02729nas a2200145 4500008003900000245012200039210006900161490000700230520216000237100003002397700002702427700002802454700002502482856007602507 2020 d00aComputerized adaptive testing to screen children for emotional and behavioral problems by preventive child healthcare0 aComputerized adaptive testing to screen children for emotional a0 v203 a

Background

Questionnaires to detect emotional and behavioral problems (EBP) in Preventive Child Healthcare (PCH) should be short which potentially affects validity and reliability. Simulation studies have shown that Computerized Adaptive Testing (CAT) could overcome these weaknesses. We studied the applicability (using the measures participation rate, satisfaction, and efficiency) and the validity of CAT in routine PCH practice.

Methods

We analyzed data on 461 children aged 10–11 years (response 41%), who were assessed during routine well-child examinations by PCH professionals. Before the visit, parents completed the CAT and the Child Behavior Checklist (CBCL). Satisfaction was measured by parent- and PCH professional-report. Efficiency of the CAT procedure was measured as number of items needed to assess whether a child has serious problems or not. Its validity was assessed using the CBCL as the criterion.

Results

Parents and PCH professionals rated the CAT on average as good. The procedure required at average 16 items to assess whether a child has serious problems or not. Agreement of scores on the CAT scales with corresponding CBCL scales was high (range of Spearman correlations 0.59–0.72). Area Under Curves (AUC) were high (range: 0.95–0.97) for the Psycat total, externalizing, and hyperactivity scales using corresponding CBCL scale scores as criterion. For the Psycat internalizing scale the AUC was somewhat lower but still high (0.86).

Conclusions

CAT is a valid procedure for the identification of emotional and behavioral problems in children aged 10–11 years. It may support the efficient and accurate identification of children with overall, and potentially also specific, emotional and behavioral problems in routine PCH.

1 aTheunissen, Meninou, H.C.1 ade Wolff, Marianne, S.1 aDeurloo, Jacqueline, A.1 aVogels, Anton, G. C. uhttps://bmcpediatr.biomedcentral.com/articles/10.1186/s12887-020-2018-101806nas a2200145 4500008003900000245012400039210006900163300001000232490000700242520130500249100001801554700002401572700001901596856004501615 2020 d00aMultidimensional Test Assembly Using Mixed-Integer Linear Programming: An Application of Kullback–Leibler Information0 aMultidimensional Test Assembly Using MixedInteger Linear Program a17-320 v443 aMany educational testing programs require different test forms with minimal or no item overlap. At the same time, the test forms should be parallel in terms of their statistical and content-related properties. A well-established method to assemble parallel test forms is to apply combinatorial optimization using mixed-integer linear programming (MILP). Using this approach, in the unidimensional case, Fisher information (FI) is commonly used as the statistical target to obtain parallelism. In the multidimensional case, however, FI is a multidimensional matrix, which complicates its use as a statistical target. Previous research addressing this problem focused on item selection criteria for multidimensional computerized adaptive testing (MCAT). Yet these selection criteria are not directly transferable to the assembly of linear parallel test forms. To bridge this gap the authors derive different statistical targets, based on either FI or the Kullback–Leibler (KL) divergence, that can be applied in MILP models to assemble multidimensional parallel test forms. Using simulated item pools and an item pool based on empirical items, the proposed statistical targets are compared and evaluated. Promising results with respect to the KL-based statistical targets are presented and discussed.1 aDebeer, Dries1 avan Rijn, Peter, W.1 aAli, Usama, S. uhttps://doi.org/10.1177/014662161982758601445nas a2200157 4500008003900000245010600039210006900145300001200214490000700226520090200233100002301135700002501158700002601183700001501209856006301224 2019 d00aEfficiency of Targeted Multistage Calibration Designs Under Practical Constraints: A Simulation Study0 aEfficiency of Targeted Multistage Calibration Designs Under Prac a121-1460 v563 aAbstract Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we investigated whether the efficiency of calibration under the Rasch model could be enhanced by improving the match between item difficulty and student ability. We introduced targeted multistage calibration designs, a design type that considers ability-related background variables and performance for assigning students to suitable items. Furthermore, we investigated whether uncertainty about item difficulty could impair the assembling of efficient designs. The results indicated that targeted multistage calibration designs were more efficient than ordinary targeted designs under optimal conditions. Limited knowledge about item difficulty reduced the efficiency of one of the two investigated targeted multistage calibration designs, whereas targeted designs were more robust.1 aBerger, Stéphanie1 aVerschoor, Angela, J1 aEggen, Theo, J. H. M.1 aMoser, Urs uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/jedm.1220300560nas a2200145 4500008003900000245008100039210006900120100002400189700002200213700001600235700001900251700002300270700002500293856009600318 2018 d00aMeasuring patient-reported outcomes adaptively: Multidimensionality matters!0 aMeasuring patientreported outcomes adaptively Multidimensionalit1 aPaap, Muirne, C. S.1 aKroeze, Karel, A.1 aGlas, C A W1 aTerwee, C., B.1 avan der Palen, Job1 aVeldkamp, Bernard, P uhttp://iacat.org/measuring-patient-reported-outcomes-adaptively-multidimensionality-matters05514nas a2200193 4500008004100000245009000041210006900131260005500200520480200255653002305057653001905080653002105099653002605120100002205146700002005168700001605188700001905204856009705223 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://iacat.org/adaptive-item-and-feedback-selection-personalized-learning-network-approach01537nas 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/10.1177/001316441665218802096nas a2200181 4500008004100000245004600041210004600087260005500133520153800188653002501726653000801751100002801759700001901787700001301806700002001819700001301839856006201852 2017 eng d00aBayesian Perspectives on Adaptive Testing0 aBayesian Perspectives on Adaptive Testing aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Although adaptive testing is usually treated from the perspective of maximum-likelihood parameter estimation and maximum-informaton item selection, a Bayesian pespective is more natural, statistically efficient, and computationally tractable. This observation not only holds for the core process of ability estimation but includes such processes as item calibration, and real-time monitoring of item security as well. Key elements of the approach are parametric modeling of each relevant process, updating of the parameter estimates after the arrival of each new response, and optimal design of the next step.

The purpose of the symposium is to illustrates the role of Bayesian statistics in this approach. The first presentation discusses a basic Bayesian algorithm for the sequential update of any parameter in adaptive testing and illustrates the idea of Bayesian optimal design for the two processes of ability estimation and online item calibration. The second presentation generalizes the ideas to the case of 62 IACAT 2017 ABSTRACTS BOOKLET adaptive testing with polytomous items. The third presentation uses the fundamental Bayesian idea of sampling from updated posterior predictive distributions (“multiple imputations”) to deal with the problem of scoring incomplete adaptive tests.

Session Video 1

Session Video 2

 

10aBayesian Perspective10aCAT1 avan der Linden, Wim, J.1 aJiang, Bingnan1 aRen, Hao1 aChoi, Seung, W.1 aDiao, Qi uhttp://iacat.org/bayesian-perspectives-adaptive-testing-003162nas a2200181 4500008004100000245010600041210006900147260005500216520249300271653000802764653001502772653002702787100002202814700002602836700001602862700001502878856008702893 2017 eng d00aEfficiency of Targeted Multistage Calibration Designs under Practical Constraints: A Simulation Study0 aEfficiency of Targeted Multistage Calibration Designs under Prac aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we focused on two related research questions. First, we investigated whether the efficiency of item calibration under the Rasch model could be enhanced by calibration designs that optimize the match between item difficulty and student ability (Berger, 1991). Therefore, we introduced targeted multistage calibration designs, a design type that refers to a combination of traditional targeted calibration designs and multistage designs. As such, targeted multistage calibration designs consider ability-related background variables (e.g., grade in school), as well as performance (i.e., outcome of a preceding test stage) for assigning students to suitable items.

Second, we explored how limited a priori knowledge about item difficulty affects the efficiency of both targeted calibration designs and targeted multistage calibration designs. When arranging items within a given calibration design, test developers need to know the item difficulties to locate items optimally within the design. However, usually, no empirical information about item difficulty is available before item calibration. Owing to missing empirical data, test developers might fail to assign all items to the most suitable location within a calibration design.

Both research questions were addressed in a simulation study in which we varied the calibration design, as well as the accuracy of item distribution across the different booklets or modules within each design (i.e., number of misplaced items). The results indicated that targeted multistage calibration designs were more efficient than ordinary targeted designs under optimal conditions. Especially, targeted multistage calibration designs provided more accurate estimates for very easy and 52 IACAT 2017 ABSTRACTS BOOKLET very difficult items. Limited knowledge about item difficulty during test construction impaired the efficiency of all designs. The loss of efficiency was considerably large for one of the two investigated targeted multistage calibration designs, whereas targeted designs were more robust.

References

Berger, M. P. F. (1991). On the efficiency of IRT models when applied to different sampling designs. Applied Psychological Measurement, 15(3), 293–306. doi:10.1177/014662169101500310

Session Video

10aCAT10aEfficiency10aMultistage Calibration1 aBerger, Stephanie1 aVerschoor, Angela, J.1 aEggen, Theo1 aMoser, Urs uhttps://drive.google.com/file/d/1ko2LuiARKqsjL_6aupO4Pj9zgk6p_xhd/view?usp=sharing01509nas a2200145 4500008004100000245003400041210003300075260005500108520107900163653002101242653001601263653001701279100002201296856004501318 2017 eng d00aGrow a Tiger out of Your CAT 0 aGrow a Tiger out of Your CAT aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The main focus in the community of test developers and researchers is on improving adaptive test procedures and methodologies. Yet, the transition from research projects to larger-scale operational CATs is facing its own challenges. Usually, these operational CATs find their origin in government tenders. “Scalability”, “Interoperability” and “Transparency” are three keywords often found in these documents. Scalability is concerned with parallel system architectures which are based upon stateless selection algorithms. Design capacities often range from 10,000 to well over 100,000 concurrent students. Interoperability is implemented in standards like QTI, standards that were not designed with adaptive testing in mind. Transparency is being realized by open source software: the adaptive test should not be a black box. These three requirements often complicate the development of an adaptive test, or sometimes even conflict.

Session Video

10ainteroparability10aScalability10atransparency1 aVerschoor, Angela uhttp://iacat.org/grow-tiger-out-your-cat03184nas a2200157 4500008004100000245010000041210006900141260005500210520260800265653002002873653001602893653001102909100001902920700001602939856007102955 2017 eng d00aThe Implementation of Nationwide High Stakes Computerized (adaptive) Testing in the Netherlands0 aImplementation of Nationwide High Stakes Computerized adaptive T aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

In this presentation the challenges of implementation of (adaptive) digital testing in the Facet system in the Netherlands is discussed. In the Netherlands there is a long tradition of implementing adaptive testing in educational settings. Already since the late nineties of the last century adaptive testing was used mostly in low stakes testing. Several CATs were implemented in student monitoring systems for primary education and in the general subjects language and arithmetic in vocational education. The only nationwide implemented high stakes CAT is the WISCAT-pabo: an arithmetic test for students in the first year of primary school teacher colleges. The psychometric advantages of item based adaptive testing are obvious. For example efficiency and high measurement precision. But there are also some disadvantages such as the impossibility of reviewing items during and after the test. During the test the student is not in control of his own test; e.q . he can only navigate forward to the next item. This is one of the reasons other methods of testing, such as multistage-testing, with adaptivity not on the item level but on subtest level, has become more popular to use in high stakes testing.

A main challenge of computerized (adaptive) testing is the implementation of the item bank and the test workflow in a digital system. Since 2014 a nationwide new digital system (Facet) was introduced in the Netherlands, with connections to the digital systems of different parties based on international standards (LTI and QTI). The first nationwide tests in the Facet-system were flexible exams Dutch and arithmetic for vocational (and secondary) education, taken as item response theory-based equated linear multiple forms tests, which are administered during 5 periods in a year. Nowadays there are some implementations of different methods of (multistage) adaptive testing in the same Facet system (DTT en Acet).

In this conference, other presenters of Cito will elaborate on the psychometric characteristics of this other adaptive testing methods. In this contribution, the system architecture and interoperability of the Facet system will be explained. The emphasis is on the implementation and the problems to be solved by using this digital system in all phases of the (adaptive) testing process: item banking, test construction, designing, publication, test taking, analyzing and reporting to the student. An evaluation of the use of the system will be presented.

Session Video

10aHigh stakes CAT10aNetherlands10aWISCAT1 avan Boxel, Mia1 aEggen, Theo uhttps://drive.google.com/open?id=1Kn1PvgioUYaOJ5pykq-_XWnwDU15rRsf00592nas a2200169 4500008003900000022001400039245011200053210006900165300001600234490000700250100002400257700002200281700002500303700002300328700002500351856004600376 2017 d a1573-264900aItem usage in a multidimensional computerized adaptive test (MCAT) measuring health-related quality of life0 aItem usage in a multidimensional computerized adaptive test MCAT a2909–29180 v261 aPaap, Muirne, C. S.1 aKroeze, Karel, A.1 aTerwee, Caroline, B.1 avan der Palen, Job1 aVeldkamp, Bernard, P uhttps://doi.org/10.1007/s11136-017-1624-300666nas a2200193 4500008003900000022001400039245008000053210006900133300001000202490000600212653004000218653002600258653003300284653002600317653002100343100002200364700002600386856006000412 2017 d a2165-659200aLatent-Class-Based Item Selection for Computerized Adaptive Progress Tests0 aLatentClassBased Item Selection for Computerized Adaptive Progre a22-430 v510acomputerized adaptive progress test10aitem selection method10aKullback-Leibler information10aLatent class analysis10alog-odds scoring1 avan Buuren, Nikky1 aEggen, Theo, J. H. M. uhttp://iacat.org/jcat/index.php/jcat/article/view/62/2903804nas a2200169 4500008004100000245008600041210006900127260005500196520318900251653000903440653000803449653002503457100002503482700001703507700001703524856009303541 2017 eng d00aNew Results on Bias in Estimates due to Discontinue Rules in Intelligence Testing0 aNew Results on Bias in Estimates due to Discontinue Rules in Int aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The presentation provides new results on a form of adaptive testing that is used frequently in intelligence testing. In these tests, items are presented in order of increasing difficulty, and the presentation of items is adaptive in the sense that each subtest session is discontinued once a test taker produces a certain number of incorrect responses in sequence. The subsequent (not observed) responses are commonly scored as wrong for that subtest, even though the test taker has not seen these. Discontinuation rules allow a certain form of adaptiveness both in paper-based and computerbased testing, and help reducing testing time.

Two lines of research that are relevant are studies that directly assess the impact of discontinuation rules, and studies that more broadly look at the impact of scoring rules on test results with a large number of not administered or not reached items. He & Wolf (2012) compared different ability estimation methods for this type of discontinuation rule adaptation of test length in a simulation study. However, to our knowledge there has been no rigorous analytical study of the underlying distributional changes of the response variables under discontinuation rules. It is important to point out that the results obtained by He & Wolf (2012) agree with results presented by, for example, DeAyala, Plake & Impara (2001) as well as Rose, von Davier & Xu (2010) and Rose, von Davier & Nagengast (2016) in that ability estimates are biased most when scoring the not observed responses as wrong. Discontinuation rules combined with scoring the non-administered items as wrong is used operationally in several major intelligence tests, so more research is needed in order to improve this particular type of adaptiveness in the testing practice.

The presentation extends existing research on adaptiveness by discontinue-rules in intelligence tests in multiple ways: First, a rigorous analytical study of the distributional properties of discontinue-rule scored items is presented. Second, an extended simulation is presented that includes additional alternative scoring rules as well as bias-corrected ability estimators that may be suitable to improve results for discontinue-rule scored intelligence tests.

References: DeAyala, R. J., Plake, B. S., & Impara, J. C. (2001). The impact of omitted responses on the accuracy of ability estimation in item response theory. Journal of Educational Measurement, 38, 213-234.

He, W. & Wolfe, E. W. (2012). Treatment of Not-Administered Items on Individually Administered Intelligence Tests. Educational and Psychological Measurement, Vol 72, Issue 5, pp. 808 – 826. DOI: 10.1177/0013164412441937

Rose, N., von Davier, M., & Xu, X. (2010). Modeling non-ignorable missing data with item response theory (IRT; ETS RR-10-11). Princeton, NJ: Educational Testing Service.

Rose, N., von Davier, M., & Nagengast, B. (2016) Modeling omitted and not-reached items in irt models. Psychometrika. doi:10.1007/s11336-016-9544-7

Session Video

10aBias10aCAT10aIntelligence Testing1 avon Davier, Matthias1 aCho, Youngmi1 aPan, Tianshu uhttp://iacat.org/new-results-bias-estimates-due-discontinue-rules-intelligence-testing-000491nas a2200133 4500008003900000022001400039245010800053210006900161300000700230490000600237100002500243700002400268856006500292 2017 d a2504-284X00aRobust Automated Test Assembly for Testlet-Based Tests: An Illustration with Analytical Reasoning Items0 aRobust Automated Test Assembly for TestletBased Tests An Illustr a630 v21 aVeldkamp, Bernard, P1 aPaap, Muirne, C. S. uhttps://www.frontiersin.org/article/10.3389/feduc.2017.0006302175nas a2200133 4500008004100000245006300041210006300104260005500167520168000222653002501902653002301927100002001950856007101970 2017 eng d00aUsing Determinantal Point Processes for Multistage Testing0 aUsing Determinantal Point Processes for Multistage Testing aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Multistage tests are a generalization of computerized adaptive tests (CATs), that allow to ask batches of questions before starting to adapt the process, instead of asking questions one by one. In order to be provided in real-world scenarios, they should be assembled on the fly, and recent models have been designed accordingly (Zheng & Chang, 2015). We will present a new algorithm for assembling multistage tests, based on a recent technique in machine learning called determinantal point processes. We will illustrate this technique on various student data that come from fraction subtraction items, or massive online open courses.

