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Optimal sequential designs for on-line item estimation

Abstract

Replenishing item pools for on-line ability testing requires innovative and efficient data collection designs. By generating localD-optimal designs for selecting individual examinees, and consistently estimating item parameters in the presence of error in the design points, sequential procedures are efficient for on-line item calibration. The estimating error in the on-line ability values is accounted for with an item parameter estimate studied by Stefanski and Carroll. LocallyD-optimaln-point designs are derived using the branch-and-bound algorithm of Welch. In simulations, the overall sequential designs appear to be considerably more efficient than random seeding of items.

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This report was prepared under the Navy Manpower, Personnel, and Training R&D Program of the Office of the Chief of Naval Research under Contract N00014-87-0696. The authors wish to acknowledge the valuable advice and consultation given by Ronald Armstrong, Charles Davis, Bradford Sympson, Zhaobo Wang, Ing-Long Wu and three anonymous reviewers.

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Jones, D.H., Jin, Z. Optimal sequential designs for on-line item estimation. Psychometrika 59, 59–75 (1994). https://doi.org/10.1007/BF02294265

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  • DOI: https://doi.org/10.1007/BF02294265

Key words

  • branch-and-bound
  • computerized adaptive test
  • exactn-pointD-optimal
  • integer programming
  • item response theory
  • measurement errors model
  • on-line testing
  • sequential design