Using diagnostic classification models to tease apart overlapping skills

Language proficiency assessments can have complex designs, with highly correlated latent constructs and items that test multiple constructs simultaneously. An appropriate statistical framework is crucial for analyzing such complex assessment structure and for providing test users with meaningful, easily interpretable reports. While item response theory (IRT) is a staple of assessment design and analysis, this framework primarily focuses on the items in an assessment and how they relate to one another. The framework of diagnostic classification models (DCMs) provides analysis of the data that estimates not only a test taker’s ability score(s) for each latent ability but also classifies each test taker as a master or non-master in each ability area and groups test takers by their skill ability profiles.