01501nas a2200133 4500008003900000245010600039210006900145300001000214490000700224520105500231100002001286700001601306856004501322 2018 d00aUsing Automatic Item Generation to Create Solutions and Rationales for Computerized Formative Testing0 aUsing Automatic Item Generation to Create Solutions and Rational a42-570 v423 aComputerized testing provides many benefits to support formative assessment. However, the advent of computerized formative testing has also raised formidable new challenges, particularly in the area of item development. Large numbers of diverse, high-quality test items are required because items are continuously administered to students. Hence, hundreds of items are needed to develop the banks necessary for computerized formative testing. One promising approach that may be used to address this test development challenge is automatic item generation. Automatic item generation is a relatively new but rapidly evolving research area where cognitive and psychometric modeling practices are used to produce items with the aid of computer technology. The purpose of this study is to describe a new method for generating both the items and the rationales required to solve the items to produce the required feedback for computerized formative testing. The method for rationale generation is demonstrated and evaluated in the medical education domain.1 aGierl, Mark, J.1 aLai, Hollis uhttps://doi.org/10.1177/014662161772678804649nas a2200145 4500008004100000245010200041210006900143260005500212520406800267653003004335653001604365653002204381100002904403856007104432 2017 eng d00aUsing Automated Item Generation in a Large-scale Medical Licensure Exam Program: Lessons Learned.0 aUsing Automated Item Generation in a Largescale Medical Licensur aNiigata, JapanbNiigata Seiryo Universityc08.20173 a

On-demand testing has become commonplace with most large-scale testing programs. Continuous testing is appealing for candidates in that it affords greater flexibility in scheduling a session at the desired location. Furthermore, the push for more comprehensive systems of assessment (e.g. CBAL) is predicated on the availability of more frequently administered tasks given the purposeful link between instruction and assessment in these frameworks. However, continuous testing models impose several challenges to programs, including overexposure of items. Robust item banks are therefore needed to support routine retirement and replenishment of items. In a traditional approach to developing items, content experts select a topic and then develop an item consisting of a stem, lead-in question, a correct answer and list of distractors. The item then undergoes review by a panel of experts to validate the content and identify any potential flaws. The process involved in developing quality MCQ items can be time-consuming as well as costly, with estimates as high as $1500-$2500 USD per item (Rudner, 2010). The Medical Council of Canada (MCC) has been exploring a novel item development process to supplement traditional approaches. Specifically, the use of automated item generation (AIG), which uses technology to generate test items from cognitive models, has been studied for over five years. Cognitive models are representations of the knowledge and skills that are required to solve any given problem. While developing a cognitive model for a medical scenario, for example, content experts are asked to deconstruct the (clinical) reasoning process involved via clearly stated variables and related elements. The latter information is then entered into a computer program that uses algorithms to generate MCQs. The MCC has been piloting AIG –based items for over five years with the MCC Qualifying Examination Part I (MCCQE I), a pre-requisite for licensure in Canada. The aim of this presentation is to provide an overview of the practical lessons learned in the use and operational rollout of AIG with the MCCQE I. Psychometrically, the quality of the items is at least equal, and in many instances superior, to that of traditionally written MCQs, based on difficulty, discrimination, and information. In fact, 96% of the AIG based items piloted in a recent administration were retained for future operational scoring based on pre-defined inclusion criteria. AIG also offers a framework for the systematic creation of plausible distractors, in that the content experts not only need to provide the clinical reasoning underlying a correct response but also the cognitive errors associated with each of the distractors (Lai et al. 2016). Consequently, AIG holds great promise in regard to improving and tailoring diagnostic feedback for remedial purposes (Pugh, De Champlain, Gierl, Lai, Touchie, 2016). Furthermore, our test development process has been greatly enhanced by the addition of AIG as it requires that item writers use metacognitive skills to describe how they solve problems. We are hopeful that sharing our experiences with attendees might not only help other testing organizations interested in adopting AIG, but also foster discussion which might benefit all participants.

References

Lai, H., Gierl, M.J., Touchie, C., Pugh, D., Boulais, A.P., & De Champlain, A.F. (2016). Using automatic item generation to improve the quality of MCQ distractors. Teaching and Learning in Medicine, 28, 166-173.

Pugh, D., De Champlain, A.F., Lai, H., Gierl, M., & Touchie, C. (2016). Using cognitive models to develop quality multiple choice questions. Medical Teacher, 38, 838-843.

Rudner, L. (2010). Implementing the Graduate Management Admission Test Computerized Adaptive Test. In W. van der Linden & C. Glass (Eds.), Elements of adaptive testing (pp. 151-165). New York, NY: Springer. 

Presentation Video

10aAutomated item generation10alarge scale10amedical licensure1 aDe Champlain, André, F. uhttps://drive.google.com/open?id=14N8hUc8qexAy5W_94TykEDABGVIJHG1h02152nas a2200157 4500008004100000245008800041210006900129260005400198520153900252653002901791653001101820100001901831700002001850700001801870856010601888 2017 eng d00aUsing Bayesian Decision Theory in Cognitive Diagnosis Computerized Adaptive Testing0 aUsing Bayesian Decision Theory in Cognitive Diagnosis Computeriz aNiigata JapanbNiigata Seiryo Universityc08/20173 a

Cognitive diagnosis computerized adaptive testing (CD-CAT) purports to provide each individual a profile about the strengths and weaknesses of attributes or skills with computerized adaptive testing. In the CD-CAT literature, researchers dedicated to evolving item selection algorithms to improve measurement efficiency, and most algorithms were developed based on information theory. By the discontinuous nature of the latent variables in CD-CAT, this study introduced an alternative for item selection, called the minimum expected cost (MEC) method, which was derived based on Bayesian decision theory. Using simulations, the MEC method was evaluated against the posterior weighted Kullback-Leibler (PWKL) information, the modified PWKL (MPWKL), and the mutual information (MI) methods by manipulating item bank quality, item selection algorithm, and termination rule. Results indicated that, regardless of item quality and termination criterion, the MEC, MPWKL, and MI methods performed very similarly and they all outperformed the PWKL method in classification accuracy and test efficiency, especially in short tests; the MEC method had more efficient item bank usage than the MPWKL and MI methods. Moreover, the MEC method could consider the costs of incorrect decisions and improve classification accuracy and test efficiency when a particular profile was of concern. All the results suggest the practicability of the MEC method in CD-CAT.