In multidimensional CATs, feature vectors are estimated for students and questions, and the probability that a student gets a question correct depends on how much their feature vector is correlated with the question feature vector. In other words, questions that are close in space lead to similar response patterns from the students. Therefore, in order to maximize the information of a batch of questions, the volume spanned by their feature vectors should be as large as possible. Determinantal point processes allow to sample efficiently batches of items from a bank that are diverse, i.e., that span a large volume: it is actually possible to draw k items among n with a O(nk3 ) complexity, which is convenient for large databases of 10,000s of items.

References

Zheng, Y., & Chang, H. H. (2015). On-the-fly assembled multistage adaptive testing. Applied Psychological Measurement, 39(2), 104-118.

Session Video

10aMultidimentional CAT10amultistage testing1 aVie, Jill-Jênn uhttps://drive.google.com/open?id=1GkJkKTEFWK3srDX8TL4ra_Xbsliemu1R01586nas a2200133 4500008003900000022001400039245008800053210006900141300001400210490000700224520115500231100002501386856004101411 2016 d a1745-398400aOn the Issue of Item Selection in Computerized Adaptive Testing With Response Times0 aIssue of Item Selection in Computerized Adaptive Testing With Re a212–2280 v533 aMany standardized tests are now administered via computer rather than paper-and-pencil format. The computer-based delivery mode brings with it certain advantages. One advantage is the ability to adapt the difficulty level of the test to the ability level of the test taker in what has been termed computerized adaptive testing (CAT). A second advantage is the ability to record not only the test taker's response to each item (i.e., question), but also the amount of time the test taker spends considering and answering each item. Combining these two advantages, various methods were explored for utilizing response time data in selecting appropriate items for an individual test taker.Four strategies for incorporating response time data were evaluated, and the precision of the final test-taker score was assessed by comparing it to a benchmark value that did not take response time information into account. While differences in measurement precision and testing times were expected, results showed that the strategies did not differ much with respect to measurement precision but that there were differences with regard to the total testing time.1 aVeldkamp, Bernard, P uhttp://dx.doi.org/10.1111/jedm.1211001549nas a2200145 4500008003900000245010400039210006900143300001200212490000700224520104200231100002601273700002601299700002501325856005301350 2016 d00aMultidimensional Computerized Adaptive Testing for Classifying Examinees With Within-Dimensionality0 aMultidimensional Computerized Adaptive Testing for Classifying E a387-4040 v403 aA classification method is presented for adaptive classification testing with a multidimensional item response theory (IRT) model in which items are intended to measure multiple traits, that is, within-dimensionality. The reference composite is used with the sequential probability ratio test (SPRT) to make decisions and decide whether testing can be stopped before reaching the maximum test length. Item-selection methods are provided that maximize the determinant of the information matrix at the cutoff point or at the projected ability estimate. A simulation study illustrates the efficiency and effectiveness of the classification method. Simulations were run with the new item-selection methods, random item selection, and maximization of the determinant of the information matrix at the ability estimate. The study also showed that the SPRT with multidimensional IRT has the same characteristics as the SPRT with unidimensional IRT and results in more accurate classifications than the latter when used for multidimensional data.1 avan Groen, Maaike, M.1 aEggen, Theo, J. H. M.1 aVeldkamp, Bernard, P uhttp://apm.sagepub.com/content/40/6/387.abstract01373nas a2200133 4500008003900000245008400039210006900123300001200192490000700204520092900211100002201140700002401162856005301186 2014 d00aComputerized Adaptive Testing for the Random Weights Linear Logistic Test Model0 aComputerized Adaptive Testing for the Random Weights Linear Logi a415-4310 v383 a

This article discusses four-item selection rules to design efficient individualized tests for the random weights linear logistic test model (RWLLTM): minimum posterior-weighted -error minimum expected posterior-weighted -error maximum expected Kullback–Leibler divergence between subsequent posteriors (KLP), and maximum mutual information (MUI). The RWLLTM decomposes test items into a set of subtasks or cognitive features and assumes individual-specific effects of the features on the difficulty of the items. The model extends and improves the well-known linear logistic test model in which feature effects are only estimated at the aggregate level. Simulations show that the efficiencies of the designs obtained with the different criteria appear to be equivalent. However, KLP and MUI are given preference over and due to their lesser complexity, which significantly reduces the computational burden.

1 aCrabbe, Marjolein1 aVandebroek, Martina uhttp://apm.sagepub.com/content/38/6/415.abstract00487nam a2200133 4500008004100000020002500041245006100066210006000127260002900187100001600216700002500232700001900257856007700276 2014 eng d a13-978-1-4665-0577-300aComputerized multistage testing: Theory and applications0 aComputerized multistage testing Theory and applications aBoca Raton FLbCRC Press1 aYan, Duanli1 avon Davier, Alina, A1 aLewis, Charles uhttp://iacat.org/computerized-multistage-testing-theory-and-applications01545nas a2200145 4500008003900000245011500039210006900154300001200223490000700235520102700242100002601269700002601295700002501321856005301346 2014 d00aItem Selection Methods Based on Multiple Objective Approaches for Classifying Respondents Into Multiple Levels0 aItem Selection Methods Based on Multiple Objective Approaches fo a187-2000 v383 a

Computerized classification tests classify examinees into two or more levels while maximizing accuracy and minimizing test length. The majority of currently available item selection methods maximize information at one point on the ability scale, but in a test with multiple cutting points selection methods could take all these points simultaneously into account. If for each cutting point one objective is specified, the objectives can be combined into one optimization function using multiple objective approaches. Simulation studies were used to compare the efficiency and accuracy of eight selection methods in a test based on the sequential probability ratio test. Small differences were found in accuracy and efficiency between different methods depending on the item pool and settings of the classification method. The size of the indifference region had little influence on accuracy but considerable influence on efficiency. Content and exposure control had little influence on accuracy and efficiency.

1 avan Groen, Maaike, M.1 aEggen, Theo, J. H. M.1 aVeldkamp, Bernard, P uhttp://apm.sagepub.com/content/38/3/187.abstract01575nas a2200145 4500008003900000245008500039210006900124300001200193490000700205520109100212100002501303700002701328700002101355856005301376 2013 d00aUncertainties in the Item Parameter Estimates and Robust Automated Test Assembly0 aUncertainties in the Item Parameter Estimates and Robust Automat a123-1390 v373 a

Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values, and uncertainty is not taken into account. As a consequence, resulting tests might be off target or less informative than expected. In this article, the process of parameter estimation is described to provide insight into the causes of uncertainty in the item parameters. The consequences of uncertainty are studied. Besides, an alternative automated test assembly algorithm is presented that is robust against uncertainties in the data. Several numerical examples demonstrate the performance of the robust test assembly algorithm, and illustrate the consequences of not taking this uncertainty into account. Finally, some recommendations about the use of robust test assembly and some directions for further research are given.

1 aVeldkamp, Bernard, P1 aMatteucci, Mariagiulia1 aJong, Martijn, G uhttp://apm.sagepub.com/content/37/2/123.abstract02588nas a2200313 4500008004100000020004600041245011200087210006900199250001500268260001900283300000700302490000600309520160100315100001901916700001801935700001701953700001801970700001701988700001402005700001602019700001502035700001402050700002602064700001602090700001302106700001502119700001902134856012102153 2011 Eng d a1477-7525 (Electronic)1477-7525 (Linking)00aCross-cultural development of an item list for computer-adaptive testing of fatigue in oncological patients0 aCrosscultural development of an item list for computeradaptive t a2011/03/31 cMarch 29, 2011 a100 v93 aABSTRACT: INTRODUCTION: Within an ongoing project of the EORTC Quality of Life Group, we are developing computerized adaptive test (CAT) measures for the QLQ-C30 scales. These new CAT measures are conceptualised to reflect the same constructs as the QLQ-C30 scales. Accordingly, the Fatigue-CAT is intended to capture physical and general fatigue. METHODS: The EORTC approach to CAT development comprises four phases (literature search, operationalisation, pre-testing, and field testing). Phases I-III are described in detail in this paper. A literature search for fatigue items was performed in major medical databases. After refinement through several expert panels, the remaining items were used as the basis for adapting items and/or formulating new items fitting the EORTC item style. To obtain feedback from patients with cancer, these English items were translated into Danish, French, German, and Spanish and tested in the respective countries. RESULTS: Based on the literature search a list containing 588 items was generated. After a comprehensive item selection procedure focusing on content, redundancy, item clarity and item difficulty a list of 44 fatigue items was generated. Patient interviews (n=52) resulted in 12 revisions of wording and translations. DISCUSSION: The item list developed in phases I-III will be further investigated within a field-testing phase (IV) to examine psychometric characteristics and to fit an item response theory model. The Fatigue CAT based on this item bank will provide scores that are backward-compatible to the original QLQ-C30 fatigue scale.1 aGiesinger, J M1 aPetersen, M A1 aGroenvold, M1 aAaronson, N K1 aArraras, J I1 aConroy, T1 aGamper, E M1 aKemmler, G1 aKing, M T1 aOberguggenberger, A S1 aVelikova, G1 aYoung, T1 aHolzner, B1 aEortc-Qlg, E O uhttp://iacat.org/content/cross-cultural-development-item-list-computer-adaptive-testing-fatigue-oncological-patients01247nas a2200181 4500008004100000245012100041210006900162260001200231520055600243653003300799653000800832653001900840653003500859100001800894700001600912700002200928856011500950 2011 eng d00aItem Selection Methods based on Multiple Objective Approaches for Classification of Respondents into Multiple Levels0 aItem Selection Methods based on Multiple Objective Approaches fo c10/20113 a

Is it possible to develop new item selection methods which take advantage of the fact that we want to classify into multiple categories? New methods: Taking multiple points on the ability scale into account; Based on multiple objective approaches.

Conclusions

10aadaptive classification test10aCAT10aitem selection10asequential classification test1 aGroen, Maaike1 aEggen, Theo1 aVeldkamp, Bernard uhttp://iacat.org/content/item-selection-methods-based-multiple-objective-approaches-classification-respondents00443nas a2200121 4500008004500000245006900045210006900114300001100183490000600194100001400200700001800214856008900232 2011 Engldsh 00aMeasuring Individual Growth With Conventional and Adaptive Tests0 aMeasuring Individual Growth With Conventional and Adaptive Tests a80-1010 v21 aWeiss, DJ1 aVon Minden, S uhttp://iacat.org/content/measuring-individual-growth-conventional-and-adaptive-tests01099nas a2200169 4500008004100000245006600041210006600107260001200173520055300185653002600738653000800764653002100772653001700793653001000810100002200820856008700842 2011 eng d00aOptimal Calibration Designs for Computerized Adaptive Testing0 aOptimal Calibration Designs for Computerized Adaptive Testing c10/20113 a

Optimaztion

How can we exploit the advantages of Balanced Block Design while keeping the logistics manageable?

Homogeneous Designs: Overlap between test booklets as regular as possible

Conclusions:

10abalanced block design10aCAT10aitem calibration10aoptimization10aRasch1 aVerschoor, Angela uhttp://iacat.org/content/optimal-calibration-designs-computerized-adaptive-testing00777nas a2200205 4500008004100000245003400041210003200075260001200107520027300119653000800392653000800400653002300408653001000431653001200441653000800453100002200461700001900483700001600502856005300518 2011 eng d00aA Test Assembly Model for MST0 aTest Assembly Model for MST c10/20113 a

This study is just a short exploration in the matter of optimization of a MST. It is extremely hard or maybe impossible to chart influence of item pool and test specifications on optimization process. Simulations are very helpful in finding an acceptable MST.

10aCAT10amst10amultistage testing10aRasch10arouting10atif1 aVerschoor, Angela1 aRadtke, Ingrid1 aEggen, Theo uhttp://iacat.org/content/test-assembly-model-mst00407nas a2200109 4500008004100000245006400041210006400105300001200169100001600181700001300197856008700210 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://iacat.org/content/adaptive-mastery-testing-using-multidimensional-irt-model01380nas a2200133 4500008004100000245006000041210006000101300001200161490000700173520093200180653003401112100001801146856008201164 2010 eng d00aBayesian item selection in constrained adaptive testing0 aBayesian item selection in constrained adaptive testing a149-1690 v313 aApplication of Bayesian item selection criteria in computerized adaptive testing might result in improvement of bias and MSE of the ability estimates. The question remains how to apply Bayesian item selection criteria in the context of constrained adaptive testing, where large numbers of specifications have to be taken into account in the item selection process. The Shadow Test Approach is a general purpose algorithm for administering constrained CAT. In this paper it is shown how the approach can be slightly modified to handle Bayesian item selection criteria. No differences in performance were found between the shadow test approach and the modifiedapproach. In a simulation study of the LSAT, the effects of Bayesian item selection criteria are illustrated. The results are compared to item selection based on Fisher Information. General recommendations about the use of Bayesian item selection criteria are provided.10acomputerized adaptive testing1 aVeldkamp, B P uhttp://iacat.org/content/bayesian-item-selection-constrained-adaptive-testing01176nas a2200145 4500008003900000245005900039210005700098300001200155490000700167520073700174100002200911700002100933700002300954856005300977 2010 d00aA Comparison of Item Selection Techniques for Testlets0 aComparison of Item Selection Techniques for Testlets a424-4370 v343 a

This study examined the performance of the maximum Fisher’s information, the maximum posterior weighted information, and the minimum expected posterior variance methods for selecting items in a computerized adaptive testing system when the items were grouped in testlets. A simulation study compared the efficiency of ability estimation among the item selection techniques under varying conditions of local-item dependency when the response model was either the three-parameter-logistic item response theory or the three-parameter-logistic testlet response theory. The item selection techniques performed similarly within any particular condition, the practical implications of which are discussed within the article.

1 aMurphy, Daniel, L1 aDodd, Barbara, G1 aVaughn, Brandon, K uhttp://apm.sagepub.com/content/34/6/424.abstract00347nas a2200097 4500008004100000245005100041210005100092300001000143100002300153856007300176 2010 eng d00aConstrained Adaptive Testing with Shadow Tests0 aConstrained Adaptive Testing with Shadow Tests a31-561 avan der Linden, WJ uhttp://iacat.org/content/constrained-adaptive-testing-shadow-tests-000363nas a2200109 4500008004100000245004600041210004600087300001200133100001800145700002300163856006700186 2010 eng d00aDesigning Item Pools for Adaptive Testing0 aDesigning Item Pools for Adaptive Testing a231-2451 aVeldkamp, B P1 avan der Linden, WJ uhttp://iacat.org/content/designing-item-pools-adaptive-testing01345nas a2200229 4500008004100000020001300041245011700054210006900171300001200240490000700252520057500259653000800834653003400842653001900876653001500895100002000910700001600930700001800946700001500964700001400979856012200993 2010 eng d a0191886900aDetection of aberrant item score patterns in computerized adaptive testing: An empirical example using the CUSUM0 aDetection of aberrant item score patterns in computerized adapti a921-9250 v483 aThe scalability of individual trait scores on a computerized adaptive test (CAT) was assessed through investigating the consistency of individual item score patterns. A sample of N = 428 persons completed a personality CAT as part of a career development procedure. To detect inconsistent item score patterns, we used a cumulative sum (CUSUM) procedure. Combined information from the CUSUM, other personality measures, and interviews showed that similar estimated trait values may have a different interpretation.Implications for computer-based assessment are discussed.10aCAT10acomputerized adaptive testing10aCUSUM approach10aperson Fit1 aEgberink, I J L1 aMeijer, R R1 aVeldkamp, B P1 aSchakel, L1 aSmid, N G uhttp://iacat.org/content/detection-aberrant-item-score-patterns-computerized-adaptive-testing-empirical-example-using02219nas a2200289 4500008004100000020004600041245010800087210006900195250001500264300001200279490000700291520131000298100001801608700001701626700001801643700001401661700001401675700001801689700001401707700001701721700001501738700001301753700001401766700001601780700001301796856012001809 2010 Eng d a1573-2649 (Electronic)0962-9343 (Linking)00aDevelopment of computerized adaptive testing (CAT) for the EORTC QLQ-C30 physical functioning dimension0 aDevelopment of computerized adaptive testing CAT for the EORTC Q a2010/10/26 a479-4900 v203 aPURPOSE: Computerized adaptive test (CAT) methods, based on item response theory (IRT), enable a patient-reported outcome instrument to be adapted to the individual patient while maintaining direct comparability of scores. The EORTC Quality of Life Group is developing a CAT version of the widely used EORTC QLQ-C30. We present the development and psychometric validation of the item pool for the first of the scales, physical functioning (PF). METHODS: Initial developments (including literature search and patient and expert evaluations) resulted in 56 candidate items. Responses to these items were collected from 1,176 patients with cancer from Denmark, France, Germany, Italy, Taiwan, and the United Kingdom. The items were evaluated with regard to psychometric properties. RESULTS: Evaluations showed that 31 of the items could be included in a unidimensional IRT model with acceptable fit and good content coverage, although the pool may lack items at the upper extreme (good PF). There were several findings of significant differential item functioning (DIF). However, the DIF findings appeared to have little impact on the PF estimation. CONCLUSIONS: We have established an item pool for CAT measurement of PF and believe that this CAT instrument will clearly improve the EORTC measurement of PF.1 aPetersen, M A1 aGroenvold, M1 aAaronson, N K1 aChie, W C1 aConroy, T1 aCostantini, A1 aFayers, P1 aHelbostad, J1 aHolzner, B1 aKaasa, S1 aSinger, S1 aVelikova, G1 aYoung, T uhttp://iacat.org/content/development-computerized-adaptive-testing-cat-eortc-qlq-c30-physical-functioning-dimension00354nam a2200121 4500008004100000245003300041210003300074260002300107300000800130100002300138700001600161856005500177 2010 eng d00aElements of Adaptive Testing0 aElements of Adaptive Testing aNew YorkbSpringer a4371 avan der Linden, WJ1 aGlas, C A W uhttp://iacat.org/content/elements-adaptive-testing00466nas a2200121 4500008004100000245007900041210006900120300001200189100001600201700002300217700001700240856008700257 2010 eng d00aEstimation of the Parameters in an Item-Cloning Model for Adaptive Testing0 aEstimation of the Parameters in an ItemCloning Model for Adaptiv a289-3141 aGlas, C A W1 avan der Linden, WJ1 aGeerlings, H uhttp://iacat.org/content/estimation-parameters-item-cloning-model-adaptive-testing00445nas a2200121 4500008004100000245006200041210006200103260002300165300000900188100002300197700001700220856008600237 2010 eng d00aItem Selection and Ability Estimation in Adaptive Testing0 aItem Selection and Ability Estimation in Adaptive Testing aNew YorkbSpringer a3-301 avan der Linden, WJ1 aPashley, P J uhttp://iacat.org/content/item-selection-and-ability-estimation-adaptive-testing-001557nas a2200157 4500008004100000020004100041245008400082210006900166250001500235300001100250490000700261520099300268100001601261700002301277856009901300 2010 eng d a0007-1102 (Print)0007-1102 (Linking)00aMarginal likelihood inference for a model for item responses and response times0 aMarginal likelihood inference for a model for item responses and a2010/01/30 a603-260 v633 a

Marginal maximum-likelihood procedures for parameter estimation and testing the fit of a hierarchical model for speed and accuracy on test items are presented. The model is a composition of two first-level models for dichotomous responses and response times along with multivariate normal models for their item and person parameters. It is shown how the item parameters can easily be estimated using Fisher's identity. To test the fit of the model, Lagrange multiplier tests of the assumptions of subpopulation invariance of the item parameters (i.e., no differential item functioning), the shape of the response functions, and three different types of conditional independence were derived. Simulation studies were used to show the feasibility of the estimation and testing procedures and to estimate the power and Type I error rate of the latter. In addition, the procedures were applied to an empirical data set from a computerized adaptive test of language comprehension.

