Chun Wang, University of Washington

In psychological and behavioral science, the increasing availability of rich survey data and the emerging needs of assessing multifaceted constructs pose great challenges to existing techniques used to handle and analyze heterogeneous assessment data. The multiple-group multidimensional IRT (MIRT) model that relaxes strict measurement invariance assumption is a viable psychometric tool to establish commensurate measures for the constructs of interest. To calibrate this model more efficiently, we developed a family of innovative Gaussian variational expectation-maximization (GVEM) methods. In this talk, I will briefly introduce GVEM followed by two latest developments, including (1) using an important-sampling enhanced GVEM algorithm to improve estimation precision in confirmatory MIRT; and (2) using a multiple-group GVEM with regularization to detect differential item functioning.

about the speaker

Dr. Chun Wang is an Associate Professor of Measurement and Statistics in the College of Education at the University of Washington, and an affiliated faculty with the Center for Statistics and the Social Sciences at UW. Her research focus is broadly situated in the field of educational and psychological measurement, with a focus on leveraging artificial intelligence and machine learning-based psychometric tools to produce assessments that are reliable and secure, fair, inclusive, and provide interpretable diagnostic feedback. She is the editor of the Journal of Educational Measurement, and associate editor of the British Journal of Mathematical and Statistical Psychology. She has received numerous awards, including the 2020 Anne Anastasi Distinguished Early Career Contributions Award from American Psychological Association (Division 5) as well as the 2017 Psychometric Society Early Career Award.