Multistage testing (MST) has many practical advantages over typical item-level computerized adaptive testing (CAT), but there is a substantial tradeoff when using MST because of its reduced level of adaptability. In typical MST, the first stage almost always performs as a routing stage in which all test takers see a linear test form. If multiple test sections measure different but moderately or highly correlated traits, then a score estimate for one section might be capable of adaptively selecting item modules for following sections without having to administer routing stages repeatedly for each section. In this article, a new framework for developing MST with intersectional routing (ISR) was proposed and evaluated under several research conditions with different MST structures, section score distributions and relationships, and types of regression models for ISR. The overall findings of the study suggested that MST with ISR approach could improve measurement efficiency and test optimality especially with tests with short lengths.
Analyzing Different Module Characteristics in Computer Adaptive Multistage Testing
