Recent advances have enabled diagnostic classification models (DCMs) to accommodate longitudinal data. These longitudinal DCMs were developed to study how examinees change, or transition, between different attribute mastery statuses over time. This study examines using longitudinal DCMs as an approach to assessing growth and serves three purposes:…

Annual student achievement data derived from state assessment programs have led to widespread enthusiasm for statistical models suitable for longitudinal analysis. The current policy environment's adherence to high stakes accountability vis-a-vis No Child Left Behind (NCLB)'s universal proficiency mandate has fostered an impoverished view of what…