1 aGlas, C A W1 avan der Linden, WJ uhttp://iacat.org/content/marginal-likelihood-inference-model-item-responses-and-response-times00446nas a2200109 4500008004100000245007500041210006900116300001200185100002500197700002400222856009000246 2010 eng d00aMATHCAT: A Flexible Testing System in Mathematics Education for Adults0 aMATHCAT A Flexible Testing System in Mathematics Education for A a137-1501 aVerschoor, Angela, J1 aStraetmans, G J J M uhttp://iacat.org/content/mathcat-flexible-testing-system-mathematics-education-adults00474nas a2200109 4500008004100000245008900041210007100130300001100201100001400212700002300226856011500249 2010 eng d00aMultidimensional Adaptive Testing with Kullback–Leibler Information Item Selection0 aMultidimensional Adaptive Testing with Kullback–Leibler Informat a77-1021 aMulder, J1 avan der Linden, WJ uhttp://iacat.org/content/multidimensional-adaptive-testing-kullback%E2%80%93leibler-information-item-selection00292nas a2200085 4500008004100000245004000041210004000081100002300121856006200144 2010 eng d00aSequencing an Adaptive Test Battery0 aSequencing an Adaptive Test Battery1 avan der Linden, WJ uhttp://iacat.org/content/sequencing-adaptive-test-battery00345nas a2200109 4500008004100000245004300041210004200084300001200126100001300138700001600151856006800167 2010 eng d00aTestlet-Based Adaptive Mastery Testing0 aTestletBased Adaptive Mastery Testing a387-4091 aVos, H J1 aGlas, C A W uhttp://iacat.org/content/testlet-based-adaptive-mastery-testing02753nas a2200241 4500008004100000020004600041245009400087210006900181250001500250300000800265490000600273520198300279100001502262700001602277700001402293700001302307700002002320700001902340700001702359700001302376700001402389856010802403 2010 eng d a1477-7525 (Electronic)1477-7525 (Linking)00aValidation of a computer-adaptive test to evaluate generic health-related quality of life0 aValidation of a computeradaptive test to evaluate generic health a2010/12/07 a1470 v83 aBACKGROUND: Health Related Quality of Life (HRQoL) is a relevant variable in the evaluation of health outcomes. Questionnaires based on Classical Test Theory typically require a large number of items to evaluate HRQoL. Computer Adaptive Testing (CAT) can be used to reduce tests length while maintaining and, in some cases, improving accuracy. This study aimed at validating a CAT based on Item Response Theory (IRT) for evaluation of generic HRQoL: the CAT-Health instrument. METHODS: Cross-sectional study of subjects aged over 18 attending Primary Care Centres for any reason. CAT-Health was administered along with the SF-12 Health Survey. Age, gender and a checklist of chronic conditions were also collected. CAT-Health was evaluated considering: 1) feasibility: completion time and test length; 2) content range coverage, Item Exposure Rate (IER) and test precision; and 3) construct validity: differences in the CAT-Health scores according to clinical variables and correlations between both questionnaires. RESULTS: 396 subjects answered CAT-Health and SF-12, 67.2% females, mean age (SD) 48.6 (17.7) years. 36.9% did not report any chronic condition. Median completion time for CAT-Health was 81 seconds (IQ range = 59-118) and it increased with age (p < 0.001). The median number of items administered was 8 (IQ range = 6-10). Neither ceiling nor floor effects were found for the score. None of the items in the pool had an IER of 100% and it was over 5% for 27.1% of the items. Test Information Function (TIF) peaked between levels -1 and 0 of HRQoL. Statistically significant differences were observed in the CAT-Health scores according to the number and type of conditions. CONCLUSIONS: Although domain-specific CATs exist for various areas of HRQoL, CAT-Health is one of the first IRT-based CATs designed to evaluate generic HRQoL and it has proven feasible, valid and efficient, when administered to a broad sample of individuals attending primary care settings.1 aRebollo, P1 aCastejon, I1 aCuervo, J1 aVilla, G1 aGarcia-Cueto, E1 aDiaz-Cuervo, H1 aZardain, P C1 aMuniz, J1 aAlonso, J uhttp://iacat.org/content/validation-computer-adaptive-test-evaluate-generic-health-related-quality-life02945nas a2200205 4500008004100000020004100041245009800082210006900180250001500249260000800264300001200272490000700284520217900291653004302470653006402513100001702577700001402594700001802608856011302626 2009 eng d a0214-9915 (Print)0214-9915 (Linking)00aComparison of methods for controlling maximum exposure rates in computerized adaptive testing0 aComparison of methods for controlling maximum exposure rates in  a2009/05/01 cMay a313-3200 v213 aThis paper has two objectives: (a) to provide a clear description of three methods for controlling the maximum exposure rate in computerized adaptive testing —the Symson-Hetter method, the restricted method, and the item-eligibility method— showing how all three can be interpreted as methods for constructing the variable sub-bank of items from which each examinee receives the items in his or her test; (b) to indicate the theoretical and empirical limitations of each method and to compare their performance. With the three methods, we obtained basically indistinguishable results in overlap rate and RMSE (differences in the third decimal place). The restricted method is the best method for controlling exposure rate, followed by the item-eligibility method. The worst method is the Sympson-Hetter method. The restricted method presents problems of sequential overlap rate. Our advice is to use the item-eligibility method, as it saves time and satisfies the goals of restricting maximum exposure. Comparación de métodos para el control de tasa máxima en tests adaptativos informatizados. Este artículo tiene dos objetivos: (a) ofrecer una descripción clara de tres métodos para el control de la tasa máxima en tests adaptativos informatizados, el método Symson-Hetter, el método restringido y el métodode elegibilidad del ítem, mostrando cómo todos ellos pueden interpretarse como métodos para la construcción del subbanco de ítems variable, del cual cada examinado recibe los ítems de su test; (b) señalar las limitaciones teóricas y empíricas de cada método y comparar sus resultados. Se obtienen resultados básicamente indistinguibles en tasa de solapamiento y RMSE con los tres métodos (diferencias en la tercera posición decimal). El método restringido es el mejor en el control de la tasa de exposición,seguido por el método de elegibilidad del ítem. El peor es el método Sympson-Hetter. El método restringido presenta un problema de solapamiento secuencial. Nuestra recomendación sería utilizar el método de elegibilidad del ítem, puesto que ahorra tiempo y satisface los objetivos de limitar la tasa máxima de exposición.10a*Numerical Analysis, Computer-Assisted10aPsychological Tests/*standards/*statistics & numerical data1 aBarrada, J R1 aAbad, F J1 aVeldkamp, B P uhttp://iacat.org/content/comparison-methods-controlling-maximum-exposure-rates-computerized-adaptive-testing01899nas a2200169 4500008004100000020004100041245008600082210006900168250001500237260000800252300001200260490000700272520131100279100001401590700002301604856010201627 2009 Eng d a0033-3123 (Print)0033-3123 (Linking)00aMultidimensional Adaptive Testing with Optimal Design Criteria for Item Selection0 aMultidimensional Adaptive Testing with Optimal Design Criteria f a2010/02/02 cJun a273-2960 v743 aSeveral criteria from the optimal design literature are examined for use with item selection in multidimensional adaptive testing. In particular, it is examined what criteria are appropriate for adaptive testing in which all abilities are intentional, some should be considered as a nuisance, or the interest is in the testing of a composite of the abilities. Both the theoretical analyses and the studies of simulated data in this paper suggest that the criteria of A-optimality and D-optimality lead to the most accurate estimates when all abilities are intentional, with the former slightly outperforming the latter. The criterion of E-optimality showed occasional erratic behavior for this case of adaptive testing, and its use is not recommended. If some of the abilities are nuisances, application of the criterion of A(s)-optimality (or D(s)-optimality), which focuses on the subset of intentional abilities is recommended. For the measurement of a linear combination of abilities, the criterion of c-optimality yielded the best results. The preferences of each of these criteria for items with specific patterns of parameter values was also assessed. It was found that the criteria differed mainly in their preferences of items with different patterns of values for their discrimination parameters.1 aMulder, J1 avan der Linden, WJ uhttp://iacat.org/content/multidimensional-adaptive-testing-optimal-design-criteria-item-selection01773nas a2200145 4500008003900000245006900039210006900108300001000177490000700187520131500194100002501509700002501534700001601559856005201575 2009 d00aMultiple Maximum Exposure Rates in Computerized Adaptive Testing0 aMultiple Maximum Exposure Rates in Computerized Adaptive Testing a58-730 v333 a

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

1 aBarrada, Juan Ramón1 aVeldkamp, Bernard, P1 aOlea, Julio uhttp://apm.sagepub.com/content/33/1/58.abstract00353nas a2200109 4500008004100000245004500041210004500086300001100131490001100142100002300153856006700176 2008 eng d00aAdaptive models of psychological testing0 aAdaptive models of psychological testing a3–110 v216(1)1 avan der Linden, WJ uhttp://iacat.org/content/adaptive-models-psychological-testing00347nas a2200109 4500008003900000245004500039210004500084300000800129490000800137100002300145856006900168 2008 d00aAdaptive Models of Psychological Testing0 aAdaptive Models of Psychological Testing a1-20 v2161 avan der Linden, WJ uhttp://iacat.org/content/adaptive-models-psychological-testing-001872nas a2200205 4500008003900000245005600039210005600095300001000151490000800161520124900169653002101418653003001439653002501469653001801494653002501512100001301537700001701550700002101567856007801588 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://iacat.org/content/computerized-adaptive-testing-personality-traits00469nas a2200121 4500008003900000245011200039210006900151300001200220490000600232100002500238700001900263856006500282 2008 d00aImplementing Sympson-Hetter Item-Exposure Control in a Shadow-Test Approach to Constrained Adaptive Testing0 aImplementing SympsonHetter ItemExposure Control in a ShadowTest  a272-2890 v81 aVeldkamp, Bernard, P1 aLinden, Wim, J uhttp://www.tandfonline.com/doi/abs/10.1080/1530505080226223301289nas a2200133 4500008004100000245005700041210005700098300000900155490000800164520084700172653003401019100002301053856007901076 2008 eng d00aSome new developments in adaptive testing technology0 aSome new developments in adaptive testing technology a3-110 v2163 a

In an ironic twist of history, modern psychological testing has returned to an adaptive format quite common when testing was not yet standardized. Important stimuli to the renewed interest in adaptive testing have been the development of item-response theory in psychometrics, which models the responses on test items using separate parameters for the items and test takers, and the use of computers in test administration, which enables us to estimate the parameter for a test taker and select the items in real time. This article reviews a selection from the latest developments in the technology of adaptive testing, such as constrained adaptive item selection, adaptive testing using rule-based item generation, multidimensional adaptive testing, adaptive use of test batteries, and the use of response times in adaptive testing.

10acomputerized adaptive testing1 avan der Linden, WJ uhttp://iacat.org/content/some-new-developments-adaptive-testing-technology00402nas a2200109 4500008004100000245006400041210006400105300001100169490000700180100002300187856008200210 2008 eng d00aUsing response times for item selection in adaptive testing0 aUsing response times for item selection in adaptive testing a5–200 v331 avan der Linden, WJ uhttp://iacat.org/content/using-response-times-item-selection-adaptive-testing01935nas a2200157 4500008004100000245011600041210006900157300001200226490000700238520135500245100001601600700001201616700001601628700001401644856011901658 2008 eng d00aUtilizing Rasch measurement models to develop a computer adaptive self-report of walking, climbing, and running0 aUtilizing Rasch measurement models to develop a computer adaptiv a458-4670 v303 aPurpose.The purpose of this paper is to show how the Rasch model can be used to develop a computer adaptive self-report of walking, climbing, and running.Method.Our instrument development work on the walking/climbing/running construct of the ICF Activity Measure was used to show how to develop a computer adaptive test (CAT). Fit of the items to the Rasch model and validation of the item difficulty hierarchy was accomplished using Winsteps software. Standard error was used as a stopping rule for the CAT. Finally, person abilities were connected to items difficulties using Rasch analysis ‘maps’.Results.All but the walking one mile item fit the Rasch measurement model. A CAT was developed which selectively presented items based on the last calibrated person ability measure and was designed to stop when standard error decreased to a pre-set criterion. Finally, person ability measures were connected to the ability to perform specific walking/climbing/running activities using Rasch maps.Conclusions.Rasch measurement models can be useful in developing CAT measures for rehabilitation and disability. In addition to CATs reducing respondent burden, the connection of person measures to item difficulties may be important for the clinical interpretation of measures.Read More: http://informahealthcare.com/doi/abs/10.1080/096382807016173171 aVelozo, C A1 aWang, Y1 aLehman, L A1 aWang, J H uhttp://iacat.org/content/utilizing-rasch-measurement-models-develop-computer-adaptive-self-report-walking-climbing01944nas a2200301 4500008004500000020001400045245012900059210006900188300001200257490000700269520093500276653002501211653002101236653002501257653003001282653003001312653001001342653001501352653002601367653002501393653002401418653001501442653001501457100001301472700001701485700002101502856011901523 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://iacat.org/content/computerized-adaptive-testing-polytomous-motivation-items-administration-mode-effects-and01415nas a2200145 4500008003900000245012900039210006900168300001200237490000700249520089000256100002001146700002301166700002701189856005301216 2007 d00aComputerized 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 a

In 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.

1 aHol, Michiel, A1 aVorst, Harrie, C M1 aMellenbergh, Gideon, J uhttp://apm.sagepub.com/content/31/5/412.abstract01361nas a2200133 4500008003900000245009700039210006900136300001200205490000700217520090200224100001901126700002501145856005701170 2007 d00aConditional Item-Exposure Control in Adaptive Testing Using Item-Ineligibility Probabilities0 aConditional ItemExposure Control in Adaptive Testing Using ItemI a398-4180 v323 a

Two conditional versions of the exposure-control method with item-ineligibility constraints for adaptive testing in van der Linden and Veldkamp (2004) are presented. The first version is for unconstrained item selection, the second for item selection with content constraints imposed by the shadow-test approach. In both versions, the exposure rates of the items are controlled using probabilities of item ineligibility given θ that adapt the exposure rates automatically to a goal value for the items in the pool. In an extensive empirical study with an adaptive version of the Law School Admission Test, the authors show how the method can be used to drive conditional exposure rates below goal values as low as 0.025. Obviously, the price to be paid for minimal exposure rates is a decrease in the accuracy of the ability estimates. This trend is illustrated with empirical data.

