The Bayesian way of accounting for the effects of error in the ability and item parameters in adaptive testing is through the joint posterior distribution of all parameters. An optimized Markov chain Monte Carlo algorithm for adaptive testing is presented, which samples this distribution in real time to score the examinee's ability and optimally…

This paper examines some characteristics of the statistics associated with the sampling distribution of the proficiency level estimate when the Rasch model is used. These characteristics allow the judgment of the meaning to be given to the proficiency level estimate obtained in adaptive testing, and as a consequence, they can illustrate the…

The present study showed that Rasch-based adaptive tests--when item domains were finite and specifiable--had greater precision in domain score estimation than test forms created by random sampling of items. Results were replicated across four data sources representing a variety of criterion-referenced, domain-based tests varying in length.…