Über den Autor
Shenyang Guo, PhD, is the Kuralt Distinguished Professor at the School of Social Work, University of North Carolina. The author of numerous articles on statistical methods and research reports in child welfare, child mental health services, welfare, and health care, Guo has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses on event history analysis, hierarchical linear modeling, growth curve modeling, and program evaluation. He has given many invited workshops on statistical methods-including event history analysis and propensity score matching-at the NIH Summer Institute, Children's Bureau, and at conferences of the Society of Social Work and Research. He led the data analysis planning for the National Survey of Child and Adolescent Well-Being (NSCAW) longitudinal analysis.
2. Counterfactual Framework and Assumptions
3. Conventional Methods for Data Balancing
4. Sample Selection and Related Methods
5. Propensity Score Matching and Related Methods
6. Matching Estimators
7. Propensity Score Analysis with Nonparametric Regression
8. Selection Bias and Sensitivity Analysis
9. Concluding Remarks
Propensity Score Matching provides readers with a systematic review of the origins, history, and statistical foundations of PSM and illustrates how to use PSM methods for solving evaluation problems. With a strong focus on practical applications, the authors explore various types of data and evaluation problems, strategies for using the methods, and the limitations of PSM. Unlike the existing textbooks on program evaluation, Guo and Fraser's Propensity Score Matching delves into statistical concepts, formulas, and models underlying the application of PSM.