02064nas a2200157 4500008003900000245009100039210006900130300001200199490000700211520157600218100001501794700001901809700001401828700001901842856004501861 2020 d00aStratified Item Selection Methods in Cognitive Diagnosis Computerized Adaptive Testing0 aStratified Item Selection Methods in Cognitive Diagnosis Compute a346-3610 v443 aCognitive diagnostic computerized adaptive testing (CD-CAT) aims to obtain more useful diagnostic information by taking advantages of computerized adaptive testing (CAT). Cognitive diagnosis models (CDMs) have been developed to classify examinees into the correct proficiency classes so as to get more efficient remediation, whereas CAT tailors optimal items to the examinee’s mastery profile. The item selection method is the key factor of the CD-CAT procedure. In recent years, a large number of parametric/nonparametric item selection methods have been proposed. In this article, the authors proposed a series of stratified item selection methods in CD-CAT, which are combined with posterior-weighted Kullback–Leibler (PWKL), nonparametric item selection (NPS), and weighted nonparametric item selection (WNPS) methods, and named S-PWKL, S-NPS, and S-WNPS, respectively. Two different types of stratification indices were used: original versus novel. The performances of the proposed item selection methods were evaluated via simulation studies and compared with the PWKL, NPS, and WNPS methods without stratification. Manipulated conditions included calibration sample size, item quality, number of attributes, number of strata, and data generation models. Results indicated that the S-WNPS and S-NPS methods performed similarly, and both outperformed the S-PWKL method. And item selection methods with novel stratification indices performed slightly better than the ones with original stratification indices, and those without stratification performed the worst.1 aYang, Jing1 aChang, Hua-Hua1 aTao, Jian1 aShi, Ningzhong uhttps://doi.org/10.1177/014662161989378300949nas a2200169 4500008003900000022001400039245011500053210006900168260000800237300001600245490000700261520040300268100001700671700002400688700002100712856004600733 2018 d a1573-264900aSome recommendations for developing multidimensional computerized adaptive tests for patient-reported outcomes0 aSome recommendations for developing multidimensional computerize cApr a1055–10630 v273 aMultidimensional item response theory and computerized adaptive testing (CAT) are increasingly used in mental health, quality of life (QoL), and patient-reported outcome measurement. Although multidimensional assessment techniques hold promises, they are more challenging in their application than unidimensional ones. The authors comment on minimal standards when developing multidimensional CATs.1 aSmits, Niels1 aPaap, Muirne, C. S.1 aBöhnke, Jan, R. uhttps://doi.org/10.1007/s11136-018-1821-803598nas a2200169 4500008004100000245004300041210004100084260005500125520306600180653000803246653002303254653002303277100001703300700002003317700002003337856007103357 2017 eng d00aScripted On-the-fly Multistage Testing0 aScripted Onthefly Multistage Testing aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

On-the-fly multistage testing (OMST) was introduced recently as a promising alternative to preassembled MST. A decidedly appealing feature of both is the reviewability of items within the current stage. However, the fundamental difference is that, instead of routing to a preassembled module, OMST adaptively assembles a module at each stage according to an interim ability estimate. This produces more individualized forms with finer measurement precision, but imposing nonstatistical constraints and controlling item exposure become more cumbersome. One recommendation is to use the maximum priority index followed by a remediation step to satisfy content constraints, and the Sympson-Hetter method with a stratified item bank for exposure control.

However, these methods can be computationally expensive, thereby impeding practical implementation. Therefore, this study investigated the script method as a simpler solution to the challenge of strict content balancing and effective item exposure control in OMST. The script method was originally devised as an item selection algorithm for CAT and generally proceeds as follows: For a test with m items, there are m slots to be filled, and an item is selected according to pre-defined rules for each slot. For the first slot, randomly select an item from a designated content area (collection). For each subsequent slot, 1) Discard any enemies of items already administered in previous slots; 2) Draw a designated number of candidate items (selection length) from the designated collection according to the current ability estimate; 3) Randomly select one item from the set of candidates. There are two distinct features of the script method. First, a predetermined sequence of collections guarantees meeting content specifications. The specific ordering may be determined either randomly or deliberately by content experts. Second, steps 2 and 3 depict a method of exposure control, in which selection length balances item usage at the possible expense of ability estimation accuracy. The adaptation of the script method to OMST is straightforward. For the first module, randomly select each item from a designated collection. For each subsequent module, the process is the same as in scripted CAT (SCAT) except the same ability estimate is used for the selection of all items within the module. A series of simulations was conducted to evaluate the performance of scripted OMST (SOMST, with 3 or 4 evenly divided stages) relative to SCAT under various item exposure restrictions. In all conditions, reliability was maximized by programming an optimization algorithm that searches for the smallest possible selection length for each slot within the constraints. Preliminary results indicated that SOMST is certainly a capable design with performance comparable to that of SCAT. The encouraging findings and ease of implementation highly motivate the prospect of operational use for large-scale assessments.

Presentation Video

10aCAT10amultistage testing10aOn-the-fly testing1 aChoe, Edison1 aWilliams, Bruce1 aLee, Sung-Hyuck uhttps://drive.google.com/open?id=1wKuAstITLXo6BM4APf2mPsth1BymNl-y01840nas a2200145 4500008004100000245010900041210006900150260005500219520128100274100001501555700001401570700001901584700002001603856007101623 2017 eng d00aA Simulation Study to Compare Classification Method in Cognitive Diagnosis Computerized Adaptive Testing0 aSimulation Study to Compare Classification Method in Cognitive D aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models that can be viewed as restricted latent class models have been developed to classify the examinees into the correct profile of skills that have been mastered and those that have not so as to get more efficient remediation. Chiu & Douglas (2013) introduces a nonparametric procedure that only requires specification of Q-matrix to classify by proximity to ideal response pattern. In this article, we compare nonparametric procedure with common profile estimation method like maximum a posterior (MAP) in CD-CAT. Simulation studies consider a variety of Q-matrix structure, the number of attributes, ways to generate attribute profiles, and item quality. Results indicate that nonparametric procedure consistently gets the higher pattern and attribute recovery rate in nearly all conditions.

References

Chiu, C.-Y., & Douglas, J. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification, 30, 225-250. doi: 10.1007/s00357-013-9132-9

Session Video

1 aYang, Jing1 aTao, Jian1 aChang, Hua-Hua1 aShi, Ning-Zhong uhttps://drive.google.com/open?id=1jCL3fPZLgzIdwvEk20D-FliZ15OTUtpr01723nas a2200145 4500008003900000245015500039210006900194300001000263490000700273520118100280100001701461700002701478700002001505856005201525 2016 d00aStochastic Curtailment of Questionnaires for Three-Level Classification: Shortening the CES-D for Assessing Low, Moderate, and High Risk of Depression0 aStochastic Curtailment of Questionnaires for ThreeLevel Classifi a22-360 v403 aIn clinical assessment, efficient screeners are needed to ensure low respondent burden. In this article, Stochastic Curtailment (SC), a method for efficient computerized testing for classification into two classes for observable outcomes, was extended to three classes. In a post hoc simulation study using the item scores on the Center for Epidemiologic Studies–Depression Scale (CES-D) of a large sample, three versions of SC, SC via Empirical Proportions (SC-EP), SC via Simple Ordinal Regression (SC-SOR), and SC via Multiple Ordinal Regression (SC-MOR) were compared at both respondent burden and classification accuracy. All methods were applied under the regular item order of the CES-D and under an ordering that was optimal in terms of the predictive power of the items. Under the regular item ordering, the three methods were equally accurate, but SC-SOR and SC-MOR needed less items. Under the optimal ordering, additional gains in efficiency were found, but SC-MOR suffered from capitalization on chance substantially. It was concluded that SC-SOR is an efficient and accurate method for clinical screening. Strengths and weaknesses of the methods are discussed.1 aSmits, Niels1 aFinkelman, Matthew, D.1 aKelderman, Henk uhttp://apm.sagepub.com/content/40/1/22.abstract01379nas a2200157 4500008003900000245012700039210006900166300001200235490000700247520082800254100001701082700002701099700001801126700002401144856005301168 2015 d00aStochastic Curtailment in Adaptive Mastery Testing: Improving the Efficiency of Confidence Interval–Based Stopping Rules0 aStochastic Curtailment in Adaptive Mastery Testing Improving the a278-2920 v393 aA well-known stopping rule in adaptive mastery testing is to terminate the assessment once the examinee’s ability confidence interval lies entirely above or below the cut-off score. This article proposes new procedures that seek to improve such a variable-length stopping rule by coupling it with curtailment and stochastic curtailment. Under the new procedures, test termination can occur earlier if the probability is high enough that the current classification decision remains the same should the test continue. Computation of this probability utilizes normality of an asymptotically equivalent version of the maximum likelihood ability estimate. In two simulation sets, the new procedures showed a substantial reduction in average test length while maintaining similar classification accuracy to the original method.1 aSie, Haskell1 aFinkelman, Matthew, D.1 aBartroff, Jay1 aThompson, Nathan, A uhttp://apm.sagepub.com/content/39/4/278.abstract02041nas a2200121 4500008003900000245007400039210006900113300001200182490000700194520163800201100002301839856005701862 2014 d00aThe Sequential Probability Ratio Test and Binary Item Response Models0 aSequential Probability Ratio Test and Binary Item Response Model a203-2300 v393 a

The sequential probability ratio test (SPRT) is a common method for terminating item response theory (IRT)-based adaptive classification tests. To decide whether a classification test should stop, the SPRT compares a simple log-likelihood ratio, based on the classification bound separating two categories, to prespecified critical values. As has been previously noted (Spray & Reckase, 1994), the SPRT test statistic is not necessarily monotonic with respect to the classification bound when item response functions have nonzero lower asymptotes. Because of nonmonotonicity, several researchers (including Spray & Reckase, 1994) have recommended selecting items at the classification bound rather than the current ability estimate when terminating SPRT-based classification tests. Unfortunately, this well-worn advice is a bit too simplistic. Items yielding optimal evidence for classification depend on the IRT model, item parameters, and location of an examinee with respect to the classification bound. The current study illustrates, in depth, the relationship between the SPRT test statistic and classification evidence in binary IRT models. Unlike earlier studies, we examine the form of the SPRT-based log-likelihood ratio while altering the classification bound and item difficulty. These investigations motivate a novel item selection algorithm based on optimizing the expected SPRT criterion given the current ability estimate. The new expected log-likelihood ratio algorithm results in test lengths noticeably shorter than current, commonly used algorithms, and with no loss in classification accuracy.

1 aNydick, Steven, W. uhttp://jeb.sagepub.com/cgi/content/abstract/39/3/20301176nas a2200121 4500008003900000245009200039210006900131300001100200490000700211520076500218100001900983856005201002 2014 d00aA Sequential Procedure for Detecting Compromised Items in the Item Pool of a CAT System0 aSequential Procedure for Detecting Compromised Items in the Item a87-1040 v383 a

To maintain the validity of a continuous testing system, such as computerized adaptive testing (CAT), items should be monitored to ensure that the performance of test items has not gone through any significant changes during their lifetime in an item pool. In this article, the author developed a sequentially monitoring procedure based on a series of statistical hypothesis tests to examine whether the statistical characteristics of individual items have changed significantly during test administration. Simulation studies show that under the simulated setting, by choosing an appropriate cutoff point, the procedure can control the rate of Type I errors at any reasonable significance level and meanwhile, has a very low rate of Type II errors.

1 aZhang, Jinming uhttp://apm.sagepub.com/content/38/2/87.abstract01676nas a2200145 4500008003900000245012600039210006900165300001200234490000700246520115400253100002601407700002201433700002201455856005301477 2014 d00aStratified Item Selection and Exposure Control in Unidimensional Adaptive Testing in the Presence of Two-Dimensional Data0 aStratified Item Selection and Exposure Control in Unidimensional a563-5760 v383 a

It is not uncommon to use unidimensional item response theory models to estimate ability in multidimensional data with computerized adaptive testing (CAT). The current Monte Carlo study investigated the penalty of this model misspecification in CAT implementations using different item selection methods and exposure control strategies. Three item selection methods—maximum information (MAXI), a-stratification (STRA), and a-stratification with b-blocking (STRB) with and without Sympson–Hetter (SH) exposure control strategy—were investigated. Calibrating multidimensional items as unidimensional items resulted in inaccurate item parameter estimates. Therefore, MAXI performed better than STRA and STRB in estimating the ability parameters. However, all three methods had relatively large standard errors. SH exposure control had no impact on the number of overexposed items. Existing unidimensional CAT implementations might consider using MAXI only if recalibration as multidimensional model is too expensive. Otherwise, building a CAT pool by calibrating multidimensional data as unidimensional is not recommended.

