Multidimensional adaptive testing with a minimum error-variance criterion

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Abstract

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 shown how the algorithm can be adapted if the interest is in a test with a "simple information structure." The statistical properties of the adaptive ML estimator are demonstrated for a two-dimensional item pool with several linear combinations of the two abilities.
Original languageUndefined
Place of PublicationEnschede, the Netherlands
PublisherUniversity of Twente, Faculty Educational Science and Technology
Publication statusPublished - 1997

Publication series

NameOMD research report
PublisherUniversity of Twente, Faculty of Educational Science and Technology
No.97-03

Keywords

  • Maximum Likelihood Statistics
  • IR-103605
  • Foreign Countries
  • Computer Assisted Testing
  • Ability
  • Algorithms
  • Adaptive Testing
  • Criteria
  • Selection

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