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/s12887-020-2018-101943nas a2200145 4500008003900000245007700039210006900116300001200185490000700197520148100204100002101685700002301706700002301729856004501752 2019 d00aComputerized Adaptive Testing for Cognitively Based Multiple-Choice Data0 aComputerized Adaptive Testing for Cognitively Based MultipleChoi a388-4010 v433 aCognitive diagnosis models (CDMs) are latent class models that hold great promise for providing diagnostic information about student knowledge profiles. The increasing use of computers in classrooms enhances the advantages of CDMs for more efficient diagnostic testing by using adaptive algorithms, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT). When multiple-choice items are involved, CD-CAT can be further improved by using polytomous scoring (i.e., considering the specific options students choose), instead of dichotomous scoring (i.e., marking answers as either right or wrong). In this study, the authors propose and evaluate the performance of the Jensen–Shannon divergence (JSD) index as an item selection method for the multiple-choice deterministic inputs, noisy “and” gate (MC-DINA) model. Attribute classification accuracy and item usage are evaluated under different conditions of item quality and test termination rule. The proposed approach is compared with the random selection method and an approximate approach based on dichotomized responses. The results show that under the MC-DINA model, JSD improves the attribute classification accuracy significantly by considering the information from distractors, even with a very short test length. This result has important implications in practical classroom settings as it can allow for dramatically reduced testing times, thus resulting in more targeted learning opportunities.1 aYigit, Hulya, D.1 aSorrel, Miguel, A.1 ade la Torre, Jimmy uhttps://doi.org/10.1177/014662161879866501598nas a2200157 4500008003900000245012200039210006900161300001200230490000700242520104300249100002401292700001601316700002001332700002501352856006301377 2019 d00aComputerized Adaptive Testing in Early Education: Exploring the Impact of Item Position Effects on Ability Estimation0 aComputerized Adaptive Testing in Early Education Exploring the I a437-4510 v563 aAbstract Studies have shown that item difficulty can vary significantly based on the context of an item within a test form. In particular, item position may be associated with practice and fatigue effects that influence item parameter estimation. The purpose of this research was to examine the relevance of item position specifically for assessments used in early education, an area of testing that has received relatively limited psychometric attention. In an initial study, multilevel item response models fit to data from an early literacy measure revealed statistically significant increases in difficulty for items appearing later in a 20-item form. The estimated linear change in logits for an increase of 1 in position was .024, resulting in a predicted change of .46 logits for a shift from the beginning to the end of the form. A subsequent simulation study examined impacts of item position effects on person ability estimation within computerized adaptive testing. Implications and recommendations for practice are discussed.1 aAlbano, Anthony, D.1 aCai, Liuhan1 aLease, Erin, M.1 aMcConnell, Scott, R. uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/jedm.1221501434nas a2200133 4500008003900000245010800039210006900147300001200216490000700228520097100235100001201206700001901218856006301237 2018 d00aA Comparison of Constraint Programming and Mixed-Integer Programming for Automated Test-Form Generation0 aComparison of Constraint Programming and MixedInteger Programmin a435-4560 v553 aAbstract The final step of the typical process of developing educational and psychological tests is to place the selected test items in a formatted form. The step involves the grouping and ordering of the items to meet a variety of formatting constraints. As this activity tends to be time-intensive, the use of mixed-integer programming (MIP) has been proposed to automate it. The goal of this article is to show how constraint programming (CP) can be used as an alternative to automate test-form generation problems with a large variety of formatting constraints, and how it compares with MIP-based form generation as for its models, solutions, and running times. Two empirical examples are presented: (i) automated generation of a computerized fixed-form; and (ii) automated generation of shadow tests for multistage testing. Both examples show that CP works well with feasible solutions and running times likely to be better than that for MIP-based applications.1 aLi, Jie1 aLinden, Wim, J uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/jedm.1218701188nas a2200133 4500008003900000245006600039210006500105300001200170490000700182520079400189100001300983700001300996856004501009 2018 d00aConstructing Shadow Tests in Variable-Length Adaptive Testing0 aConstructing Shadow Tests in VariableLength Adaptive Testing a538-5520 v423 aImposing content constraints is very important in most operational computerized adaptive testing (CAT) programs in educational measurement. Shadow test approach to CAT (Shadow CAT) offers an elegant solution to imposing statistical and nonstatistical constraints by projecting future consequences of item selection. The original form of Shadow CAT presumes fixed test lengths. The goal of the current study was to extend Shadow CAT to tests under variable-length termination conditions and evaluate its performance relative to other content balancing approaches. The study demonstrated the feasibility of constructing Shadow CAT with variable test lengths and in operational CAT programs. The results indicated the superiority of the approach compared with other content balancing methods.1 aDiao, Qi1 aRen, Hao uhttps://doi.org/10.1177/014662161775373601267nas a2200157 4500008003900000245009900039210006900138300001200207490000700219520076400226100001800990700001501008700001801023700002301041856004501064 2018 d00aA Continuous a-Stratification Index for Item Exposure Control in Computerized Adaptive Testing0 aContinuous aStratification Index for Item Exposure Control in Co a523-5370 v423 aThe method of a-stratification aims to reduce item overexposure in computerized adaptive testing, as items that are administered at very high rates may threaten the validity of test scores. In existing methods of a-stratification, the item bank is partitioned into a fixed number of nonoverlapping strata according to the items’a, or discrimination, parameters. This article introduces a continuous a-stratification index which incorporates exposure control into the item selection index itself and thus eliminates the need for fixed discrete strata. The new continuous a-stratification index is compared with existing stratification methods via simulation studies in terms of ability estimation bias, mean squared error, and control of item exposure rates.1 aHuebner, Alan1 aWang, Chun1 aDaly, Bridget1 aPinkelman, Colleen uhttps://doi.org/10.1177/014662161875828903113nas a2200145 4500008004100000245004900041210004500090260005500135520264100190653002802831653000802859653002102867100001702888856006202905 2017 eng d00aIs CAT Suitable for Automated Speaking Test?0 aCAT Suitable for Automated Speaking Test aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

We have developed automated scoring system of Japanese speaking proficiency, namely SJ-CAT (Speaking Japanese Computerized Adaptive Test), which is operational for last few months. One of the unique features of the test is an adaptive test base on polytomous IRT.

SJ-CAT consists of two sections; Section 1 has sentence reading aloud tasks and a multiple choicereading tasks and Section 2 has sentence generation tasks and an open answer tasks. In reading aloud tasks, a test taker reads a phoneme-balanced sentence on the screen after listening to a model reading. In a multiple choice-reading task, a test taker sees a picture and reads aloud one sentence among three sentences on the screen, which describe the scene most appropriately. In a sentence generation task, a test taker sees a picture or watches a video clip and describes the scene with his/her own words for about ten seconds. In an open answer tasks, the test taker expresses one’s support for or opposition to e.g., a nuclear power generation with reasons for about 30 seconds.

In the course of the development of the test, we found many unexpected and unique characteristics of speaking CAT, which are not found in usual CATs with multiple choices. In this presentation, we will discuss some of such factors that are not previously noticed in our previous project of developing dichotomous J-CAT (Japanese Computerized Adaptive Test), which consists of vocabulary, grammar, reading, and listening. Firstly, we will claim that distribution of item difficulty parameters depends on the types of items. An item pool with unrestricted types of items such as open questions is difficult to achieve ideal distributions, either normal distribution or uniform distribution. Secondly, contrary to our expectations, open questions are not necessarily more difficult to operate in automated scoring system than more restricted questions such as sentence reading, as long as if one can set up suitable algorithm for open question scoring. Thirdly, we will show that the speed of convergence of standard deviation of posterior distribution, or standard error of theta parameter in polytomous IRT used for SJCAT is faster than dichotomous IRT used in J-CAT. Fourthly, we will discuss problems in equation of items in SJ-CAT, and suggest introducing deep learning with reinforcement learning instead of equation. And finally, we will discuss the issues of operation of SJ-CAT on the web, including speed of scoring, operation costs, security among others.

Session Video

10aAutomated Speaking Test10aCAT10alanguage testing1 aImai, Shingo uhttp://www.iacat.org/cat-suitable-automated-speaking-test05044nas a2200145 4500008004100000245008900041210006900130260005500199520447200254653000804726653002904734100001804763700001504781856010204796 2017 eng d00aComparison of Pretest Item Calibration Methods in a Computerized Adaptive Test (CAT)0 aComparison of Pretest Item Calibration Methods in a Computerized aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Calibration methods for pretest items in a computerized adaptive test (CAT) are not a new area of research inquiry. After decades of research on CAT, the fixed item parameter calibration (FIPC) method has been widely accepted and used by practitioners to address two CAT calibration issues: (a) a restricted ability range each item is exposed to, and (b) a sparse response data matrix. In FIPC, the parameters of the operational items are fixed at their original values, and multiple expectation maximization (EM) cycles are used to estimate parameters of the pretest items with prior ability distribution being updated multiple times (Ban, Hanson, Wang, Yi, & Harris, 2001; Kang & Peterson, 2009; Pommerich & Segall, 2003).

Another calibration method is the fixed person parameter calibration (FPPC) method proposed by Stocking (1988) as “Method A.” Under this approach, candidates’ ability estimates are fixed in the calibration of pretest items and they define the scale on which the parameter estimates are reported. The logic of FPPC is suitable for CAT applications because the person parameters are estimated based on operational items and available for pretest item calibration. In Stocking (1988), the FPPC was evaluated using the LOGIST computer program developed by Wood, Wingersky, and Lord (1976). He reported that “Method A” produced larger root mean square errors (RMSEs) in the middle ability range than “Method B,” which required the use of anchor items (administered non-adaptively) and linking steps to attempt to correct for the potential scale drift due to the use of imperfect ability estimates.

Since then, new commercial software tools such as BILOG-MG and flexMIRT (Cai, 2013) have been developed to handle the FPPC method with different implementations (e.g., the MH-RM algorithm with flexMIRT). The performance of the FPPC method with those new software tools, however, has rarely been researched in the literature.

In our study, we evaluated the performance of two pretest item calibration methods using flexMIRT, the new software tool. The FIPC and FPPC are compared under various CAT settings. Each simulated exam contains 75% operational items and 25% pretest items, and real item parameters are used to generate the CAT data. This study also addresses the lack of guidelines in existing CAT item calibration literature regarding population ability shift and exam length (more accurate theta estimates are expected in longer exams). Thus, this study also investigates the following four factors and their impact on parameter estimation accuracy, including: (1) candidate population changes (3 ability distributions); (2) exam length (20: 15 OP + 5 PT, 40: 30 OP + 10 PT, and 60: 45 OP + 15 PT); (3) data model fit (3PL and 3PL with fixed C), and (4) pretest item calibration sample sizes (300, 500, and 1000). This study’s findings will fill the gap in this area of research and thus provide new information on which practitioners can base their decisions when selecting a pretest calibration method for their exams.

References

Ban, J. C., Hanson, B. A., Wang, T., Yi, Q., & Harris, D. J. (2001). A comparative study of online pretest item—Calibration/scaling methods in computerized adaptive testing. Journal of Educational Measurement, 38(3), 191–212.

Cai, L. (2013). flexMIRT® Flexible Multilevel Multidimensional Item Analysis and Test Scoring (Version 2) [Computer software]. Chapel Hill, NC: Vector Psychometric Group.

Kang, T., & Petersen, N. S. (2009). Linking item parameters to a base scale (Research Report No. 2009– 2). Iowa City, IA: ACT.

Pommerich, M., & Segall, D.O. (2003, April). Calibrating CAT pools and online pretest items using marginal maximum likelihood methods. Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago, IL.

Stocking, M. L. (1988). Scale drift in online calibration (Research Report No. 88–28). Princeton, NJ: Educational Testing Service.

Wood, R. L., Wingersky, M. S., & Lord, F. M. (1976). LOGIST: A computer program for estimating examinee ability and item characteristic curve parameters (RM76-6) [Computer program]. Princeton, NJ: Educational Testing Service.

Session Video

10aCAT10aPretest Item Calibration1 aMeng, Huijuan1 aHan, Chris uhttp://www.iacat.org/comparison-pretest-item-calibration-methods-computerized-adaptive-test-cat-001722nas a2200133 4500008004100000245009200041210006900133260005500202520120200257653000801459653001601467100001801483856008701501 2017 eng d00aA Comparison of Three Empirical Reliability Estimates for Computerized Adaptive Testing0 aComparison of Three Empirical Reliability Estimates for Computer aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Reliability estimates in Computerized Adaptive Testing (CAT) are derived from estimated thetas and standard error of estimated thetas. In practical, the observed standard error (OSE) of estimated thetas can be estimated by test information function for each examinee with respect to Item response theory (IRT). Unlike classical test theory (CTT), OSEs in IRT are conditional values given each estimated thetas so that those values should be marginalized to consider test reliability. Arithmetic mean, Harmonic mean, and Jensen equality were applied to marginalize OSEs to estimate CAT reliability. Based on different marginalization method, three empirical CAT reliabilities were compared with true reliabilities. Results showed that three empirical CAT reliabilities were underestimated compared to true reliability in short test length (< 40), whereas the magnitude of CAT reliabilities was followed by Jensen equality, Harmonic mean, and Arithmetic mean in long test length (> 40). Specifically, Jensen equality overestimated true reliability across all conditions in long test length (>50).

Session Video 

10aCAT10aReliability1 aSeo, Dong, Gi uhttps://drive.google.com/file/d/1gXgH-epPIWJiE0LxMHGiCAxZZAwy4dAH/view?usp=sharing02166nas a2200157 4500008003900000245007600039210006900115300001200184490000700196520167900203100002001882700001801902700001801920700002501938856004501963 2017 d00aIs a Computerized Adaptive Test More Motivating Than a Fixed-Item Test?0 aComputerized Adaptive Test More Motivating Than a FixedItem Test a495-5110 v413 aComputer adaptive tests provide important measurement advantages over traditional fixed-item tests, but research on the psychological reactions of test takers to adaptive tests is lacking. In particular, it has been suggested that test-taker engagement, and possibly test performance as a consequence, could benefit from the control that adaptive tests have on the number of test items examinees answer correctly. However, previous research on this issue found little support for this possibility. This study expands on previous research by examining this issue in the context of a mathematical ability assessment and by considering the possible effect of immediate feedback of response correctness on test engagement, test anxiety, time on task, and test performance. Middle school students completed a mathematics assessment under one of three test type conditions (fixed, adaptive, or easier adaptive) and either with or without immediate feedback about the correctness of responses. Results showed little evidence for test type effects. The easier adaptive test resulted in higher engagement and lower anxiety than either the adaptive or fixed-item tests; however, no significant differences in performance were found across test types, although performance was significantly higher across all test types when students received immediate feedback. In addition, these effects were not related to ability level, as measured by the state assessment achievement levels. The possibility that test experiences in adaptive tests may not in practice be significantly different than in fixed-item tests is raised and discussed to explain the results of this and previous studies.1 aLing, Guangming1 aAttali, Yigal1 aFinn, Bridgid1 aStone, Elizabeth, A. uhttps://doi.org/10.1177/014662161770755604446nas a2200157 4500008004100000245009700041210006900138260005500207520381800262653001104080653002804091100002004119700001804139700002104157856011004178 2017 eng d00aComputerized Adaptive Testing for Cognitive Diagnosis in Classroom: A Nonparametric Approach0 aComputerized Adaptive Testing for Cognitive Diagnosis in Classro aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

In the past decade, CDMs of educational test performance have received increasing attention among educational researchers (for details, see Fu & Li, 2007, and Rupp, Templin, & Henson, 2010). CDMs of educational test performance decompose the ability domain of a given test into specific skills, called attributes, each of which an examinee may or may not have mastered. The resulting attribute profile documents the individual’s strengths and weaknesses within the ability domain. The Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) has been suggested by researchers as a diagnostic tool for assessment and evaluation (e.g., Cheng & Chang, 2007; Cheng, 2009; Liu, You, Wang, Ding, & Chang, 2013; Tatsuoka & Tatsuoka, 1997). While model-based CD-CAT is relatively well-researched in the context of large-scale assessments, this type of system has not received the same degree of development in small-scale settings, where it would be most useful. The main challenge is that the statistical estimation techniques successfully applied to the parametric CD-CAT require large samples to guarantee the reliable calibration of item parameters and accurate estimation of examinees’ attribute profiles. In response to the challenge, a nonparametric approach that does not require any parameter calibration, and thus can be used in small educational programs, is proposed. The proposed nonparametric CD-CAT relies on the same principle as the regular CAT algorithm, but uses the nonparametric classification method (Chiu & Douglas, 2013) to assess and update the student’s ability state while the test proceeds. Based on a student’s initial responses, 2 a neighborhood of candidate proficiency classes is identified, and items not characteristic of the chosen proficiency classes are precluded from being chosen next. The response to the next item then allows for an update of the skill profile, and the set of possible proficiency classes is further narrowed. In this manner, the nonparametric CD-CAT cycles through item administration and update stages until the most likely proficiency class has been pinpointed. The simulation results show that the proposed method outperformed the compared parametric CD-CAT algorithms and the differences were significant when the item parameter calibration was not optimal.

References

Cheng, Y. (2009). When cognitive diagnosis meets computerized adaptive testing: CD-CAT. Psychometrika, 74, 619-632.

Cheng, Y., & Chang, H. (2007). The modified maximum global discrimination index method for cognitive diagnostic CAT. In D. Weiss (Ed.) Proceedings of the 2007 GMAC Computerized Adaptive Testing Conference.

Chiu, C.-Y., & Douglas, J. A. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification, 30, 225-250.

Fu, J., & Li, Y. (2007). An integrative review of cognitively diagnostic psychometric models. Paper presented at the Annual Meeting of the National Council on Measurement in Education. Chicago, Illinois.

Liu, H., You, X., Wang, W., Ding, S., & Chang, H. (2013). The development of computerized adaptive testing with cognitive diagnosis for an English achievement test in China. Journal of Classification, 30, 152-172.

Rupp, A. A., & Templin, J. L., & Henson, R. A. (2010). Diagnostic Measurement. Theory, Methods, and Applications. New York: Guilford.

Tatsuoka, K.K., & Tatsuoka, M.M. (1997), Computerized cognitive diagnostic adaptive testing: Effect on remedial instruction as empirical validation. Journal of Educational Measurement, 34, 3–20.

Session Video

10aCD-CAT10anon-parametric approach1 aChang, Yuan-Pei1 aChiu, Chia-Yi1 aTsai, Rung-Ching uhttp://www.iacat.org/computerized-adaptive-testing-cognitive-diagnosis-classroom-nonparametric-approach-001506nas a2200133 4500008004100000245006000041210005900101260005500160520103000215653001501245653002001260100002101280856007101301 2017 eng d00aConcerto 5 Open Source CAT Platform: From Code to Nodes0 aConcerto 5 Open Source CAT Platform From Code to Nodes aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Concerto 5 is the newest version of the Concerto open source R-based Computer-Adaptive Testing platform, which is currently used in educational testing and in clinical trials. In our quest to make CAT accessible to all, the latest version uses flowchart nodes to connect different elements of a test, so that CAT test creation is an intuitive high-level process that does not require writing code.

A test creator might connect an Info Page node, to a Consent Page node, to a CAT node, to a Feedback node. And after uploading their items, their test is done.

This talk will show the new flowchart interface, and demonstrate the creation of a CAT test from scratch in less than 10 minutes.

Concerto 5 also includes a new Polytomous CAT node, so CATs with Likert items can be easily created in the flowchart interface. This node is currently used in depression and anxiety tests in a clinical trial.

Session Video

10aConcerto 510aOpen Source CAT1 aStillwell, David uhttps://drive.google.com/open?id=11eu1KKILQEoK5c-CYO1P1AiJgiQxX0E003452nas a2200157 4500008004100000245008400041210006900125260005500194520283600249653001203085653002503097653002803122100002303150700002003173856010103193 2017 eng d00aConsiderations in Performance Evaluations of Computerized Formative Assessments0 aConsiderations in Performance Evaluations of Computerized Format aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Computerized adaptive instruments have been widely established and used in the context of summative assessments for purposes including licensure, admissions and proficiency testing. The benefits of examinee tailored examinations, which can provide estimates of performance that are more reliable and valid, have in recent years attracted a greater audience (i.e. patient oriented outcomes, test prep, etc.). Formative assessment, which are most widely understood in their implementation as diagnostic tools, have recently started to expand to lesser known areas of computerized testing such as in implementations of instructional designs aiming to maximize examinee learning through targeted practice.

Using a CAT instrument within the framework of evaluating repetitious examinee performances (in such settings as a Quiz Bank practices for example) poses unique challenges not germane to summative assessments. The scale on which item parameters (and subsequently examinee performance estimates such as Maximum Likelihood Estimates) are determined usually do not take change over time under consideration. While vertical scaling features resolve the learning acquisition problem, most content practice engines do not make use of explicit practice windows which could be vertically aligned. Alternatively, the Multidimensional (MIRT)- and Hierarchical Item Response Theory (HIRT) models allow for the specification of random effects associated with change over time in examinees’ skills, but are often complex and require content and usage resources not often observed.

The research submitted for consideration simulated examinees’ repeated variable length Quiz Bank practice in algebra using a 500 1-PL operational item pool. The stability simulations sought to determine with which rolling item interval size ability estimates would provide the most informative insight into the examinees’ learning progression over time. Estimates were evaluated in terms of reduction in estimate uncertainty, bias and RMSD with the true and total item based ability estimates. It was found that rolling item intervals between 20-25 items provided the best reduction of uncertainty around the estimate without compromising the ability to provide informed performance estimates to students. However, while asymptotically intervals of 20-25 items tended to provide adequate estimates of performance, changes over shorter periods of time assessed with shorter quizzes could not be detected as those changes would be suppressed in lieu of the performance based on the full interval considered. Implications for infrastructure (such as recommendation engines, etc.), product and scale development are discussed.

Session video

10aalgebra10aFormative Assessment10aPerformance Evaluations1 aChajewski, Michael1 aHarnisher, John uhttp://www.iacat.org/considerations-performance-evaluations-computerized-formative-assessments-001967nas a2200133 4500008004100000245008000041210006900121260005500190520145700245653002001702653002101722100001901743856007101762 2017 eng d00aConstruction of Gratitude Scale Using Polytomous Item Response Theory Model0 aConstruction of Gratitude Scale Using Polytomous Item Response T aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Various studies have shown that gratitude is essential to increase the happiness and quality of life of every individual. Unfortunately, research on gratitude still received little attention, and there is no standardized measurement for it. Gratitude measurement scale was developed overseas, and has not adapted to the Indonesian culture context. Moreover, the scale development is generally performed with classical theory approach, which has some drawbacks. This research will develop a gratitude scale using polytomous Item Response Theory model (IRT) by applying the Partial Credit Model (PCM).

The pilot study results showed that the gratitude scale (with 44 items) is a reliable measure (α = 0.944) and valid (meet both convergent and discriminant validity requirements). The pilot study results also showed that the gratitude scale satisfies unidimensionality assumptions.

The test results using the PCM model showed that the gratitude scale had a fit model. Of 44 items, there was one item that does not fit, so it was eliminated. Second test results for the remaining 43 items showed that they fit the model, and all items were fit to measure gratitude. Analysis using Differential Item Functioning (DIF) showed four items have a response bias based on gender. Thus, there are 39 items remaining in the scale.

Session Video 

10aGratitude Scale10apolytomous items1 aArbiyah, Nurul uhttps://drive.google.com/open?id=1pHhO4cq2-wh24ht3nBAoXNHv7234_mjH01793nas a2200121 4500008003900000245010500039210006900144300001200213490000700225520137100232100001501603856005301618 2016 d00aA Comparison of Constrained Item Selection Methods in Multidimensional Computerized Adaptive Testing0 aComparison of Constrained Item Selection Methods in Multidimensi a346-3600 v403 aThe construction of assessments in computerized adaptive testing (CAT) usually involves fulfilling a large number of statistical and non-statistical constraints to meet test specifications. To improve measurement precision and test validity, the multidimensional priority index (MPI) and the modified MPI (MMPI) can be used to monitor many constraints simultaneously under a between-item and a within-item multidimensional framework, respectively. As both item selection methods can be implemented easily and computed efficiently, they are important and useful for operational CATs; however, no thorough simulation study has compared the performance of these two item selection methods under two different item bank structures. The purpose of this study was to investigate the efficiency of the MMPI and the MPI item selection methods under the between-item and within-item multidimensional CAT through simulations. The MMPI and the MPI item selection methods yielded similar performance in measurement precision for both multidimensional pools and yielded similar performance in exposure control and constraint management for the between-item multidimensional pool. For the within-item multidimensional pool, the MPI method yielded slightly better performance in exposure control but yielded slightly worse performance in constraint management than the MMPI method.1 aSu, Ya-Hui uhttp://apm.sagepub.com/content/40/5/346.abstract01809nas a2200133 4500008003900000245010300039210006900142300001200211490000700223520134300230100002201573700002701595856005301622 2016 d00aOn Computing the Key Probability in the Stochastically Curtailed Sequential Probability Ratio Test0 aComputing the Key Probability in the Stochastically Curtailed Se a142-1560 v403 aThe Stochastically Curtailed Sequential Probability Ratio Test (SCSPRT) is a termination criterion for computerized classification tests (CCTs) that has been shown to be more efficient than the well-known Sequential Probability Ratio Test (SPRT). The performance of the SCSPRT depends on computing the probability that at a given stage in the test, an examinee’s current interim classification status will not change before the end of the test. Previous work discusses two methods of computing this probability, an exact method in which all potential responses to remaining items are considered and an approximation based on the central limit theorem (CLT) requiring less computation. Generally, the CLT method should be used early in the test when the number of remaining items is large, and the exact method is more appropriate at later stages of the test when few items remain. However, there is currently a dearth of information as to the performance of the SCSPRT when using the two methods. For the first time, the exact and CLT methods of computing the crucial probability are compared in a simulation study to explore whether there is any effect on the accuracy or efficiency of the CCT. The article is focused toward practitioners and researchers interested in using the SCSPRT as a termination criterion in an operational CCT.1 aHuebner, Alan, R.1 aFinkelman, Matthew, D. uhttp://apm.sagepub.com/content/40/2/142.abstract01433nas a2200145 4500008003900000245011700039210006900156300001200225490000700237520092600244100002601170700001401196700002401210856005301234 2015 d00aComparing Simple Scoring With IRT Scoring of Personality Measures: The Navy Computer Adaptive Personality Scales0 aComparing Simple Scoring With IRT Scoring of Personality Measure a144-1540 v393 a

This article analyzes data from U.S. Navy sailors (N = 8,956), with the central measure being the Navy Computer Adaptive Personality Scales (NCAPS). Analyses and results from this article extend and qualify those from previous research efforts by examining the properties of the NCAPS and its adaptive structure in more detail. Specifically, this article examines item exposure rates, the efficiency of item use based on item response theory (IRT)–based Expected A Posteriori (EAP) scoring, and a comparison of IRT-EAP scoring with much more parsimonious scoring methods that appear to work just as well (stem-level scoring and dichotomous scoring). The cutting-edge nature of adaptive personality testing will necessitate a series of future efforts like this: to examine the benefits of adaptive scoring schemes and novel measurement methods continually, while pushing testing technology further ahead.

1 aOswald, Frederick, L.1 aShaw, Amy1 aFarmer, William, L. uhttp://apm.sagepub.com/content/39/2/144.abstract01505nas a2200157 4500008003900000022001400039245008900053210006900142300001200211490000700223520101800230100001701248700001501265700002601280856004101306 2015 d a1745-398400aA Comparison of IRT Proficiency Estimation Methods Under Adaptive Multistage Testing0 aComparison of IRT Proficiency Estimation Methods Under Adaptive  a70–790 v523 aThis inquiry is an investigation of item response theory (IRT) proficiency estimators’ accuracy under multistage testing (MST). We chose a two-stage MST design that includes four modules (one at Stage 1, three at Stage 2) and three difficulty paths (low, middle, high). We assembled various two-stage MST panels (i.e., forms) by manipulating two assembly conditions in each module, such as difficulty level and module length. For each panel, we investigated the accuracy of examinees’ proficiency levels derived from seven IRT proficiency estimators. The choice of Bayesian (prior) versus non-Bayesian (no prior) estimators was of more practical significance than the choice of number-correct versus item-pattern scoring estimators. The Bayesian estimators were slightly more efficient than the non-Bayesian estimators, resulting in smaller overall error. Possible score changes caused by the use of different proficiency estimators would be nonnegligible, particularly for low- and high-performing examinees.1 aKim, Sooyeon1 aMoses, Tim1 aYoo, Hanwook, (Henry) uhttp://dx.doi.org/10.1111/jedm.1206301278nas a2200121 4500008003900000245009800039210006900137490000700206520079800213100001701011700001901028856010901047 2015 d00aConsidering the Use of General and Modified Assessment Items in Computerized Adaptive Testing0 aConsidering the Use of General and Modified Assessment Items in 0 v283 aThis article used several data sets from a large-scale state testing program to examine the feasibility of combining general and modified assessment items in computerized adaptive testing (CAT) for different groups of students. Results suggested that several of the assumptions made when employing this type of mixed-item CAT may not be met for students with disabilities that have typically taken alternate assessments based on modified achievement standards (AA-MAS). A simulation study indicated that the abilities of AA-MAS students can be underestimated or overestimated by the mixed-item CAT, depending on students’ location on the underlying ability scale. These findings held across grade levels and test lengths. The mixed-item CAT appeared to function well for non-AA-MAS students.1 aWyse, A., E.1 aAlbano, A., D. uhttp://www.iacat.org/considering-use-general-and-modified-assessment-items-computerized-adaptive-testing00532nas a2200145 4500008004500000245013100045210006900176300001000245490000600255100001900261700001100280700001600291700001500307856006400322 2014 Engldsh 00aCognitive Diagnostic Models and Computerized Adaptive Testing: Two New Item-Selection Methods That Incorporate Response Times0 aCognitive Diagnostic Models and Computerized Adaptive Testing Tw a59-760 v21 aFinkelman, M D1 aKim, W1 aWeissman, A1 aCook, R.J. uhttp://www.iacat.org/jcat/index.php/jcat/article/view/43/2101689nas a2200145 4500008003900000245007800039210006900117300001200186490000700198520124300205100001201448700001301460700001701473856005301490 2014 d00aA Comparison of Four Item-Selection Methods for Severely Constrained CATs0 aComparison of Four ItemSelection Methods for Severely Constraine a677-6960 v743 a

This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several constraints at the same time). The procedures examined in the study included the weighted deviation model (WDM), the weighted penalty model (WPM), the maximum priority index (MPI), and the shadow test approach (STA). In addition, two modified versions of the MPI procedure were introduced to deal with an edge case condition that results in the item selection procedure becoming dysfunctional during a test. The results suggest that the STA worked best among all candidate methods in terms of measurement accuracy and constraint management. For the other three heuristic approaches, they did not differ significantly in measurement accuracy and constraint management at the lower bound level. However, the WPM method appears to perform considerably better in overall constraint management than either the WDM or MPI method. Limitations and future research directions were also discussed.

1 aHe, Wei1 aDiao, Qi1 aHauser, Carl uhttp://epm.sagepub.com/content/74/4/677.abstract00553nas a2200133 4500008004500000245011300045210006900158260001200227300001000239490000600249100002700255700001700282856012000299 2014 Engldsh 00aA Comparison of Multi-Stage and Linear Test Designs for Medium-Size Licensure and Certification Examinations0 aComparison of MultiStage and Linear Test Designs for MediumSize  c02-2014 a18-360 v21 aBrossman, Bradley., G.1 aGuille, R.A. uhttp://www.iacat.org/content/comparison-multi-stage-and-linear-test-designs-medium-size-licensure-and-certification01373nas 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.abstract00491nam a2200133 4500008004100000020002500041245006100066210006000127260002900187100001600216700002500232700001900257856008100276 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://www.iacat.org/computerized-multistage-testing-theory-and-applications02024nas a2200121 4500008003900000245011000039210006900149300000900218490000700227520160200234100001501836856005101851 2013 d00aComparing the Performance of Five Multidimensional CAT Selection Procedures With Different Stopping Rules0 aComparing the Performance of Five Multidimensional CAT Selection a3-230 v373 a

Through simulated data, five multidimensional computerized adaptive testing (MCAT) selection procedures with varying test lengths are examined and compared using different stopping rules. Fixed item exposure rates are used for all the items, and the Priority Index (PI) method is used for the content constraints. Two stopping rules, standard error (SE) and predicted standard error reduction (PSER), are proposed; each MCAT selection process is stopped if either the required precision has been achieved or the selected number of items has reached the maximum limit. The five procedures are as follows: minimum angle (Ag), volume (Vm), minimize the error variance of the linear combination (V 1), minimize the error variance of the composite score with the optimized weight (V 2), and Kullback–Leibler (KL) information. The recovery for the domain scores or content scores and their overall score, test length, and test reliability are compared across the five MCAT procedures and between the two stopping rules. It is found that the two stopping rules are implemented successfully and that KL uses the least number of items to reach the same precision level, followed by Vm; Ag uses the largest number of items. On average, to reach a precision of SE = .35, 40, 55, 63, 63, and 82 items are needed for KL, Vm, V 1, V 2, and Ag, respectively, for the SE stopping rule. PSER yields 38, 45, 53, 58, and 68 items for KL, Vm, V 1, V 2, and Ag, respectively; PSER yields only slightly worse results than SE, but with much fewer items. Overall, KL is recommended for varying-length MCAT.

1 aYao, Lihua uhttp://apm.sagepub.com/content/37/1/3.abstract00520nas a2200121 4500008004500000245011300045210006900158300001000227490000600237100001300243700001900256856012300275 2013 Engldsh 00aA Comparison of Computerized Classification Testing and Computerized Adaptive Testing in Clinical Psychology0 aComparison of Computerized Classification Testing and Computeriz a19-370 v11 aSmits, N1 aFinkelman, M D uhttp://www.iacat.org/content/comparison-computerized-classification-testing-and-computerized-adaptive-testing-clinical00504nas a2200133 4500008003900000245011400039210006900153300001200222490000700234100001900241700001800260700002400278856006800302 2013 d00aA Comparison of Exposure Control Procedures in CAT Systems Based on Different Measurement Models for Testlets0 aComparison of Exposure Control Procedures in CAT Systems Based o a113-1350 v261 aBoyd, Aimee, M1 aDodd, Barbara1 aFitzpatrick, Steven uhttp://www.tandfonline.com/doi/abs/10.1080/08957347.2013.76543401640nas a2200157 4500008003900000245007600039210006900115300001200184490000700196520114800203100002301351700001801374700001601392700002101408856005301429 2013 d00aA Comparison of Exposure Control Procedures in CATs Using the 3PL Model0 aComparison of Exposure Control Procedures in CATs Using the 3PL  a857-8740 v733 a

This study compares the progressive-restricted standard error (PR-SE) exposure control procedure to three commonly used procedures in computerized adaptive testing, the randomesque, Sympson–Hetter (SH), and no exposure control methods. The performance of these four procedures is evaluated using the three-parameter logistic model under the manipulated conditions of item pool size (small vs. large) and stopping rules (fixed-length vs. variable-length). PR-SE provides the advantage of similar constraints to SH, without the need for a preceding simulation study to execute it. Overall for the large and small item banks, the PR-SE method administered almost all of the items from the item pool, whereas the other procedures administered about 52% or less of the large item bank and 80% or less of the small item bank. The PR-SE yielded the smallest amount of item overlap between tests across conditions and administered fewer items on average than SH. PR-SE obtained these results with similar, and acceptable, measurement precision compared to the other exposure control procedures while vastly improving on item pool usage.

1 aLeroux, Audrey, J.1 aLopez, Myriam1 aHembry, Ian1 aDodd, Barbara, G uhttp://epm.sagepub.com/content/73/5/857.abstract00583nas a2200121 4500008004600000245017700046210006900223300001100292490000600303100001100309700001700320856012400337 2013 Engldish 00aA Comparison of Four Methods for Obtaining Information Functions for Scores From Computerized Adaptive Tests With Normally Distributed Item Difficulties and Discriminations0 aComparison of Four Methods for Obtaining Information Functions f a88-1070 v11 aIto, K1 aSegall, D.O. uhttp://www.iacat.org/content/comparison-four-methods-obtaining-information-functions-scores-computerized-adaptive-tests01847nas a2200169 4500008004100000245012200041210006900163300001200232490000700244520124800251100001201499700001101511700001401522700001101536700001401547856011601561 2012 eng d00aComparison Between Dichotomous and Polytomous Scoring of Innovative Items in a Large-Scale Computerized Adaptive Test0 aComparison Between Dichotomous and Polytomous Scoring of Innovat a493-5090 v723 a

This study explored the impact of partial credit scoring of one type of innovative items (multiple-response items) in a computerized adaptive version of a large-scale licensure pretest and operational test settings. The impacts of partial credit scoring on the estimation of the ability parameters and classification decisions in operational test settings were explored in one real data analysis and two simulation studies when two different polytomous scoring algorithms, automated polytomous scoring and rater-generated polytomous scoring, were applied. For the real data analyses, the ability estimates from dichotomous and polytomous scoring were highly correlated; the classification consistency between different scoring algorithms was nearly perfect. Information distribution changed slightly in the operational item bank. In the two simulation studies comparing each polytomous scoring with dichotomous scoring, the ability estimates resulting from polytomous scoring had slightly higher measurement precision than those resulting from dichotomous scoring. The practical impact related to classification decision was minor because of the extremely small number of items that could be scored polytomously in this current study.

1 aJiao, H1 aLiu, J1 aHaynie, K1 aWoo, A1 aGorham, J uhttp://www.iacat.org/content/comparison-between-dichotomous-and-polytomous-scoring-innovative-items-large-scale01906nas a2200133 4500008003900000245013200039210006900171300001200240490000700252520142100259100001801680700002101698856005301719 2012 d00aComparison of Exposure Controls, Item Pool Characteristics, and Population Distributions for CAT Using the Partial Credit Model0 aComparison of Exposure Controls Item Pool Characteristics and Po a159-1750 v723 a

This study investigated item exposure control procedures under various combinations of item pool characteristics and ability distributions in computerized adaptive testing based on the partial credit model. Three variables were manipulated: item pool characteristics (120 items for each of easy, medium, and hard item pools), two ability distributions (normally distributed and negatively skewed data), and three exposure control procedures (randomesque procedure, progressive–restricted procedure, and maximum information procedure). A number of measurement precision indexes such as descriptive statistics, correlations between known and estimated ability levels, bias, root mean squared error, and average absolute difference, exposure rates, item usage, and item overlap were computed to assess the impact of matched or nonmatched item pool and ability distributions on the accuracy of ability estimation and the performance of exposure control procedures. As expected, the medium item pool produced better precision of measurement than both the easy and hard item pools. The progressive–restricted procedure performed better in terms of maximum exposure rates, item average overlap, and pool utilization than both the randomesque procedure and the maximum information procedure. The easy item pool with the negatively skewed data as a mismatched condition produced the worst performance.

