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Optimal Sequential Designs for On-line Item Estimation

Published online by Cambridge University Press:  01 January 2025

Douglas H. Jones and
Zhiying Jin
Douglas H. Jones*
Affiliation:
Graduate School of Management, Rutgers, The State University of New Jersey
Zhiying Jin
Affiliation:
Graduate School of Management, Rutgers, The State University of New Jersey
*
Requests for reprints should be sent to Douglas H. Jones; Graduate School of Management; Rutgers, The State University; 92 New Street; Newark, NJ 07102.
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Abstract

Replenishing item pools for on-line ability testing requires innovative and efficient data collection designs. By generating local D-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. Locally D-optimal n-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.

Keywords

branch-and-boundcomputerized adaptive testexact n-point D-optimalinteger programmingitem response theorymeasurement errors modelon-line testingsequential design

Information

Type
Original Paper
Information
Psychometrika , Volume 59 , Issue 1 , March 1994, pp. 59 - 75
Copyright
Copyright © 1994 The Psychometric Society

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Footnotes

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-C-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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