Using Propensity Scores in Quasi-Experimental Designs

June 26, 2013|
Using Propensity Scores in Quasi-Experimental Designs
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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.

Title:Using Propensity Scores in Quasi-Experimental DesignsFormat:Kobo ebookPublished:June 26, 2013Publisher:SAGE PublicationsLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:148332124X

ISBN - 13:9781483321240

Appropriate for ages: All ages

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