Wanyi Wang, Ph.D.


Center for Research Design and Analysis

Wanyi Wang, Ph.D.


IHSH 10116


Wanyi Wang serves as a Biostatistician for the Center for Research Design and Analysis (CRDA) under the Office of Research and Sponsored Programs for Texas Woman's University on the Houston campus. All CRDA statisticians and analysts provide research development and analysis support to faculty and students across all three campuses.


Dr. Wang received her PhD in Exercise Science and MS in Statistics and Data Science from University of Texas at Austin. Dr. Wang specializes in grant proposal consultation, research design, and analysis. She has strong experience in research design, especially in the areas of biomedical and health sciences. She is also experienced in proposal development, sample size determination, data analysis, and manuscript preparation, as well as extensive knowledge of advanced statistical methods, including regression, structural equation modeling (SEM), and mixed models. She is fluent in SPSS, Amos, GraphPad Prism, and MedCalc, and experienced in R, SAS, Stata, and Tableau. Dr. Wang also provides workshops for grant development, research design, and statistical software, such as SPSS.


Dr. Wang’s personal research interest focuses on public health, nutritional supplementation, and exercise metabolism. Her most recent research about carbohydrate and protein supplementations on mTOR signaling pathways post resistance exercise was published in PlosOne. Currently and over the past several years, she also works with faculties and students in Health Science on the design and statistical analysis, and published several more articles. One of her primary goals is to support faculty increasing their publications and help them to be more competitive in external grant applications, particularly federal grants.


Ph.D., Exercise Science, University of Texas at Austin, 2016


Exercise Science; Statistics; Data Science; Grant Proposals; Research Design; Analysis; Biomedical and Health Sciences; Sample Size Determination; Manuscript Preparation; Structural Equation Modeling; Mixed Models