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Ge Jiang


Dr. Jiang completed her doctorate in 2018 in quantitative psychology from the University of Notre Dame and earned her master's degree in 2017 in applied and computational mathematics and statistics. Dr. Jiang is interested in the development and applications of structural equation modeling, statistical learning, item factor analysis, and experimental design in education, psychology, and related social science.


EPSY 574: Quasi-Experimental Design (EPSY 574) Intermediate course for graduate students in education and related fields. Goal is to prepare students to design and conduct quasi-experimental studies and critique the work of others in an informed, systematic way. Students will read and discuss foundational and contemporary issues in design, validity, sampling and loss, regression artifacts, analysis and causal inferences.

EPSY 579: Structural Equation Modeling (EPSY 579) Structural Equation Modeling (SEM) is a general class of multivariate techniques that models relationships between latent variables and observed variables (?measurement models?) and relationships among latent variables (?structural models?) simultaneously. Students will learn the theoretical background of SEM as well as the techniques using programming language R. Topics covered in this class include mediation/moderation model; confirmatory factor analysis; model fit evaluation; multi-group SEM; latent growth modeling; MTMM model; and SEM with categorical variables.

EPSY 580: Statistical Inference in Education (EPSY 580) Intermediate statistical methods in education; includes probability theory, distribution theory, interval estimation, hypothesis testing, regression and correlational analysis, and analysis of variance.

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