Biography

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.

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Education

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

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.

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Courses

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) Intermediate statistical methods in education; includes probability theory, distribution theory, interval estimation, hypothesis testing, regression and correlational analysis, and analysis of variance.

Structural Equation Modeling (EPSY 590) Seminar in educational psychology; topics relate to the areas of specialization represented by the various divisions within the department. 0 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated to a maximum of 8 hours in the same or separate semesters, if topics vary.

Jiang, Ge (Gabriella)

Assistant Professor, Educational Psychology

Contact

Office

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

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