1 aKalinowski, Kevin, E.1 aNatesan, Prathiba1 aHenson, Robin, K. uhttp://apm.sagepub.com/content/38/7/563.abstract01630nas a2200157 4500008003900000245010100039210006900140300001200209490000700221520111200228100001501340700001601355700001901371700002501390856005701415 2013 d00aA Semiparametric Model for Jointly Analyzing Response Times and Accuracy in Computerized Testing0 aSemiparametric Model for Jointly Analyzing Response Times and Ac a381-4170 v383 a

The item response times (RTs) collected from computerized testing represent an underutilized type of information about items and examinees. In addition to knowing the examinees’ responses to each item, we can investigate the amount of time examinees spend on each item. Current models for RTs mainly focus on parametric models, which have the advantage of conciseness, but may suffer from reduced flexibility to fit real data. We propose a semiparametric approach, specifically, the Cox proportional hazards model with a latent speed covariate to model the RTs, embedded within the hierarchical framework proposed by van der Linden to model the RTs and response accuracy simultaneously. This semiparametric approach combines the flexibility of nonparametric modeling and the brevity and interpretability of the parametric modeling. A Markov chain Monte Carlo method for parameter estimation is given and may be used with sparse data obtained by computerized adaptive testing. Both simulation studies and real data analysis are carried out to demonstrate the applicability of the new model.

1 aWang, Chun1 aFan, Zhewen1 aChang, Hua-Hua1 aDouglas, Jeffrey, A. uhttp://jeb.sagepub.com/cgi/content/abstract/38/4/38101189nas a2200133 4500008003900000245003700039210003700076300001200113490000700125520082900132100001900961700001800980856005700998 2013 d00aSpeededness and Adaptive Testing0 aSpeededness and Adaptive Testing a418-4380 v383 a

Two simple constraints on the item parameters in a response–time model are proposed to control the speededness of an adaptive test. As the constraints are additive, they can easily be included in the constraint set for a shadow-test approach (STA) to adaptive testing. Alternatively, a simple heuristic is presented to control speededness in plain adaptive testing without any constraints. Both types of control are easy to implement and do not require any other real-time parameter estimation during the test than the regular update of the test taker’s ability estimate. Evaluation of the two approaches using simulated adaptive testing showed that the STA was especially effective. It guaranteed testing times that differed less than 10 seconds from a reference test across a variety of conditions.

1 aLinden, Wim, J1 aXiong, Xinhui uhttp://jeb.sagepub.com/cgi/content/abstract/38/4/41801281nas a2200133 4500008003900000245009500039210006900134300001200203490000700215520083800222100001801060700001601078856005301094 2012 d00aA Stochastic Method for Balancing Item Exposure Rates in Computerized Classification Tests0 aStochastic Method for Balancing Item Exposure Rates in Computeri a181-1880 v363 a

Computerized classification tests (CCTs) classify examinees into categories such as pass/fail, master/nonmaster, and so on. This article proposes the use of stochastic methods from sequential analysis to address item overexposure, a practical concern in operational CCTs. Item overexposure is traditionally dealt with in CCTs by the Sympson-Hetter (SH) method, but this method is unable to restrict the exposure of the most informative items to the desired level. The authors’ new method of stochastic item exposure balance (SIEB) works in conjunction with the SH method and is shown to greatly reduce the number of overexposed items in a pool and improve overall exposure balance while maintaining classification accuracy comparable with using the SH method alone. The method is demonstrated using a simulation study.

1 aHuebner, Alan1 aLi, Zhushan uhttp://apm.sagepub.com/content/36/3/181.abstract01281nas a2200133 4500008003900000245009500039210006900134300001200203490000700215520083800222100001801060700001601078856005301094 2012 d00aA Stochastic Method for Balancing Item Exposure Rates in Computerized Classification Tests0 aStochastic Method for Balancing Item Exposure Rates in Computeri a181-1880 v363 a

Computerized classification tests (CCTs) classify examinees into categories such as pass/fail, master/nonmaster, and so on. This article proposes the use of stochastic methods from sequential analysis to address item overexposure, a practical concern in operational CCTs. Item overexposure is traditionally dealt with in CCTs by the Sympson-Hetter (SH) method, but this method is unable to restrict the exposure of the most informative items to the desired level. The authors’ new method of stochastic item exposure balance (SIEB) works in conjunction with the SH method and is shown to greatly reduce the number of overexposed items in a pool and improve overall exposure balance while maintaining classification accuracy comparable with using the SH method alone. The method is demonstrated using a simulation study.

1 aHuebner, Alan1 aLi, Zhushan uhttp://apm.sagepub.com/content/36/3/181.abstract00313nas a2200109 4500008004100000245003200041210003100073653000800104653001600112100001800128856005700146 2011 eng d00aSmall-Sample Shadow Testing0 aSmallSample Shadow Testing10aCAT10ashadow test1 aJudd, Wallace uhttp://iacat.org/content/small-sample-shadow-testing00292nas a2200085 4500008004100000245004000041210004000081100002300121856006200144 2010 eng d00aSequencing an Adaptive Test Battery0 aSequencing an Adaptive Test Battery1 avan der Linden, WJ uhttp://iacat.org/content/sequencing-adaptive-test-battery00336nas a2200085 4500008004100000245009100041210006900132100001300201856003600214 2010 eng d00aSimulCAT: Windows application that simulates computerized adaptive test administration0 aSimulCAT Windows application that simulates computerized adaptiv1 aHan, K T uhttp://www.hantest.net/simulcat00515nas a2200133 4500008004100000245009400041210006900135300001200204490000700216100001200223700001400235700001600249856011600265 2010 Eng d00aStratified and maximum information item selection procedures in computer adaptive testing0 aStratified and maximum information item selection procedures in  a202-2260 v471 aDeng, H1 aAnsley, T1 aChang, H -H uhttp://iacat.org/content/stratified-and-maximum-information-item-selection-procedures-computer-adaptive-testing01719nas a2200157 4500008003900000022001400039245009400053210006900147300001400216490000700230520121600237100001401453700002001467700001901487856005501506 2010 d a1745-398400aStratified and Maximum Information Item Selection Procedures in Computer Adaptive Testing0 aStratified and Maximum Information Item Selection Procedures in  a202–2260 v473 a

In this study we evaluated and compared three item selection procedures: the maximum Fisher information procedure (F), the a-stratified multistage computer adaptive testing (CAT) (STR), and a refined stratification procedure that allows more items to be selected from the high a strata and fewer items from the low a strata (USTR), along with completely random item selection (RAN). The comparisons were with respect to error variances, reliability of ability estimates and item usage through CATs simulated under nine test conditions of various practical constraints and item selection space. The results showed that F had an apparent precision advantage over STR and USTR under unconstrained item selection, but with very poor item usage. USTR reduced error variances for STR under various conditions, with small compromises in item usage. Compared to F, USTR enhanced item usage while achieving comparable precision in ability estimates; it achieved a precision level similar to F with improved item usage when items were selected under exposure control and with limited item selection space. The results provide implications for choosing an appropriate item selection procedure in applied settings.

1 aDeng, Hui1 aAnsley, Timothy1 aChang, Hua-Hua uhttp://dx.doi.org/10.1111/j.1745-3984.2010.00109.x01380nas a2200133 4500008003900000245013100039210006900170300001200239490000700251520087800258100002701136700003001163856005301193 2009 d00aStudying the Equivalence of Computer-Delivered and Paper-Based Administrations of the Raven Standard Progressive Matrices Test0 aStudying the Equivalence of ComputerDelivered and PaperBased Adm a855-8670 v693 a

This study investigates the effect of mode of administration of the Raven Standard Progressive Matrices test on distribution, accuracy, and meaning of raw scores. A random sample of high school students take counterbalanced paper-and-pencil and computer-based administrations of the test and answer a questionnaire surveying preferences for computer-delivered test administrations. Administration mode effect is studied with repeated measures multivariate analysis of variance, internal consistency reliability estimates, and confirmatory factor analysis approaches. Results show a lack of test mode effect on distribution, accuracy, and meaning of raw scores. Participants indicate their preferences for the computer-delivered administration of the test. The article discusses findings in light of previous studies of the Raven Standard Progressive Matrices test.

1 aArce-Ferrer, Alvaro, J1 aMartínez Guzmán, Elvira uhttp://epm.sagepub.com/content/69/5/855.abstract01559nas a2200145 4500008003900000245009000039210006900129300001200198490000700210520109400217100001201311700001801323700001901341856005301360 2008 d00aSeverity of Organized Item Theft in Computerized Adaptive Testing: A Simulation Study0 aSeverity of Organized Item Theft in Computerized Adaptive Testin a543-5580 v323 a

Criteria had been proposed for assessing the severity of possible test security violations for computerized tests with high-stakes outcomes. However, these criteria resulted from theoretical derivations that assumed uniformly randomized item selection. This study investigated potential damage caused by organized item theft in computerized adaptive testing (CAT) for two realistic item selection methods, maximum item information and a-stratified with content blocking, using the randomized method as a baseline for comparison. Damage caused by organized item theft was evaluated by the number of compromised items each examinee could encounter and the impact of the compromised items on examinees' ability estimates. Severity of test security violation was assessed under self-organized and organized item theft simulation scenarios. Results indicated that though item theft could cause severe damage to CAT with either item selection method, the maximum item information method was more vulnerable to the organized item theft simulation than was the a-stratified method.

1 aQing Yi1 aJinming Zhang1 aChang, Hua-Hua uhttp://apm.sagepub.com/content/32/7/543.abstract01289nas a2200133 4500008004100000245005700041210005700098300000900155490000800164520084700172653003401019100002301053856007901076 2008 eng d00aSome new developments in adaptive testing technology0 aSome new developments in adaptive testing technology a3-110 v2163 a

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

10acomputerized adaptive testing1 avan der Linden, WJ uhttp://iacat.org/content/some-new-developments-adaptive-testing-technology01828nas a2200229 4500008004100000020004100041245010800082210006900190250001500259300000900274490000600283520101700289653001601306653001501322653005701337653001101394653003001405653001801435100001401453700001401467856011701481 2008 eng d a1529-7713 (Print)1529-7713 (Linking)00aStrategies for controlling item exposure in computerized adaptive testing with the partial credit model0 aStrategies for controlling item exposure in computerized adaptiv a2008/01/09 a1-170 v93 aExposure control research with polytomous item pools has determined that randomization procedures can be very effective for controlling test security in computerized adaptive testing (CAT). The current study investigated the performance of four procedures for controlling item exposure in a CAT under the partial credit model. In addition to a no exposure control baseline condition, the Kingsbury-Zara, modified-within-.10-logits, Sympson-Hetter, and conditional Sympson-Hetter procedures were implemented to control exposure rates. The Kingsbury-Zara and the modified-within-.10-logits procedures were implemented with 3 and 6 item candidate conditions. The results show that the Kingsbury-Zara and modified-within-.10-logits procedures with 6 item candidates performed as well as the conditional Sympson-Hetter in terms of exposure rates, overlap rates, and pool utilization. These two procedures are strongly recommended for use with partial credit CATs due to their simplicity and strength of their results.10a*Algorithms10a*Computers10a*Educational Measurement/statistics & numerical data10aHumans10aQuestionnaires/*standards10aUnited States1 aDavis, LL1 aDodd, B G uhttp://iacat.org/content/strategies-controlling-item-exposure-computerized-adaptive-testing-partial-credit-model01614nas a2200145 4500008003900000245009500039210006900134300001200203490000700215520113800222100001801360700001801378700001901396856005301415 2008 d00aA Strategy for Controlling Item Exposure in Multidimensional Computerized Adaptive Testing0 aStrategy for Controlling Item Exposure in Multidimensional Compu a215-2320 v683 a

Although computerized adaptive tests have enjoyed tremendous growth, solutions for important problems remain unavailable. One problem is the control of item exposure rate. Because adaptive algorithms are designed to select optimal items, they choose items with high discriminating power. Thus, these items are selected more often than others, leading to both overexposure and underutilization of some parts of the item pool. Overused items are often compromised, creating a security problem that could threaten the validity of a test. Building on a previously proposed stratification scheme to control the exposure rate for one-dimensional tests, the authors extend their method to multidimensional tests. A strategy is proposed based on stratification in accordance with a functional of the vector of the discrimination parameter, which can be implemented with minimal computational overhead. Both theoretical and empirical validation studies are provided. Empirical results indicate significant improvement over the commonly used method of controlling exposure rate that requires only a reasonable sacrifice in efficiency.

