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.The currently available programs for matching do not generally addressthis issue in documentation. For most of the examples given, 1a1 matchingis used tomore easily illustrate the matching process. When 1amany ... With quasiexperimental and natural experiment designs, however, such individualsmaybeover or underrepresented inthe treatment or comparisongroup. ... Ifonewanted to lookat the effectofreligious affiliationon suchactivity, one would haveto use quota matching.
|Title||:||Using Propensity Scores in Quasi-Experimental Designs|
|Author||:||William M. Holmes|
|Publisher||:||SAGE Publications - 2013-06-10|