Shiyu Wang

This research project will advance statistical methods for estimation and inference on attribute hierarchy within the framework of cognitive diagnosis models (CDM). CDMs have been widely applied to the field of educational assessment, psychiatric diagnosis, and other social sciences. In conjunction with diagnostic assessments, this type of model uses subjects’ observed responses to specifically designed diagnostic items to determine the fine-grained classification of the underlying latent attribute patterns. Attribute hierarchy, or the relationship among attributes, plays an important role in designing an effective diagnostic assessment. However, there is a lack of efficient statistical tools for estimating attribute hierarchy from observed data. This project will develop a series of Bayesian approaches for estimating attribute hierarchy. The project will contribute to the newly developed interdisciplinary field that integrates artificial intelligence with psychometrics. The new methods will be useful for applied research in education and psychology, as well as other social science disciplines. The investigators will apply the new methods to educational data sets. Graduate students will participate in the conduct of this research, and publicly available software will be developed.