Session Video

10aBayesian Decision Theory10aCD-CAT1 aHsu, Chia-Ling1 aWang, Wen-Chung1 aChen, ShuYing uhttp://www.iacat.org/using-bayesian-decision-theory-cognitive-diagnosis-computerized-adaptive-testing04519nas a2200181 4500008004100000245010800041210006900149260005500218520381200273653000804085653002404093653002604117100001604143700001204159700001604171700002004187856013004207 2017 eng d00aUsing Computerized Adaptive Testing to Detect Students’ Misconceptions: Exploration of Item Selection0 aUsing Computerized Adaptive Testing to Detect Students Misconcep aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Owning misconceptions impedes learning, thus detecting misconceptions through assessments is crucial to facilitate teaching. However, most computerized adaptive testing (CAT) applications to diagnose examinees’ attribute profiles focus on whether examinees mastering correct concepts or not. In educational scenario, teachers and students have to figure out the misconceptions underlying incorrect answers after obtaining the scores from assessments and then correct the corresponding misconceptions. The Scaling Individuals and Classifying Misconceptions (SICM) models proposed by Bradshaw and Templin (2014) fill this gap. SICMs can identify a student’s misconceptions directly from the distractors of multiple-choice questions and report whether s/he own the misconceptions or not. Simultaneously, SICM models are able to estimate a continuous ability within the item response theory (IRT) framework to fulfill the needs of policy-driven assessment systems relying on scaling examinees’ ability. However, the advantage of providing estimations for two types of latent variables also causes complexity of model estimation. More items are required to achieve the same accuracies for both classification and estimation compared to dichotomous DCMs and to IRT, respectively. Thus, we aim to develop a CAT using the SICM models (SICM-CAT) to estimate students’ misconceptions and continuous abilities simultaneously using fewer items than a linear test.

To achieve this goal, in this study, our research questions mainly focus on establishing several item selection rules that target on providing both accurate classification results and continuous ability estimations using SICM-CAT. The first research question is which information criterion to be used. The Kullback–Leibler (KL) divergence is the first choice, as it can naturally combine the continuous and discrete latent variables. Based on this criterion, we propose an item selection index that can nicely integrate the two types of information. Based on this index, the items selected in real time could discriminate the examinee’s current misconception profile and ability estimates from other possible estimates to the most extent. The second research question is about how to adaptively balance the estimations of the misconception profile and the continuous latent ability. Mimic the idea of the Hybrid Design proposed by Wang et al. (2016), we propose a design framework which makes the item selection transition from the group-level to the item-level. We aim to explore several design questions, such as how to select the transiting point and which latent variable estimation should be targeted first.

Preliminary results indicated that the SICM-CAT based on the proposed item selection index could classify examinees into different latent classes and measure their latent abilities compared with the random selection method more accurately and reliably under all the simulation conditions. We plan to compare different CAT designs based on our proposed item selection rules with the best linear test as the next step. We expect that the SICM-CAT is able to use shorter test length while retaining the same accuracies and reliabilities.

References

Bradshaw, L., & Templin, J. (2014). Combining item response theory and diagnostic classification models: A psychometric model for scaling ability and diagnosing misconceptions. Psychometrika, 79(3), 403-425.

Wang, S., Lin, H., Chang, H. H., & Douglas, J. (2016). Hybrid computerized adaptive testing: from group sequential design to fully sequential design. Journal of Educational Measurement, 53(1), 45-62.

Session Video

10aCAT10aincorrect answering10aStudent Misconception1 aShen, Yawei1 aBao, Yu1 aWang, Shiyu1 aBradshaw, Laine uhttp://www.iacat.org/using-computerized-adaptive-testing-detect-students%E2%80%99-misconceptions-exploration-item-selection-002175nas 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_Xbsliemu1R01699nas a2200145 4500008003900000245009300039210006900132490000700201520118000208100001301388700001901401700001501420700001101435856010701446 2016 d00aUsing Response Time to Detect Item Preknowledge in Computer?Based Licensure Examinations0 aUsing Response Time to Detect Item Preknowledge in ComputerBased0 v353 aThis article addresses the issue of how to detect item preknowledge using item response time data in two computer-based large-scale licensure examinations. Item preknowledge is indicated by an unexpected short response time and a correct response. Two samples were used for detecting item preknowledge for each examination. The first sample was from the early stage of the operational test and was used for item calibration. The second sample was from the late stage of the operational test, which may feature item preknowledge. The purpose of this research was to explore whether there was evidence of item preknowledge and compromised items in the second sample using the parameters estimated from the first sample. The results showed that for one nonadaptive operational examination, two items (of 111) were potentially exposed, and two candidates (of 1,172) showed some indications of preknowledge on multiple items. For another licensure examination that featured computerized adaptive testing, there was no indication of item preknowledge or compromised items. Implications for detected aberrant examinees and compromised items are discussed in the article.1 aH., Qian1 aStaniewska, D.1 aReckase, M1 aWoo, A uhttp://www.iacat.org/using-response-time-detect-item-preknowledge-computerbased-licensure-examinations01273nas a2200121 4500008003900000245006200039210006000101490000700161520088200168100001201050700001201062856007701074 2015 d00aUsing Out-of-Level Items in Computerized Adaptive Testing0 aUsing OutofLevel Items in Computerized Adaptive Testing0 v153 aOut-of-level testing refers to the practice of assessing a student with a test that is intended for students at a higher or lower grade level. Although the appropriateness of out-of-level testing for accountability purposes has been questioned by educators and policymakers, incorporating out-of-level items in formative assessments for accurate feedback is recommended. This study made use of a commercial item bank with vertically scaled items across grades and simulated student responses in a computerized adaptive testing (CAT) environment. Results of the study suggested that administration of out-of-level items improved measurement accuracy and test efficiency for students who perform significantly above or below their grade-level peers. This study has direct implications with regards to the relevance, applicability, and benefits of using out-of-level items in CAT.1 aWei, H.1 aLin, J. uhttp://www.iacat.org/using-out-level-items-computerized-adaptive-testing01461nas a2200157 4500008003900000245006800039210006800107300001200175490000700187520097800194100001701172700002701189700001701216700001701233856005301250 2015 d00aUtilizing Response Times in Computerized Classification Testing0 aUtilizing Response Times in Computerized Classification Testing a389-4050 v393 aA well-known approach in computerized mastery testing is to combine the Sequential Probability Ratio Test (SPRT) stopping rule with item selection to maximize Fisher information at the mastery threshold. This article proposes a new approach in which a time limit is defined for the test and examinees’ response times are considered in both item selection and test termination. Item selection is performed by maximizing Fisher information per time unit, rather than Fisher information itself. The test is terminated once the SPRT makes a classification decision, the time limit is exceeded, or there is no remaining item that has a high enough probability of being answered before the time limit. In a simulation study, the new procedure showed a substantial reduction in average testing time while slightly improving classification accuracy compared with the original method. In addition, the new procedure reduced the percentage of examinees who exceeded the time limit.1 aSie, Haskell1 aFinkelman, Matthew, D.1 aRiley, Barth1 aSmits, Niels uhttp://apm.sagepub.com/content/39/5/389.abstract01548nas a2200145 4500008003900000245008200039210006900121300001200190490000700202520108300209100001501292700002001307700002201327856005301349 2014 d00aUsing Multidimensional CAT to Administer a Short, Yet Precise, Screening Test0 aUsing Multidimensional CAT to Administer a Short Yet Precise Scr a614-6310 v383 a

