01349nas a2200229   4500008004100000020001300041245011700054210006900171300001200240490000700252520057500259653000800834653003400842653001900876653001500895100002000910700001600930700001800946700001500964700001400979856012600993       2010                        eng d  a0191886900aDetection of aberrant item score patterns in computerized adaptive testing: An empirical example using the CUSUM0 aDetection of aberrant item score patterns in computerized adapti  a921-9250 v483 aThe scalability of individual trait scores on a computerized adaptive test (CAT) was assessed through investigating the consistency of individual item score patterns. A sample of N = 428 persons completed a personality CAT as part of a career development procedure. To detect inconsistent item score patterns, we used a cumulative sum (CUSUM) procedure. Combined information from the CUSUM, other personality measures, and interviews showed that similar estimated trait values may have a different interpretation.Implications for computer-based assessment are discussed.10aCAT10acomputerized adaptive testing10aCUSUM approach10aperson Fit1 aEgberink, I J L1 aMeijer, R R1 aVeldkamp, B P1 aSchakel, L1 aSmid, N G  uhttp://www.iacat.org/content/detection-aberrant-item-score-patterns-computerized-adaptive-testing-empirical-example-using01879nas a2200169   4500008004100000245013100041210006900172300001200241490000700253520122700260653003001487653002501517653001501542653001601557100001601573856012001589       2004                        eng d00aUsing patterns of summed scores in paper-and-pencil tests and computer-adaptive tests to detect misfitting item score patterns0 aUsing patterns of summed scores in paperandpencil tests and comp  a119-1360 v413 aTwo new methods have been proposed to determine unexpected sum scores on subtests (testlets) both for paper-and-pencil tests and computer adaptive tests. A method based on a conservative bound using the hypergeometric distribution, denoted ρ, was compared with a method where the probability for each score combination was calculated using a highest density region (HDR). Furthermore, these methods were compared with the standardized log-likelihood statistic with and without a correction for the estimated latent trait value (denoted as l-super(*)-sub(z) and l-sub(z), respectively). Data were simulated on the basis of the one-parameter logistic model, and both parametric and nonparametric logistic regression was used to obtain estimates of the latent trait. Results showed that it is important to take the trait level into account when comparing subtest scores. In a nonparametric item response theory (IRT) context, on adapted version of the HDR method was a powerful alterative to ρ. In a parametric IRT context, results showed that l-super(*)-sub(z) had the highest power when the data were simulated conditionally on the estimated latent trait level. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aComputer Assisted Testing10aItem Response Theory10aperson Fit10aTest Scores1 aMeijer, R R  uhttp://www.iacat.org/content/using-patterns-summed-scores-paper-and-pencil-tests-and-computer-adaptive-tests-detect01631nas 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-testing01859nas a2200193   4500008004100000245012400041210007100165300001200236490000700248520110700255653002101362653002601383653002201409653001401431653005901445100001601504700001701520856012801537       2001                        eng d00aNouveaux développements dans le domaine du testing informatisé [New developments in the area of computerized testing]0 aNouveaux développements dans le domaine du testing informatisé N  a221-2300 v463 aL'usage de l'évaluation assistée par ordinateur s'est fortement développé depuis la première formulation de ses principes de base dans les années soixante et soixante-dix. Cet article offre une introduction aux derniers développements dans le domaine de l'évaluation assistée par ordinateur, en particulier celui du testing adaptative informatisée (TAI). L'estimation de l'aptitude, la sélection des items et le développement d'une base d'items dans le cas du TAI sont discutés. De plus, des exemples d'utilisations innovantes de l'ordinateur dans des systèmes intégrés de testing et de testing via Internet sont présentés. L'article se termine par quelques illustrations de nouvelles applications du testing informatisé et des suggestions pour des recherches futures.Discusses the latest developments in computerized psychological assessment, with emphasis on computerized adaptive testing (CAT). Ability estimation, item selection, and item pool development in CAT are described. Examples of some innovative approaches to CAT are presented. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Applications10aComputer Assisted10aDiagnosis10aPsychological Assessment computerized adaptive testing1 aMeijer, R R1 aGrégoire, J  uhttp://www.iacat.org/content/nouveaux-d%C3%A9veloppements-dans-le-domaine-du-testing-informatis%C3%A9-new-developments-area00915nas a2200145   4500008004100000245006100041210006000102300001100162490000700173520043400180653003400614100001600648700001600664856008900680       1999                        eng d00aComputerized Adaptive Testing: Overview and Introduction0 aComputerized Adaptive Testing Overview and Introduction  a187-940 v233 aUse of computerized adaptive testing (CAT) has increased substantially since it was first formulated in the 1970s. This paper provides an overview of CAT and introduces the contributions to this Special Issue. The elements of CAT discussed here include item selection procedures, estimation of the latent trait, item exposure, measurement precision, and item bank development. Some topics for future research are also presented. 10acomputerized adaptive testing1 aMeijer, R R1 aNering, M L  uhttp://www.iacat.org/content/computerized-adaptive-testing-overview-and-introduction