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 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.

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