1 aLee, Yi-Hsuan1 aIp, Edward, H1 aFuh, Cheng-Der uhttp://epm.sagepub.com/content/68/2/215.abstract00514nas a2200097 4500008004100000245008600041210006900127260009700196100002300293856010000316 2007 eng d00aThe shadow-test approach: A universal framework for implementing adaptive testing0 ashadowtest approach A universal framework for implementing adapt aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 avan der Linden, WJ uhttp://iacat.org/content/shadow-test-approach-universal-framework-implementing-adaptive-testing00469nas a2200097 4500008004100000245006700041210006700108260009700175100001300272856008600285 2007 eng d00aSome thoughts on controlling item exposure in adaptive testing0 aSome thoughts on controlling item exposure in adaptive testing aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aLewis, C uhttp://iacat.org/content/some-thoughts-controlling-item-exposure-adaptive-testing00470nas a2200109 4500008004100000245004400041210004400085260012600129100002300255700001600278856006600294 2007 eng d00aStatistical aspects of adaptive testing0 aStatistical aspects of adaptive testing aC. R. Rao and S. Sinharay (Eds.), Handbook of statistics (Vol. 27: Psychometrics) (pp. 801838). Amsterdam: North-Holland.1 avan der Linden, WJ1 aGlas, C A W uhttp://iacat.org/content/statistical-aspects-adaptive-testing02038nas a2200253 4500008004100000020002200041245007400063210006900137250001500206300001100221490000700232520121000239653002201449653001101471653005501482653005101537100001501588700002001603700002301623700001801646700001301664700001701677856009001694 2007 eng d a0885-3924 (Print)00aA system for interactive assessment and management in palliative care0 asystem for interactive assessment and management in palliative c a2007/03/16 a745-550 v333 aThe availability of psychometrically sound and clinically relevant screening, diagnosis, and outcome evaluation tools is essential to high-quality palliative care assessment and management. Such data will enable us to improve patient evaluations, prognoses, and treatment selections, and to increase patient satisfaction and quality of life. To accomplish these goals, medical care needs more precise, efficient, and comprehensive tools for data acquisition, analysis, interpretation, and management. We describe a system for interactive assessment and management in palliative care (SIAM-PC), which is patient centered, model driven, database derived, evidence based, and technology assisted. The SIAM-PC is designed to reliably measure the multiple dimensions of patients' needs for palliative care, and then to provide information to clinicians, patients, and the patients' families to achieve optimal patient care, while improving our capacity for doing palliative care research. This system is innovative in its application of the state-of-the-science approaches, such as item response theory and computerized adaptive testing, to many of the significant clinical problems related to palliative care.10a*Needs Assessment10aHumans10aMedical Informatics/*organization & administration10aPalliative Care/*organization & administration1 aChang, C-H1 aBoni-Saenz, A A1 aDurazo-Arvizu, R A1 aDesHarnais, S1 aLau, D T1 aEmanuel, L L uhttp://iacat.org/content/system-interactive-assessment-and-management-palliative-care00574nas a2200133 4500008003900000245014600039210006900185300001200254490000700266100001500273700002500288700001000313856011700323 2006 d00aSensitivity of a computer adaptive assessment for measuring functional mobility changes in children enrolled in a community fitness programme0 aSensitivity of a computer adaptive assessment for measuring func a616-6220 v201 aHaley, S M1 aFragala-Pinkham, M A1 aNi, P uhttp://iacat.org/content/sensitivity-computer-adaptive-assessment-measuring-functional-mobility-changes-children00395nas a2200109 4500008003900000245007400039210007200113300001000185490000600195100001800201856006600219 2006 d00aSequential Computerized Mastery Tests—Three Simulation Studies0 aSequential Computerized Mastery Tests†Three Simulation Studies a41-550 v61 aWiberg, Marie uhttp://www.tandfonline.com/doi/abs/10.1207/s15327574ijt0601_302342nas a2200217 4500008004100000020002200041245008000063210006900143260002600212300001000238490000700248520160400255653003001859653002101889653003201910653003001942653002501972100001501997700001502012856009702027 2006 eng d a0146-6216 (Print)00aSIMCAT 1.0: A SAS computer program for simulating computer adaptive testing0 aSIMCAT 10 A SAS computer program for simulating computer adaptiv bSage Publications: US a60-610 v303 aMonte Carlo methodologies are frequently applied to study the sampling distribution of the estimated proficiency level in adaptive testing. These methods eliminate real situational constraints. However, these Monte Carlo methodologies are not currently supported by the available software programs, and when these programs are available, their flexibility is limited. SIMCAT 1.0 is aimed at the simulation of adaptive testing sessions under different adaptive expected a posteriori (EAP) proficiency-level estimation methods (Blais & Raîche, 2005; Raîche & Blais, 2005) based on the one-parameter Rasch logistic model. These methods are all adaptive in the a priori proficiency-level estimation, the proficiency-level estimation bias correction, the integration interval, or a combination of these factors. The use of these adaptive EAP estimation methods diminishes considerably the shrinking, and therefore biasing, effect of the estimated a priori proficiency level encountered when this a priori is fixed at a constant value independently of the computed previous value of the proficiency level. SIMCAT 1.0 also computes empirical and estimated skewness and kurtosis coefficients, such as the standard error, of the estimated proficiency-level sampling distribution. In this way, the program allows one to compare empirical and estimated properties of the estimated proficiency-level sampling distribution under different variations of the EAP estimation method: standard error and bias, like the skewness and kurtosis coefficients. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputer adaptive testing10acomputer program10aestimated proficiency level10aMonte Carlo methodologies10aRasch logistic model1 aRaîche, G1 aBlais, J-G uhttp://iacat.org/content/simcat-10-sas-computer-program-simulating-computer-adaptive-testing00591nas a2200145 4500008004100000245013800041210006900179300001200248490000700260100001200267700001600279700001500295700001700310856011800327 2006 eng d00aSimulated computerized adaptive test for patients with lumbar spine impairments was efficient and produced valid measures of function0 aSimulated computerized adaptive test for patients with lumbar sp a947-9560 v591 aHart, D1 aMioduski, J1 aWerenke, M1 aStratford, P uhttp://iacat.org/content/simulated-computerized-adaptive-test-patients-lumbar-spine-impairments-was-efficient-and02107nas a2200229 4500008004100000245013800041210006900179300001400248490000700262520127000269653003101539653003401570653002501604653001701629653001901646653002401665100001401689700001801703700001701721700001901738856012001757 2006 eng d00aSimulated computerized adaptive test for patients with lumbar spine impairments was efficient and produced valid measures of function0 aSimulated computerized adaptive test for patients with lumbar sp a947–9560 v593 aObjective: To equate physical functioning (PF) items with Back Pain Functional Scale (BPFS) items, develop a computerized adaptive test (CAT) designed to assess lumbar spine functional status (LFS) in people with lumbar spine impairments, and compare discriminant validity of LFS measures (qIRT) generated using all items analyzed with a rating scale Item Response Theory model (RSM) and measures generated using the simulated CAT (qCAT). Methods: We performed a secondary analysis of retrospective intake rehabilitation data. Results: Unidimensionality and local independence of 25 BPFS and PF items were supported. Differential item functioning was negligible for levels of symptom acuity, gender, age, and surgical history. The RSM fit the data well. A lumbar spine specific CAT was developed that was 72% more efficient than using all 25 items to estimate LFS measures. qIRT and qCAT measures did not discriminate patients by symptom acuity, age, or gender, but discriminated patients by surgical history in similar clinically logical ways. qCAT measures were as precise as qIRT measures. Conclusion: A body part specific simulated CAT developed from an LFS item bank was efficient and produced precise measures of LFS without eroding discriminant validity.10aBack Pain Functional Scale10acomputerized adaptive testing10aItem Response Theory10aLumbar spine10aRehabilitation10aTrue-score equating1 aHart, D L1 aMioduski, J E1 aWerneke, M W1 aStratford, P W uhttp://iacat.org/content/simulated-computerized-adaptive-test-patients-lumbar-spine-impairments-was-efficient-and-002068nas a2200217 4500008004500000245013400045210006900179300001200248490000700260520127300267653003401540653004201574653002501616653001901641100001401660700001301674700001801687700001401705700001501719856011601734 2006 Engldsh 00aSimulated computerized adaptive test for patients with shoulder impairments was efficient and produced valid measures of function0 aSimulated computerized adaptive test for patients with shoulder  a290-2980 v593 a

Background and Objective: To test unidimensionality and local independence of a set of shoulder functional status (SFS) items,
develop a computerized adaptive test (CAT) of the items using a rating scale item response theory model (RSM), and compare discriminant validity of measures generated using all items (qIRT) and measures generated using the simulated CAT (qCAT).
Study Design and Setting: We performed a secondary analysis of data collected prospectively during rehabilitation of 400 patients
with shoulder impairments who completed 60 SFS items.
Results: Factor analytic techniques supported that the 42 SFS items formed a unidimensional scale and were locally independent. Except for five items, which were deleted, the RSM fit the data well. The remaining 37 SFS items were used to generate the CAT. On average, 6 items on were needed to estimate precise measures of function using the SFS CAT, compared with all 37 SFS items. The qIRT and qCAT measures were highly correlated (r 5 .96) and resulted in similar classifications of patients.
Conclusion: The simulated SFS CAT was efficient and produced precise, clinically relevant measures of functional status with good
discriminating ability. 

