Using Propensity Scores in Quasi-Experimental Designs

Using Propensity Scores in Quasi-Experimental Designs

By: William M. Holmes (author)Paperback

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The author covers a wider range of software that is used with doing such analysis, and presents how propensity scores can be used to address issues in analyzing data from quasi-experimental designs, and which techniques should be used, and when. This book will clearly help students to understand the underlying concepts behind statistics, when they are used and how they are used.

About Author

William Holmes is a faculty member at the University of Massachusetts, Boston, in the College of Public and Community Services. He has evaluated criminal justice and community programs serving families, children, individuals who have suffered abuse, and those with substance abuse problems. He coauthored with Kay Kitson Portrait of Divorce, which won the William Goode Award from the Family Section of the American Sociological Association, and coauthored Family Abuse: Consequences, Theories, and Responses with Calvin Larsen and Sylvia Mignon. Dr. Holmes has conducted research funded by the U.S. Bureau of Justice Statistics, the National Institute of Justice, the National Institute of Mental Health, the National Center on Child Abuse and Neglect, the U.S. Children's Bureau, United Way, foundations, and many community agencies. He received a merit award from the Office of Justice Programs for evaluation of criminal justice programs, as well as the G. Paul Sylvester Award for contributions to criminal justice statistics.


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

Product Details

  • ISBN13: 9781452205267
  • Format: Paperback
  • Number Of Pages: 360
  • ID: 9781452205267
  • weight: 600
  • ISBN10: 1452205264

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