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Using Propensity Scores in Quasi-Experimental Designs

Using Propensity Scores in Quasi-Experimental Designs

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July 2013 | 360 pages | SAGE Publications, Inc
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.
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
Author Index
Subject Index


Student Study Site

See the companion website for commands useful for propensity analysis in SPSS, SAS, Stata, and R.  The following videos are also available on the companion website: 

Overview of Propensity Scores
Installing R Programs for Propensity Score Matching
Example is on a MAC, but procedures apply to Windows systems as well.

Assessing Covariate Balance
Using r command plot ()
Nearest-Neighbor Greedy Matching
Using Matchit program
Full Matching
Using Matchit program
Optimal Matching
Using Matchit program 

“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
University of Central Florida

“The worked up examples in different software programs are a definite strength.”

Tina Savla
Virginia Tech

“The discussion of alternatives in order to control sources of influence is very good.”     

Michael A. Milburn
University of Massachusetts, Boston

“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 Luke
Washington University in St. Louis

The text was an excellent supplement for advanced students working on thesis research projects.

Dr Christopher John Godfrey
Psychology Dept, Pace University
June 17, 2014

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ISBN: 9781452205267

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