10acomputerized adaptive testing10aFlexilevel Scale of Shoulder Function10aItem Response Theory10aRehabilitation1 aHart, D L1 aCook, KF1 aMioduski, J E1 aTeal, C R1 aCrane, P K uhttp://iacat.org/content/simulated-computerized-adaptive-test-patients-shoulder-impairments-was-efficient-and-002649nas a2200409 4500008004100000245013400041210006900175300001000244490000700254520123100261653002501492653003201517653003101549653001001580653000901590653002201599653003301621653001101654653001101665653000901676653001601685653002401701653003101725653004101756653004501797653006801842653006101910653003001971653002802001653002202029100001402051700001302065700001802078700001402096700001502110856011402125 2006 eng d00aSimulated computerized adaptive test for patients with shoulder impairments was efficient and produced valid measures of function0 aSimulated computerized adaptive test for patients with shoulder  a290-80 v593 aBACKGROUND AND OBJECTIVE: To test unidimensionality and local independence of a set of shoulder functional status (SFS) items, develop a computerized adaptive test (CAT) of the items using a rating scale item response theory model (RSM), and compare discriminant validity of measures generated using all items (theta(IRT)) and measures generated using the simulated CAT (theta(CAT)). STUDY DESIGN AND SETTING: We performed a secondary analysis of data collected prospectively during rehabilitation of 400 patients with shoulder impairments who completed 60 SFS items. RESULTS: Factor analytic techniques supported that the 42 SFS items formed a unidimensional scale and were locally independent. Except for five items, which were deleted, the RSM fit the data well. The remaining 37 SFS items were used to generate the CAT. On average, 6 items were needed to estimate precise measures of function using the SFS CAT, compared with all 37 SFS items. The theta(IRT) and theta(CAT) measures were highly correlated (r = .96) and resulted in similar classifications of patients. CONCLUSION: The simulated SFS CAT was efficient and produced precise, clinically relevant measures of functional status with good discriminating ability.10a*Computer Simulation10a*Range of Motion, Articular10aActivities of Daily Living10aAdult10aAged10aAged, 80 and over10aFactor Analysis, Statistical10aFemale10aHumans10aMale10aMiddle Aged10aProspective Studies10aReproducibility of Results10aResearch Support, N.I.H., Extramural10aResearch Support, U.S. Gov't, Non-P.H.S.10aShoulder Dislocation/*physiopathology/psychology/rehabilitation10aShoulder Pain/*physiopathology/psychology/rehabilitation10aShoulder/*physiopathology10aSickness Impact Profile10aTreatment Outcome1 aHart, D L1 aCook, KF1 aMioduski, J E1 aTeal, C R1 aCrane, P K uhttp://iacat.org/content/simulated-computerized-adaptive-test-patients-shoulder-impairments-was-efficient-and02716nas a2200373 4500008004100000245017500041210006900216300001100285490000700296520137800303653003001681653003101711653001501742653001001757653000901767653002201776653003201798653004201830653001101872653003001883653001101913653005101924653003101975653003702006653000902043653001602052653004102068653004102109653002602150100001402176700001802190700001902208856011502227 2005 eng d00aSimulated computerized adaptive tests for measuring functional status were efficient with good discriminant validity in patients with hip, knee, or foot/ankle impairments0 aSimulated computerized adaptive tests for measuring functional s a629-380 v583 aBACKGROUND AND OBJECTIVE: To develop computerized adaptive tests (CATs) designed to assess lower extremity functional status (FS) in people with lower extremity impairments using items from the Lower Extremity Functional Scale and compare discriminant validity of FS measures generated using all items analyzed with a rating scale Item Response Theory model (theta(IRT)) and measures generated using the simulated CATs (theta(CAT)). METHODS: Secondary analysis of retrospective intake rehabilitation data. RESULTS: Unidimensionality of items was strong, and local independence of items was adequate. Differential item functioning (DIF) affected item calibration related to body part, that is, hip, knee, or foot/ankle, but DIF did not affect item calibration for symptom acuity, gender, age, or surgical history. Therefore, patients were separated into three body part specific groups. The rating scale model fit all three data sets well. Three body part specific CATs were developed: each was 70% more efficient than using all LEFS items to estimate FS measures. theta(IRT) and theta(CAT) measures discriminated patients by symptom acuity, age, and surgical history in similar ways. theta(CAT) measures were as precise as theta(IRT) measures. CONCLUSION: Body part-specific simulated CATs were efficient and produced precise measures of FS with good discriminant validity.10a*Health Status Indicators10aActivities of Daily Living10aAdolescent10aAdult10aAged10aAged, 80 and over10aAnkle Joint/physiopathology10aDiagnosis, Computer-Assisted/*methods10aFemale10aHip Joint/physiopathology10aHumans10aJoint Diseases/physiopathology/*rehabilitation10aKnee Joint/physiopathology10aLower Extremity/*physiopathology10aMale10aMiddle Aged10aResearch Support, N.I.H., Extramural10aResearch Support, U.S. Gov't, P.H.S.10aRetrospective Studies1 aHart, D L1 aMioduski, J E1 aStratford, P W uhttp://iacat.org/content/simulated-computerized-adaptive-tests-measuring-functional-status-were-efficient-good01472nas a2200193 4500008004100000245020200041210006900243300001200312490000700324520066400331653002100995653003001016653005501046653001101101653001801112100001401130700001701144856011701161 2005 eng d00aSomministrazione di test computerizzati di tipo adattivo: Un' applicazione del modello di misurazione di Rasch [Administration of computerized and adaptive tests: An application of the Rasch Model]0 aSomministrazione di test computerizzati di tipo adattivo Un appl a131-1490 v123 aThe aim of the present study is to describe the characteristics of a procedure for administering computerized and adaptive tests (Computer Adaptive Testing or CAT). Items to be asked to the individuals are interactively chosen and are selected from a "bank" in which they were previously calibrated and recorded on the basis of their difficulty level. The selection of items is performed by increasingly more accurate estimates of the examinees' ability. The building of an item-bank on Psychometrics and the implementation of this procedure allow a first validation through Monte Carlo simulations. (PsycINFO Database Record (c) 2006 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Response Theory computerized adaptive testing10aModels10aPsychometrics1 aMiceli, R1 aMolinengo, G uhttp://iacat.org/content/somministrazione-di-test-computerizzati-di-tipo-adattivo-un-applicazione-del-modello-di00531nas a2200109 4500008004100000245010800041210006900149260005800218100001400276700001200290856011900302 2005 eng d00aStrategies for controlling item exposure in computerized adaptive testing with the partial credit model0 aStrategies for controlling item exposure in computerized adaptiv aPearson Educational Measurement Research Report 05-011 aDavis, LL1 aDodd, B uhttp://iacat.org/content/strategies-controlling-item-exposure-computerized-adaptive-testing-partial-credit-model-002884nas a2200301 4500008004100000020002200041245013700063210006900200250001500269260000800284300001000292490000700302520186600309653001102175653003302186653001102219653004602230653002402276653002902300653003002329653002902359100001502388700001602403700001602419700001602435700001002451856012102461 2004 eng d a0003-9993 (Print)00aScore comparability of short forms and computerized adaptive testing: Simulation study with the activity measure for post-acute care0 aScore comparability of short forms and computerized adaptive tes a2004/04/15 cApr a661-60 v853 aOBJECTIVE: To compare simulated short-form and computerized adaptive testing (CAT) scores to scores obtained from complete item sets for each of the 3 domains of the Activity Measure for Post-Acute Care (AM-PAC). DESIGN: Prospective study. SETTING: Six postacute health care networks in the greater Boston metropolitan area, including inpatient acute rehabilitation, transitional care units, home care, and outpatient services. PARTICIPANTS: A convenience sample of 485 adult volunteers who were receiving skilled rehabilitation services. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Inpatient and community-based short forms and CAT applications were developed for each of 3 activity domains (physical & mobility, personal care & instrumental, applied cognition) using item pools constructed from new items and items from existing postacute care instruments. RESULTS: Simulated CAT scores correlated highly with score estimates from the total item pool in each domain (4- and 6-item CAT r range,.90-.95; 10-item CAT r range,.96-.98). Scores on the 10-item short forms constructed for inpatient and community settings also provided good estimates of the AM-PAC item pool scores for the physical & movement and personal care & instrumental domains, but were less consistent in the applied cognition domain. Confidence intervals around individual scores were greater in the short forms than for the CATs. CONCLUSIONS: Accurate scoring estimates for AM-PAC domains can be obtained with either the setting-specific short forms or the CATs. The strong relationship between CAT and item pool scores can be attributed to the CAT's ability to select specific items to match individual responses. The CAT may have additional advantages over short forms in practicality, efficiency, and the potential for providing more precise scoring estimates for individuals.10aBoston10aFactor Analysis, Statistical10aHumans10aOutcome Assessment (Health Care)/*methods10aProspective Studies10aQuestionnaires/standards10aRehabilitation/*standards10aSubacute Care/*standards1 aHaley, S M1 aCoster, W J1 aAndres, P L1 aKosinski, M1 aNi, P uhttp://iacat.org/content/score-comparability-short-forms-and-computerized-adaptive-testing-simulation-study-activity00452nas a2200109 4500008004100000245007900041210006900120260001700189100002300206700001800229856009500247 2004 eng d00aA sequential Bayesian procedure for item calibration in multistage testing0 asequential Bayesian procedure for item calibration in multistage aSan Diego CA1 avan der Linden, WJ1 aMead, Alan, D uhttp://iacat.org/content/sequential-bayesian-procedure-item-calibration-multistage-testing01567nas a2200133 4500008004100000020001300041245007500054210006900129300001200198490000800210520110300218100001501321856009701336 2004 eng d a0378375800aSequential estimation in variable length computerized adaptive testing0 aSequential estimation in variable length computerized adaptive t a249-2640 v1213 aWith the advent of modern computer technology, there have been growing e3orts in recent years to computerize standardized tests, including the popular Graduate Record Examination (GRE), the Graduate Management Admission Test (GMAT) and the Test of English as a Foreign Language (TOEFL). Many of such computer-based tests are known as the computerized adaptive tests, a major feature of which is that, depending on their performance in the course of testing, di3erent examinees may be given with di3erent sets of items (questions). In doing so, items can be e>ciently utilized to yield maximum accuracy for estimation of examinees’ ability traits. We consider, in this article, one type of such tests where test lengths vary with examinees to yield approximately same predetermined accuracy for all ability traits. A comprehensive large sample theory is developed for the expected test length and the sequential point and interval estimates of the latent trait. Extensive simulations are conducted with results showing that the large sample approximations are adequate for realistic sample sizes. 1 aChang, I Y uhttp://iacat.org/content/sequential-estimation-variable-length-computerized-adaptive-testing01260nas a2200133 4500008004100000245007500041210006900116260000800185300001200193490000700205520080400212100001601016856009401032 2004 eng d00aA sharing item response theory model for computerized adaptive testing0 asharing item response theory model for computerized adaptive tes cWin a439-4600 v293 aA new sharing item response theory (SIRT) model is presented which explicitly models the effects of sharing item content between informants and testtakers. This model is used to construct adaptive item selection and scoring rules that provide increased precision and reduced score gains in instances where sharing occurs. The adaptive item selection rules are expressed as functions of the item’s exposure rate in addition to other commonly used properties (characterized by difficulty, discrimination, and guessing parameters). Based on the results of simulated item responses, the new item selection and scoring algorithms compare favorably to the Sympson-Hetter exposure control method. The new SIRT approach provides higher reliability and lower score gains in instances where sharing occurs.1 aSegall, D O uhttp://iacat.org/content/sharing-item-response-theory-model-computerized-adaptive-testing00537nas a2200181 4500008004100000245005000041210004800091300001000139490000700149653003400156100001400190700001500204700001500219700001400234700002600248700001300274856006800287 2004 eng d00aSiette: a web-based tool for adaptive testing0 aSiette a webbased tool for adaptive testing a29-610 v1410acomputerized adaptive testing1 aConejo, R1 aGuzmán, E1 aMillán, E1 aTrella, M1 aPérez-De-La-Cruz, JL1 aRíos, A uhttp://iacat.org/content/siette-web-based-tool-adaptive-testing00602nas a2200109 4500008004100000245009200041210006900133260014600202100001800348700001900366856010700385 2004 eng d00aState-of-the-art and adaptive open-closed items in adaptive foreign language assessment0 aStateoftheart and adaptive openclosed items in adaptive foreign  aProceedings 4th Hellenic Conference with ternational Participation: Informational and Communication Technologies in Education, Athens,747-7561 aGiouroglou, H1 aEconomides, AA uhttp://iacat.org/content/state-art-and-adaptive-open-closed-items-adaptive-foreign-language-assessment00425nas a2200121 4500008004100000022001400041245006600055210006600121490000600187100001000193700001800203856008200221 2004 eng d a1575-910500aStatistics for detecting disclosed items in a CAT environment0 aStatistics for detecting disclosed items in a CAT environment0 v51 aLu, Y1 aHambleton, RK uhttp://iacat.org/content/statistics-detecting-disclosed-items-cat-environment01590nas a2200121 4500008003900000245012000039210006900159300001200228490000700240520114100247100002701388856005301415 2004 d00aStrategies for Controlling Item Exposure in Computerized Adaptive Testing With the Generalized Partial Credit Model0 aStrategies for Controlling Item Exposure in Computerized Adaptiv a165-1850 v283 a

Choosing a strategy for controlling item exposure has become an integral part of test development for computerized adaptive testing (CAT). This study investigated the performance of six procedures for controlling item exposure in a series of simulated CATs under the generalized partial credit model. In addition to a no-exposure control baseline condition, the randomesque, modified-within-.10-logits, Sympson-Hetter, conditional Sympson-Hetter, a-stratified with multiple-stratification, and enhanced a-stratified with multiple-stratification procedures were implemented to control exposure rates. Two variations of the randomesque and modified-within-.10-logits procedures were examined, which varied the size of the item group from which the next item to be administered was randomly selected. The results indicate that although the conditional Sympson-Hetter provides somewhat lower maximum exposure rates, the randomesque and modified-within-.10-logits procedures with the six-item group variation have great utility for controlling overlap rates and increasing pool utilization and should be given further consideration.

