Using Propensity Scores in Quasi-Experimental Designs

1. Auflage 2013
ISBN: 978-1-4522-0526-7
Verlag: Sage Publications, Inc

Buch, Englisch, 360 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 672 g

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.

Autoren/Hrsg.

Holmes, William M.

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung

Inhaltsverzeichnis

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

Über Autor(innen)

Holmes, William M.
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.