Using Propensity Scores in Quasi-Experimental Designs
Nonfiction, Reference & Language, Reference, Research, Social & Cultural Studies, Social Science
| Author: | William M. Holmes | ISBN: | 9781483321240 |
| Publisher: | SAGE Publications | Publication: | June 10, 2013 |
| Imprint: | SAGE Publications, Inc | Language: | English |
| Author: | William M. Holmes |
| ISBN: | 9781483321240 |
| Publisher: | SAGE Publications |
| Publication: | June 10, 2013 |
| Imprint: | SAGE Publications, Inc |
| Language: | English |
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.
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.
