Computerized adaptive testing (CAT) comes with many advantages. Unfortunately, it still is quite expensive to develop and maintain an operational CAT. In this paper, various steps involved in developing an operational CAT are described and literature on these topics is reviewed. Bayesian CAT is introduced as an alternative, and the use of empirical priors is proposed for estimating item and person parameters to reduce the costs of CAT. Methods to elicit empirical priors are presented and a two small examples are presented that illustrate the advantages of Bayesian CAT. Implications of the use of empirical priors are discussed, limitations are mentioned and some suggestions for further research are formulated.
Bayesian IRT modeling; Computerized Adaptive Testing; Eliciting priors; Item Response Theory; Item selection; Parameter estimation
