Dr. Jiang is interested in (1) modeling the relationships among latent variables using structural equation modeling (SEM); (2) estimating the causal effects of interventions using quasi-experimental methods; (3) leveraging the power of data mining methods to account for nonlinear relationships; and (4) programming statistical methods into accessible tools and applications.

In the area of SEM, Dr. Jiang focuses on increasing SEM’s robustness to violations of assumptions. Specifically, she has worked on model fit statistics, effect sizes, measurement invariance, and item factor analysis. In the area of causal analysis, Dr. Jiang focuses on developing methods for observational studies where random assignment is not feasible. Specifically, she has developed an integrated framework using propensity score analysis and data mining methods to estimate the effects of interventions in the presence of nonlinear relationships. In the area of program development, Dr. Jiang is the author and maintainer of multiple R packages.

Ph.D. in Quantitative Psychology, University of Notre Dame, 2018

M.S. in Applied and Computational Mathematics and Statistics, University of Notre Dame, 2017

B.S. in Psychology, Central University of Finance and Economics, 2013

Dr. Jiang is an assistant professor in the QUERIES division of the Department of Educational Psychology. She primarily studies structural equation modeling and quasi-experimental methods and is broadly interested in quantitative methods in social sciences.