Using Propensity Scores in Quasi-Experimental Designs
1st Edition
Published on 23. July 2013
Book
Paperback/Softback
360 pages
978-1-4522-0526-7 (ISBN)
Description
Using an accessible approach perfect for social and behavioral science students (requiring minimal use of matrix and vector algebra), Holmes examines how propensity scores can be used to both reduce bias with different kinds of quasi-experimental designs and fix or improve broken experiments. This unique book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of social and behavioral science disciplines.
Reviews / Votes
"I find the accessibility of propensity scores to be the most appealing contribution of this text. As the authors pointed out, many articles on propensity scores use statistical equations and programs that many users are unfamiliar with. Most students that take workshops from me want how-to instructions for computing and using propensity scores. I like that this book would present them from a methodological and applied approach, rather than the more-common theoretical approach." -- M. H. Clark "The worked up examples in different software programs are a definite strength." -- Tina Savla "The discussion of alternatives in order to control sources of influence is very good." -- Michael A. Milburn "I was most intrigued by some of the material covered near the end of the outline, in particular the chapters on missing data and repairing broken experiments. It is one thing to cover the statistical theory, but in my experience students really need guidance in how to handle messy research design and data situations. In the same vein, I liked seeing how many of the chapters appear to end with sections on assessing the adequacy and sufficiency of the techniques covered in those chapters." -- Douglas LukeMore details
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 19 mm
Weight
672 gr
ISBN-13
978-1-4522-0526-7 (9781452205267)
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
Person
William Holmes is a faculty member at the University of Massachusetts, Boston, in the College of Public and Community Services. He has evaluated criminal justice and community programs serving families, children, individuals who have suffered abuse, and those with substance abuse problems. He coauthored with Kay Kitson Portrait of Divorce, which won the William Goode Award from the Family Section of the American Sociological Association, and coauthored Family Abuse: Consequences, Theories, and Responses with Calvin Larsen and Sylvia Mignon. Dr. Holmes has conducted research funded by the U.S. Bureau of Justice Statistics, the National Institute of Justice, the National Institute of Mental Health, the National Center on Child Abuse and Neglect, the U.S. Children's Bureau, United Way, foundations, and many community agencies. He received a merit award from the Office of Justice Programs for evaluation of criminal justice programs, as well as the G. Paul Sylvester Award for contributions to criminal justice statistics.
Content
Preface
Acknowledgments
About the Author
Chapter 1. Quasi-Experiments and Nonequivalent Groups
Chapter 2. Causal Inference Using Control Variables
Chapter 3. Causal Inference Using Counterfactual Designs
Chapter 4. Propensity Approaches for Quasi-Experiments
Chapter 5. Propensity Matching
Chapter 6. Propensity Score Optimized Matching
Chapter 7. Propensities and Weighted Least Squares Regression
Chapter 8. Propensities and Covariate Controls
Chapter 9. Use With Generalized Linear Models
Chapter 10. Propensity With Correlated Samples
Chapter 11. Handling Missing Data
Chapter 12. Repairing Broken Experiments
Appendix A. Stata Commands for Propensity Use
Appendix B. R Commands for Propensity Use
Appendix C. SPSS Commands for Propensity Use
Appendix D. SAS Commands for Propensity Use
References
Author Index
Subject Index
Acknowledgments
About the Author
Chapter 1. Quasi-Experiments and Nonequivalent Groups
Chapter 2. Causal Inference Using Control Variables
Chapter 3. Causal Inference Using Counterfactual Designs
Chapter 4. Propensity Approaches for Quasi-Experiments
Chapter 5. Propensity Matching
Chapter 6. Propensity Score Optimized Matching
Chapter 7. Propensities and Weighted Least Squares Regression
Chapter 8. Propensities and Covariate Controls
Chapter 9. Use With Generalized Linear Models
Chapter 10. Propensity With Correlated Samples
Chapter 11. Handling Missing Data
Chapter 12. Repairing Broken Experiments
Appendix A. Stata Commands for Propensity Use
Appendix B. R Commands for Propensity Use
Appendix C. SPSS Commands for Propensity Use
Appendix D. SAS Commands for Propensity Use
References
Author Index
Subject Index