1 aDavis, Laurie Laughlin uhttp://apm.sagepub.com/content/28/3/165.abstract01891nas a2200181 4500008004100000020002200041245012000063210006900183260002600252300001200278490000700290520119300297653003401490653003701524653001801561100001401579856011601593 2004 eng d a0146-6216 (Print)00aStrategies for controlling item exposure in computerized adaptive testing with the generalized partial credit model0 aStrategies for controlling item exposure in computerized adaptiv bSage Publications: US a165-1850 v283 aChoosing a strategy for controlling item exposure has become an integral part of test development for computerized adaptive testing (CAT). This study investigated the performance of six procedures for controlling item exposure in a series of simulated CATs under the generalized partial credit model. In addition to a no-exposure control baseline condition, the randomesque, modified-within-.10-logits, Sympson-Hetter, conditional Sympson-Hetter, a-stratified with multiple-stratification, and enhanced a-stratified with multiple-stratification procedures were implemented to control exposure rates. Two variations of the randomesque and modified-within-.10-logits procedures were examined, which varied the size of the item group from which the next item to be administered was randomly selected. The results indicate that although the conditional Sympson-Hetter provides somewhat lower maximum exposure rates, the randomesque and modified-within-.10-logits procedures with the six-item group variation have great utility for controlling overlap rates and increasing pool utilization and should be given further consideration. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive testing10ageneralized partial credit model10aitem exposure1 aDavis, LL uhttp://iacat.org/content/strategies-controlling-item-exposure-computerized-adaptive-testing-generalized-partial03118nas a2200121 4500008004100000245009500041210006900136300000900205490000700214520263700221100002502858856011302883 2004 eng d00aStrategies for controlling testlet exposure rates in computerized adaptive testing systems0 aStrategies for controlling testlet exposure rates in computerize a58350 v643 aExposure control procedures in computerized adaptive testing (CAT) systems protect item pools from being compromised, however, this impacts measurement precision. Previous research indicates that exposure control procedures perform differently for dichotomously scored versus polytomously scored CAT systems. For dichotomously scored CATs, conditional selection procedures are often the optimal choice, while randomization procedures perform best for polytomously scored CATs. CAT systems modeled with testlet response theory have not been examined to determine optimal exposure control procedures. This dissertation examined various exposure control procedures in testlet-based CAT systems using the three-parameter logistic testlet response theory model and the partial credit model. The exposure control procedures were the randomesque procedure, the modified within .10 logits procedure, two levels of the progressive restricted procedure, and two levels of the Sympson-Hetter procedure. Each of these was compared to a baseline no exposure control procedure, maximum information. The testlets were reading passages with six to ten multiple-choice items. The CAT systems consisted of maximum information testlet selection contingent on an exposure control procedure and content balancing for passage type and the number of items per passage; expected a posteriori ability estimation; and a fixed length stopping rule of seven testlets totaling fifty multiple-choice items. Measurement precision and exposure rates were examined to evaluate the effectiveness of the exposure control procedures for each measurement model. The exposure control procedures yielded similar results for measurement precision within the models. The exposure rates distinguished which exposure control procedures were most effective. The Sympson-Hetter conditions, which are conditional procedures, maintained the pre-specified maximum exposure rate, but performed very poorly in terms of pool utilization. The randomization procedures, randomesque and modified within .10 logits, yielded low maximum exposure rates, but used only about 70% of the testlet pool. Surprisingly, the progressive restricted procedure, which is a combination of both a conditional and randomization procedure, yielded the best results in its ability to maintain and control the maximum exposure rate and it used the entire testlet pool. The progressive restricted conditions were the optimal procedures for both the partial credit CAT systems and the three-parameter logistic testlet response theory CAT systems. (PsycINFO Database Record (c) 2004 APA, all rights reserved).1 aBoyd, Aimee Michelle uhttp://iacat.org/content/strategies-controlling-testlet-exposure-rates-computerized-adaptive-testing-systems00376nas a2200109 4500008004100000245005200041210005000093260001700143100001900160700001500179856007200194 2004 eng d00aA study of multiple stage adaptive test designs0 astudy of multiple stage adaptive test designs aSan Diego CA1 aArmstrong, R D1 aEdmonds, J uhttp://iacat.org/content/study-multiple-stage-adaptive-test-designs00461nas a2200109 4500008004100000245007700041210006900118260003000187100002300217700001800240856009300258 2003 eng d00aA sequential Bayes procedure for item calibration in multi-stage testing0 asequential Bayes procedure for item calibration in multistage te aManuscript in preparation1 avan der Linden, WJ1 aMead, Alan, D uhttp://iacat.org/content/sequential-bayes-procedure-item-calibration-multi-stage-testing00453nas a2200121 4500008004100000245007300041210006900114260001500183100001000198700001900208700001500227856008900242 2003 eng d00aA simulation study to compare CAT strategies for cognitive diagnosis0 asimulation study to compare CAT strategies for cognitive diagnos aChicago IL1 aXu, X1 aChang, Hua-Hua1 aDouglas, J uhttp://iacat.org/content/simulation-study-compare-cat-strategies-cognitive-diagnosis01707nas a2200169 4500008004100000245015800041210006900199300001000268490000700278520105400285100001901339700001801358700001601376700001201392700001601404856011701420 2003 eng d00aSmall sample estimation in dichotomous item response models: Effect of priors based on judgmental information on the accuracy of item parameter estimates0 aSmall sample estimation in dichotomous item response models Effe a27-510 v273 aLarge item banks with properly calibrated test items are essential for ensuring the validity of computer-based tests. At the same time, item calibrations with small samples are desirable to minimize the amount of pretesting and limit item exposure. Bayesian estimation procedures show considerable promise with small examinee samples. The purposes of the study were (a) to examine how prior information for Bayesian item parameter estimation can be specified and (b) to investigate the relationship between sample size and the specification of prior information on the accuracy of item parameter estimates. The results of the simulation study were clear: Estimation of item response theory (IRT) model item parameters can be improved considerably. Improvements in the one-parameter model were modest; considerable improvements with the two- and three-parameter models were observed. Both the study of different forms of priors and ways to improve the judgmental data used in forming the priors appear to be promising directions for future research. 1 aSwaminathan, H1 aHambleton, RK1 aSireci, S G1 aXing, D1 aRizavi, S M uhttp://iacat.org/content/small-sample-estimation-dichotomous-item-response-models-effect-priors-based-judgmental01516nas a2200157 4500008004100000245009500041210006900136300001200205490000700217520090100224653002101125653003001146653004501176100002301221856011401244 2003 eng d00aSome alternatives to Sympson-Hetter item-exposure control in computerized adaptive testing0 aSome alternatives to SympsonHetter itemexposure control in compu a249-2650 v283 aTheHetter and Sympson (1997; 1985) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic set of values for the examinees’ ability parameter. Formal properties of the method are identified that help us explain why this iterative process can be slow and does not guarantee admissibility. In addition, some alternatives to the SH method are introduced. The behavior of these alternatives was estimated for an adaptive test from an item pool from the Law School Admission Test (LSAT). Two of the alternatives showed attractive behavior and converged smoothly to admissibility for all items in a relatively small number of iteration steps. 10aAdaptive Testing10aComputer Assisted Testing10aTest Items computerized adaptive testing1 avan der Linden, WJ uhttp://iacat.org/content/some-alternatives-sympson-hetter-item-exposure-control-computerized-adaptive-testing00399nas a2200097 4500008004100000245006100041210005900102260004100161100001600202856008300218 2003 eng d00aStandard-setting issues in computerized-adaptive testing0 aStandardsetting issues in computerizedadaptive testing aHalifax, Nova Scotia, May 30th, 20031 aGushta, M M uhttp://iacat.org/content/standard-setting-issues-computerized-adaptive-testing02976nas a2200121 4500008004100000245010700041210006900148300000900217490000700226520249100233100001202724856011802736 2003 eng d00aStatistical detection and estimation of differential item functioning in computerized adaptive testing0 aStatistical detection and estimation of differential item functi a27360 v643 aDifferential item functioning (DIF) is an important issue in large scale standardized testing. DIF refers to the unexpected difference in item performances among groups of equally proficient examinees, usually classified by ethnicity or gender. Its presence could seriously affect the validity of inferences drawn from a test. Various statistical methods have been proposed to detect and estimate DIF. This dissertation addresses DIF analysis in the context of computerized adaptive testing (CAT), whose item selection algorithm adapts to the ability level of each individual examinee. In a CAT, a DIF item may be more consequential and more detrimental be cause fewer items are administered in a CAT than in a traditional paper-and-pencil test and because the remaining sequence of items presented to examinees depends in part on their responses to the DIF item. Consequently, an efficient, stable and flexible method to detect and estimate CAT DIF becomes necessary and increasingly important. We propose simultaneous implementations of online calibration and DIF testing. The idea is to perform online calibration of an item of interest separately in the focal and reference groups. Under any specific parametric IRT model, we can use the (online) estimated latent traits as covariates and fit a nonlinear regression model to each of the two groups. Because of the use of the estimated, not the true , the regression fit has to adjust for the covariate "measurement errors". It turns out that this situation fits nicely into the framework of nonlinear error-in-variable modelling, which has been extensively studied in statistical literature. We develop two bias-correction methods using asymptotic expansion and conditional score theory. After correcting the bias caused by measurement error, one can perform a significance test to detect DIF with the parameter estimates for different groups. This dissertation also discusses some general techniques to handle measurement error modelling with different IRT models, including the three-parameter normal ogive model and polytomous response models. Several methods of estimating DIF are studied as well. Large sample properties are established to justify the proposed methods. Extensive simulation studies show that the resulting methods perform well in terms of Type-I error rate control, accuracy in estimating DIF and power against both unidirectional and crossing DIF. (PsycINFO Database Record (c) 2004 APA, all rights reserved).1 aFeng, X uhttp://iacat.org/content/statistical-detection-and-estimation-differential-item-functioning-computerized-adaptive00475nas a2200097 4500008004100000245012000041210006900161260001500230100001400245856011800259 2003 eng d00aStrategies for controlling item exposure in computerized adaptive testing with the generalized partial credit model0 aStrategies for controlling item exposure in computerized adaptiv aChicago IL1 aDavis, LL uhttp://iacat.org/content/strategies-controlling-item-exposure-computerized-adaptive-testing-generalized-partial-002963nas a2200121 4500008004100000245010900041210006900150300000800219490000700227520247100234100001402705856012202719 2003 eng d00aStrategies for controlling item exposure in computerized adaptive testing with polytomously scored items0 aStrategies for controlling item exposure in computerized adaptiv a4580 v643 aChoosing a strategy for controlling the exposure of items to examinees has become an integral part of test development for computerized adaptive testing (CAT). Item exposure can be controlled through the use of a variety of algorithms which modify the CAT item selection process. This may be done through a randomization, conditional selection, or stratification approach. The effectiveness of each procedure as well as the degree to which measurement precision is sacrificed has been extensively studied with dichotomously scored item pools. However, only recently have researchers begun to examine these procedures in polytomously scored item pools. The current study investigated the performance of six different exposure control mechanisms under three polytomous IRT models in terms of measurement precision, test security, and ease of implementation. The three models examined in the current study were the partial credit, generalized partial credit, and graded response models. In addition to a no exposure control baseline condition, the randomesque, within .10 logits, Sympson-Hetter, conditional Sympson-Hetter, a-Stratified, and enhanced a-Stratified procedures were implemented to control item exposure rates. The a-Stratified and enhanced a-Stratified procedures were not evaluated with the partial credit model. Two variations of the randomesque and within .10 logits procedures were also examined which varied the size of the item group from which the next item to be administered was randomly selected. The results of this study were remarkably similar for all three models and indicated that the randomesque and within .10 logits procedures, when implemented with the six item group variation, provide the best option for controlling exposure rates when impact to measurement precision and ease of implementation are considered. The three item group variations of the procedures were, however, ineffective in controlling exposure, overlap, and pool utilization rates to desired levels. The Sympson-Hetter and conditional Sympson-Hetter procedures were difficult and time consuming to implement, and while they did control exposure rates to the target level, their performance in terms of item overlap (for the Sympson-Hetter) and pool utilization were disappointing. The a-Stratified and enhanced a-Stratified procedures both turned in surprisingly poor performances across all