Optimal Sequential Designs for On-line Item Estimation
- 1 March 1994
- journal article
- research article
- Published by Cambridge University Press (CUP) in Psychometrika
- Vol. 59 (1) , 59-75
- https://doi.org/10.1007/bf02294265
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
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