Multidimensional computerized adaptive testing (MCAT) provides a mechanism by which the simultaneous goals of accurate prediction and minimal testing time for a screening test could both be met. This article demonstrates the use of MCAT to administer a screening test for the Computerized Adaptive Testing–Armed Services Vocational Aptitude Battery (CAT-ASVAB) under a variety of manipulated conditions. CAT-ASVAB is a test battery administered via unidimensional CAT (UCAT) that is used to qualify applicants for entry into the U.S. military and assign them to jobs. The primary research question being evaluated is whether the use of MCAT to administer a screening test can lead to significant reductions in testing time from the full-length selection test, without significant losses in score precision. Different stopping rules, item selection methods, content constraints, time constraints, and population distributions for the MCAT administration are evaluated through simulation, and compared with results from a regular full-length UCAT administration.

1 aYao, Lihua1 aPommerich, Mary1 aSegall, Daniel, O uhttp://apm.sagepub.com/content/38/8/614.abstract00445nas a2200109 4500008004500000245008700045210006900132300000900201490000600210100002000216856009900236 2014 Engldsh 00aThe Utility of Adaptive Testing in Addressing the Problem of Unmotivated Examinees0 aUtility of Adaptive Testing in Addressing the Problem of Unmotiv a1-170 v21 aWise, Steven, L uhttp://www.iacat.org/content/utility-adaptive-testing-addressing-problem-unmotivated-examinees01575nas 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.abstract00476nas a2200121 4500008004500000245008000045210006900125300001200194490000700206100001700213700001600230856010800246 2011 Engldsh 00aUnproctored Internet test verification: Using adaptive confirmation testing0 aUnproctored Internet test verification Using adaptive confirmati a608-6300 v141 aMakransky, G1 aGlas, C A W uhttp://www.iacat.org/content/unproctored-internet-test-verification-using-adaptive-confirmation-testing02120nas a2200193 4500008004100000245007900041210006900120260001200189520146800201653002801669653000801697653001801705100002001723700001701743700002301760700002301783700002301806856009701829 2011 eng d00aThe Use of Decision Trees for Adaptive Item Selection and Score Estimation0 aUse of Decision Trees for Adaptive Item Selection and Score Esti c10/20113 a

Conducted post-hoc simulations comparing the relative efficiency, and precision of decision trees (using CHAID and CART) vs. IRT-based CAT.

Conclusions

Decision tree methods were more efficient than CAT

But,...

Conclusions

CAT selects items based on two criteria: Item location relative to current estimate of theta, Item discrimination

Decision Trees select items that best discriminate between groups defined by the total score.

CAT is optimal only when trait level is well estimated.
Findings suggest that combining decision tree followed by CAT item selection may be advantageous.

10aadaptive item selection10aCAT10adecision tree1 aRiley, Barth, B1 aFunk, Rodney1 aDennis, Michael, L1 aLennox, Richard, D1 aFinkelman, Matthew uhttp://www.iacat.org/content/use-decision-trees-adaptive-item-selection-and-score-estimation01352nas a2200133 4500008003900000245008000039210006900119300001200188490000700200520091200207100001901119700002301138856005701161 2011 d00aUsing Item Response Theory and Adaptive Testing in Online Career Assessment0 aUsing Item Response Theory and Adaptive Testing in Online Career a274-2860 v193 a

The present article describes the potential utility of item response theory (IRT) and adaptive testing for scale evaluation and for web-based career assessment. The article describes the principles of both IRT and adaptive testing and then illustrates these with reference to data analyses and simulation studies of the Career Confidence Inventory (CCI). The kinds of information provided by IRT are shown to give a more precise look at scale quality across the trait continuum and also to permit the use of adaptive testing, where the items administered are tailored to the individual being tested. Such tailoring can significantly reduce testing time while maintaining high quality of measurement. This efficiency is especially useful when multiscale inventories and/or a large number of scales are to be administered. Readers are encouraged to consider using these advances in career assessment.