variables. (PsycINFO Database Record (c) 2004 APA, all rights reserved).1 aDavis, LL uhttp://iacat.org/content/strategies-controlling-item-exposure-computerized-adaptive-testing-polytomously-scored-items00503nam a2200097 4500008004100000245009500041210006900136260007100205100001400276856011500290 2003 eng d00aStrategies for controlling testlet exposure rates in computerized adaptive testing systems0 aStrategies for controlling testlet exposure rates in computerize aUnpublished Ph.D. Dissertation, The University of Texas at Austin.1 aBoyd, A M uhttp://iacat.org/content/strategies-controlling-testlet-exposure-rates-computerized-adaptive-testing-systems-000819nas a2200133 4500008004100000245005300041210005300094300001200147490000700159520041300166100001300579700001500592856007800607 2003 eng d00aStudent modeling and ab initio language learning0 aStudent modeling and ab initio language learning a519-5350 v313 aProvides examples of student modeling techniques that have been employed in computer-assisted language learning over the past decade. Describes two systems for learning German: "German Tutor" and "Geroline." Shows how a student model can support computerized adaptive language testing for diagnostic purposes in a Web-based language learning environment that does not rely on parsing technology. (Author/VWL)1 aHeift, T1 aSchulze, M uhttp://iacat.org/content/student-modeling-and-ab-initio-language-learning00576nas a2200145 4500008004100000245012200041210006900163300001300232490000800245100001700253700001500270700001400285700001200299856011900311 2003 eng d00aA study of the feasibility of Internet administration of a computerized health survey: The Headache Impact Test (HIT)0 astudy of the feasibility of Internet administration of a compute a 953-9610 v 121 aBayliss, M S1 aDewey, J E1 aDunlap, I1 aet. al. uhttp://iacat.org/content/study-feasibility-internet-administration-computerized-health-survey-headache-impact-test00368nas a2200133 4500008004100000245003800041210003600079300001200115490000700127100001400134700001500148700001200163856005900175 2002 eng d00aSelf-adapted testing: An overview0 aSelfadapted testing An overview a107-1220 v121 aWise, S L1 aPonsoda, V1 aOlea, J uhttp://iacat.org/content/self-adapted-testing-overview00506nas a2200097 4500008004100000245014600041210006900187100001500256700001500271856012200286 2002 eng d00aSome features of the estimated sampling distribution of the ability estimate in computerized adaptive testing according to two stopping rules0 aSome features of the estimated sampling distribution of the abil1 aRaîche, G1 aBlais, J G uhttp://iacat.org/content/some-features-estimated-sampling-distribution-ability-estimate-computerized-adaptive-testing00527nas a2200109 4500008004100000245013600041210006900177260002000246100001500266700001400281856012200295 2002 eng d00aSome features of the sampling distribution of the ability estimate in computerized adaptive testing according to two stopping rules0 aSome features of the sampling distribution of the ability estima aNew Orleans, LA1 aBlais, J-G1 aRaiche, G uhttp://iacat.org/content/some-features-sampling-distribution-ability-estimate-computerized-adaptive-testing-according00445nas a2200097 4500008004100000245007500041210006900116260003300185100003000218856009900248 2002 eng d00aSTAR Math 2 Computer-Adaptive Math Test and Database: Technical Manual0 aSTAR Math 2 ComputerAdaptive Math Test and Database Technical Ma aWisconsin Rapids, WI: Author1 aRenaissance-Learning-Inc. uhttp://iacat.org/content/star-math-2-computer-adaptive-math-test-and-database-technical-manual00500nas a2200121 4500008004100000245009700041210006900138260001900207100001100226700001000237700001300247856011800260 2002 eng d00aStatistical indexes for monitoring item behavior under computer adaptive testing environment0 aStatistical indexes for monitoring item behavior under computer  aNew Orleans LA1 aZhu, R1 aYu, F1 aLiu, S M uhttp://iacat.org/content/statistical-indexes-monitoring-item-behavior-under-computer-adaptive-testing-environment00520nam a2200097 4500008004100000245010900041210006900150260006700219100001400286856012200300 2002 eng d00aStrategies for controlling item exposure in computerized adaptive testing with polytomously scored items0 aStrategies for controlling item exposure in computerized adaptiv aUnpublished doctoral dissertation, University of Texas, Austin1 aDavis, LL uhttp://iacat.org/content/strategies-controlling-item-exposure-computerized-adaptive-testing-polytomously-scored-ite-000558nas a2200121 4500008004100000245009500041210006900136260008200205100001300287700001200300700001300312856011100325 2002 eng d00aA strategy for controlling item exposure in multidimensional computerized adaptive testing0 astrategy for controlling item exposure in multidimensional compu aAvailable from http://www3. tat.sinica.edu.tw/library/c_tec_rep/c-2002-11.pdf1 aLee, Y H1 aIp, E H1 aFuh, C D uhttp://iacat.org/content/strategy-controlling-item-exposure-multidimensional-computerized-adaptive-testing02030nas a2200253 4500008004100000245010900041210006900150300000900219490000600228520114100234653002101375653001501396653003901411653002201450653002501472653001801497653002201515653005501537653001501592653001201607100001701619700002501636856011501661 2002 eng d00aA structure-based approach to psychological measurement: Matching measurement models to latent structure0 astructurebased approach to psychological measurement Matching me a4-160 v93 aThe present article sets forth the argument that psychological assessment should be based on a construct's latent structure. The authors differentiate dimensional (continuous) and taxonic (categorical) structures at the latent and manifest levels and describe the advantages of matching the assessment approach to the latent structure of a construct. A proper match will decrease measurement error, increase statistical power, clarify statistical relationships, and facilitate the location of an efficient cutting score when applicable. Thus, individuals will be placed along a continuum or assigned to classes more accurately. The authors briefly review the methods by which latent structure can be determined and outline a structure-based approach to assessment that builds on dimensional scaling models, such as item response theory, while incorporating classification methods as appropriate. Finally, the authors empirically demonstrate the utility of their approach and discuss its compatibility with traditional assessment methods and with computerized adaptive testing. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aAssessment10aClassification (Cognitive Process)10aComputer Assisted10aItem Response Theory10aPsychological10aScaling (Testing)10aStatistical Analysis computerized adaptive testing10aTaxonomies10aTesting1 aRuscio, John1 aRuscio, Ayelet Meron uhttp://iacat.org/content/structure-based-approach-psychological-measurement-matching-measurement-models-latent00527nas a2200121 4500008004100000245009200041210006900133260004600202100001300248700001700261700001600278856011100294 2001 eng d00aScoring alternatives for incomplete computerized adaptive tests (Research Report 01-20)0 aScoring alternatives for incomplete computerized adaptive tests  aPrinceton NJ: Educational Testing Service1 aWay, W D1 aGawlick, L A1 aEignor, D R uhttp://iacat.org/content/scoring-alternatives-incomplete-computerized-adaptive-tests-research-report-01-2000459nas a2200097 4500008004100000245008200041210006900123260003300192100003000225856010600255 2001 eng d00aSTAR Early Literacy Computer-Adaptive Diagnostic Assessment: Technical Manual0 aSTAR Early Literacy ComputerAdaptive Diagnostic Assessment Techn aWisconsin Rapids, WI: Author1 aRenaissance-Learning-Inc. uhttp://iacat.org/content/star-early-literacy-computer-adaptive-diagnostic-assessment-technical-manual00313nas a2200097 4500008004100000245004500041210004100086260001500127100001600142856005700158 2001 eng d00aA system for on-the-fly adaptive testing0 asystem for onthefly adaptive testing aSeattle WA1 aWagner, M E uhttp://iacat.org/content/system-fly-adaptive-testing00590nas a2200133 4500008004100000245013300041210006900174260003400243100000900277700001600286700001600302700001700318856012100335 2000 eng d00aA selection procedure for polytomous items in computerized adaptive testing (Measurement and Research Department Reports 2000-5)0 aselection procedure for polytomous items in computerized adaptiv aArnhem, The Netherlands: Cito1 aRijn1 aEggen, Theo1 aHemker, B T1 aSanders, P F uhttp://iacat.org/content/selection-procedure-polytomous-items-computerized-adaptive-testing-measurement-and-research00492nas a2200121 4500008004100000245008600041210006900127260002100196100001500217700001900232700001300251856010600264 2000 eng d00aSolving complex constraints in a-stratified computerized adaptive testing designs0 aSolving complex constraints in astratified computerized adaptive aNew Orleans, USA1 aLeung, C-K1 aChang, Hua-Hua1 aHau, K-T uhttp://iacat.org/content/solving-complex-constraints-stratified-computerized-adaptive-testing-designs00502nas a2200097 4500008004100000245014600041210006900187260001900256100001600275856011300291 2000 eng d00aSome considerations for improving accuracy of estimation of item characteristic curves in online calibration of computerized adaptive testing0 aSome considerations for improving accuracy of estimation of item aNew Orleans LA1 aSamejima, F uhttp://iacat.org/content/some-considerations-improving-accuracy-estimation-item-characteristic-curves-online00395nas a2200109 4500008004100000245006100041210006100102260001600163100001300179700001100192856008200203 2000 eng d00aSpecific information item selection for adaptive testing0 aSpecific information item selection for adaptive testing aNew Orleans1 aDavey, T1 aFan, M uhttp://iacat.org/content/specific-information-item-selection-adaptive-testing00457nas a2200097 4500008004100000245008100041210006900122260003300191100003000224856010500254 2000 eng d00aSTAR Reading 2 Computer-Adaptive Reading Test and Database: Technical Manual0 aSTAR Reading 2 ComputerAdaptive Reading Test and Database Techni aWisconsin Rapids, WI: Author1 aRenaissance-Learning-Inc. uhttp://iacat.org/content/star-reading-2-computer-adaptive-reading-test-and-database-technical-manual00556nas a2200121 4500008004100000245013100041210006900172260001900241100001800260700001700278700001700295856012200312 2000 eng d00aSufficient simplicity or comprehensive complexity? A comparison of probabilitic and stratification methods of exposure control0 aSufficient simplicity or comprehensive complexity A comparison o aNew Orleans LA1 aParshall, C G1 aKromrey, J D1 aHogarty, K Y uhttp://iacat.org/content/sufficient-simplicity-or-comprehensive-complexity-comparison-probabilitic-and-stratification00350nas a2200085 4500008004100000245006300041210006300104100001200167856008500179 1999 eng d00aSome relationship among issues in CAT item pool management0 aSome relationship among issues in CAT item pool management1 aWang, T uhttp://iacat.org/content/some-relationship-among-issues-cat-item-pool-management01178nas a2200133 4500008004100000245006300041210006300104300001100167490000700178520073600185100002000921700001900941856008400960 1999 eng d00aSome reliability estimates for computerized adaptive tests0 aSome reliability estimates for computerized adaptive tests a239-470 v233 aThree reliability estimates are derived for the Bayes modal estimate (BME) and the maximum likelihood estimate (MLE) of θin computerized adaptive tests (CAT). Each reliability estimate is a function of test information. Two of the estimates are shown to be upper bounds to true reliability. The three reliability estimates and the true reliabilities of both MLE and BME were computed for seven simulated CATs. Results showed that the true reliabilities for MLE and BME were nearly identical in all seven tests. The three reliability estimates never differed from the true reliabilities by more than .02 (.01 in most cases). A simple implementation of one reliability estimate was found to accurately estimate reliability in CATs. 1 aNicewander, W A1 aThomasson, G L uhttp://iacat.org/content/some-reliability-estimates-computerized-adaptive-tests00399nas a2200097 4500008004100000245006700041210006700108260002100175100001900196856008600215 1999 eng d00aStandard errors of proficiency estimates in stratum scored CAT0 aStandard errors of proficiency estimates in stratum scored CAT aMontreal, Canada1 aKingsbury, G G uhttp://iacat.org/content/standard-errors-proficiency-estimates-stratum-scored-cat00484nas a2200121 4500008004100000245008200041210006900123260002100192100001700213700001900230700001500249856009800264 1999 eng d00aStudy of methods to detect aberrant response patterns in computerized testing0 aStudy of methods to detect aberrant response patterns in compute aMontreal, Canada1 aIwamoto, C K1 aNungester, R J1 aLuecht, RM uhttp://iacat.org/content/study-methods-detect-aberrant-response-patterns-computerized-testing00555nas a2200109 4500008004100000245008700041210006900128260011900197100001000316700001400326856010500340 1998 eng d00aSimulating nonmodel-fitting responses in a CAT Environment (Research Report 98-10)0 aSimulating nonmodelfitting responses in a CAT Environment Resear aIowa City IA: ACT Inc. (Also presented at National Council on Measurement in Education, 1999: ERIC No. ED 427 042)1 aYi, Q1 aNering, L uhttp://iacat.org/content/simulating-nonmodel-fitting-responses-cat-environment-research-report-98-1000650nas a2200109 4500008004100000245012200041210006900163260014400232100001600376700002700392856012100419 1998 eng d00aSimulating the null distribution of person-fit statistics for conventional and adaptive tests (Research Report 98-02)0 aSimulating the null distribution of personfit statistics for con aEnschede, The Netherlands: University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aMeijer, R R1 aKrimpen-Stoop, E M L A uhttp://iacat.org/content/simulating-null-distribution-person-fit-statistics-conventional-and-adaptive-tests-research01294nas a2200157 4500008004100000245007500041210006900116300001000185490000700195520076100202653003400963100001800997700001401015700001701029856009001046 1998 eng d00aSimulating the use of disclosed items in computerized adaptive testing0 aSimulating the use of disclosed items in computerized adaptive t a48-680 v353 aRegular use of questions previously made available to the public (i.e., disclosed items) may provide one way to meet the requirement for large numbers of questions in a continuous testing environment, that is, an environment in which testing is offered at test taker convenience throughout the year rather than on a few prespecified test dates. First it must be shown that such use has effects on test scores small enough to be acceptable. In this study simulations are used to explore the use of disclosed items under a worst-case scenario which assumes that disclosed items are always answered correctly. Some item pool and test designs were identified in which the use of disclosed items produces effects on test scores that may be viewed as negligible.10acomputerized adaptive testing1 aStocking, M L1 aWard, W C1 aPotenza, M T uhttp://iacat.org/content/simulating-use-disclosed-items-computerized-adaptive-testing00431nas a2200085 