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Eggen, Theo J. H. M. – Educational Research and Evaluation, 2011
If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…
Descriptors: Test Length, Adaptive Testing, Classification, Item Analysis
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Thompson, Nathan A. – Practical Assessment, Research & Evaluation, 2011
Computerized classification testing (CCT) is an approach to designing tests with intelligent algorithms, similar to adaptive testing, but specifically designed for the purpose of classifying examinees into categories such as "pass" and "fail." Like adaptive testing for point estimation of ability, the key component is the…
Descriptors: Adaptive Testing, Computer Assisted Testing, Classification, Probability
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Finkelman, Matthew David – Applied Psychological Measurement, 2010
In sequential mastery testing (SMT), assessment via computer is used to classify examinees into one of two mutually exclusive categories. Unlike paper-and-pencil tests, SMT has the capability to use variable-length stopping rules. One approach to shortening variable-length tests is stochastic curtailment, which halts examination if the probability…
Descriptors: Mastery Tests, Computer Assisted Testing, Adaptive Testing, Test Length
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Ramon Barrada, Juan; Veldkamp, Bernard P.; Olea, Julio – Applied Psychological Measurement, 2009
Computerized adaptive testing is subject to security problems, as the item bank content remains operative over long periods and administration time is flexible for examinees. Spreading the content of a part of the item bank could lead to an overestimation of the examinees' trait level. The most common way of reducing this risk is to impose a…
Descriptors: Item Banks, Adaptive Testing, Item Analysis, Psychometrics
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Bradlow, Eric T.; Weiss, Robert E. – Journal of Educational and Behavioral Statistics, 2001
Compares four methods that map outlier statistics to a familiarity probability scale (a "P" value). Explored these methods in the context of computerized adaptive test data from a 1995 nationally administered computerized examination for professionals in the medical industry. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Probability, Test Construction
Zwick, Rebecca – 1994
The Mantel Haenszel (MH; 1959) approach of Holland and Thayer (1988) is a well-established method for assessing differential item functioning (DIF). The formula for the variance of the MH DIF statistic is based on work by Phillips and Holland (1987) and Robins, Breslow, and Greenland (1986). Recent simulation studies showed that the MH variances…
Descriptors: Adaptive Testing, Evaluation Methods, Item Bias, Measurement Techniques
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van der Linden, Wim J.; Veldkamp, Bernard P. – Journal of Educational and Behavioral Statistics, 2004
Item-exposure control in computerized adaptive testing is implemented by imposing item-ineligibility constraints on the assembly process of the shadow tests. The method resembles Sympson and Hetter's (1985) method of item-exposure control in that the decisions to impose the constraints are probabilistic. The method does not, however, require…
Descriptors: Probability, Law Schools, Admission (School), Adaptive Testing
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Eggen, Theo J. H. M.; Verschoor, Angela J. – Applied Psychological Measurement, 2006
Computerized adaptive tests (CATs) are individualized tests that, from a measurement point of view, are optimal for each individual, possibly under some practical conditions. In the present study, it is shown that maximum information item selection in CATs using an item bank that is calibrated with the one- or the two-parameter logistic model…
Descriptors: Adaptive Testing, Difficulty Level, Test Items, Item Response Theory
Patsula, Liane N.; Steffen, Mandred – 1997
One challenge associated with computerized adaptive testing (CAT) is the maintenance of test and item security while allowing for daily testing. An alternative to continually creating new pools containing an independent set of items would be to consider each CAT pool as a sample of items from a larger collection (referred to as a VAT) rather than…
Descriptors: Adaptive Testing, Computer Assisted Testing, Item Banks, Multiple Choice Tests
Zwick, Rebecca – 1995
This paper describes a study, now in progress, of new methods for representing the sampling variability of Mantel-Haenszel differential item functioning (DIF) results, based on the system for categorizing the severity of DIF that is now in place at the Educational Testing Service. The methods, which involve a Bayesian elaboration of procedures…
Descriptors: Adaptive Testing, Bayesian Statistics, Classification, Computer Assisted Testing
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Meijer, Rob R. – Journal of Educational Measurement, 2004
Two new methods have been proposed to determine unexpected sum scores on sub-tests (testlets) both for paper-and-pencil tests and computer adaptive tests. A method based on a conservative bound using the hypergeometric distribution, denoted p, was compared with a method where the probability for each score combination was calculated using a…
Descriptors: Probability, Adaptive Testing, Item Response Theory, Scores
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Frick, Theodore W. – American Educational Research Journal, 1990
Analysis of patterns in time (APT) is presented as a method for gathering information about observable phenomena so that probabilities of temporal patterns of events can be estimated empirically. APT is illustrated through a classroom study of 25 mentally handicapped students and in a study of 2 computer-based tests. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Data Analysis, Disabilities
Papa, Frank J.; Schumacker, Randall E. – 1995
Measures of the robustness of disease class-specific diagnostic concepts could play a central role in training programs designed to assure the development of diagnostic competence. In the pilot study, the authors used disease/sign-symptom conditional probability estimates, Monte Carlo procedures, and artificial intelligence (AI) tools to create…
Descriptors: Adaptive Testing, Artificial Intelligence, Classification, Clinical Diagnosis