This study investigates measurement decision theory (MDT) as an underlying model for computer adaptive testing when the goal is to classify examinees into one of a finite number of groups. The first analysis compares MDT with a popular item response theory model and finds little difference in terms of the percentage of correct classifications. The…

In measurement research, data simulations are a commonly used analytical technique. While simulation designs have many benefits, it is unclear if these artificially generated datasets are able to accurately capture real examinee item response behaviors. This potential lack of comparability may have important implications for administration of…

Problem Statement: Computerized adaptive testing (CAT) is a sophisticated and efficient way of delivering examinations. In CAT, items for each examinee are selected from an item bank based on the examinee's responses to the items. In this way, the difficulty level of the test is adjusted based on the examinee's ability level. Instead of…

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the…

The purpose of this study was to use realistically simulated data to evaluate various CAT designs for use with the verbal reasoning measure of the Medical College Admissions Test (MCAT). Factors such as item pool depth, content constraints, and item formats often cause repeated adaptive administrations of an item at ability levels that are not…