Using Propensity Scores in Quasi-Experimental Designs

by

Subject categories
ISBNs
  • 9781452205267
  • 9781483310817
  • 9781483321240
Using Propensity Scores in Quasi-Experimental Designs, by William M. Holmes, examines how propensity scores can be used to reduce bias with different kinds of quasi-experimental designs and to fix or improve broken experiments. Requiring minimal use of matrix and vector algebra, the 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 disciplines.
  • SAGE Publications; June 2013
  • ISBN 9781483310817
  • Read online, or download in secure PDF or secure EPUB format
  • Title: Using Propensity Scores in Quasi-Experimental Designs
  • Author: William M. Holmes
  • Imprint: SAGE Publications, Inc
Subject categories
ISBNs
  • 9781452205267
  • 9781483310817
  • 9781483321240

In The Press

“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.”

Subject categories
ISBNs
  • 9781452205267
  • 9781483310817
  • 9781483321240