Using Propensity Scores in Quasi-Experimental Designs - William M. Holmes

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Using Propensity Scores in Quasi-Experimental Designs

By: William M. Holmes

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Paperback | 10 June 2013

At a Glance

Paperback
360 Pages

Dimensions(cm)
23.5 x 19.05 x 1.91

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$185.90

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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.

Industry Reviews

"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 Luke

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