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.
Quasi-Experiments and Nonequivalent Groups
A propensity score is the probability that a person will be in a treatment group, given his or her specific characteristics. This text describes how propensity scores can be used in a variety of experimental and quasi-experimental designs to improve the validity and reduce the bias of estimates of the effect of treatments, interventions, and other causal influences.
Explaining how experiments and quasi-experiments benefit from propensity score use depends on noting how actual research differs from the ideal of well-designed experiments. This chapter will summarize the ways in which such designs affect the ability to make reliable causal inferences from the results.
Experiments are regarded by many as the gold standard by which the quality of research is judged. This ...