1 aLinden, Wim, J1 aVeldkamp, Bernard, P uhttp://jeb.sagepub.com/cgi/content/abstract/32/4/39800492nas a2200109 4500008004100000245006600041210006200107260009700169100002000266700001800286856007800304 2007 eng d00aThe development of a computerized adaptive test for integrity0 adevelopment of a computerized adaptive test for integrity aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aEgberink, I J L1 aVeldkamp, B P uhttp://iacat.org/content/development-computerized-adaptive-test-integrity00634nas a2200145 4500008004100000245009700041210006900138260009700207100001600304700001200320700001900332700001300351700001300364856011100377 2007 eng d00aDevelopment of a multiple-component CAT for measuring foreign language proficiency (SIMTEST)0 aDevelopment of a multiplecomponent CAT for measuring foreign lan aD. J. Weiss (Ed.). Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aSumbling, M1 aSanz, P1 aViladrich, M C1 aDoval, E1 aRiera, L uhttp://iacat.org/content/development-multiple-component-cat-measuring-foreign-language-proficiency-simtest02408nas a2200361 4500008004500000020001400045245011100059210006900170300001200239490000700251520135900258653001601617653002001633653001301653653002401666653002501690653001101715653001401726653001301740653001801753653002701771653001001798653001101808100001501819700001201834700001601846700001201862700001601874700001301890700001301903700001401916856011601930 2007 Engldsh  a1057-924900aThe initial development of an item bank to assess and screen for psychological distress in cancer patients0 ainitial development of an item bank to assess and screen for psy a724-7320 v163 aPsychological distress is a common problem among cancer patients. Despite the large number of instruments that have been developed to assess distress, their utility remains disappointing. This study aimed to use Rasch models to develop an item-bank which would provide the basis for better means of assessing psychological distress in cancer patients. An item bank was developed from eight psychological distress questionnaires using Rasch analysis to link common items. Items from the questionnaires were added iteratively with common items as anchor points and misfitting items (infit mean square > 1.3) removed, and unidimensionality assessed. A total of 4914 patients completed the questionnaires providing an initial pool of 83 items. Twenty items were removed resulting in a final pool of 63 items. Good fit was demonstrated and no additional factor structure was evident from the residuals. However, there was little overlap between item locations and person measures, since items mainly targeted higher levels of distress. The Rasch analysis allowed items to be pooled and generated a unidimensional instrument for measuring psychological distress in cancer patients. Additional items are required to more accurately assess patients across the whole continuum of psychological distress. (PsycINFO Database Record (c) 2007 APA ) (journal abstract)10a3293 Cancer10acancer patients10aDistress10ainitial development10aItem Response Theory10aModels10aNeoplasms10aPatients10aPsychological10apsychological distress10aRasch10aStress1 aSmith, A B1 aRush, R1 aVelikova, G1 aWall, L1 aWright, E P1 aStark, D1 aSelby, P1 aSharpe, M uhttp://iacat.org/content/initial-development-item-bank-assess-and-screen-psychological-distress-cancer-patients00555nas a2200121 4500008004100000245011100041210006900152260003400221100001800255700002500273700001600298856011900314 2007 eng d00aA multiple objective test assembly approach for exposure control problems in computerized adaptive testing0 amultiple objective test assembly approach for exposure control p aArnhem, The NetherlandsbCito1 aVeldkamp, B P1 aVerschoor, Angela, J1 aEggen, Theo uhttp://iacat.org/content/multiple-objective-test-assembly-approach-exposure-control-problems-computerized-adaptive00514nas a2200097 4500008004100000245008600041210006900127260009700196100002300293856010000316 2007 eng d00aThe shadow-test approach: A universal framework for implementing adaptive testing0 ashadowtest approach A universal framework for implementing adapt aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 avan der Linden, WJ uhttp://iacat.org/content/shadow-test-approach-universal-framework-implementing-adaptive-testing00470nas a2200109 4500008004100000245004400041210004400085260012600129100002300255700001600278856006600294 2007 eng d00aStatistical aspects of adaptive testing0 aStatistical aspects of adaptive testing aC. R. Rao and S. Sinharay (Eds.), Handbook of statistics (Vol. 27: Psychometrics) (pp. 801838). Amsterdam: North-Holland.1 avan der Linden, WJ1 aGlas, C A W uhttp://iacat.org/content/statistical-aspects-adaptive-testing01959nas a2200265 4500008004100000020002200041245008200063210006900145260002600214300001000240490000700250520112900257653001501386653003401401653001401435653001701449653002401466653001501490653002201505653001501527100002301542700001301565700001801578856009701596 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://iacat.org/content/assembling-computerized-adaptive-testing-item-pool-set-linear-tests01580nas a2200205 4500008004100000020002200041245005000063210005000113260002600163300001200189490000700201520094200208653003401150653002801184653001901212653002001231653003301251100002301284856006701307 2006 eng d a0146-6216 (Print)00aEquating scores from adaptive to linear tests0 aEquating scores from adaptive to linear tests bSage Publications: US a493-5080 v303 aTwo local methods for observed-score equating are applied to the problem of equating an adaptive test to a linear test. In an empirical study, the methods were evaluated against a method based on the test characteristic function (TCF) of the linear test and traditional equipercentile equating applied to the ability estimates on the adaptive test for a population of test takers. The two local methods were generally best. Surprisingly, the TCF method performed slightly worse than the equipercentile method. Both methods showed strong bias and uniformly large inaccuracy, but the TCF method suffered from extra error due to the lower asymptote of the test characteristic function. It is argued that the worse performances of the two methods are a consequence of the fact that they use a single equating transformation for an entire population of test takers and therefore have to compromise between the individual score distributions. 10acomputerized adaptive testing10aequipercentile equating10alocal equating10ascore reporting10atest characteristic function1 avan der Linden, WJ uhttp://iacat.org/content/equating-scores-adaptive-linear-tests02394nas a2200301 4500008004100000020002200041245010800063210006900171260002400240300000900264490000600273520142200279653002201701653003401723653002301757653003201780653001801812653002101830653001801851100001401869700001301883700001401896700001901910700001501929700001701944700001301961856011801974 2006 eng d a1529-7713 (Print)00aExpansion of a physical function item bank and development of an abbreviated form for clinical research0 aExpansion of a physical function item bank and development of an bRichard M Smith: US a1-150 v73 aWe expanded an existing 33-item physical function (PF) item bank with a sufficient number of items to enable computerized adaptive testing (CAT). Ten items were written to expand the bank and the new item pool was administered to 295 people with cancer. For this analysis of the new pool, seven poorly performing items were identified for further examination. This resulted in a bank with items that define an essentially unidimensional PF construct, cover a wide range of that construct, reliably measure the PF of persons with cancer, and distinguish differences in self-reported functional performance levels. We also developed a 5-item (static) assessment form ("BriefPF") that can be used in clinical research to express scores on the same metric as the overall bank. The BriefPF was compared to the PF-10 from the Medical Outcomes Study SF-36. Both short forms significantly differentiated persons across functional performance levels. While the entire bank was more precise across the PF continuum than either short form, there were differences in the area of the continuum in which each short form was more precise: the BriefPF was more precise than the PF-10 at the lower functional levels and the PF-10 was more precise than the BriefPF at the higher levels. Future research on this bank will include the development of a CAT version, the PF-CAT. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aclinical research10acomputerized adaptive testing10aperformance levels10aphysical function item bank10aPsychometrics10atest reliability10aTest Validity1 aBode, R K1 aLai, J-S1 aDineen, K1 aHeinemann, A W1 aShevrin, D1 aVon Roenn, J1 aCella, D uhttp://iacat.org/content/expansion-physical-function-item-bank-and-development-abbreviated-form-clinical-research00461nas a2200121 4500008004100000245006900041210006900110260002200179100001700201700001800218700001200236856009100248 2006 eng d00aMultiple maximum exposure rates in computerized adaptive testing0 aMultiple maximum exposure rates in computerized adaptive testing aBudapest, Hungary1 aBarrada, J R1 aVeldkamp, B P1 aOlea, J uhttp://iacat.org/content/multiple-maximum-exposure-rates-computerized-adaptive-testing01617nas a2200217 4500008004100000020002200041245008200063210006900145260002600214300001200240490000700252520088700259653002801146653002501174653002501199653001901224653001601243100001601259700002501275856009901300 2006 eng d a0146-6216 (Print)00aOptimal testing with easy or difficult items in computerized adaptive testing0 aOptimal testing with easy or difficult items in computerized ada bSage Publications: US a379-3930 v303 aComputerized adaptive tests (CATs) are individualized tests that, from a measurement point of view, are optimal for each individual, possibly under some practical conditions. In the present study, it is shown that maximum information item selection in CATs using an item bank that is calibrated with the one- or the two-parameter logistic model results in each individual answering about 50% of the items correctly. Two item selection procedures giving easier (or more difficult) tests for students are presented and evaluated. Item selection on probability points of items yields good results only with the one-parameter logistic model and not with the two-parameter logistic model. An alternative selection procedure, based on maximum information at a shifted ability level, gives satisfactory results with both models. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputer adaptive tests10aindividualized tests10aItem Response Theory10aitem selection10aMeasurement1 aEggen, Theo1 aVerschoor, Angela, J uhttp://iacat.org/content/optimal-testing-easy-or-difficult-items-computerized-adaptive-testing01352nas a2200133 4500008003900000245008200039210006900121300001200190490000700202520091500209100001601124700002501140856005301165 2006 d00aOptimal Testing With Easy or Difficult Items in Computerized Adaptive Testing0 aOptimal Testing With Easy or Difficult Items in Computerized Ada a379-3930 v303 a

Computerized adaptive tests (CATs) are individualized tests that, from a measurement point of view, are optimal for each individual, possibly under some practical conditions. In the present study, it is shown that maximum information item selection in CATs using an item bank that is calibrated with the one or the two-parameter logistic model results in each individual answering about 50% of the items correctly. Two item selection procedures giving easier (or more difficult) tests for students are presented and evaluated. Item selection on probability points of items yields good results only with the one-parameter logistic model and not with the two-parameter logistic model. An alternative selection procedure, based on maximum information at a shifted ability level, gives satisfactory results with both models. Index terms: computerized adaptive testing, item selection, item response theory

1 aEggen, Theo1 aVerschoor, Angela, J uhttp://apm.sagepub.com/content/30/5/379.abstract01482nas a2200145 4500008003900000245006500039210006500104300001200169490000700181520102700188100002001215700002501235700002301260856005301283 2006 d00aOptimal Testlet Pool Assembly for Multistage Testing Designs0 aOptimal Testlet Pool Assembly for Multistage Testing Designs a204-2150 v303 a

Computerized multistage testing (MST) designs require sets of test questions (testlets) to be assembled to meet strict, often competing criteria. Rules that govern testlet assembly may dictate the number of questions on a particular subject or may describe desirable statistical properties for the test, such as measurement precision. In an MST design, testlets of differing difficulty levels must be created. Statistical properties for assembly of the testlets can be expressed using item response theory (IRT) parameters. The testlet test information function (TIF) value can be maximized at a specific point on the IRT ability scale. In practical MST designs, parallel versions of testlets are needed, so sets of testlets with equivalent properties are built according to equivalent specifications. In this project, the authors study the use of a mathematical programming technique to simultaneously assemble testlets to ensure equivalence and fairness to candidates who may be administered different testlets.

1 aAriel, Adelaide1 aVeldkamp, Bernard, P1 aBreithaupt, Krista uhttp://apm.sagepub.com/content/30/3/204.abstract00443nas 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_800530nas a2200133 4500008003900000245010500039210006900144300001200213490000700225100001700232700001600249700001500265856011600280 2005 d00aA closer look at using judgments of item difficulty to change answers on computerized adaptive tests0 acloser look at using judgments of item difficulty to change answ a331-3500 v421 aVispoel, W P1 aClough, S J1 aBleiler, T uhttp://iacat.org/content/closer-look-using-judgments-item-difficulty-change-answers-computerized-adaptive-tests00449nas a2200109 4500008004100000245008700041210006900128300001200197490000700209100002300216856010000239 2005 eng d00aA comparison of item-selection methods for adaptive tests with content constraints0 acomparison of itemselection methods for adaptive tests with cont a283-3020 v421 avan der Linden, WJ uhttp://iacat.org/content/comparison-item-selection-methods-adaptive-tests-content-constraints-002148nas a2200229 4500008004100000020002200041245008700063210006900150260004100219300001200260490000700272520135500279653002101634653001501655653002401670653002601694653002501720653002101745653003101766100002301797856009801820 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://iacat.org/content/comparison-item-selection-methods-adaptive-tests-content-constraints00511nas a2200109 4500008004100000245008200041210006900123260006700192100002300259700001800282856010100300 2005 eng d00aConstraining item exposure in computerized adaptive testing with shadow tests0 aConstraining item exposure in computerized adaptive testing with aLaw School Admission Council Computerized Testing Report 02-031 avan der Linden, WJ1 aVeldkamp, B P uhttp://iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests-100549nas a2200109 4500008004100000245010300041210006900144260006800213100002300281700001900304856011600323 2005 eng d00aImplementing content constraints in alpha-stratified adaptive testing using a shadow test approach0 aImplementing content constraints in alphastratified adaptive tes aLaw School Admission Council, Computerized Testing Report 01-091 avan der Linden, WJ1 aChang, Hua-Hua uhttp://iacat.org/content/implementing-content-constraints-alpha-stratified-adaptive-testing-using-shadow-test-002016nas a2200193 4500008004100000245009300041210006900134300001200203490000700215520136400222653001501586653002401601653001101625653002201636100001801658700001801676700001901694856010901713 2005 eng d00aInfeasibility in automated test assembly models: A comparison study of different methods0 aInfeasibility in automated test assembly models A comparison stu a223-2430 v423 aSeveral techniques exist to automatically put together a test meeting a number of specifications. In an item bank, the items are stored with their characteristics. A test is constructed by selecting a set of items that fulfills the specifications set by the test assembler. Test assembly problems are often formulated in terms of a model consisting of restrictions and an objective to be maximized or minimized. A problem arises when it is impossible to construct a test from the item pool that meets all specifications, that is, when the model is not feasible. Several methods exist to handle these infeasibility problems. In this article, test assembly models resulting from two practical testing programs were reconstructed to be infeasible. These models were analyzed using methods that forced a solution (Goal Programming, Multiple-Goal Programming, Greedy Heuristic), that analyzed the causes (Relaxed and Ordered Deletion Algorithm (RODA), Integer Randomized Deletion Algorithm (IRDA), Set Covering (SC), and Item Sampling), or that analyzed the causes and used this information to force a solution (Irreducible Infeasible Set-Solver). Specialized methods such as the IRDA and the Irreducible Infeasible Set-Solver performed best. Recommendations about the use of different methods are given. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAlgorithms10aItem Content (Test)10aModels10aTest Construction1 aHuitzing, H A1 aVeldkamp, B P1 aVerschoor, A J uhttp://iacat.org/content/infeasibility-automated-test-assembly-models-comparison-study-different-methods02439nas a2200373 4500008004100000020004100041245008200082210006900164250001500233260000800248300001000256490000700266520136700273653001001640653000901650653002201659653003301681653003301714653001101747653001101758653000901769653001601778653004001794653001801834653001901852100001301871700001301884700001401897700001201911700001701923700001601940700001501956856009401971 2005 eng d a0895-4356 (Print)0895-4356 (Linking)00aAn item bank was created to improve the measurement of cancer-related fatigue0 aitem bank was created to improve the measurement of cancerrelate a2005/02/01 cFeb a190-70 v583 aOBJECTIVE: Cancer-related fatigue (CRF) is one of the most common unrelieved symptoms experienced by patients. CRF is underrecognized and undertreated due to a lack of clinically sensitive instruments that integrate easily into clinics. Modern computerized adaptive testing (CAT) can overcome these obstacles by enabling precise assessment of fatigue without requiring the administration of a large number of questions. A working item bank is essential for development of a CAT platform. The present report describes the building of an operational item bank for use in clinical settings with the ultimate goal of improving CRF identification and treatment. STUDY DESIGN AND SETTING: The sample included 301 cancer patients. Psychometric properties of items were examined by using Rasch analysis, an Item Response Theory (IRT) model. RESULTS AND CONCLUSION: The final bank includes 72 items. These 72 unidimensional items explained 57.5% of the variance, based on factor analysis results. Excellent internal consistency (alpha=0.99) and acceptable item-total correlation were found (range: 0.51-0.85). The 72 items covered a reasonable range of the fatigue continuum. No significant ceiling effects, floor effects, or gaps were found. A sample short form was created for demonstration purposes. The resulting bank is amenable to the development of a CAT platform.10aAdult10aAged10aAged, 80 and over10aFactor Analysis, Statistical10aFatigue/*etiology/psychology10aFemale10aHumans10aMale10aMiddle Aged10aNeoplasms/*complications/psychology10aPsychometrics10aQuestionnaires1 aLai, J-S1 aCella, D1 aDineen, K1 aBode, R1 aVon Roenn, J1 aGershon, RC1 aShevrin, D uhttp://iacat.org/content/item-bank-was-created-improve-measurement-cancer-related-fatigue01739nas a2200229 4500008004100000245008300041210006900124300001100193490000700204520103000211653003401241100001301275700001401288700001501302700001701317700001501334700001501349700001401364700001301378700001301391856010501404 2005 eng d00aAn item response theory-based pain item bank can enhance measurement precision0 aitem response theorybased pain item bank can enhance measurement a278-880 v303 aCancer-related pain is often under-recognized and undertreated. This is partly due to the lack of appropriate assessments, which need to be comprehensive and precise yet easily integrated into clinics. Computerized adaptive testing (CAT) can enable precise-yet-brief assessments by only selecting the most informative items from a calibrated item bank. The purpose of this study was to create such a bank. The sample included 400 cancer patients who were asked to complete 61 pain-related items. Data were analyzed using factor analysis and the Rasch model. The final bank consisted of 43 items which satisfied the measurement requirement of factor analysis and the Rasch model, demonstrated high internal consistency and reasonable item-total correlations, and discriminated patients with differing degrees of pain. We conclude that this bank demonstrates good psychometric properties, is sensitive to pain reported by patients, and can be used as the foundation for a CAT pain-testing platform for use in clinical practice.10acomputerized adaptive testing1 aLai, J-S1 aDineen, K1 aReeve, B B1 aVon Roenn, J1 aShervin, D1 aMcGuire, M1 aBode, R K1 aPaice, J1 aCella, D uhttp://iacat.org/content/item-response-theory-based-pain-item-bank-can-enhance-measurement-precision01733nas a2200181 4500008004100000245014500041210006900186300001200255490000700267520104600274653003001320653002401350653001501374100001301389700001701402700002101419856011101440 2005 eng d00aA randomized experiment to compare conventional, computerized, and computerized adaptive administration of ordinal polytomous attitude items0 arandomized experiment to compare conventional computerized and c a159-1830 v293 aA total of 520 high school students were randomly assigned to a paper-and-pencil test (PPT), a computerized standard test (CST), or a computerized adaptive test (CAT) version of the Dutch School Attitude Questionnaire (SAQ), consisting of ordinal polytomous items. The CST administered items in the same order as the PPT. The CAT administered all items of three SAQ subscales in adaptive order using Samejima's graded response model, so that six different stopping rule settings could be applied afterwards. School marks were used as external criteria. Results showed significant but small multivariate administration mode effects on conventional raw scores and small to medium effects on maximum likelihood latent trait estimates. When the precision of CAT latent trait estimates decreased, correlations with grade point average in general decreased. However, the magnitude of the decrease was not very large as compared to the PPT, the CST, and the CAT without the stopping rule. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aComputer Assisted Testing10aTest Administration10aTest Items1 aHol, A M1 aVorst, H C M1 aMellenbergh, G J uhttp://iacat.org/content/randomized-experiment-compare-conventional-computerized-and-computerized-adaptive01542nas a2200145 4500008003900000245014500039210006900184300001200253490000700265520100100272100002001273700002301293700002701316856005301343 2005 d00aA Randomized Experiment to Compare Conventional, Computerized, and Computerized Adaptive Administration of Ordinal Polytomous Attitude Items0 aRandomized Experiment to Compare Conventional Computerized and C a159-1830 v293 a

A total of 520 high school students were randomly assigned to a paper-and-pencil test (PPT), a computerized standard test (CST), or a computerized adaptive test (CAT) version of the Dutch School Attitude Questionnaire (SAQ), consisting of ordinal polytomous items. The CST administered items in the same order as the PPT. The CAT administered all items of three SAQ subscales in adaptive order using Samejima’s graded response model, so that six different stopping rule settings could be applied afterwards. School marks were used as external criteria. Results showed significant but small multivariate administration mode effects on conventional raw scores and small to medium effects on maximum likelihood latent trait estimates. When the precision of CAT latent trait estimates decreased, correlations with grade point average in general decreased. However, the magnitude of the decrease was not very large as compared to the PPT, the CST, and the CAT without the stopping rule.

