Computerized adaptive testing with item clones

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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 three-parameter logistic model describing response behavior are sampled from a multivariate normal distribution associated with a parent item. In this approach to item calibration, only distributions of item parameters are estimated. Therefore, the savings in item calibration costs for the item cloning model are potentially enormous. A marginal maximum likelihood and a Bayesian item-calibration procedure are formulated. Further, a two-stage item selection procedure for computerized adaptive testing is, presented. First, a set of items cloned from the same parent item is selected to be optimal at the ability estimate. Second, a random item from this set is administered. Simulation studies illustrate the accuracy of the item pool calibration and ability estimation procedures. An appendix describes Bayes model estimates for the item cloning model.
Original languageEnglish
Place of PublicationEnschede
PublisherUniversity of Twente
Number of pages23
Publication statusPublished - 2001

Publication series

NameResearch Report TO/OMD
PublisherUniversity of Twente, Faculty of Educational Science and Technology


  • item clones
  • Computerized Adaptive Testing
  • IR-103568
  • marginal maximum likelihood
  • item shells
  • METIS-203898
  • Bayesian item selection
  • multilevel item response theory


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