Includes bibliographical references (pages 320-326) and index.

Preface -- Approach of the book -- chapter 1. Quasi-experiments and nonequivalent groups -- chapter 2. Causal inference using control variables -- chapter 3. Causal inference using counterfactual designs -- chapter 4. Propensity approaches of quasi-experiments -- chapter 5. Propensity matching -- chapter 6. Propensity score optimized matching -- chapter 7. Propensities and weighted least squares regression -- chapter 8. Propensities and covariate controls -- chapter 9. Use with generalized linear models -- chapter 10. Propensity with correlated samples -- chapter 11. Handling missing data -- chapter 12. Repairing broken experiments -- A. STATA commands for propensity use -- B. R commands for propensity use -- SPSS commands for propensity use -- SAS commands for propensity use -- References -- Index.

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.

Description based on MARC record for print version.

Licensed e-book