Propensity Score Methods and Applications
1st Edition
Published on 29. December 2018
Book
Paperback/Softback
136 pages
978-1-5063-7805-3 (ISBN)
Description
A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method.
Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.
Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.
Reviews / Votes
"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." -- Adam Seth Litwin "This volume provides a thorough introduction to propensity score methods while taking care to not overwhelm the reader with dense mathematics. Simple examples, straightforward language, and a catalog of software options make this a fine primer for researchers seeking to incorporate propensity score methods into their own research plans, and an excellent desk reference." -- Christopher Michael Sedelmaier "This book contains excellent descriptions of propensity score matching with practical examples and clear guides using different software programs." -- Mido ChangMore details
Series
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 216 mm
Width: 140 mm
Weight
181 gr
ISBN-13
978-1-5063-7805-3 (9781506378053)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
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.
Content
Series Editor's Introduction
About the Authors
Acknowledgments
1. Basic Concepts of Propensity Score Methods
1.1 Causal Inference
1.2 Propensity Scores
1.3 Assumptions
1.4 Summary of the Chapter
2. Covariate Selection and Propensity Score Estimation
2.1 Covariate Selection
2.2 Propensity Score Estimation
2.3 Summary of the Chapter
2.4 An Example
3. Propensity Score Adjustment Methods
3.1 Propensity Score Matching
3.2 Other Propensity Score Adjustment Methods
3.3 Summary of the Chapter
3.4 An Example
4. Covariate Evaluation and Causal Effect Estimation
4.1 Evaluating the Balance of Covariate Distributions
4.2 Causal Effect Estimation
4.3 Sensitivity Analysis
4.4 Summary of the Chapter
4.5 An Example
5. Conclusion
5.1 Limitations of the Propensity Score Methods and How to Address Them
5.2 Summary of Propensity Score Procedures
5.3 Final Comments
References
Index
About the Authors
Acknowledgments
1. Basic Concepts of Propensity Score Methods
1.1 Causal Inference
1.2 Propensity Scores
1.3 Assumptions
1.4 Summary of the Chapter
2. Covariate Selection and Propensity Score Estimation
2.1 Covariate Selection
2.2 Propensity Score Estimation
2.3 Summary of the Chapter
2.4 An Example
3. Propensity Score Adjustment Methods
3.1 Propensity Score Matching
3.2 Other Propensity Score Adjustment Methods
3.3 Summary of the Chapter
3.4 An Example
4. Covariate Evaluation and Causal Effect Estimation
4.1 Evaluating the Balance of Covariate Distributions
4.2 Causal Effect Estimation
4.3 Sensitivity Analysis
4.4 Summary of the Chapter
4.5 An Example
5. Conclusion
5.1 Limitations of the Propensity Score Methods and How to Address Them
5.2 Summary of Propensity Score Procedures
5.3 Final Comments
References
Index