Using propensity scores in quasi-experimental designs

著者

書誌事項

Using propensity scores in quasi-experimental designs

William M. Holmes

Sage, c2014

  • : pbk

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注記

Includes bibliographical references (p. 320-326) and indexes

内容説明・目次

内容説明

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.

目次

Preface Acknowledgments About the Author 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 for 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 Appendix A. Stata Commands for Propensity Use Appendix B. R Commands for Propensity Use Appendix C. SPSS Commands for Propensity Use Appendix D. SAS Commands for Propensity Use References Author Index Subject Index

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