1 aBetz, Nancy, E1 aTurner, Brandon, M uhttp://jca.sagepub.com/cgi/content/abstract/19/3/27401857nas a2200181 4500008004100000020001400041245011000055210006900165300001200234490000700246520122300253100001601476700001601492700001601508700001201524700001301536856012601549 2010 eng d a1529-771300aThe use of PROMIS and assessment center to deliver patient-reported outcome measures in clinical research0 ause of PROMIS and assessment center to deliver patientreported o a304-3140 v113 aThe Patient-Reported Outcomes Measurement Information System (PROMIS) was developed as one of the first projects funded by the NIH Roadmap for Medical Research Initiative to re-engineer the clinical research enterprise. The primary goal of PROMIS is to build item banks and short forms that measure key health outcome domains that are manifested in a variety of chronic diseases which could be used as a "common currency" across research projects. To date, item banks, short forms and computerized adaptive tests (CAT) have been developed for 13 domains with relevance to pediatric and adult subjects. To enable easy delivery of these new instruments, PROMIS built a web-based resource (Assessment Center) for administering CATs and other self-report data, tracking item and instrument development, monitoring accrual, managing data, and storing statistical analysis results. Assessment Center can also be used to deliver custom researcher developed content, and has numerous features that support both simple and complicated accrual designs (branching, multiple arms, multiple time points, etc.). This paper provides an overview of the development of the PROMIS item banks and details Assessment Center functionality.1 aGershon, RC1 aRothrock, N1 aHanrahan, R1 aBass, M1 aCella, D uhttp://www.iacat.org/content/use-promis-and-assessment-center-deliver-patient-reported-outcome-measures-clinical-research00525nas a2200121 4500008004100000245006800041210006800109260009700177100001100274700001300285700001500298856009000313 2009 eng d00aUsing automatic item generation to address item demands for CAT0 aUsing automatic item generation to address item demands for CAT aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aLai, H1 aAlves, C1 aGierl, M J uhttp://www.iacat.org/content/using-automatic-item-generation-address-item-demands-cat02021nas a2200109 4500008004100000245007400041210006900115260009700184520151800281100001801799856009401817 2009 eng d00aUtilizing the generalized likelihood ratio as a termination criterion0 aUtilizing the generalized likelihood ratio as a termination crit aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.3 aComputer-based testing can be used to classify examinees into mutually exclusive groups. Currently, the predominant psychometric algorithm for designing computerized classification tests (CCTs) is the sequential probability ratio test (SPRT; Reckase, 1983) based on item response theory (IRT). The SPRT has been shown to be more efficient than confidence intervals around θ estimates as a method for CCT delivery (Spray & Reckase, 1996; Rudner, 2002). More recently, it was demonstrated that the SPRT, which only uses fixed values, is less efficient than a generalized form which tests whether a given examinee’s θ is below θ1or above θ2 (Thompson, 2007). This formulation allows the indifference region to vary based on observed data. Moreover, this composite hypothesis formulation better represents the conceptual purpose of the test, which is to test whether θ is above or below the cutscore. The purpose of this study was to explore the specifications of the new generalized likelihood ratio (GLR; Huang, 2004). As with the SPRT, the efficiency of the procedure depends on the nominal error rates and the distance between θ1 and θ2 (Eggen, 1999). This study utilized a monte-carlo approach, with 10,000 examinees simulated under each condition, to evaluate differences in efficiency and accuracy due to hypothesis structure, nominal error rate, and indifference region size. The GLR was always at least as efficient as the fixed-point SPRT while maintaining equivalent levels of accuracy. 1 aThompson, N A uhttp://www.iacat.org/content/utilizing-generalized-likelihood-ratio-termination-criterion03157nas a2200493 4500008004100000020002200041245008900063210006900152250001500221260000800236300001000244490000700254520169600261653003401957653002001991653001502011653001002026653000902036653002602045653003202071653003102103653001102134653001102145653000902156653003202165653001602197653002902213653004402242653002902286653003102315653003102346653001702377100001702394700001402411700001602425700001302441700001702454700002202471700001702493700001402510700001402524700001702538856010802555 2008 eng d a1075-2730 (Print)00aUsing computerized adaptive testing to reduce the burden of mental health assessment0 aUsing computerized adaptive testing to reduce the burden of ment a2008/04/02 cApr a361-80 v593 aOBJECTIVE: This study investigated the combination of item response theory and computerized adaptive testing (CAT) for psychiatric measurement as a means of reducing the burden of research and clinical assessments. METHODS: Data were from 800 participants in outpatient treatment for a mood or anxiety disorder; they completed 616 items of the 626-item Mood and Anxiety Spectrum Scales (MASS) at two times. The first administration was used to design and evaluate a CAT version of the MASS by using post hoc simulation. The second confirmed the functioning of CAT in live testing. RESULTS: Tests of competing models based on item response theory supported the scale's bifactor structure, consisting of a primary dimension and four group factors (mood, panic-agoraphobia, obsessive-compulsive, and social phobia). Both simulated and live CAT showed a 95% average reduction (585 items) in items administered (24 and 30 items, respectively) compared with administration of the full MASS. The correlation between scores on the full MASS and the CAT version was .93. For the mood disorder subscale, differences in scores between two groups of depressed patients--one with bipolar disorder and one without--on the full scale and on the CAT showed effect sizes of .63 (p<.003) and 1.19 (p<.001) standard deviation units, respectively, indicating better discriminant validity for CAT. CONCLUSIONS: Instead of using small fixed-length tests, clinicians can create item banks with a large item pool, and a small set of the items most relevant for a given individual can be administered with no loss of information, yielding a dramatic reduction in administration time and patient and clinician burden.10a*Diagnosis, Computer-Assisted10a*Questionnaires10aAdolescent10aAdult10aAged10aAgoraphobia/diagnosis10aAnxiety Disorders/diagnosis10aBipolar Disorder/diagnosis10aFemale10aHumans10aMale10aMental Disorders/*diagnosis10aMiddle Aged10aMood Disorders/diagnosis10aObsessive-Compulsive Disorder/diagnosis10aPanic Disorder/diagnosis10aPhobic Disorders/diagnosis10aReproducibility of Results10aTime Factors1 aGibbons, R D1 aWeiss, DJ1 aKupfer, D J1 aFrank, E1 aFagiolini, A1 aGrochocinski, V J1 aBhaumik, D K1 aStover, A1 aBock, R D1 aImmekus, J C uhttp://www.iacat.org/content/using-computerized-adaptive-testing-reduce-burden-mental-health-assessment02410nas a2200193 4500008004100000020002200041245011700063210006900180250001500249300001100264490000600275520172700281100001502008700001702023700001602040700001802056700001802074856012402092 2008 eng d a1740-7745 (Print)00aUsing item banks to construct measures of patient reported outcomes in clinical trials: investigator perceptions0 aUsing item banks to construct measures of patient reported outco a2008/11/26 a575-860 v53 aBACKGROUND: Item response theory (IRT) promises more sensitive and efficient measurement of patient-reported outcomes (PROs) than traditional approaches; however, the selection and use of PRO measures from IRT-based item banks differ from current methods of using PRO measures. PURPOSE: To anticipate barriers to the adoption of IRT item banks into clinical trials. METHODS: We conducted semistructured telephone or in-person interviews with 42 clinical researchers who published results from clinical trials in the Journal of the American Medical Association, the New England Journal of Medicine, or other leading clinical journals from July 2005 through May 2006. Interviews included a brief tutorial on IRT item banks. RESULTS: After the tutorial, 39 of 42 participants understood the novel products available from an IRT item bank, namely customized short forms and computerized adaptive testing. Most participants (38/42) thought that item banks could be useful in their clinical trials, but they mentioned several potential barriers to adoption, including economic and logistical constraints, concerns about whether item banks are better than current PRO measures, concerns about how to convince study personnel or statisticians to use item banks, concerns about FDA or sponsor acceptance, and the lack of availability of item banks validated in specific disease populations. LIMITATIONS: Selection bias might have led to more positive responses to the concept of item banks in clinical trials. CONCLUSIONS: Clinical investigators are open to a new method of PRO measurement offered in IRT item banks, but bank developers must address investigator and stakeholder concerns before widespread adoption can be expected.1 aFlynn, K E1 aDombeck, C B1 aDeWitt, E M1 aSchulman, K A1 aWeinfurt, K P uhttp://www.iacat.org/content/using-item-banks-construct-measures-patient-reported-outcomes-clinical-trials-investigator00406nas a2200109 4500008004100000245006400041210006400105300001100169490000700180100002300187856008600210 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://www.iacat.org/content/using-response-times-item-selection-adaptive-testing00442nas a2200109 4500008004100000245008600041210006900127300000800196490000700204100001900211856010200230 2008 eng d00aOn using stochastic curtailment to shorten the SPRT in sequential mastery testing0 ausing stochastic curtailment to shorten the SPRT in sequential m a4420 v331 aFinkelman, M D uhttp://www.iacat.org/content/using-stochastic-curtailment-shorten-sprt-sequential-mastery-testing01939nas a2200157 4500008004100000245011600041210006900157300001200226490000700238520135500245100001601600700001201616700001601628700001401644856012301658 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://www.iacat.org/content/utilizing-rasch-measurement-models-develop-computer-adaptive-self-report-walking-climbing00585nas a2200121 4500008004100000245009300041210006900134260009700203100001300300700001600313700001900329856011500348 2007 eng d00aUp-and-down procedures for approximating optimal designs using person-response functions0 aUpanddown procedures for approximating optimal designs using per aD. J. Weiss (Ed.). Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aSheng, Y1 aFlournoy, N1 aOsterlind, S J uhttp://www.iacat.org/content/and-down-procedures-approximating-optimal-designs-using-person-response-functions00376nas a2200097 4500008004100000245003400041210003400075260009700109100001500206856005700221 2007 eng d00aUse of CAT in dynamic testing0 aUse of CAT in dynamic testing aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aDe Beer, M uhttp://www.iacat.org/content/use-cat-dynamic-testing00586nas a2200121 4500008004100000245011800041210006900159260006500228100001600293700001500309700001600324856012400340 2007 eng d00aThe use of computerized adaptive testing to assess psychopathology using the Global Appraisal of Individual Needs0 ause of computerized adaptive testing to assess psychopathology u aPortland, OR USAbAmerican Evaluation Association cNovember1 aConrad, K J1 aRiley, B B1 aDennis, M L uhttp://www.iacat.org/content/use-computerized-adaptive-testing-assess-psychopathology-using-global-appraisal-individual00540nas a2200121 4500008004100000020003800041245007000079210006500149260007200214100001600286700002700302856008900329 2005 eng d aComputerized Testing Report 97-1400aThe use of person-fit statistics in computerized adaptive testing0 ause of personfit statistics in computerized adaptive testing aNewton, PA. USAbLaw School Administration CouncilcSeptember, 20051 aMeijer, R R1 aKrimpen-Stoop, E M L A uhttp://www.iacat.org/content/use-person-fit-statistics-computerized-adaptive-testing00543nas a2200097 4500008004100000245008700041210006900128260012200197100001900319856010700338 2004 eng d00aUnderstanding computerized adaptive testing: From Robbins-Munro to Lord and beyond0 aUnderstanding computerized adaptive testing From RobbinsMunro to aD. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 117-133). New York: Sage.1 aChang, Hua-Hua uhttp://www.iacat.org/content/understanding-computerized-adaptive-testing-robbins-munro-lord-and-beyond01879nas a2200169 4500008004100000245013100041210006900172300001200241490000700253520122700260653003001487653002501517653001501542653001601557100001601573856012001589 2004 eng d00aUsing patterns of summed scores in paper-and-pencil tests and computer-adaptive tests to detect misfitting item score patterns0 aUsing patterns of summed scores in paperandpencil tests and comp a119-1360 v413 aTwo new methods have been proposed to determine unexpected sum scores on subtests (testlets) both for paper-and-pencil tests and computer adaptive tests. A method based on a conservative bound using the hypergeometric distribution, denoted ρ, was compared with a method where the probability for each score combination was calculated using a highest density region (HDR). Furthermore, these methods were compared with the standardized log-likelihood statistic with and without a correction for the estimated latent trait value (denoted as l-super(*)-sub(z) and l-sub(z), respectively). Data were simulated on the basis of the one-parameter logistic model, and both parametric and nonparametric logistic regression was used to obtain estimates of the latent trait. Results showed that it is important to take the trait level into account when comparing subtest scores. In a nonparametric item response theory (IRT) context, on adapted version of the HDR method was a powerful alterative to ρ. In a parametric IRT context, results showed that l-super(*)-sub(z) had the highest power when the data were simulated conditionally on the estimated latent trait level. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aComputer Assisted Testing10aItem Response Theory10aperson Fit10aTest Scores1 aMeijer, R R uhttp://www.iacat.org/content/using-patterns-summed-scores-paper-and-pencil-tests-and-computer-adaptive-tests-detect01413nas a2200121 4500008003900000245011400039210006900153300001200222490000700234520097400241100002301215856005301238 2004 d00aUsing Set Covering with Item Sampling to Analyze the Infeasibility of Linear Programming Test Assembly Models0 aUsing Set Covering with Item Sampling to Analyze the Infeasibili a355-3750 v283 a