1 aLee, HwaYoung1 aDodd, Barbara, G uhttp://epm.sagepub.com/content/72/1/159.abstract02776nas a2200229 4500008004100000022001400041245015400055210006900209260000900278300000800287490000700295520193000302653001802232653003702250653001102287653002702298653002302325653003702348100002002385700001902405856012202424 2012 eng d a1471-228800aComparison of two Bayesian methods to detect mode effects between paper-based and computerized adaptive assessments: a preliminary Monte Carlo study.0 aComparison of two Bayesian methods to detect mode effects betwee c2012 a1240 v123 a

BACKGROUND: Computerized adaptive testing (CAT) is being applied to health outcome measures developed as paper-and-pencil (P&P) instruments. Differences in how respondents answer items administered by CAT vs. P&P can increase error in CAT-estimated measures if not identified and corrected.

METHOD: Two methods for detecting item-level mode effects are proposed using Bayesian estimation of posterior distributions of item parameters: (1) a modified robust Z (RZ) test, and (2) 95% credible intervals (CrI) for the CAT-P&P difference in item difficulty. A simulation study was conducted under the following conditions: (1) data-generating model (one- vs. two-parameter IRT model); (2) moderate vs. large DIF sizes; (3) percentage of DIF items (10% vs. 30%), and (4) mean difference in θ estimates across modes of 0 vs. 1 logits. This resulted in a total of 16 conditions with 10 generated datasets per condition.

RESULTS: Both methods evidenced good to excellent false positive control, with RZ providing better control of false positives and with slightly higher power for CrI, irrespective of measurement model. False positives increased when items were very easy to endorse and when there with mode differences in mean trait level. True positives were predicted by CAT item usage, absolute item difficulty and item discrimination. RZ outperformed CrI, due to better control of false positive DIF.

CONCLUSIONS: Whereas false positives were well controlled, particularly for RZ, power to detect DIF was suboptimal. Research is needed to examine the robustness of these methods under varying prior assumptions concerning the distribution of item and person parameters and when data fail to conform to prior assumptions. False identification of DIF when items were very easy to endorse is a problem warranting additional investigation.

10aBayes Theorem10aData Interpretation, Statistical10aHumans10aMathematical Computing10aMonte Carlo Method10aOutcome Assessment (Health Care)1 aRiley, Barth, B1 aCarle, Adam, C uhttp://www.iacat.org/content/comparison-two-bayesian-methods-detect-mode-effects-between-paper-based-and-computerized01663nas a2200097 4500008004500000245007600045210006900121520126100190100001601451856009801467 2012 Engldsh 00aComputerized Adaptive Testing for Student Selection to Higher Education0 aComputerized Adaptive Testing for Student Selection to Higher Ed3 a

The purpose of the present study is to discuss applicability of computerized adaptive testing format as an alternative for current student selection examinations to higher education in Turkey. In the study, first problems associated with current student selection system are given. These problems exerts pressure on students that results in test anxiety, produce measurement experiences that can be criticized, and lessen credibility of student selection system. Next, computerized adaptive test are introduced and advantages they provide are presented. Then results of a study that used two research designs (simulation and live testing) were presented. Results revealed that (i) computerized adaptive format provided a reduction up to 80% in the number of items given to students compared to paper and pencil format of student selection examination, (ii) ability estimations have high reliabilities. Correlations between ability estimations obtained from simulation and traditional format were higher than 0.80. At the end of the study solutions provided by computerized adaptive testing implementation to the current problems were discussed. Also some issues for application of CAT format for student selection examinations in Turkey are given.

1 aKalender, I uhttp://www.iacat.org/content/computerized-adaptive-testing-student-selection-higher-education00479nas a2200109 4500008004100000245007800041210006900119260005000188490001000238100001700248856010400265 2012 eng d00aComputerized adaptive testing in industrial and organizational psychology0 aComputerized adaptive testing in industrial and organizational p aTwente, The NetherlandsbUniversity of Twente0 vPh.D.1 aMakransky, G uhttp://www.iacat.org/content/computerized-adaptive-testing-industrial-and-organizational-psychology01240nas a2200145 4500008003900000245009000039210006900129300001200198490000700210520076800217100001900985700001701004700002001021856005301041 2012 d00aComputerized Adaptive Testing Using a Class of High-Order Item Response Theory Models0 aComputerized Adaptive Testing Using a Class of HighOrder Item Re a689-7060 v363 a

In the human sciences, a common assumption is that latent traits have a hierarchical structure. Higher order item response theory models have been developed to account for this hierarchy. In this study, computerized adaptive testing (CAT) algorithms based on these kinds of models were implemented, and their performance under a variety of situations was examined using simulations. The results showed that the CAT algorithms were very effective. The progressive method for item selection, the Sympson and Hetter method with online and freeze procedure for item exposure control, and the multinomial model for content balancing can simultaneously maintain good measurement precision, item exposure control, content balance, test security, and pool usage.

1 aHuang, Hung-Yu1 aChen, Po-Hsi1 aWang, Wen-Chung uhttp://apm.sagepub.com/content/36/8/689.abstract01165nas a2200157 4500008004100000245005700041210005600098520065200154653002100806653003400827653001500861653002500876100001300901700001500914856007800929 2011 eng d00acatR: An R Package for Computerized Adaptive Testing0 acatR An R Package for Computerized Adaptive Testing3 a

Computerized adaptive testing (CAT) is an active current research field in psychometrics and educational measurement. However, there is very little software available to handle such adaptive tasks. The R package catR was developed to perform adaptive testing with as much flexibility as possible, in an attempt to provide a developmental and testing platform to the interested user. Several item-selection rules and ability estimators are implemented. The item bank can be provided by the user or randomly generated from parent distributions of item parameters. Three stopping rules are available. The output can be graphically displayed.

10acomputer program10acomputerized adaptive testing10aEstimation10aItem Response Theory1 aMagis, D1 aRaîche, G uhttp://www.iacat.org/content/catr-r-package-computerized-adaptive-testing01233nas a2200121 4500008004500000245007300045210006900118300001200187490000700199520079700206100001401003856009401017 2011 Engldsh 00aA Comment on Early Student Blunders on Computer-Based Adaptive Tests0 aComment on Early Student Blunders on ComputerBased Adaptive Test a165-1740 v353 a

This article refutes a recent claim that computer-based tests produce biased scores for very proficient test takers who make mistakes on one or two initial items and that they can be reduced by using a four-parameter IRT model. Because the same effect occurs with pattern scores on nonadaptive tests, the effect results from IRT scoring, not from adaptive testing. Because very proficient test takers rarely err on items of middle difficulty, the so-called bias is one of selective data analysis. Furthermore, the apparently large score penalty for one error on an otherwise perfect response pattern is shown to result from the relative stretching of the IRT scale at very high and very low proficiencies. The recommended use of a four-parameter IRT model is shown to have drawbacks.

1 aGreen, BF uhttp://www.iacat.org/content/comment-early-student-blunders-computer-based-adaptive-tests01223nas a2200121 4500008003900000245007300039210006900112300001200181490000700193520082900200100001901029856005301048 2011 d00aA Comment on Early Student Blunders on Computer-Based Adaptive Tests0 aComment on Early Student Blunders on ComputerBased Adaptive Test a165-1740 v353 a

This article refutes a recent claim that computer-based tests produce biased scores for very proficient test takers who make mistakes on one or two initial items and that the ‘‘bias’’ can be reduced by using a four-parameter IRT model. Because the same effect occurs with pattern scores on nonadaptive tests, the effect results from IRT scoring, not from adaptive testing. Because very proficient test takers rarely err on items of middle difficulty, the so-called bias is one of selective data analysis. Furthermore, the apparently large score penalty for one error on an otherwise perfect response pattern is shown to result from the relative stretching of the IRT scale at very high and very low proficiencies. The recommended use of a four-parameter IRT model is shown to have drawbacks.

1 aGreen, Bert, F uhttp://apm.sagepub.com/content/35/2/165.abstract01006nas a2200121 4500008004500000245008100045210006900126490000700195520054900202100001600751700001100767856010600778 2011 Engldsh 00aComputer adaptive testing for small scale programs and instructional systems0 aComputer adaptive testing for small scale programs and instructi0 v123 a

This study investigates measurement decision theory (MDT) as an underlying model for computer adaptive testing when the goal is to classify examinees into one of a finite number of groups. The first analysis compares MDT with a popular item response theory model and finds little difference in terms of the percentage of correct classifications. The second analysis examines the number of examinees needed to calibrate MDT item parameters and finds accurate classifications even with calibration sample sizes as small as 100 examinees.

1 aRudner, L M1 aGuo, F uhttp://www.iacat.org/content/computer-adaptive-testing-small-scale-programs-and-instructional-systems01960nas a2200193 4500008004100000020001400041245009500055210006900150300001200219490000700231520131000238100001501548700001801563700001701581700001401598700001301612700001901625856012201644 2011 eng d a0022-389100aComputerized adaptive assessment of personality disorder: Introducing the CAT–PD project0 aComputerized adaptive assessment of personality disorder Introdu a380-3890 v933 aAssessment of personality disorders (PD) has been hindered by reliance on the problematic categorical model embodied in the most recent Diagnostic and Statistical Model of Mental Disorders (DSM), lack of consensus among alternative dimensional models, and inefficient measurement methods. This article describes the rationale for and early results from a multiyear study funded by the National Institute of Mental Health that was designed to develop an integrative and comprehensive model and efficient measure of PD trait dimensions. To accomplish these goals, we are in the midst of a 5-phase project to develop and validate the model and measure. The results of Phase 1 of the project—which was focused on developing the PD traits to be assessed and the initial item pool—resulted in a candidate list of 59 PD traits and an initial item pool of 2,589 items. Data collection and structural analyses in community and patient samples will inform the ultimate structure of the measure, and computerized adaptive testing will permit efficient measurement of the resultant traits. The resultant Computerized Adaptive Test of Personality Disorder (CAT–PD) will be well positioned as a measure of the proposed DSM–5 PD traits. Implications for both applied and basic personality research are discussed.1 aSimms, L J1 aGoldberg, L R1 aRoberts, J E1 aWatson, D1 aWelte, J1 aRotterman, J H uhttp://www.iacat.org/content/computerized-adaptive-assessment-personality-disorder-introducing-cat%E2%80%93pd-project00466nas a2200121 4500008003900000245009900039210006900138300001200207490000700219100001900226700003100245856006800276 2011 d00aComputerized Adaptive Testing with the Zinnes and Griggs Pairwise Preference Ideal Point Model0 aComputerized Adaptive Testing with the Zinnes and Griggs Pairwis a231-2470 v111 aStark, Stephen1 aChernyshenko, Oleksandr, S uhttp://www.tandfonline.com/doi/abs/10.1080/15305058.2011.56145901379nas a2200133 4500008003900000245008500039210006900124300001200193490000700205520094200212100002001154700001801174856005301192 2011 d00aComputerized Classification Testing Under the Generalized Graded Unfolding Model0 aComputerized Classification Testing Under the Generalized Graded a114-1280 v713 a

The generalized graded unfolding model (GGUM) has been recently developed to describe item responses to Likert items (agree—disagree) in attitude measurement. In this study, the authors (a) developed two item selection methods in computerized classification testing under the GGUM, the current estimate/ability confidence interval method and the cut score/sequential probability ratio test method and (b) evaluated their accuracy and efficiency in classification through simulations. The results indicated that both methods were very accurate and efficient. The more points each item had and the fewer the classification categories, the more accurate and efficient the classification would be. However, the latter method may yield a very low accuracy in dichotomous items with a short maximum test length. Thus, if it is to be used to classify examinees with dichotomous items, the maximum text length should be increased.

1 aWang, Wen-Chung1 aLiu, Chen-Wei uhttp://epm.sagepub.com/content/71/1/114.abstract01976nas a2200133 4500008003900000245011600039210006900155300001200224490000700236520150500243100002001748700002101768856005301789 2011 d00aComputerized Classification Testing Under the One-Parameter Logistic Response Model With Ability-Based Guessing0 aComputerized Classification Testing Under the OneParameter Logis a925-9410 v713 a

The one-parameter logistic model with ability-based guessing (1PL-AG) has been recently developed to account for effect of ability on guessing behavior in multiple-choice items. In this study, the authors developed algorithms for computerized classification testing under the 1PL-AG and conducted a series of simulations to evaluate their performances. Four item selection methods (the Fisher information, the Fisher information with a posterior distribution, the progressive method, and the adjusted progressive method) and two termination criteria (the ability confidence interval [ACI] method and the sequential probability ratio test [SPRT]) were developed. In addition, the Sympson–Hetter online method with freeze (SHOF) was implemented for item exposure control. Major results include the following: (a) when no item exposure control was made, all the four item selection methods yielded very similar correct classification rates, but the Fisher information method had the worst item bank usage and the highest item exposure rate; (b) SHOF can successfully maintain the item exposure rate at a prespecified level, without compromising substantial accuracy and efficiency in classification; (c) once SHOF was implemented, all the four methods performed almost identically; (d) ACI appeared to be slightly more efficient than SPRT; and (e) in general, a higher weight of ability in guessing led to a slightly higher accuracy and efficiency, and a lower forced classification rate.

1 aWang, Wen-Chung1 aHuang, Sheng-Yun uhttp://epm.sagepub.com/content/71/6/925.abstract01695nas a2200217 4500008004100000020004600041245012100087210006900208250001500277300001100292490000700303520094700310100001701257700001301274700001601287700001001303700001201313700001601325700001801341856011801359 2011 eng d a1541-3144 (Electronic)0194-2638 (Linking)00aContent range and precision of a computer adaptive test of upper extremity function for children with cerebral palsy0 aContent range and precision of a computer adaptive test of upper a2010/10/15 a90-1020 v313 aThis article reports on the content range and measurement precision of an upper extremity (UE) computer adaptive testing (CAT) platform of physical function in children with cerebral palsy. Upper extremity items representing skills of all abilities were administered to 305 parents. These responses were compared with two traditional standardized measures: Pediatric Outcomes Data Collection Instrument and Functional Independence Measure for Children. The UE CAT correlated strongly with the upper extremity component of these measures and had greater precision when describing individual functional ability. The UE item bank has wider range with items populating the lower end of the ability spectrum. This new UE item bank and CAT have the capability to quickly assess children of all ages and abilities with good precision and, most importantly, with items that are meaningful and appropriate for their age and level of physical function.1 aMontpetit, K1 aHaley, S1 aBilodeau, N1 aNi, P1 aTian, F1 aGorton, 3rd1 aMulcahey, M J uhttp://www.iacat.org/content/content-range-and-precision-computer-adaptive-test-upper-extremity-function-children01633nas a2200145 4500008004100000245005200041210005000093260001200143520119300155653000801348653001601356653002001372100002301392856007201415 2011 eng d00aContinuous Testing (an avenue for CAT research)0 aContinuous Testing an avenue for CAT research c10/20113 a

Publishing an Adaptive Test

Problems with Publishing

Research Questions

10aCAT10aitem filter10aitem filtration1 aKingsbury, Gage, G uhttp://www.iacat.org/content/continuous-testing-avenue-cat-research01600nas a2200121 4500008004500000245011000045210006900155490000700224520114200231100001901373700001401392856007201406 2011 Engldsh 00aCreating a K-12 Adaptive Test: Examining the Stability of Item Parameter Estimates and Measurement Scales0 aCreating a K12 Adaptive Test Examining the Stability of Item Par0 v123 a

Development of adaptive tests used in K-12 settings requires the creation of stable measurement scales to measure the growth of individual students from one grade to the next, and to measure change in groups from one year to the next. Accountability systems
like No Child Left Behind require stable measurement scales so that accountability has meaning across time. This study examined the stability of the measurement scales used with the Measures of Academic Progress. Difficulty estimates for test questions from the reading and mathematics scales were examined over a period ranging from 7 to 22 years. Results showed high correlations between item difficulty estimates from the time at which they where originally calibrated and the current calibration. The average drift in item difficulty estimates was less than .01 standard deviations. The average impact of change in item difficulty estimates was less than the smallest reported difference on the score scale for two actual tests. The findings of the study indicate that an IRT scale can be stable enough to allow consistent measurement of student achievement.

1 aKingsbury, G G1 aWise, S L uhttp://www.testpublishers.org/journal-of-applied-testing-technology02592nas a2200313 4500008004100000020004600041245011200087210006900199250001500268260001900283300000700302490000600309520160100315100001901916700001801935700001701953700001801970700001701988700001402005700001602019700001502035700001402050700002602064700001602090700001302106700001502119700001902134856012502153 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://www.iacat.org/content/cross-cultural-development-item-list-computer-adaptive-testing-fatigue-oncological-patients01767nas a2200157 4500008004100000020002300041245020500064210006900269300001200338490000700350520108200357100001501439700001601454700001601470856012301486 2010 eng d a0146-62161552-349700aA comparison of content-balancing procedures for estimating multiple clinical domains in computerized adaptive testing: Relative precision, validity, and detection of persons with misfitting responses0 acomparison of contentbalancing procedures for estimating multipl a410-4230 v343 aThis simulation study sought to compare four different computerized adaptive testing (CAT) content-balancing procedures designed for use in a multidimensional assessment with respect to measurement precision, symptom severity classification, validity of clinical diagnostic recommendations, and sensitivity to atypical responding. The four content-balancing procedures were (a) no content balancing, (b) screener-based, (c) mixed (screener plus content balancing), and (d) full content balancing. In full content balancing and in mixed content balancing following administration of the screener items, item selection was based on (a) whether the target numberof items for the item’s subscale was reached and (b) the item’s information function. Mixed and full content balancing provided the best representation of items from each of the main subscales of the Internal Mental Distress Scale. These procedures also resulted in higher CAT to full-scale correlations for the Trauma and Homicidal/Suicidal Thought subscales and improved detection of atypical responding.Keywords1 aRiley, B B1 aDennis, M L1 aConrad, K J uhttp://www.iacat.org/content/comparison-content-balancing-procedures-estimating-multiple-clinical-domains-computerized01688nas a2200145 4500008003900000245020500039210006900244300001200313490000700325520109200332100002001424700002301444700002201467856005301489 2010 d00aA Comparison of Content-Balancing Procedures for Estimating Multiple Clinical Domains in Computerized Adaptive Testing: Relative Precision, Validity, and Detection of Persons With Misfitting Responses0 aComparison of ContentBalancing Procedures for Estimating Multipl a410-4230 v343 a

This simulation study sought to compare four different computerized adaptive testing (CAT) content-balancing procedures designed for use in a multidimensional assessment with respect to measurement precision, symptom severity classification, validity of clinical diagnostic recommendations, and sensitivity to atypical responding. The four content-balancing procedures were (a) no content balancing, (b) screener-based, (c) mixed (screener plus content balancing), and (d) full content balancing. In full content balancing and in mixed content balancing following administration of the screener items, item selection was based on (a) whether the target number of items for the item’s subscale was reached and (b) the item’s information function. Mixed and full content balancing provided the best representation of items from each of the main subscales of the Internal Mental Distress Scale. These procedures also resulted in higher CAT to full-scale correlations for the Trauma and Homicidal/Suicidal Thought subscales and improved detection of atypical responding.

1 aRiley, Barth, B1 aDennis, Michael, L1 aConrad, Kendon, J uhttp://apm.sagepub.com/content/34/6/410.abstract01176nas 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.abstract00465nas a2200133 4500008004100000245005800041210005800099260004200157300001200199490000700211100001200218700001700230856008400247 2010 eng d00aComputerized adaptive testing based on decision trees0 aComputerized adaptive testing based on decision trees aSousse, TunisiabIEEE Computer Sience a191-1930 v581 aUeno, M1 aSongmuang, P uhttp://www.iacat.org/content/computerized-adaptive-testing-based-decision-trees00351nas a2200097 4500008004100000245005100041210005100092300001000143100002300153856007700176 2010 eng d00aConstrained Adaptive Testing with Shadow Tests0 aConstrained Adaptive Testing with Shadow Tests a31-561 avan der Linden, WJ uhttp://www.iacat.org/content/constrained-adaptive-testing-shadow-tests-000492nas a2200121 4500008004100000245008500041210006900126260001800195100001700213700001600230700001600246856010800262 2009 eng d00aComparing methods to recalibrate drifting items in computerized adaptive testing0 aComparing methods to recalibrate drifting items in computerized  aSan Diego, CA1 aMasters, J S1 aMuckle, T J1 aBontempo, B uhttp://www.iacat.org/content/comparing-methods-recalibrate-drifting-items-computerized-adaptive-testing00578nas a2200109 4500008004100000245011400041210006900155260009700224100001200321700001500333856012000348 2009 eng d00aComparison of ability estimation and item selection methods in multidimensional computerized adaptive testing0 aComparison of ability estimation and item selection methods in m aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aDiao, Q1 aReckase, M uhttp://www.iacat.org/content/comparison-ability-estimation-and-item-selection-methods-multidimensional-computerized00572nas a2200097 4500008004100000245013000041210006900171260009700240100001400337856012300351 2009 eng d00aComparison of adaptive Bayesian estimation and weighted Bayesian estimation in multidimensional computerized adaptive testing0 aComparison of adaptive Bayesian estimation and weighted Bayesian aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aChen, P H uhttp://www.iacat.org/content/comparison-adaptive-bayesian-estimation-and-weighted-bayesian-estimation-multidimensional00437nas a2200121 4500008004100000245006700041210006700108300001400175490000700189100001400196700001600210856008900226 2009 eng d00aComparison of CAT item selection criteria for polytomous items0 aComparison of CAT item selection criteria for polytomous items a419–4400 v331 aChoi, S W1 aSwartz, R J uhttp://www.iacat.org/content/comparison-cat-item-selection-criteria-polytomous-items01148nas a2200133 4500008003900000245006700039210006700106300001200173490000700185520072700192100001900919700002300938856005300961 2009 d00aComparison of CAT Item Selection Criteria for Polytomous Items0 aComparison of CAT Item Selection Criteria for Polytomous Items a419-4400 v333 a

Item selection is a core component in computerized adaptive testing (CAT). Several studies have evaluated new and classical selection methods; however, the few that have applied such methods to the use of polytomous items have reported conflicting results. To clarify these discrepancies and further investigate selection method properties, six different selection methods are compared systematically. The results showed no clear benefit from more sophisticated selection criteria and showed one method previously believed to be superior—the maximum expected posterior weighted information (MEPWI)—to be mathematically equivalent to a simpler method, the maximum posterior weighted information (MPWI).

1 aChoi, Seung, W1 aSwartz, Richard, J uhttp://apm.sagepub.com/content/33/6/419.abstract02949nas a2200205 4500008004100000020004100041245009800082210006900180250001500249260000800264300001200272490000700284520217900291653004302470653006402513100001702577700001402594700001802608856011702626 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://www.iacat.org/content/comparison-methods-controlling-maximum-exposure-rates-computerized-adaptive-testing00595nas a2200133 4500008004100000245008600041210006900127260009700196100001500293700001600308700001700324700001700341856010300358 2009 eng d00aA comparison of three methods of item selection for computerized adaptive testing0 acomparison of three methods of item selection for computerized a aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aCosta, D R1 aKarino, C A1 aMoura, F A S1 aAndrade, D F uhttp://www.iacat.org/content/comparison-three-methods-item-selection-computerized-adaptive-testing02278nas a2200109 4500008004100000245008100041210006900122260009700191520175400288100001902042856010702061 2009 eng d00aComputerized adaptive testing by mutual information and multiple imputations0 aComputerized adaptive testing by mutual information and multiple aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.3 aOver the years, most computerized adaptive testing (CAT) systems have used score estimation procedures from item response theory (IRT). IRT models have salutary properties for score estimation, error reporting, and next-item selection. However, some testing purposes favor scoring approaches outside IRT. Where a criterion metric is readily available and more relevant than the assessed construct, for example in the selection of job applicants, a predictive model might be appropriate (Scarborough & Somers, 2006). In these cases, neither IRT scoring nor a unidimensional assessment structure can be assumed. Yet, the primary benefit of CAT remains desirable: shorter assessments with minimal loss of accuracy due to unasked items. In such a case, it remains possible to create a CAT system that produces an estimated score from a subset of available items, recognizes differential item information given the emerging item response pattern, and optimizes the accuracy of the score estimated at every successive item. The method of multiple imputations (Rubin, 1987) can be used to simulate plausible scores given plausible response patterns to unasked items (Thissen-Roe, 2005). Mutual information can then be calculated in order to select an optimally informative next item (or set of items). Previously observed response patterns to two complete neural network-scored assessments were resampled according to MIMI CAT item selection. The reproduced CAT scores were compared to full-length assessment scores. Approximately 95% accurate assignment of examinees to one of three score categories was achieved with a 70%-80% reduction in median test length. Several algorithmic factors influencing accuracy and computational performance were examined.1 aThissen-Roe, A uhttp://www.iacat.org/content/computerized-adaptive-testing-mutual-information-and-multiple-imputations00448nas a2200097 4500008004100000245005800041210005800099260009700157100001300254856008300267 2009 eng d00aComputerized adaptive testing for cognitive diagnosis0 aComputerized adaptive testing for cognitive diagnosis aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aCheng, Y uhttp://www.iacat.org/content/computerized-adaptive-testing-cognitive-diagnosis00459nas a2200097 4500008004100000245010100041210006900142100001500211700001400226856012100240 2009 eng d00aComputerized adaptive testing using the two parameter logistic model with ability-based guessing0 aComputerized adaptive testing using the two parameter logistic m1 aShih, H -J1 aWang, W-C uhttp://www.iacat.org/content/computerized-adaptive-testing-using-two-parameter-logistic-model-ability-based-guessing00566nas a2200109 4500008004100000245010000041210006900141260009700210100001500307700001600322856011800338 2009 eng d00aComputerized classification testing in more than two categories by using stochastic curtailment0 aComputerized classification testing in more than two categories  aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aWouda, J T1 aEggen, Theo uhttp://www.iacat.org/content/computerized-classification-testing-more-two-categories-using-stochastic-curtailment00495nas a2200133 4500008004100000245008000041210006900121300001100190490000700201100001700208700001600225700001700241856010300258 2009 eng d00aA conditional exposure control method for multidimensional adaptive testing0 aconditional exposure control method for multidimensional adaptiv a84-1030 v461 aFinkelman, M1 aNering, M L1 aRoussos, L A uhttp://www.iacat.org/content/conditional-exposure-control-method-multidimensional-adaptive-testing01700nas a2200157 4500008003900000022001400039245008000053210006900133300001300202490000700215520119700222100002301419700002301442700002201465856005501487 2009 d a1745-398400aA Conditional Exposure Control Method for Multidimensional Adaptive Testing0 aConditional Exposure Control Method for Multidimensional Adaptiv a84–1030 v463 a

In computerized adaptive testing (CAT), ensuring the security of test items is a crucial practical consideration. A common approach to reducing item theft is to define maximum item exposure rates, i.e., to limit the proportion of examinees to whom a given item can be administered. Numerous methods for controlling exposure rates have been proposed for tests employing the unidimensional 3-PL model. The present article explores the issues associated with controlling exposure rates when a multidimensional item response theory (MIRT) model is utilized and exposure rates must be controlled conditional upon ability. This situation is complicated by the exponentially increasing number of possible ability values in multiple dimensions. The article introduces a new procedure, called the generalized Stocking-Lewis method, that controls the exposure rate for students of comparable ability as well as with respect to the overall population. A realistic simulation set compares the new method with three other approaches: Kullback-Leibler information with no exposure control, Kullback-Leibler information with unconditional Sympson-Hetter exposure control, and random item selection.

1 aFinkelman, Matthew1 aNering, Michael, L1 aRoussos, Louis, A uhttp://dx.doi.org/10.1111/j.1745-3984.2009.01070.x01452nas a2200205 4500008004100000020004100041245016200082210006900244250001500313300001100328490000700339520065200346653002500998653001501023653003701038653002401075100001401099700001501113856011801128 2009 eng d a1529-7713 (Print)1529-7713 (Linking)00aConsiderations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval0 aConsiderations about expected a posteriori estimation in adaptiv a2009/07/01 a138-560 v103 aIn a computerized adaptive test, we would like to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Unfortunately, decreasing the number of items is accompanied by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. The authors suggest that it is possible to reduced the bias, and even the standard error of the estimate, by applying to each provisional estimation one or a combination of the following strategies: adaptive correction for bias proposed by Bock and Mislevy (1982), adaptive a priori estimate, and adaptive integration interval.10a*Bias (Epidemiology)10a*Computers10aData Interpretation, Statistical10aModels, Statistical1 aRaiche, G1 aBlais, J G uhttp://www.iacat.org/content/considerations-about-expected-posteriori-estimation-adaptive-testing-adaptive-priori00513nas a2200109 4500008004100000245006900041210006900110260009700179100001500276700001600291856009600307 2009 eng d00aConstrained item selection using a stochastically curtailed SPRT0 aConstrained item selection using a stochastically curtailed SPRT aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.1 aWouda, J T1 aEggen, Theo uhttp://www.iacat.org/content/constrained-item-selection-using-stochastically-curtailed-sprt01651nas a2200157 4500008003900000245010700039210006900146300001000215490000700225520113900232100001501371700001901386700002101405700001501426856005201441 2009 d00aConstraint-Weighted a-Stratification for Computerized Adaptive Testing With Nonstatistical Constraints0 aConstraintWeighted aStratification for Computerized Adaptive Tes a35-490 v693 a

a-stratification is a method that utilizes items with small discrimination (a) parameters early in an exam and those with higher a values when more is learned about the ability parameter. It can achieve much better item usage than the maximum information criterion (MIC). To make a-stratification more practical and more widely applicable, a method for weighting the item selection process in a-stratification as a means of satisfying multiple test constraints is proposed. This method is studied in simulation against an analogous method without stratification as well as a-stratification using descending-rather than ascending-a procedures. In addition, a variation of a-stratification that allows for unbalanced usage of a parameters is included in the study to examine the trade-off between efficiency and exposure control. Finally, MIC and randomized item selection are included as baseline measures. Results indicate that the weighting mechanism successfully addresses the constraints, that stratification helps to a great extent balancing exposure rates, and that the ascending-a design improves measurement precision.

1 aYing Cheng1 aChang, Hua-Hua1 aDouglas, Jeffrey1 aFanmin Guo uhttp://epm.sagepub.com/content/69/1/35.abstract00618nas a2200145 4500008004100000245016200041210006900203300001000272490000700282100001300289700001900302700001500321700001100336856012500347 2009 eng d00aConstraint-weighted a-stratification for computerized adaptive testing with nonstatistical constraints: Balancing measurement efficiency and exposure control0 aConstraintweighted astratification for computerized adaptive tes a35-490 v691 aCheng, Y1 aChang, Hua-Hua1 aDouglas, J1 aGuo, F uhttp://www.iacat.org/content/constraint-weighted-stratification-computerized-adaptive-testing-nonstatistical-constraints01893nas a2200157 4500008004100000245007800041210006900119260009700188520126000285100001801545700001801563700002001581700001701601700001601618856010101634 2009 eng d00aCriterion-related validity of an innovative CAT-based personality measure0 aCriterionrelated validity of an innovative CATbased personality  aD. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.3 aThis paper describes development and initial criterion-related validation of the PreVisor Computer Adaptive Personality Scales (PCAPS), a computerized adaptive testing-based personality measure that uses an ideal point IRT model based on forced-choice, paired-comparison responses. Based on results from a large consortium study, a composite of six PCAPS scales identified as relevant to the population of interest (first-line supervisors) had an estimated operational validity against an overall job performance criterion of ρ = .25. Uncorrected and corrected criterion-related validity results for each of the six PCAPS scales making up the composite are also reported. Because the PCAPS algorithm computes intermediate scale scores until a stopping rule is triggered, we were able to graph number of statement-pairs presented against criterion-related validities. Results showed generally monotonically increasing functions. However, asymptotic validity levels, or at least a reduction in the rate of increase in slope, were often reached after 5-7 statement-pairs were presented. In the case of the composite measure, there was some evidence that validities decreased after about six statement-pairs. A possible explanation for this is provided.1 aSchneider, RJ1 aMcLellan, R A1 aKantrowitz, T M1 aHouston, J S1 aBorman, W C uhttp://www.iacat.org/content/criterion-related-validity-innovative-cat-based-personality-measure00450nas a2200133 4500008003900000245006000039210005800099300001000157490000600167100002100173700001800194700001900212856008500231 2008 d00aCAT-MD: Computerized adaptive testing on mobile devices0 aCATMD Computerized adaptive testing on mobile devices a13-200 v31 aTriantafillou, E1 aGeorgiadou, E1 aEconomides, AA uhttp://www.iacat.org/content/cat-md-computerized-adaptive-testing-mobile-devices02582nas a2200241 4500008004100000020002200041245009000063210006900153250001500222260000800237300001100245490000700256520178300263653001502046653001502061653002502076653002902101653005002130653001102180100001602191700001902207856011402226 2008 eng d a1554-351X (Print)00aCombining computer adaptive testing technology with cognitively diagnostic assessment0 aCombining computer adaptive testing technology with cognitively  a2008/08/14 cAug a808-210 v403 aA major advantage of computerized adaptive testing (CAT) is that it allows the test to home in on an examinee's ability level in an interactive manner. The aim of the new area of cognitive diagnosis is to provide information about specific content areas in which an examinee needs help. The goal of this study was to combine the benefit of specific feedback from cognitively diagnostic assessment with the advantages of CAT. In this study, three approaches to combining these were investigated: (1) item selection based on the traditional ability level estimate (theta), (2) item selection based on the attribute mastery feedback provided by cognitively diagnostic assessment (alpha), and (3) item selection based on both the traditional ability level estimate (theta) and the attribute mastery feedback provided by cognitively diagnostic assessment (alpha). The results from these three approaches were compared for theta estimation accuracy, attribute mastery estimation accuracy, and item exposure control. The theta- and alpha-based condition outperformed the alpha-based condition regarding theta estimation, attribute mastery pattern estimation, and item exposure control. Both the theta-based condition and the theta- and alpha-based condition performed similarly with regard to theta estimation, attribute mastery estimation, and item exposure control, but the theta- and alpha-based condition has an additional advantage in that it uses the shadow test method, which allows the administrator to incorporate additional constraints in the item selection process, such as content balancing, item type constraints, and so forth, and also to select items on the basis of both the current theta and alpha estimates, which can be built on top of existing 3PL testing programs.10a*Cognition10a*Computers10a*Models, Statistical10a*User-Computer Interface10aDiagnosis, Computer-Assisted/*instrumentation10aHumans1 aMcGlohen, M1 aChang, Hua-Hua uhttp://www.iacat.org/content/combining-computer-adaptive-testing-technology-cognitively-diagnostic-assessment01669nas a2200169 4500008003900000245009500039210007100134300000900205490000700214520113900221100001701360700001401377700002201391700001901413700001601432856005101448 2008 d00aComparability of Computer-Based and Paper-and-Pencil Testing in K–12 Reading Assessments0 aComparability of ComputerBased and PaperandPencil Testing in K–1 a5-240 v683 a

In recent years, computer-based testing (CBT) has grown in popularity, is increasingly being implemented across the United States, and will likely become the primary mode for delivering tests in the future. Although CBT offers many advantages over traditional paper-and-pencil testing, assessment experts, researchers, practitioners, and users have expressed concern about the comparability of scores between the two test administration modes. To help provide an answer to this issue, a meta-analysis was conducted to synthesize the administration mode effects of CBTs and paper-and-pencil tests on K—12 student reading assessments. Findings indicate that the administration mode had no statistically significant effect on K—12 student reading achievement scores. Four moderator variables—study design, sample size, computer delivery algorithm, and computer practice—made statistically significant contributions to predicting effect size. Three moderator variables—grade level, type of test, and computer delivery method—did not affect the differences in reading scores between test modes.

1 aShudong Wang1 aHong Jiao1 aYoung, Michael, J1 aBrooks, Thomas1 aOlson, John uhttp://epm.sagepub.com/content/68/1/5.abstract01435nas a2200169 4500008003900000245009000039210006900129300001000198490000800208520081300216653002901029653003601058653003001094100001501124700001201139856011401151 2008 d00aComputer Adaptive-Attribute Testing A New Approach to Cognitive Diagnostic Assessment0 aComputer AdaptiveAttribute Testing A New Approach to Cognitive D a29-390 v2163 a

The influence of interdisciplinary forces stemming from developments in cognitive science,mathematical statistics, educational
psychology, and computing science are beginning to appear in educational and psychological assessment. Computer adaptive-attribute testing (CA-AT) is one example. The concepts and procedures in CA-AT can be found at the intersection between computer adaptive testing and cognitive diagnostic assessment. CA-AT allows us to fuse the administrative benefits of computer adaptive testing with the psychological benefits of cognitive diagnostic assessment to produce an innovative psychologically-based adaptive testing approach. We describe the concepts behind CA-AT as well as illustrate how it can be used to promote formative, computer-based, classroom assessment.

10acognition and assessment10acognitive diagnostic assessment10acomputer adaptive testing1 aGierl, M J1 aZhou, J uhttp://www.iacat.org/content/computer-adaptive-attribute-testing-new-approach-cognitive-diagnostic-assessment01113nas a2200133 4500008003900000245011000039210006900149300001200218490000700230520065500237100001700892700001700909856005300926 2008 d00aComputer-Based and Paper-and-Pencil Administration Mode Effects on a Statewide End-of-Course English Test0 aComputerBased and PaperandPencil Administration Mode Effects on  a554-5700 v683 a

The current study compared student performance between paper-and-pencil testing (PPT) and computer-based testing (CBT) on a large-scale statewide end-of-course English examination. Analyses were conducted at both the item and test levels. The overall results suggest that scores obtained from PPT and CBT were comparable. However, at the content domain level, a rather large difference in the reading comprehension section suggests that reading comprehension test may be more affected by the test administration mode. Results from the confirmatory factor analysis suggest that the administration mode did not alter the construct of the test.

