This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).

Wei Pan, PhD, is Associate Professor and Biostatistician in the School of Nursing at Duke University. His research interests include causal inference (confounding, propensity score analysis, and resampling), advanced modeling (multilevel, structural, and mediation and moderation), meta-analysis, and their applications in the social, behavioral, and health sciences. Dr. Pan has published over 50 articles in refereed journals, as well as other publications, and has served on the editorial boards of several journals.

Haiyan Bai, PhD, is Associate Professor of Quantitative Research Methodology at the University of Central Florida. Her interests include resampling methods, propensity score analysis, research design, measurement and evaluation, and the applications of statistical methods in the educational and behavioral sciences. She has published a book on resampling methods as well as numerous articles in refereed journals, and has served on the editorial boards of several journals. Dr. Bai is a Fellow of the Academy for Teaching, Learning, and Leadership and a Faculty Fellow at the University of Central Florida.

I. Fundamentals of Propensity Score Analysis
1. Propensity Score Analysis: Concepts and Issues, Wei Pan & Haiyan Bai
2. Overview of Implementing Propensity Score Analysis in Statistical Software, Megan Schuler
II. Propensity Score Estimation, Matching, and Covariate Balance
3. Propensity Score Estimation with Boosted Regression, Lane F. Burgette, Daniel F. McCaffrey, & Beth Ann Griffin
4. Methodological Considerations in Implementing Propensity Score Matching, Haiyan Bai
5. Evaluating Covariate Balance, Cassandra W. Pattanayak
III. Weighting Schemes and Other Strategies for Outcome Analysis after Matching
6. Propensity Score Adjustment Methods, M. H. Clark
7. Propensity Score Analysis with Matching Weights, Liang Li, Tom H. Greene, & Brian C. Sauer
8. Robust Outcome Analysis for Propensity-Matched Designs, Scott F. Kosten, Joseph W. McKean, & Bradley E. Huitema
IV. Propensity Score Analysis on Complex Data
9. Latent Growth Modeling of Longitudinal Data with Propensity-Score-Matched Groups, Walter L. Leite
10. Propensity Score Matching on Multilevel Data, Qiu Wang
11. Propensity Score Analysis with Complex Survey Samples, Debbie L. Hahs-Vaughn
V. Sensitivity Analysis and Extensions Related to Propensity Score Analysis
12. Missing Data in Propensity Scores, Robin Mitra
13. Unobserved Confounding in Propensity Score Analysis, Rolf H. H. Groenwold & Olaf H. Klungel
14. Propensity-Score-Based Sensitivity Analysis, Lingling Li, Changyu Shen, & Xiaochun Li
15. Prognostic Scores in Clustered Settings, Ben Kelcey & Christopher M. Swoboda
Author Index
Subject Index
About the Editors
Contributors