This article shows how set covering with item sampling (SCIS) methods can be used in the analysis and preanalysis of linear programming models for test assembly (LPTA). LPTA models can construct tests, fulfilling a set of constraints set by the test assembler. Sometimes, no solution to the LPTA model exists. The model is then said to be infeasible. Causes of infeasibility can be difficult to find. A method is proposed that constitutes a helpful tool for test assemblers to detect infeasibility before hand and, in the case of infeasibility, give insight into its causes. This method is based on SCIS. Although SCIS can help to detect feasibility or infeasibility, its power lies in pinpointing causes of infeasibility such as irreducible infeasible sets of constraints. Methods to resolve infeasibility are also given, minimizing the model deviations. A simulation study is presented, offering a guide to test assemblers to analyze and solve infeasibility.

1 aHuitzing, Hiddo, A uhttp://apm.sagepub.com/content/28/5/355.abstract00545nas a2200097 4500008004100000245014400041210006900185260006700254100001100321856011500332 2003 eng d00aUsing moving averages to assess test and item security in computer-based testing (Center for Educational Assessment Research Report No 468)0 aUsing moving averages to assess test and item security in comput aAmherst, MA: University of Massachusetts, School of Education.1 aHan, N uhttp://www.iacat.org/content/using-moving-averages-assess-test-and-item-security-computer-based-testing-center01516nas a2200229 4500008004100000245008700041210006900128300001200197490000700209520075300216653002100969653001300990653003001003653003401033653001101067653001501078653001501093653001801108100002301126700002701149856011001176 2003 eng d00aUsing response times to detect aberrant responses in computerized adaptive testing0 aUsing response times to detect aberrant responses in computerize a251-2650 v683 aA lognormal model for response times is used to check response times for aberrances in examinee behavior on computerized adaptive tests. Both classical procedures and Bayesian posterior predictive checks are presented. For a fixed examinee, responses and response times are independent; checks based on response times offer thus information independent of the results of checks on response patterns. Empirical examples of the use of classical and Bayesian checks for detecting two different types of aberrances in response times are presented. The detection rates for the Bayesian checks outperformed those for the classical checks, but at the cost of higher false-alarm rates. A guideline for the choice between the two types of checks is offered.10aAdaptive Testing10aBehavior10aComputer Assisted Testing10acomputerized adaptive testing10aModels10aperson Fit10aPrediction10aReaction Time1 avan der Linden, WJ1 aKrimpen-Stoop, E M L A uhttp://www.iacat.org/content/using-response-times-detect-aberrant-responses-computerized-adaptive-testing00454nas a2200133 4500008004100000245005900041210005900100260001900159100001500178700001400193700001200207700001300219856008800232 2002 eng d00aUpdated item parameter estimates using sparse CAT data0 aUpdated item parameter estimates using sparse CAT data aNew Orleans LA1 aSmith, R L1 aRizavi, S1 aPaez, R1 aRotou, O uhttp://www.iacat.org/content/updated-item-parameter-estimates-using-sparse-cat-data00517nas a2200121 4500008004100000245009900041210006900140260001900209100001700228700001600245700001500261856011900276 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://www.iacat.org/content/using-judgments-item-difficulty-change-answers-computerized-adaptive-vocabulary-tests00509nas a2200109 4500008004100000245011100041210006900152260001900221100002000240700001500260856012400275 2002 eng d00aUsing testlet response theory to evaluate the equivalence of automatically generated multiple-choice items0 aUsing testlet response theory to evaluate the equivalence of aut aNew Orleans LA1 aWilliamson, D M1 aBejar, I I uhttp://www.iacat.org/content/using-testlet-response-theory-evaluate-equivalence-automatically-generated-multiple-choice00538nas a2200097 4500008004100000245016800041210006900209260002700278100001500305856012000320 2002 eng d00aUtility of Learning Potential Computerised Adaptive Test (LPCAT) scores in predicting academic performance of bridging students: A comparison with other predictors0 aUtility of Learning Potential Computerised Adaptive Test LPCAT s aPretoria, South Africa1 aDe Beer, M uhttp://www.iacat.org/content/utility-learning-potential-computerised-adaptive-test-lpcat-scores-predicting-academic00567nas a2200121 4500008004100000245012200041210006900163260004700232100001200279700001700291700001100308856012600319 2001 eng d00aUser's guide for SCORIGHT (version 1): A computer program for scoring tests built of testlets (Research Report 01-06)0 aUsers guide for SCORIGHT version 1 A computer program for scorin aPrinceton NJ: Educational Testing Service.1 aWang, X1 aBradlow, E T1 aWainer uhttp://www.iacat.org/content/users-guide-scoright-version-1-computer-program-scoring-tests-built-testlets-research-report00567nas a2200121 4500008004100000245012200041210006900163260004700232100001200279700001700291700001100308856012600319 2001 eng d00aUsers guide for SCORIGHT (version 2) : A computer program for scoring tests built of testlets (Research Report 01-06)0 aUsers guide for SCORIGHT version 2 A computer program for scorin aPrinceton NJ: Educational Testing Service.1 aWang, X1 aBradlow, E T1 aWainer uhttp://www.iacat.org/content/users-guide-scoright-version-2-computer-program-scoring-tests-built-testlets-research-report00480nas a2200109 4500008004100000245008600041210006900127260001500196100002300211700002700234856010900261 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://www.iacat.org/content/using-response-times-detect-aberrant-behavior-computerized-adaptive-testing00546nas a2200133 4500008004100000245005900041210005900100260009900159100001400258700001400272700002700286700001400313856008500327 2000 eng d00aUsing Bayesian Networks in Computerized Adaptive Tests0 aUsing Bayesian Networks in Computerized Adaptive Tests aM. Ortega and J. Bravo (Eds.),Computers and Education in the 21st Century. Kluwer, pp. 217228.1 aMillan, E1 aTrella, M1 aPerez-de-la-Cruz, J -L1 aConejo, R uhttp://www.iacat.org/content/using-bayesian-networks-computerized-adaptive-tests00379nas a2200097 4500008004100000245006000041210006000101100001800161700001600179856008600195 2000 eng d00aUsing constraints to develop and deliver adaptive tests0 aUsing constraints to develop and deliver adaptive tests1 aAbdullah, S C1 aCooley, R E uhttp://www.iacat.org/content/using-constraints-develop-and-deliver-adaptive-tests00649nas a2200109 4500008004100000245011000041210006900151260014400220100002300364700002700387856012500414 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://www.iacat.org/content/using-response-times-detect-aberrant-behavior-computerized-adaptive-testing-research-report00408nas a2200097 4500008004100000245007200041210006900113260002100182100001600203856009100219 1999 eng d00aUse of conditional item exposure methodology for an operational CAT0 aUse of conditional item exposure methodology for an operational  aMontreal, Canada1 aAnderson, D uhttp://www.iacat.org/content/use-conditional-item-exposure-methodology-operational-cat00356nas a2200097 4500008004100000245005900041210005200100260002100152100001500173856007000188 1999 eng