A priori reliability of tests with cut score

The theoretical probability of misclassification in a mastery test is exactly computed using the raw score probability distribution (in the Rasch model) as a function of the examinee's latent ability. The resulting misclassification probability curve, together with the latent ability distribution in the group of examinees, completely determines the expected rate of classification errors. It is shown that several distinct ability thresholds, playing different roles in connection to classification reliability, can be associated to a test with a single cut score. In particular, it is possible to define (and compute) two relevant ability intervals, which encapsulate the functioning of a mastery test (about and far from the cut score, respectively); the dependence of these intervals on the item difficulty spectrum is investigated. Extension to the 2PL model is also discussed, with emphasis on the effects of weighted scoring.