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van der Linden, Wim J. – Applied Psychological Measurement, 2009
An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the…
Descriptors: Simulation, Adaptive Testing, Vocational Aptitude, Bayesian Statistics
Peer reviewedvan der Linden, Wim J. – Psychometrika, 1998
This paper suggests several item selection criteria for adaptive testing that are all based on the use of the true posterior. Some of the ability estimators produced by these criteria are discussed and empirically criticized. (SLD)
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing
Peer reviewedvan der Linden, Wim J. – Applied Psychological Measurement, 1999
Proposes a procedure for empirical initialization of the trait (theta) estimator in adaptive testing that is based on the statistical relation between theta and background variables known prior to test administration. Illustrates the procedure for an adaptive version of a test from the Dutch General Aptitude Battery. (SLD)
Descriptors: Adaptive Testing, Aptitude Tests, Bayesian Statistics, Computer Assisted Testing
Glas, Cees A. W.; van der Linden, Wim J. – 2001
To reduce the cost of item writing and to enhance the flexibility of item presentation, items can be generated by item-cloning techniques. An important consequence of cloning is that it may cause variability on the item parameters. Therefore, a multilevel item response model is presented in which it is assumed that the item parameters of a…
Descriptors: Adaptive Testing, Bayesian Statistics, Computer Assisted Testing, Costs
van der Linden, Wim J. – 1996
R. J. Owen (1975) proposed an approximate empirical Bayes procedure for item selection in adaptive testing. The procedure replaces the true posterior by a normal approximation with closed-form expressions for its first two moments. This approximation was necessary to minimize the computational complexity involved in a fully Bayesian approach, but…
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computation

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