Abstract

In this paper, empirical prior information is introduced in computer adaptive testing. Despite its increasing use, the method suffers from a weak measurement precision, especially under particular conditions. Therefore, it is shown how the inclusion of background variables both in the initialization and the ability estimation is able to improve the accuracy of ability estimates. In particular, a Gibbs sampler scheme is proposed in the phases of interim and final ability estimation. By using simulated data, it is demonstrated that the method produces more accurate ability estimates, especially for short tests and when reproducing boundary abilities.