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Propensity Score Analysis provides readers with a systematic review of the origins, history, and statistical foundations of PSA and illustrates how it can be used for solving evaluation problems. With a strong focus on practical applications, the authors explore various types of data and evaluation problems related to, strategies for employing, and the limitations of PSA. Unlike the existing textbooks on program evaluation, Propensity Score Analysis delves into statistical concepts, formulas, and models underlying the application.
Key Features
- Presents key information on model derivations
- Summarizes complex statistical arguments but omits their proofs
- Links each method found in this book to specific Stata programs and provides empirical examples
- Guides readers using two conceptual frameworks: the Neyman-Rubin counterfactual framework and the Heckman econometric model of causality
- Contains examples representing real challenges commonly found in social behavioral research
- Utilizes data simulation and Monte Carlo studies to illustrate key points
- Presents descriptions of new statistical approaches necessary for understanding the four evaluation methods incorporated throughout the text
Intended Audience
This text is appropriate for graduate and doctoral students taking Evaluation, Quantitative Methods, Survey Research, and Research Design courses across business, social work, public policy, psychology, sociology, and health/medicine disciplines.
- Sales Rank: #1138951 in Books
- Brand: Brand: SAGE Publications, Inc
- Published on: 2009-07-16
- Original language: English
- Number of items: 1
- Dimensions: .90" h x 6.10" w x 9.10" l, 1.40 pounds
- Binding: Hardcover
- 392 pages
- Used Book in Good Condition
Review
"The approach the authors take in writing this book is very effective for novices and experiences users...This balance between the practical and applied approach is a useful model for researchers to understand the process and interpretation of these analyses...[it] goes a long way in making propensity score analysis techniques more accessible, understandable, and useful to psychologists." (Karl N. Kelley PsycCRITIQUES 2011-07-06)
"Guo and Fraser’s book Propensity Score Analysis: Statistical Methods and Applications is the first comprehensive book that discusses and compares different PS techniques from theoretical and practical points of view. One of the book’s strengths is its focus on the application of PS to real data.
[T]his textbook gives a good introduction to PS matching techniques and some alternative approaches for estimating causal treatment effects. With its many examples in Stata, it may be useful for graduate students and applied researchers who have no or limited experience with PS methods but are familiar with basic regression methods and mathematical/statistical notation." (Peter M. Steiner PSYCHOMETRIKA—VOL. 75, NO. 4, 775–777 2010-12-08)
About the Author
Shenyang Guo, PhD, is the Kuralt Distinguished Professor at the School of Social Work, University of North Carolina. The author of numerous articles on statistical methods and research reports in child welfare, child mental health services, welfare, and health care, Guo has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses on event history analysis, hierarchical linear modeling, growth curve modeling, and program evaluation. He has given many invited workshops on statistical methods—including event history analysis and propensity score matching—at the NIH Summer Institute, Children’s Bureau, and at conferences of the Society of Social Work and Research. He led the data analysis planning for the National Survey of Child and Adolescent Well-Being (NSCAW) longitudinal analysis.
Mark W. Fraser, PhD, holds the Tate Distinguished Professorship at the School of Social Work, University of North Carolina at Chapel Hill, where he serves as associate dean for research. He has written numerous chapters and articles on risk and resilience, child behavior, child and family services, and research methods. With colleagues, he is the co-author or editor of eight books, including Families in Crisis, Evaluating Family-Based Services, Risk and Resilience in Childhood, Making Choices, The Context of Youth Violence, and Intervention with Children and Adolescents. His award-winning text Social Policy for Children and Families reviews the bases for public policy in child welfare, juvenile justice, mental health, developmental disabilities, and health. His most recent book, Intervention Research: Developing Social Programs, describes a design perspective on the development of innovative social and health programs.
Most helpful customer reviews
10 of 10 people found the following review helpful.
Excellent overview for matched sampling, propensity score analysis, and causal modeling
By Sitting in Seattle
Researchers often want to make inferences about treatments in cases where experimental manipulation is not possible. For instance, what will happen if we pass a particular social policy? We can't run an experiment to test it. And similarly, we often wish to understand possible causes in cases where the data are confounded. For instance, with smoking and lung cancer: there are many differences between smokers and non-smokers as groups in terms of health, education, socioeconomic status, and so forth. Since the samples differ in systematic ways, and we can't randomly assign people to smoke, how can we make inference about the effects of smoking?
Classic experimental design would say that the answers are unknowable. Without random assignment, one cannot determine a treatment effect. Without sample equivalence, one cannot compare groups. Without random sampling, assignment, control, and longitudinal measurement, one can say nothing about causation. However, over the past four decades, several groups of researchers in public policy, statistics, and econometrics have developed a family of similar methods to address these issues.
It turns out that random assignment is not always required, that one can say a lot about treatment effects given naturally occurring variation. And similarly, even when samples are drawn from distinct subpopulations, one can tease apart at least some of the treatment effect from the contributions of sample differences (bias). The key is to use other information that is available to help control and reduce bias, and to make the contribution of treatment effect as unbiased as possible.
So, what about the book? In a nutshell, it shows how to do just those kinds of models. It provides an outstanding overview of the theoretical issues and general structure of methods from Heckman, Rubin, Pearl, and others. It provides particular depth on the Heckman approach in econometrics, the Rubin causal model that is perhaps more approachable for social researchers, and the key differences between the two. The illustrative examples are extremely well-chosen, and at several points the basic concepts are illustrated with brilliantly clear, small data sets that can be appreciated in simple presentation in the book itself. In each case, it discusses both the mathematics behind the method (which can be comfortably skipped by those so inclined) and available software to estimate it.
It is more readable than 90% of statistics texts (I would estimate), and I expect it could be appreciated by most folks with a PhD related to any social or economics research field.
The main drawback to it, in my opinion, is that the software discussion primarily focuses on Stata. There is somewhat less coverage of R, which is a shame because (IMHO) R is quickly becoming the platform of choice for advanced researchers who use emerging methods. However, there is enough discussion of R analyses to get one started, and the Stata analyses are syntactically quite similar anyway. So this is a limitation but is not severe.
If you are a researcher interested in causal models or measurement of effects in non-experimental settings, and want an applied book rather than a math tome, this is the right book to get started. Thank you to the authors!
1 of 1 people found the following review helpful.
This book is like a bible
By Seongsu K.
This book is like a bible. 100% satisfaction guaranteed! Yet, I must say to fully understand this book, the reader first should know the statistics behind.
0 of 0 people found the following review helpful.
Five Stars
By Joseph H Schroeder
Excellent book for any empirical researcher's shelf. Great way to get up to speed on PSM.
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