4500008004100000245010200041210006900143100001600212856011700228 1998 eng d00aSome considerations for eliminating biases in ability estimation in computerized adaptive testing0 aSome considerations for eliminating biases in ability estimation1 aSamejima, F uhttp://iacat.org/content/some-considerations-eliminating-biases-ability-estimation-computerized-adaptive-testing00489nas a2200097 4500008004100000245013400041210006900175260001500244100001500259856011700274 1998 eng d00aSome item response theory to provide scale scores based on linear combinations of testlet scores, for computerized adaptive tests0 aSome item response theory to provide scale scores based on linea aUrbana, IL1 aThissen, D uhttp://iacat.org/content/some-item-response-theory-provide-scale-scores-based-linear-combinations-testlet-scores00451nas a2200121 4500008004100000245007200041210006900113300001200182490000700194100001500201700001900216856009400235 1998 eng d00aSome practical examples of computerized adaptive sequential testing0 aSome practical examples of computerized adaptive sequential test a229-2490 v351 aLuecht, RM1 aNungester, R J uhttp://iacat.org/content/some-practical-examples-computerized-adaptive-sequential-testing00418nas a2200109 4500008004100000245006400041210006400105260001500169100002000184700001900204856008500223 1998 eng d00aSome reliability estimators for computerized adaptive tests0 aSome reliability estimators for computerized adaptive tests aUrbana, IL1 aNicewander, W A1 aThomasson, G L uhttp://iacat.org/content/some-reliability-estimators-computerized-adaptive-tests00646nas a2200121 4500008004100000245009700041210006900138260014500207100001600352700001600368700002700384856011300411 1998 eng d00aStatistical tests for person misfit in computerized adaptive testing (Research Report 98-01)0 aStatistical tests for person misfit in computerized adaptive tes aEnschede, The Netherlands : University of Twente, Faculty of Educational Science and Technology, Department of Measurement and Data Analysis1 aGlas, C A W1 aMeijer, R R1 aKrimpen-Stoop, E M L A uhttp://iacat.org/content/statistical-tests-person-misfit-computerized-adaptive-testing-research-report-98-0100592nas a2200145 4500008004100000020001000041245007300051210006900124260010000193300000700293100001600300700001600316700002300332856009100355 1998 eng d a98-0100aStatistical tests for person misfit in computerized adaptive testing0 aStatistical tests for person misfit in computerized adaptive tes aEnschede, The NetherlandsbFaculty of Educational Science and Technology, Univeersity of Twente a281 aGlas, C A W1 aMeijer, R R1 aKrimpen-Stoop, E M uhttp://iacat.org/content/statistical-tests-person-misfit-computerized-adaptive-testing00488nas a2200109 4500008004100000245010500041210006900146300001200215490000700227100002300234856012100257 1998 eng d00aStochastic order in dichotomous item response models for fixed, adaptive, and multidimensional tests0 aStochastic order in dichotomous item response models for fixed a a211-2260 v631 avan der Linden, WJ uhttp://iacat.org/content/stochastic-order-dichotomous-item-response-models-fixed-adaptive-and-multidimensional-tests00519nas a2200121 4500008004100000245012000041210006900161300001200230490000600242100001600248700001700264856011600281 1998 eng d00aSwedish Enlistment Battery: Construct validity and latent variable estimation of cognitive abilities by the CAT-SEB0 aSwedish Enlistment Battery Construct validity and latent variabl a107-1140 v61 aMardberg, B1 aCarlstedt, B uhttp://iacat.org/content/swedish-enlistment-battery-construct-validity-and-latent-variable-estimation-cognitive01518nas a2200121 4500008004100000245008800041210006900129300001100198490001000209520105600219100001501275856010601290 1997 eng d00aSelf-adapted testing: Improving performance by modifying tests instead of examinees0 aSelfadapted testing Improving performance by modifying tests ins a83-1040 v10(1)3 aThis paper describes self-adapted testing and some of the evidence concerning its effects, presents possible theoretical explanations for those effects, and discusses some of the practical concerns regarding self-adapted testing. Self-adapted testing is a variant of computerized adapted testing in which the examine makes dynamic choices about the difficulty of the items he or she attempts. Self-adapted testing generates scores that are, in constrast to computerized adapted test and fixed-item tests, uncorrelated with a measure of trait test anxiety. This lack of correlation with an irrelevant attribute of the examine is evidence of an improvement in the construct validity of the scores. This improvement comes at the cost of a decrease in testing efficiency. The interaction between test anxiety and test administration mode is more consistent with an interference theory of test anxiety than a deficit theory. Some of the practical concerns regarding self-adapted testing can be ruled out logically, but others await empirical investigation.1 aRocklin, T uhttp://iacat.org/content/self-adapted-testing-improving-performance-modifying-tests-instead-examinees00538nas a2200121 4500008004100000245009900041210006900140260004600209100001800255700001400273700001700287856011200304 1997 eng d00aSimulating the use of disclosed items in computerized adaptive testing (Research Report 97-10)0 aSimulating the use of disclosed items in computerized adaptive t aPrinceton NJ: Educational Testing Service1 aStocking, M L1 aWard, W C1 aPotenza, M T uhttp://iacat.org/content/simulating-use-disclosed-items-computerized-adaptive-testing-research-report-97-1000380nas a2200121 4500008004100000245004400041210004400085260001800129100001400147700001800161700001300179856006600192 1997 eng d00aSimulation of realistic ability vectors0 aSimulation of realistic ability vectors aGatlinburg TN1 aNering, M1 aThompson, T D1 aDavey, T uhttp://iacat.org/content/simulation-realistic-ability-vectors00543nas a2200121 4500008004100000245013100041210006900172260001500241100001400256700001500270700001700285856011900302 1997 eng d00aA simulation study of the use of the Mantel-Haenszel and logistic regression procedures for assessing DIF in a CAT environment0 asimulation study of the use of the MantelHaenszel and logistic r aChicago IL1 aRoss, L P1 aNandakumar1 aClauser, B E uhttp://iacat.org/content/simulation-study-use-mantel-haenszel-and-logistic-regression-procedures-assessing-dif-cat00410nas a2200121 4500008004100000245005800041210005800099300001200157490000700169100001300176700001800189856008100207 1997 eng d00aSome new item selection criteria for adaptive testing0 aSome new item selection criteria for adaptive testing a203-2260 v221 aVeerkamp1 aBerger, M P F uhttp://iacat.org/content/some-new-item-selection-criteria-adaptive-testing-000408nas a2200121 4500008004100000245005800041210005800099300001200157490000700169100001100176700002000187856007900207 1997 eng d00aSome new item selection criteria for adaptive testing0 aSome new item selection criteria for adaptive testing a203-2260 v221 aBerger1 aVeerkamp, W J J uhttp://iacat.org/content/some-new-item-selection-criteria-adaptive-testing00460nas a2200097 4500008004100000245010700041210006900148260001500217100001900232856011100251 1997 eng d00aSome questions that must be addressed to develop and maintain an item pool for use in an adaptive test0 aSome questions that must be addressed to develop and maintain an aChicago IL1 aKingsbury, G G uhttp://iacat.org/content/some-questions-must-be-addressed-develop-and-maintain-item-pool-use-adaptive-test00441nam a2200097 4500008004100000245005800041210005800099260008700157100002000244856007900264 1997 eng d00aStatistical methods for computerized adaptive testing0 aStatistical methods for computerized adaptive testing aUnpublished doctoral dissertation, University of Twente, Enschede, The Netherlands1 aVeerkamp, W J J uhttp://iacat.org/content/statistical-methods-computerized-adaptive-testing00505nas a2200109 4500008004100000245012400041210006900165260001300234100001300247700001300260856012200273 1996 eng d00aA search procedure to determine sets of decision points when using testlet-based Bayesian sequential testing procedures0 asearch procedure to determine sets of decision points when using aNew York1 aSmith, R1 aLewis, C uhttp://iacat.org/content/search-procedure-determine-sets-decision-points-when-using-testlet-based-bayesian-sequential00508nas a2200109 4500008004100000245009000041210006900131260005400200100001500254700001900269856011000288 1996 eng d00aSome practical examples of computerized adaptive sequential testing (Internal Report)0 aSome practical examples of computerized adaptive sequential test aPhiladelphia: National Board of Medical Examiners1 aLuecht, RM1 aNungester, R J uhttp://iacat.org/content/some-practical-examples-computerized-adaptive-sequential-testing-internal-report00428nas a2200121 4500008004100000245006500041210006500106260001400171100001300185700001200198700001300210856008300223 1996 eng d00aStrategies for managing item pools to maximize item security0 aStrategies for managing item pools to maximize item security aSan Diego1 aWay, W D1 aZara, A1 aLeahy, J uhttp://iacat.org/content/strategies-managing-item-pools-maximize-item-security00365nas a2200085 4500008004100000245006700041210006700108100001800175856008600193 1995 eng d00aShortfall of questions curbs use of computerized graduate exam0 aShortfall of questions curbs use of computerized graduate exam1 aJacobson, R L uhttp://iacat.org/content/shortfall-questions-curbs-use-computerized-graduate-exam00439nas a2200097 4500008004100000245006900041210006700110260005700177100001500234856009200249 1995 eng d00aSome alternative CAT item selection heuristics (Internal report)0 aSome alternative CAT item selection heuristics Internal report aPhiladelphia PA: National Board of Medical Examiners1 aLuecht, RM uhttp://iacat.org/content/some-alternative-cat-item-selection-heuristics-internal-report00396nas a2200109 4500008004100000245005800041210005800099260001900157100001600176700001500192856007900207 1995 eng d00aSome new methods for content balancing adaptive tests0 aSome new methods for content balancing adaptive tests aMinneapolis MN1 aSegall, D O1 aDavey, T C uhttp://iacat.org/content/some-new-methods-content-balancing-adaptive-tests01693nas a2200229 4500008004100000020004100041245006400082210006200146250001500208260000800223300001100231490000700242520102000249653003101269653002501300653001001325653001101335653001101346653000901357100001601366856008101382 1995 jpn d a0021-5236 (Print)0021-5236 (Linking)00aA study of psychologically optimal level of item difficulty0 astudy of psychologically optimal level of item difficulty a1995/02/01 cFeb a446-530 v653 aFor the purpose of selecting items in a test, this study presented a viewpoint of psychologically optimal difficulty level, as well as measurement efficiency, of items. A paper-and-pencil test (P & P) composed of hard, moderate and easy subtests was administered to 298 students at a university. A computerized adaptive test (CAT) was also administered to 79 students. The items of both tests were selected from Shiba's Word Meaning Comprehension Test, for which the estimates of parameters of two-parameter item response model were available. The results of P & P research showed that the psychologically optimal success level would be such that the proportion of right answers is somewhere between .75 and .85. A similar result was obtained from CAT research, where the proportion of about .8 might be desirable. Traditionally a success rate of .5 has been recommended in adaptive testing. In this study, however, it was suggested that the items of such level would be too hard psychologically for many examinees.10a*Adaptation, Psychological10a*Psychological Tests10aAdult10aFemale10aHumans10aMale1 aFujimori, S uhttp://iacat.org/content/study-psychologically-optimal-level-item-difficulty00442nas a2200109 4500008004100000245008200041210006900123260001900192100001700211700001500228856008900243 1994 eng d00aThe selection of test items for decision making with a computer adaptive test0 aselection of test items for decision making with a computer adap aNew Orleans LA1 aReckase, M D1 aSpray, J A uhttp://iacat.org/content/selection-test-items-decision-making-computer-adaptive-test00444nas a2200109 4500008004100000245008200041210006900123260001900192100001500211700001700226856009100243 1994 eng d00aThe selection of test items for decision making with a computer adaptive test0 aselection of test items for decision making with a computer adap aNew Orleans LA1 aSpray, J A1 aReckase, M D uhttp://iacat.org/content/selection-test-items-decision-making-computer-adaptive-test-000282nas a2200109 4500008004100000245002500041210002400066300000900090490000600099100001700105856005000122 1994 eng d00aSelf-adapted testing0 aSelfadapted testing a3-140 v71 aRocklin, T R uhttp://iacat.org/content/self-adapted-testing00431nas a2200097 4500008004100000245006800041210006600109260005100175100002000226856008700246 1994 eng d00aA simple and fast item selection procedure for adaptive testing0 asimple and fast item selection procedure for adaptive testing aResearch (Report 94-13). University of Twente.1 aVeerkamp, W J J uhttp://iacat.org/content/simple-and-fast-item-selection-procedure-adaptive-testing00544nas a2200133 4500008004500000245010900045210006900154300001200223490000700235100001300242700001600255700001700271856012200288 1994 Engldsh 00aA Simulation Study of Methods for Assessing Differential Item Functioning in Computerized Adaptive Tests0 aSimulation Study of Methods for Assessing Differential Item Func a121-1400 v181 aZwick, R1 aThayer, D T1 aWingersky, M uhttp://iacat.org/content/simulation-study-methods-assessing-differential-item-functioning-computerized-adaptive-tests00513nas a2200097 4500008004100000245012400041210006900165260004600234100001300280856012200293 1994 eng d00aA simulation study of the Mantel-Haenszel procedure for detecting DIF with the NCLEX using CAT (Technical Report xx-xx)0 asimulation study of the MantelHaenszel procedure for detecting D aPrinceton NJ: Educational Testing Service1 aWay, W D uhttp://iacat.org/content/simulation-study-mantel-haenszel-procedure-detecting-dif-nclex-using-cat-technical-report-xx00541nas a2200109 4500008004100000245007800041210006900119260011000188100001800298700001800316856009700334 1994 eng d00aSome new item selection criteria for adaptive testing (Research Rep 94-6)0 aSome new item selection criteria for adaptive testing Research R aEnschede, The Netherlands: University of Twente, Department of Educational Measurement and Data Analysis.1 aVeerkamp, W J1 aBerger, M P F uhttp://iacat.org/content/some-new-item-selection-criteria-adaptive-testing-research-rep-94-600522nas a2200121 4500008004100000245011500041210006900156260001200225100001800237700001700255700001400272856011400286 1993 eng d00aA simulated comparison of testlets and a content balancing procedure for an adaptive certification examination0 asimulated comparison of testlets and a content balancing procedu aAtlanta1 aReshetar, R A1 aNorcini, J J1 aShea, J A uhttp://iacat.org/content/simulated-comparison-testlets-and-content-balancing-procedure-adaptive-certification00559nas a2200121 4500008004100000245014400041210006900185260001200254100001800266700001700284700001400301856012200315 1993 