1 aKim, Do-Hong1 aHuynh, Huynh uhttp://epm.sagepub.com/content/68/4/554.abstract03041nas a2200481 4500008004100000020004600041245012200087210006900209250001500278260000800293300001200301490000700313520155700320653003201877653003101909653002201940653002001962653001001982653000901992653002202001653002802023653003302051653001102084653001102095653002502106653000902131653001602140653004602156653002202202653002402224653003002248653002902278100001502307700001402322700001502336700002402351700001802375700001102393700001602404700001002420700001502430856011402445 2008 eng d a1532-821X (Electronic)0003-9993 (Linking)00aComputerized adaptive testing for follow-up after discharge from inpatient rehabilitation: II. Participation outcomes0 aComputerized adaptive testing for followup after discharge from  a2008/01/30 cFeb a275-2830 v893 aOBJECTIVES: To measure participation outcomes with a computerized adaptive test (CAT) and compare CAT and traditional fixed-length surveys in terms of score agreement, respondent burden, discriminant validity, and responsiveness. DESIGN: Longitudinal, prospective cohort study of patients interviewed approximately 2 weeks after discharge from inpatient rehabilitation and 3 months later. SETTING: Follow-up interviews conducted in patient's home setting. PARTICIPANTS: Adults (N=94) with diagnoses of neurologic, orthopedic, or medically complex conditions. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Participation domains of mobility, domestic life, and community, social, & civic life, measured using a CAT version of the Participation Measure for Postacute Care (PM-PAC-CAT) and a 53-item fixed-length survey (PM-PAC-53). RESULTS: The PM-PAC-CAT showed substantial agreement with PM-PAC-53 scores (intraclass correlation coefficient, model 3,1, .71-.81). On average, the PM-PAC-CAT was completed in 42% of the time and with only 48% of the items as compared with the PM-PAC-53. Both formats discriminated across functional severity groups. The PM-PAC-CAT had modest reductions in sensitivity and responsiveness to patient-reported change over a 3-month interval as compared with the PM-PAC-53. CONCLUSIONS: Although continued evaluation is warranted, accurate estimates of participation status and responsiveness to change for group-level analyses can be obtained from CAT administrations, with a sizeable reduction in respondent burden.10a*Activities of Daily Living10a*Adaptation, Physiological10a*Computer Systems10a*Questionnaires10aAdult10aAged10aAged, 80 and over10aChi-Square Distribution10aFactor Analysis, Statistical10aFemale10aHumans10aLongitudinal Studies10aMale10aMiddle Aged10aOutcome Assessment (Health Care)/*methods10aPatient Discharge10aProspective Studies10aRehabilitation/*standards10aSubacute Care/*standards1 aHaley, S M1 aGandek, B1 aSiebens, H1 aBlack-Schaffer, R M1 aSinclair, S J1 aTao, W1 aCoster, W J1 aNi, P1 aJette, A M uhttp://www.iacat.org/content/computerized-adaptive-testing-follow-after-discharge-inpatient-rehabilitation-ii00581nas a2200145 4500008004100000245012000041210006900161300001400230490000700244100001400251700001400265700001900279700001800298856011900316 2008 eng d00aComputerized adaptive testing for patients with knee inpairments produced valid and responsive measures of function0 aComputerized adaptive testing for patients with knee inpairments a1113-11240 v611 aHart, D L1 aWang, Y-C1 aStratford, P W1 aMioduski, J E uhttp://www.iacat.org/content/computerized-adaptive-testing-patients-knee-inpairments-produced-valid-and-responsive03313nas a2200433 4500008004100000020004600041245007700087210006900164250001500233260001100248300001200259490000700271520203200278653002702310653003002337653002102367653001002388653000902398653001502407653003602422653002102458653004402479653002402523653001102547653001102558653001302569653000902582653001602591653003002607653003002637653003102667100001502698700001302713700001502726700001402741700001502755700001402770856009502784 2008 eng d a1528-1159 (Electronic)0362-2436 (Linking)00aComputerized adaptive testing in back pain: Validation of the CAT-5D-QOL0 aComputerized adaptive testing in back pain Validation of the CAT a2008/05/23 cMay 20 a1384-900 v333 aSTUDY DESIGN: We have conducted an outcome instrument validation study. OBJECTIVE: Our objective was to develop a computerized adaptive test (CAT) to measure 5 domains of health-related quality of life (HRQL) and assess its feasibility, reliability, validity, and efficiency. SUMMARY OF BACKGROUND DATA: Kopec and colleagues have recently developed item response theory based item banks for 5 domains of HRQL relevant to back pain and suitable for CAT applications. The domains are Daily Activities (DAILY), Walking (WALK), Handling Objects (HAND), Pain or Discomfort (PAIN), and Feelings (FEEL). METHODS: An adaptive algorithm was implemented in a web-based questionnaire administration system. The questionnaire included CAT-5D-QOL (5 scales), Modified Oswestry Disability Index (MODI), Roland-Morris Disability Questionnaire (RMDQ), SF-36 Health Survey, and standard clinical and demographic information. Participants were outpatients treated for mechanical back pain at a referral center in Vancouver, Canada. RESULTS: A total of 215 patients completed the questionnaire and 84 completed a retest. On average, patients answered 5.2 items per CAT-5D-QOL scale. Reliability ranged from 0.83 (FEEL) to 0.92 (PAIN) and was 0.92 for the MODI, RMDQ, and Physical Component Summary (PCS-36). The ceiling effect was 0.5% for PAIN compared with 2% for MODI and 5% for RMQ. The CAT-5D-QOL scales correlated as anticipated with other measures of HRQL and discriminated well according to the level of satisfaction with current symptoms, duration of the last episode, sciatica, and disability compensation. The average relative discrimination index was 0.87 for PAIN, 0.67 for DAILY and 0.62 for WALK, compared with 0.89 for MODI, 0.80 for RMDQ, and 0.59 for PCS-36. CONCLUSION: The CAT-5D-QOL is feasible, reliable, valid, and efficient in patients with back pain. This methodology can be recommended for use in back pain research and should improve outcome assessment, facilitate comparisons across studies, and reduce patient burden.10a*Disability Evaluation10a*Health Status Indicators10a*Quality of Life10aAdult10aAged10aAlgorithms10aBack Pain/*diagnosis/psychology10aBritish Columbia10aDiagnosis, Computer-Assisted/*standards10aFeasibility Studies10aFemale10aHumans10aInternet10aMale10aMiddle Aged10aPredictive Value of Tests10aQuestionnaires/*standards10aReproducibility of Results1 aKopec, J A1 aBadii, M1 aMcKenna, M1 aLima, V D1 aSayre, E C1 aDvorak, M uhttp://www.iacat.org/content/computerized-adaptive-testing-back-pain-validation-cat-5d-qol01876nas a2200205 4500008003900000245005600039210005600095300001000151490000800161520124900169653002101418653003001439653002501469653001801494653002501512100001301537700001701550700002101567856008201588 2008 d00aComputerized Adaptive Testing of Personality Traits0 aComputerized Adaptive Testing of Personality Traits a12-210 v2163 a

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

10aAdaptive Testing10acmoputer-assisted testing10aItem Response Theory10aLikert scales10aPersonality Measures1 aHol, A M1 aVorst, H C M1 aMellenbergh, G J uhttp://www.iacat.org/content/computerized-adaptive-testing-personality-traits01922nas a2200217 4500008004100000020004100041245009100082210006900173250001500242260000800257300001100265490000700276520119200283653004001475653002701515653001101542100001401553700001301567700001401580856011001594 2008 eng d a0007-1102 (Print)0007-1102 (Linking)00aControlling item exposure and test overlap on the fly in computerized adaptive testing0 aControlling item exposure and test overlap on the fly in compute a2007/07/26 cNov a471-920 v613 aThis paper proposes an on-line version of the Sympson and Hetter procedure with test overlap control (SHT) that can provide item exposure control at both the item and test levels on the fly without iterative simulations. The on-line procedure is similar to the SHT procedure in that exposure parameters are used for simultaneous control of item exposure rates and test overlap rate. The exposure parameters for the on-line procedure, however, are updated sequentially on the fly, rather than through iterative simulations conducted prior to operational computerized adaptive tests (CATs). Unlike the SHT procedure, the on-line version can control item exposure rate and test overlap rate without time-consuming iterative simulations even when item pools or examinee populations have been changed. Moreover, the on-line procedure was found to perform better than the SHT procedure in controlling item exposure and test overlap for examinees who take tests earlier. Compared with two other on-line alternatives, this proposed on-line method provided the best all-around test security control. Thus, it would be an efficient procedure for controlling item exposure and test overlap in CATs.10a*Decision Making, Computer-Assisted10a*Models, Psychological10aHumans1 aChen, S-Y1 aLei, P W1 aLiao, W H uhttp://www.iacat.org/content/controlling-item-exposure-and-test-overlap-fly-computerized-adaptive-testing00423nas a2200097 4500008004100000245004900041210004600090260009700136100001100233856008100244 2007 eng d00aCAT Security: A practitioner’s perspective0 aCAT Security A practitioner s perspective aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aGuo, F uhttp://www.iacat.org/content/cat-security-practitioner%E2%80%99s-perspective00460nas a2200097 4500008004100000245006400041210006400105260009700169100001600266856008000282 2007 eng d00aChoices in CAT models in the context of educational testing0 aChoices in CAT models in the context of educational testing aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aEggen, Theo uhttp://www.iacat.org/content/choices-cat-models-context-educational-testing00436nas a2200097 4500008004100000245006500041210006500106260007000171100001600241856008100257 2007 eng d00aChoices in CAT models in the context of educattional testing0 aChoices in CAT models in the context of educattional testing aSt. Paul, MNbGraduate Management Admission CouncilcJune 7, 20071 aEggen, Theo uhttp://www.iacat.org/content/choices-cat-models-context-educattional-testing00573nas a2200109 4500008004100000245010200041210006900143260009700212100001600309700001400325856012400339 2007 eng d00aComparison of computerized adaptive testing and classical methods for measuring individual change0 aComparison of computerized adaptive testing and classical method aD. J. Weiss (Ed.). Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aKim-Kang, G1 aWeiss, DJ uhttp://www.iacat.org/content/comparison-computerized-adaptive-testing-and-classical-methods-measuring-individual-change00494nas a2200109 4500008004100000245011700041210006900158300001200227490000700239100001400246856012400260 2007 eng d00aThe comparison of maximum likelihood estimation and expected a posteriori in CAT using the graded response model0 acomparison of maximum likelihood estimation and expected a poste a339-3710 v191 aChen, S-K uhttp://www.iacat.org/content/comparison-maximum-likelihood-estimation-and-expected-posteriori-cat-using-graded-response00516nam a2200097 4500008004100000245010500041210006900146260006300215100001800278856012200296 2007 eng d00aA comparison of two methods of polytomous computerized classification testing for multiple cutscores0 acomparison of two methods of polytomous computerized classificat aUnpublished doctoral dissertation, University of Minnesota1 aThompson, N A uhttp://www.iacat.org/content/comparison-two-methods-polytomous-computerized-classification-testing-multiple-cutscores02308nas a2200301 4500008004100000020002200041245011900063210006900182250001500251260000800266300001000274490000700284520125000291653001501541653001001556653006201566653001101628653001101639653000901650653003801659653005601697653004601753653002101799653003101820100001601851700002001867856011901887 2007 eng d a1040-3590 (Print)00aComputerized adaptive personality testing: A review and illustration with the MMPI-2 Computerized Adaptive Version0 aComputerized adaptive personality testing A review and illustrat a2007/03/21 cMar a14-240 v193 aComputerized adaptive testing in personality assessment can improve efficiency by significantly reducing the number of items administered to answer an assessment question. Two approaches have been explored for adaptive testing in computerized personality assessment: item response theory and the countdown method. In this article, the authors review the literature on each and report the results of an investigation designed to explore the utility, in terms of item and time savings, and validity, in terms of correlations with external criterion measures, of an expanded countdown method-based research version of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), the MMPI-2 Computerized Adaptive Version (MMPI-2-CA). Participants were 433 undergraduate college students (170 men and 263 women). Results indicated considerable item savings and corresponding time savings for the adaptive testing modalities compared with a conventional computerized MMPI-2 administration. Furthermore, computerized adaptive administration yielded comparable results to computerized conventional administration of the MMPI-2 in terms of both test scores and their validity. Future directions for computerized adaptive personality testing are discussed.10aAdolescent10aAdult10aDiagnosis, Computer-Assisted/*statistics & numerical data10aFemale10aHumans10aMale10aMMPI/*statistics & numerical data10aPersonality Assessment/*statistics & numerical data10aPsychometrics/statistics & numerical data10aReference Values10aReproducibility of Results1 aForbey, J D1 aBen-Porath, Y S uhttp://www.iacat.org/content/computerized-adaptive-personality-testing-review-and-illustration-mmpi-2-computerized01477nas a2200241 4500008004100000020002200041245007800063210006900141250001500210260001100225300001200236490000700248520069100255653002300946653002500969653002100994653006201015653001101077653001601088100001701104700001401121856010001135 2007 eng d a0277-6715 (Print)00aComputerized adaptive testing for measuring development of young children0 aComputerized adaptive testing for measuring development of young a2006/11/30 cJun 15 a2629-380 v263 aDevelopmental indicators that are used for routine measurement in The Netherlands are usually chosen to optimally identify delayed children. Measurements on the majority of children without problems are therefore quite imprecise. This study explores the use of computerized adaptive testing (CAT) to monitor the development of young children. CAT is expected to improve the measurement precision of the instrument. We do two simulation studies - one with real data and one with simulated data - to evaluate the usefulness of CAT. It is shown that CAT selects developmental indicators that maximally match the individual child, so that all children can be measured to the same precision.10a*Child Development10a*Models, Statistical10aChild, Preschool10aDiagnosis, Computer-Assisted/*statistics & numerical data10aHumans10aNetherlands1 aJacobusse, G1 aBuuren, S uhttp://www.iacat.org/content/computerized-adaptive-testing-measuring-development-young-children01948nas a2200301 4500008004500000020001400045245012900059210006900188300001200257490000700269520093500276653002501211653002101236653002501257653003001282653003001312653001001342653001501352653002601367653002501393653002401418653001501442653001501457100001301472700001701485700002101502856012301523 2007 Engldsh  a0146-621600aComputerized adaptive testing for polytomous motivation items: Administration mode effects and a comparison with short forms0 aComputerized adaptive testing for polytomous motivation items Ad a412-4290 v313 aIn a randomized experiment (n=515), a computerized and a computerized adaptive test (CAT) are compared. The item pool consists of 24 polytomous motivation items. Although items are carefully selected, calibration data show that Samejima's graded response model did not fit the data optimally. A simulation study is done to assess possible consequences of model misfit. CAT efficiency was studied by a systematic comparison of the CAT with two types of conventional fixed length short forms, which are created to be good CAT competitors. Results showed no essential administration mode effects. Efficiency analyses show that CAT outperformed the short forms in almost all aspects when results are aggregated along the latent trait scale. The real and the simulated data results are very similar, which indicate that the real data results are not affected by model misfit. (PsycINFO Database Record (c) 2007 APA ) (journal abstract)10a2220 Tests & Testing10aAdaptive Testing10aAttitude Measurement10acomputer adaptive testing10aComputer Assisted Testing10aitems10aMotivation10apolytomous motivation10aStatistical Validity10aTest Administration10aTest Forms10aTest Items1 aHol, A M1 aVorst, H C M1 aMellenbergh, G J uhttp://www.iacat.org/content/computerized-adaptive-testing-polytomous-motivation-items-administration-mode-effects-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.abstract00473nas a2200109 4500008004100000245005800041210005800099260009700157100001400254700001700268856007800285 2007 eng d00aComputerized adaptive testing with the bifactor model0 aComputerized adaptive testing with the bifactor model aD. J. Weiss (Ed.). Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aWeiss, DJ1 aGibbons, R D uhttp://www.iacat.org/content/computerized-adaptive-testing-bifactor-model00608nas a2200121 4500008004100000245012500041210006900166260009700235100001200332700001500344700001100359856011600370 2007 eng d00aComputerized attribute-adaptive testing: A new computerized adaptive testing approach incorporating cognitive psychology0 aComputerized attributeadaptive testing A new computerized adapti aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aZhou, J1 aGierl, M J1 aCui, Y uhttp://www.iacat.org/content/computerized-attribute-adaptive-testing-new-computerized-adaptive-testing-approach00504nas a2200121 4500008004100000245006600041210006600107260005700173653003400230100001800264700001000282856009000292 2007 eng d00aComputerized classification testing with composite hypotheses0 aComputerized classification testing with composite hypotheses aSt. Paul, MNbGraduate Management Admissions Council10acomputerized adaptive testing1 aThompson, N A1 aRo, S uhttp://www.iacat.org/content/computerized-classification-testing-composite-hypotheses00500nas a2200109 4500008004100000245006600041210006600107260009700173100001800270700001000288856009200298 2007 eng d00aComputerized classification testing with composite hypotheses0 aComputerized classification testing with composite hypotheses aD. J. Weiss (Ed.). Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aThompson, N A1 aRo, S uhttp://www.iacat.org/content/computerized-classification-testing-composite-hypotheses-001052nas a2200145 4500008003900000245005000039210005000089300001200139490000700151520063300158100002100791700002200812700001900834856005300853 2007 d00aComputerizing Organizational Attitude Surveys0 aComputerizing Organizational Attitude Surveys a658-6780 v673 a

Two quasi-experimental field studies were conducted to evaluate the psychometric equivalence of computerized and paper-and-pencil job satisfaction measures. The present research extends previous work in the area by providing better control of common threats to validity in quasi-experimental research on test mode effects and by evaluating a more comprehensive measurement model for job attitudes. Results of both studies demonstrated substantial equivalence of the computerized measure with the paper-and-pencil version. Implications for the practical use of computerized organizational attitude surveys are discussed.

1 aMueller, Karsten1 aLiebig, Christian1 aHattrup, Keith uhttp://epm.sagepub.com/content/67/4/658.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/32/4/39800469nas a2200109 4500008004100000245008900041210006900130260001700199100001000216700001800226856011500244 2007 eng d00aCutscore location and classification accuracy in computerized classification testing0 aCutscore location and classification accuracy in computerized cl aTokyo, Japan1 aRo, S1 aThompson, N A uhttp://www.iacat.org/content/cutscore-location-and-classification-accuracy-computerized-classification-testing00347nas a2200097 4500008004100000245004000041210003800081260005500119100001300174856006200187 2006 eng d00aA CAT with personality and attitude0 aCAT with personality and attitude aEnschede, The Netherlands: PrintPartners Ipskamp B1 aHol, A M uhttp://www.iacat.org/content/cat-personality-and-attitude01526nas a2200157 4500008003900000022001400039245011200053210006900165300001400234490000700248520101100255100001601266700001901282700001201301856005501313 2006 d a1745-398400aComparing Methods of Assessing Differential Item Functioning in a Computerized Adaptive Testing Environment0 aComparing Methods of Assessing Differential Item Functioning in  a245–2640 v433 a

Mantel-Haenszel and SIBTEST, which have known difficulty in detecting non-unidirectional differential item functioning (DIF), have been adapted with some success for computerized adaptive testing (CAT). This study adapts logistic regression (LR) and the item-response-theory-likelihood-ratio test (IRT-LRT), capable of detecting both unidirectional and non-unidirectional DIF, to the CAT environment in which pretest items are assumed to be seeded in CATs but not used for trait estimation. The proposed adaptation methods were evaluated with simulated data under different sample size ratios and impact conditions in terms of Type I error, power, and specificity in identifying the form of DIF. The adapted LR and IRT-LRT procedures are more powerful than the CAT version of SIBTEST for non-unidirectional DIF detection. The good Type I error control provided by IRT-LRT under extremely unequal sample sizes and large impact is encouraging. Implications of these and other findings are discussed.

1 aLei, Pui-Wa1 aChen, Shu-Ying1 aYu, Lan uhttp://dx.doi.org/10.1111/j.1745-3984.2006.00015.x01947nas a2200241 4500008004100000020002200041245011200063210006900175260004100244300001200285490000700297520102300304653003401327653002401361653004701385653002401432653002101456653007001477100001301547700001401560700001001574856012101584 2006 eng d a0022-0655 (Print)00aComparing methods of assessing differential item functioning in a computerized adaptive testing environment0 aComparing methods of assessing differential item functioning in  bBlackwell Publishing: United Kingdom a245-2640 v433 aMantel-Haenszel and SIBTEST, which have known difficulty in detecting non-unidirectional differential item functioning (DIF), have been adapted with some success for computerized adaptive testing (CAT). This study adapts logistic regression (LR) and the item-response-theory-likelihood-ratio test (IRT-LRT), capable of detecting both unidirectional and non-unidirectional DIF, to the CAT environment in which pretest items are assumed to be seeded in CATs but not used for trait estimation. The proposed adaptation methods were evaluated with simulated data under different sample size ratios and impact conditions in terms of Type I error, power, and specificity in identifying the form of DIF. The adapted LR and IRT-LRT procedures are more powerful than the CAT version of SIBTEST for non-unidirectional DIF detection. The good Type I error control provided by IRT-LRT under extremely unequal sample sizes and large impact is encouraging. Implications of these and other findings are discussed. all rights reserved)10acomputerized adaptive testing10aeducational testing10aitem response theory likelihood ratio test10alogistic regression10atrait estimation10aunidirectional & non-unidirectional differential item functioning1 aLei, P-W1 aChen, S-Y1 aYu, L uhttp://www.iacat.org/content/comparing-methods-assessing-differential-item-functioning-computerized-adaptive-testing02403nas a2200241 4500008004100000020002200041245008500063210006900148260002500217300001200242490000700254520161500261653000801876653003401884653002601918653003001944653002601974100001402000700001402014700001602028700001502044856010202059 2006 eng d a0439-755X (Print)00aThe comparison among item selection strategies of CAT with multiple-choice items0 acomparison among item selection strategies of CAT with multiplec bScience Press: China a778-7830 v383 aThe initial purpose of comparing item selection strategies for CAT was to increase the efficiency of tests. As studies continued, however, it was found that increasing the efficiency of item bank using was also an important goal of comparing item selection strategies. These two goals often conflicted. The key solution was to find a strategy with which both goals could be accomplished. The item selection strategies for graded response model in this study included: the average of the difficulty orders matching with the ability; the medium of the difficulty orders matching with the ability; maximum information; A stratified (average); and A stratified (medium). The evaluation indexes used for comparison included: the bias of ability estimates for the true; the standard error of ability estimates; the average items which the examinees have administered; the standard deviation of the frequency of items selected; and sum of the indices weighted. Using the Monte Carlo simulation method, we obtained some data and computer iterated the data 20 times each under the conditions that the item difficulty parameters followed the normal distribution and even distribution. The results were as follows; The results indicated that no matter difficulty parameters followed the normal distribution or even distribution. Every type of item selection strategies designed in this research had its strong and weak points. In general evaluation, under the condition that items were stratified appropriately, A stratified (medium) (ASM) had the best effect. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aCAT10acomputerized adaptive testing10agraded response model10aitem selection strategies10amultiple choice items1 aHai-qi, D1 aDe-zhi, C1 aShuliang, D1 aTaiping, D uhttp://www.iacat.org/content/comparison-among-item-selection-strategies-cat-multiple-choice-items00420nas a2200121 4500008004100000245005700041210005500098260002200153100001600175700001300191700001900204856007500223 2006 eng d00aA comparison of online calibration methods for a CAT0 acomparison of online calibration methods for a CAT aSan Francisco, CA1 aMorgan, D L1 aWay, W D1 aAugemberg, K E uhttp://www.iacat.org/content/comparison-online-calibration-methods-cat00526nas a2200133 4500008003900000245013200039210006900171300001200240490000700252100002300259700001900282700002500301856006600326 2006 d00aComparison of the Psychometric Properties of Several Computer-Based Test Designs for Credentialing Exams With Multiple Purposes0 aComparison of the Psychometric Properties of Several ComputerBas a203-2200 v191 aJodoin, Michael, G1 aZenisky, April1 aHambleton, Ronald, K uhttp://www.tandfonline.com/doi/abs/10.1207/s15324818ame1903_302652nas a2200397 4500008004100000020002200041245013500063210006900198250001500267260000800282300001200290490000700302520140700309653002601716653003101742653001501773653001001788653000901798653002201807653002501829653003301854653001101887653001101898653000901909653001601918653004601934653003001980653003102010653001302041100001502054700001002069700001802079700001602097700001502113856012602128 2006 eng d a0895-4356 (Print)00aComputer adaptive testing improved accuracy and precision of scores over random item selection in a physical functioning item bank0 aComputer adaptive testing improved accuracy and precision of sco a2006/10/10 cNov a1174-820 v593 aBACKGROUND AND OBJECTIVE: Measuring physical functioning (PF) within and across postacute settings is critical for monitoring outcomes of rehabilitation; however, most current instruments lack sufficient breadth and feasibility for widespread use. Computer adaptive testing (CAT), in which item selection is tailored to the individual patient, holds promise for reducing response burden, yet maintaining measurement precision. We calibrated a PF item bank via item response theory (IRT), administered items with a post hoc CAT design, and determined whether CAT would improve accuracy and precision of score estimates over random item selection. METHODS: 1,041 adults were interviewed during postacute care rehabilitation episodes in either hospital or community settings. Responses for 124 PF items were calibrated using IRT methods to create a PF item bank. We examined the accuracy and precision of CAT-based scores compared to a random selection of items. RESULTS: CAT-based scores had higher correlations with the IRT-criterion scores, especially with short tests, and resulted in narrower confidence intervals than scores based on a random selection of items; gains, as expected, were especially large for low and high performing adults. CONCLUSION: The CAT design may have important precision and efficiency advantages for point-of-care functional assessment in rehabilitation practice settings.10a*Recovery of Function10aActivities of Daily Living10aAdolescent10aAdult10aAged10aAged, 80 and over10aConfidence Intervals10aFactor Analysis, Statistical10aFemale10aHumans10aMale10aMiddle Aged10aOutcome Assessment (Health Care)/*methods10aRehabilitation/*standards10aReproducibility of Results10aSoftware1 aHaley, S M1 aNi, P1 aHambleton, RK1 aSlavin, M D1 aJette, A M uhttp://www.iacat.org/content/computer-adaptive-testing-improved-accuracy-and-precision-scores-over-random-item-selectio-000642nas a2200169 4500008004100000020001300041245013500054210006900189300001400258490000700272100001300279700001000292700001800302700001400320700001300334856012500347 2006 eng d a0895435600aComputer adaptive testing improved accuracy and precision of scores over random item selection in a physical functioning item bank0 aComputer adaptive testing improved accuracy and precision of sco a1174-11820 v591 aHaley, S1 aNi, P1 aHambleton, RK1 aSlavin, M1 aJette, A uhttp://www.iacat.org/content/computer-adaptive-testing-improved-accuracy-and-precision-scores-over-random-item-selection01470nas a2200205 4500008004100000245002700041210002600068260006000094300001100154490000800165520087300173653005101046653003001097653002001127653001801147653001301165100001501178700001501193856005601208 2006 eng d00aComputer-based testing0 aComputerbased testing aWashington D.C. USAbAmerican Psychological Association a87-1000 vxiv3 a(From the chapter) There has been a proliferation of research designed to explore and exploit opportunities provided by computer-based assessment. This chapter provides an overview of the diverse efforts by researchers in this area. It begins by describing how paper-and-pencil tests can be adapted for administration by computers. Computerization provides the important advantage that items can be selected so they are of appropriate difficulty for each examinee. Some of the psychometric theory needed for computerized adaptive testing is reviewed. Then research on innovative computerized assessments is summarized. These assessments go beyond multiple-choice items by using formats made possible by computerization. Then some hardware and software issues are described, and finally, directions for future work are outlined. (PsycINFO Database Record (c) 2006 APA )10aAdaptive Testing computerized adaptive testing10aComputer Assisted Testing10aExperimentation10aPsychometrics10aTheories1 aDrasgow, F1 aChuah, S C uhttp://www.iacat.org/content/computer-based-testing03329nas a2200469 4500008004100000020002200041245011600063210006900179250001500248260000800263300001200271490000700283520189400290653003202184653003102216653002202247653002002269653001002289653000902299653002202308653002802330653003302358653001102391653001102402653002502413653000902438653001602447653004602463653002202509653002402531653003002555653002902585100001502614700001502629700001602644700001102660700002402671700001402695700001802709700001002727856012202737 2006 eng d a0003-9993 (Print)00aComputerized adaptive testing for follow-up after discharge from inpatient rehabilitation: I. Activity outcomes0 aComputerized adaptive testing for followup after discharge from  a2006/08/01 cAug a1033-420 v873 aOBJECTIVE: To examine score agreement, precision, validity, efficiency, and responsiveness of a computerized adaptive testing (CAT) version of the Activity Measure for Post-Acute Care (AM-PAC-CAT) in a prospective, 3-month follow-up sample of inpatient rehabilitation patients recently discharged home. DESIGN: Longitudinal, prospective 1-group cohort study of patients followed approximately 2 weeks after hospital discharge and then 3 months after the initial home visit. SETTING: Follow-up visits conducted in patients' home setting. PARTICIPANTS: Ninety-four adults who were recently discharged from inpatient rehabilitation, with diagnoses of neurologic, orthopedic, and medically complex conditions. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Summary scores from AM-PAC-CAT, including 3 activity domains of movement and physical, personal care and instrumental, and applied cognition were compared with scores from a traditional fixed-length version of the AM-PAC with 66 items (AM-PAC-66). RESULTS: AM-PAC-CAT scores were in good agreement (intraclass correlation coefficient model 3,1 range, .77-.86) with scores from the AM-PAC-66. On average, the CAT programs required 43% of the time and 33% of the items compared with the AM-PAC-66. Both formats discriminated across functional severity groups. The standardized response mean (SRM) was greater for the movement and physical fixed form than the CAT; the effect size and SRM of the 2 other AM-PAC domains showed similar sensitivity between CAT and fixed formats. Using patients' own report as an anchor-based measure of change, the CAT and fixed length formats were comparable in responsiveness to patient-reported change over a 3-month interval. CONCLUSIONS: Accurate estimates for functional activity group-level changes can be obtained from CAT administrations, with a considerable reduction in administration time.10a*Activities of Daily Living10a*Adaptation, Physiological10a*Computer Systems10a*Questionnaires10aAdult10aAged10aAged, 80 and over10aChi-Square Distribution10aFactor Analysis, Statistical10aFemale10aHumans10aLongitudinal Studies10aMale10aMiddle Aged10aOutcome Assessment (Health Care)/*methods10aPatient Discharge10aProspective Studies10aRehabilitation/*standards10aSubacute Care/*standards1 aHaley, S M1 aSiebens, H1 aCoster, W J1 aTao, W1 aBlack-Schaffer, R M1 aGandek, B1 aSinclair, S J1 aNi, P uhttp://www.iacat.org/content/computerized-adaptive-testing-follow-after-discharge-inpatient-rehabilitation-i-activity03166nas a2200361 4500008004100000020002200041245013600063210006900199250001500268260000800283300001200291490000700303520206800310653001502378653002402393653002102417653001002438653000902448653002902457653003402486653002402520653001102544653001102555653001302566653000902579653001602588100001602604700001302620700002302633700001202656700001102668856012502679 2006 eng d a0962-9343 (Print)00aComputerized adaptive testing of diabetes impact: a feasibility study of Hispanics and non-Hispanics in an active clinic population0 aComputerized adaptive testing of diabetes impact a feasibility s a2006/10/13 cNov a1503-180 v153 aBACKGROUND: Diabetes is a leading cause of death and disability in the US and is twice as common among Hispanic Americans as non-Hispanics. The societal costs of diabetes provide an impetus for developing tools that can improve patient care and delay or prevent diabetes complications. METHODS: We implemented a feasibility study of a Computerized Adaptive Test (CAT) to measure diabetes impact using a sample of 103 English- and 97 Spanish-speaking patients (mean age = 56.5, 66.5% female) in a community medical center with a high proportion of minority patients (28% African-American). The 37 items of the Diabetes Impact Survey were translated using forward-backward translation and cognitive debriefing. Participants were randomized to receive either the full-length tool or the Diabetes-CAT first, in the patient's native language. RESULTS: The number of items and the amount of time to complete the survey for the CAT was reduced to one-sixth the amount for the full-length tool in both languages, across disease severity. Confirmatory Factor Analysis confirmed that the Diabetes Impact Survey is unidimensional. The Diabetes-CAT demonstrated acceptable internal consistency reliability, construct validity, and discriminant validity in the overall sample, although subgroup analyses suggested that the English sample data evidenced higher levels of reliability and validity than the Spanish sample and issues with discriminant validity in the Spanish sample. Differential Item Function analysis revealed differences in responses tendencies by language group in 3 of the 37 items. Participant interviews suggested that the Spanish-speaking patients generally preferred the paper survey to the computer-assisted tool, and were twice as likely to experience difficulties understanding the items. CONCLUSIONS: While the Diabetes-CAT demonstrated clear advantages in reducing respondent burden as compared to the full-length tool, simplifying the item bank will be necessary for enhancing the feasibility of the Diabetes-CAT for use with low literacy patients.10a*Computers10a*Hispanic Americans10a*Quality of Life10aAdult10aAged10aData Collection/*methods10aDiabetes Mellitus/*psychology10aFeasibility Studies10aFemale10aHumans10aLanguage10aMale10aMiddle Aged1 aSchwartz, C1 aWelch, G1 aSantiago-Kelley, P1 aBode, R1 aSun, X uhttp://www.iacat.org/content/computerized-adaptive-testing-diabetes-impact-feasibility-study-hispanics-and-non-hispanics00429nas a2200121 4500008003900000245006500039210006500104300001200169490000700181100001000188700001500198856009400213 2006 d00aComputerized adaptive testing under nonparametric IRT models0 aComputerized adaptive testing under nonparametric IRT models a121-1370 v711 aXu, X1 aDouglas, J uhttp://www.iacat.org/content/computerized-adaptive-testing-under-nonparametric-irt-models00535nas a2200121 4500008004100000245011700041210006900158260001800227100001300245700001900258700001400277856012200291 2006 eng d00aConstraints-weighted information method for item selection of severely constrained computerized adaptive testing0 aConstraintsweighted information method for item selection of sev aSan Francisco1 aCheng, Y1 aChang, Hua-Hua1 aWang, X B uhttp://www.iacat.org/content/constraints-weighted-information-method-item-selection-severely-constrained-computerized00534nas a2200133 4500008003900000245010500039210006900144300001200213490000700225100001700232700001600249700001500265856012000280 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://www.iacat.org/content/closer-look-using-judgments-item-difficulty-change-answers-computerized-adaptive-tests00519nam a2200097 4500008004100000245009600041210006900137260008000206100002400286856011100310 2005 eng d00aA comparison of adaptive mastery testing using testlets with the 3-parameter logistic model0 acomparison of adaptive mastery testing using testlets with the 3 aUnpublished doctoral dissertation, University of Minnesota, Minneapolis, MN1 aJacobs-Cassuto, M S uhttp://www.iacat.org/content/comparison-adaptive-mastery-testing-using-testlets-3-parameter-logistic-model02152nas a2200229 4500008004100000020002200041245008700063210006900150260004100219300001200260490000700272520135500279653002101634653001501655653002401670653002601694653002501720653002101745653003101766100002301797856010201820 2005 eng d a0022-0655 (Print)00aA comparison of item-selection methods for adaptive tests with content constraints0 acomparison of itemselection methods for adaptive tests with cont bBlackwell Publishing: United Kingdom a283-3020 v423 aIn test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee's ability estimate, such as the test information function in computerized adaptive testing. But it leads to the non-trivial problem of how to realize a set of content constraints on the test—a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of the future consequences of selecting an item. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test and the latter a projection of the test itself. The pros and cons of these methods are analyzed. An empirical comparison between the WDM and STA was conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM.10aAdaptive Testing10aAlgorithms10acontent constraints10aitem selection method10ashadow test approach10aspiraling method10aweighted deviations method1 avan der Linden, WJ uhttp://www.iacat.org/content/comparison-item-selection-methods-adaptive-tests-content-constraints00453nas a2200109 4500008004100000245008700041210006900128300001200197490000700209100002300216856010400239 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://www.iacat.org/content/comparison-item-selection-methods-adaptive-tests-content-constraints-000305nas a2200109 4500008004100000245003000041210003000071300001100101490000600112100001600118856006100134 2005 eng d00aComputer adaptive testing0 aComputer adaptive testing a109-270 v61 aGershon, RC uhttp://www.iacat.org/content/computer-adaptive-testing-001605nas a2200253 4500008004100000020002200041245003000063210003000093250001500123300001100138490000600149520095200155653001401107653002501121653002901146653001801175653001901193653001101212653001401223653001901237653002001256100001601276856005901292 2005 eng d a1529-7713 (Print)00aComputer adaptive testing0 aComputer adaptive testing a2005/02/11 a109-270 v63 aThe creation of item response theory (IRT) and Rasch models, inexpensive accessibility to high speed desktop computers, and the growth of the Internet, has led to the creation and growth of computerized adaptive testing or CAT. This form of assessment is applicable for both high stakes tests such as certification or licensure exams, as well as health related quality of life surveys. This article discusses the historical background of CAT including its many advantages over conventional (typically paper and pencil) alternatives. The process of CAT is then described including descriptions of the specific differences of using CAT based upon 1-, 2- and 3-parameter IRT and various Rasch models. Numerous specific topics describing CAT in practice are described including: initial item selection, content balancing, test difficulty, test length and stopping rules. The article concludes with the author's reflections regarding the future of CAT.10a*Internet10a*Models, Statistical10a*User-Computer Interface10aCertification10aHealth Surveys10aHumans10aLicensure10aMicrocomputers10aQuality of Life1 aGershon, RC uhttp://www.iacat.org/content/computer-adaptive-testing02791nas a2200469 4500008004100000020002200041245010400063210006900167250001500236260000800251300001200259490000700271520132800278653002201606653003101628653001501659653001601674653001001690653003401700653002101734653002401755653002501779653001501804653001101819653005301830653002901883653001101912653001101923653002001934653000901954653003101963653004601994653003102040653001402071653003202085100001502117700001002132700002502142700001702167700001302184856012402197 2005 eng d a0012-1622 (Print)00aA computer adaptive testing approach for assessing physical functioning in children and adolescents0 acomputer adaptive testing approach for assessing physical functi a2005/02/15 cFeb a113-1200 v473 aThe purpose of this article is to demonstrate: (1) the accuracy and (2) the reduction in amount of time and effort in assessing physical functioning (self-care and mobility domains) of children and adolescents using computer-adaptive testing (CAT). A CAT algorithm selects questions directly tailored to the child's ability level, based on previous responses. Using a CAT algorithm, a simulation study was used to determine the number of items necessary to approximate the score of a full-length assessment. We built simulated CAT (5-, 10-, 15-, and 20-item versions) for self-care and mobility domains and tested their accuracy in a normative sample (n=373; 190 males, 183 females; mean age 6y 11mo [SD 4y 2m], range 4mo to 14y 11mo) and a sample of children and adolescents with Pompe disease (n=26; 21 males, 5 females; mean age 6y 1mo [SD 3y 10mo], range 5mo to 14y 10mo). Results indicated that comparable score estimates (based on computer simulations) to the full-length tests can be achieved in a 20-item CAT version for all age ranges and for normative and clinical samples. No more than 13 to 16% of the items in the full-length tests were needed for any one administration. These results support further consideration of using CAT programs for accurate and efficient clinical assessments of physical functioning.10a*Computer Systems10aActivities of Daily Living10aAdolescent10aAge Factors10aChild10aChild Development/*physiology10aChild, Preschool10aComputer Simulation10aConfidence Intervals10aDemography10aFemale10aGlycogen Storage Disease Type II/physiopathology10aHealth Status Indicators10aHumans10aInfant10aInfant, Newborn10aMale10aMotor Activity/*physiology10aOutcome Assessment (Health Care)/*methods10aReproducibility of Results10aSelf Care10aSensitivity and Specificity1 aHaley, S M1 aNi, P1 aFragala-Pinkham, M A1 aSkrinar, A M1 aCorzo, D uhttp://www.iacat.org/content/computer-adaptive-testing-approach-assessing-physical-functioning-children-and-adolescents00512nas a2200097 4500008004100000245005100041210005100092260017200143100001900315856008000334 2005 eng d00aComputer adaptive testing quality requirements0 aComputer adaptive testing quality requirements aProceedings E-Learn 2005 World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 288-295, Vancouver, Canada, AACE, October 2005.1 aEconomides, AA uhttp://www.iacat.org/content/computer-adaptive-testing-quality-requirements02352nas a2200193 4500008004100000245008700041210006900128300001200197490000700209520170500216653003001921653002201951653001401973653002801987653001302015100001002028700001302038856010702051 2005 eng d00aA computer-assisted test design and diagnosis system for use by classroom teachers0 acomputerassisted test design and diagnosis system for use by cla a419-4290 v213 aComputer-assisted assessment (CAA) has become increasingly important in education in recent years. A variety of computer software systems have been developed to help assess the performance of students at various levels. However, such systems are primarily designed to provide objective assessment of students and analysis of test items, and focus has been mainly placed on higher and further education. Although there are commercial professional systems available for use by primary and secondary educational institutions, such systems are generally expensive and require skilled expertise to operate. In view of the rapid progress made in the use of computer-based assessment for primary and secondary students by education authorities here in the UK and elsewhere, there is a need to develop systems which are economic and easy to use and can provide the necessary information that can help teachers improve students' performance. This paper presents the development of a software system that provides a range of functions including generating items and building item banks, designing tests, conducting tests on computers and analysing test results. Specifically, the system can generate information on the performance of students and test items that can be easily used to identify curriculum areas where students are under performing. A case study based on data collected from five secondary schools in Hong Kong involved in the Curriculum, Evaluation and Management Centre's Middle Years Information System Project, Durham University, UK, has been undertaken to demonstrate the use of the system for diagnostic and performance analysis. (PsycINFO Database Record (c) 2006 APA ) (journal abstract)10aComputer Assisted Testing10aComputer Software10aDiagnosis10aEducational Measurement10aTeachers1 aHe, Q1 aTymms, P uhttp://www.iacat.org/content/computer-assisted-test-design-and-diagnosis-system-use-classroom-teachers02219nas a2200241 4500008004100000020004100041245009600082210006900178250001500247260000800262300001100270490000700281520139600288653002701684653003701711653002701748653001101775653002601786100001501812700001901827700001301846856011801859 2005 eng d a0007-1102 (Print)0007-1102 (Linking)00aComputerized adaptive testing: a mixture item selection approach for constrained situations0 aComputerized adaptive testing a mixture item selection approach  a2005/11/19 cNov a239-570 v583 aIn computerized adaptive testing (CAT), traditionally the most discriminating items are selected to provide the maximum information so as to attain the highest efficiency in trait (theta) estimation. The maximum information (MI) approach typically results in unbalanced item exposure and hence high item-overlap rates across examinees. Recently, Yi and Chang (2003) proposed the multiple stratification (MS) method to remedy the shortcomings of MI. In MS, items are first sorted according to content, then difficulty and finally discrimination parameters. As discriminating items are used strategically, MS offers a better utilization of the entire item pool. However, for testing with imposed non-statistical constraints, this new stratification approach may not maintain its high efficiency. Through a series of simulation studies, this research explored the possible benefits of a mixture item selection approach (MS-MI), integrating the MS and MI approaches, in testing with non-statistical constraints. In all simulation conditions, MS consistently outperformed the other two competing approaches in item pool utilization, while the MS-MI and the MI approaches yielded higher measurement efficiency and offered better conformity to the constraints. Furthermore, the MS-MI approach was shown to perform better than MI on all evaluation criteria when control of item exposure was imposed.10a*Computer-Aided Design10a*Educational Measurement/methods10a*Models, Psychological10aHumans10aPsychometrics/methods1 aLeung, C K1 aChang, Hua-Hua1 aHau, K T uhttp://www.iacat.org/content/computerized-adaptive-testing-mixture-item-selection-approach-constrained-situations00601nas a2200145 4500008004100000245014400041210006900185300001200254490000700266100001300273700001400286700002100300700001500321856011900336 2005 eng d00aComputerized adaptive testing with the partial credit model: Estimation procedures, population distributions, and item pool characteristics0 aComputerized adaptive testing with the partial credit model Esti a533-5460 v291 aGorin, J1 aDodd, B G1 aFitzpatrick, S J1 aShieh, Y Y uhttp://www.iacat.org/content/computerized-adaptive-testing-partial-credit-model-estimation-procedures-population-001564nas a2200157 4500008003900000245014400039210006900183300001200252490000700264520099200271100002101263700002101284700002701305700002101332856005301353 2005 d00aComputerized Adaptive Testing With the Partial Credit Model: Estimation Procedures, Population Distributions, and Item Pool Characteristics0 aComputerized Adaptive Testing With the Partial Credit Model Esti a433-4560 v293 a