d00aThe use of linear-on-the-fly testing for TOEFL Reading0 ause of linearonthefly testing for TOEFL Reading aMontreal, Canada1 aCarey, P A uhttp://www.iacat.org/content/use-linear-fly-testing-toefl-reading01910nas a2200229 4500008004100000245008600041210006900127300001000196490000700206520111400213653003201327653003701359653001001396653003401406653003001440653002901470653003201499100001601531700001801547700001301565856010201578 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://www.iacat.org/content/use-rasch-analysis-produce-scale-free-measurement-functional-ability00429nas a2200109 4500008004100000245007300041210006900114300001400183490001000197100001300207856009900220 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://www.iacat.org/content/using-bayesian-decision-theory-design-computerized-mastery-test-001190nas a2200157 4500008004100000245010900041210006900150300001200219490000700231520058300238653003400821100002300855700001600878700001800894856012000912 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://www.iacat.org/content/using-response-time-constraints-control-differential-speededness-computerized-adaptive00677nas a2200121 4500008004100000245012000041210006900161260014400230100002300374700001600397700001800413856012400431 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://www.iacat.org/content/using-response-time-constraints-control-differential-speededness-adaptive-testing-research00542nas a2200121 4500008004500000245012600045210007100171300000800242490000600250100001600256700001500272856013300287 1997 Spandsh 00aUna solución a la estimatión inicial en los tests adaptivos informatizados [A solution to initial estimation in CATs.] 0 aUna solución a la estimatión inicial en los tests adaptivos info a1-60 v21 aRevuelta, J1 aPonsoda, V uhttp://www.iacat.org/content/una-soluci%C3%B3n-la-estimati%C3%B3n-inicial-en-los-tests-adaptivos-informatizados-solution-initial00608nas a2200133 4500008004100000245014700041210006900188260002600257100001500283700002200298700001600320700001200336856012600348 1997 eng d00aUnidimensional approximations for a computerized adaptive test when the item pool and latent space are multidimensional (Research Report 97-5)0 aUnidimensional approximations for a computerized adaptive test w aIowa City IA: ACT Inc1 aSpray, J A1 aAbdel-Fattah, A A1 aHuang, C -Y1 aLau, CA uhttp://www.iacat.org/content/unidimensional-approximations-computerized-adaptive-test-when-item-pool-and-latent-space-are00425nam a2200097 4500008004100000245006200041210006000103260004700163100003600210856008100246 1996 eng d00aUsers manual for the MicroCAT testing system, Version 3.50 aUsers manual for the MicroCAT testing system Version 35 aSt Paul MN: Assessment Systems Corporation1 aAssessment-Systems-Corporation. uhttp://www.iacat.org/content/users-manual-microcat-testing-system-version-3500515nas a2200121 4500008004100000245010200041210006900143260001400212100001200226700002200238700001500260856011800275 1996 eng d00aUsing unidimensional IRT models for dichotomous classification via CAT with multidimensional data0 aUsing unidimensional IRT models for dichotomous classification v aBoston MA1 aLau, CA1 aAbdel-Fattah, A A1 aSpray, J A uhttp://www.iacat.org/content/using-unidimensional-irt-models-dichotomous-classification-cat-multidimensional-data00478nas a2200109 4500008004100000245009900041210006900140260001600209100001100225700001100236856012100247 1996 eng d00aUtility of Fisher information, global information and different starting abilities in mini CAT0 aUtility of Fisher information global information and different s aNew York NY1 aFan, M1 aHsu, Y uhttp://www.iacat.org/content/utility-fisher-information-global-information-and-different-starting-abilities-mini-cat00506nas a2200109 4500008004100000245009300041210006900134260005300203100001300256700001500269856011200284 1995 eng d00aUsing simulation to select an adaptive testing strategy: An item bank evaluation program0 aUsing simulation to select an adaptive testing strategy An item  aUnpublished manuscript, University of Pittsburgh1 aHsu, T C1 aTseng, F L uhttp://www.iacat.org/content/using-simulation-select-adaptive-testing-strategy-item-bank-evaluation-program00420nas a2200109 4500008004100000245007300041210006900114300001000183490000600193100001400199856009700213 1994 eng d00aUnderstanding self-adapted testing: The perceived control hypothesis0 aUnderstanding selfadapted testing The perceived control hypothes a15-240 v71 aWise, S L uhttp://www.iacat.org/content/understanding-self-adapted-testing-perceived-control-hypothesis00679nas a2200097 4500008004100000245010900041210007000150260021800220100001500438856012800453 1994 eng d00aUtilisation de la simulation en tant que méthodologie de recherche [Simulation methodology in research]0 aUtilisation de la simulation en tant que méthodologie de recherc aAssociation pour la recherche au collégial (Ed.) : L'en-quête de la créativité [In quest of creativity]. Proceeding of the 6th Congress of the ARC. Montréal: Association pour la recherche au collégial (ARC).1 aRaîche, G uhttp://www.iacat.org/content/utilisation-de-la-simulation-en-tant-que-m%C3%A9thodologie-de-recherche-simulation-methodology00478nas a2200097 4500008004100000245007100041210006900112260008600181100001500267856009800282 1993 eng d00aUn test adaptatif en langue seconde : la perception des apprenants0 aUn test adaptatif en langue seconde la perception des apprenants aR.Hivon (Éd.),L’évaluation des apprentissages. Sherbrooke : Éditions du CRP.1 aLaurier, M uhttp://www.iacat.org/content/un-test-adaptatif-en-langue-seconde-la-perception-des-apprenants01606nas a2200121 4500008004100000245010800041210006900149260003700218520108800255100001501343700001301358856011301371 1991 eng d00aThe use of the graded response model in computerized adaptive testing of the attitudes to science scale0 ause of the graded response model in computerized adaptive testin aChicago, IL USAcApril 3-7, 19913 aThe graded response model for two-stage testing was applied to an attitudes toward science scale using real-data simulation. The 48-item scale was administered to 920 students at a grade-8 equivalent in Singapore. A two-stage 16-item computerized adaptive test was developed. In two-stage testing an initial, or routing, test is followed by a second-stage testlet of greater or lesser difficulty based on performance. A conventional test of the same length as the adaptive two-stage test was selected from the 48-item pool. Responses to the conventional test, the routing test, and a testlet were simulated. The algorithm of E. Balas (1965) and the multidimensional knapsack problem of optimization theory were used in test development. The simulation showed the efficiency and accuracy of the two-stage test with the graded response model in estimating attitude trait levels, as evidenced by better results from the two-stage test than its conventional counterpart and the reduction to one-third of the length of the original measure. Six tables and three graphs are included. (SLD)1 aFoong, Y-Y1 aLam, T-L uhttp://www.iacat.org/content/use-graded-response-model-computerized-adaptive-testing-attitudes-science-scale00469nas a2200109 4500008004500000245009600045210006900141300001000210490000700220100001800227856011400245 1991 Engldsh 00aThe Use of Unidimensional Parameter Estimates of Multidimensional Items in Adaptive Testing0 aUse of Unidimensional Parameter Estimates of Multidimensional It a13-240 