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.

Handling Missing Data

This chapter examines problems of missing data when using propensity scores. Procedures for identifying missing data and their risk to the analysis are described. Strategies for handling, estimating, and imputing missing data are presented and discussed. Criteria are presented for judging the results when missing data are present.

It is common to regard missing data as being either random or nonrandom. If it is random, researchers might ignore it or use any of a variety of techniques for estimating or replacing it—often, without clear theoretical or substantive reasons for choosing a technique, other than the fact that “it seems to work.” Using propensity scores, however, requires a more carefully considered response to missing data. Rubin (1976a) distinguishes between three types of missing ...

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