The primary purpose of this research is to examine the impact of estimation methods, actual latent trait distributions, and item pool characteristics on the performance of a simulated computerized adaptive testing (CAT) system. In this study, three estimation procedures are compared for accuracy of estimation: maximum likelihood estimation (MLE), expected a priori (EAP), and Warm's weighted likelihood estimation (WLE). Some research has shown that MLE and EAP perform equally well under certain conditions in polytomous CAT systems, such that they match the actual latent trait distribution. However, little research has compared these methods when prior estimates of. distributions are extremely poor. In general, it appears that MLE, EAP, and WLE procedures perform equally well when using an optimal item pool. However, the use of EAP procedures may be advantageous under nonoptimal testing conditions when the item pool is not appropriately matched to the examinees.

1 aGorin, Joanna, S1 aDodd, Barbara, G1 aFitzpatrick, Steven, J1 aShieh, Yann Yann uhttp://apm.sagepub.com/content/29/6/433.abstract00541nas a2200121 4500008004100000020001400041245014400055210006900199300001200268490000700280100001500287856011700302 2005 eng d a0146-621600aComputerized adaptive testing with the partial credit model: Estimation procedures, population distributions, and item pool characteristics0 aComputerized adaptive testing with the partial credit model Esti a433-4560 v291 aGorin, J S uhttp://www.iacat.org/content/computerized-adaptive-testing-partial-credit-model-estimation-procedures-population00685nas a2200121 4500008004100000245017000041210006900211260011000280100001800390700002000408700001600428856011900444 2005 eng d00aComputerizing statewide assessments in Minnesota: A report on the feasibility of converting the Minnesota Comprehensive Assessments to a computerized adaptive format0 aComputerizing statewide assessments in Minnesota A report on the bOffice of Educational Accountability, College of Education and Human Development, University of Minnesota1 aPeterson, K A1 aL., Davison., M1 aHjelseth, L uhttp://www.iacat.org/content/computerizing-statewide-assessments-minnesota-report-feasibility-converting-minnesota00515nas a2200109 4500008004100000245008200041210006900123260006700192100002300259700001800282856010500300 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://www.iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests-100447nas a2200121 4500008003900000245009000039210006900129300001200198490000700210100002200217700002000239856006600259 2005 d00aConstructing a Computerized Adaptive Test for University Applicants With Disabilities0 aConstructing a Computerized Adaptive Test for University Applica a381-4050 v181 aMoshinsky, Avital1 aKazin, Cathrael uhttp://www.tandfonline.com/doi/abs/10.1207/s15324818ame1804_302123nas a2200253 4500008004100000245007900041210006900120300001200189490000700201520113300208653002701341653004601368653005201414653002901466653001101495653005601506653002501562653004101587653004501628653006201673100001501735700001501750856010401765 2005 eng d00aContemporary measurement techniques for rehabilitation outcomes assessment0 aContemporary measurement techniques for rehabilitation outcomes  a339-3450 v373 aIn this article, we review the limitations of traditional rehabilitation functional outcome instruments currently in use within the rehabilitation field to assess Activity and Participation domains as defined by the International Classification of Function, Disability, and Health. These include a narrow scope of functional outcomes, data incompatibility across instruments, and the precision vs feasibility dilemma. Following this, we illustrate how contemporary measurement techniques, such as item response theory methods combined with computer adaptive testing methodology, can be applied in rehabilitation to design functional outcome instruments that are comprehensive in scope, accurate, allow for compatibility across instruments, and are sensitive to clinically important change without sacrificing their feasibility. Finally, we present some of the pressing challenges that need to be overcome to provide effective dissemination and training assistance to ensure that current and future generations of rehabilitation professionals are familiar with and skilled in the application of contemporary outcomes measurement.10a*Disability Evaluation10aActivities of Daily Living/classification10aDisabled Persons/classification/*rehabilitation10aHealth Status Indicators10aHumans10aOutcome Assessment (Health Care)/*methods/standards10aRecovery of Function10aResearch Support, N.I.H., Extramural10aResearch Support, U.S. Gov't, Non-P.H.S.10aSensitivity and Specificity computerized adaptive testing1 aJette, A M1 aHaley, S M uhttp://www.iacat.org/content/contemporary-measurement-techniques-rehabilitation-outcomes-assessment01559nas a2200169 4500008004100000245008000041210006900121300001200190490000700202520094200209653002101151653003001172653005401202100001401256700001301270856010601283 2005 eng d00aControlling item exposure and test overlap in computerized adaptive testing0 aControlling item exposure and test overlap in computerized adapt a204-2170 v293 aThis article proposes an item exposure control method, which is the extension of the Sympson and Hetter procedure and can provide item exposure control at both the item and test levels. Item exposure rate and test overlap rate are two indices commonly used to track item exposure in computerized adaptive tests. By considering both indices, item exposure can be monitored at both the item and test levels. To control the item exposure rate and test overlap rate simultaneously, the modified procedure attempted to control not only the maximum value but also the variance of item exposure rates. Results indicated that the item exposure rate and test overlap rate could be controlled simultaneously by implementing the modified procedure. Item exposure control was improved and precision of trait estimation decreased when a prespecified maximum test overlap rate was stringent. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Content (Test) computerized adaptive testing1 aChen, S-Y1 aLei, P-W uhttp://www.iacat.org/content/controlling-item-exposure-and-test-overlap-computerized-adaptive-testing00471nas a2200121 4500008004100000245008000041210006900121300001400190490001000204100001400214700001300228856010800241 2005 eng d00aControlling item exposure and test overlap in computerized adaptive testing0 aControlling item exposure and test overlap in computerized adapt a204–2170 v29(2)1 aChen, S Y1 aLei, P W uhttp://www.iacat.org/content/controlling-item-exposure-and-test-overlap-computerized-adaptive-testing-001320nas a2200133 4500008003900000245008000039210006900119300001200188490000700200520089100207100001901098700001601117856005301133 2005 d00aControlling Item Exposure and Test Overlap in Computerized Adaptive Testing0 aControlling Item Exposure and Test Overlap in Computerized Adapt a204-2170 v293 a

This article proposes an item exposure control method, which is the extension of the Sympson and Hetter procedure and can provide item exposure control at both the item and test levels. Item exposure rate and test overlap rate are two indices commonly used to track item exposure in computerized adaptive tests. By considering both indices, item exposure can be monitored at both the item and test levels. To control the item exposure rate and test overlap rate simultaneously, the modified procedure attempted to control not only the maximum value but also the variance of item exposure rates. Results indicated that the item exposure rate and test overlap rate could be controlled simultaneously by implementing the modified procedure. Item exposure control was improved and precision of trait estimation decreased when a prespecified maximum test overlap rate was stringent.

1 aChen, Shu-Ying1 aLei, Pui-Wa uhttp://apm.sagepub.com/content/29/3/204.abstract00506nas a2200121 4500008004100000245009000041210006900131260001700200100001700217700001900234700001500253856011600268 2004 eng d00aCombining computer adaptive testing technology with cognitively diagnostic assessment0 aCombining computer adaptive testing technology with cognitively  aSan Diego CA1 aMcGlohen, MK1 aChang, Hua-Hua1 aWills, J T uhttp://www.iacat.org/content/combining-computer-adaptive-testing-technology-cognitively-diagnostic-assessment-001801nas a2200361 4500008004100000020002200041245009500063210006900158250001500227260001100242300001000253490000700263520066300270653002500933653002900958653001000987653000900997653002201006653004501028653003701073653001101110653001101121653000901132653001601141653003601157653003001193653003401223100001601257700002401273700001001297700001501307856011701322 2004 eng d a1074-9357 (Print)00aComputer adaptive testing: a strategy for monitoring stroke rehabilitation across settings0 aComputer adaptive testing a strategy for monitoring stroke rehab a2004/05/01 cSpring a33-390 v113 aCurrent functional assessment instruments in stroke rehabilitation are often setting-specific and lack precision, breadth, and/or feasibility. Computer adaptive testing (CAT) offers a promising potential solution by providing a quick, yet precise, measure of function that can be used across a broad range of patient abilities and in multiple settings. CAT technology yields a precise score by selecting very few relevant items from a large and diverse item pool based on each individual's responses. We demonstrate the potential usefulness of a CAT assessment model with a cross-sectional sample of persons with stroke from multiple rehabilitation settings.10a*Computer Simulation10a*User-Computer Interface10aAdult10aAged10aAged, 80 and over10aCerebrovascular Accident/*rehabilitation10aDisabled Persons/*classification10aFemale10aHumans10aMale10aMiddle Aged10aMonitoring, Physiologic/methods10aSeverity of Illness Index10aTask Performance and Analysis1 aAndres, P L1 aBlack-Schaffer, R M1 aNi, P1 aHaley, S M uhttp://www.iacat.org/content/computer-adaptive-testing-strategy-monitoring-stroke-rehabilitation-across-settings00415nas a2200109 4500008004100000245006300041210006300104260001700167100001900184700001400203856008800217 2004 eng d00aComputer adaptive testing and the No Child Left Behind Act0 aComputer adaptive testing and the No Child Left Behind Act aSan Diego CA1 aKingsbury, G G1 aHauser, C uhttp://www.iacat.org/content/computer-adaptive-testing-and-no-child-left-behind-act00379nas a2200097 4500008004100000245003000041210002900071260010500100100001500205856006100220 2004 eng d00aComputer-adaptive testing0 aComputeradaptive testing aB. Everett, and D. Howell (Eds.), Encyclopedia of statistics in behavioral science. New York: Wiley.1 aLuecht, RM uhttp://www.iacat.org/content/computer-adaptive-testing-200528nas a2200109 4500008004100000245009800041210006900139260006200208100001800270700001200288856011800300 2004 eng d00aComputer-based test designs with optimal and non-optimal tests for making pass-fail decisions0 aComputerbased test designs with optimal and nonoptimal tests for aResearch Report, University of Massachusetts, Amherst, MA1 aHambleton, RK1 aXing, D uhttp://www.iacat.org/content/computer-based-test-designs-optimal-and-non-optimal-tests-making-pass-fail-decisions01904nas a2200217 4500008004100000245010000041210006900141260000800210300001100218490000700229520117300236653002201409653001501431653003701446653003101483653001101514653001901525100001201544700001501556856011501571 2004 eng d00aA computerized adaptive knowledge test as an assessment tool in general practice: a pilot study0 acomputerized adaptive knowledge test as an assessment tool in ge cMar a178-830 v263 aAdvantageous to assessment in many fields, CAT (computerized adaptive testing) use in general practice has been scarce. In adapting CAT to general practice, the basic assumptions of item response theory and the case specificity must be taken into account. In this context, this study first evaluated the feasibility of converting written extended matching tests into CAT. Second, it questioned the content validity of CAT. A stratified sample of students was invited to participate in the pilot study. The items used in this test, together with their parameters, originated from the written test. The detailed test paths of the students were retained and analysed thoroughly. Using the predefined pass-fail standard, one student failed the test. There was a positive correlation between the number of items and the candidate's ability level. The majority of students were presented with questions in seven of the 10 existing domains. Although proved to be a feasible test format, CAT cannot substitute for the existing high-stakes large-scale written test. It may provide a reliable instrument for identifying candidates who are at risk of failing in the written test.10a*Computer Systems10aAlgorithms10aEducational Measurement/*methods10aFamily Practice/*education10aHumans10aPilot Projects1 aRoex, A1 aDegryse, J uhttp://www.iacat.org/content/computerized-adaptive-knowledge-test-assessment-tool-general-practice-pilot-study02593nas a2200469 4500008004100000245007200041210006900113300001000182490000600192520108600198653002501284653001001309653001501319653002101334653002201355653005901377653007001436653003301506653001101539653001101550653001301561653000901574653002701583653002201610653005501632653001901687653001501706653006601721653001801787653003701805653004101842653003001883653001301913100001501926700001301941700001801954700001501972700001401987700001402001700001302015856009502028 2004 eng d00aComputerized adaptive measurement of depression: A simulation study0 aComputerized adaptive measurement of depression A simulation stu a13-230 v43 aBackground: Efficient, accurate instruments for measuring depression are increasingly importantin clinical practice. We developed a computerized adaptive version of the Beck DepressionInventory (BDI). We examined its efficiency and its usefulness in identifying Major DepressiveEpisodes (MDE) and in measuring depression severity.Methods: Subjects were 744 participants in research studies in which each subject completed boththe BDI and the SCID. In addition, 285 patients completed the Hamilton Depression Rating Scale.Results: The adaptive BDI had an AUC as an indicator of a SCID diagnosis of MDE of 88%,equivalent to the full BDI. The adaptive BDI asked fewer questions than the full BDI (5.6 versus 21items). The adaptive latent depression score correlated r = .92 with the BDI total score and thelatent depression score correlated more highly with the Hamilton (r = .74) than the BDI total scoredid (r = .70).Conclusions: Adaptive testing for depression may provide greatly increased efficiency withoutloss of accuracy in identifying MDE or in measuring depression severity.10a*Computer Simulation10aAdult10aAlgorithms10aArea Under Curve10aComparative Study10aDepressive Disorder/*diagnosis/epidemiology/psychology10aDiagnosis, Computer-Assisted/*methods/statistics & numerical data10aFactor Analysis, Statistical10aFemale10aHumans10aInternet10aMale10aMass Screening/methods10aPatient Selection10aPersonality Inventory/*statistics & numerical data10aPilot Projects10aPrevalence10aPsychiatric Status Rating Scales/*statistics & numerical data10aPsychometrics10aResearch Support, Non-U.S. Gov't10aResearch Support, U.S. Gov't, P.H.S.10aSeverity of Illness Index10aSoftware1 aGardner, W1 aShear, K1 aKelleher, K J1 aPajer, K A1 aMammen, O1 aBuysse, D1 aFrank, E uhttp://www.iacat.org/content/computerized-adaptive-measurement-depression-simulation-study00344nas a2200097 4500008004100000245003400041210003400075260005600109100001600165856006500181 2004 eng d00aComputerized adaptive testing0 aComputerized adaptive testing aEncyclopedia of social measurement. Academic Press.1 aSegall, D O uhttp://www.iacat.org/content/computerized-adaptive-testing-100511nas a2200133 4500008004100000245005100041210005100092260009900143100001700242700001600259700001400275700000800289856008000297 2004 eng d00aComputerized adaptive testing and item banking0 aComputerized adaptive testing and item banking aP. M. Fayers and R. D. Hays (Eds.) Assessing Quality of Life. Oxford: Oxford University Press.1 aBjorner, J B1 aKosinski, M1 aWare, J E1 aJr. uhttp://www.iacat.org/content/computerized-adaptive-testing-and-item-banking00481nas a2200109 4500008004500000245010200045210006900147300001000216490000700226100001400233856012400247 2004 Engldsh 00aComputerized Adaptive Testing for Effective and Efficient Measurement in Counseling and Education0 aComputerized Adaptive Testing for Effective and Efficient Measur a70-840 v371 aWeiss, DJ uhttp://www.iacat.org/content/computerized-adaptive-testing-effective-and-efficient-measurement-counseling-and-education01251nas a2200157 4500008003900000245006400039210006300103300001200166490000700178520076400185100002500949700002200974700002200996700002201018856005301040 2004 d00aComputerized Adaptive Testing With Multiple-Form Structures0 aComputerized Adaptive Testing With MultipleForm Structures a147-1640 v283 a

A multiple-form structure (MFS) is an orderedcollection or network of testlets (i.e., sets of items).An examinee’s progression through the networkof testlets is dictated by the correctness of anexaminee’s answers, thereby adapting the test tohis or her trait level. The collection of pathsthrough the network yields the set of all possibletest forms, allowing test specialists the opportunityto review them before they are administered. Also,limiting the exposure of an individual MFS to aspecific period of time can enhance test security.This article provides an overview of methods thathave been developed to generate parallel MFSs.The approach is applied to the assembly of anexperimental computerized Law School Admission Test (LSAT).

