Overview

Using real-world data and exercises at the end of each chapter, this text focuses on the procedures for implementing propensity score methods for research in social sciences, instead of merely demonstrating the effectiveness of the method. Haiyan Bai and M. H. Clark cover the basic concepts, assumptions, procedures, available software packages and step-by-step examples for implementing PSM.

Full Product Details

Author:   Haiyan Bai ,  M. H. Clark
Publisher:   SAGE Publications Inc
Imprint:   SAGE Publications Inc
Weight:   0.180kg
ISBN:  

9781506378053

ISBN 10:   1506378056
Pages:   136
Publication Date:   29 December 2018
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

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

Reviews

""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 Chang

This book contains excellent descriptions of propensity score matching with practical examples and clear guides using different software programs. -- Mido Chang 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 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

Author Information

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.

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