1 aHol, Michiel, A1 aVorst, Harrie, C M1 aMellenbergh, Gideon, J uhttp://apm.sagepub.com/content/29/3/159.abstract10197nas a2200553 4500008004100000245016300041210006900204300001400273490000700287520862500294653001808919653003208937100001608969700001608985700001409001700001609015700001409031700001509045700001609060700001409076700001509090700001509105700001409120700001709134700001709151700001709168700001609185700001709201700001609218700001509234700001609249700001709265700002309282700001609305700002209321700001609343700001609359700001709375700001309392700001509405700001309420700001609433700001209449700001409461700001609475700002009491700001209511856012009523 2005 eng d00aToward efficient and comprehensive measurement of the alcohol problems continuum in college students: The Brief Young Adult Alcohol Consequences Questionnaire0 aToward efficient and comprehensive measurement of the alcohol pr a1180-11890 v293 aBackground: Although a number of measures of alcohol problems in college students have been studied, the psychometric development and validation of these scales have been limited, for the most part, to methods based on classical test theory. In this study, we conducted analyses based on item response theory to select a set of items for measuring the alcohol problem severity continuum in college students that balances comprehensiveness and efficiency and is free from significant gender bias., Method: We conducted Rasch model analyses of responses to the 48-item Young Adult Alcohol Consequences Questionnaire by 164 male and 176 female college students who drank on at least a weekly basis. An iterative process using item fit statistics, item severities, item discrimination parameters, model residuals, and analysis of differential item functioning by gender was used to pare the items down to those that best fit a Rasch model and that were most efficient in discriminating among levels of alcohol problems in the sample., Results: The process of iterative Rasch model analyses resulted in a final 24-item scale with the data fitting the unidimensional Rasch model very well. The scale showed excellent distributional properties, had items adequately matched to the severity of alcohol problems in the sample, covered a full range of problem severity, and appeared highly efficient in retaining all of the meaningful variance captured by the original set of 48 items., Conclusions: The use of Rasch model analyses to inform item selection produced a final scale that, in both its comprehensiveness and its efficiency, should be a useful tool for researchers studying alcohol problems in college students. To aid interpretation of raw scores, examples of the types of alcohol problems that are likely to be experienced across a range of selected scores are provided., (C)2005Research Society on AlcoholismAn important, sometimes controversial feature of all psychological phenomena is whether they are categorical or dimensional. A conceptual and psychometric framework is described for distinguishing whether the latent structure behind manifest categories (e.g., psychiatric diagnoses, attitude groups, or stages of development) is category-like or dimension-like. Being dimension-like requires (a) within-category heterogeneity and (b) between-category quantitative differences. Being category-like requires (a) within-category homogeneity and (b) between-category qualitative differences. The relation between this classification and abrupt versus smooth differences is discussed. Hybrid structures are possible. Being category-like is itself a matter of degree; the authors offer a formalized framework to determine this degree. Empirical applications to personality disorders, attitudes toward capital punishment, and stages of cognitive development illustrate the approach., (C) 2005 by the American Psychological AssociationThe authors conducted Rasch model ( G. Rasch, 1960) analyses of items from the Young Adult Alcohol Problems Screening Test (YAAPST; S. C. Hurlbut & K. J. Sher, 1992) to examine the relative severity and ordering of alcohol problems in 806 college students. Items appeared to measure a single dimension of alcohol problem severity, covering a broad range of the latent continuum. Items fit the Rasch model well, with less severe symptoms reliably preceding more severe symptoms in a potential progression toward increasing levels of problem severity. However, certain items did not index problem severity consistently across demographic subgroups. A shortened, alternative version of the YAAPST is proposed, and a norm table is provided that allows for a linking of total YAAPST scores to expected symptom expression., (C) 2004 by the American Psychological AssociationA didactic on latent growth curve modeling for ordinal outcomes is presented. The conceptual aspects of modeling growth with ordinal variables and the notion of threshold invariance are illustrated graphically using a hypothetical example. The ordinal growth model is described in terms of 3 nested models: (a) multivariate normality of the underlying continuous latent variables (yt) and its relationship with the observed ordinal response pattern (Yt), (b) threshold invariance over time, and (c) growth model for the continuous latent variable on a common scale. Algebraic implications of the model restrictions are derived, and practical aspects of fitting ordinal growth models are discussed with the help of an empirical example and Mx script ( M. C. Neale, S. M. Boker, G. Xie, & H. H. Maes, 1999). The necessary conditions for the identification of growth models with ordinal data and the methodological implications of the model of threshold invariance are discussed., (C) 2004 by the American Psychological AssociationRecent research points toward the viability of conceptualizing alcohol problems as arrayed along a continuum. Nevertheless, modern statistical techniques designed to scale multiple problems along a continuum (latent trait modeling; LTM) have rarely been applied to alcohol problems. This study applies LTM methods to data on 110 problems reported during in-person interviews of 1,348 middle-aged men (mean age = 43) from the general population. The results revealed a continuum of severity linking the 110 problems, ranging from heavy and abusive drinking, through tolerance and withdrawal, to serious complications of alcoholism. These results indicate that alcohol problems can be arrayed along a dimension of severity and emphasize the relevance of LTM to informing the conceptualization and assessment of alcohol problems., (C) 2004 by the American Psychological AssociationItem response theory (IRT) is supplanting classical test theory as the basis for measures development. This study demonstrated the utility of IRT for evaluating DSM-IV diagnostic criteria. Data on alcohol, cannabis, and cocaine symptoms from 372 adult clinical participants interviewed with the Composite International Diagnostic Interview-Expanded Substance Abuse Module (CIDI-SAM) were analyzed with Mplus ( B. Muthen & L. Muthen, 1998) and MULTILOG ( D. Thissen, 1991) software. Tolerance and legal problems criteria were dropped because of poor fit with a unidimensional model. Item response curves, test information curves, and testing of variously constrained models suggested that DSM-IV criteria in the CIDI-SAM discriminate between only impaired and less impaired cases and may not be useful to scale case severity. IRT can be used to study the construct validity of DSM-IV diagnoses and to identify diagnostic criteria with poor performance., (C) 2004 by the American Psychological AssociationThis study examined the psychometric characteristics of an index of substance use involvement using item response theory. The sample consisted of 292 men and 140 women who qualified for a Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; American Psychiatric Association, 1987) substance use disorder (SUD) diagnosis and 293 men and 445 women who did not qualify for a SUD diagnosis. The results indicated that men had a higher probability of endorsing substance use compared with women. The index significantly predicted health, psychiatric, and psychosocial disturbances as well as level of substance use behavior and severity of SUD after a 2-year follow-up. Finally, this index is a reliable and useful prognostic indicator of the risk for SUD and the medical and psychosocial sequelae of drug consumption., (C) 2002 by the American Psychological AssociationComparability, validity, and impact of loss of information of a computerized adaptive administration of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) were assessed in a sample of 140 Veterans Affairs hospital patients. The countdown method ( Butcher, Keller, & Bacon, 1985) was used to adaptively administer Scales L (Lie) and F (Frequency), the 10 clinical scales, and the 15 content scales. Participants completed the MMPI-2 twice, in 1 of 2 conditions: computerized conventional test-retest, or computerized conventional-computerized adaptive. Mean profiles and test-retest correlations across modalities were comparable. Correlations between MMPI-2 scales and criterion measures supported the validity of the countdown method, although some attenuation of validity was suggested for certain health-related items. Loss of information incurred with this mode of adaptive testing has minimal impact on test validity. Item and time savings were substantial., (C) 1999 by the American Psychological Association10aPsychometrics10aSubstance-Related Disorders1 aKahler, C W1 aStrong, D R1 aRead, J P1 aDe Boeck, P1 aWilson, M1 aActon, G S1 aPalfai, T P1 aWood, M D1 aMehta, P D1 aNeale, M C1 aFlay, B R1 aConklin, C A1 aClayton, R R1 aTiffany, S T1 aShiffman, S1 aKrueger, R F1 aNichol, P E1 aHicks, B M1 aMarkon, K E1 aPatrick, C J1 aIacono, William, G1 aMcGue, Matt1 aLangenbucher, J W1 aLabouvie, E1 aMartin, C S1 aSanjuan, P M1 aBavly, L1 aKirisci, L1 aChung, T1 aVanyukov, M1 aDunn, M1 aTarter, R1 aHandel, R W1 aBen-Porath, Y S1 aWatt, M uhttp://iacat.org/content/toward-efficient-and-comprehensive-measurement-alcohol-problems-continuum-college-students02772nas a2200421 4500008004100000020004600041245011200087210006900199250001500268260001000283300000700293490000600300520144100306653002701747653003001774653004701804653001001851653000901861653002201870653002801892653001101920653001101931653002001942653000901962653001601971653001601987653001902003653001602022653003502038653002902073653004002102653003002142100001402172700001702186700001702203700001402220856011602234 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://iacat.org/content/amc-linear-disability-score-project-population-requiring-residential-care-psychometric00492nas a2200121 4500008004100000245009100041210006900132260001700201100001800218700001300236700001600249856010500265 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://iacat.org/content/automated-simultaneous-assembly-multi-stage-testing-uniform-cpa-examination01608nas a2200217 4500008004100000020002200041245008200063210006900145260004300214300001200257490000700269520084600276653003401122653002601156653003501182653001601217653001701233100002301250700001801273856009901291 2004 eng d a1076-9986 (Print)00aConstraining item exposure in computerized adaptive testing with shadow tests0 aConstraining item exposure in computerized adaptive testing with bAmerican Educational Research Assn: US a273-2910 v293 aItem-exposure control in computerized adaptive testing is implemented by imposing item-ineligibility constraints on the assembly process of the shadow tests. The method resembles Sympson and Hetter’s (1985) method of item-exposure control in that the decisions to impose the constraints are probabilistic. The method does not, however, require time-consuming simulation studies to set values for control parameters before the operational use of the test. Instead, it can set the probabilities of item ineligibility adaptively during the test using the actual item-exposure rates. An empirical study using an item pool from the Law School Admission Test showed that application of the method yielded perfect control of the item-exposure rates and had negligible impact on the bias and mean-squared error functions of the ability estimator. 10acomputerized adaptive testing10aitem exposure control10aitem ineligibility constraints10aProbability10ashadow tests1 avan der Linden, WJ1 aVeldkamp, B P uhttp://iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests01302nas a2200133 4500008003900000245008200039210006900121300001200190490000700202520085800209100001901067700002501086856005701111 2004 d00aConstraining Item Exposure in Computerized Adaptive Testing With Shadow Tests0 aConstraining Item Exposure in Computerized Adaptive Testing With a273-2910 v293 a

Item-exposure control in computerized adaptive testing is implemented by imposing item-ineligibility constraints on the assembly process of the shadow tests. The method resembles Sympson and Hetter’s (1985) method of item-exposure control in that the decisions to impose the constraints are probabilistic. The method does not, however, require time-consuming simulation studies to set values for control parameters before the operational use of the test. Instead, it can set the probabilities of item ineligibility adaptively during the test using the actual item-exposure rates. An empirical study using an item pool from the Law School Admission Test showed that application of the method yielded perfect control of the item-exposure rates and had negligible impact on the bias and mean-squared error functions of the ability estimator.

1 aLinden, Wim, J1 aVeldkamp, Bernard, P uhttp://jeb.sagepub.com/cgi/content/abstract/29/3/27301903nas a2200217 4500008004100000020002200041245007000063210006900133260004100202300001200243490000700255520117100262653003201433653003301465653001801498653002401516100001301540700001801553700002301571856009101594 2004 eng d a0022-0655 (Print)00aConstructing rotating item pools for constrained adaptive testing0 aConstructing rotating item pools for constrained adaptive testin bBlackwell Publishing: United Kingdom a345-3590 v413 aPreventing items in adaptive testing from being over- or underexposed is one of the main problems in computerized adaptive testing. Though the problem of overexposed items can be solved using a probabilistic item-exposure control method, such methods are unable to deal with the problem of underexposed items. Using a system of rotating item pools, on the other hand, is a method that potentially solves both problems. In this method, a master pool is divided into (possibly overlapping) smaller item pools, which are required to have similar distributions of content and statistical attributes. These pools are rotated among the testing sites to realize desirable exposure rates for the items. A test assembly model, motivated by Gulliksen's matched random subtests method, was explored to help solve the problem of dividing a master pool into a set of smaller pools. Different methods to solve the model are proposed. An item pool from the Law School Admission Test was used to evaluate the performances of computerized adaptive tests from systems of rotating item pools constructed using these methods. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive tests10aconstrained adaptive testing10aitem exposure10arotating item pools1 aAriel, A1 aVeldkamp, B P1 avan der Linden, WJ uhttp://iacat.org/content/constructing-rotating-item-pools-constrained-adaptive-testing00717nas a2200205 4500008004100000020002200041245010800063210006900171260003300240300000900273490000900282100002400291700002300315700002700338700002900365700002100394700002300415700002300438856005000461 2004 eng d a978-3-540-22948-300aA Learning Environment for English for Academic Purposes Based on Adaptive Tests and Task-Based Systems0 aLearning Environment for English for Academic Purposes Based on  bSpringer Berlin / Heidelberg a1-110 v32201 aGonçalves, Jean, P1 aAluisio, Sandra, M1 aOliveira, Leandro, H M1 aOliveira Jr., Osvaldo, N1 aLester, James, C1 aVicari, Rosa Maria1 aParaguaçu, Fábio uhttp://dx.doi.org/10.1007/978-3-540-30139-4_101441nas a2200157 4500008003900000245006700039210006700106300001200173490000700185520094700192100002901139700002301168700001901191700002001210856005301230 2004 d00aMokken Scale Analysis Using Hierarchical Clustering Procedures0 aMokken Scale Analysis Using Hierarchical Clustering Procedures a332-3540 v283 a

