Dr. Haiyan Bai is a Professor at the University of Central Florida. She earned her Ph.D. in quantitative research methodology at the University of Cincinnati. Her research interests include issues that revolve around statistical/quantitative methods, specifically, propensity score methods, resampling techniques, research design, measurement, and the application of statistical methods in social and behavioral sciences.Dr. M. H. Clark is an Associate Lecturer, statistical consultant, and program evaluator at the University of Central Florida. She has a Ph.D. in Experimental Psychology with a specialization in research design and statistics from the University of Memphis. Her specific areas of expertise are in causal inference, selection bias in non-randomized experiments, and propensity score methods.

• Series Editor’s IntroductionAbout the AuthorsAcknowledgments1. Basic Concepts of Propensity Score Methods1.1 Causal Inference1.2 Propensity Scores1.3 Assumptions1.4 Summary of the Chapter2. Covariate Selection and Propensity Score Estimation2.1 Covariate Selection2.2 Propensity Score Estimation2.3 Summary of the Chapter2.4 An Example3. Propensity Score Adjustment Methods3.1 Propensity Score Matching3.2 Other Propensity Score Adjustment Methods3.3 Summary of the Chapter3.4 An Example4. Covariate Evaluation and Causal Effect Estimation4.1 Evaluating the Balance of Covariate Distributions4.2 Causal Effect Estimation4.3 Sensitivity Analysis4.4 Summary of the Chapter4.5 An Example5. Conclusion5.1 Limitations of the Propensity Score Methods and How to Address Them5.2 Summary of Propensity Score Procedures5.3 Final CommentsReferencesIndex

"Haiyan Bai and M.H. Clark have delivered a readable and easily applicable guide for eager researchers with data-in-hand, chomping at the bit to determine whether and how their empirical challenges might be addressed through the careful application of propensity score methods."