1 aLiou, M uhttp://www.iacat.org/content/estimation-trait-level-computerized-adaptive-testing02315nas a2200205 4500008004100000245012100041210006900162300000800231490000700239520159200246653002101838653003001859653002201889653001801911653001601929653000901945653001801954100001401972856012301986 2000 eng d00aAn examination of the reliability and validity of performance ratings made using computerized adaptive rating scales0 aexamination of the reliability and validity of performance ratin a5700 v613 aThis study compared the psychometric properties of performance ratings made using recently-developed computerized adaptive rating scales (CARS) to the psyc hometric properties of ratings made using more traditional paper-and-pencil rati ng formats, i.e., behaviorally-anchored and graphic rating scales. Specifically, the reliability, validity and accuracy of the performance ratings from each for mat were examined. One hundred twelve participants viewed six 5-minute videotape s of office situations and rated the performance of a target person in each vide otape on three contextual performance dimensions-Personal Support, Organizationa l Support, and Conscientious Initiative-using CARS and either behaviorally-ancho red or graphic rating scales. Performance rating properties were measured using Shrout and Fleiss's intraclass correlation (2, 1), Borman's differential accurac y measure, and Cronbach's accuracy components as indexes of rating reliability, validity, and accuracy, respectively. Results found that performance ratings mad e using the CARS were significantly more reliable and valid than performance rat ings made using either of the other formats. Additionally, CARS yielded more acc urate performance ratings than the paper-and-pencil formats. The nature of the C ARS system (i.e., its adaptive nature and scaling methodology) and its paired co mparison judgment task are offered as possible reasons for the differences found in the psychometric properties of the performance ratings made using the variou s rating formats. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aPerformance Tests10aRating Scales10aReliability10aTest10aTest Validity1 aBuck, D E uhttp://www.iacat.org/content/examination-reliability-and-validity-performance-ratings-made-using-computerized-adaptive00867nas a2200145 4500008004100000245006600041210006600107300001200173490000700185520036100192653002100553653004400574100001500618856008800633 2000 eng d00aOverview of the computerized adaptive testing special section0 aOverview of the computerized adaptive testing special section a115-1200 v213 aThis paper provides an overview of the five papers included in the Psicologica special section on computerized adaptive testing. A short introduction to this topic is presented as well. The main results, the links between the five papers and the general research topic to which they are more related are also shown. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputers computerized adaptive testing1 aPonsoda, V uhttp://www.iacat.org/content/overview-computerized-adaptive-testing-special-section01733nas a2200169 4500008004100000245009900041210006900140300001200209490000700221520112300228653002101351653003001372653000801402653002301410100001601433856011401449 1997 eng d00aThe distribution of indexes of person fit within the computerized adaptive testing environment0 adistribution of indexes of person fit within the computerized ad a115-1270 v213 aThe extent to which a trait estimate represents the underlying latent trait of interest can be estimated by using indexes of person fit. Several statistical methods for indexing person fit have been proposed to identify nonmodel-fitting response vectors. These person-fit indexes have generally been found to follow a standard normal distribution for conventionally administered tests. The present investigation found that within the context of computerized adaptive testing (CAT) these indexes tended not to follow a standard normal distribution. As the item pool became less discriminating, as the CAT termination criterion became less stringent, and as the number of items in the pool decreased, the distributions of the indexes approached a standard normal distribution. It was determined that under these conditions the indexes' distributions approached standard normal distributions because more items were being administered. However, even when over 50 items were administered in a CAT the indexes were distributed in a fashion that was different from what was expected. (PsycINFO Database Record (c) 2006 APA )10aAdaptive Testing10aComputer Assisted Testing10aFit10aPerson Environment1 aNering, M L uhttp://www.iacat.org/content/distribution-indexes-person-fit-within-computerized-adaptive-testing-environment