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

1 aAbswoude, Alexandra, A H1 aVermunt, Jeroen, K1 aHemker, Bas, T1 aArk, Andries, L uhttp://apm.sagepub.com/content/28/5/332.abstract00538nas a2200109 4500008004100000245012100041210006900162260004000231100001600271700001900287856012200306 2004 eng d00aOptimal testing with easy items in computerized adaptive testing (Measurement and Research Department Report 2004-2)0 aOptimal testing with easy items in computerized adaptive testing aArnhem, The Netherlands: Cito Group1 aEggen, Theo1 aVerschoor, A J uhttp://iacat.org/content/optimal-testing-easy-items-computerized-adaptive-testing-measurement-and-research-department00452nas a2200109 4500008004100000245007900041210006900120260001700189100002300206700001800229856009500247 2004 eng d00aA sequential Bayesian procedure for item calibration in multistage testing0 asequential Bayesian procedure for item calibration in multistage aSan Diego CA1 avan der Linden, WJ1 aMead, Alan, D uhttp://iacat.org/content/sequential-bayesian-procedure-item-calibration-multistage-testing00470nas a2200121 4500008004100000245008000041210006900121300001200190490000700202100002300209700001900232856009700251 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://iacat.org/content/alpha-stratified-adaptive-testing-large-numbers-content-constraints01223nas a2200193 4500008004100000245002900041210002900070260003200099300001200131520066800143653003000811653003200841653001400873653004500887100001200932700001500944700001600959856005400975 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://iacat.org/content/assessing-question-banks00563nas a2200097 4500008004100000245008000041210006900121260015300190100002300343856009900366 2003 eng d00aBayesian checks on outlying response times in computerized adaptive testing0 aBayesian checks on outlying response times in computerized adapt aH. Yanai, A. Okada, K. Shigemasu, Y. Kano, Y. and J. J. Meulman, (Eds.), New developments in psychometrics (pp. 215-222). New York: Springer-Verlag.1 avan der Linden, WJ uhttp://iacat.org/content/bayesian-checks-outlying-response-times-computerized-adaptive-testing00521nas a2200145 4500008004100000245008400041210006900125300001200194490000700206100001300213700001400226700001300240700001700253856010500270 2003 eng d00aCan an item response theory-based pain item bank enhance measurement precision?0 aCan an item response theorybased pain item bank enhance measurem aD34-D360 v251 aLai, J-S1 aDineen, K1 aCella, D1 aVon Roenn, J uhttp://iacat.org/content/can-item-response-theory-based-pain-item-bank-enhance-measurement-precision01587nas a2200145 4500008004100000245005200041210005200093300001200145490000700157520113200164653003401296100001601330700002301346856007201369 2003 eng d00aComputerized adaptive testing with item cloning0 aComputerized adaptive testing with item cloning a247-2610 v273 a(from the journal abstract) To increase the number of items available for adaptive testing and reduce the cost of item writing, the use of techniques of item cloning has been proposed. An important consequence of item cloning is possible variability between the item parameters. To deal with this variability, a multilevel item response (IRT) model is presented which allows for differences between the distributions of item parameters of families of item clones. A marginal maximum likelihood and a Bayesian procedure for estimating the hyperparameters are presented. In addition, an item-selection procedure for computerized adaptive testing with item cloning is presented which has the following two stages: First, a family of item clones is selected to be optimal at the estimate of the person parameter. Second, an item is randomly selected from the family for administration. Results from simulation studies based on an item pool from the Law School Admission Test (LSAT) illustrate the accuracy of these item pool calibration and adaptive testing procedures. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aGlas, C A W1 avan der Linden, WJ uhttp://iacat.org/content/computerized-adaptive-testing-item-cloning00459nas a2200109 4500008004100000245008200041210006900123260001500192100002300207700001800230856010100248 2003 eng d00aConstraining item exposure in computerized adaptive testing with shadow tests0 aConstraining item exposure in computerized adaptive testing with aChicago IL1 avan der Linden, WJ1 aVeldkamp, B P uhttp://iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests-000462nas a2200121 4500008004100000245007000041210006900111260001500180100001300195700001600208700002300224856009300247 2003 eng d00aConstructing rotating item pools for constrained adaptive testing0 aConstructing rotating item pools for constrained adaptive testin aChicago IL1 aAriel, A1 aVeldkamp, B1 avan der Linden, WJ uhttp://iacat.org/content/constructing-rotating-item-pools-constrained-adaptive-testing-000439nas a2200097 4500008004100000245008400041210006900125100002300194700001800217856010600235 2003 eng d00aControlling item exposure and item eligibility in computerized adaptive testing0 aControlling item exposure and item eligibility in computerized a1 avan der Linden, WJ1 aVeldkamp, B P uhttp://iacat.org/content/controlling-item-exposure-and-item-eligibility-computerized-adaptive-testing00500nas a2200109 4500008004100000245010400041210006900145260001500214100001800229700002300247856012000270 2003 eng d00aImplementing an alternative to Sympson-Hetter item-exposure control in constrained adaptive testing0 aImplementing an alternative to SympsonHetter itemexposure contro aChicago IL1 aVeldkamp, B P1 avan der Linden, WJ uhttp://iacat.org/content/implementing-alternative-sympson-hetter-item-exposure-control-constrained-adaptive-testing00510nas a2200121 4500008004100000245010300041210006900144300001200213490000700225100002300232700001900255856011400274 2003 eng d00aImplementing content constraints in alpha-stratified adaptive testing using a shadow test approach0 aImplementing content constraints in alphastratified adaptive tes a107-1200 v271 avan der Linden, WJ1 aChang, Hua-Hua uhttp://iacat.org/content/implementing-content-constraints-alpha-stratified-adaptive-testing-using-shadow-test00516nas a2200169 4500008004100000245003700041210003700078260004900115300001400164653003400178100001800212700001300230700001700243700001200260700001500272856005900287 2003 eng d00aItem selection in polytomous CAT0 aItem selection in polytomous CAT aTokyo, JapanbPsychometric Society, Springer a207–21410acomputerized adaptive testing1 aVeldkamp, B P1 aOkada, A1 aShigenasu, K1 aKano, Y1 aMeulman, J uhttp://iacat.org/content/item-selection-polytomous-cat00444nas a2200097 4500008004100000245003700041210003700078260015200115100001800267856006100285 2003 eng d00aItem selection in polytomous CAT0 aItem selection in polytomous CAT aH. Yanai, A. Okada, K. Shigemasu, Y Kano, and J. J. Meulman (eds.), New developments in psychometrics (pp. 207-214). Tokyo, Japan: Springer-Verlag.1 aVeldkamp, B P uhttp://iacat.org/content/item-selection-polytomous-cat-001432nas a2200205 4500008004100000245008800041210007000129300001200199490000700211520067700218653002100895653003000916653002400946653002500970653002600995653005201021100001901073700002301092856011101115 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://iacat.org/content/optimal-stratification-item-pools-%CE%B1-stratified-computerized-adaptive-testing00426nas a2200109 4500008004100000245006900041210006900110260001800179100001600197700001700213856008600230 2003 eng d00aOptimal testing with easy items in computerized adaptive testing0 aOptimal testing with easy items in computerized adaptive testing aManchester UK1 aEggen, Theo1 aVerschoor, A uhttp://iacat.org/content/optimal-testing-easy-items-computerized-adaptive-testing00461nas a2200109 4500008004100000245007700041210006900118260003000187100002300217700001800240856009300258 2003 eng d00aA sequential Bayes procedure for item calibration in multi-stage testing0 asequential Bayes procedure for item calibration in multistage te aManuscript in preparation1 avan der Linden, WJ1 aMead, Alan, D uhttp://iacat.org/content/sequential-bayes-procedure-item-calibration-multi-stage-testing01516nas a2200157 4500008004100000245009500041210006900136300001200205490000700217520090100224653002101125653003001146653004501176100002301221856011401244 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://iacat.org/content/some-alternatives-sympson-hetter-item-exposure-control-computerized-adaptive-testing01512nas a2200229 4500008004100000245008700041210006900128300001200197490000700209520075300216653002100969653001300990653003001003653003401033653001101067653001501078653001501093653001801108100002301126700002701149856010601176 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://iacat.org/content/using-response-times-detect-aberrant-responses-computerized-adaptive-testing00614nas a2200157 4500008004100000245012800041210006900169300001300238490000700251100001700258700001600275700001500291700002100306700001300327856011600340 2002 eng d00aCan examinees use judgments of item difficulty to improve proficiency estimates on computerized adaptive vocabulary tests? 0 aCan examinees use judgments of item difficulty to improve profic a 311-3300 v391 aVispoel, W P1 aClough, S J1 aBleiler, T1 aHendrickson, A B1 aIhrig, D uhttp://iacat.org/content/can-examinees-use-judgments-item-difficulty-improve-proficiency-estimates-computerized00534nas a2200109 4500008004100000245011000041210006900151260004200220100002300262700001800285856012100303 2002 eng d00aConstraining item exposure in computerized adaptive testing with shadow tests (Research Report No. 02-06)0 aConstraining item exposure in computerized adaptive testing with aUniversity of Twente, The Netherlands1 avan der Linden, WJ1 aVeldkamp, B P uhttp://iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests-research-report-no-0201689nas a2200277 4500008004100000020001000041245006500051210006400116260009700180300001100277520074500288653002101033653002201054653002501076653002801101653002501129653001601154653001801170653005501188653001501243653001201258100001801270700002301288700001301311856008701324 2002 eng d a02-0900aMathematical-programming approaches to test item pool design0 aMathematicalprogramming approaches to test item pool design aTwente, The NetherlandsbUniversity of Twente, Faculty of Educational Science and Technology a93-1083 a(From the chapter) This paper presents an approach to item pool design that has the potential to improve on the quality of current item pools in educational and psychological testing and hence to increase both measurement precision and validity. The approach consists of the application of mathematical programming techniques to calculate optimal blueprints for item pools. These blueprints can be used to guide the item-writing process. Three different types of design problems are discussed, namely for item pools for linear tests, item pools computerized adaptive testing (CAT), and systems of rotating item pools for CAT. The paper concludes with an empirical example of the problem of designing a system of rotating item pools for CAT.10aAdaptive Testing10aComputer Assisted10aComputer Programming10aEducational Measurement10aItem Response Theory10aMathematics10aPsychometrics10aStatistical Rotation computerized adaptive testing10aTest Items10aTesting1 aVeldkamp, B P1 avan der Linden, WJ1 aAriel, A uhttp://iacat.org/content/mathematical-programming-approaches-test-item-pool-design00495nas a2200097 4500008004100000245010600041210006900147260004100216100002300257856011700280 2002 eng d00aModifications of the Sympson-Hetter method for item-exposure control in computerized adaptive testing0 aModifications of the SympsonHetter method for itemexposure contr aManuscript submitted for publication1 avan der Linden, WJ uhttp://iacat.org/content/modifications-sympson-hetter-method-item-exposure-control-computerized-adaptive-testing01155nas a2200133 4500008004100000245007100041210006900112300001200181490000700193520069200200100001800892700002300910856008800933 2002 eng d00aMultidimensional adaptive testing with constraints on test content0 aMultidimensional adaptive testing with constraints on test conte a575-5880 v673 aThe case of adaptive testing under a multidimensional response model with large numbers of constraints on the content of the test is addressed. The items in the test are selected using a shadow test approach. The 0–1 linear programming model that assembles the shadow tests maximizes posterior expected Kullback-Leibler information in the test. The procedure is illustrated for five different cases of multidimensionality. These cases differ in (a) the numbers of ability dimensions that are intentional or should be considered as ldquonuisance dimensionsrdquo and (b) whether the test should or should not display a simple structure with respect to the intentional ability dimensions.1 aVeldkamp, B P1 avan der Linden, WJ uhttp://iacat.org/content/multidimensional-adaptive-testing-constraints-test-content00513nas a2200121 4500008004100000245009900041210006900140260001900209100001700228700001600245700001500261856011500276 2002 eng d00aUsing judgments of item difficulty to change answers on computerized adaptive vocabulary tests0 aUsing judgments of item difficulty to change answers on computer aNew Orleans LA1 aVispoel, W P1 aClough, S J1 aBleiler, T uhttp://iacat.org/content/using-judgments-item-difficulty-change-answers-computerized-adaptive-vocabulary-tests00568nas a2200133 4500008004100000245012700041210006900168260001600237100001700253700001600270700001700286700001300303856011800316 2001 eng d00aCan examinees use judgments of item difficulty to improve proficiency estimates on computerized adaptive vocabulary tests?0 aCan examinees use judgments of item difficulty to improve profic a Seattle WA1 aVispoel, W P1 aClough, S J1 aBleiler, A B1 aIhrig, D uhttp://iacat.org/content/can-examinees-use-judgments-item-difficulty-improve-proficiency-estimates-computerized-001201nas a2200121 4500008004100000245007000041210006900111300001200180490000700192520076700199100002300966856009000989 2001 eng d00aComputerized adaptive testing with equated number-correct scoring0 aComputerized adaptive testing with equated numbercorrect scoring a343-3550 v253 aA constrained computerized adaptive testing (CAT) algorithm is presented that can be used to equate CAT number-correct (NC) scores to a reference test. As a result, the CAT NC scores also are equated across administrations. The constraints are derived from van der Linden & Luecht’s (1998) set of conditions on item response functions that guarantees identical observed NC score distributions on two test forms. An item bank from the Law School Admission Test was used to compare the results of the algorithm with those for equipercentile observed-score equating, as well as the prediction of NC scores on a reference test using its test response function. The effects of the constraints on the statistical properties of the θ estimator in CAT were examined. 1 avan der Linden, WJ uhttp://iacat.org/content/computerized-adaptive-testing-equated-number-correct-scoring01613nas a2200193 4500008004100000245008600041210006900127300001200196490000700208520094500215653002101160653003001181653004101211653000901252653001701261100001601278700001701294856010801311 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://iacat.org/content/differences-between-self-adapted-and-computerized-adaptive-tests-meta-analysis00416nas a2200097 4500008004100000245007200041210006900113260003000182100001800212856008800230 2001 eng d00aImplementing constrained CAT with shadow tests for large item pools0 aImplementing constrained CAT with shadow tests for large item po aSubmitted for publication1 aVeldkamp, B P uhttp://iacat.org/content/implementing-constrained-cat-shadow-tests-large-item-pools00585nas a2200109 4500008004100000245012400041210006900165260008200234100001900316700002300335856011700358 2001 eng d00aImplementing content constraints in a-stratified adaptive testing using a shadow test approach (Research Report 01-001)0 aImplementing content constraints in astratified adaptive testing aUniversity of Twente, Department of Educational Measurement and Data Analysis1 aChang, Hua-Hua1 avan der Linden, WJ uhttp://iacat.org/content/implementing-content-constraints-stratified-adaptive-testing-using-shadow-test-approach00444nas a2200109 4500008004100000245009100041210006900132300001200201490000700213100001300220856010100233 2001 eng d00aA minimax procedure in the context of sequential testing problems in psychodiagnostics0 aminimax procedure in the context of sequential testing problems  a139-1590 v541 aVos, H J uhttp://iacat.org/content/minimax-procedure-context-sequential-testing-problems-psychodiagnostics00377nas a2200109 4500008004100000245005100041210005100092260001500143100001600158700002300174856007000197 2001 eng d00aModeling variability in item parameters in CAT0 aModeling variability in item parameters in CAT aSeattle WA1 aGlas, C A W1 avan der Linden, WJ uhttp://iacat.org/content/modeling-variability-item-parameters-cat00420nas a2200109 4500008004100000245006700041210006600108260001500174100001300189700001600202856009200218 2001 eng d00aMultidimensional IRT-based adaptive sequential mastery testing0 aMultidimensional IRTbased adaptive sequential mastery testing aSeattle WA1 aVos, H J1 aGlas, C E W uhttp://iacat.org/content/multidimensional-irt-based-adaptive-sequential-mastery-testing01587nas a2200205 4500008004100000245016500041210006900206300001200275490000700287520076900294653002101063653002601084653003001110653002501140653005101165100001301216700001701229700002101246856011401267 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://iacat.org/content/toepassing-van-een-computergestuurde-adaptieve-testprocedure-op-persoonlijkheidsdata00476nas a2200109 4500008004100000245008600041210006900127260001500196100002300211700002700234856010500261 2001 eng d00aUsing response times to detect aberrant behavior in computerized adaptive testing0 aUsing response times to detect aberrant behavior in computerized aSeattle WA1 avan der Linden, WJ1 aKrimpen-Stoop, E M L A uhttp://iacat.org/content/using-response-times-detect-aberrant-behavior-computerized-adaptive-testing00641nas a2200109 4500008004100000245012800041210006900169260014400238100001600382700001300398856012000411 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://iacat.org/content/adaptive-mastery-testing-using-multidimensional-irt-model-and-bayesian-sequential-decision01256nas a2200145 4500008004100000245006500041210006500106300001000171490000700181520076500188653003400953100002300987700001601010856008401026 2000 eng d00aCapitalization on item calibration error in adaptive testing0 aCapitalization on item calibration error in adaptive testing a35-530 v133 a(from the journal abstract) In adaptive testing, item selection is sequentially optimized during the test. Because the optimization takes place over a pool of items calibrated with estimation error, capitalization on chance is likely to occur. How serious the consequences of this phenomenon are depends not only on the distribution of the estimation errors in the pool or the conditional ratio of the test length to the pool size given ability, but may also depend on the structure of the item selection criterion used. A simulation study demonstrated a dramatic impact of capitalization on estimation errors on ability estimation. Four different strategies to minimize the likelihood of capitalization on error in computerized adaptive testing are discussed.10acomputerized adaptive testing1 avan der Linden, WJ1 aGlas, C A W uhttp://iacat.org/content/capitalization-item-calibration-error-adaptive-testing00437nam a2200109 4500008004100000245005500041210005400096260005900150100002300209700001600232856007900248 2000 eng d00aComputerized adaptive testing: Theory and practice0 aComputerized adaptive testing Theory and practice aDordrecht, The NetherlandsbKluwer Academic Publishers1 avan der Linden, WJ1 aGlas, C A W uhttp://iacat.org/content/computerized-adaptive-testing-theory-and-practice00469nas a2200097 4500008004100000245005100041210005100092260013400143100002300277856007100300 2000 eng d00aConstrained adaptive testing with shadow tests0 aConstrained adaptive testing with shadow tests aW. J. van der Linden and C. A. W. Glas (eds.), Computerized adaptive testing: Theory and practice (pp.27-52). Norwell MA: Kluwer.1 avan der Linden, WJ uhttp://iacat.org/content/constrained-adaptive-testing-shadow-tests00544nas a2200109 4500008004100000245006700041210006600108260013200174100002300306700001600329856008900345 