01472nas a2200217   4500008004100000245004600041210004600087300001200133490000700145520085100152653001801003653002501021653003201046653001301078653002201091653002401113653001501137100001601152700001501168856007101183       2005                        eng d00aTest construction for cognitive diagnosis0 aTest construction for cognitive diagnosis  a262-2770 v293 aAlthough cognitive diagnostic models (CDMs) can be useful in the analysis and interpretation of existing tests, little has been developed to specify how one might construct a good test using aspects of the CDMs. This article discusses the derivation of a general CDM index based on Kullback-Leibler information that will serve as a measure of how informative an item is for the classification of examinees. The effectiveness of the index is examined for items calibrated using the deterministic input noisy "and" gate model (DINA) and the reparameterized unified model (RUM) by implementing a simple heuristic to construct a test from an item bank. When compared to randomly constructed tests from the same item bank, the heuristic shows significant improvement in classification rates. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Measurement)10aCognitive Assessment10aItem Analysis (Statistical)10aProfiles10aTest Construction10aTest Interpretation10aTest Items1 aHenson, R K1 aDouglas, J  uhttp://www.iacat.org/content/test-construction-cognitive-diagnosis01923nas a2200241   4500008004100000245009400041210006900135300001200204490000700216520110100223653002101324653001301345653003001358653005701388653000901445653003201454653002601486653002001512100001401532700001301546700001501559856010701574       2003                        eng d00aA Bayesian method for the detection of item preknowledge in computerized adaptive testing0 aBayesian method for the detection of item preknowledge in comput  a121-1370 v273 aWith the increased use of continuous testing in computerized adaptive testing, new concerns about test security have evolved, such as how to ensure that items in an item pool are safeguarded from theft. In this article, procedures to detect test takers using item preknowledge are explored. When test takers use item preknowledge, their item responses deviate from the underlying item response theory (IRT) model, and estimated abilities may be inflated. This deviation may be detected through the use of person-fit indices. A Bayesian posterior log odds ratio index is proposed for detecting the use of item preknowledge. In this approach to person fit, the estimated probability that each test taker has preknowledge of items is updated after each item response. These probabilities are based on the IRT parameters, a model specifying the probability that each item has been memorized, and the test taker's item responses. Simulations based on an operational computerized adaptive test (CAT) pool are used to demonstrate the use of the odds ratio index. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aCheating10aComputer Assisted Testing10aIndividual Differences computerized adaptive testing10aItem10aItem Analysis (Statistical)10aMathematical Modeling10aResponse Theory1 aMcLeod, L1 aLewis, C1 aThissen, D  uhttp://www.iacat.org/content/bayesian-method-detection-item-preknowledge-computerized-adaptive-testing01840nas a2200205   4500008004100000245009000041210006900131300001100200490000700211520111400218653002101332653003001353653001601383653003201399653001601431653004501447100001501492700001601507856011101523       2003                        eng d00aA comparative study of item exposure control methods in computerized adaptive testing0 acomparative study of item exposure control methods in computeriz  a71-1030 v403 aThis study compared the properties of five methods of item exposure control within the purview of estimating examinees' abilities in a computerized adaptive testing (CAT) context. Each exposure control algorithm was incorporated into the item selection procedure and the adaptive testing progressed based on the CAT design established for this study. The merits and shortcomings of these strategies were considered under different item pool sizes and different desired maximum exposure rates and were evaluated in light of the observed maximum exposure rates, the test overlap rates, and the conditional standard errors of measurement. Each method had its advantages and disadvantages, but no one possessed all of the desired characteristics. There was a clear and logical trade-off between item exposure control and measurement precision. The M. L. Stocking and C. Lewis conditional multinomial procedure and, to a slightly lesser extent, the T. Davey and C. G. Parshall method seemed to be the most promising considering all of the factors that this study addressed. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aEducational10aItem Analysis (Statistical)10aMeasurement10aStrategies computerized adaptive testing1 aChang, S-W1 aAnsley, T N  uhttp://www.iacat.org/content/comparative-study-item-exposure-control-methods-computerized-adaptive-testing01631nas a2200241   4500008004100000245005900041210005800100300001200158490000700170520087100177653002101048653003401069653002801103653002001131653003201151653002501183653001501208653002701223653002201250653001601272100001601288856008501304       2002                        eng d00aOutlier detection in high-stakes certification testing0 aOutlier detection in highstakes certification testing  a219-2330 v393 aDiscusses recent developments of person-fit analysis in computerized adaptive testing (CAT). Methods from statistical process control are presented that have been proposed to classify an item score pattern as fitting or misfitting the underlying item response theory model in CAT Most person-fit research in CAT is restricted to simulated data. In this study, empirical data from a certification test were used. Alternatives are discussed to generate norms so that bounds can be determined to classify an item score pattern as fitting or misfitting. Using bounds determined from a sample of a high-stakes certification test, the empirical analysis showed that different types of misfit can be distinguished Further applications using statistical process control methods to detect misfitting item score patterns are discussed. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10acomputerized adaptive testing10aEducational Measurement10aGoodness of Fit10aItem Analysis (Statistical)10aItem Response Theory10aperson Fit10aStatistical Estimation10aStatistical Power10aTest Scores1 aMeijer, R R  uhttp://www.iacat.org/content/outlier-detection-high-stakes-certification-testing02407nas a2200193   4500008004100000245010700041210006900148300000900217490000700226520169400233653003201927653002501959653002001984653001802004653002602022653001402048100002602062856012502088       2000                        eng d00aAn exploratory analysis of item parameters and characteristics that influence item level response time0 aexploratory analysis of item parameters and characteristics that  a18120 v613 aThis research examines the relationship between item level response time and (1) item discrimination, (2) item difficulty, (3) word count, (4) item type, and (5) whether a figure is included in an item. Data are from the Graduate Management Admission Test, which is currently offered only as a computerized adaptive test. Analyses revealed significant differences in response time between the five item types: problem solving, data sufficiency, sentence correction, critical reasoning, and reading comprehension. For this reason, the planned pairwise and complex analyses were run within each item type. Pairwise curvilinear regression analyses explored the relationship between response time and item discrimination, item difficulty, and word count. Item difficulty significantly contributed to the prediction of response time for each item type; two of the relationships were significantly quadratic. Item discrimination significantly contributed to the prediction of response time for only two of the item types; one revealed a quadratic relationship and the other a cubic relationship. Word count had significant linear relationship with response time for all the item types except reading comprehension, for which there was no significant relationship. Multiple regression analyses using word count, item difficulty, and item discrimination predicted between 35.4% and 71.4% of the variability in item response time across item types. The results suggest that response time research should consider the type of item that is being administered and continue to explore curvilinear relationships between response time and its predictor variables. (PsycINFO Database Record (c) 2005 APA )10aItem Analysis (Statistical)10aItem Response Theory10aProblem Solving10aReaction Time10aReading Comprehension10aReasoning1 aSmith, Russell Winsor  uhttp://www.iacat.org/content/exploratory-analysis-item-parameters-and-characteristics-influence-item-level-response-time