v151 aAckerman, T A uhttp://www.iacat.org/content/use-unidimensional-parameter-estimates-multidimensional-items-adaptive-testing-000463nas a2200109 4500008004100000245009600041210006900137300001000206490000700216100001800223856011200241 1991 eng d00aThe use of unidimensional parameter estimates of multidimensional items in adaptive testing0 ause of unidimensional parameter estimates of multidimensional it a13-240 v151 aAckerman, T A uhttp://www.iacat.org/content/use-unidimensional-parameter-estimates-multidimensional-items-adaptive-testing00446nas a2200121 4500008004100000245007300041210006900114300001200183490000700195100001000202700001500212856009700227 1990 eng d00aUsing Bayesian decision theory to design a computerized mastery test0 aUsing Bayesian decision theory to design a computerized mastery  a367-3860 v141 aLewis1 aSheehan, K uhttp://www.iacat.org/content/using-bayesian-decision-theory-design-computerized-mastery-test00455nas a2200121 4500008004500000245007300045210006900118300001200187490000700199100001300206700001500219856009900234 1990 Engldsh 00aUsing Bayesian Decision Theory to Design a Computerized Mastery Test0 aUsing Bayesian Decision Theory to Design a Computerized Mastery  a367-3860 v141 aLewis, C1 aSheehan, K uhttp://www.iacat.org/content/using-bayesian-decision-theory-design-computerized-mastery-test-100552nas a2200109 4500008004100000245011200041210006900153260006900222100001400291700001600305856012100321 1990 eng d00aUtility of predicting starting abilities in sequential computer-based adaptive tests (Research Report 90-1)0 aUtility of predicting starting abilities in sequential computerb aBaltimore MD: Johns Hopkins University, Department of Psychology1 aGreen, BF1 aThomas, T J uhttp://www.iacat.org/content/utility-predicting-starting-abilities-sequential-computer-based-adaptive-tests-research00398nam a2200097 4500008004100000245006000041210005900101260002500160100003500185856008000220 1988 eng d00aUsers manual for the MicroCAT Testing System, Version 30 aUsers manual for the MicroCAT Testing System Version 3 aSt. Paul MN: Author.1 aAssessment-Systems-Corporation uhttp://www.iacat.org/content/users-manual-microcat-testing-system-version-301146nas a2200133 4500008004100000020001000041245010100051210006900152260004000221300000700261520060900268100001800877856011700895 1987 eng d a87-1300aThe use of unidimensional item parameter estimates of multidimensional items in adaptive testing0 ause of unidimensional item parameter estimates of multidimension aIowa City, IAbACTcSeptember, 1987 a333 aInvestigated the effect of using multidimensional (MDN) items in a computer adaptive test setting that assumes a unidimensional item response theory model in 2 experiments, using generated and real data in which difficulty was known to be confounded with dimensionality. Results from simulations suggest that univariate calibration of MDN data filtered out multidimensionality. The closer an item's MDN composite aligned itself with the calibrated univariate ability scale's orientation, the larger was the estimated discrimination parameter. (PsycINFO Database Record (c) 2003 APA, all rights reserved).1 aAckerman, T A uhttp://www.iacat.org/content/use-unidimensional-item-parameter-estimates-multidimensional-items-adaptive-testing00397nas a2200109 4500008004100000245006200041210006100103300001000164490000700174100001800181856008800199 1986 eng d00aUsing microcomputer-based assessment in career counseling0 aUsing microcomputerbased assessment in career counseling a50-560 v231 aThompson, D L uhttp://www.iacat.org/content/using-microcomputer-based-assessment-career-counseling00535nas a2200109 4500008004100000245007200041210006900113260011600182300001200298100001800310856009700328 1985 eng d00aUnidimensional and multidimensional models for item response theory0 aUnidimensional and multidimensional models for item response the aMinneapolis, MN. USAbUniversity of Minnesota, Department of Psychology, Psychometrics Methods Programc06/1982 a127-1481 aMcDonald, R P uhttp://www.iacat.org/content/unidimensional-and-multidimensional-models-item-response-theory00366nam a2200097 4500008004100000245004900041210004900090260002400139100003500163856007000198 1984 eng d00aUsers manual for the MicroCAT Testing System0 aUsers manual for the MicroCAT Testing System aSt. Paul MN: Author1 aAssessment-Systems-Corporation uhttp://www.iacat.org/content/users-manual-microcat-testing-system00345nas a2200109 4500008004100000245004500041210004500086300001000131490000900141100001400150856007100164 1984 eng d00aUsing microcomputers to administer tests0 aUsing microcomputers to administer tests a16-200 v3(2)1 aWard, W C uhttp://www.iacat.org/content/using-microcomputers-administer-tests00419nas a2200109 4500008004100000245007300041210006900114300001000183490000900193100001500202856009200217 1984 eng d00aUsing microcomputers to administer tests: An alternate point of view0 aUsing microcomputers to administer tests An alternate point of v a20-210 v3(2)1 aMillman, J uhttp://www.iacat.org/content/using-microcomputers-administer-tests-alternate-point-view00622nas a2200097 4500008004100000245008500041210006900126260020700195100001800402856010400420 1982 eng d00aUse of Sequential Testing to Prescreen Prospective Entrants to Military Service.0 aUse of Sequential Testing to Prescreen Prospective Entrants to M aD. J. Weiss (Ed.), Proceedings of the 1982 Item Response Theory and Computerized Adaptive Testing Conference Minneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aWeitzman, R A uhttp://www.iacat.org/content/use-sequential-testing-prescreen-prospective-entrants-military-service00569nas a2200097 4500008004100000245014500041210006900186260007800255100001700333856012100350 1981 eng d00aThe use of the sequential probability ratio test in making grade classifications in conjunction with tailored testing (Research Report 81-4)0 ause of the sequential probability ratio test in making grade cla aColumbia MO: University of Missouri, Department of Educational Psychology1 aReckase, M D uhttp://www.iacat.org/content/use-sequential-probability-ratio-test-making-grade-classifications-conjunction-tailored00374nas a2200109 4500008004100000245005800041210005600099300001200155490000600167100001600173856007500189 1977 En d00aA Use of the Information Function in Tailored Testing0 aUse of the Information Function in Tailored Testing a233-2470 v11 aSamejima, F uhttp://www.iacat.org/content/use-information-function-tailored-testing00646nas a2200097 4500008004100000245014800041210006900189260015300258100001400411856012300425 1976 eng d00aUsing computerized tests to add new dimensions to the measurement of abilities which are important for on-job performance: An exploratory study0 aUsing computerized tests to add new dimensions to the measuremen aC. K. Clark (Ed.), Proceedings of the First Conference on Computerized Adaptive Testing (pp. 64-74). Washington DC: U.S. Government Printing Office.1 aCory, C H uhttp://www.iacat.org/content/using-computerized-tests-add-new-dimensions-measurement-abilities-which-are-important-job00430nas a2200109 4500008004100000245008500041210006900126300001200195490000700207100001700214856008900231 1969 eng d00aUse of an on-line computer for psychological testing with the up-and-down method0 aUse of an online computer for psychological testing with the upa a207-2110 v241 aKappauf, W E uhttp://www.iacat.org/content/use-line-computer-psychological-testing-and-down-method