2000 eng d00aCross-validating item parameter estimation in adaptive testing0 aCrossvalidating item parameter estimation in adaptive testing aA. Boorsma, M. A. J. van Duijn, and T. A. B. Snijders (Eds.) (pp. 205-219), Essays on item response theory. New York: Springer.1 avan der Linden, WJ1 aGlas, C A W uhttp://iacat.org/content/cross-validating-item-parameter-estimation-adaptive-testing00475nas a2200121 4500008004100000245005900041210005900100260005900159300001400218100001800232700002300250856008000273 2000 eng d00aDesigning item pools for computerized adaptive testing0 aDesigning item pools for computerized adaptive testing aDendrecht, The NetherlandsbKluwer Academic Publishers a149–1621 aVeldkamp, B P1 avan der Linden, WJ uhttp://iacat.org/content/designing-item-pools-computerized-adaptive-testing00481nas a2200121 4500008004100000245009100041210006900132300001200201490000700213100002000220700001600240856010300256 2000 eng d00aDetection of known items in adaptive testing with a statistical quality control method0 aDetection of known items in adaptive testing with a statistical  a373-3890 v251 aVeerkamp, W J J1 aGlas, C E W uhttp://iacat.org/content/detection-known-items-adaptive-testing-statistical-quality-control-method01168nas a2200205 4500008004100000245005600041210005300097300001200150490000700162520055300169653002200722653002500744653002500769653002200794653001500816100002300831700001800854700001500872856007500887 2000 eng d00aAn integer programming approach to item bank design0 ainteger programming approach to item bank design a139-1500 v243 aAn integer programming approach to item bank design is presented that can be used to calculate an optimal blueprint for an item bank, in order to support an existing testing program. The results are optimal in that they minimize the effort involved in producing the items as revealed by current item writing patterns. Also presented is an adaptation of the models, which can be used as a set of monitoring tools in item bank management. The approach is demonstrated empirically for an item bank that was designed for the Law School Admission Test. 10aAptitude Measures10aItem Analysis (Test)10aItem Response Theory10aTest Construction10aTest Items1 avan der Linden, WJ1 aVeldkamp, B P1 aReese, L M uhttp://iacat.org/content/integer-programming-approach-item-bank-design00481nas a2200121 4500008004100000245006200041210006200103260005900165300001100224100002300235700001700258856008400275 2000 eng d00aItem selection and ability estimation in adaptive testing0 aItem selection and ability estimation in adaptive testing aDordrecht, The NetherlandsbKluwer Academic Publishers a1–251 avan der Linden, WJ1 aPashley, P J uhttp://iacat.org/content/item-selection-and-ability-estimation-adaptive-testing00551nas a2200133 4500008004100000245011800041210006900159300001000228490000700238100001700245700002100262700001500283856011900298 2000 eng d00aLimiting answer review and change on computerized adaptive vocabulary tests: Psychometric and attitudinal results0 aLimiting answer review and change on computerized adaptive vocab a21-380 v371 aVispoel, W P1 aHendrickson, A B1 aBleiler, T uhttp://iacat.org/content/limiting-answer-review-and-change-computerized-adaptive-vocabulary-tests-psychometric-and00514nas a2200097 4500008004100000245006200041210006000103260015900163100001300322856008100335 2000 eng d00aA minimax solution for sequential classification problems0 aminimax solution for sequential classification problems aH. A. L. Kiers, J.-P.Rasson, P. J. F. Groenen, and M. Schader (Eds.), Data analysis, classification, and related methods (pp. 121-126). Berlin: Springer. 1 aVos, H J uhttp://iacat.org/content/minimax-solution-sequential-classification-problems00614nas a2200097 4500008004100000245012400041210006900165260014400234100001800378856012000396 2000 eng d00aModifications of the branch-and-bound algorithm for application in constrained adaptive testing (Research Report 00-05)0 aModifications of the branchandbound algorithm for application in aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aVeldkamp, B P uhttp://iacat.org/content/modifications-branch-and-bound-algorithm-application-constrained-adaptive-testing-research00610nas a2200109 4500008004100000245009500041210006900136260014400205100001800349700002300367856011000390 2000 eng d00aMultidimensional adaptive testing with constraints on test content (Research Report 00-11)0 aMultidimensional adaptive testing with constraints on test conte aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aVeldkamp, B P1 avan der Linden, WJ uhttp://iacat.org/content/multidimensional-adaptive-testing-constraints-test-content-research-report-00-1100606nas a2200097 4500008004100000245011100041210006900152260014400221100002300365856012000388 2000 eng d00aOptimal stratification of item pools in a-stratified computerized adaptive testing (Research Report 00-07)0 aOptimal stratification of item pools in astratified computerized aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 avan der Linden, WJ uhttp://iacat.org/content/optimal-stratification-item-pools-stratified-computerized-adaptive-testing-research-report01195nas a2200133 4500008004100000245008300041210006900124300001200193490000700205520069300212653003400905100002000939856010200959 2000 eng d00aTaylor approximations to logistic IRT models and their use in adaptive testing0 aTaylor approximations to logistic IRT models and their use in ad a307-3430 v253 aTaylor approximation can be used to generate a linear approximation to a logistic ICC and a linear ability estimator. For a specific situation it will be shown to result in a special case of a Robbins-Monro item selection procedure for adaptive testing. The linear estimator can be used for the situation of zero and perfect scores when maximum likelihood estimation fails to come up with a finite estimate. It is also possible to use this estimator to generate starting values for maximum likelihood and weighted likelihood estimation. Approximations to the expectation and variance of the linear estimator for a sequence of Robbins-Monro item selections can be determined analytically. 10acomputerized adaptive testing1 aVeerkamp, W J J uhttp://iacat.org/content/taylor-approximations-logistic-irt-models-and-their-use-adaptive-testing00455nas a2200109 4500008004100000245004600041210004400087260011500131100001300246700001600259856007000275 2000 eng d00aTestlet-based adaptive mastery testing, W0 aTestletbased adaptive mastery testing W aJ. van der Linden (Ed.), Computerized adaptive testing: Theory and practice (pp. 289-309). Norwell MA: Kluwer.1 aVos, H J1 aGlas, C A W uhttp://iacat.org/content/testlet-based-adaptive-mastery-testing-w00645nas a2200109 4500008004100000245011000041210006900151260014400220100002300364700002700387856012100414 2000 eng d00aUsing response times to detect aberrant behavior in computerized adaptive testing (Research Report 00-09)0 aUsing response times to detect aberrant behavior in computerized aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 avan der Linden, WJ1 aKrimpen-Stoop, E M L A uhttp://iacat.org/content/using-response-times-detect-aberrant-behavior-computerized-adaptive-testing-research-report00555nas a2200097 4500008004100000245008100041210006900122260014400191100002300335856009900358 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://iacat.org/content/adaptive-testing-equated-number-correct-scoring-research-report-99-0200571nas a2200145 4500008004100000245011800041210006900159300001200228490000700240100001700247700001700264700001200281700001500293856011700308 1999 eng d00aCan examinees use a review option to obtain positively biased ability estimates on a computerized adaptive test? 0 aCan examinees use a review option to obtain positively biased ab a141-1570 v361 aVispoel, W P1 aRocklin, T R1 aWang, T1 aBleiler, T uhttp://iacat.org/content/can-examinees-use-review-option-obtain-positively-biased-ability-estimates-computerized00572nas a2200097 4500008004100000245010300041210006900144260012200213100001700335856012200352 1999 eng d00aCreating computerized adaptive tests of music aptitude: Problems, solutions, and future directions0 aCreating computerized adaptive tests of music aptitude Problems  aF. Drasgow and J. B. Olson-Buchanan (Eds.), Innovations in computerized assessment (pp. 151-176). Mahwah NJ: Erlbaum.1 aVispoel, W P uhttp://iacat.org/content/creating-computerized-adaptive-tests-music-aptitude-problems-solutions-and-future-directions00591nas a2200109 4500008004100000245008400041210006900125260014400194100001800338700002300356856010200379 1999 eng d00aDesigning item pools for computerized adaptive testing (Research Report 99-03 )0 aDesigning item pools for computerized adaptive testing Research  aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aVeldkamp, B P1 avan der Linden, WJ uhttp://iacat.org/content/designing-item-pools-computerized-adaptive-testing-research-report-99-0300419nas a2200109 4500008004100000245007200041210006900113300001000182490000700192100002300199856008700222 1999 eng d00aEmpirical initialization of the trait estimator in adaptive testing0 aEmpirical initialization of the trait estimator in adaptive test a21-290 v231 avan der Linden, WJ uhttp://iacat.org/content/empirical-initialization-trait-estimator-adaptive-testing00596nas a2200097 4500008004100000245006700041210006500108260021200173100002300385856009000408 1999 eng d00aHet ontwerpen van adaptieve examens [Designing adaptive tests]0 aHet ontwerpen van adaptieve examens Designing adaptive tests aJ. M Pieters, Tj. Plomp, and L.E. Odenthal (Eds.), Twintig jaar Toegepaste Onderwijskunde: Een kaleidoscopisch overzicht van Twents onderwijskundig onderzoek (pp. 249-267). Enschede: Twente University Press.1 avan der Linden, WJ uhttp://iacat.org/content/het-ontwerpen-van-adaptieve-examens-designing-adaptive-tests00456nas a2200109 4500008004100000245004100041210004100082260011900123100001600242700002000258856006800278 1999 eng d00aItem calibration and parameter drift0 aItem calibration and parameter drift aW. J. van der Linden and C. A. W. Glas (Eds.), Computer adaptive testing: Theory and practice. Norwell MA: Kluwer.1 aGlas, C A W1 aVeerkamp, W J J uhttp://iacat.org/content/item-calibration-and-parameter-drift-000623nas a2200157 4500008004100000245011800041210006900159260002100228100001700249700001900266700001500285700001600300700001500316700001300331856012100344 1999 eng d00aLimiting answer review and change on computerized adaptive vocabulary tests: Psychometric and attitudinal results0 aLimiting answer review and change on computerized adaptive vocab aMontreal, Canada1 aVispoel, W P1 aHendrickson, A1 aBleiler, T1 aWidiatmo, H1 aShrairi, S1 aIhrig, D uhttp://iacat.org/content/limiting-answer-review-and-change-computerized-adaptive-vocabulary-tests-psychometric-and-000562nas a2200097 4500008004100000245009300041210006900134260014400203100001300347856010400360 1999 eng d00aA minimax procedure in the context of sequential mastery testing (Research Report 99-04)0 aminimax procedure in the context of sequential mastery testing R aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aVos, H J uhttp://iacat.org/content/minimax-procedure-context-sequential-mastery-testing-research-report-99-0401086nas a2200133 4500008004100000245007800041210006900119300001200188490000700200520059200207653003400799100002300833856009600856 1999 eng d00aMultidimensional adaptive testing with a minimum error-variance criterion0 aMultidimensional adaptive testing with a minimum errorvariance c a398-4120 v243 aAdaptive testing under a multidimensional logistic response model is addressed. An algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to evaluate. In addition, it is shown how the algorithm can be modified if the interest is in a test with a "simple ability structure". The statistical properties of the adaptive ML estimator are demonstrated for a two-dimensional item pool with several linear combinations of the abilities. 10acomputerized adaptive testing1 avan der Linden, WJ uhttp://iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion01906nas a2200229 4500008004100000245008600041210006900127300001000196490000700206520111400213653003201327653003701359653001001396653003401406653003001440653002901470653003201499100001601531700001801547700001301565856009801578 1999 eng d00aThe use of Rasch analysis to produce scale-free measurement of functional ability0 ause of Rasch analysis to produce scalefree measurement of functi a83-900 v533 aInnovative applications of Rasch analysis can lead to solutions for traditional measurement problems and can produce new assessment applications in occupational therapy and health care practice. First, Rasch analysis is a mechanism that translates scores across similar functional ability assessments, thus enabling the comparison of functional ability outcomes measured by different instruments. This will allow for the meaningful tracking of functional ability outcomes across the continuum of care. Second, once the item-difficulty order of an instrument or item bank is established by Rasch analysis, computerized adaptive testing can be used to target items to the patient's ability level, reducing assessment length by as much as one half. More importantly, Rasch analysis can provide the foundation for "equiprecise" measurement or the potential to have precise measurement across all levels of functional ability. The use of Rasch analysis to create scale-free measurement of functional ability demonstrates how this methodlogy can be used in practical applications of clinical and outcome assessment.10a*Activities of Daily Living10aDisabled Persons/*classification10aHuman10aOccupational Therapy/*methods10aPredictive Value of Tests10aQuestionnaires/standards10aSensitivity and Specificity1 aVelozo, C A1 aKielhofner, G1 aLai, J-S uhttp://iacat.org/content/use-rasch-analysis-produce-scale-free-measurement-functional-ability00425nas a2200109 4500008004100000245007300041210006900114300001400183490001000197100001300207856009500220 1999 eng d00aUsing Bayesian decision theory to design a computerized mastery test0 aUsing Bayesian decision theory to design a computerized mastery  a271–2920 v24(3)1 aVos, H J uhttp://iacat.org/content/using-bayesian-decision-theory-design-computerized-mastery-test-001186nas a2200157 4500008004100000245010900041210006900150300001200219490000700231520058300238653003400821100002300855700001600878700001800894856011600912 1999 eng d00aUsing response-time constraints to control for differential speededness in computerized adaptive testing0 aUsing responsetime constraints to control for differential speed a195-2100 v233 aAn item-selection algorithm is proposed for neutralizing the differential effects of time limits on computerized adaptive test scores. The method is based on a statistical model for distributions of examinees’ response times on items in a bank that is updated each time an item is administered. Predictions from the model are used as constraints in a 0-1 linear programming model for constrained adaptive testing that maximizes the accuracy of the trait estimator. The method is demonstrated empirically using an item bank from the Armed Services Vocational Aptitude Battery. 10acomputerized adaptive testing1 avan der Linden, WJ1 aScrams, D J1 aSchnipke, D L uhttp://iacat.org/content/using-response-time-constraints-control-differential-speededness-computerized-adaptive00629nas a2200109 4500008004100000245011500041210006900156260014400225100001600369700001300385856012100398 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://iacat.org/content/adaptive-mastery-testing-using-rasch-model-and-bayesian-sequential-decision-theory-research00390nas a2200109 4500008004100000245005800041210005800099300001200157490000700169100002300176856008100199 1998 eng d00aBayesian item selection criteria for adaptive testing0 aBayesian item selection criteria for adaptive testing a201-2160 v631 avan der Linden, WJ uhttp://iacat.org/content/bayesian-item-selection-criteria-adaptive-testing-000598nas a2200109 4500008004100000245008900041210006900130260014400199100002300343700001600366856010600382 1998 eng d00aCapitalization on item calibration error in adaptive testing (Research Report 98-07)0 aCapitalization on item calibration error in adaptive testing Res aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 avan der Linden, WJ1 aGlas, C A W uhttp://iacat.org/content/capitalization-item-calibration-error-adaptive-testing-research-report-98-0701435nas a2200145 4500008004100000245005300041210005100094300001200145490000700157520098100164653003401145100002301179700001501202856007201217 1998 eng d00aA model for optimal constrained adaptive testing0 amodel for optimal constrained adaptive testing a259-2700 v223 aA model for constrained computerized adaptive testing is proposed in which the information in the test at the trait level (0) estimate is maximized subject to a number of possible constraints on the content of the test. At each item-selection step, a full test is assembled to have maximum information at the current 0 estimate, fixing the items already administered. Then the item with maximum in-formation is selected. All test assembly is optimal because a linear programming (LP) model is used that automatically updates to allow for the attributes of the items already administered and the new value of the 0 estimator. The LP model also guarantees that each adaptive test always meets the entire set of constraints. A simulation study using a bank of 753 items from the Law School Admission Test showed that the 0 estimator for adaptive tests of realistic lengths did not suffer any loss of efficiency from the presence of 433 constraints on the item selection process. 