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 Matching

This chapter focuses on 1–1 and 1-many matching. It compares matching by propensities and by distance measures. It explains greedy matching and the use of calipers. It discusses problems of dropped cases, unequal cases in the groups, and assessing adequacy and sufficiency of these forms of matching.

Propensity matching is a highly efficient way of matching. It solves the dimensionality problem when multiple variables are used to match cases. It reduces the number of dimensions from the number of matching variables to that of a single dimension (Rosenbaum & Rubin, 1983). It is a balancing measure in the sense that it can be used to balance the distribution of characteristics between two groups so that they are the same (Austin, Grootendorst, & Anderson, ...

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