1 aArmstrong, Ronald, D1 aJones, Douglas, H1 aKoppel, Nicole, B1 aPashley, Peter, J uhttp://apm.sagepub.com/content/28/3/147.abstract01543nas a2200229 4500008004100000020002200041245006400063210006300127260002600190300001200216490000700228520081800235653003401053653003001087653002801117653001301145100001901158700001501177700001601192700001701208856008801225 2004 eng d a0146-6216 (Print)00aComputerized adaptive testing with multiple-form structures0 aComputerized adaptive testing with multipleform structures bSage Publications: US a147-1640 v283 aA multiple-form structure (MFS) is an ordered collection or network of testlets (i.e., sets of items). An examinee's progression through the network of testlets is dictated by the correctness of an examinee's answers, thereby adapting the test to his or her trait level. The collection of paths through the network yields the set of all possible test forms, allowing test specialists the opportunity to review them before they are administered. Also, limiting the exposure of an individual MFS to a specific period of time can enhance test security. This article provides an overview of methods that have been developed to generate parallel MFSs. The approach is applied to the assembly of an experimental computerized Law School Admission Test (LSAT). (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive testing10aLaw School Admission Test10amultiple-form structure10atestlets1 aArmstrong, R D1 aJones, D H1 aKoppel, N B1 aPashley, P J uhttp://www.iacat.org/content/computerized-adaptive-testing-multiple-form-structures01908nas a2200217 4500008004100000020004600041245010100087210006900188260002600257300001200283490000700295520111100302653002401413653003201437653001301469653003801482100001701520700001501537700001401552856012401566 2004 eng d a0021-9762 (Print); 1097-4679 (Electronic)00aComputers in clinical assessment: Historical developments, present status, and future challenges0 aComputers in clinical assessment Historical developments present bJohn Wiley & Sons: US a331-3450 v603 aComputerized testing methods have long been regarded as a potentially powerful asset for providing psychological assessment services. Ever since computers were first introduced and adapted to the field of assessment psychology in the 1950s, they have been a valuable aid for scoring, data processing, and even interpretation of test results. The history and status of computer-based personality and neuropsychological tests are discussed in this article. Several pertinent issues involved in providing test interpretation by computer are highlighted. Advances in computer-based test use, such as computerized adaptive testing, are described and problems noted. Today, there is great interest in expanding the availability of psychological assessment applications on the Internet. Although these applications show great promise, there are a number of problems associated with providing psychological tests on the Internet that need to be addressed by psychologists before the Internet can become a major medium for psychological service delivery. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aclinical assessment10acomputerized testing method10aInternet10apsychological assessment services1 aButcher, J N1 aPerry, J L1 aHahn, J A uhttp://www.iacat.org/content/computers-clinical-assessment-historical-developments-present-status-and-future-challenges01302nas 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/29/3/27301612nas a2200217 4500008004100000020002200041245008200063210006900145260004300214300001200257490000700269520084600276653003401122653002601156653003501182653001601217653001701233100002301250700001801273856010301291 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://www.iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests01907nas a2200217 4500008004100000020002200041245007000063210006900133260004100202300001200243490000700255520117100262653003201433653003301465653001801498653002401516100001301540700001801553700002301571856009501594 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://www.iacat.org/content/constructing-rotating-item-pools-constrained-adaptive-testing00512nas a2200109 4500008004100000245012200041210006900163260001700232100001200249700001700261856012400278 2004 eng d00aThe context effects of multidimensional CAT on the accuracy of multidimensional abilities and the item exposure rates0 acontext effects of multidimensional CAT on the accuracy of multi aSan Diego CA1 aLi, Y H1 aSchafer, W D uhttp://www.iacat.org/content/context-effects-multidimensional-cat-accuracy-multidimensional-abilities-and-item-exposure00436nam a2200097 4500008003900000245007800039210006900117260003900186100001600225856009700241 2004 d00aContributions to the theory and practice of computerized adaptive testing0 aContributions to the theory and practice of computerized adaptiv aArnhem, The Netherlands: Citogroep1 aEggen, Theo uhttp://www.iacat.org/content/contributions-theory-and-practice-computerized-adaptive-testing00418nas a2200097 4500008004100000245007500041210006900116260001500185100001600200856010400216 2003 eng d00aCalibrating CAT item pools and online pretest items using MCMC methods0 aCalibrating CAT item pools and online pretest items using MCMC m aChicago IL1 aSegall, D O uhttp://www.iacat.org/content/calibrating-cat-item-pools-and-online-pretest-items-using-mcmc-methods00483nas a2200109 4500008004100000245009300041210006900134260001500203100001700218700001600235856012200251 2003 eng d00aCalibrating CAT pools and online pretest items using marginal maximum likelihood methods0 aCalibrating CAT pools and online pretest items using marginal ma aChicago IL1 aPommerich, M1 aSegall, D O uhttp://www.iacat.org/content/calibrating-cat-pools-and-online-pretest-items-using-marginal-maximum-likelihood-methods00508nas a2200109 4500008004100000245012000041210006900161260001500230100001500245700001600260856012200276 2003 eng d00aCalibrating CAT pools and online pretest items using nonparametric and adjusted marginal maximum likelihood methods0 aCalibrating CAT pools and online pretest items using nonparametr aChicago IL1 aKrass, I A1 aWilliams, B uhttp://www.iacat.org/content/calibrating-cat-pools-and-online-pretest-items-using-nonparametric-and-adjusted-marginal02654nas a2200385 4500008004100000245014400041210006900185300001200254490000700266520138100273653002101654653003301675653002901708653001501737653001001752653000901762653002201771653002601793653003301819653002501852653001901877653001001896653002501906653001601931653002401947653002601971653002701997653003202024653001302056653002802069100001702097700001602114700001402130856012402144 2003 eng d00aCalibration of an item pool for assessing the burden of headaches: an application of item response theory to the Headache Impact Test (HIT)0 aCalibration of an item pool for assessing the burden of headache a913-9330 v123 aBACKGROUND: Measurement of headache impact is important in clinical trials, case detection, and the clinical monitoring of patients. Computerized adaptive testing (CAT) of headache impact has potential advantages over traditional fixed-length tests in terms of precision, relevance, real-time quality control and flexibility. OBJECTIVE: To develop an item pool that can be used for a computerized adaptive test of headache impact. METHODS: We analyzed responses to four well-known tests of headache impact from a population-based sample of recent headache sufferers (n = 1016). We used confirmatory factor analysis for categorical data and analyses based on item response theory (IRT). RESULTS: In factor analyses, we found very high correlations between the factors hypothesized by the original test constructers, both within and between the original questionnaires. These results suggest that a single score of headache impact is sufficient. We established a pool of 47 items which fitted the generalized partial credit IRT model. By simulating a computerized adaptive health test we showed that an adaptive test of only five items had a very high concordance with the score based on all items and that different worst-case item selection scenarios did not lead to bias. CONCLUSION: We have established a headache impact item pool that can be used in CAT of headache impact.10a*Cost of Illness10a*Decision Support Techniques10a*Sickness Impact Profile10aAdolescent10aAdult10aAged10aComparative Study10aDisability Evaluation10aFactor Analysis, Statistical10aHeadache/*psychology10aHealth Surveys10aHuman10aLongitudinal Studies10aMiddle Aged10aMigraine/psychology10aModels, Psychological10aPsychometrics/*methods10aQuality of Life/*psychology10aSoftware10aSupport, Non-U.S. Gov't1 aBjorner, J B1 aKosinski, M1 aWare, Jr. uhttp://www.iacat.org/content/calibration-item-pool-assessing-burden-headaches-application-item-response-theory-headache00525nas a2200145 4500008004100000245008400041210006900125300001200194490000700206100001300213700001400226700001300240700001700253856010900270 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://www.iacat.org/content/can-item-response-theory-based-pain-item-bank-enhance-measurement-precision00429nas a2200097 4500008004100000245008100041210006900122260001900191100001700210856010400227 2003 eng d00aCan We Assess Pre-K Kids With Computer-Based Tests: STAR Early Literacy Data0 aCan We Assess PreK Kids With ComputerBased Tests STAR Early Lite aSan Antonio TX1 aMcBride, J R uhttp://www.iacat.org/content/can-we-assess-pre-k-kids-computer-based-tests-star-early-literacy-data00478nas a2200109 4500008004100000245007400041210006800115260005500183100001600238700001400254856010000268 2003 eng d00aCAT-ASVAB prototype Internet delivery system: Final report (FR-03-06)0 aCATASVAB prototype Internet delivery system Final report FR0306 aArlington VA: Human Resources Rsearch Organization1 aSticha, P J1 aBarber, G uhttp://www.iacat.org/content/cat-asvab-prototype-internet-delivery-system-final-report-fr-03-0600433nas a2200109 4500008004100000245004900041210004900090260007200139100001800211700001900229856007500248 2003 eng d00aCognitive CAT in foreign language assessment0 aCognitive CAT in foreign language assessment aPowerful ICT Tools for Learning and Teaching, PEG '03, CD-ROM, 20031 aGiouroglou, H1 aEconomides, AA uhttp://www.iacat.org/content/cognitive-cat-foreign-language-assessment01840nas a2200205 4500008004100000245009000041210006900131300001100200490000700211520111400218653002101332653003001353653001601383653003201399653001601431653004501447100001501492700001601507856011101523 2003 eng d00aA comparative study of item exposure control methods in computerized adaptive testing0 acomparative study of item exposure control methods in computeriz a71-1030 v403 aThis study compared the properties of five methods of item exposure control within the purview of estimating examinees' abilities in a computerized adaptive testing (CAT) context. Each exposure control algorithm was incorporated into the item selection procedure and the adaptive testing progressed based on the CAT design established for this study. The merits and shortcomings of these strategies were considered under different item pool sizes and different desired maximum exposure rates and were evaluated in light of the observed maximum exposure rates, the test overlap rates, and the conditional standard errors of measurement. Each method had its advantages and disadvantages, but no one possessed all of the desired characteristics. There was a clear and logical trade-off between item exposure control and measurement precision. The M. L. Stocking and C. Lewis conditional multinomial procedure and, to a slightly lesser extent, the T. Davey and C. G. Parshall method seemed to be the most promising considering all of the factors that this study addressed. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aEducational10aItem Analysis (Statistical)10aMeasurement10aStrategies computerized adaptive testing1 aChang, S-W1 aAnsley, T N uhttp://www.iacat.org/content/comparative-study-item-exposure-control-methods-computerized-adaptive-testing00576nas a2200121 4500008004100000245016100041210006900202260001500271100001400286700001400300700002100314856011900335 2003 eng d00aA comparison of exposure control procedures in CAT systems based on different measurement models for testlets using the verbal reasoning section of the MCAT0 acomparison of exposure control procedures in CAT systems based o aChicago IL1 aBoyd, A M1 aDodd, B G1 aFitzpatrick, S J uhttp://www.iacat.org/content/comparison-exposure-control-procedures-cat-systems-based-different-measurement-models00553nas a2200133 4500008004100000245011800041210006900159260001500228100001400243700001400257700001400271700001400285856012000299 2003 eng d00aA comparison of item exposure control procedures using a CAT system based on the generalized partial credit model0 acomparison of item exposure control procedures using a CAT syste aChicago IL1 aBurt, W M1 aKim, S -J1 aDavis, LL1 aDodd, B G uhttp://www.iacat.org/content/comparison-item-exposure-control-procedures-using-cat-system-based-generalized-partial00418nas a2200097 4500008004100000245007700041210006900118260002000187100001500207856009800222 2003 eng d00aA comparison of learning potential results at various educational levels0 acomparison of learning potential results at various educational  a25-27 June 20031 aDe Beer, M uhttp://www.iacat.org/content/comparison-learning-potential-results-various-educational-levels00509nas a2200133 4500008004100000245008600041210006900127260001500196100001300211700001500224700001500239700001400254856010700268 2003 eng d00aComparison of multi-stage tests with computer adaptive and paper and pencil tests0 aComparison of multistage tests with computer adaptive and paper  aChicago IL1 aRotou, O1 aPatsula, L1 aSteffen, M1 aRizavi, S uhttp://www.iacat.org/content/comparison-multi-stage-tests-computer-adaptive-and-paper-and-pencil-tests00452nas a2200109 4500008003900000245008900039210006900128300001200197490000700209100001700216856010900233 2003 d00aA computer adaptive testing simulation applied to the FIM instrument motor component0 acomputer adaptive testing simulation applied to the FIM instrume a384-3930 v841 aDijkers, M P uhttp://www.iacat.org/content/computer-adaptive-testing-simulation-applied-fim-instrument-motor-component03603nas a2200121 4500008004100000245008800041210006900129300000800198490000700206520312600213100002903339856011303368 2003 eng d00aComputer-adaptive test for measuring personality factors using item response theory0 aComputeradaptive test for measuring personality factors using it a9990 v643 aThe aim of the present research was to develop a computer adaptive test with the graded response model to measure the Five Factor Model of personality attributes. In the first of three studies, simulated items and simulated examinees were used to investigate systematically the impact of several variables on the accuracy and efficiency of a computer adaptive test. Item test banks containing more items, items with greater trait discrimination, and more response options resulted in increased accuracy and efficiency of the computer adaptive test. It was also found that large stopping rule values required fewer items before stopping but had less accuracy compared to smaller stopping rule values. This demonstrated a trade-off between accuracy and efficiency such that greater measurement accuracy can be obtained at a cost of decreased test efficiency. In the second study, the archival responses of 501 participants to five 30-item test banks measuring the Five Factor Model of personality were utilized in simulations of a computer adaptive personality test. The computer adaptive test estimates of participant trait scores were highly correlated with the item response theory trait estimates, and the magnitude of the correlation was related directly to the stopping rule value with higher correlations and less measurement error being associated with smaller stopping rule values. It was also noted that the performance of the computer adaptive test was dependent on the personality factor being measured whereby Conscientiousness required the most number of items to be administered and Neuroticism required the least. The results confirmed that a simulated computer adaptive test using archival personality data could accurately and efficiently attain trait estimates. In the third study, 276 student participants selected response options with a click of a mouse in a computer adaptive personality test (CAPT) measuring the Big Five factors of the Five Factor Model of personality structure. Participant responses to alternative measures of the Big Five were also collected using conventional paper-and-pencil personality questionnaires. It was found that the CAPT obtained trait estimates that were very accurate even with very few administered items. Similarly, the CAPT trait estimates demonstrated moderate to high concurrent validity with the alternative Big Five measures, and the strength of the estimates varied as a result of the similarity of the personality items and assessment methodology. It was also found that the computer adaptive test was accurately able to detect, with relatively few items, the relations between the measured personality traits and several socially interesting variables such as smoking behavior, alcohol consumption rating, and number of dates per month. Implications of the results of this research are discussed in terms of the utility of computer adaptive testing of personality characteristics. As well, methodological limitations of the studies are noted and directions for future research are considered. (PsycINFO Database Record (c) 2004 APA, all rights reserved).1 aMacdonald, Paul Lawrence uhttp://www.iacat.org/content/computer-adaptive-test-measuring-personality-factors-using-item-response-theory01761nas a2200313 4500008004100000245007700041210006900118300001200187490000700199520083400206653002101040653001501061653001701076653001601093653003001109653001701139653001501156653002501171653002001196653001501216653002501231653001801256653000901274100001801283700001201301700001601313700001601329856010201345 2003 eng d00aComputerized adaptive rating scales for measuring managerial performance0 aComputerized adaptive rating scales for measuring managerial per a237-2460 v113 aComputerized adaptive rating scales (CARS) had been developed to measure contextual or citizenship performance. This rating format used a paired-comparison protocol, presenting pairs of behavioral statements scaled according to effectiveness levels, and an iterative item response theory algorithm to obtain estimates of ratees' citizenship performance (W. C. Borman et al, 2001). In the present research, we developed CARS to measure the entire managerial performance domain, including task and citizenship performance, thus addressing a major limitation of the earlier CARS. The paper describes this development effort, including an adjustment to the algorithm that reduces substantially the number of item pairs required to obtain almost as much precision in the performance estimates. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aAlgorithms10aAssociations10aCitizenship10aComputer Assisted Testing10aConstruction10aContextual10aItem Response Theory10aJob Performance10aManagement10aManagement Personnel10aRating Scales10aTest1 aSchneider, RJ1 aGoff, M1 aAnderson, S1 aBorman, W C uhttp://www.iacat.org/content/computerized-adaptive-rating-scales-measuring-managerial-performance00405nas a2200109 4500008004100000245003400041210003400075260009400109100001500203700001200218856006500230 2003 eng d00aComputerized adaptive testing0 aComputerized adaptive testing aR. Fernández-Ballesteros (Ed.): Encyclopaedia of Psychological Assessment. London: Sage.1 aPonsoda, V1 aOlea, J uhttp://www.iacat.org/content/computerized-adaptive-testing-000509nas a2200133 4500008004100000245008300041210006900124260001500193100001500208700001900223700001300242700001200255856010800267 2003 eng d00aComputerized adaptive testing: A comparison of three content balancing methods0 aComputerized adaptive testing A comparison of three content bala aChicago IL1 aLeung, C-K1 aChang, Hua-Hua1 aHau, K-T1 aWen, z. uhttp://www.iacat.org/content/computerized-adaptive-testing-comparison-three-content-balancing-methods-001114nas a2200145 4500008004100000245008300041210006900124300000900193490000600202520060700208100001500815700001900830700001300849856010600862 2003 eng d00aComputerized adaptive testing: A comparison of three content balancing methods0 aComputerized adaptive testing A comparison of three content bala a1-150 v23 aContent balancing is often a practical consideration in the design of computerized adaptive testing (CAT). This study compared three content balancing methods, namely, the constrained CAT (CCAT), the modified constrained CAT (MCCAT), and the modified multinomial model (MMM), under various conditions of test length and target maximum exposure rate. Results of a series of simulation studies indicate that there is no systematic effect of content balancing method in measurement efficiency and pool utilization. However, among the three methods, the MMM appears to consistently over-expose fewer items.1 aLeung, C-K1 aChang, Hua-Hua1 aHau, K-T uhttp://www.iacat.org/content/computerized-adaptive-testing-comparison-three-content-balancing-methods00449nas a2200121 4500008004100000245007200041210006900113300001200182490000700194100001500201700001200216856009900228 2003 eng d00aComputerized adaptive testing using the nearest-neighbors criterion0 aComputerized adaptive testing using the nearestneighbors criteri a204-2160 v271 aCheng, P E1 aLiou, M uhttp://www.iacat.org/content/computerized-adaptive-testing-using-nearest-neighbors-criterion-001931nas a2200229 4500008004100000245007200041210006900113300001200182490000700194520116000201653001801361653002101379653003001400653001801430653002501448653002501473653005701498653002201555100001501577700001201592856009701604 2003 eng d00aComputerized adaptive testing using the nearest-neighbors criterion0 aComputerized adaptive testing using the nearestneighbors criteri a204-2160 v273 aItem selection procedures designed for computerized adaptive testing need to accurately estimate every taker's trait level (θ) and, at the same time, effectively use all items in a bank. Empirical studies showed that classical item selection procedures based on maximizing Fisher or other related information yielded highly varied item exposure rates; with these procedures, some items were frequently used whereas others were rarely selected. In the literature, methods have been proposed for controlling exposure rates; they tend to affect the accuracy in θ estimates, however. A modified version of the maximum Fisher information (MFI) criterion, coined the nearest neighbors (NN) criterion, is proposed in this study. The NN procedure improves to a moderate extent the undesirable item exposure rates associated with the MFI criterion and keeps sufficient precision in estimates. The NN criterion will be compared with a few other existing methods in an empirical study using the mean squared errors in θ estimates and plots of item exposure rates associated with different distributions. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aStatistical Analysis10aStatistical Estimation computerized adaptive testing10aStatistical Tests1 aCheng, P E1 aLiou, M uhttp://www.iacat.org/content/computerized-adaptive-testing-using-nearest-neighbors-criterion01591nas a2200145 4500008004100000245005200041210005200093300001200145490000700157520113200164653003401296100001601330700002301346856007601369 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://www.iacat.org/content/computerized-adaptive-testing-item-cloning00463nas a2200109 4500008004100000245008200041210006900123260001500192100002300207700001800230856010500248 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://www.iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests-000466nas a2200121 4500008004100000245007000041210006900111260001500180100001300195700001600208700002300224856009700247 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://www.iacat.org/content/constructing-rotating-item-pools-constrained-adaptive-testing-000443nas a2200097 4500008004100000245008400041210006900125100002300194700001800217856011000235 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://www.iacat.org/content/controlling-item-exposure-and-item-eligibility-computerized-adaptive-testing00493nas a2200097 4500008004100000245013500041210006900176260001500245100001600260856011900276 2003 eng d00aCriterion item characteristic curve function for evaluating the differential weight procedure adjusted to on-line item calibration0 aCriterion item characteristic curve function for evaluating the  aChicago IL1 aSamejima, F uhttp://www.iacat.org/content/criterion-item-characteristic-curve-function-evaluating-differential-weight-procedure00618nas a2200157 4500008004100000245012800041210006900169300001300238490000700251100001700258700001600275700001500291700002100306700001300327856012000340 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://www.iacat.org/content/can-examinees-use-judgments-item-difficulty-improve-proficiency-estimates-computerized00536nas a2200121 4500008004100000245011500041210006900156260001900225100001500244700001900259700001300278856012300291 2002 eng d00aComparing three item selection approaches for computerized adaptive testing with content balancing requirement0 aComparing three item selection approaches for computerized adapt aNew Orleans LA1 aLeung, C-K1 aChang, Hua-Hua1 aHau, K-T uhttp://www.iacat.org/content/comparing-three-item-selection-approaches-computerized-adaptive-testing-content-balancing00441nas a2200109 4500008004100000245007500041210006900116260001900185100001500204700001300219856009900232 2002 eng d00aA comparison of computer mastery models when pool characteristics vary0 acomparison of computer mastery models when pool characteristics  aNew Orleans LA1 aSmith, R L1 aLewis, C uhttp://www.iacat.org/content/comparison-computer-mastery-models-when-pool-characteristics-vary02085nas a2200229 4500008004100000245012900041210006900170300001200239490000700251520124000258653001801498653002101516653004501537653003001582653001801612653002501630653002601655100001601681700001401697700001901711856012501730 2002 eng d00aA comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model0 acomparison of item selection techniques and exposure control mec a147-1630 v263 aThe use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aAlgorithms computerized adaptive testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aMathematical Modeling1 aPastor, D A1 aDodd, B G1 aChang, Hua-Hua uhttp://www.iacat.org/content/comparison-item-selection-techniques-and-exposure-control-mechanisms-cats-using-generalized00450nas a2200109 4500008004100000245009000041210006900131300001200200490000700212100001800219856010300237 2002 eng d00aA comparison of non-deterministic procedures for the adaptive assessment of knowledge0 acomparison of nondeterministic procedures for the adaptive asses a495-5030 v441 aHockemeyer, C uhttp://www.iacat.org/content/comparison-non-deterministic-procedures-adaptive-assessment-knowledge00533nas a2200121 4500008004100000245010900041210006900150260001900219100001400238700001700252700001800269856012400287 2002 eng d00aComparison of the psychometric properties of several computer-based test designs for credentialing exams0 aComparison of the psychometric properties of several computerbas aNew Orleans LA1 aJodoin, M1 aZenisky, A L1 aHambleton, RK uhttp://www.iacat.org/content/comparison-psychometric-properties-several-computer-based-test-designs-credentialing-exams01502nas a2200133 4500008004100000245011800041210006900159300000900228490000700237520094700244653003401191100001801225856012501243 2002 eng d00aComputer adaptive testing: The impact of test characteristics on perceived performance and test takers' reactions0 aComputer adaptive testing The impact of test characteristics on  a34100 v623 aThis study examined the relationship between characteristics of adaptive testing and test takers' subsequent reactions to the test. Participants took a computer adaptive test in which two features, the difficulty of the initial item and the difficulty of subsequent items, were manipulated. These two features of adaptive testing determined the number of items answered correctly by examinees and their subsequent reactions to the test. The data show that the relationship between test characteristics and reactions was fully mediated by perceived performance on the test. In addition, the impact of feedback on reactions to adaptive testing was also evaluated. In general, feedback that was consistent with perceptions of performance had a positive impact on reactions to the test. Implications for adaptive test design concerning maximizing test takers' reactions are discussed. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aTonidandel, S uhttp://www.iacat.org/content/computer-adaptive-testing-impact-test-characteristics-perceived-performance-and-test-takers00567nas a2200133 4500008003900000245012000039210006900159300001200228490000700240100001800247700001900265700001500284856013400299 2002 d00aComputer-adaptive testing: The impact of test characteristics on perceived performance and test takers’ reactions0 aComputeradaptive testing The impact of test characteristics on p a320-3320 v871 aTonidandel, S1 aQuiñones, M A1 aAdams, A A uhttp://www.iacat.org/content/computer-adaptive-testing-impact-test-characteristics-perceived-performance-and-test-takers%E2%80%9900689nas a2200145 4500008004100000245003400041210003400075300001100109490000700120520029200127653003400419100001200453700001500465856006300480 2002 eng d00aComputerised adaptive testing0 aComputerised adaptive testing a619-220 v333 aConsiders the potential of computer adaptive testing (CAT). Discusses the use of CAT instead of traditional paper and pencil tests, identifies decisions that impact the efficacy of CAT, and concludes that CAT is beneficial when used to its full potential on certain types of tests. (LRW)10acomputerized adaptive testing1 aLatu, E1 aChapman, E uhttp://www.iacat.org/content/computerised-adaptive-testing00493nas a2200097 4500008004100000245012700041210006900168260002000237100001600257856012200273 2002 eng d00aConfirmatory item factor analysis using Markov chain Monte Carlo estimation with applications to online calibration in CAT0 aConfirmatory item factor analysis using Markov chain Monte Carlo aNew Orleans, LA1 aSegall, D O uhttp://www.iacat.org/content/confirmatory-item-factor-analysis-using-markov-chain-monte-carlo-estimation-applications00538nas a2200109 4500008004100000245011000041210006900151260004200220100002300262700001800285856012500303 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://www.iacat.org/content/constraining-item-exposure-computerized-adaptive-testing-shadow-tests-research-report-no-0200466nas a2200109 4500008004100000245008400041210006900125260001900194100001600213700001700229856011000246 2002 eng d00aContent-stratified random item selection in computerized classification testing0 aContentstratified random item selection in computerized classifi aNew Orleans LA1 aGuille, R S1 aNorcini, J J uhttp://www.iacat.org/content/content-stratified-random-item-selection-computerized-classification-testing00578nas a2200109 4500008004100000245006000041210006000101260018900161100001300350700001400363856009100377 2002 eng d00aControlling item exposure and maintaining item security0 aControlling item exposure and maintaining item security aC. N. Mills, M. T. Potenza, and J. J. Fremer (Eds.), Computer-Based Testing: Building the Foundation for Future Assessments (pp. 165-191). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.1 aDavey, T1 aNering, M uhttp://www.iacat.org/content/controlling-item-exposure-and-maintaining-item-security-000572nas a2200133 4500008004100000245012700041210006900168260001600237100001700253700001600270700001700286700001300303856012200316 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://www.iacat.org/content/can-examinees-use-judgments-item-difficulty-improve-proficiency-estimates-computerized-000554nas a2200109 4500008004100000245013000041210006900171260004500240100001800285700001500303856012600318 2001 eng d00aCATSIB: A modified SIBTEST procedure to detect differential item functioning in computerized adaptive tests (Research report)0 aCATSIB A modified SIBTEST procedure to detect differential item  aNewton, PAbLaw School Admission Council1 aNandakumar, R1 aRoussos, L uhttp://www.iacat.org/content/catsib-modified-sibtest-procedure-detect-differential-item-functioning-computerized-adapti-000485nas a2200097 4500008004100000245009100041210006900132260005800201100001800259856011000277 2001 eng d00aCB BULATS: Examining the reliability of a computer based test using test-retest method0 aCB BULATS Examining the reliability of a computer based test usi aCambridge ESOL Research Notes, Issue 5, July 2001, pp1 aGeranpayeh, A uhttp://www.iacat.org/content/cb-bulats-examining-reliability-computer-based-test-using-test-retest-method01945nas a2200169 4500008004100000245011100041210007100152300001200223490000700235520133200242100001301574700001601587700001201603700001001615700001601625856013401641 2001 eng d00aA comparative study of on line pretest item—Calibration/scaling methods in computerized adaptive testing0 acomparative study of on line pretest item—Calibrationscaling met a191-2120 v383 aThe purpose of this study was to compare and evaluate five on-line pretest item-calibration/scaling methods in computerized adaptive testing (CAT): marginal maximum likelihood estimate with one EM cycle (OEM), marginal maximum likelihood estimate with multiple EM cycles (MEM), Stocking's Method A, Stocking's Method B, and BILOG/Prior. The five methods were evaluated in terms ofitem-parameter recovery, using three different sample sizes (300, 1000 and 3000). The MEM method appeared to be the best choice among these, because it produced the smallest parameter-estimation errors for all sample size conditions. MEM and OEM are mathematically similar, although the OEM method produced larger errors. MEM also was preferable to OEM, unless the amount of timeinvolved in iterative computation is a concern. Stocking's Method B also worked very well, but it required anchor items that either would increase test lengths or require larger sample sizes depending on test administration design. Until more appropriate ways of handling sparse data are devised, the BILOG/Prior method may not be a reasonable choice for small sample sizes. Stocking's Method A hadthe largest weighted total error, as well as a theoretical weakness (i.e., treating estimated ability as true ability); thus, there appeared to be little reason to use it1 aBan, J C1 aHanson, B A1 aWang, T1 aYi, Q1 aHarris, D J uhttp://www.iacat.org/content/comparative-study-line-pretest-item%E2%80%94calibrationscaling-methods-computerized-adaptive-testing00492nas a2200133 4500008004100000245007900041210006900120260001500189100001000204700001400214700001600228700001600244856009800260 2001 eng d00aComparison of the SPRT and CMT procedures in computerized adaptive testing0 aComparison of the SPRT and CMT procedures in computerized adapti aSeattle WA1 aYi, Q1 aHanson, B1 aWidiatmo, H1 aHarris, D J uhttp://www.iacat.org/content/comparison-sprt-and-cmt-procedures-computerized-adaptive-testing01205nas a2200121 4500008004100000245007000041210006900111300001200180490000700192520076700199100002300966856009400989 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://www.iacat.org/content/computerized-adaptive-testing-equated-number-correct-scoring01699nas a2200229 4500008004100000245007800041210006900119300001200188490000700200520094500207653002501152653005101177653003001228653001801258653001101276653002701287653001101314100001701325700001101342700001801353856009801371 2001 eng d00aComputerized adaptive testing with the generalized graded unfolding model0 aComputerized adaptive testing with the generalized graded unfold a177-1960 v253 aExamined the use of the generalized graded unfolding model (GGUM) in computerized adaptive testing. The objective was to minimize the number of items required to produce equiprecise estimates of person locations. Simulations based on real data about college student attitudes toward abortion and on data generated to fit the GGUM were used. It was found that as few as 7 or 8 items were needed to produce accurate and precise person estimates using an expected a posteriori procedure. The number items in the item bank (20, 40, or 60 items) and their distribution on the continuum (uniform locations or item clusters in moderately extreme locations) had only small effects on the accuracy and precision of the estimates. These results suggest that adaptive testing with the GGUM is a good method for achieving estimates with an approximately uniform level of precision using a small number of items. (PsycINFO Database Record (c) 2005 APA )10aAttitude Measurement10aCollege Students computerized adaptive testing10aComputer Assisted Testing10aItem Response10aModels10aStatistical Estimation10aTheory1 aRoberts, J S1 aLin, Y1 aLaughlin, J E uhttp://www.iacat.org/content/computerized-adaptive-testing-generalized-graded-unfolding-model00496nas a2200097 4500008004100000245012000041210006900161490002600230100002000256856012200276 2001 eng d00aComputerized-adaptive versus paper-and-pencil testing environments: An experimental analysis of examinee experience0 aComputerizedadaptive versus paperandpencil testing environments 0 vDoctoral dissertation1 aBringsjord, E L uhttp://www.iacat.org/content/computerized-adaptive-versus-paper-and-pencil-testing-environments-experimental-analysis01039nas a2200145 4500008004100000245021400041210006900255300001100324490000600335520038100341100001600722700001600738700001700754856012200771 2001 eng d00aConcerns with computerized adaptive oral proficiency assessment. A commentary on "Comparing examinee attitudes Toward computer-assisted and other oral proficient assessments": Response to the Norris Commentary0 aConcerns with computerized adaptive oral proficiency assessment  a95-1080 v53 aResponds to an article on computerized adaptive second language (L2) testing, expressing concerns about the appropriateness of such tests for informing language educators about the language skills of L2 learners and users and fulfilling the intended purposes and achieving the desired consequences of language test use.The authors of the original article respond. (Author/VWL)1 aNorris, J M1 aKenyon, D M1 aMalabonga, V uhttp://www.iacat.org/content/concerns-computerized-adaptive-oral-proficiency-assessment-commentary-comparing-examinee00425nas a2200121 4500008004100000245005900041210005700100300001200157490000700169100002700176700001600203856008400219 2001 eng d00aCUSUM-based person-fit statistics for adaptive testing0 aCUSUMbased personfit statistics for adaptive testing a199-2180 v261 aKrimpen-Stoop, E M L A1 aMeijer, R R uhttp://www.iacat.org/content/cusum-based-person-fit-statistics-adaptive-testing01260nas a2200145 4500008004100000245006500041210006500106300001000171490000700181520076500188653003400953100002300987700001601010856008801026 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://www.iacat.org/content/capitalization-item-calibration-error-adaptive-testing01587nas a2200241 4500008004100000245005800041210005800099300001200157490000600169520081000175653001400985653001400999653004801013653005701061653001101118653001801129653003101147653003701178100001301215700001701228700001601245856008401261 2000 eng d00aCAT administration of language placement examinations0 aCAT administration of language placement examinations a292-3020 v13 aThis article describes the development of a computerized adaptive test for Cegep de Jonquiere, a community college located in Quebec, Canada. Computerized language proficiency testing allows the simultaneous presentation of sound stimuli as the question is being presented to the test-taker. With a properly calibrated bank of items, the language proficiency test can be offered in an adaptive framework. By adapting the test to the test-taker's level of ability, an assessment can be made with significantly fewer items. We also describe our initial attempt to detect instances in which "cheating low" is occurring. In the "cheating low" situation, test-takers deliberately answer questions incorrectly, questions that they are fully capable of answering correctly had they been taking the test honestly.10a*Language10a*Software10aAptitude Tests/*statistics & numerical data10aEducational Measurement/*statistics & numerical data10aHumans10aPsychometrics10aReproducibility of Results10aResearch Support, Non-U.S. Gov't1 aStahl, J1 aBergstrom, B1 aGershon, RC uhttp://www.iacat.org/content/cat-administration-language-placement-examinations00597nas a2200109 4500008004100000245010000041210006900141260012600210100001100336700001600347856012400363 2000 eng d00aCaveats, pitfalls, and unexpected consequences of implementing large-scale computerized testing0 aCaveats pitfalls and unexpected consequences of implementing lar aWainer, H. (Ed). Computerized adaptive testing: A primer (2nd ed.). pp. 271-299. Mahwah, NJ: Lawrence Erlbaum Associates.1 aWainer1 aEignor, D R uhttp://www.iacat.org/content/caveats-pitfalls-and-unexpected-consequences-implementing-large-scale-computerized-testing00467nas a2200097 4500008004100000245007700041210006900118260006600187100001300253856010300266 2000 eng d00aCBTS: Computer-based testing simulation and analysis [computer software]0 aCBTS Computerbased testing simulation and analysis computer soft aAmherst, MA: University of Massachusetts, School of Education1 aRobin, F uhttp://www.iacat.org/content/cbts-computer-based-testing-simulation-and-analysis-computer-software00405nas a2200097 4500008004100000245008000041210006900121260000700190100001500197856009500212 2000 eng d00aChange in distribution of latent ability with item position in CAT sequence0 aChange in distribution of latent ability with item position in C aLA1 aKrass, I A uhttp://www.iacat.org/content/change-distribution-latent-ability-item-position-cat-sequence00450nas a2200145 4500008004100000245005800041210005400099300000900153490000700162100001700169700001500186700001200201700001400213856007700227 2000 eng d00aThe choice of item difficulty in self adapted testing0 achoice of item difficulty in self adapted testing a3-120 v161 aHontangas, P1 aPonsoda, V1 aOlea, J1 aWise, S L uhttp://www.iacat.org/content/choice-item-difficulty-self-adapted-testing00562nas a2200133 4500008004100000245012400041210006900165260001900234100001300253700001200266700001400278700001500292856012100307 2000 eng d00aClassification accuracy and test security for a computerized adaptive mastery test calibrated with different IRT models0 aClassification accuracy and test security for a computerized ada aNew Orleans LA1 aRobin, F1 aXing, D1 aScrams, D1 aPotenza, M uhttp://www.iacat.org/content/classification-accuracy-and-test-security-computerized-adaptive-mastery-test-calibrated02654nas a2200133 4500008004100000245007300041210006900114300000900183490000700192520217300199653003402372100001702406856009702423 2000 eng d00aA comparison of computerized adaptive testing and multistage testing0 acomparison of computerized adaptive testing and multistage testi a58290 v603 aThere is considerable evidence to show that computerized-adaptive testing (CAT) and multi-stage testing (MST) are viable frameworks for testing. With many testing organizations looking to move towards CAT or MST, it is important to know what framework is superior in different situations and at what cost in terms of measurement. What was needed is a comparison of the different testing procedures under various realistic testing conditions. This dissertation addressed the important problem of the increase or decrease in accuracy of ability estimation in using MST rather than CAT. The purpose of this study was to compare the accuracy of ability estimates produced by MST and CAT while keeping some variables fixed and varying others. A simulation study was conducted to investigate the effects of several factors on the accuracy of ability estimation using different CAT and MST designs. The factors that were manipulated are the number of stages, the number of subtests per stage, and the number of items per subtest. Kept constant were test length, distribution of subtest information, method of determining cut-points on subtests, amount of overlap between subtests, and method of scoring total test. The primary question of interest was, given a fixed test length, how many stages and many subtests per stage should there be to maximize measurement precision? Furthermore, how many items should there be in each subtest? Should there be more in the routing test or should there be more in the higher stage tests? Results showed that, in general, increasing the number of stages from two to three decreased the amount of errors in ability estimation. Increasing the number of subtests from three to five increased the accuracy of ability estimates as well as the efficiency of the MST designs relative to the P&P and CAT designs at most ability levels (-.75 to 2.25). Finally, at most ability levels (-.75 to 2.25), varying the number of items per stage had little effect on either the resulting accuracy of ability estimates or the relative efficiency of the MST designs to the P&P and CAT designs. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aPatsula, L N uhttp://www.iacat.org/content/comparison-computerized-adaptive-testing-and-multistage-testing01389nas a2200193 4500008004100000245009400041210006900135300001200204490000700216520067900223653002100902653003000923653002500953653005700978100001401035700001901049700001901068856010801087 2000 eng d00aA comparison of item selection rules at the early stages of computerized adaptive testing0 acomparison of item selection rules at the early stages of comput a241-2550 v243 aThe effects of 5 item selection rules--Fisher information (FI), Fisher interval information (FII), Fisher information with a posterior distribution (FIP), Kullback-Leibler information (KL), and Kullback-Leibler information with a posterior distribution (KLP)--were compared with respect to the efficiency and precision of trait (θ) estimation at the early stages of computerized adaptive testing (CAT). FII, FIP, KL, and KLP performed marginally better than FI at the early stages of CAT for θ=-3 and -2. For tests longer than 10 items, there appeared to be no precision advantage for any of the selection rules. