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.

Propensities and Covariate Controls

This chapter examines the use of propensity scores with control variables and adjusting for bias. The trade-offs between matching and adjustment are discussed, as well as joint matching and adjustment. Criteria for judging the adequacy of the result of control variables are also presented and discussed.

Using statistical procedures to control prior group differences has a long history in the social and biological sciences. Data from quasi-experimental and experimental designs may have differences between intervention and comparison groups that confound estimation of treatment effects. Rosenbaum and Rubin (1983) showed that bias from prior differences can be removed by adjusting for differences in propensity scores. Such scores are a function of the prior confounding variables. Controlling differences in propensity scores also controls ...

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