Using response-time constraints in item selection to control for differential speededness in computerized adaptive testing

This paper proposes an item selection algorithm that can be used to neutralize the effect of time limits in computer adaptive testing. The method is based on a statistical model for the response-time distributions of the test takers on the items in the pool that is updated each time a new item has been administered. Predictions from the model are used as constraints in a 0-1 linear programming model for constrained adaptive testing that maximizes the accuracy of the ability estimator. The method is demonstrated empirically using an item pool from an operational, large-scale computer adaptive test.