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis (Test)10aStatistical Estimation computerized adaptive testing1 aChen, S-Y1 aAnkenmann, R D1 aChang, Hua-Hua uhttp://www.iacat.org/content/comparison-item-selection-rules-early-stages-computerized-adaptive-testing00519nas a2200133 4500008004100000245009400041210006900135300001200204490000700216100001400223700001900237700001900256856011000275 2000 eng d00aA comparison of item selection rules at the early stages of computerized adaptive testing0 acomparison of item selection rules at the early stages of comput a241-2550 v241 aChen, S Y1 aAnkenmann, R D1 aChang, Hua-Hua uhttp://www.iacat.org/content/comparison-item-selection-rules-early-stages-computerized-adaptive-testing-000470nas a2200109 4500008004100000245004100041210004000082260013400122100001500256700001900271856007000290 2000 eng d00aComputer-adaptive sequential testing0 aComputeradaptive sequential testing aW. J. van der Linden (Ed.), Computerized Adaptive Testing: Theory and Practice (pp. 289-209). Dordrecht, The Netherlands: Kluwer.1 aLuecht, RM1 aNungester, R J uhttp://www.iacat.org/content/computer-adaptive-sequential-testing00544nas a2200157 4500008004100000245006500041210006300106260002700169490000700196653003400203100001700237700001200254700001200266700001700278856009100295 2000 eng d00aComputer-adaptive testing: A methodology whose time has come0 aComputeradaptive testing A methodology whose time has come aChicago, IL. USAbMESA0 v6910acomputerized adaptive testing1 aLinacre, J M1 aKang, U1 aJean, E1 aLinacre, J M uhttp://www.iacat.org/content/computer-adaptive-testing-methodology-whose-time-has-come00490nas a2200097 4500008004100000245008700041210006900128260006600197100001700263856011200280 2000 eng d00aComputer-adaptive testing: A methodology whose time has come. MESA Memorandum No 90 aComputeradaptive testing A methodology whose time has come MESA  aChicago : MESA psychometric laboratory, Unversity of Chicago.1 aLinacre, J M uhttp://www.iacat.org/content/computer-adaptive-testing-methodology-whose-time-has-come-mesa-memorandum-no-901563nas a2200229 4500008004100000245006400041210006300105300001100168490000600179520083800185653002701023653001501050653001501065653004201080653001101122653002601133653002601159653003101185100001501216700001601231856008601247 2000 eng d00aComputerization and adaptive administration of the NEO PI-R0 aComputerization and adaptive administration of the NEO PIR a347-640 v73 aThis study asks, how well does an item response theory (IRT) based computerized adaptive NEO PI-R work? To explore this question, real-data simulations (N = 1,059) were used to evaluate a maximum information item selection computerized adaptive test (CAT) algorithm. Findings indicated satisfactory recovery of full-scale facet scores with the administration of around four items per facet scale. Thus, the NEO PI-R could be reduced in half with little loss in precision by CAT administration. However, results also indicated that the CAT algorithm was not necessary. We found that for many scales, administering the "best" four items per facet scale would have produced similar results. In the conclusion, we discuss the future of computerized personality assessment and describe the role IRT methods might play in such assessments.10a*Personality Inventory10aAlgorithms10aCalifornia10aDiagnosis, Computer-Assisted/*methods10aHumans10aModels, Psychological10aPsychometrics/methods10aReproducibility of Results1 aReise, S P1 aHenson, J M uhttp://www.iacat.org/content/computerization-and-adaptive-administration-neo-pi-r00484nas a2200133 4500008004100000245007600041210006900117300001100186490000700197100001500204700001600219700001700235856009800252 2000 eng d00aComputerized adaptive administration of the self-evaluation examination0 aComputerized adaptive administration of the selfevaluation exami a226-310 v681 aLaVelle, T1 aZaglaniczny1 aSpitzer, L E uhttp://www.iacat.org/content/computerized-adaptive-administration-self-evaluation-examination00586nas a2200133 4500008004100000245009000041210006900131260007900200100001600279700001600295700001800311700001400329856010900343 2000 eng d00aComputerized adaptive rating scales (CARS): Development and evaluation of the concept0 aComputerized adaptive rating scales CARS Development and evaluat a(Institute Rep No. 350). Tampa FL: Personnel Decisions Research Institute.1 aBorman, W C1 aHanson, M A1 aKubisiak, U C1 aBuck, D E uhttp://www.iacat.org/content/computerized-adaptive-rating-scales-cars-development-and-evaluation-concept00587nam a2200181 4500008004100000245005800041210005500099260005100154100001100205700001400216700001600230700001600246700001400262700001500276700001700291700001500308856008200323 2000 eng d00aComputerized adaptive testing: A primer (2nd edition)0 aComputerized adaptive testing A primer 2nd edition aHillsdale, N. J. : Lawrence Erlbaum Associates1 aWainer1 aDorans, N1 aEignor, D R1 aFlaugher, R1 aGreen, BF1 aMislevy, R1 aSteinberg, L1 aThissen, D uhttp://www.iacat.org/content/computerized-adaptive-testing-primer-2nd-edition01598nas a2200157 4500008004100000245008200041210006900123300001100192490000700203520101400210653003401224653004001258100001601298700002401314856010201338 2000 eng d00aComputerized adaptive testing for classifying examinees into three categories0 aComputerized adaptive testing for classifying examinees into thr a713-340 v603 aThe objective of this study was to explore the possibilities for using computerized adaptive testing in situations in which examinees are to be classified into one of three categories.Testing algorithms with two different statistical computation procedures are described and evaluated. The first computation procedure is based on statistical testing and the other on statistical estimation. Item selection methods based on maximum information (MI) considering content and exposure control are considered. The measurement quality of the proposed testing algorithms is reported. The results of the study are that a reduction of at least 22% in the mean number of items can be expected in a computerized adaptive test (CAT) compared to an existing paper-and-pencil placement test. Furthermore, statistical testing is a promising alternative to statistical estimation. Finally, it is concluded that imposing constraints on the MI selection strategy does not negatively affect the quality of the testing algorithms10acomputerized adaptive testing10aComputerized classification testing1 aEggen, Theo1 aStraetmans, G J J M uhttp://www.iacat.org/content/computerized-adaptive-testing-classifying-examinees-three-categories00441nam a2200109 4500008004100000245005500041210005400096260005900150100002300209700001600232856008300248 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://www.iacat.org/content/computerized-adaptive-testing-theory-and-practice00462nas a2200097 4500008004100000245009400041210006900135260001900204100001700223856012400240 2000 eng d00aComputerized testing – the adolescent years: Juvenile delinquent or positive role model0 aComputerized testing the adolescent years Juvenile delinquent or aNew Orleans LA1 aReckase, M D uhttp://www.iacat.org/content/computerized-testing-%E2%80%93-adolescent-years-juvenile-delinquent-or-positive-role-model00473nas a2200097 4500008004100000245005100041210005100092260013400143100002300277856007500300 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://www.iacat.org/content/constrained-adaptive-testing-shadow-tests00542nam a2200097 4500008004100000245011800041210006900159260008400228100001500312856011700327 2000 eng d00aThe construction and evaluation of a dynamic computerised adaptive test for the measurement of learning potential0 aconstruction and evaluation of a dynamic computerised adaptive t aUnpublished D. Litt et Phil dissertation. University of South Africa, Pretoria.1 aDe Beer, M uhttp://www.iacat.org/content/construction-and-evaluation-dynamic-computerised-adaptive-test-measurement-learning00473nas a2200121 4500008004100000245007400041210006900115260002000184100001500204700001900219700001300238856010000251 2000 eng d00aContent balancing in stratified computerized adaptive testing designs0 aContent balancing in stratified computerized adaptive testing de aNew Orleans, LA1 aLeung, C-K1 aChang, Hua-Hua1 aHau, K-T uhttp://www.iacat.org/content/content-balancing-stratified-computerized-adaptive-testing-designs00548nas a2200109 4500008004100000245006700041210006600108260013200174100002300306700001600329856009300345 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://www.iacat.org/content/cross-validating-item-parameter-estimation-adaptive-testing00575nas a2200145 4500008004100000245011800041210006900159300001200228490000700240100001700247700001700264700001200281700001500293856012100308 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://www.iacat.org/content/can-examinees-use-review-option-obtain-positively-biased-ability-estimates-computerized00409nas a2200121 4500008004100000245005100041210005100092260002100143100001300164700001600177700001700193856007700210 1999 eng d00aCAT administration of language placement exams0 aCAT administration of language placement exams aMontreal, Canada1 aStahl, J1 aGershon, RC1 aBergstrom, B uhttp://www.iacat.org/content/cat-administration-language-placement-exams01276nas a2200145 4500008004100000245004000041210004000081260004600121300001000167520081600177653003400993100002401027700001401051856006501065 1999 eng d00aCAT for certification and licensure0 aCAT for certification and licensure aMahwah, N.J.bLawrence Erlbaum Associates a67-913 a(from the chapter) This chapter discusses implementing computerized adaptive testing (CAT) for high-stakes examinations that determine whether or not a particular candidate will be certified or licensed. The experience of several boards who have chosen to administer their licensure or certification examinations using the principles of CAT illustrates the process of moving into this mode of administration. Examples of the variety of options that can be utilized within a CAT administration are presented, the decisions that boards must make to implement CAT are discussed, and a timetable for completing the tasks that need to be accomplished is provided. In addition to the theoretical aspects of CAT, practical issues and problems are reviewed. (PsycINFO Database Record (c) 2002 APA, all rights reserved).10acomputerized adaptive testing1 aBergstrom, Betty, A1 aLunz, M E uhttp://www.iacat.org/content/cat-certification-and-licensure00494nas a2200109 4500008004100000245010000041210006900141260002000210100001700230700001800247856011900265 1999 eng d00aA comparative study of ability estimates from computer-adaptive testing and multi-stage testing0 acomparative study of ability estimates from computeradaptive tes aMontreal Canada1 aPatsula, L N1 aHambleton, RK uhttp://www.iacat.org/content/comparative-study-ability-estimates-computer-adaptive-testing-and-multi-stage-testing00475nam a2200097 4500008004100000245007400041210006900115260007800184100001700262856009800279 1999 eng d00aA comparison of computerized-adaptive testing and multi-stage testing0 acomparison of computerizedadaptive testing and multistage testin aUnpublished doctoral dissertation, University of Massachusetts at Amherst1 aPatsula, L N uhttp://www.iacat.org/content/comparison-computerized-adaptive-testing-and-multi-stage-testing00490nas a2200109 4500008004100000245009900041210006900140260002100209100001900230700001200249856011900261 1999 eng d00aA comparison of conventional and adaptive testing procedures for making single-point decisions0 acomparison of conventional and adaptive testing procedures for m aMontreal, Canada1 aKingsbury, G G1 aZara, A uhttp://www.iacat.org/content/comparison-conventional-and-adaptive-testing-procedures-making-single-point-decisions00392nas a2200097 4500008004100000245006400041210006400105260002100169100001400190856009000204 1999 eng d00aComparison of stratum scored and maximum likelihood scoring0 aComparison of stratum scored and maximum likelihood scoring aMontreal, Canada1 aWise, S L uhttp://www.iacat.org/content/comparison-stratum-scored-and-maximum-likelihood-scoring00517nas a2200109 4500008004100000245012200041210006900163260002300232100001800255700001500273856011900288 1999 eng d00aA comparison of testlet-based test designs for computerized adaptive testing (LSAC Computerized Testing Report 97-01)0 acomparison of testletbased test designs for computerized adaptiv aNewtown, PA: LSAC.1 aSchnipke, D L1 aReese, L M uhttp://www.iacat.org/content/comparison-testlet-based-test-designs-computerized-adaptive-testing-lsac-computerized00525nas a2200121 4500008004100000245012600041210006900167260001600236100001100252700001200263700001000275856011800285 1999 eng d00aComparison of the a-stratified method, the Sympson-Hetter method, and the matched difficulty method in CAT administration0 aComparison of the astratified method the SympsonHetter method an aLawrence KS1 aBan, J1 aWang, T1 aYi, Q uhttp://www.iacat.org/content/comparison-stratified-method-sympson-hetter-method-and-matched-difficulty-method-cat01772nas a2200265 4500008004100000245004900041210004800090300001000138490000600148520092600154653002501080653005701105653001501162653001201177653001001189653002101199653006401220653001101284653002201295653001101317653000901328653007801337100001701415856007401432 1999 eng d00aCompetency gradient for child-parent centers0 aCompetency gradient for childparent centers a35-520 v33 aThis report describes an implementation of the Rasch model during the longitudinal evaluation of a federally-funded early childhood preschool intervention program. An item bank is described for operationally defining a psychosocial construct called community life-skills competency, an expected teenage outcome of the preschool intervention. This analysis examined the position of teenage students on this scale structure, and investigated a pattern of cognitive operations necessary for students to pass community life-skills test items. Then this scale structure was correlated with nationally standardized reading and math achievement scores, teacher ratings, and school records to assess its validity as a measure of the community-related outcome goal for this intervention. The results show a functional relationship between years of early intervention and magnitude of effect on the life-skills competency variable.10a*Models, Statistical10aActivities of Daily Living/classification/psychology10aAdolescent10aChicago10aChild10aChild, Preschool10aEarly Intervention (Education)/*statistics & numerical data10aFemale10aFollow-Up Studies10aHumans10aMale10aOutcome and Process Assessment (Health Care)/*statistics & numerical data1 aBezruczko, N uhttp://www.iacat.org/content/competency-gradient-child-parent-centers00478nas a2200133 4500008004100000245007500041210006900116300001200185490000700197100001600204700002000220700001400240856009000254 1999 eng d00aComputerized adaptive assessment with the MMPI-2 in a clinical setting0 aComputerized adaptive assessment with the MMPI2 in a clinical se a369-3800 v111 aHandel, R W1 aBen-Porath, Y S1 aWatt, M E uhttp://www.iacat.org/content/computerized-adaptive-assessment-mmpi-2-clinical-setting00359nas a2200097 4500008004100000245005200041210005200093260002700145100001500172856007400187 1999 eng d00aComputerized adaptive testing in the Bundeswehr0 aComputerized adaptive testing in the Bundeswehr aUnpublished manuscript1 aStorm, E G uhttp://www.iacat.org/content/computerized-adaptive-testing-bundeswehr00915nas a2200145 4500008004100000245006100041210006000102300001100162490000700173520043400180653003400614100001600648700001600664856008900680 1999 eng d00aComputerized Adaptive Testing: Overview and Introduction0 aComputerized Adaptive Testing Overview and Introduction a187-940 v233 aUse of computerized adaptive testing (CAT) has increased substantially since it was first formulated in the 1970s. This paper provides an overview of CAT and introduces the contributions to this Special Issue. The elements of CAT discussed here include item selection procedures, estimation of the latent trait, item exposure, measurement precision, and item bank development. Some topics for future research are also presented. 10acomputerized adaptive testing1 aMeijer, R R1 aNering, M L uhttp://www.iacat.org/content/computerized-adaptive-testing-overview-and-introduction00426nas a2200121 4500008004100000245006100041210006000102300001200162490000700174100001600181700001600197856009100213 1999 eng d00aComputerized adaptive testing: Overview and introduction0 aComputerized adaptive testing Overview and introduction a187-1940 v231 aMeijer, R R1 aNering, M L uhttp://www.iacat.org/content/computerized-adaptive-testing-overview-and-introduction-000461nas a2200109 4500008004100000245009200041210006900133260001300202100000800215700001200223856011600235 1999 eng d00aComputerized classification testing under practical constraints with a polytomous model0 aComputerized classification testing under practical constraints  aMontreal1 aLau1 aWang, T uhttp://www.iacat.org/content/computerized-classification-testing-under-practical-constraints-polytomous-model-000488nas a2200109 4500008004100000245009200041210006900133260003800202100001200240700001200252856011400264 1999 eng d00aComputerized classification testing under practical constraints with a polytomous model0 aComputerized classification testing under practical constraints  aMontreal, Quebec, Canadac04/19991 aLau, CA1 aWang, T uhttp://www.iacat.org/content/computerized-classification-testing-under-practical-constraints-polytomous-model00492nas a2200133 4500008004100000245007400041210006700115260001300182100001600195700001300211700001300224700001400237856010700251 1999 eng d00aComputerized testing – Issues and applications (Mini-course manual)0 aComputerized testing Issues and applications Minicourse manual aMontreal1 aParshall, C1 aDavey, T1 aSpray, J1 aKalohn, J uhttp://www.iacat.org/content/computerized-testing-%E2%80%93-issues-and-applications-mini-course-manual00442nas a2200121 4500008004100000245006600041210006600107260001300173100001100186700001600197700001300213856009400226 1999 eng d00aConstructing adaptive tests to parallel conventional programs0 aConstructing adaptive tests to parallel conventional programs aMontreal1 aFan, M1 aThompson, T1 aDavey, T uhttp://www.iacat.org/content/constructing-adaptive-tests-parallel-conventional-programs-000576nas a2200097 4500008004100000245010300041210006900144260012200213100001700335856012600352 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://www.iacat.org/content/creating-computerized-adaptive-tests-music-aptitude-problems-solutions-and-future-directions00505nas a2200097 4500008004100000245008000041210006900121260009600190100001700286856010400303 1999 eng d00aCurrent and future research in multi-stage testing (Research Report No 370)0 aCurrent and future research in multistage testing Research Repor aAmherst MA: University of Massachusetts, Laboratory of Pychometric and Evaluative Research.1 aZenisky, A L uhttp://www.iacat.org/content/current-and-future-research-multi-stage-testing-research-report-no-37000427nas a2200121 4500008004100000245005900041210005700100300001200157490000700169100002700176700001600203856008600219 1999 eng d00aCUSUM-based person-fit statistics for adaptive testing0 aCUSUMbased personfit statistics for adaptive testing a199-2180 v261 aKrimpen-Stoop, E M L A1 aMeijer, R R uhttp://www.iacat.org/content/cusum-based-person-fit-statistics-adaptive-testing-000596nas a2200109 4500008004100000245008300041210006900124260014400193100002700337700001600364856010600380 1999 eng d00aCUSUM-based person-fit statistics for adaptive testing (Research Report 99-05)0 aCUSUMbased personfit statistics for adaptive testing Research Re aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aKrimpen-Stoop, E M L A1 aMeijer, R R uhttp://www.iacat.org/content/cusum-based-person-fit-statistics-adaptive-testing-research-report-99-0500602nas a2200109 4500008004100000245008900041210006900130260014400199100002300343700001600366856011000382 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://www.iacat.org/content/capitalization-item-calibration-error-adaptive-testing-research-report-98-0700336nas a2200097 4500008004100000245003300041210003100074260005800105100001500163856006000178 1998 eng d00aCASTISEL [Computer software]0 aCASTISEL Computer software aPhiladelphia, PA: National Board of Medical Examiners1 aLuecht, RM uhttp://www.iacat.org/content/castisel-computer-software00324nas a2200121 4500008004100000245002500041210002500066260001400091100001100105700001800116700001400134856005400148 1998 eng d00aCAT item calibration0 aCAT item calibration aSan Diego1 aHsu, Y1 aThompson, T D1 aChen, W-H uhttp://www.iacat.org/content/cat-item-calibration00481nas a2200097 4500008004100000245011200041210006900153260001800222100001900240856012400259 1998 eng d00aCAT Item exposure control: New evaluation tools, alternate methods and integration into a total CAT program0 aCAT Item exposure control New evaluation tools alternate methods aSan Diego, CA1 aThomasson, G L uhttp://www.iacat.org/content/cat-item-exposure-control-new-evaluation-tools-alternate-methods-and-integration-total-cat00687nas a2200169 4500008004100000020003000041245009600071210006900167260006400236100001900300700001700319700002100336700001300357700001700370700001500387856011500402 1998 eng d aETS Research Report 98-3800aComparability of paper-and-pencil and computer adaptive test scores on the GRE General Test0 aComparability of paperandpencil and computer adaptive test score aPrinceton, N.J.bEducational Testing ServicescAugust, 19981 aSchaeffer, G A1 aBridgeman, B1 aGolub-Smith, M L1 aLewis, C1 aPotenza, M T1 aSteffen, M uhttp://www.iacat.org/content/comparability-paper-and-pencil-and-computer-adaptive-test-scores-gre-general-test00729nas a2200157 4500008004100000245018900041210006900230260004700299100001700346700001700363700002100380700001300401700001700414700001500431856012500446 1998 eng d00aComparability of paper-and-pencil and computer adaptive test scores on the GRE General Test (GRE Board Professional Report No 95-08P; Educational Testing Service Research Report 98-38)0 aComparability of paperandpencil and computer adaptive test score aPrinceton, NJ: Educational Testing Service1 aSchaeffer, G1 aBridgeman, B1 aGolub-Smith, M L1 aLewis, C1 aPotenza, M T1 aSteffen, M uhttp://www.iacat.org/content/comparability-paper-and-pencil-and-computer-adaptive-test-scores-gre-general-test-gre-board00521nas a2200109 4500008004100000245009000041210006900131260007000200100001500270700001300285856011300298 1998 eng d00aA comparative study of item exposure control methods in computerized adaptive testing0 acomparative study of item exposure control methods in computeriz aResearch Report Series 98-3, Iowa City: American College Testing.1 aChang, S-W1 aTwu, B-Y uhttp://www.iacat.org/content/comparative-study-item-exposure-control-methods-computerized-adaptive-testing-000499nam a2200097 4500008004100000245009000041210006900131260007300200100001500273856011300288 1998 eng d00aA comparative study of item exposure control methods in computerized adaptive testing0 acomparative study of item exposure control methods in computeriz aUnpublished doctoral dissertation, University of Iowa , Iowa City IA1 aChang, S-W uhttp://www.iacat.org/content/comparative-study-item-exposure-control-methods-computerized-adaptive-testing-100496nas a2200109 4500008004100000245012000041210006900161260001400230100001200244700001200256856011800268 1998 eng d00aComparing and combining dichotomous and polytomous items with SPRT procedure in computerized classification testing0 aComparing and combining dichotomous and polytomous items with SP aSan Diego1 aLau, CA1 aWang, T uhttp://www.iacat.org/content/comparing-and-combining-dichotomous-and-polytomous-items-sprt-procedure-computerized00466nas a2200121 4500008004100000245008300041210006900124300001200193490000700205100001300212700001500225856010400240 1998 eng d00aA comparison of item exposure control methods in computerized adaptive testing0 acomparison of item exposure control methods in computerized adap a311-3270 v351 aRevuelta1 aPonsoda, V uhttp://www.iacat.org/content/comparison-item-exposure-control-methods-computerized-adaptive-testing00544nas a2200133 4500008004100000245012500041210006900166300001200235490000700247100001200254700001100266700001400277856011900291 1998 eng d00aA comparison of maximum likelihood estimation and expected a posteriori estimation in CAT using the partial credit model0 acomparison of maximum likelihood estimation and expected a poste a569-5950 v581 aChen, S1 aHou, L1 aDodd, B G uhttp://www.iacat.org/content/comparison-maximum-likelihood-estimation-and-expected-posteriori-estimation-cat-using00470nas a2200109 4500008004100000245009400041210006900135100001200204700001300216700001900229856011200248 1998 eng d00aA comparison of two methods of controlling item exposure in computerized adaptive testing0 acomparison of two methods of controlling item exposure in comput1 aTang, L1 aJiang, H1 aChang, Hua-Hua uhttp://www.iacat.org/content/comparison-two-methods-controlling-item-exposure-computerized-adaptive-testing00506nas a2200109 4500008004100000245008000041210006900121260006100190100001900251700001500270856011100285 1998 eng d00aComputer adaptive testing – Approaches for item selection and measurement0 aComputer adaptive testing Approaches for item selection and meas aRutgers Center for Operations Research, New Brunswick NJ1 aArmstrong, R D1 aJones, D H uhttp://www.iacat.org/content/computer-adaptive-testing-%E2%80%93-approaches-item-selection-and-measurement00413nas a2200109 4500008004100000245006600041210006500107300001300172490000800185100001500193856009500208 1998 eng d00aComputer-assisted test assembly using optimization heuristics0 aComputerassisted test assembly using optimization heuristics a224-236.0 v22 1 aLuecht, RM uhttp://www.iacat.org/content/computer-assisted-test-assembly-using-optimization-heuristics00557nas a2200157 4500008004100000245007600041210006900117260001400186100001600200700001600216700002000232700001500252700001400267700001800281856010000299 1998 eng d00aComputerized adaptive rating scales that measure contextual performance0 aComputerized adaptive rating scales that measure contextual perf aDallas TX1 aBorman, W C1 aHanson, M A1 aMontowidlo, S J1 aDrasgow, F1 aFoster, L1 aKubisiak, U C uhttp://www.iacat.org/content/computerized-adaptive-rating-scales-measure-contextual-performance00879nas a2200133 4500008004100000245006300041210006200104300001000166490000700176520043400183100002400617700001600641856008800657 1998 eng d00aComputerized adaptive testing: What it is and how it works0 aComputerized adaptive testing What it is and how it works a45-520 v383 aDescribes the workings of computerized adaptive testing (CAT). Focuses on the key concept of information and then discusses two important components of a CAT system: the calibrated item bank and the testing algorithm. Describes a CAT that was designed for making placement decisions on the basis of two typical test administrations and notes the most significant differences between traditional paper-based testing and CAT. (AEF)1 aStraetmans, G J J M1 aEggen, Theo uhttp://www.iacat.org/content/computerized-adaptive-testing-what-it-and-how-it-works00446nas a2200121 4500008004100000245006400041210006400105260001500169100001900184700001500203700001600218856009000234 1998 eng d00aComputerized adaptive testing with multiple form structures0 aComputerized adaptive testing with multiple form structures aUrbana, IL1 aArmstrong, R D1 aJones, D H1 aBerliner, N uhttp://www.iacat.org/content/computerized-adaptive-testing-multiple-form-structures-000448nas a2200121 4500008004100000245006600041210006600107260001400173100001600187700001300203700001600216856009400232 1998 eng d00aConstructing adaptive tests to parallel conventional programs0 aConstructing adaptive tests to parallel conventional programs aSan Diego1 aThompson, T1 aDavey, T1 aNering, M L uhttp://www.iacat.org/content/constructing-adaptive-tests-parallel-conventional-programs-100462nas a2200121 4500008004100000245007300041210006900114260001500183100001600198700001300214700001600227856009700243 1998 eng d00aConstructing passage-based tests that parallel conventional programs0 aConstructing passagebased tests that parallel conventional progr aUrbana, IL1 aThompson, T1 aDavey, T1 aNering, M L uhttp://www.iacat.org/content/constructing-passage-based-tests-parallel-conventional-programs00413nas a2200109 4500008004100000245006000041210006000101260002400161100001300185700001600198856008900214 1998 eng d00aControlling item exposure and maintaining item security0 aControlling item exposure and maintaining item security a” Philadelphia PA1 aDavey, T1 aNering, M L uhttp://www.iacat.org/content/controlling-item-exposure-and-maintaining-item-security01273nas a2200133 4500008004100000245008600041210006900127300001000196490000700206520078600213100001800999700001301017856010901030 1998 eng d00aControlling item exposure conditional on ability in computerized adaptive testing0 aControlling item exposure conditional on ability in computerized a57-750 v233 aThe interest in the application of large-scale adaptive testing for secure tests has served to focus attention on issues that arise when theoretical advances are made operational. One such issue is that of ensuring item and pool security in the continuous testing environment made possible by the computerized admin-istration of a test, as opposed to the more periodic testing environment typically used for linear paper-and-pencil tests. This article presents a new method of controlling the exposure rate of items conditional on ability level in this continuous testing environment. The properties of such conditional control on the exposure rates of items, when used in conjunction with a particular adaptive testing algorithm, are explored through studies with simulated data. 1 aStocking, M L1 aLewis, C uhttp://www.iacat.org/content/controlling-item-exposure-conditional-ability-computerized-adaptive-testing00521nas a2200121 4500008004100000245010200041210006900143260001800212100001500230700001800245700001600263856012000279 1997 eng d00aCalibration of CAT items administered online for classification: Assumption of local independence0 aCalibration of CAT items administered online for classification  aGatlinburg TN1 aSpray, J A1 aParshall, C G1 aHuang, C -H uhttp://www.iacat.org/content/calibration-cat-items-administered-online-classification-assumption-local-independence00414nas a2200109 4500008004100000245003600041210003500077260009900112100001700211700001600228856006000244 1997 eng d00aCAST 5 for Windows users' guide0 aCAST 5 for Windows users guide aContract No. "MDA903-93-D-0032, DO 0054. Alexandria, VA: Human Resources Research Organization1 aMcBride, J R1 aCooper, R R uhttp://www.iacat.org/content/cast-5-windows-users-guide00535nas a2200121 4500008004100000245004000041210003900081260017700120100001400297700001600311700001700327856006900344 1997 eng d00aCAT-ASVAB cost and benefit analyses0 aCATASVAB cost and benefit analyses aW. A. Sands, B. K. Waters, and J. R. McBride (Eds.), Computer adaptive testing: From inquiry to operation (pp. 227-236). Washington, DC: American Psychological Association.1 aWise, L L1 aCurran, L T1 aMcBride, J R uhttp://www.iacat.org/content/cat-asvab-cost-and-benefit-analyses00500nas a2200097 4500008004100000245004600041210004500087260017900132100001600311856007500327 1997 eng d00aCAT-ASVAB operational test and evaluation0 aCATASVAB operational test and evaluation aW. A. Sands, B. K. Waters, and . R. McBride (Eds.), Computerized adaptive testing: From inquiry to operation (pp. 199-205). Washington DC: American Psychological Association.1 aMoreno, K E uhttp://www.iacat.org/content/cat-asvab-operational-test-and-evaluation00555nas a2200109 4500008004100000245013000041210006900171260004600240100001800286700001500304856012600319 1997 eng d00aCATSIB: A modified SIBTEST procedure to detect differential item functioning in computerized adaptive tests (Research report)0 aCATSIB A modified SIBTEST procedure to detect differential item  aNewtown, PA: Law School Admission Council1 aNandakumar, R1 aRoussos, L uhttp://www.iacat.org/content/catsib-modified-sibtest-procedure-detect-differential-item-functioning-computerized-adaptive00491nas a2200109 4500008004100000245010400041210006900145100001600214700002000230700001200250856011900262 1997 eng d00aComparability and validity of computerized adaptive testing with the MMPI-2 using a clinical sample0 aComparability and validity of computerized adaptive testing with1 aHandel, R W1 aBen-Porath, Y S1 aWatt, M uhttp://www.iacat.org/content/comparability-and-validity-computerized-adaptive-testing-mmpi-2-using-clinical-sample02915nas a2200133 4500008004100000245016300041210006900204300000800273490000700281520232300288653003402611100001402645856012202659 1997 eng d00aA comparison of maximum likelihood estimation and expected a posteriori estimation in computerized adaptive testing using the generalized partial credit model0 acomparison of maximum likelihood estimation and expected a poste a4530 v583 aA simulation study was conducted to investigate the application of expected a posteriori (EAP) trait estimation in computerized adaptive tests (CAT) based on the generalized partial credit model (Muraki, 1992), and to compare the performance of EAP with maximum likelihood trait estimation (MLE). The performance of EAP was evaluated under different conditions: the number of quadrature points (10, 20, and 30), and the type of prior distribution (normal, uniform, negatively skewed, and positively skewed). The relative performance of the MLE and EAP estimation methods were assessed under two distributional forms of the latent trait, one normal and the other negatively skewed. Also, both the known item parameters and estimated item parameters were employed in the simulation study. Descriptive statistics, correlations, scattergrams, accuracy indices, and audit trails were used to compare the different methods of trait estimation in CAT. The results showed that, regardless of the latent trait distribution, MLE and EAP with a normal prior, a uniform prior, or the prior that matches the latent trait distribution using either 20 or 30 quadrature points provided relatively accurate estimation in CAT based on the generalized partial credit model. However, EAP using only 10 quadrature points did not work well in the generalized partial credit CAT. Also, the study found that increasing the number of quadrature points from 20 to 30 did not increase the accuracy of EAP estimation. Therefore, it appears 20 or more quadrature points are sufficient for accurate EAP estimation. The results also showed that EAP with a negatively skewed prior and positively skewed prior performed poorly for the normal data set, and EAP with positively skewed prior did not provide accurate estimates for the negatively skewed data set. Furthermore, trait estimation in CAT using estimated item parameters produced results similar to those obtained using known item parameters. In general, when at least 20 quadrature points are used, EAP estimation with a normal prior, a uniform prior or the prior that matches the latent trait distribution appears to be a good alternative to MLE in the application of polytomous CAT based on the generalized partial credit model. (PsycINFO Database Record (c) 2003 APA, all rights reserved).10acomputerized adaptive testing1 aChen, S-K uhttp://www.iacat.org/content/comparison-maximum-likelihood-estimation-and-expected-posteriori-estimation-computerized00451nas a2200109 4500008004100000245008100041210006900122260001600191100001800207700001500225856010100240 1997 eng d00aA comparison of testlet-based test designs for computerized adaptive testing0 acomparison of testletbased test designs for computerized adaptiv aChicago, IL1 aSchnipke, D L1 aReese, L M uhttp://www.iacat.org/content/comparison-testlet-based-test-designs-computerized-adaptive-testing00402nas a2200109 4500008004100000245006200041210006200103260001500165100001400180700001500194856008300209 1997 eng d00aComputer assembly of tests so that content reigns supreme0 aComputer assembly of tests so that content reigns supreme aChicago IL1 aCase, S M1 aLuecht, RM uhttp://www.iacat.org/content/computer-assembly-tests-so-content-reigns-supreme00435nas a2200097 4500008004100000245008900041210006900130490001300199100001600212856010900228 1997 eng d00aComputer-adaptive testing of listening comprehension: A blueprint of CAT Development0 aComputeradaptive testing of listening comprehension A blueprint 0 vno. 10. 1 aDunkel, P A uhttp://www.iacat.org/content/computer-adaptive-testing-listening-comprehension-blueprint-cat-development00567nas a2200133 4500008004100000245013800041210006900179300001000248490000700258100001700265700001200282700001500294856012400309 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://www.iacat.org/content/computerized-adaptive-and-fixed-item-testing-music-listening-skill-comparison-efficiency-000533nas a2200121 4500008004100000245013800041210006900179300001000248490000700258100001200265700001200277856012200289 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://www.iacat.org/content/computerized-adaptive-and-fixed-item-testing-music-listening-skill-comparison-efficiency01686nam a2200145 4500008004100000245006100041210006000102260006200162520115300224653003401377100001501411700001601426700001701442856008101459 1997 eng d00aComputerized adaptive testing: From inquiry to operation0 aComputerized adaptive testing From inquiry to operation aWashington, D.C., USAbAmerican Psychological Association3 a(from the cover) This book traces the development of computerized adaptive testing (CAT) from its origins in the 1960s to its integration with the Armed Services Vocational Aptitude Battery (ASVAB) in the 1990s. A paper-and-pencil version of the battery (P&P-ASVAB) has been used by the Defense Department since the 1970s to measure the abilities of applicants for military service. The test scores are used both for initial qualification and for classification into entry-level training opportunities. /// This volume provides the developmental history of the CAT-ASVAB through its various stages in the Joint-Service arena. Although the majority of the book concerns the myriad technical issues that were identified and resolved, information is provided on various political and funding support challenges that were successfully overcome in developing, testing, and implementing the battery into one of the nation's largest testing programs. The book provides useful information to professionals in the testing community and everyone interested in personnel assessment and evaluation. (PsycINFO Database Record (c) 2004 APA, all rights reserved).10acomputerized adaptive testing1 aSands, W A1 aWaters, B K1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing-inquiry-operation02024nas a2200289 4500008004100000245012700041210006900168300001300237490000800250520105800258653003401316653003301350653002301383653001501406653001001421653002601431653001001457653001501467653001801482653003101500100001501531700001601546700001401562700001701576700001601593856012501609 1997 eng d00aA computerized adaptive testing system for speech discrimination measurement: The Speech Sound Pattern Discrimination Test0 acomputerized adaptive testing system for speech discrimination m a2289-2980 v1013 aA computerized, adaptive test-delivery system for the measurement of speech discrimination, the Speech Sound Pattern Discrimination Test, is described and evaluated. Using a modified discrimination task, the testing system draws on a pool of 130 items spanning a broad range of difficulty to estimate an examinee's location along an underlying continuum of speech processing ability, yet does not require the examinee to possess a high level of English language proficiency. The system is driven by a mathematical measurement model which selects only test items which are appropriate in difficulty level for a given examinee, thereby individualizing the testing experience. Test items were administered to a sample of young deaf adults, and the adaptive testing system evaluated in terms of respondents' sensory and perceptual capabilities, acoustic and phonetic dimensions of speech, and theories of speech perception. Data obtained in this study support the validity, reliability, and efficiency of this test as a measure of speech processing ability.10a*Diagnosis, Computer-Assisted10a*Speech Discrimination Tests10a*Speech Perception10aAdolescent10aAdult10aAudiometry, Pure-Tone10aHuman10aMiddle Age10aPsychometrics10aReproducibility of Results1 aBochner, J1 aGarrison, W1 aPalmer, L1 aMacKenzie, D1 aBraveman, A uhttp://www.iacat.org/content/computerized-adaptive-testing-system-speech-discrimination-measurement-speech-sound-pattern00352nas a2200085 4500008004100000245006100041210006100102100001700163856008600180 1997 eng d00aComputerized adaptive testing through the World Wide Web0 aComputerized adaptive testing through the World Wide Web1 aShermis, M D uhttp://www.iacat.org/content/computerized-adaptive-testing-through-world-wide-web00468nas a2200133 4500008004100000245006100041210006100102260002400163100001700187700001500204700001300219700001400232856008800246 1997 eng d00aComputerized adaptive testing through the World Wide Web0 aComputerized adaptive testing through the World Wide Web a(ERIC No. ED414536)1 aShermis, M D1 aMzumara, H1 aBrown, M1 aLillig, C uhttp://www.iacat.org/content/computerized-adaptive-testing-through-world-wide-web-000548nas a2200097 4500008003900000245009000039210006900129260012700198100001400325856011100339 1997 d00aComputerized adaptive testing using the partial credit model for attitude measurement0 aComputerized adaptive testing using the partial credit model for aM. Wilson, G. Engelhard Jr and K. Draney (Eds.), Objective measurement: Theory into practice, volume 4. Norwood NJ: Ablex.1 aBaek, S G uhttp://www.iacat.org/content/computerized-adaptive-testing-using-partial-credit-model-attitude-measurement00500nas a2200121 4500008004100000245008900041210006900130260001500199100001700214700001500231700001500246856011700261 1997 eng d00aControlling test and computer anxiety: Test performance under CAT and SAT conditions0 aControlling test and computer anxiety Test performance under CAT aChicago IL1 aShermis, M D1 aMzumara, H1 aBublitz, S uhttp://www.iacat.org/content/controlling-test-and-computer-anxiety-test-performance-under-cat-and-sat-conditions00493nas a2200109 4500008004100000245003400041210003400075260017900109100001600288700001600304856006300320 1997 eng d00aCurrent and future challenges0 aCurrent and future challenges aW. A. Sands, B. K. Waters, and J. R. McBride (Eds.). Computerized adaptive testing: From inquiry to operation (pp 257-269). Washington DC: American Psychological Association.1 aSegall, D O1 aMoreno, K E uhttp://www.iacat.org/content/current-and-future-challenges00647nas a2200133 4500008004100000245020300041210006900244260001600313100001700329700001700346700001200363700001700375856012100392 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://www.iacat.org/content/can-examinees-use-review-option-positively-bias-their-scores-computerized-adaptive-test00530nam a2200097 4500008004100000245011800041210006900159260006300228100001600291856012500307 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://www.iacat.org/content/comparison-adaptive-self-referenced-testing-and-classical-approaches-measurement-individual00534nas a2200121 4500008004100000245012700041210006900168300001200237490000700249100001500256700001700271856012400288 1996 eng d00aComparison of SPRT and sequential Bayes procedures for classifying examinees into two categories using a computerized test0 aComparison of SPRT and sequential Bayes procedures for classifyi a405-4140 v211 aSpray, J A1 aReckase, M D uhttp://www.iacat.org/content/comparison-sprt-and-sequential-bayes-procedures-classifying-examinees-two-categories-using00561nas a2200121 4500008004100000245011900041210006900160260002100229653003400250653001900284100001400303856012200317 1996 eng d00aA comparison of the traditional maximum information method and the global information method in CAT item selection0 acomparison of the traditional maximum information method and the aNew York, NY USA10acomputerized adaptive testing10aitem selection1 aTang, K L uhttp://www.iacat.org/content/comparison-traditional-maximum-information-method-and-global-information-method-cat-item00484nas a2200133 4500008004100000245007400041210006900115300001000184490001000194100001700204700001700221700001400238856009800252 1996 eng d00aComputerized adaptive skill assessment in a statewide testing program0 aComputerized adaptive skill assessment in a statewide testing pr a49-670 v29(1)1 aShermis, M D1 aStemmer, P M1 aWebb, P M uhttp://www.iacat.org/content/computerized-adaptive-skill-assessment-statewide-testing-program00541nas a2200109 4500008004100000245012900041210006900170260003400239100001600273700002400289856011800313 1996 eng d00aComputerized adaptive testing for classifying examinees into three categories (Measurement and Research Department Rep 96-3)0 aComputerized adaptive testing for classifying examinees into thr aArnhem, The Netherlands: Cito1 aEggen, Theo1 aStraetmans, G J J M uhttp://www.iacat.org/content/computerized-adaptive-testing-classifying-examinees-three-categories-measurement-and00450nas a2200109 4500008004100000245008300041210006900124300001000193100001700203700001200220856010800232 1996 eng d00aComputerized adaptive testing for reading assessment and diagnostic assessment0 aComputerized adaptive testing for reading assessment and diagnos a18-201 aShermis, M D1 aet. al. uhttp://www.iacat.org/content/computerized-adaptive-testing-reading-assessment-and-diagnostic-assessment00395nas a2200085 4500008004100000245007700041210006900118100002400187856009800211 1996 eng d00aComputerized adaptive testing for the national certification examination0 aComputerized adaptive testing for the national certification exa1 aBergstrom, Betty, A uhttp://www.iacat.org/content/computerized-adaptive-testing-national-certification-examination00440nas a2200109 4500008004100000245007700041210006900118300001200187490000700199100002400206856010000230 1996 eng d00aComputerized adaptive testing for the national certification examination0 aComputerized adaptive testing for the national certification exa a119-1240 v641 aBergstrom, Betty, A uhttp://www.iacat.org/content/computerized-adaptive-testing-national-certification-examination-000439nas a2200109 4500008004100000245007200041210006900113260001600182100001600198700001800214856009700232 1996 eng d00aComputing scores for incomplete GRE General computer adaptive tests0 aComputing scores for incomplete GRE General computer adaptive te aNew York NY1 aSlater, S C1 aSchaffer, G A