10acomputerized adaptive testing1 avan der Linden, WJ1 aReese, L M uhttp://iacat.org/content/model-optimal-constrained-adaptive-testing00386nas a2200109 4500008004100000245006000041210005900101300001200160490001000172100001300182856008100195 1998 eng d00aOptimal sequential rules for computer-based instruction0 aOptimal sequential rules for computerbased instruction a133-1540 v19(2)1 aVos, H J uhttp://iacat.org/content/optimal-sequential-rules-computer-based-instruction00410nas a2200109 4500008004100000245006500041210006500106300001200171490000700183100002300190856008700213 1998 eng d00aOptimal test assembly of psychological and educational tests0 aOptimal test assembly of psychological and educational tests a195-2110 v221 avan der Linden, WJ uhttp://iacat.org/content/optimal-test-assembly-psychological-and-educational-tests00455nas a2200121 4500008004100000245007800041210006900119300001200188490000700200100001200207700001700219856009700236 1998 eng d00aProperties of ability estimation methods in computerized adaptive testing0 aProperties of ability estimation methods in computerized adaptiv a109-1350 v351 aWang, T1 aVispoel, W P uhttp://iacat.org/content/properties-ability-estimation-methods-computerized-adaptive-testing00518nas a2200109 4500008004100000245013500041210006900176300002300245490000700268100001700275856011600292 1998 eng d00aPsychometric characteristics of computer-adaptive and self-adaptive vocabulary tests: The role of answer feedback and test anxiety0 aPsychometric characteristics of computeradaptive and selfadaptiv a328-347 or 155-1670 v351 aVispoel, W P uhttp://iacat.org/content/psychometric-characteristics-computer-adaptive-and-self-adaptive-vocabulary-tests-role00460nas a2200109 4500008004100000245009100041210006900132300001200201490000700213100001700220856011300237 1998 eng d00aReviewing and changing answers on computer-adaptive and self-adaptive vocabulary tests0 aReviewing and changing answers on computeradaptive and selfadapt a328-3450 v351 aVispoel, W P uhttp://iacat.org/content/reviewing-and-changing-answers-computer-adaptive-and-self-adaptive-vocabulary-tests00488nas a2200109 4500008004100000245010500041210006900146300001200215490000700227100002300234856012100257 1998 eng d00aStochastic order in dichotomous item response models for fixed, adaptive, and multidimensional tests0 aStochastic order in dichotomous item response models for fixed a a211-2260 v631 avan der Linden, WJ uhttp://iacat.org/content/stochastic-order-dichotomous-item-response-models-fixed-adaptive-and-multidimensional-tests00673nas a2200121 4500008004100000245012000041210006900161260014400230100002300374700001600397700001800413856012000431 1998 eng d00aUsing response-time constraints to control for differential speededness in adaptive testing (Research Report 98-06)0 aUsing responsetime constraints to control for differential speed aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 avan der Linden, WJ1 aScrams, D J1 aSchnipke, D L uhttp://iacat.org/content/using-response-time-constraints-control-differential-speededness-adaptive-testing-research00521nas a2200097 4500008004100000245009900041210006900140260008500209100001300294856011600307 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://iacat.org/content/applications-bayesian-decision-theory-sequential-mastery-testing-research-report-97-0600529nas a2200121 4500008004100000245013800041210006900179300001000248490000700258100001200265700001200277856011800289 1997 eng d00aComputerized adaptive and fixed-item testing of music listening skill: A comparison of efficiency, precision, and concurrent validity0 aComputerized adaptive and fixeditem testing of music listening s a43-630 v341 aVispoel1 aWang, T uhttp://iacat.org/content/computerized-adaptive-and-fixed-item-testing-music-listening-skill-comparison-efficiency00563nas a2200133 4500008004100000245013800041210006900179300001000248490000700258100001700265700001200282700001500294856012000309 1997 eng d00aComputerized adaptive and fixed-item testing of music listening skill: A comparison of efficiency, precision, and concurrent validity0 aComputerized adaptive and fixeditem testing of music listening s a43-630 v341 aVispoel, W P1 aWang, T1 aBleiler, T uhttp://iacat.org/content/computerized-adaptive-and-fixed-item-testing-music-listening-skill-comparison-efficiency-000349nas a2200097 4500008004100000245005100041210005100092260001500143100002300158856007000181 1997 eng d00aDetection of aberrant response patterns in CAT0 aDetection of aberrant response patterns in CAT aChicago IL1 avan der Linden, WJ uhttp://iacat.org/content/detection-aberrant-response-patterns-cat00459nas a2200097 4500008004100000245009100041210006900132260003700201100001700238856010600255 1997 eng d00aImproving the quality of music aptitude tests through adaptive administration of items0 aImproving the quality of music aptitude tests through adaptive a aUniversity of Iowa, Iowa City IA1 aVispoel, W P uhttp://iacat.org/content/improving-quality-music-aptitude-tests-through-adaptive-administration-items00541nas a2200097 4500008004100000245011500041210006900156260008500225100001300310856012000323 1997 eng d00aA minimax sequential procedure in the context of computerized adaptive mastery testing (Research Report 97-07)0 aminimax sequential procedure in the context of computerized adap aTwente, The Netherlands: Department of Educational Measurement and Data Analysis1 aVos, H J uhttp://iacat.org/content/minimax-sequential-procedure-context-computerized-adaptive-mastery-testing-research-report00419nas a2200097 4500008004100000245007800041210006900119260001200188100002300200856009800223 1997 eng d00aMultidimensional adaptive testing with a minimum error-variance criterion0 aMultidimensional adaptive testing with a minimum errorvariance c aChicago1 avan der Linden, WJ uhttp://iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion-000560nas a2200097 4500008004100000245010200041210006900143260010900212100002300321856011800344 1997 eng d00aMultidimensional adaptive testing with a minimum error-variance criterion (Research Report 97-03)0 aMultidimensional adaptive testing with a minimum errorvariance c aEnschede, The Netherlands: University of Twente, Department of Educational Measurement and Data Analysis1 avan der Linden, WJ uhttp://iacat.org/content/multidimensional-adaptive-testing-minimum-error-variance-criterion-research-report-97-0300410nas a2200121 4500008004100000245005800041210005800099300001200157490000700169100001300176700001800189856008100207 1997 eng d00aSome new item selection criteria for adaptive testing0 aSome new item selection criteria for adaptive testing a203-2260 v221 aVeerkamp1 aBerger, M P F uhttp://iacat.org/content/some-new-item-selection-criteria-adaptive-testing-000408nas a2200121 4500008004100000245005800041210005800099300001200157490000700169100001100176700002000187856007900207 1997 eng d00aSome new item selection criteria for adaptive testing0 aSome new item selection criteria for adaptive testing a203-2260 v221 aBerger1 aVeerkamp, W J J uhttp://iacat.org/content/some-new-item-selection-criteria-adaptive-testing00441nam a2200097 4500008004100000245005800041210005800099260008700157100002000244856007900264 1997 eng d00aStatistical methods for computerized adaptive testing0 aStatistical methods for computerized adaptive testing aUnpublished doctoral dissertation, University of Twente, Enschede, The Netherlands1 aVeerkamp, W J J uhttp://iacat.org/content/statistical-methods-computerized-adaptive-testing00613nas a2200145 4500008004100000245005200041210005100093260016700144100001600311700001600327700002100343700001600364700001700380856007000397 1997 eng d00aValidation of the experimental CAT-ASVAB system0 aValidation of the experimental CATASVAB system aW. A. Sands, B. K. Waters, and J. R. McBride (Eds.), Computerized adaptive testing: From inquiry to operation. Washington, DC: American Psychological Association.1 aSegall, D O1 aMoreno, K E1 aKieckhaefer, W F1 aVicino, F L1 aMcBride, J R uhttp://iacat.org/content/validation-experimental-cat-asvab-system00388nas a2200109 4500008004100000245005800041210005800099300001200157490000700169100002300176856007900199 1996 eng d00aBayesian item selection criteria for adaptive testing0 aBayesian item selection criteria for adaptive testing a201-2160 v631 avan der Linden, WJ uhttp://iacat.org/content/bayesian-item-selection-criteria-adaptive-testing00499nas a2200097 4500008004100000245008200041210006900123260008500192100002300277856010100300 1996 eng d00aBayesian item selection criteria for adaptive testing (Research Report 96-01)0 aBayesian item selection criteria for adaptive testing Research R aTwente, The Netherlands: Department of Educational Measurement and Data Analysis1 avan der Linden, WJ uhttp://iacat.org/content/bayesian-item-selection-criteria-adaptive-testing-research-report-96-0100643nas a2200133 4500008004100000245020300041210006900244260001600313100001700329700001700346700001200363700001700375856011700392 1996 eng d00aCan examinees use a review option to positively bias their scores on a computerized adaptive test? Paper presented at the annual meeting of the National council on Measurement in Education, New York0 aCan examinees use a review option to positively bias their score aNew York NY1 aRocklin, T R1 aVispoel, W P1 aWang, T1 aBleiler, T L uhttp://iacat.org/content/can-examinees-use-review-option-positively-bias-their-scores-computerized-adaptive-test00526nam a2200097 4500008004100000245011800041210006900159260006300228100001600291856012100307 1996 eng d00aA comparison of adaptive self-referenced testing and classical approaches to the measurement of individual change0 acomparison of adaptive selfreferenced testing and classical appr aUnpublished doctoral dissertation, University of Minnesota1 aVanLoy, W J uhttp://iacat.org/content/comparison-adaptive-self-referenced-testing-and-classical-approaches-measurement-individual00566nas a2200121 4500008004100000245015700041210006900198100001700267700001600284700001300300700001500313856011600328 1996 eng d00aEffects of answer feedback and test anxiety on the psychometric and motivational characteristics of computer-adaptive and self-adaptive vocabulary tests0 aEffects of answer feedback and test anxiety on the psychometric 1 aVispoel, W P1 aBrunsman, B1 aForte, E1 aBleiler, T uhttp://iacat.org/content/effects-answer-feedback-and-test-anxiety-psychometric-and-motivational-characteristics00553nas a2200121 4500008004100000245015500041210006900196260001300265100001500278700001300293700001100306856011400317 1996 eng d00aEffects of answer review and test anxiety on the psychometric and motivational characteristics of computer-adaptive and self-adaptive vocabulary tests0 aEffects of answer review and test anxiety on the psychometric an aNew York1 aVispoel, W1 aForte, E1 aBoo, J uhttp://iacat.org/content/effects-answer-review-and-test-anxiety-psychometric-and-motivational-characteristics00347nas a2200097 4500008004100000245004800041210004800089260001900137100002300156856007000179 1995 eng d00aBayesian item selection in adaptive testing0 aBayesian item selection in adaptive testing aMinneapolis MN1 avan der Linden, WJ uhttp://iacat.org/content/bayesian-item-selection-adaptive-testing00434nas a2200097 4500008004100000245003900041210003900080260014300119100001400262856006000276 1995 eng d00aComputerized testing for licensure0 aComputerized testing for licensure aJ. Impara (ed.), Licensure testing: Purposes, procedures, and Practices (pp. 291-320). Lincoln NE: Buros Institute of Mental Measurements.1 aVale, C D uhttp://iacat.org/content/computerized-testing-licensure00438nas a2200109 4500008004100000245007700041210006900118260001600187100001200203700001700215856009600232 1995 eng d00aPrecision of ability estimation methods in computerized adaptive testing0 aPrecision of ability estimation methods in computerized adaptive aMinneapolis1 aWang, T1 aVispoel, W P uhttp://iacat.org/content/precision-ability-estimation-methods-computerized-adaptive-testing00494nas a2200121 4500008004100000245010100041210006900142300001000211490000600221100001200227700001700239856011600256 1994 eng d00aComputerized-adaptive and self-adapted music-listening tests: Features and motivational benefits0 aComputerizedadaptive and selfadapted musiclistening tests Featur a25-510 v71 aVispoel1 aCoffman, D D uhttp://iacat.org/content/computerized-adaptive-and-self-adapted-music-listening-tests-features-and-motivational00520nas a2200121 4500008004100000245011800041210006900159300001200228490000700240100001800247700001600265856011700281 1994 eng d00aThe incomplete equivalence of the paper-and-pencil and computerized versions of the General Aptitude Test Battery0 aincomplete equivalence of the paperandpencil and computerized ve a852-8590 v791 aVan de Vijver1 aHarsveld, M uhttp://iacat.org/content/incomplete-equivalence-paper-and-pencil-and-computerized-versions-general-aptitude-test00567nas a2200133 4500008004100000245013900041210006900180300001000249490000600259100001700265700001700282700001200299856012200311 1994 eng d00aIndividual differences and test administration procedures: A comparison of fixed-item, computerized adaptive, and self-adapted testing0 aIndividual differences and test administration procedures A comp a53-790 v71 aVispoel, W P1 aRocklin, T R1 aWang, T uhttp://iacat.org/content/individual-differences-and-test-administration-procedures-comparison-fixed-item-computerized00431nas a2200097 4500008004100000245006800041210006600109260005100175100002000226856008700246 1994 eng d00aA simple and fast item selection procedure for adaptive testing0 asimple and fast item selection procedure for adaptive testing aResearch (Report 94-13). University of Twente.1 aVeerkamp, W J J uhttp://iacat.org/content/simple-and-fast-item-selection-procedure-adaptive-testing00541nas a2200109 4500008004100000245007800041210006900119260011000188100001800298700001800316856009700334 1994 eng d00aSome new item selection criteria for adaptive testing (Research Rep 94-6)0 aSome new item selection criteria for adaptive testing Research R aEnschede, The Netherlands: University of Twente, Department of Educational Measurement and Data Analysis.1 aVeerkamp, W J1 aBerger, M P F uhttp://iacat.org/content/some-new-item-selection-criteria-adaptive-testing-research-rep-94-600430nas a2200109 4500008004100000245007800041210006900119300001200188490000700200100001700207856009600224 1993 eng d00aComputerized adaptive and fixed-item versions of the ITED Vocabulary test0 aComputerized adaptive and fixeditem versions of the ITED Vocabul a779-7880 v531 aVispoel, W P uhttp://iacat.org/content/computerized-adaptive-and-fixed-item-versions-ited-vocabulary-test00435nas a2200109 4500008004100000245008300041210006900124300001200193490000700205100001700212856009600229 1993 eng d00aThe development and evaluation of a computerized adaptive test of tonal memory0 adevelopment and evaluation of a computerized adaptive test of to a111-1360 v411 aVispoel, W P uhttp://iacat.org/content/development-and-evaluation-computerized-adaptive-test-tonal-memory00529nas a2200121 4500008004100000245011600041210006900157260001500226100001700241700001200258700001500270856012200285 1993 eng d00aThe efficiency, reliability, and concurrent validity of adaptive and fixed-item tests of music listening skills0 aefficiency reliability and concurrent validity of adaptive and f aAtlanta GA1 aVispoel, W P1 aWang, T1 aBleiler, T uhttp://iacat.org/content/efficiency-reliability-and-concurrent-validity-adaptive-and-fixed-item-tests-music-listening00517nas a2200109 4500008004100000245012600041210006900167260001500236100001700251700001700268856012200285 1993 eng d00aIndividual differences and test administration procedures: A comparison of fixed-item, adaptive, and self-adapted testing0 aIndividual differences and test administration procedures A comp aAtlanta GA1 aVispoel, W P1 aRocklin, T R uhttp://iacat.org/content/individual-differences-and-test-administration-procedures-comparison-fixed-item-adaptive-and00405nas a2200121 4500008004100000245005800041210005700099300001000156490000800166100001200174700001700186856008000203 1992 eng d00aComputerized adaptive testing of music-related skills0 aComputerized adaptive testing of musicrelated skills a29-490 v1121 aVispoel1 aCoffman, D D uhttp://iacat.org/content/computerized-adaptive-testing-music-related-skills00569nas a2200145 4500008004100000245009700041210006900138260002100207100001700228700001200245700001900257700001500276700001300291856011900304 1992 eng d00aHow review options and administration mode influence scores on computerized vocabulary tests0 aHow review options and administration mode influence scores on c aSan Francisco CA1 aVispoel, W P1 aWang, T1 aDe la Torre, R1 aBleiler, T1 aDings, J uhttp://iacat.org/content/how-review-options-and-administration-mode-influence-scores-computerized-vocabulary-tests00425nas a2200109 4500008004100000245007900041210006900120300001000189490000700199100001700206856009200223 1992 eng d00aImproving the measurement of tonal memory with computerized adaptive tests0 aImproving the measurement of tonal memory with computerized adap a73-890 v111 aVispoel, W P uhttp://iacat.org/content/improving-measurement-tonal-memory-computerized-adaptive-tests00495nam a2200109 4500008004100000245008800041210006900129260004600198100001800244700002000262856010300282 1992 eng d00aManual for the General Scholastic Aptitude Test (Senior) Computerized adaptive test0 aManual for the General Scholastic Aptitude Test Senior Computeri aPretoria: Human Sciences Research Council1 aVon Tonder, M1 aClaasswn, N C W uhttp://iacat.org/content/manual-general-scholastic-aptitude-test-senior-computerized-adaptive-test00441nas a2200109 4500008004100000245007700041210006900118260001500187100001900202700001700221856009300238 1991 eng d00aThe development and evaluation of a computerized adaptive testing system0 adevelopment and evaluation of a computerized adaptive testing sy aChicago IL1 aDe la Torre, R1 aVispoel, W P uhttp://iacat.org/content/development-and-evaluation-computerized-adaptive-testing-system00414nas a2200097 4500008004100000245007600041210006900117260001800186100001700204856009500221 1990 eng d00aComputerized adaptive music tests: A new solution to three old problems0 aComputerized adaptive music tests A new solution to three old pr aWashington DC1 aVispoel, W P uhttp://iacat.org/content/computerized-adaptive-music-tests-new-solution-three-old-problems00591nas a2200109 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Columbus OH: Association for the Development of Computer-Based Instructional Systems.1 aVispoel, W T1 aTwing, J S uhttp://iacat.org/content/creating-adaptive-tests-musical-ability-limited-size-item-pools00448nas a2200121 4500008004100000245006800041210006700109260001700176100001700193700001500210700001500225856008600240 1990 eng d00aMusicCAT: An adaptive testing program to assess musical ability0 aMusicCAT An adaptive testing program to assess musical ability aSan Diego CA1 aVispoel, W P1 aCoffman, D1 aScriven, D uhttp://iacat.org/content/musiccat-adaptive-testing-program-assess-musical-ability00404nas a2200121 4500008004100000245005300041210005300094300001200147490001000159100002300169700001600192856007400208 1989 eng d00aSome procedures for computerized ability testing0 aSome procedures for computerized ability testing a175-1870 v13(2)1 avan der Linden, WJ1 aZwarts, M A uhttp://iacat.org/content/some-procedures-computerized-ability-testing00557nam a2200097 4500008004100000245011600041210006900157260009800226100001700324856011800341 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. 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Minneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laborat1 aVale, C D uhttp://iacat.org/content/design-microcomputer-based-adaptive-testing-system00480nas a2200109 4500008004100000245010900041210006900150300001200219490000700231100001600238856011600254 1982 eng d00aPros and cons of tailored testing: An examination of issues highlighted with an automated testing system0 aPros and cons of tailored testing An examination of issues highl a301-3040 v171 aVolans, P J uhttp://iacat.org/content/pros-and-cons-tailored-testing-examination-issues-highlighted-automated-testing-system00431nas a2200109 4500008004100000245007900041210006900120300001200189490000700201100001400208856009900222 1981 eng d00aDesign and implementation of a microcomputer-based adaptive testing system0 aDesign and implementation of a microcomputerbased adaptive testi a399-4060 v131 aVale, C D uhttp://iacat.org/content/design-and-implementation-microcomputer-based-adaptive-testing-system00568nam a2200097 4500008004100000245011100041210006900152260012300221100001400344856011200358 1980 eng d00aDevelopment and evaluation of an adaptive testing strategy for use in multidimensional interest assessment0 aDevelopment and evaluation of an adaptive testing strategy for u aUnpublished doctoral dissertation, University of Minnesota. Dissertational Abstract International, 42(11-B), 4248-42491 aVale, C D uhttp://iacat.org/content/development-and-evaluation-adaptive-testing-strategy-use-multidimensional-interest00468nas a2200121 4500008004100000245008900041210006900130300001200199490000600211100001400217700001400231856010100245 1978 eng d00aThe stratified adaptive ability test as a tool for personnel selection and placement0 astratified adaptive ability test as a tool for personnel selecti a135-1510 v81 aVale, C D1 aWeiss, DJ uhttp://iacat.org/content/stratified-adaptive-ability-test-tool-personnel-selection-and-placement00523nas a2200097 4500008004100000245005500041210005500096260018700151100001400338856007300352 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://iacat.org/content/adaptive-testing-and-problem-classification00536nas a2200109 4500008004100000245008700041210006900128260009700197100001400294700001400308856010400322 1977 eng d00aA rapid item search procedure for Bayesian adaptive testing (Research Report 77-4)0 arapid item search procedure for Bayesian adaptive testing Resear aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aVale, C D1 aWeiss, DJ uhttp://iacat.org/content/rapid-item-search-procedure-bayesian-adaptive-testing-research-report-77-400517nas a2200109 4500008004100000245007700041210006900118260009700187100001400284700001400298856009500312 1975 eng d00aA simulation study of stradaptive ability testing (Research Report 75-6)0 asimulation study of stradaptive ability testing Research Report  aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aVale, C D1 aWeiss, DJ uhttp://iacat.org/content/simulation-study-stradaptive-ability-testing-research-report-75-600534nas a2200097 4500008004100000245004900041210004900090260021500139100001400354856006800368 1975 eng d00aStrategies of branching through an item pool0 aStrategies of branching through an item pool aD. J. Weiss (Ed.), Computerized adaptive trait measurement: Problems and Prospects (Research Report 75-5), pp. 1-16. Minneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aVale, C D uhttp://iacat.org/content/strategies-branching-through-item-pool00539nas a2200109 4500008004100000245008800041210006900129260009700198100001400295700001400309856010600323 1975 eng d00aA study of computer-administered stradaptive ability testing (Research Report 75-4)0 astudy of computeradministered stradaptive ability testing Resear aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aVale, C D1 aWeiss, DJ uhttp://iacat.org/content/study-computer-administered-stradaptive-ability-testing-research-report-75-4