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).
|Publisher:||Guilford Publications, Inc.|
|Product dimensions:||6.00(w) x 9.40(h) x 1.00(d)|
About the Author
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. He is the recipient of several awards for excellence in research, teaching, and service. 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, where she has been the recipient of several awards for excellence in research and teaching.
Table of Contents
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
Applied researchers and graduate students in psychology, education, management, sociology, and public health. Serves as a core text for a graduate seminar in Propensity Score Analysis, or as a supplement in such courses as Advanced Quantitative Methods, Research Design, and Causal Modeling.
Most Helpful Customer Reviews
The authors do a wonderful job constructing the book in a manner that first delineates the fundamentals of propensity score analysis. The authors then provide explanations of how propensity score methods can assist researchers in reducing bias created by confounding variables. Furthermore, as statistical methodology continues to advance, the authors provide a detailed discussion about current developments in propensity score analysis. Overall, this book is an excellent resource for scholars who want to conduct sound, yet robust social-science and/or health related research.
This book presents several topics on propensity score methods and techniques. The topics are presented in a way concepts and fundamentals of propensity score come first and then estimation and matching methods for the score are detailed. There are a few features I really like. Concepts, methods, issues and solutions related to the propensity score are easy to understand and learn. SAS, Mplus or STATA code has been demonstrated for some basic topics on how to estimate the propensity score and match the units in different groups. It is a good book for professionals and academic researchers in social, psychological, and health/medical sciences.