Using Propensity Scores in Quasi-Experimental Designs
eBook - ePub

Using Propensity Scores in Quasi-Experimental Designs

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Using Propensity Scores in Quasi-Experimental Designs

About this book

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.

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Yes, you can access Using Propensity Scores in Quasi-Experimental Designs by William M. Holmes in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Halftitle
  3. Title
  4. Copyright
  5. Brief Contents
  6. Detailed Contents
  7. Preface
  8. Acknowledgments
  9. About the Author
  10. 1 Quasi-Experiments and Nonequivalent Groups
  11. 2 Causal Inference Using Control Variables
  12. 3 Causal Inference Using Counterfactual Designs
  13. 4 Propensity Approaches for Quasi-Experiments
  14. 5 Propensity Matching
  15. 6 Propensity Score Optimized Matching
  16. 7 Propensities and Weighted Least Squares Regression
  17. 8 Propensities and Covariate Controls
  18. 9 Use With Generalized Linear Models
  19. 10 Propensity With Correlated Samples
  20. 11 Handling Missing Data
  21. 12 Repairing Broken Experiments
  22. Appendixes
  23. Appendix A: Stata Commands for Propensity Use
  24. Appendix B: R Commands for Propensity Use
  25. Appendix C: SPSS Commands for Propensity Use
  26. Appendix D: SAS Commands for Propensity Use
  27. References
  28. Author Index
  29. Subject Index
  30. Advertisement