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 Approaches for Quasi-Experiments

This chapter discusses the basics of propensity score estimation and use. It presents tests for the effectiveness of propensity scores for reducing imbalance or bias in the groups compared. It discusses how to improve propensity score estimates and how to judge whether the imbalance reduction is adequate for analysis. In addition, it reviews alternative propensity uses and how they may be combined and discusses trade-offs between the uses.

Estimating Propensity Scores

The probability that a given case will fall in a specified group is affected by the process of selecting cases into the groups. When randomization is used for the selection process, all cases have an equal chance of being in the intervention or control groups. The treatment effect is easily estimated ...

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