A procedure for empirical initialization of adaptive testing algorithms

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In constrained adaptive testing, the numbers of constraints needed to control the content of the tests can easily run into the hundreds. Proper initialization of the algorithm becomes a requirement because the presence of large numbers of constraints slows down the convergence of the ability estimator. In this paper, an empirical initialization of the algorithm is proposed based on the statistical relation between the ability variable and background variables known prior to the test. The relation is modeled using a two-parameter logistic version of an item response theory (IRT) model with manifest predictors discussed in A.H. Zwinderman (1991). An empirical example shows how an (incomplete) sample of response data and data on background variables can be used to derive an initial ability estimate or an empirical prior distribution for the ability parameter. An appendix gives the derivation of an equation for the estimator.
Original languageEnglish
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


  • Ability
  • Adaptive testing
  • Item response theory
  • Test construction
  • Computer assisted testing
  • Foreign countries
  • Algorithms


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