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.

Repairing Broken Experiments

This chapter discusses problems with experimental and quasi-experimental data when random assignment is either impossible or not achieved. It considers departures from research protocol. Nonrandomization, noncompliance, and nonresponse are the principal consequences when research protocol is broken. This results in problems for analysis and inference. The chapter presents procedures for diagnosing the magnitude of these problems and for reducing their impact.

Broken experiment is a term coined by Barnard, Du, Hill, and Rubin (1998). It means that deviations from the ideal standard of random assignment and complete data raise questions about the internal validity of a study. These deviations may or may not introduce bias into the analysis. The data must be examined to determine whether the deviations bias the results.

Problems of ...

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