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.

Propensity with Correlated Samples

This chapter examines issues unique to correlated samples that arise with propensity score use. The correlation of samples may be cross-sectional or across time. Propensity scores may be used in either situation, but adjustments need to be made in the procedures used. The correlation of the samples can alter the variances and covariances between the outcomes, the confounders, and the intervention. It can bias the estimate of the treatment effect. Propensity scores can control some of the bias resulting from correlated data. Additional procedures, however, may be necessary to obtain consistent and unbiased estimates of intervention effects.

The issues associated with using propensity scores with various correlated samples are examined for different types of correlated samples. The chapter covers paired samples ...

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