Workshops on Foundational and Cutting-Edge Methods
of Statistical Analysis
June 12th to June 21st, 2024
Xi’an, China
[Introduction]
Big Data, Machine Learning, Social Computing, and Artificial Intelligence are currently the cutting-edge methods and applications to address research questions of social behavioral, health, and policy research. Through an introduction to the basics of statistical methods and cutting-edge topics, students will be able to understand the transformation that quantitative research methods are undergoing during the information revolution. This workshop is going to discuss how to integrate the changes brought about by the computer technology revolution with the existing results of traditional methodological science, enhance students’ interest in statistical learning and big data research, and help acquire ideas and orientation for further study of more advanced methodological science.
[Course Leader]
Shenyang Guo is the Frank J. Bruno Distinguished Professor of Social Work Research at Washington University in St. Louis, USA, and Guest Professor at Xi’an Jiaotong University. He is a fellow of the American Academy of Social Work and Social Welfare and Vice President of the Society of Social Work Research for 2021. He has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses that address event history analysis, hierarchical linear modeling, growth curve modeling, propensity score analysis, and program evaluation. He has published over 100 peer-reviewed papers in the field of social evidence-based research and social work. He is one of the top 2% of scientists in the world in 2021, as selected by researchers at PLOS Biology and Stanford University, and one of the top 100 contributors to social work in the world, ranked 37th by Sage magazine in 2022.
[Curriculum System]
Based on general knowledge of statistical methods, the workshops discuss the algebra of expectations, likelihood functions, generalized boosted regression, general structural equation models, unobserved heterogeneity, and causality. The workshops introduce interesting topics about the opportunities and challenges of statistical models. It will also review the social impact and theoretical iteration of statistical methods, explore the content of Big Data, Machine Learning, Social Computing, and Artificial Intelligence, and provide new insights for future academic research.
[Curriculum Format]
Synchronous online and offline teaching.
[Curriculum Arrangement]
Part |
Date |
Content |
Hours |
1 |
June 12th B-205 |
The Algebra of Expectations and Its Applications |
19:00-22:00 3h |
2 |
June 13th B-205 |
The Maximum Likelihood and Its Applications |
19:00-22:00 3h |
3 |
June 14th B-205 |
The Generalized Linear Model and Its Explanations: Odds Ratio vs Predicted Probabilities |
19:00-22:00 3h |
4 |
June 16th B-205 |
The Results of Cox Regression |
19:00-22:00 3h |
5 |
June 17th B-205 |
The Complication and Simplification of Statistical Modeling: Examples of Structural Equation Models |
19:00-22:00 3h |
6 |
June 18th B-205 |
The Propensity Score Subclassification |
19:00-22:00 3h |
7 |
June 19th B-205 |
The Unobserved Heterogeneity |
19:00-22:00 3h |
8 |
June 20th B-205 |
The Argument and Challenge of Causal Inference in the Past Forty Years (I) |
19:00-22:00 3h |
9 |
June 21st B-205 |
The Argument and Challenge of Causal Inference in the Past Forty Years (II) |
19:00-22:00 3h |
[Recorded Courses Information]
At present, the online live broadcasting website is available after registration.
[Enrollment]
Graduate students with unlimited grades as well as undergraduates. An unlimited number of students offline and online. Undergraduate and international students are expected to register.
[Application]
Students who are interested in applying to attend this workshop can scan the OR code link below. Or send email to 18710878638@163.com