uhttp://www.iacat.org/content/computing-scores-incomplete-gre-general-computer-adaptive-tests00446nas a2200145 4500008004100000245005100041210005000092300001200142490000700154100001400161700001400175700001400189700001700203856008000220 1996 eng d00aConducting self-adapted testing using MicroCAT0 aConducting selfadapted testing using MicroCAT a821-8270 v561 aRoos, L L1 aWise, S L1 aYoes, M E1 aRocklin, T R uhttp://www.iacat.org/content/conducting-self-adapted-testing-using-microcat00415nas a2200109 4500008004100000245006600041210006600107260001300173100001300186700001400199856009200213 1996 eng d00aConstructing adaptive tests to parallel conventional programs0 aConstructing adaptive tests to parallel conventional programs aNew York1 aDavey, T1 aThomas, L uhttp://www.iacat.org/content/constructing-adaptive-tests-parallel-conventional-programs00672nas a2200097 4500008004100000245008600041210006900127260025400196100001500450856010900465 1996 eng d00aA content-balanced adaptive testing algorithm for computer-based training systems0 acontentbalanced adaptive testing algorithm for computerbased tra aFrasson, C. and Gauthier, G. and Lesgold, A. (Eds.), Intelligent Tutoring Systems, Third International Conference, ITS'96, Montr�al, Canada, June 1996 Proceedings. Lecture Notes in Computer Science 1086. Berlin Heidelberg: Springer-Verlag 306-314.1 aHuang, S X uhttp://www.iacat.org/content/content-balanced-adaptive-testing-algorithm-computer-based-training-systems00450nas a2200097 4500008004100000245010100041210006900142260001300211100001400224856011400238 1996 eng d00aA critical analysis of the argument for and against item review in computerized adaptive testing0 acritical analysis of the argument for and against item review in aNew York1 aWise, S L uhttp://www.iacat.org/content/critical-analysis-argument-and-against-item-review-computerized-adaptive-testing00450nas a2200085 4500008004100000245011100041210006900152100001700221856012600238 1996 eng d00aCurrent research in computer-based testing for personnel selection and classification in the United States0 aCurrent research in computerbased testing for personnel selectio1 aMcBride, J R uhttp://www.iacat.org/content/current-research-computer-based-testing-personnel-selection-and-classification-united-states01549nas a2200181 4500008004100000020002200041245008000063210006900143250001500212260000800227300001100235490000700246520096300253100001501216700002001231700001701251856009901268 1995 eng d a0022-3891 (Print)00aComparability and validity of computerized adaptive testing with the MMPI-20 aComparability and validity of computerized adaptive testing with a1995/10/01 cOct a358-710 v653 aThe comparability and validity of a computerized adaptive (CA) Minnesota Multiphasic Personality Inventory-2 (MMPI-2) were assessed in a sample of 571 undergraduate college students. The CA MMPI-2 administered adaptively Scales L, E the 10 clinical scales, and the 15 content scales, utilizing the countdown method (Butcher, Keller, & Bacon, 1985). All subjects completed the MMPI-2 twice, with three experimental conditions: booklet test-retest, booklet-CA, and conventional computerized (CC)-CA. Profiles across administration modalities show a high degree of similarity, providing evidence for the comparability of the three forms. Correlations between MMPI-2 scales and other psychometric measures (Beck Depression Inventory; Symptom Checklist-Revised; State-Trait Anxiety and Anger Scales; and the Anger Expression Scale) support the validity of the CA MMPI-2. Substantial item savings may be realized with the implementation of the countdown procedure.1 aRoper, B L1 aBen-Porath, Y S1 aButcher, J N uhttp://www.iacat.org/content/comparability-and-validity-computerized-adaptive-testing-mmpi-2-000447nas a2200109 4500008004100000245007900041210006900120260001800189100001600207700001800223856009600241 1995 eng d00aComparability studies for the GRE CAT General Test and the NCLEX using CAT0 aComparability studies for the GRE CAT General Test and the NCLEX aSan Francisco1 aEignor, D R1 aSchaffer, G A uhttp://www.iacat.org/content/comparability-studies-gre-cat-general-test-and-nclex-using-cat00542nas a2200121 4500008004100000245011400041210006900155260002100224100001500245700001800260700001800278856012400296 1995 eng d00aA comparison of classification agreement between adaptive and full-length test under the 1-PL and 2-PL models0 acomparison of classification agreement between adaptive and full aSan Francisco CA1 aLewis, M J1 aSubhiyah, R G1 aMorrison, C A uhttp://www.iacat.org/content/comparison-classification-agreement-between-adaptive-and-full-length-test-under-1-pl-and-200522nas a2200109 4500008004100000245012500041210006900166260002100235100001700256700001900273856012000292 1995 eng d00aA comparison of gender differences on paper-and-pencil and computer-adaptive versions of the Graduate Record Examination0 acomparison of gender differences on paperandpencil and computera aSan Francisco CA1 aBridgeman, B1 aSchaeffer, G A uhttp://www.iacat.org/content/comparison-gender-differences-paper-and-pencil-and-computer-adaptive-versions-graduate00445nas a2200121 4500008004100000245007300041210006900114300001200183490000700195100001300202700001400215856009400229 1995 eng d00aA comparison of item selection routines in linear and adaptive tests0 acomparison of item selection routines in linear and adaptive tes a227-2420 v321 aSchnipke1 aGreen, BF uhttp://www.iacat.org/content/comparison-item-selection-routines-linear-and-adaptive-tests00533nas a2200121 4500008004100000245011800041210006900159260001800228100001300246700001300259700001500272856012400287 1995 eng d00aA comparison of two IRT-based models for computerized mastery testing when item parameter estimates are uncertain0 acomparison of two IRTbased models for computerized mastery testi aSan Francisco1 aWay, W D1 aLewis, C1 aSmith, R L uhttp://www.iacat.org/content/comparison-two-irt-based-models-computerized-mastery-testing-when-item-parameter-estimates00535nas a2200109 4500008004100000245013700041210006900178260001800247100001800265700001900283856012300302 1995 eng d00aComputer adaptive testing in a medical licensure setting: A comparison of outcomes under the one- and two- parameter logistic models0 aComputer adaptive testing in a medical licensure setting A compa aSan Francisco1 aMorrison, C A1 aNungester, R J uhttp://www.iacat.org/content/computer-adaptive-testing-medical-licensure-setting-comparison-outcomes-under-one-and-two00454nas a2200145 4500008004100000245005700041210005500098300001400153490000700167100001200174700001400186700001300200700001300213856008200226 1995 eng d00aComputer-adaptive testing: A new breed of assessment0 aComputeradaptive testing A new breed of assessment a1326-13270 v951 aRuiz, B1 aFitz, P A1 aLewis, C1 aReidy, C uhttp://www.iacat.org/content/computer-adaptive-testing-new-breed-assessment-000452nas a2200145 4500008004100000245005700041210005500098300001400153490000700167100001200174700001400186700001300200700001300213856008000226 1995 eng d00aComputer-adaptive testing: A new breed of assessment0 aComputeradaptive testing A new breed of assessment a1326-13270 v951 aRuiz, B1 aFitz, P A1 aLewis, C1 aReidy, C uhttp://www.iacat.org/content/computer-adaptive-testing-new-breed-assessment00432nas a2200109 4500008004100000245007800041210006900119300000800188490000600196100001700202856010300219 1995 eng d00aComputer-adaptive testing: CAT: A Bayesian maximum-falsification approach0 aComputeradaptive testing CAT A Bayesian maximumfalsification app a4120 v91 aLinacre, J M uhttp://www.iacat.org/content/computer-adaptive-testing-cat-bayesian-maximum-falsification-approach00474nam a2200097 4500008004100000245007400041210006900115260007900184100001400263856009900277 1995 eng d00aComputerized adaptive attitude testing using the partial credit model0 aComputerized adaptive attitude testing using the partial credit  aDissertation Abstracts International-A, 55(7-A), 1922 (UMI No. AAM9430378)1 aBaek, S G uhttp://www.iacat.org/content/computerized-adaptive-attitude-testing-using-partial-credit-model01388nas a2200133 4500008004100000245007200041210006900113300001200182490000700194520091500201100001401116700002401130856010001154 1995 eng d00aComputerized adaptive testing: Tracking candidate response patterns0 aComputerized adaptive testing Tracking candidate response patter a151-1620 v133 aTracked the effect of candidate response patterns on a computerized adaptive test. Data were from a certification examination in laboratory science administered in 1992 to 155 candidates, using a computerized adaptive algorithm. The 90-item certification examination was divided into 9 units of 10 items each to track the pattern of initial responses and response alterations on ability estimates and test precision across the 9 test units. The precision of the test was affected most by response alterations during early segments of the test. While candidates generally benefited from altering responses, individual candidates showed different patterns of response alterations across test segments. Test precision was minimally affected, suggesting that the tailoring of computerized adaptive testing is minimally affected by response alterations. (PsycINFO Database Record (c) 2002 APA, all rights reserved).1 aLunz, M E1 aBergstrom, Betty, A uhttp://www.iacat.org/content/computerized-adaptive-testing-tracking-candidate-response-patterns01008nas a2200145 4500008004100000245005600041210005600097300001200153490000700165520056200172100001400734700002000748700001400768856008000782 1995 eng d00aComputerized adaptive testing with polytomous items0 aComputerized adaptive testing with polytomous items a5–22.0 v193 aDiscusses polytomous item response theory models and the research that has been conducted to investigate a variety of possible operational procedures (item bank, item selection, trait estimation, stopping rule) for polytomous model-based computerized adaptive testing (PCAT). Studies are reviewed that compared PCAT systems based on competing item response theory models that are appropriate for the same measurement objective, as well as applications of PCAT in marketing and educational psychology. Directions for future research using PCAT are suggested.1 aDodd, B G1 ade Ayala, R. J.1 aKoch, W R uhttp://www.iacat.org/content/computerized-adaptive-testing-polytomous-items00436nas a2200133 4500008004500000245005600045210005600101300000900157490000700166100001400173700001800187700001500205856008200220 1995 Engldsh 00aComputerized Adaptive Testing With Polytomous Items0 aComputerized Adaptive Testing With Polytomous Items a5-220 v191 aDodd, B G1 aDe Ayala, R J1 aKoch. W.R. uhttp://www.iacat.org/content/computerized-adaptive-testing-polytomous-items-000438nas a2200097 4500008004100000245003900041210003900080260014300119100001400262856006400276 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://www.iacat.org/content/computerized-testing-licensure00533nas a2200109 4500008004100000245011000041210006900151260004700220100001800267700001300285856012500298 1995 eng d00aControlling item exposure conditional on ability in computerized adaptive testing (Research Report 95-25)0 aControlling item exposure conditional on ability in computerized aPrinceton NJ: Educational Testing Service.1 aStocking, M L1 aLewis, C uhttp://www.iacat.org/content/controlling-item-exposure-conditional-ability-computerized-adaptive-testing-research-report00355nam a2200097 4500008004100000245004200041210004100083260004700124100001600171856007000187 1994 eng d00aCAT software system [computer program0 aCAT software system computer program aChicago IL: Computer Adaptive Technologies1 aGershon, RC uhttp://www.iacat.org/content/cat-software-system-computer-program00344nas a2200097 4500008004100000245003200041210003100073260006500104100001600169856006100185 1994 eng d00aCAT-GATB simulation studies0 aCATGATB simulation studies aSan Diego CA: Navy Personnel Research and Development Center1 aSegall, D O uhttp://www.iacat.org/content/cat-gatb-simulation-studies00534nas a2200121 4500008004100000245011500041210006900156260001900225100001400244700001400258700001500272856012500287 1994 eng d00aComparing computerized adaptive and self-adapted tests: The influence of examinee achievement locus of control0 aComparing computerized adaptive and selfadapted tests The influe aNew Orleans LA1 aWise, S L1 aRoos, L L1 aPlake, B S uhttp://www.iacat.org/content/comparing-computerized-adaptive-and-self-adapted-tests-influence-examinee-achievement-locus00464nas a2200121 4500008004500000245007600045210006900121490000700190100001600197700001600213700001600229856009700245 1994 Engldsh 00aA Comparison of Item Calibration Media in Computerized Adaptive Testing0 aComparison of Item Calibration Media in Computerized Adaptive Te0 v181 aHetter, R D1 aSegall, D O1 aBloxom, B M uhttp://www.iacat.org/content/comparison-item-calibration-media-computerized-adaptive-testing00480nas a2200133 4500008004100000245007400041210006900115300001200184490000700196100001600203700001600219700001600235856009500251 1994 eng d00aA comparison of item calibration media in computerized adaptive tests0 acomparison of item calibration media in computerized adaptive te a197-2040 v181 aHetter, R D1 aSegall, D O1 aBloxom, B M uhttp://www.iacat.org/content/comparison-item-calibration-media-computerized-adaptive-tests00368nas a2200133 4500008004100000245003000041210003000071300001200101490000600113100001400119700002400133700001600157856006100173 1994 eng d00aComputer adaptive testing0 aComputer adaptive testing a623-6340 v61 aLunz, M E1 aBergstrom, Betty, A1 aGershon, RC uhttp://www.iacat.org/content/computer-adaptive-testing-100405nas a2200109 4500008004100000245006600041210006500107300001200172490001100184100001500195856008500210 1994 eng d00aComputer adaptive testing: A shift in the evaluation paradigm0 aComputer adaptive testing A shift in the evaluation paradigm a213-2240 v22 (3)1 aCarlson, R uhttp://www.iacat.org/content/computer-adaptive-testing-shift-evaluation-paradigm00398nas a2200121 4500008004100000245005600041210005500097300000800152490001000160100001400170700001500184856007700199 1994 eng d00aComputer adaptive testing: Assessment of the future0 aComputer adaptive testing Assessment of the future a1-30 v4 (2)1 aDiones, R1 aEverson, H uhttp://www.iacat.org/content/computer-adaptive-testing-assessment-future00496nas a2200109 4500008004100000245010200041210006900143260001900212100001700231700001600248856012200264 1994 eng d00aComputerized adaptive testing exploring examinee response time using hierarchical linear modeling0 aComputerized adaptive testing exploring examinee response time u aNew Orleans LA1 aBergstrom, B1 aGershon, RC uhttp://www.iacat.org/content/computerized-adaptive-testing-exploring-examinee-response-time-using-hierarchical-linear00452nas a2200121 4500008004100000245006600041210006600107300001000173490001600183100002400199700001600223856009100239 1994 eng d00aComputerized adaptive testing for licensure and certification0 aComputerized adaptive testing for licensure and certification a25-270 vWinter 19941 aBergstrom, Betty, A1 aGershon, RC uhttp://www.iacat.org/content/computerized-adaptive-testing-licensure-and-certification00426nas a2200109 4500008004100000245007100041210006900112300001000181490001100191100001500202856009900217 1994 eng d00aComputerized adaptive testing: Revolutionizing academic assessment0 aComputerized adaptive testing Revolutionizing academic assessmen a32-350 v65 (1)1 aSmittle, P uhttp://www.iacat.org/content/computerized-adaptive-testing-revolutionizing-academic-assessment00523nas a2200121 4500008004100000245008900041210006900130260004600199100001000245700001300255700001700268856011600285 1994 eng d00aComputerized mastery testing using fuzzy set decision theory (Research Report 94-37)0 aComputerized mastery testing using fuzzy set decision theory Res aPrinceton NJ: Educational Testing Service1 aDu, Y1 aLewis, C1 aPashley, P J uhttp://www.iacat.org/content/computerized-mastery-testing-using-fuzzy-set-decision-theory-research-report-94-3700373nas a2200097 4500008004100000245005000041210004600091260004600137100001600183856007600199 1994 eng d00aComputerized Testing (Research Report 94-22).0 aComputerized Testing Research Report 9422 aPrinceton NJ: Educational Testing Service1 aOltman, P K uhttp://www.iacat.org/content/computerized-testing-research-report-94-2200498nas a2200121 4500008004100000245010100041210006900142300001000211490000600221100001200227700001700239856012000256 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://www.iacat.org/content/computerized-adaptive-and-self-adapted-music-listening-tests-features-and-motivational00568nas a2200133 4500008004100000245009600041210006900137260004600206100001600252700001800268700001300286700001500299856012000314 1993 eng d00aCase studies in computer adaptive test design through simulation (Research Report RR-93-56)0 aCase studies in computer adaptive test design through simulation aPrinceton NJ: Educational Testing Service1 aEignor, D R1 aStocking, M L1 aWay, W D1 aSteffen, M uhttp://www.iacat.org/content/case-studies-computer-adaptive-test-design-through-simulation-research-report-rr-93-5600568nas a2200133 4500008004100000245009700041210006900138260004600207100001600253700001300269700001600282700001500298856012100313 1993 eng d00aCase studies in computerized adaptive test design through simulation (Research Report 93-56)0 aCase studies in computerized adaptive test design through simula aPrinceton NJ: Educational Testing Service1 aEignor, D R1 aWay, W D1 aStocking, M1 aSteffen, M uhttp://www.iacat.org/content/case-studies-computerized-adaptive-test-design-through-simulation-research-report-93-5600474nas a2200121 4500008004100000245008000041210006900121300000900190490000700199653003400206100001500240856009700255 1993 eng d00aComparability and validity of computerized adaptive testing with the MMPI-20 aComparability and validity of computerized adaptive testing with a37910 v5310acomputerized adaptive testing1 aRoper, B L uhttp://www.iacat.org/content/comparability-and-validity-computerized-adaptive-testing-mmpi-200433nam a2200097 4500008004100000245006600041210006400107260006100171100001300232856009000245 1993 eng d00aA comparison of computer adaptive test administration methods0 acomparison of computer adaptive test administration methods aUnpublished doctoral dissertation, University of Chicago1 aDolan, S uhttp://www.iacat.org/content/comparison-computer-adaptive-test-administration-methods00490nas a2200097 4500008004100000245012400041210006900165100001500234700001700249856012600266 1993 eng d00aComparison of SPRT and sequential Bayes procedures for classifying examinees into two categories using an adaptive test0 aComparison of SPRT and sequential Bayes procedures for classifyi1 aSpray, J A1 aReckase, M D uhttp://www.iacat.org/content/comparison-sprt-and-sequential-bayes-procedures-classifying-examinees-two-categories-using-000576nas a2200121 4500008004100000245015100041210006900192300000900261490000700270653003400277100001700311856012600328 1993 eng d00aComputer adaptive testing: A comparison of four item selection strategies when used with the golden section search strategy for estimating ability0 aComputer adaptive testing A comparison of four item selection st a17720 v5410acomputerized adaptive testing1 aCarlson, R D uhttp://www.iacat.org/content/computer-adaptive-testing-comparison-four-item-selection-strategies-when-used-golden-section00334nas a2200109 4500008004100000245004100041210004000082300000900122490001100131100001500142856006700157 1993 eng d00aComputer adaptive testing: A new era0 aComputer adaptive testing A new era a8-100 v17 (1)1 aSmittle, P uhttp://www.iacat.org/content/computer-adaptive-testing-new-era00434nas a2200109 4500008004100000245007800041210006900119300001200188490000700200100001700207856010000224 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://www.iacat.org/content/computerized-adaptive-and-fixed-item-versions-ited-vocabulary-test00482nas a2200109 4500008004100000245009500041210006900136260002000205100001300225700001400238856012000252 1993 eng d00aComputerized adaptive testing in computer science: assessing student programming abilities0 aComputerized adaptive testing in computer science assessing stud aIndianapolis IN1 aSyang, A1 aDale, N B uhttp://www.iacat.org/content/computerized-adaptive-testing-computer-science-assessing-student-programming-abilities00412nas a2200121 4500008004100000245006000041210006000101300001000161490001000171100001000181700001300191856008600204 1993 eng d00aComputerized adaptive testing in instructional settings0 aComputerized adaptive testing in instructional settings a47-620 v41(3)1 aWelch1 aFrick, T uhttp://www.iacat.org/content/computerized-adaptive-testing-instructional-settings00561nam a2200097 4500008004100000245016500041210006900206260005500275100001200330856012100342 1993 eng d00aComputerized adaptive testing strategies: Golden section search, dichotomous search, and Z-score strategies (Doctoral dissertation, Iowa State University, 1990)0 aComputerized adaptive testing strategies Golden section search d aDissertation Abstracts International, 54-03B, 17201 aXiao, B uhttp://www.iacat.org/content/computerized-adaptive-testing-strategies-golden-section-search-dichotomous-search-and-z00733nas a2200241 4500008004100000020002200041245005700063210005600120250001500176260000800191300001100199490000700210653003500217653002400252653002900276653001900305653001100324653002700335653001800362100001800380700001500398856007800413 1993 eng d a0276-5284 (Print)00aComputerized adaptive testing: the future is upon us0 aComputerized adaptive testing the future is upon us a1993/09/01 cSep a378-850 v1410a*Computer-Assisted Instruction10a*Education, Nursing10a*Educational Measurement10a*Reaction Time10aHumans10aPharmacology/education10aPsychometrics1 aHalkitis, P N1 aLeahy, J M uhttp://www.iacat.org/content/computerized-adaptive-testing-future-upon-us01367nas a2200145 4500008004100000245013200041210006900173300001100242490000700253520078900260100001401049700001401063700002001077856012401097 1993 eng d00aComputerized adaptive testing using the partial credit model: Effects of item pool characteristics and different stopping rules0 aComputerized adaptive testing using the partial credit model Eff a61-77.0 v533 aSimulated datasets were used to research the effects of the systematic variation of three major variables on the performance of computerized adaptive testing (CAT) procedures for the partial credit model. The three variables studied were the stopping rule for terminating the CATs, item pool size, and the distribution of the difficulty of the items in the pool. Results indicated that the standard error stopping rule performed better across the variety of CAT conditions than the minimum information stopping rule. In addition it was found that item pools that consisted of as few as 30 items were adequate for CAT provided that the item pool was of medium difficulty. The implications of these findings for implementing CAT systems based on the partial credit model are discussed. 1 aDodd, B G1 aKoch, W R1 ade Ayala, R. J. uhttp://www.iacat.org/content/computerized-adaptive-testing-using-partial-credit-model-effects-item-pool-characteristics00457nas a2200133 4500008004100000245006500041210006500106300001200171490000600183100001000189700001300199700001700212856009400229 1993 eng d00aComputerized mastery testing using fuzzy set decision theory0 aComputerized mastery testing using fuzzy set decision theory a181-1930 v61 aDu, Y1 aLewis, C1 aPashley, P J uhttp://www.iacat.org/content/computerized-mastery-testing-using-fuzzy-set-decision-theory00494nas a2200097 4500008004100000245010000041210006900141260004600210100001800256856012200274 1993 eng d00aControlling item exposure rates in a realistic adaptive testing paradigm (Research Report 93-2)0 aControlling item exposure rates in a realistic adaptive testing  aPrinceton NJ: Educational Testing Service1 aStocking, M L uhttp://www.iacat.org/content/controlling-item-exposure-rates-realistic-adaptive-testing-paradigm-research-report-93-200306nas a2200121 4500008004100000245002400041210002300065300001000088490000600098100001100104700001600115856005300131 1992 eng d00aCAT-ASVAB precision0 aCATASVAB precision a22-260 v11 aMoreno1 aSegall, D O uhttp://www.iacat.org/content/cat-asvab-precision00490nas a2200097 4500008004100000245012700041210006900168260002100237100001200258856012200270 1992 eng d00aA comparison of computerized adaptive and paper-and-pencil versions of the national registered nurse licensure examination0 acomparison of computerized adaptive and paperandpencil versions  aSan Francisco CA1 aZara, A uhttp://www.iacat.org/content/comparison-computerized-adaptive-and-paper-and-pencil-versions-national-registered-nurse00441nas a2200109 4500008004100000245007300041210006900114260002100183100001700204700001600221856009400237 1992 eng d00aComparison of item targeting strategies for pass/fail adaptive tests0 aComparison of item targeting strategies for passfail adaptive te aSan Francisco CA1 aBergstrom, B1 aGershon, RC uhttp://www.iacat.org/content/comparison-item-targeting-strategies-passfail-adaptive-tests00455nam a2200097 4500008004100000245008000041210006900121260005900190100001300249856009500262 1992 eng d00aA comparison of methods for adaptive estimation of a multidimensional trait0 acomparison of methods for adaptive estimation of a multidimensio aUnpublished doctoral dissertation, Columbia University1 aTam, S S uhttp://www.iacat.org/content/comparison-methods-adaptive-estimation-multidimensional-trait00513nas a2200145 4500008004100000245007700041210006900118300001200187490000700199100001400206700001500220700001700235700001400252856010100266 1992 eng d00aA comparison of self-adapted and computerized adaptive achievement tests0 acomparison of selfadapted and computerized adaptive achievement  a329-3390 v291 aWise, S L1 aPlake, S S1 aJohnson, P L1 aRoos, S L uhttp://www.iacat.org/content/comparison-self-adapted-and-computerized-adaptive-achievement-tests00521nas a2200133 4500008004100000245009900041210006900140300001000209490000600219100001800225700001400243700001400257856011600271 1992 eng d00aA comparison of the partial credit and graded response models in computerized adaptive testing0 acomparison of the partial credit and graded response models in c a17-340 v51 aDe Ayala, R J1 aDodd, B G1 aKoch, W R uhttp://www.iacat.org/content/comparison-partial-credit-and-graded-response-models-computerized-adaptive-testing00482nas a2200133 4500008004100000245008200041210006900123300001200192490000700204100001100211700001400222700001300236856009900249 1992 eng d00aA comparison of the performance of simulated hierarchical and linear testlets0 acomparison of the performance of simulated hierarchical and line a243-2510 v291 aWainer1 aKaplan, B1 aLewis, C uhttp://www.iacat.org/content/comparison-performance-simulated-hierarchical-and-linear-testlets00400nam a2200097 4500008004100000245005200041210005000093260006100143100001700204856008100221 1992 eng d00aComputer adaptive versus paper-and-pencil tests0 aComputer adaptive versus paperandpencil tests aUnpublished doctoral dissertation, University of Chicago1 aBergstrom, B uhttp://www.iacat.org/content/computer-adaptive-versus-paper-and-pencil-tests00419nas a2200109 4500008004100000245007000041210006900111300001000180490000700190100001600197856009600213 1992 eng d00aComputer-based adaptive testing in music research and instruction0 aComputerbased adaptive testing in music research and instruction a49-630 v101 aBowers, D R uhttp://www.iacat.org/content/computer-based-adaptive-testing-music-research-and-instruction00443nas a2200097 4500008004100000245008200041210006900123260003000192100001500222856010800237 1992 eng d00aComputerized adaptive assessment of cognitive abilities among disabled adults0 aComputerized adaptive assessment of cognitive abilities among di aERIC Document No ED3012741 aEngdahl, B uhttp://www.iacat.org/content/computerized-adaptive-assessment-cognitive-abilities-among-disabled-adults00385nas a2200109 4500008004100000245005800041210005800099300001300157490000600170100001500176856008400191 1992 eng d00aComputerized adaptive mastery tests as expert systems0 aComputerized adaptive mastery tests as expert systems a187-213.0 v81 aFrick, T W uhttp://www.iacat.org/content/computerized-adaptive-mastery-tests-expert-systems00389nas a2200109 4500008004100000245005800041210005800099300001200157490000900169100001500178856008600193 1992 eng d00aComputerized adaptive mastery tests as expert systems0 aComputerized adaptive mastery tests as expert systems a187-2130 v8(2)1 aFrick, T W uhttp://www.iacat.org/content/computerized-adaptive-mastery-tests-expert-systems-000614nas a2200205 4500008004100000020002200041245004700063210004600110250001500156260000800171300000900179490000700188653001500195653002800210653003700238653001100275653003400286100001600320856007200336 1992 eng d a0022-3867 (Print)00aComputerized adaptive testing for NCLEX-PN0 aComputerized adaptive testing for NCLEXPN a1992/06/01 cJun a8-100 v4210a*Licensure10a*Programmed Instruction10aEducational Measurement/*methods10aHumans10aNursing, Practical/*education1 aFields, F A uhttp://www.iacat.org/content/computerized-adaptive-testing-nclex-pn00497nas a2200109 4500008004100000245011500041210006900156300001200225490000600237100001900243856012500262 1992 eng d00aComputerized adaptive testing: Its potential substantive contribution to psychological research and assessment0 aComputerized adaptive testing Its potential substantive contribu a129-1330 v11 aEmbretson, S E uhttp://www.iacat.org/content/computerized-adaptive-testing-its-potential-substantive-contribution-psychological-research00409nas a2200121 4500008004100000245005800041210005700099300001000156490000800166100001200174700001700186856008400203 1992 eng d00aComputerized adaptive testing of music-related skills0 aComputerized adaptive testing of musicrelated skills a29-490 v1121 aVispoel1 aCoffman, D D uhttp://www.iacat.org/content/computerized-adaptive-testing-music-related-skills00399nas a2200121 4500008004100000245005600041210005600097300001000153490001100163100000900174700001400183856008000197 1992 eng d00aComputerized adaptive testing with different groups0 aComputerized adaptive testing with different groups a23-270 v11 (2)1 aLegg1 aBuhr, D C uhttp://www.iacat.org/content/computerized-adaptive-testing-different-groups00527nas a2200109 4500008004100000245012700041210006900168260001900237100002000256700001500276856012600291 1992 eng d00aComputerized adaptive testing with the MMPI-2: Reliability, validity, and comparability to paper and pencil administration0 aComputerized adaptive testing with the MMPI2 Reliability validit aMinneapolis MN1 aBen-Porath, Y S1 aRoper, B L uhttp://www.iacat.org/content/computerized-adaptive-testing-mmpi-2-reliability-validity-and-comparability-paper-and-pencil00412nas a2200121 4500008004100000245006100041210006100102300001000163490000700173100001200180700001300192856008500205 1992 eng d00aComputerized mastery testing with nonequivalent testlets0 aComputerized mastery testing with nonequivalent testlets a65-760 v161 aSheehan1 aLewis, C uhttp://www.iacat.org/content/computerized-mastery-testing-nonequivalent-testlets00421nas a2200121 4500008004500000245006100045210006100106300001000167490000700177100001500184700001300199856008700212 1992 Engldsh 00aComputerized Mastery Testing With Nonequivalent Testlets0 aComputerized Mastery Testing With Nonequivalent Testlets a65-760 v161 aSheehan, K1 aLewis, C uhttp://www.iacat.org/content/computerized-mastery-testing-nonequivalent-testlets-000477nas a2200109 4500008004100000245009400041210006900135300001200204490001000216100002400226856011700250 1992 eng d00aConfidence in pass/fail decisions for computer adaptive and paper and pencil examinations0 aConfidence in passfail decisions for computer adaptive and paper a435-4640 v15(4)1 aBergstrom, Betty, A uhttp://www.iacat.org/content/confidence-passfail-decisions-computer-adaptive-and-paper-and-pencil-examinations-101198nas a2200133 4500008004100000245009400041210006900135300001200204490000700216520068800223100002400911700001400935856011500949 1992 eng d00aConfidence in pass/fail decisions for computer adaptive and paper and pencil examinations0 aConfidence in passfail decisions for computer adaptive and paper a453-4640 v153 aCompared the level of confidence in pass/fail decisions obtained with computer adaptive tests (CADTs) and pencil-and-paper tests (PPTs). 600 medical technology students took a variable-length CADT and 2 fixed-length PPTs. The CADT was stopped when the examinee ability estimate was either 1.3 times the standard error of measurement above or below the pass/fail point or when a maximum test length was reached. Results show that greater confidence in the accuracy of the pass/fail decisions was obtained for more examinees when the CADT implemented a 90% confidence stopping rule than with PPTs of comparable test length. (PsycINFO Database Record (c) 2002 APA, all rights reserved).1 aBergstrom, Betty, A1 aLunz, M E uhttp://www.iacat.org/content/confidence-passfail-decisions-computer-adaptive-and-paper-and-pencil-examinations00474nas a2200109 4500008004100000245009400041210006900135300001200204490000700216100002400223856011700247 1992 eng d00aConfidence in pass/fail decisions for computer adaptive and paper and pencil examinations0 aConfidence in passfail decisions for computer adaptive and paper a435-4640 v151 aBergstrom, Betty, A uhttp://www.iacat.org/content/confidence-passfail-decisions-computer-adaptive-and-paper-and-pencil-examinations-000535nas a2200133 4500008004100000245007400041210006900115260006600184100001500250700001400265700001600279700001300295856009300308 1991 eng d00aCollected works on the legal aspects of computerized adaptive testing0 aCollected works on the legal aspects of computerized adaptive te aChicago, IL: National Council of State Boards of Nursing, Inc1 aStenson, H1 aGraves, P1 aGardiner, J1 aDally, L uhttp://www.iacat.org/content/collected-works-legal-aspects-computerized-adaptive-testing01284nas a2200181 4500008004100000020002200041245008400063210006900147250001500216260000800231300001200239490000700251520069100258100001500949700002000964700001700984856010101001 1991 eng d a0022-3891 (Print)00aComparability of computerized adaptive and conventional testing with the MMPI-20 aComparability of computerized adaptive and conventional testing  a1991/01/01 cOct a278-2900 v573 aA computerized adaptive version and the standard version of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) were administered 1 week apart to a sample of 155 college students to assess the comparability of the two versions. The countdown method was used to adaptively administer Scales L, F, the I0 clinical scales, and the 15 new content scales. Profiles across administration modalities show a high degree of similarity, providing evidence for the comparability of computerized adaptive and conventional testing with the MMPI-2. Substantial item savings were found with the adaptive version. Future directions in the study of adaptive testing with the MMPI-2 are discussed.1 aRoper, B L1 aBen-Porath, Y S1 aButcher, J N uhttp://www.iacat.org/content/comparability-computerized-adaptive-and-conventional-testing-mmpi-200465nas a2200121 4500008004100000245007800041210006900119300001000188490000700198100001400205700002400219856010000243 1991 eng d00aComparability of decisions for computer adaptive and written examinations0 aComparability of decisions for computer adaptive and written exa a15-230 v201 aLunz, M E1 aBergstrom, Betty, A uhttp://www.iacat.org/content/comparability-decisions-computer-adaptive-and-written-examinations00584nas a2200121 4500008004100000245016000041210006900201300000900270490000700279653003400286100002000320856012200340 1991 eng d00aA comparison of paper-and-pencil, computer-administered, computerized feedback, and computerized adaptive testing methods for classroom achievement testing0 acomparison of paperandpencil computeradministered computerized f a17190 v5210acomputerized adaptive testing1 aKuan, Tsung Hao uhttp://www.iacat.org/content/comparison-paper-and-pencil-computer-administered-computerized-feedback-and-computerized00367nas a2200085 4500008004100000245006800041210006500109100001900174856008800193 1991 eng d00aA comparison of procedures for content-sensitive item selection0 acomparison of procedures for contentsensitive item selection1 aKingsbury, G G uhttp://www.iacat.org/content/comparison-procedures-content-sensitive-item-selection00496nas a2200121 4500008004100000245009900041210006900140300001200209490000600221100001900227700001200246856011600258 1991 eng d00aA comparison of procedures for content-sensitive item selection in computerized adaptive tests0 acomparison of procedures for contentsensitive item selection in  a241-2610 v41 aKingsbury, G G1 aZara, A uhttp://www.iacat.org/content/comparison-procedures-content-sensitive-item-selection-computerized-adaptive-tests00465nas a2200109 4500008004100000245006400041210006400105260005800169100002400227700001400251856009000265 1991 eng d00aComparisons of computer adaptive and pencil and paper tests0 aComparisons of computer adaptive and pencil and paper tests aChicago IL: American Society of Clinical Pathologists1 aBergstrom, Betty, A1 aLunz, M E uhttp://www.iacat.org/content/comparisons-computer-adaptive-and-pencil-and-paper-tests00599nas a2200121 4500008004100000245007100041210006800112260014900180100001800329700001400347700001900361856009700380 1991 eng d00aComputerized adaptive testing: Theory, applications, and standards0 aComputerized adaptive testing Theory applications and standards aR. K. Hambleton and J. N. Zaal (Eds.), Advances in educational and psychological testing: Theory and Applications (pp. 341-366). Boston: Kluwer.1 aHambleton, RK1 aZaal, J N1 aPieters, J P M uhttp://www.iacat.org/content/computerized-adaptive-testing-theory-applications-and-standards00479nas a2200109 4500008004100000245009400041210006900135260001500204100001900219700001400238856011700252 1991 eng d00aConfidence in pass/fail decisions for computer adaptive and paper and pencil examinations0 aConfidence in passfail decisions for computer adaptive and paper aChicago IL1 aBergstrom, B B1 aLunz, M E uhttp://www.iacat.org/content/confidence-passfail-decisions-computer-adaptive-and-paper-and-pencil-examinations-200501nas a2200109 4500008004100000245009900041210006900140260003300209100001500242700001800257856011600275 1991 eng d00aConstruction and validation of the SON-R 5-17, the Snijders-Oomen non-verbal intelligence test0 aConstruction and validation of the SONR 517 the SnijdersOomen no aGroningen: Wolters-Noordhoff1 aLaros, J A1 aTellegen, P J uhttp://www.iacat.org/content/construction-and-validation-son-r-5-17-snijders-oomen-non-verbal-intelligence-test00499nas a2200145 4500008004100000245007200041210006900113300001000182490000600192100001700198700001400215700001400229700001500243856009500258 1991 eng d00aCorrelates of examinee item choice behavior in self-adapted testing0 aCorrelates of examinee item choice behavior in selfadapted testi a25-280 v41 aJohnson, J L1 aRoos, L L1 aWise, S L1 aPlake, B S uhttp://www.iacat.org/content/correlates-examinee-item-choice-behavior-self-adapted-testing00491nas a2200109 4500008004100000245009500041210006900136260002700205100001600232700001700248856011600265 1990 eng d00aA comparison of Rasch and three-parameter logistic models in computerized adaptive testing0 acomparison of Rasch and threeparameter logistic models in comput aUnpublished manuscript1 aParker, S B1 aMcBride, J R uhttp://www.iacat.org/content/comparison-rasch-and-three-parameter-logistic-models-computerized-adaptive-testing00462nas a2200109 4500008004100000245009800041210006900139300001200208490000600220100001500226856011100241 1990 eng d00aA comparison of three decision models for adapting the length of computer-based mastery tests0 acomparison of three decision models for adapting the length of c a479-5130 v61 aFrick, T W uhttp://www.iacat.org/content/comparison-three-decision-models-adapting-length-computer-based-mastery-tests00388nas a2200109 4500008004100000245005800041210005700099300001000156490001000166100001600176856008600192 1990 eng d00aComputer testing: Pragmatic issues and research needs0 aComputer testing Pragmatic issues and research needs a19-200 v9 (2)1 aRudner, L M uhttp://www.iacat.org/content/computer-testing-pragmatic-issues-and-research-needs00420nas a2200133 4500008004100000245005100041210005100092300001000143490000700153100001400160700001400174700002100188856007700209 1990 eng d00aComputerized adaptive measurement of attitudes0 aComputerized adaptive measurement of attitudes a20-300 v231 aKoch, W R1 aDodd, B G1 aFitzpatrick, S J uhttp://www.iacat.org/content/computerized-adaptive-measurement-attitudes00418nas a2200097 4500008004100000245007600041210006900117260001800186100001700204856009900221 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://www.iacat.org/content/computerized-adaptive-music-tests-new-solution-three-old-problems00515nam a2200169 4500008003900000245005100039210004700090260002600137100001100163700001600174700001600190700001400206700001700220700001700237700001500254856007600269 1990 d00aComputerized adaptive testing: A primer (Eds.)0 aComputerized adaptive testing A primer Eds aHillsdale NJ: Erlbaum1 aWainer1 aDorans, N J1 aFlaugher, R1 aGreen, BF1 aMislevy, R J1 aSteinberg, L1 aThissen, D uhttp://www.iacat.org/content/computerized-adaptive-testing-primer-eds-200358nas a2200109 4500008004100000245005200041210004700093300001200140490000700152100001500159856007400174 1990 eng d00aThe construction of customized two-staged tests0 aconstruction of customized twostaged tests a241-2530 v271 aAdema, J J uhttp://www.iacat.org/content/construction-customized-two-staged-tests00595nas a2200109 4500008004100000245007600041210006900117260017000186100001700356700001500373856009700388 1990 eng d00aCreating adaptive tests of musical ability with limited-size item pools0 aCreating adaptive tests of musical ability with limitedsize item aD. Dalton (Ed.), ADCIS 32nd International Conference Proceedings (pp. 105-112). Columbus OH: Association for the Development of Computer-Based Instructional Systems.1 aVispoel, W T1 aTwing, J S uhttp://www.iacat.org/content/creating-adaptive-tests-musical-ability-limited-size-item-pools00334nam a2200097 4500008004100000245004100041210003900082260003100121100001600152856006800168 1989 eng d00aCAT administrator [Computer program]0 aCAT administrator Computer program aChicago: Micro Connections1 aGershon, RC uhttp://www.iacat.org/content/cat-administrator-computer-program00386nas a2200097 4500008004100000245006100041210006100102260002100163100001700184856008700201 1989 eng d00aCommercial applications of computerized adaptive testing0 aCommercial applications of computerized adaptive testing aSan Antonio, TX1 aMcBride, J R uhttp://www.iacat.org/content/commercial-applications-computerized-adaptive-testing00512nas a2200097 4500008004100000245009700041210006900138260008000207100001500287856011200302 1989 eng d00aA comparison of an expert systems approach to computerized adaptive testing and an IRT model0 acomparison of an expert systems approach to computerized adaptiv aUnpublished manuscript (submitted to American Educational Research Journal)1 aFrick, T W uhttp://www.iacat.org/content/comparison-expert-systems-approach-computerized-adaptive-testing-and-irt-model00502nas a2200109 4500008004100000245011900041210006900160300001200229490000700241100002000248856012400268 1989 eng d00aA comparison of the nominal response model and the three-parameter logistic model in computerized adaptive testing0 acomparison of the nominal response model and the threeparameter  a789-8050 v491 ade Ayala, R. J. uhttp://www.iacat.org/content/comparison-nominal-response-model-and-three-parameter-logistic-model-computerized-adaptive00421nas a2200109 4500008004100000245006900041210006700110260001800177100001000195700001300205856009300218 1989 eng d00aA comparison of three adaptive testing strategies using MicroCAT0 acomparison of three adaptive testing strategies using MicroCAT aSan Francisco1 aHo, R1 aHsu, T C uhttp://www.iacat.org/content/comparison-three-adaptive-testing-strategies-using-microcat01130nas a2200157 4500008004100000245010500041210006900146300001200215490000600227520055900233100001500792700001600807700001500823700001000838856012400848 1989 eng d00aComparisons of paper-administered, computer-administered and computerized adaptive achievement tests0 aComparisons of paperadministered computeradministered and comput a311-3260 v53 aThis research study was designed to compare student achievement scores from three different testing methods: paper-administered testing, computer-administered testing, and computerized adaptive testing. The three testing formats were developed from the California Assessment Program (CAP) item banks for grades three and six. The paper-administered and the computer-administered tests were identical in item content, format, and sequence. The computerized adaptive test was a tailored or adaptive sequence of the items in the computer-administered test. 1 aOlson, J B1 aMaynes, D D1 aSlawson, D1 aHo, K uhttp://www.iacat.org/content/comparisons-paper-administered-computer-administered-and-computerized-adaptive-achievement00365nas a2200097 4500008004100000245005500041210005300096260001900149100001700168856008200185 1989 eng d00aA computerized adaptive mathematics screening test0 acomputerized adaptive mathematics screening test aBurlingame, CA1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-mathematics-screening-test00399nam a2200097 4500008004100000245004900041210004900090260006800139100001600207856007800223 1989 eng d00aComputerized adaptive personality assessment0 aComputerized adaptive personality assessment aUnpublished master’s thesis, Harvard University, Cambridge MA1 aWaller, N G uhttp://www.iacat.org/content/computerized-adaptive-personality-assessment00396nas a2200121 4500008004100000245003200041210003200073260007000105100001300175700001600188700000900204856006100213 1989 eng d00aComputerized adaptive tests0 aComputerized adaptive tests aERIC Clearinghouse on Tests, Measurement, and Evaluation, no. 1071 aGrist, S1 aRudner, L M1 aWise uhttp://www.iacat.org/content/computerized-adaptive-tests00452nas a2200097 4500008004100000245007900041210006900120260004600189100001900235856010000254 1989 eng d00aA consideration for variable length adaptive tests (Research Report 89-40)0 aconsideration for variable length adaptive tests Research Report aPrinceton NJ: Educational Testing Service1 aWingersky, M S uhttp://www.iacat.org/content/consideration-variable-length-adaptive-tests-research-report-89-4000470nas a2200109 4500008004100000245010000041210006900141300001200210490000600222100001400228856011800242 