On the Use of Mcmc Cat with Empirical Prior Information to Improve the Efficiency of Cat

In this paper, empirical prior information about the candidate is applied in computerized adaptive testing (CAT). The main objective of CAT is to improve efficiency of test administration. In this paper, 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 both simulated and real data, it is demonstrated that the method produces more accurate ability estimates, especially for short tests and when reproducing boundary abilities. This implies that operational problems of CAT related to weak measurement precision under particular conditions, can be reduced as well. In the empirical example, the methods were applied to CAT for intelligence testing in the area of personnel selection. Other promising applications would be in the medical world, where testing efficiency is of paramount importance as well.