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Propensity Score Analysis, by Wei Pan
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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).
- Sales Rank: #1088758 in eBooks
- Published on: 2015-03-18
- Released on: 2015-03-18
- Format: Kindle eBook
Review
"Pan and Bai have assembled a comprehensive volume on all aspects of propensity score methods. Both the user and the statistician will find something to like in this book. I recommend it."--William R. Shadish, PhD, Distinguished Professor of Psychology, University of California, Merced
"This book effectively synthesizes general principles of PSA with recent developments regarding complex issues such as estimation techniques, covariate balance, weighting, complex datasets, and sensitivity analysis. The discussion of statistical software and examples of computer code are helpful additions. This book will be useful to graduate students and applied researchers who are interested in learning about PSA for the first time or who have some knowledge and would like to learn about issues and recent developments. I recommend it as a textbook for graduate-level courses in methods of causal inference or as a reference for researchers in the social and biomedical sciences."--Suzanne E. Graham, EdD, Department of Education, University of New Hampshire
"There is no question that this book will serve as an excellent resource for those who want to add PSA to their repertoire of analytical methods. The chapters provide sufficient materials and examples to help both newbies and seasoned analysts deal with the methodological and practical challenges of applying PSA in research work."--Xitao Fan, PhD, Chair Professor and Dean, Faculty of Education, University of Macau, China
"This book is a go-to guide for designing and analyzing observational data. The editors have produced a brilliant work that addresses both methodological and practical issues in propensity score analysis. A 'must read' for all biostatisticians as well as applied researchers in the social, behavioral, and health sciences."--Ding-Geng (Din) Chen, PhD, School of Nursing and Department of Biostatistics and Computational Biology, University of Rochester Medical Center
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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
My reading view
By rose
Several features are included in this book. 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. More interestingly, 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. It is a good book for professionals and academic researchers in social, psychological, and health/medical sciences.
1 of 1 people found the following review helpful.
The authors do a wonderful job constructing the book in a manner that first ...
By Samir Patel
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.
1 of 1 people found the following review helpful.
the basic user can get a good understanding of the major concepts
By Xray04
The book does a thorough job of covering all of the contemporary issues surrounding Propensity Score Analysis. Although the description is on a relatively high technical level, the basic user can get a good understanding of the major concepts, while the expert user, can obtain a thorough treatment of the topic.
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