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Using Propensity Scores In Quasiexperimental Designs,Used
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Using Propensity Scores in QuasiExperimental Designs, by William M. Holmes, examines how propensity scores can be used to reduce bias with different kinds of quasiexperimental 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, twostage 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.
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