Using an accessible approach perfect for social and behavioral science students (requiring minimal use of matrix and vector algebra), Holmes examines how propensity scores can be used to both reduce bias with different kinds of quasi-experimental designs and fix or improve broken experiments. This unique 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 social and behavioral science disciplines.

Use with Generalized Linear Models

This chapter examines the use of generalized linear models when propensity scores are used. It identifies issues for this procedure that arise with using propensity scores and discusses strategies for dealing with those issues. It presents and discusses criteria for judging the adequacy of the analysis when propensity scores are used.

Ordinary least squares (OLS) regression has been extended to the general linear model (GLM), which includes analysis of variance (ANOVA), analysis of covariance (ANCOVA), t tests, and maximum likelihood estimation (MLE). GLM is a family of approaches used in analyzing data with propensity scores. GLM is applied mainly to outcomes that are normally or symmetrically distributed. GLM, in turn, has been generalized by Nelder and Wedderbom (1972) to apply ...

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