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.


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

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

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

Awards, Honors, Associations

College of Education Course Development Funding, College of Education, 2020 - 2020

List of Teachers Ranked as Excellent by Their Students, University of Illinois at Urbana-Champaign, 2019 - 2019

List of Teachers Ranked as Outstanding by Their Students, University of Illinois at Urbana-Champaign, 2019 - 2019

Workshop Award, Meta-Analysis Training Institute (MATI), Institute of Education Sciences (IES), 2019 - 2019

Research & Service

The broad objective of Dr. Jiang's research is to develop quantitative methods for analyzing complex data in educational and psychological research, including non-normally distributed data, small samples, high-dimensional data, and categorical data.

Dr. Jiang's research centers around two areas: (1) structural equation modeling (SEM) and (2) statistical learning. In the area of SEM, she studies test statistics, fit indices, robust estimation methods, measurement invariance, and equivalence testing. In the area of statistical learning, she studies regularization methods (lasso, ridge, elastic net) in the contexts of clustering, with applications on genomic datasets. Substantively, she is interested in developing software packages to facilitate the use of quantitative methods and applying them to educational and psychological research.

Dr. Jiang is open to mentoring students in the Fall of 2020.


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.

Statistical Inference in Educ (EPSY 580) Students must have taken EPSY 480 or equivalent introductory statistics course.

Structural Equation Modeling (EPSY 590) Introduction to a general class of multivariate techniques that are known as structural equation modeling (SEM). Students will learn the techniques of SEM using the computer software R and Mplus, as well as have the opportunity to apply the methods to real datasets in education, psychology, and related fields. Topics that will be covered include mediation/moderation model; path analysis; confirmatory factor analysis; multi-group SEM; SEM with categorical variables; model identification and estimation; and assessing goodness-of-fit. Other topics that might be covered include asymptotic theory, robust inference, and missing data in SEM. Prerequisite: EPSY 580 and 581, or equivalents.

Profile Picture for Ge (Gabriella) Jiang

Assistant Professor, Quantitative and Evaluative Research Methodologies



226C Education Building
1310 S. Sixth St.
Champaign, IL 61820

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