eng d00aA simulated comparison of two content balancing and maximum information item selection procedures for an adaptive certification examination0 asimulated comparison of two content balancing and maximum inform aAtlanta1 aReshetar, R A1 aNorcini, J J1 aShea, J A uhttp://iacat.org/content/simulated-comparison-two-content-balancing-and-maximum-information-item-selection-procedures00601nas a2200121 4500008004100000245016000041210006900201260004700270100001300317700001400330700001700344856011800361 1993 eng d00aA simulation study of methods for assessing differential item functioning in computer-adaptive tests (Educational Testing Service Research Rep No RR 93-11)0 asimulation study of methods for assessing differential item func aPrinceton NJ: Educational Testing Service.1 aZwick, R1 aThayer, D1 aWingersky, M uhttp://iacat.org/content/simulation-study-methods-assessing-differential-item-functioning-computer-adaptive-tests00430nas a2200097 4500008004100000245008800041210006900129260001700198100001300215856010400228 1993 eng d00aSome initial experiments with adaptive survey designs for structured questionnaires0 aSome initial experiments with adaptive survey designs for struct aCambridge MA1 aSingh, J uhttp://iacat.org/content/some-initial-experiments-adaptive-survey-designs-structured-questionnaires00471nas a2200109 4500008004100000245010100041210006900142300001000211490001100221100001100232856011800243 1993 eng d00aSome practical considerations when converting a linearly administered test to an adaptive format0 aSome practical considerations when converting a linearly adminis a15-200 v12 (1)1 aWainer uhttp://iacat.org/content/some-practical-considerations-when-converting-linearly-administered-test-adaptive-format00449nas a2200109 4500008004100000245007800041210006900119260001900188100002100207700001500228856009600243 1992 eng d00aScaling of two-stage adaptive test configurations for achievement testing0 aScaling of twostage adaptive test configurations for achievement aNew Orleans LA1 aHendrickson, A B1 aKolen, M J uhttp://iacat.org/content/scaling-two-stage-adaptive-test-configurations-achievement-testing00517nas a2200097 4500008004100000245013200041210006900173260004600242100001100288856012000299 1992 eng d00aSome practical considerations when converting a linearly administered test to an adaptive format (Research Report 92-21 or 13?)0 aSome practical considerations when converting a linearly adminis aPrinceton NJ: Educational Testing Service1 aWainer uhttp://iacat.org/content/some-practical-considerations-when-converting-linearly-administered-test-adaptive-format-000453nas a2200121 4500008004100000245006700041210006600108260002100174100001300195700001700208700001400225856009200239 1992 eng d00aStudent attitudes toward computer-adaptive test administration0 aStudent attitudes toward computeradaptive test administration aSan Francisco CA1 aBaghi, H1 aFerrara, S F1 aGabrys, R uhttp://iacat.org/content/student-attitudes-toward-computer-adaptive-test-administration00465nas a2200109 4500008004100000245007800041210006900119260005300188100001700241700001300258856008400271 1991 eng d00aA simulation study of some simple approaches to the study of DIF for CATs0 asimulation study of some simple approaches to the study of DIF f aInternal memorandum, Educational Testing Service1 aHolland, P W1 aZwick, R uhttp://iacat.org/content/simulation-study-some-simple-approaches-study-dif-cats00518nas a2200121 4500008004100000245007700041210006900118260007500187100001100262700001400273700001300287856009600300 1991 eng d00aSome empirical guidelines for building testlets (Technical Report 91-56)0 aSome empirical guidelines for building testlets Technical Report aPrinceton NJ: Educational Testing Service, Program Statistics Research1 aWainer1 aKaplan, B1 aLewis, C uhttp://iacat.org/content/some-empirical-guidelines-building-testlets-technical-report-91-5600378nas a2200109 4500008003900000245006100039210006100100300001000161490000700171100001200178856007800190 1990 d00aSequential item response models with an ordered response0 aSequential item response models with an ordered response a39-550 v431 aTutz, G uhttp://iacat.org/content/sequential-item-response-models-ordered-response01504nas a2200157 4500008004100000245008900041210006900130300001200199490000700211520093700218653003401155100002001189700001401209700001401223856010901237 1990 eng d00aA simulation and comparison of flexilevel and Bayesian computerized adaptive testing0 asimulation and comparison of flexilevel and Bayesian computerize a227-2390 v273 aComputerized adaptive testing (CAT) is a testing procedure that adapts an examination to an examinee's ability by administering only items of appropriate difficulty for the examinee. In this study, the authors compared Lord's flexilevel testing procedure (flexilevel CAT) with an item response theory-based CAT using Bayesian estimation of ability (Bayesian CAT). Three flexilevel CATs, which differed in test length (36, 18, and 11 items), and three Bayesian CATs were simulated; the Bayesian CATs differed from one another in the standard error of estimate (SEE) used for terminating the test (0.25, 0.10, and 0.05). Results showed that the flexilevel 36- and 18-item CATs produced ability estimates that may be considered as accurate as those of the Bayesian CAT with SEE = 0.10 and comparable to the Bayesian CAT with SEE = 0.05. The authors discuss the implications for classroom testing and for item response theory-based CAT.10acomputerized adaptive testing1 ade Ayala, R. J.1 aDodd, B G1 aKoch, W R uhttp://iacat.org/content/simulation-and-comparison-flexilevel-and-bayesian-computerized-adaptive-testing00373nas a2200109 4500008004100000245005500041210005400096300001000150490000600160100001800166856007900184 1990 eng d00aSoftware review: MicroCAT Testing System Version 30 aSoftware review MicroCAT Testing System Version 3 a82-880 v71 aPatience, W M uhttp://iacat.org/content/software-review-microcat-testing-system-version-300466nas a2200109 4500008004100000245008900041210006900130260001500199100002400214700001400238856010400252 1990 eng d00aThe stability of Rasch pencil and paper item calibrations on computer adaptive tests0 astability of Rasch pencil and paper item calibrations on compute aChicago IL1 aBergstrom, Betty, A1 aLunz, M E uhttp://iacat.org/content/stability-rasch-pencil-and-paper-item-calibrations-computer-adaptive-tests00404nas a2200121 4500008004100000245005300041210005300094300001200147490001000159100002300169700001600192856007400208 1989 eng d00aSome procedures for computerized ability testing0 aSome procedures for computerized ability testing a175-1870 v13(2)1 avan der Linden, WJ1 aZwarts, M A uhttp://iacat.org/content/some-procedures-computerized-ability-testing00424nas a2200097 4500008004100000245007000041210006400111260004600175100001800221856008700239 1988 eng d00aScale drift in on-line calibration (Research Report RR-88-28-ONR)0 aScale drift in online calibration Research Report RR8828ONR aPrinceton NJ: Educational Testing Service1 aStocking, M L uhttp://iacat.org/content/scale-drift-line-calibration-research-report-rr-88-28-onr00423nas a2200097 4500008004100000245006900041210006400110260004900174100001800223856008400241 1988 eng d00aScale drift in on-line calibration (Tech Rep. No. ERIC ED389710)0 aScale drift in online calibration Tech Rep No ERIC ED389710 aEducational Testing Service, Princeton, N.J.1 aStocking, M L uhttp://iacat.org/content/scale-drift-line-calibration-tech-rep-no-eric-ed38971000395nas a2200097 4500008004100000245006800041210006500109260001900174100001700193856008700210 1988 eng d00aSimple and effective algorithms [for] computer-adaptive testing0 aSimple and effective algorithms for computeradaptive testing aNew Orleans LA1 aLinacre, J M uhttp://iacat.org/content/simple-and-effective-algorithms-computer-adaptive-testing00476nas a2200097 4500008004100000245009200041210006900133260004600202100001800248856011200266 1988 eng d00aSome considerations in maintaining adaptive test item pools (Research Report 88-33-ONR)0 aSome considerations in maintaining adaptive test item pools Rese aPrinceton NJ: Educational Testing Service1 aStocking, M L uhttp://iacat.org/content/some-considerations-maintaining-adaptive-test-item-pools-research-report-88-33-onr00481nas a2200097 4500008004100000245009400041210006900135260004900204100001800253856011200271 1988 eng d00aSome considerations in maintaining adaptive test item pools (Tech Rep. No. ERIC ED391814)0 aSome considerations in maintaining adaptive test item pools Tech aEducational Testing Service, Princeton, N.J.1 aStocking, M L uhttp://iacat.org/content/some-considerations-maintaining-adaptive-test-item-pools-tech-rep-no-eric-ed39181400489nas a2200121 4500008004100000245009300041210006900134300001200203490000700215100001200222700002100234856011200255 1987 eng d00aSelf-adapted testing: A performance improving variation of computerized adaptive testing0 aSelfadapted testing A performance improving variation of compute a315-3190 v791 aRocklin1 aO’Donnell, A M uhttp://iacat.org/content/self-adapted-testing-performance-improving-variation-computerized-adaptive-testing00458nas a2200109 4500008004500000245008500045210006900130300001200199490000700211100002400218856010600242 1986 Engldsh 00aSome Applications of Optimization Algorithms in Test Design and Adaptive Testing0 aSome Applications of Optimization Algorithms in Test Design and  a381-3890 v101 aTheunissen, T J J M uhttp://iacat.org/content/some-applications-optimization-algorithms-test-design-and-adaptive-testing-000452nas a2200109 4500008004100000245008500041210006900126300001200195490000700207100002400214856010400238 1986 eng d00aSome applications of optimization algorithms in test design and adaptive testing0 aSome applications of optimization algorithms in test design and  a381-3890 v101 aTheunissen, T J J M uhttp://iacat.org/content/some-applications-optimization-algorithms-test-design-and-adaptive-testing00376nam a2200097 4500008004100000245005600041210005500097260003000152100001600182856008000198 1985 eng d00aSequential analysis: Tests and confidence intervals0 aSequential analysis Tests and confidence intervals aNew York: Springer-Verlag1 aSiegmund, D uhttp://iacat.org/content/sequential-analysis-tests-and-confidence-intervals01650nas a2200121 4500008004100000245007900041210006900120300001200189490000700201520121400208100001401422856009201436 1985 eng d00aA structural comparison of conventional and adaptive versions of the ASVAB0 astructural comparison of conventional and adaptive versions of t a305-3220 v203 aExamined several structural models of similarity between the Armed Services Vocational Aptitude Battery (ASVAB) and a battery of computerized adaptive tests designed to measure the same aptitudes. 12 plausible models were fitted to sample data in a double cross-validation design. 1,411 US Navy recruits completed 10 ASVAB subtests. A computerized adaptive test version of the ASVAB subtests was developed on item pools of approximately 200 items each. The items were pretested using applicants from military entrance processing stations across the US, resulting in a total calibration sample size of approximately 60,000 for the computerized adaptive tests. Three of the 12 models provided reasonable summaries of the data. One model with a multiplicative structure (M. W. Browne; see record 1984-24964-001) performed quite well. This model provides an estimate of the disattenuated method correlation between conventional testing and adaptive testing. In the present data, this correlation was estimated to be 0.97 and 0.98 in the 2 halves of the data. Results support computerized adaptive tests as replacements for conventional tests. (33 ref) (PsycINFO Database Record (c) 2004 APA, all rights reserved).1 aCudeck, R uhttp://iacat.org/content/structural-comparison-conventional-and-adaptive-versions-asvab00432nas a2200109 4500008004100000245007700041210006900118260001900187100001500206700001700221856008400238 1984 eng d00aThe selection of items for decision making with a computer adaptive test0 aselection of items for decision making with a computer adaptive  aNew Orleans LA1 aSpray, J A1 aReckase, M D uhttp://iacat.org/content/selection-items-decision-making-computer-adaptive-test00365nas a2200121 4500008004100000245003400041210003400075260003800109300001000147100001300157700001400170856005900184 1983 eng d00aSmall N justifies Rasch model0 aSmall N justifies Rasch model aNew York, NY. USAbAcademic Press a51-611 aLord, FM1 aBock, R D uhttp://iacat.org/content/small-n-justifies-rasch-model00438nam a2200109 4500008004100000245006600041210006200107260004200169100001800211700001500229856008400244 1983 eng d00aThe stochastic modeling of elementary psychological processes0 astochastic modeling of elementary psychological processes aCambridge: Cambridge University Press1 aTownsend, J T1 aAshby, G F uhttp://iacat.org/content/stochastic-modeling-elementary-psychological-processes00506nas a2200097 4500008004100000245007700041210006900118260010900187100001400296856009800310 1983 eng d00aThe stratified adaptive computerized ability test (Research Report 73-3)0 astratified adaptive computerized ability test Research Report 73 aMinneapolis: University of Minnesota, Department of Psychology, Computerized Adaptive Testing Laboratory1 aWeiss, DJ uhttp://iacat.org/content/stratified-adaptive-computerized-ability-test-research-report-73-3-000325nas a2200109 4500008004500000245003700045210003700082300001200119490000600131100001800137856006000155 1982 Engldsh 00aSequential Testing for Selection0 aSequential Testing for Selection a337-3510 v61 aWeitzman, R A uhttp://iacat.org/content/sequential-testing-selection-000319nas a2200109 4500008004100000245003700041210003700078300001200115490000600127100001800133856005800151 1982 eng d00aSequential testing for selection0 aSequential testing for selection a337-3510 v61 aWeitzman, R A uhttp://iacat.org/content/sequential-testing-selection00365nas a2200133 4500008003900000245003800039210003600077300001200113490000700125100001400132700001300146700001400159856005800173 1980 d00aA simple form of tailored testing0 asimple form of tailored testing a301-3030 v501 aNisbet, J1 aAdams, M1 aArthur, J uhttp://iacat.org/content/simple-form-tailored-testing00590nas a2200097 4500008004100000245005900041210005900100260024100159100001700400856007500417 1980 eng d00aSome decision procedures for use with tailored testing0 aSome decision procedures for use with tailored testing aD. J. Weiss (Ed.), Proceedings of the 1979 Computerized Adaptive Testing Conference (pp. 79-100). Minneapolis MN: University of Minnesota, Department of Psychology, Psychometric Methods Program, Computerized Adaptive Testing Laboratory.1 aReckase, M D uhttp://iacat.org/content/some-decision-procedures-use-tailored-testing00585nas a2200097 4500008004100000245005400041210005400095260025000149100001300399856007500412 1980 eng d00aSome how and which for practical tailored testing0 aSome how and which for practical tailored testing aL. J. T. van der Kamp, W. F. Langerak and D.N.M. de Gruijter (Eds): Psychometrics for educational debates (pp. 189-206). New York: John Wiley and Sons. Computer-Assisted Instruction, Testing, and Guidance (pp. 139-183). 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