Shenyang Guo, Ph.D., is the Frank J. Bruno Distinguished Professor of Social Work Research in the George Warren Brown School of Social Work at Washington University in St. Louis. He is an expert on the application of advanced statistical models to the solution of social welfare problems.
Guo is the author (with Mark Fraser) of Propensity Score Analysis: Statistical Methods and Applications (2015), a comprehensive guide to the many ways that propensity scores can be used to improve causal inference. Other books include Survival Analysis (2010) and Structural Equation Modeling (2011) (with Natasha Bowen).
Guo has published more than 100 articles in peer-reviewed journals and is among the top 2% of most cited scientists in the world, according to a recent study published on PLoS Biology by experts at Stanford University. He is the Vice President of American Society of Social Work and Research. He is the Fellow of American Academy of Social Work and Social Welfare. He is on the editorial boards of Social Service Review, Journal of the Society for Social Work and Children and Youth Services Review.
Guo was previously a faculty member at the University of North Carolina at Chapel Hill where he taught graduate courses on event history analysis, hierarchical linear modeling, growth curve modeling, structural equation modeling, and program evaluation. There he received many awards for his teaching:
- Dean’s Recognition of Teaching Excellence in the School of Social Work at UNC at Chapel Hill for academic years 2004-05, 2005-06, 2006-07, 2008-09, 2009-10.
- 2010 Distinguished Teaching Award for Post-Baccalaureate Instruction, UNC at Chapel Hill. Read more.
You can visit his university webpage here.
Dr. Guo’s books can be found below:
Structural Equation Modeling (2011)
Survival Analysis (2010)
Propensity Score Analysis: Basics
Thursday, March 14 –
Saturday, March 16, 2024
Propensity score analysis is a relatively new and innovative class of statistical methods that has proven useful for evaluating the effects of treatments or interventions when using nonexperimental or observational data. Although regression analysis is most often used to adjust...View Details
Propensity Score Analysis: Advanced
Thursday, April 4 –
Saturday, April 6, 2024
Propensity score analysis is a relatively new and innovative class of statistical methods that has proven useful for evaluating the effects of treatments or interventions when using nonexperimental or observational data. This seminar is a follow-up of Propensity Score Analysis:...View Details