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:…

A sample (N=37) of currently published achievement tests was surveyed as to the availability of five types of technical data: (1) item selection techniques; (2) standardization; (3) types of norms; (4) types of validating data; and (5) types of reliability data. Recommendations for publishers and cautions for test users are given. (BS)