1988 eng d00aThe College Board computerized placement tests: An application of computerized adaptive testing0 aCollege Board computerized placement tests An application of com a271-2820 v21 aWard, W C uhttp://www.iacat.org/content/college-board-computerized-placement-tests-application-computerized-adaptive-testing00509nas a2200109 4500008004100000245011200041210006900153260001900222100001900241700001600260856012300276 1988 eng d00aA comparison of achievement level estimates from computerized adaptive testing and paper-and-pencil testing0 acomparison of achievement level estimates from computerized adap aNew Orleans LA1 aKingsbury, G G1 aHouser, R L uhttp://www.iacat.org/content/comparison-achievement-level-estimates-computerized-adaptive-testing-and-paper-and-pencil00524nas a2200133 4500008004100000245009300041210006900134260001500203100002000218700001600238700001700254700001700271856010200288 1988 eng d00aA comparison of two methods for the adaptive administration of the MMPI-2 content scales0 acomparison of two methods for the adaptive administration of the aAtlanta GA1 aBen-Porath, Y S1 aWaller, N G1 aSlutske, W S1 aButcher, J N uhttp://www.iacat.org/content/comparison-two-methods-adaptive-administration-mmpi-2-content-scales00524nas a2200121 4500008004100000245010800041210006900149260001600218100001400234700001400248700002000262856012000282 1988 eng d00aComputerized adaptive attitude measurement: A comparison of the graded response and rating scale models0 aComputerized adaptive attitude measurement A comparison of the g aNew Orleans1 aDodd, B G1 aKoch, W R1 ade Ayala, R. J. uhttp://www.iacat.org/content/computerized-adaptive-attitude-measurement-comparison-graded-response-and-rating-scale00533nas a2200121 4500008004100000245010800041210006900149300000900218490000700227653003400234100002000268856012300288 1988 eng d00aComputerized adaptive testing: A comparison of the nominal response model and the three parameter model0 aComputerized adaptive testing A comparison of the nominal respon a31480 v4810acomputerized adaptive testing1 ade Ayala, R. J. uhttp://www.iacat.org/content/computerized-adaptive-testing-comparison-nominal-response-model-and-three-parameter-model00479nas a2200121 4500008004100000245008700041210006900128300001000197490001100207100001900218700001200237856010800249 1988 eng d00aComputerized adaptive testing: A four-year-old pilot study shows that CAT can work0 aComputerized adaptive testing A fouryearold pilot study shows th a73-760 v16 (4)1 aKingsbury, G G1 aet. al. uhttp://www.iacat.org/content/computerized-adaptive-testing-four-year-old-pilot-study-shows-cat-can-work00431nas a2200097 4500008004100000245008000041210006900121260002800190100001700218856009800235 1988 eng d00aComputerized adaptive testing: a good idea waiting for the right technology0 aComputerized adaptive testing a good idea waiting for the right  aNew Orleans, April 19881 aReckase, M D uhttp://www.iacat.org/content/computerized-adaptive-testing-good-idea-waiting-right-technology00442nas a2200097 4500008004100000245008900041210006900130100001800199700001300217856011400230 1988 eng d00aComputerized adaptive testing program at Miami-Dade Community College, South Campous0 aComputerized adaptive testing program at MiamiDade Community Col1 aSchinoff, R B1 aStead, L uhttp://www.iacat.org/content/computerized-adaptive-testing-program-miami-dade-community-college-south-campous00494nas a2200097 4500008004100000245009800041210006900139260002800208100005100236856010900287 1988 eng d00aComputerized adaptive testing: The state of the art in assessment at three community colleges0 aComputerized adaptive testing The state of the art in assessment aLaguna Hills CA: Author1 aLeague-for-Innovation-in-the-Community-College uhttp://www.iacat.org/content/computerized-adaptive-testing-state-art-assessment-three-community-colleges00501nam a2200097 4500008004100000245009800041210006900139260006800208100001600276856011100292 1988 eng d00aComputerized adaptive testing: The state of the art in assessment at three community colleges0 aComputerized adaptive testing The state of the art in assessment aLaguna Hills CA: League for Innovation in the Community College1 aDoucette, D uhttp://www.iacat.org/content/computerized-adaptive-testing-state-art-assessment-three-community-colleges-000402nas a2200097 4500008004100000245007100041210006900112260001500181100001700196856009100213 1988 eng d00aA computerized adaptive version of the Differential Aptitude Tests0 acomputerized adaptive version of the Differential Aptitude Tests aAtlanta GA1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-version-differential-aptitude-tests00337nas a2200121 4500008004100000245003300041210003300074300001200107490000600119100001300125700001500138856006200153 1988 eng d00aComputerized mastery testing0 aComputerized mastery testing a283-2860 v21 aLewis, C1 aSheehan, K uhttp://www.iacat.org/content/computerized-mastery-testing00405nas a2200097 4500008004100000245004700041210004600088260008600134100001400220856007300234 1988 eng d00aConstruct validity of computer-based tests0 aConstruct validity of computerbased tests aH. Wainer and H. Braun (Eds.), Test validity (pp. 77-103). Hillsdale NJ: Erlbaum.1 aGreen, BF uhttp://www.iacat.org/content/construct-validity-computer-based-tests00411nas a2200109 4500008004100000245007100041210006500112300001200177490000600189100001400195856009200209 1988 eng d00aCritical problems in computer-based psychological measurement, , ,0 aCritical problems in computerbased psychological measurement a223-2310 v11 aGreen, BF uhttp://www.iacat.org/content/critical-problems-computer-based-psychological-measurement00486nas a2200121 4500008004100000245009700041210006900138300001200207490000700219100001100226700001500237856011200252 1987 eng d00aCATS, testlets, and test construction: A rationale for putting test developers back into CAT0 aCATS testlets and test construction A rationale for putting test a185-2020 v321 aWainer1 aKiely, G L uhttp://www.iacat.org/content/cats-testlets-and-test-construction-rationale-putting-test-developers-back-cat00468nas a2200097 4500008004100000245008500041210006900126260005500195100001700250856010300267 1987 eng d00aA computer program for adaptive testing by microcomputer (MESA Memorandum No 40)0 acomputer program for adaptive testing by microcomputer MESA Memo aChicago: University of Chicago. (ERIC ED 280 895.)1 aLinacre, J M uhttp://www.iacat.org/content/computer-program-adaptive-testing-microcomputer-mesa-memorandum-no-4000469nas a2200097 4500008004100000245006900041210006800110260008200178100001600260856009500276 1987 eng d00aComputerized adaptive language testing: A Spanish placement exam0 aComputerized adaptive language testing A Spanish placement exam aIn Language Testing Research Selected Papers from the Colloquium, Monterey CA1 aLarson, J W uhttp://www.iacat.org/content/computerized-adaptive-language-testing-spanish-placement-exam00515nas a2200109 4500008004100000245011700041210006900158260001800227100002000245700001400265856012600279 1987 eng d00aComputerized adaptive testing: A comparison of the nominal response model and the three-parameter logistic model0 aComputerized adaptive testing A comparison of the nominal respon aWashington DC1 ade Ayala, R. J.1 aKoch, W R uhttp://www.iacat.org/content/computerized-adaptive-testing-comparison-nominal-response-model-and-three-parameter-logistic00580nas a2200109 4500008004100000245009200041210006900133260012300202100001400325700001400339856011700353 1987 eng d00aComputerized adaptive testing for measuring abilities and other psychological variables0 aComputerized adaptive testing for measuring abilities and other  aJ. N. Butcher (Ed.), Computerized personality measurement: A practitioners guide (pp. 325-343). New York: Basic Books.1 aWeiss, DJ1 aVale, C D uhttp://www.iacat.org/content/computerized-adaptive-testing-measuring-abilities-and-other-psychological-variables00487nas a2200097 4500008004100000245011900041210006900160260002200229100001700251856012100268 1987 eng d00aComputerized adaptive testing made practical: The Computerized Adaptive Edition of the Differential Aptitude Tests0 aComputerized adaptive testing made practical The Computerized Ad aSan Francisco, CA1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing-made-practical-computerized-adaptive-edition-differential00371nas a2200097 4500008004100000245006200041210006200103260001200165100001400177856008200191 1987 eng d00aComputerized adaptive testing with the rating scale model0 aComputerized adaptive testing with the rating scale model aChicago1 aDodd, B G uhttp://www.iacat.org/content/computerized-adaptive-testing-rating-scale-model00443nas a2200133 4500008004100000245006200041210006100103300001000164490000600174100001000180700001500190700001400205856009000219 1987 eng d00aComputerized psychological testing: Overview and critique0 aComputerized psychological testing Overview and critique a42-510 v11 aBurke1 aNormand, J1 aRaju, N M uhttp://www.iacat.org/content/computerized-psychological-testing-overview-and-critique00565nas a2200109 4500008004100000245012200041210006900163260007500232100001100307700001500318856012200333 1986 eng d00aCATs, testlets, and test construction: A rationale for putting test developers back into CAT (Technical Report 86-71)0 aCATs testlets and test construction A rationale for putting test aPrinceton NJ: Educational Testing Service, Program Statistics Research1 aWainer1 aKiely, G L uhttp://www.iacat.org/content/cats-testlets-and-test-construction-rationale-putting-test-developers-back-cat-technical00432nas a2200097 4500008004100000245005700041210005500098260007900153100001800232856008400250 1986 eng d00aA cognitive error diagnostic adaptive testing system0 acognitive error diagnostic adaptive testing system athe 28th ADCIS International Conference Proceedings. Washington DC: ADCIS.1 aTatsuoka, K K uhttp://www.iacat.org/content/cognitive-error-diagnostic-adaptive-testing-system00567nas a2200121 4500008004100000245011900041210006900160260004800229100001400277700001500291700001600306856012300322 1986 eng d00aCollege Board computerized placement tests: Validation of an adaptive test of basic skills (Research Report 86-29)0 aCollege Board computerized placement tests Validation of an adap aPrinceton NJ: Educational Testing Service.1 aWard, W C1 aKline, R G1 aFlaugher, J uhttp://www.iacat.org/content/college-board-computerized-placement-tests-validation-adaptive-test-basic-skills-research00566nas a2200133 4500008004100000245012100041210006900162260002100231100001500252700001600267700001500283700001000298856012400308 1986 eng d00aComparison and equating of paper-administered, computer-administered, and computerized adaptive tests of achievement0 aComparison and equating of paperadministered computeradministere aSan Francisco CA1 aOlsen, J B1 aMaynes, D D1 aSlawson, D1 aHo, K uhttp://www.iacat.org/content/comparison-and-equating-paper-administered-computer-administered-and-computerized-adaptive00386nas a2200097 4500008004100000245006300041210006000104260002100164100001700185856008600202 1986 eng d00aA computer-adaptive placement test for college mathematics0 acomputeradaptive placement test for college mathematics aSan Francisco CA1 aShermis, M D uhttp://www.iacat.org/content/computer-adaptive-placement-test-college-mathematics00404nas a2200109 4500008004100000245005900041210005800100260002100158100001700179700001300196856008500209 1986 eng d00aComputerized adaptive achievement testing: A prototype0 aComputerized adaptive achievement testing A prototype aSan Francisco CA1 aMcBride, J R1 aMoe, K C uhttp://www.iacat.org/content/computerized-adaptive-achievement-testing-prototype00405nas a2200097 4500008004100000245007100041210006900112260001600181100001700197856009300214 1986 eng d00aA computerized adaptive edition of the Differential Aptitude Tests0 acomputerized adaptive edition of the Differential Aptitude Tests aBoulder, CO1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-edition-differential-aptitude-tests-000405nas a2200097 4500008004100000245007100041210006900112260001800181100001700199856009100216 1986 eng d00aA computerized adaptive edition of the Differential Aptitude Tests0 acomputerized adaptive edition of the Differential Aptitude Tests aWashington DC1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-edition-differential-aptitude-tests00518nas a2200097 4500008004100000245005100041210005000092260018200142100001900324856007700343 1986 eng d00aComputerized adaptive testing: A pilot project0 aComputerized adaptive testing A pilot project aW. C. Ryan (ed.), Proceedings: NECC 86, National Educational Computing Conference (pp.172-176). Eugene OR: University of Oregon, International Council on Computers in Education.1 aKingsbury, G G uhttp://www.iacat.org/content/computerized-adaptive-testing-pilot-project00319nas a2200109 4500008004100000245003600041210003600077300001000113490000600123100001500129856006500144 1986 eng d00aComputerized testing technology0 aComputerized testing technology a71-780 v41 aWolfe, J H uhttp://www.iacat.org/content/computerized-testing-technology00361nas a2200109 4500008004100000245004700041210004700088260001200135100001400147700001400161856007600175 1985 eng d00aComputerized adaptive attitude measurement0 aComputerized adaptive attitude measurement aChicago1 aKoch, W R1 aDodd, B G uhttp://www.iacat.org/content/computerized-adaptive-attitude-measurement00316nas a2200109 4500008004100000245003400041210003400075300001000109490000700119100001700126856006300143 1985 eng d00aComputerized adaptive testing0 aComputerized adaptive testing a25-280 v431 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing00379nas a2200097 4500008004100000245006200041210006100103260001600164100001700180856008400197 1985 eng d00aComputerized adaptive testing: An overview and an example0 aComputerized adaptive testing An overview and an example aBoulder, CO1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing-overview-and-example00479nas a2200121 4500008004100000245008600041210006900127300001000196490000700206100001700213700001600230856011100246 1985 Eng d00aControlling item exposure conditional on ability in computerized adaptive testing0 aControlling item exposure conditional on ability in computerized a57-750 v231 aSympson, J B1 aHetter, R D uhttp://www.iacat.org/content/controlling-item-exposure-conditional-ability-computerized-adaptive-testing-000572nas a2200109 4500008004100000245006900041210006800110260015600178100001700334700001600351856009500367 1985 eng d00aControlling item-exposure rates in computerized adaptive testing0 aControlling itemexposure rates in computerized adaptive testing aProceedings of the 27th annual meeting of the Military Testing Association (pp. 973-977). San Diego CA: Navy Personnel Research and Development Center.1 aSympson, J B1 aHetter, R D uhttp://www.iacat.org/content/controlling-item-exposure-rates-computerized-adaptive-testing01634nas a2200145 4500008004100000245008600041210006900127300001200196490000700208520111300215100001701328700001601345700001501361856011201376 1985 eng d00aCurrent developments and future directions in computerized personality assessment0 aCurrent developments and future directions in computerized perso a803-8150 v533 aAlthough computer applications in personality assessment have burgeoned rapidly in recent years, the majority of these uses capitalize on the computer's speed, accuracy, and memory capacity rather than its potential for the development of new, flexible assessment strategies. A review of current examples of computer usage in personality assessment reveals wide acceptance of automated clerical tasks such as test scoring and even test administration. The computer is also assuming tasks previously reserved for expert clinicians, such as writing narrative interpretive reports from test results. All of these functions represent automation of established assessment devices and interpretive strategies. The possibility also exists of harnessing some of the computer's unique adaptive capabilities to alter standard devices and even develop new ones. Three proposed strategies for developing computerized adaptive personality tests are described, with the conclusion that the computer's potential in this area justifies a call for further research efforts., (C) 1985 by the American Psychological Association1 aButcher, J N1 aKeller, L S1 aBacon, S F uhttp://www.iacat.org/content/current-developments-and-future-directions-computerized-personality-assessment00486nam a2200097 4500008004100000245009300041210006900134260006300203100001400266856010800280 1984 eng d00aA comparison of the maximum likelihood strategy and stradaptive test on a micro-computer0 acomparison of the maximum likelihood strategy and stradaptive te aUnpublished M.S. thesis, University of Wisconsin, Madison.1 aBill, B C uhttp://www.iacat.org/content/comparison-maximum-likelihood-strategy-and-stradaptive-test-micro-computer00397nas a2200109 4500008004100000245006500041210006500106300000600171490000600177100001700183856008700200 1984 eng d00aComputerized adaptive testing in the Maryland Public Schools0 aComputerized adaptive testing in the Maryland Public Schools a10 v11 aStevenson, J uhttp://www.iacat.org/content/computerized-adaptive-testing-maryland-public-schools00352nas a2200121 4500008003900000245003600039210003600075300001200111490000700123100001800130700001700148856006500165 1984 d00aComputerized diagnostic testing0 aComputerized diagnostic testing a391-3970 v211 aMCArthur, D L1 aChoppin, B H uhttp://www.iacat.org/content/computerized-diagnostic-testing00535nas a2200121 4500008004100000245009900041210006900140260003900209300001200248100001900260700001400279856012000293 1983 eng d00aA comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure.0 acomparison of IRTbased adaptive mastery testing and a sequential aNew York, NY. USAbAcademic Press. a258-2831 aKingsbury, G G1 aWeiss, DJ uhttp://www.iacat.org/content/comparison-irt-based-adaptive-mastery-testing-and-sequential-mastery-testing-procedure00621nas a2200109 4500008004100000245009800041210006900139260014800208100001900356700001400375856012200389 1983 eng d00aA comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure0 acomparison of IRTbased adaptive mastery testing and a sequential aD. J. Weiss (Ed.), New horizons in testing: Latent trait test theory and computerized adaptive testing (pp. 257-283). New York: Academic Press.1 aKingsbury, G G1 aWeiss, DJ uhttp://www.iacat.org/content/comparison-irt-based-adaptive-mastery-testing-and-sequential-mastery-testing-procedure-000613nas a2200109 4500008004100000245009800041210006900139260013900208100002000347700001400367856012200381 1983 eng d00aA comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure0 acomparison of IRTbased adaptive mastery testing and a sequential aD. J. Weiss (Ed.), New horizons in testing: Latent trait theory and computerized adaptive testing (pp. 1-8). New York: Academic Press.1 aKingsbury, G.G.1 aWeiss, DJ uhttp://www.iacat.org/content/comparison-irt-based-adaptive-mastery-testing-and-sequential-mastery-testing-procedure-100412nas a2200097 4500008004100000245005200041210005200093260007500145100001600220856007800236 1982 eng d00aComparison of live and simulated adaptive tests0 aComparison of live and simulated adaptive tests aBrooks Air Force Base, TexasbAir Force Systems CommandcDecember 19821 aHUnter, D R uhttp://www.iacat.org/content/comparison-live-and-simulated-adaptive-tests00509nas a2200097 4500008004100000245008900041210006900130260008000199100001700279856011500296 1982 eng d00aComputerized adaptive testing project: Objectives and requirements (Tech Note 82-22)0 aComputerized adaptive testing project Objectives and requirement aSan Diego CA: Navy Personnel Research and Development Center. (AD A118 447)1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing-project-objectives-and-requirements-tech-note-82-2200533nas a2200097 4500008004100000245010400041210006900145260008000214100001500294856012600309 1982 eng d00aComputerized adaptive testing system design: Preliminary design considerations (Tech. Report 82-52)0 aComputerized adaptive testing system design Preliminary design c aSan Diego CA: Navy Personnel Research and Development Center. (AD A118 495)1 aCroll, P R uhttp://www.iacat.org/content/computerized-adaptive-testing-system-design-preliminary-design-considerations-tech-report-8200631nas a2200097 4500008004100000245007700041210006900118260022400187100001700411856010500428 1982 eng d00aComputerized Adaptive Testing system development and project management.0 aComputerized Adaptive Testing system development and project man aMinutes of the ASVAB (Armed Services Vocational Aptitude Battery) Steering Committee. Washington, DC: Office of the Assistant Secretary of Defense (Manpower, Reserve Affairs and Logistics), Accession Policy Directorate.1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing-system-development-and-project-management00593nas a2200109 4500008004100000245006500041210006100106260019200167100001700359700001700376856009000393 1982 eng d00aThe computerized adaptive testing system development project0 acomputerized adaptive testing system development project aD. J. Weiss (Ed.), Proceedings of the 1982 Item Response Theory and Computerized Adaptive Testing Conference (pp. 342-349). Minneapolis: University of Minnesota, Department of Psychology.1 aMcBride, J R1 aSympson, J B uhttp://www.iacat.org/content/computerized-adaptive-testing-system-development-project00641nas a2200097 4500008004100000245008800041210006900129260022100198100001700419856010700436 1982 eng d00aComputerized testing in the German Federal Armed Forces (FAF): Empirical approaches0 aComputerized testing in the German Federal Armed Forces FAF Empi aD. J. Weiss (Ed.), Proceedings of the 1982 Item Response Theory and Computerized Adaptive Testing Conference (pp.353-359). Minneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aWildgrube, W uhttp://www.iacat.org/content/computerized-testing-german-federal-armed-forces-faf-empirical-approaches00552nas a2200109 4500008004100000245008300041210006900124260011600193100001300309700001700322856010300339 1981 eng d00aA comparison of a Bayesian and a maximum likelihood tailored testing procedure0 acomparison of a Bayesian and a maximum likelihood tailored testi aColumbia MObUniversity of Missouri, Department of Educational Psychology, Tailored Testing Research Laboratory1 aMcKinley1 aReckase, M D uhttp://www.iacat.org/content/comparison-bayesian-and-maximum-likelihood-tailored-testing-procedure00483nas a2200109 4500008004100000245009800041210006900139260001900208100001500227700001700242856011400259 1981 eng d00aA comparison of a maximum likelihood and a Bayesian estimation procedure for tailored testing0 acomparison of a maximum likelihood and a Bayesian estimation pro aLos Angeles CA1 aRosso, M A1 aReckase, M D uhttp://www.iacat.org/content/comparison-maximum-likelihood-and-bayesian-estimation-procedure-tailored-testing00487nas a2200121 4500008004100000245006900041210006600110260005000176100002000226700001500246700001500261856008900276 1981 eng d00aA comparison of two methods of interactive testing Final report.0 acomparison of two methods of interactive testing Final report aNational Institute of Education Grant 79-10451 aNicewander, W A1 aChang, H S1 aDoody, E N uhttp://www.iacat.org/content/comparison-two-methods-interactive-testing-final-report00538nas a2200109 4500008004100000245011800041210006900159260005000228490001000278100001400288856012600302 1980 eng d00aA comparative evaluation of two Bayesian adaptive ability estimation procedures with a conventional test strategy0 acomparative evaluation of two Bayesian adaptive ability estimati aWashington DCbCatholic University of America0 vPh.D.1 aGorman, S uhttp://www.iacat.org/content/comparative-evaluation-two-bayesian-adaptive-ability-estimation-procedures-conventional-test00488nam a2200097 4500008004100000245008400041210006900125260007400194100001400268856010800282 1980 eng d00aA comparative evaluation of two Bayesian adaptive ability estimation procedures0 acomparative evaluation of two Bayesian adaptive ability estimati aUnpublished doctoral dissertation, the Catholic University of America1 aGorman, S uhttp://www.iacat.org/content/comparative-evaluation-two-bayesian-adaptive-ability-estimation-procedures00608nas a2200109 4500008004100000245012300041210006900164260011400233100001900347700001400366856011800380 1980 eng d00aA comparison of adaptive, sequential, and conventional testing strategies for mastery decisions (Research Report 80-4)0 acomparison of adaptive sequential and conventional testing strat aMinneapolis, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory1 aKingsbury, G G1 aWeiss, DJ uhttp://www.iacat.org/content/comparison-adaptive-sequential-and-conventional-testing-strategies-mastery-decisions00706nas a2200109 4500008004100000245009600041210006900137260024100206100001900447700001400466856011600480 1980 eng d00aA comparison of ICC-based adaptive mastery testing and the Waldian probability ratio method0 acomparison of ICCbased adaptive mastery testing and the Waldian  aD. J. Weiss (Ed.). Proceedings of the 1979 Computerized Adaptive Testing Conference (pp. 120-139). Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory1 aKingsbury, G G1 aWeiss, DJ uhttp://www.iacat.org/content/comparison-icc-based-adaptive-mastery-testing-and-waldian-probability-ratio-method00653nas a2200097 4500008004100000245010500041210006900146260021000215100001400425856011600439 1980 eng d00aA comparison of the accuracy of Bayesian adaptive and static tests using a correction for regression0 acomparison of the accuracy of Bayesian adaptive and static tests aD. J. Weiss (Ed.), Proceedings of the 1979 Computerized Adaptive Testing Conference (pp. 35-50). Minneapolis MN: University of Minnesota, Department of Psychology, Computerized Adaptive Testing Laboratory.1 aGorman, S uhttp://www.iacat.org/content/comparison-accuracy-bayesian-adaptive-and-static-tests-using-correction-regression00436nas a2200109 4500008004100000245008200041210006900123300001200192490000700204100001400211856010100225 1980 eng d00aComputer applications in audiology and rehabilitation of the hearing impaired0 aComputer applications in audiology and rehabilitation of the hea a471-4810 v131 aLevitt, H uhttp://www.iacat.org/content/computer-applications-audiology-and-rehabilitation-hearing-impaired00373nas a2200121 4500008004100000245004500041210004500086300001200131490000700143100001300150700001700163856007100180 1980 eng d00aComputer applications to ability testing0 aComputer applications to ability testing a193-2030 v131 aMcKinley1 aReckase, M D uhttp://www.iacat.org/content/computer-applications-ability-testing00620nas a2200097 4500008004100000245011300041210006900154260015700223100001700380856012500397 1980 eng d00aComputerized instructional adaptive testing model: Formulation and validation (AFHRL-TR-79-33, Final Report)0 aComputerized instructional adaptive testing model Formulation an aBrooks Air Force Base TX: Air Force Human Resources Laboratory", Also Catalog of Selected Documents in Psychology, February 1981, 11, 20 (Ms. No, 2217) 1 aKalisch, S J uhttp://www.iacat.org/content/computerized-instructional-adaptive-testing-model-formulation-and-validation-afhrl-tr-79-3300632nas a2200097 4500008004100000245006600041210006400107260026000171100001700431856008600448 1980 eng d00aComputerized testing in the German Federal Armed Forces (FAF)0 aComputerized testing in the German Federal Armed Forces FAF aD. J. Weiss (Ed.), Proceedings of the 1979 Item Response Theory and Computerized Adaptive Testing Conference (pp. 68-77). Minneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laborator1 aWildgrube, W uhttp://www.iacat.org/content/computerized-testing-german-federal-armed-forces-faf00585nas a2200109 4500008004100000245008500041210006900126260013900195100001800334700001400352856010900366 1980 eng d00aCriterion-related validity of adaptive testing strategies (Research Report 80-3)0 aCriterionrelated validity of adaptive testing strategies Researc aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory1 aThompson, J G1 aWeiss, DJ uhttp://www.iacat.org/content/criterion-related-validity-adaptive-testing-strategies-research-report-80-300506nas a2200121 4500008004100000245010500041210006900146300000900215490000700224100001600231700001500247856012200262 1979 eng d00aA comparison of a standard and a computerized adaptive paradigm in Bekesy fixed-frequency audiometry0 acomparison of a standard and a computerized adaptive paradigm in a1-220 v191 aHarris, J D1 aSmith, P F uhttp://www.iacat.org/content/comparison-standard-and-computerized-adaptive-paradigm-bekesy-fixed-frequency-audiometry00495nas a2200097 4500008004100000245008300041210006900124260008900193100001700282856009800299 1979 eng d00aComputerized adaptive testing: The state of the art (ARI Technical Report 423)0 aComputerized adaptive testing The state of the art ARI Technical aAlexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing-state-art-ari-technical-report-42300449nas a2200097 4500008004100000245009200041210006900133260001900202100001700221856011300238 1979 eng d00aCriterion-related validity of conventional and adaptive tests in a military environment0 aCriterionrelated validity of conventional and adaptive tests in  aMinneapolis MN1 aSympson, J B uhttp://www.iacat.org/content/criterion-related-validity-conventional-and-adaptive-tests-military-environment00512nas a2200133 4500008004100000245009100041210006900132300001200201490000700213100001600220700001600236700001600252856011000268 1978 eng d00aCombining auditory and visual stimuli in the adaptive testing of speech discrimination0 aCombining auditory and visual stimuli in the adaptive testing of a115-1220 v431 aSteele, J A1 aBinnie, C A1 aCooper, W A uhttp://www.iacat.org/content/combining-auditory-and-visual-stimuli-adaptive-testing-speech-discrimination00510nam a2200097 4500008004100000245009200041210006900133260008100202100001800283856011100301 1978 eng d00aA comparison of Bayesian and maximum likelihood scoring in a simulated stradaptive test0 acomparison of Bayesian and maximum likelihood scoring in a simul aUnpublished Masters thesis, St. Mary’s University of Texas, San Antonio TX1 aMaurelli, V A uhttp://www.iacat.org/content/comparison-bayesian-and-maximum-likelihood-scoring-simulated-stradaptive-test00568nas a2200109 4500008004100000245010400041210006900145260009700214100001400311700001400325856011900339 1978 eng d00aA comparison of the fairness of adaptive and conventional testing strategies (Research Report 78-1)0 acomparison of the fairness of adaptive and conventional testing  aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aPine, S M1 aWeiss, DJ uhttp://www.iacat.org/content/comparison-fairness-adaptive-and-conventional-testing-strategies-research-report-78-100485nas a2200133 4500008004100000245007400041210006900115300001200184490000700196100001700203700001400220700001500234856010200249 1978 eng d00aComputer-assisted tailored testing: Examinee reactions and evaluation0 aComputerassisted tailored testing Examinee reactions and evaluat a265-2730 v381 aSchmidt, F L1 aUrry, V W1 aGugel, J F uhttp://www.iacat.org/content/computer-assisted-tailored-testing-examinee-reactions-and-evaluation00403nas a2200109 4500008004100000245006100041210006000102300001200162490001000174100002000184856008900204 1978 eng d00aComputerized adaptive testing: Principles and directions0 aComputerized adaptive testing Principles and directions a319-3290 v2 (4)1 aKreitzberg, C B uhttp://www.iacat.org/content/computerized-adaptive-testing-principles-and-directions00453nas a2200133 4500008004100000245006100041210006000102300001200162490000600174100002000180700001300200700001500213856009100228 1978 eng d00aComputerized adaptive testing: Principles and directions0 aComputerized adaptive testing Principles and directions a319-3290 v21 aKreitzberg, C B1 aStocking1 aSwanson, L uhttp://www.iacat.org/content/computerized-adaptive-testing-principles-and-directions-000575nas a2200109 4500008003900000245008200039210006900121260014200190100001500332700001400347856010400361 1978 d00aA construct validation of adaptive achievement testing (Research Report 78-4)0 aconstruct validation of adaptive achievement testing Research Re aMinneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory1 aBejar, I I1 aWeiss, DJ uhttp://www.iacat.org/content/construct-validation-adaptive-achievement-testing-research-report-78-400560nas a2200121 4500008004100000245009900041210006900140260007200209100001500281700001400296700001900310856010900329 1977 eng d00aCalibration of an item pool for the adaptive measurement of achievement (Research Report 77-5)0 aCalibration of an item pool for the adaptive measurement of achi aMinneapolis: Department of Psychology, Psychometric Methods Program1 aBejar, I I1 aWeiss, DJ1 aKingsbury, G G uhttp://www.iacat.org/content/calibration-item-pool-adaptive-measurement-achievement-research-report-77-500560nas a2200097 4500008004100000245006600041210006400107260018600171100001500357856009000372 1977 eng d00aA comparison of conventional and adaptive achievement testing0 acomparison of conventional and adaptive achievement testing aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aBejar, I I uhttp://www.iacat.org/content/comparison-conventional-and-adaptive-achievement-testing00525nam a2200097 4500008004100000245011600041210006900157260006500226100001400291856012200305 1977 eng d00aA comparison of the classification of students by two methods of administration of a mathematics placement test0 acomparison of the classification of students by two methods of a aUnpublished doctoral dissertation, Syracuse University, 19771 aBrooks, S uhttp://www.iacat.org/content/comparison-classification-students-two-methods-administration-mathematics-placement-test00589nam a2200121 4500008004100000024005800041050003600099245007700135210006900212260007300281100001900354856009400373 1977 eng d aDissertation abstracts International, 1978, 38, 4993B aUniversity Microfims No.78-450300aA computer adaptive approach to the measurement of personality variables0 acomputer adaptive approach to the measurement of personality var aUnpublished doctoral dissertation, University of Maryland, Baltimore1 aSapinkopf, R C uhttp://www.iacat.org/content/computer-adaptive-approach-measurement-personality-variables01595nas a2200133 4500008004100000245010600041210006900147300001200216490000700228520106500235100001701300700001801317856012601335 1977 eng d00aA computer simulation study of tailored testing strategies for objective-based instructional programs0 acomputer simulation study of tailored testing strategies for obj a139-1580 v373 aOne possible way of reducing the amount of time spent testing in . objective-based instructional programs would involve the implementation of a tailored testing strategy. Our purpose was to provide some additional data on the effectiveness of various tailored testing strategies for different testing situations. The three factors of a tailored testing strategy under study with various hypothetical distributions of abilities across two learning hierarchies were test length, mastery cutting score, and starting point. Overall, our simulation results indicate that it is possible to obtain a reduction of more than 50% in testing time without any loss in decision-making accuracy, when compared to a conventional testing procedure, by implementing a tailored testing strategy. In addition, our study of starting points revealed that it was generally best to begin testing in the middle of the learning hierarchy. Finally we observed a 40% reduction in errors of classification as the number of items for testing each objective was increased from one to five.1 aSpineti, J P1 aHambleton, RK uhttp://www.iacat.org/content/computer-simulation-study-tailored-testing-strategies-objective-based-instructional-programs00572nas a2200121 4500008004100000245008700041210006900128260009400197100001700291700001400308700001500322856011300337 1977 eng d00aComputer-assisted tailored testing: Examinee reactions and evaluation (PB-276 748)0 aComputerassisted tailored testing Examinee reactions and evaluat aWashington DC: U. S. Civil Service Commission, Personnel Research and Development Center.1 aSchmidt, F L1 aUrry, V W1 aGugel, J F uhttp://www.iacat.org/content/computer-assisted-tailored-testing-examinee-reactions-and-evaluation-pb-276-74800590nas a2200097 4500008003900000245007500039210006900114260018600183100001900369856010400388 1977 d00aComputerized Adaptive Testing and Personnel Accessioning System Design0 aComputerized Adaptive Testing and Personnel Accessioning System  aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aUnderwood, M A uhttp://www.iacat.org/content/computerized-adaptive-testing-and-personnel-accessioning-system-design00525nas a2200097 4500008004100000245005900041210005900100260016300159100001700322856008800339 1977 eng d00aComputerized Adaptive Testing research and development0 aComputerized Adaptive Testing research and development aH. Taylor, Proceedings of the Second Training and Personnel Technology Conference. Washington, DC: Office of the Director of Defense Research and Engineering.1 aMcBride, J R uhttp://www.iacat.org/content/computerized-adaptive-testing-research-and-development00541nas a2200097 4500008003900000245006100039210006100100260018500161100001400346856008300360 1977 d00aComputerized Adaptive Testing with a Military Population0 aComputerized Adaptive Testing with a Military Population aD. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolls MN: University of Minnesota, Department of Psychology, Psychometric Methods Program1 aGorman, S uhttp://www.iacat.org/content/computerized-adaptive-testing-military-population00576nas a2200109 4500008004100000245007700041210006900118260015300187100001800340700001400358856009400372 1976 eng d00aComputer-assisted testing: An orderly transition from theory to practice0 aComputerassisted testing An orderly transition from theory to pr aC. K. Clark (Ed.), Proceedings of the First Conference on Computerized Adaptive Testing (pp. 95-96). Washington DC: U.S. Government Printing Office.1 aMcKillip, R H1 aUrry, V W uhttp://www.iacat.org/content/computer-assisted-testing-orderly-transition-theory-practice00503nas a2200097 4500008004100000245008700041210006900128260009300197100001400290856010100304 1976 eng d00aComputer-assisted testing with live examinees: A rendezvous with reality (TN 75-3)0 aComputerassisted testing with live examinees A rendezvous with r aWashington DC: U. S. Civil Service Commission, Personnel Research and Development Center1 aUrry, V W uhttp://www.iacat.org/content/computer-assisted-testing-live-examinees-rendezvous-reality-tn-75-300445nas a2200109 4500008004100000245008400041210006900125300001200194490000700206100001500213856010700228 1975 eng d00aComplete orders from incomplete data: Interactive ordering and tailored testing0 aComplete orders from incomplete data Interactive ordering and ta a259-3020 v821 aCliff, N A uhttp://www.iacat.org/content/complete-orders-incomplete-data-interactive-ordering-and-tailored-testing00348nas a2200109 4500008004100000245004600041210004600087300000900133490000700142100001400149856007500163 1975 eng d00aComputerized adaptive ability measurement0 aComputerized adaptive ability measurement a1-180 v281 aWeiss, DJ uhttp://www.iacat.org/content/computerized-adaptive-ability-measurement00528nas a2200097 4500008004100000245009100041210006900132260009800201100001400299856011700313 1975 eng d00aComputerized adaptive trait measurement: Problems and prospects (Research Report 75-5)0 aComputerized adaptive trait measurement Problems and prospects R aMinneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program.1 aWeiss, DJ uhttp://www.iacat.org/content/computerized-adaptive-trait-measurement-problems-and-prospects-research-report-75-500507nam a2200097 4500008004100000245010500041210006900146260006400215100001700279856011300296 1974 eng d00aThe comparison of two tailored testing models and the effects of the models variables on actual loss0 acomparison of two tailored testing models and the effects of the aUnpublished doctoral dissertation, Florida State University1 aKalisch, S J uhttp://www.iacat.org/content/comparison-two-tailored-testing-models-and-effects-models-variables-actual-loss00558nas a2200109 4500008004100000245008700041210006900128260011200197100001700309700001400326856010800340 1974 eng d00aA computer software system for adaptive ability measurement (Research Report 74-1)0 acomputer software system for adaptive ability measurement Resear aMinneapolis MN: University of Minnesota, Department of Psychology, Computerized Adaptive Testing Laboratory1 aDe Witt, J J1 aWeiss, DJ uhttp://www.iacat.org/content/computer-software-system-adaptive-ability-measurement-research-report-74-100552nas a2200097 4500008004100000245011200041210006900153260009300222100001400315856012500329 1974 eng d00aComputer-assisted testing: The calibration and evaluation of the verbal ability bank (Technical Study 74-3)0 aComputerassisted testing The calibration and evaluation of the v aWashington DC: U. S. Civil Service Commission, Personnel Research and Development Center1 aUrry, V W uhttp://www.iacat.org/content/computer-assisted-testing-calibration-and-evaluation-verbal-ability-bank-technical-study-7400740nas a2200145 4500008004100000245018400041210006900225260010800294100001600402700001700418700001500435700001100450700001200461856012100473 1974 eng d00aComputer-based adaptive testing models for the Air Force technical training environment: Phase I: Development of a computerized measurement system for Air Force technical Training0 aComputerbased adaptive testing models for the Air Force technica aJSAS Catalogue of Selected Documents in Psychology, 5, 1-86 (MS No. 882). AFHRL Technical Report 74-48.1 aHansen, D N1 aJohnson, B F1 aFagan, R L1 aTan, P1 aDick, W uhttp://www.iacat.org/content/computer-based-adaptive-testing-models-air-force-technical-training-environment-phase-i00441nas a2200109 4500008004100000245004100041210004000082260011100122100001300233700001500246856007000261 1973 eng d00aComputer-based psychological testing0 aComputerbased psychological testing aA. Elithorn and D. Jones (Eds.), Artificial and human thinking (pp. 83-93). San Francisco CA: Jossey-Bass.1 aJones, D1 aWeinman, J uhttp://www.iacat.org/content/computer-based-psychological-testing00452nas a2200121 4500008004100000245007200041210006900113300001200182490000700194100001600201700001700217856009600234 1971 eng d00aA comparison of computer-simulated conventional and branching tests0 acomparison of computersimulated conventional and branching tests a125-1360 v311 aWaters, C J1 aBayroff, A G uhttp://www.iacat.org/content/comparison-computer-simulated-conventional-and-branching-tests00521nas a2200097 4500008003900000245011200039210006900151260006400220100001400284856012500298 1971 d00aA comparison of four methods of selecting items for computer-assisted testing (Technical Bulletin STB 72-5)0 acomparison of four methods of selecting items for computerassist aSan Diego: Naval Personnel and Training Research Laboratory1 aBryson, R uhttp://www.iacat.org/content/comparison-four-methods-selecting-items-computer-assisted-testing-technical-bulletin-stb-7200411nas a2200097 4500008004100000245005600041210005600097260005900153100001800212856008300230 1971 eng d00aComputer assistance for individualizing measurement0 aComputer assistance for individualizing measurement aPittsburgh PA: University of Pittsburgh R and D Center1 aFerguson, R L uhttp://www.iacat.org/content/computer-assistance-individualizing-measurement-100378nam a2200097 4500008004100000245004500041210004500086260006100131100001400192856007400206 1971 eng d00aComputerized adaptive sequential testing0 aComputerized adaptive sequential testing aUnpublished doctoral dissertation, University of Chicago1 aWood, R L uhttp://www.iacat.org/content/computerized-adaptive-sequential-testing00399nas a2200097 4500008004100000245003300041210003300074260012100107100001400228856005900242 1970 eng d00aComments on tailored testing0 aComments on tailored testing aW. H. Holtzman, (Ed.), Computer-assisted instruction, testing, and guidance (pp. 184-197). New York: Harper and Row.1 aGreen, BF uhttp://www.iacat.org/content/comments-tailored-testing00384nas a2200109 4500008004100000245005600041210005600097300000700153490001500160100001800175856008100193 1970 eng d00aComputer assistance for individualizing measurement0 aComputer assistance for individualizing measurement a190 vMarch 19701 aFerguson, R L uhttp://www.iacat.org/content/computer-assistance-individualizing-measurement00438nas a2200097 4500008004100000245005600041210005600097260008600153100001800239856008300257 1970 eng d00aComputer assistance for individualizing measurement0 aComputer assistance for individualizing measurement aPittsburgh PA: University of Pittsburgh, Learning Research and Development Center1 aFerguson, R L uhttp://www.iacat.org/content/computer-assistance-individualizing-measurement-000516nas a2200097 4500008004100000245007700041210006900118260010900187100001800296856010400314 1969 eng d00aComputer-assisted criterion-referenced measurement (Working Paper No 49)0 aComputerassisted criterionreferenced measurement Working Paper N aPittsburgh PA: University of Pittsburgh, Learning and Research Development Center. (ERIC No. ED 037 089)1 aFerguson, R L uhttp://www.iacat.org/content/computer-assisted-criterion-referenced-measurement-working-paper-no-4900386nam a2200121 4500008004100000245003700041210003300078260004600111100001600157700001400173700001400187856006300201 1968 eng d00aComputer-assisted testing (Eds.)0 aComputerassisted testing Eds aPrinceton NJ: Educational Testing Service1 aHarman, H H1 aHelm, C E1 aLoye, D E uhttp://www.iacat.org/content/computer-assisted-testing-eds00541nas a2200121 4500008004100000245008400041210006900125260006900194100001700263700001600280700001800296856010500314 1960 eng d00aConstruction of an experimental sequential item test (Research Memorandum 60-1)0 aConstruction of an experimental sequential item test Research Me aWashington DC: Personnel Research Branch, Department of the Army1 aBayroff, A G1 aThomas, J J1 aAnderson, A A uhttp://www.iacat.org/content/construction-experimental-sequential-item-test-research-memorandum-60-100445nas a2200109 4500008004100000245008900041210006900130300001100199490000700210100001400217856010400231 1947 eng d00aA clinical study of consecutive and adaptive testing with the revised Stanford-Binet0 aclinical study of consecutive and adaptive testing with the revi a93-1030 v111 aHutt, M L uhttp://www.iacat.org/content/clinical-study-consecutive-and-adaptive-testing-revised-stanford-binet