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van der Linden, Wim J. – Journal of Educational and Behavioral Statistics, 2009
A bivariate lognormal model for the distribution of the response times on a test by a pair of test takers is presented. As the model has parameters for the item effects on the response times, its correlation parameter automatically corrects for the spuriousness in the observed correlation between the response times of different test takers because…
Descriptors: Cheating, Models, Reaction Time, Correlation
van der Linden, Wim J. – 1997
The case of adaptive testing under a multidimensional logistic response model is addressed. An adaptive algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood (ML) estimator of a linear combination of abilities of interest. The item selection criterion is a simple expression in closed form. In addition, it is…
Descriptors: Ability, Adaptive Testing, Algorithms, Computer Assisted Testing
Peer reviewedvan der Linden, Wim J. – Journal of Educational and Behavioral Statistics, 1999
Proposes an algorithm that minimizes the asymptotic variance of the maximum-likelihood (ML) estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to evaluate. Also shows how the algorithm can be modified if the interest is in a test with a "simple ability structure."…
Descriptors: Ability, Adaptive Testing, Algorithms, 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. – 1988
Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with multiple item parameters. The models are able to cope…
Descriptors: Ability Identification, Computer Assisted Testing, Elementary Education, Elementary School Students

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