Using Propensity Scores in Quasi-Experimental Designs

Using Propensity Scores in Quasi-Experimental Designs

By: William M. Holmes (author)Paperback

Only 1 in stock


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.


PrefaceAcknowledgmentsAbout the AuthorChapter 1. Quasi-Experiments and Nonequivalent GroupsChapter 2. Causal Inference Using Control VariablesChapter 3. Causal Inference Using Counterfactual DesignsChapter 4. Propensity Approaches for Quasi-ExperimentsChapter 5. Propensity MatchingChapter 6. Propensity Score Optimized MatchingChapter 7. Propensities and Weighted Least Squares RegressionChapter 8. Propensities and Covariate ControlsChapter 9. Use With Generalized Linear ModelsChapter 10. Propensity With Correlated SamplesChapter 11. Handling Missing DataChapter 12. Repairing Broken ExperimentsAppendix A. Stata Commands for Propensity UseAppendix B. R Commands for Propensity UseAppendix C. SPSS Commands for Propensity UseAppendix D. SAS Commands for Propensity UseReferencesAuthor IndexSubject Index

Product Details

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

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