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Bai / Clark

Propensity Score Methods and Applications

Medium: Buch
ISBN: 978-1-5063-7805-3
Verlag: SAGE Publications Inc
Erscheinungstermin: 29.12.2018
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A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method.

Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.

Produkteigenschaften

  • Artikelnummer: 9781506378053
  • Medium: Buch
  • ISBN: 978-1-5063-7805-3
  • Verlag: SAGE Publications Inc
  • Erscheinungstermin: 29.12.2018
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2018
  • Serie: Quantitative Applications in the Social Sciences
  • Produktform: Kartoniert
  • Gewicht: 170 g
  • Seiten: 136
  • Format (B x H x T): 138 x 215 x 7 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

Bai, Haiyan

Dr. Haiyan Bai is a Professor at the University of Central Florida. She earned her Ph.D. in quantitative research methodology at the University of Cincinnati. Her research interests include issues that revolve around statistical/quantitative methods, specifically, propensity score methods, resampling techniques, research design, measurement, and the application of statistical methods in social and behavioral sciences.

Clark, M. H.

Dr. M. H. Clark is an Associate Lecturer, statistical consultant, and program evaluator at the University of Central Florida. She has a Ph.D. in Experimental Psychology with a specialization in research design and statistics from the University of Memphis. Her specific areas of expertise are in causal inference, selection bias in non-randomized experiments, and propensity score methods.

Series Editor’s Introduction
About the Authors
Acknowledgments
1. Basic Concepts of Propensity Score Methods
1.1 Causal Inference
1.2 Propensity Scores
1.3 Assumptions
1.4 Summary of the Chapter
2. Covariate Selection and Propensity Score Estimation
2.1 Covariate Selection
2.2 Propensity Score Estimation
2.3 Summary of the Chapter
2.4 An Example
3. Propensity Score Adjustment Methods
3.1 Propensity Score Matching
3.2 Other Propensity Score Adjustment Methods
3.3 Summary of the Chapter
3.4 An Example
4. Covariate Evaluation and Causal Effect Estimation
4.1 Evaluating the Balance of Covariate Distributions
4.2 Causal Effect Estimation
4.3 Sensitivity Analysis
4.4 Summary of the Chapter
4.5 An Example
5. Conclusion
5.1 Limitations of the Propensity Score Methods and How to Address Them
5.2 Summary of Propensity Score Procedures
5.3 Final Comments
References
Index