Propensity Score Methods and Applications (häftad)
Häftad (Paperback)
Antal sidor
SAGE Publications, Inc
Clark, M. H.
211 x 140 x 8 mm
159 g
Antal komponenter
Propensity Score Methods and Applications (häftad)

Propensity Score Methods and Applications

Häftad Engelska, 2018-12-29
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Fler böcker av Haiyan Bai

  • Propensity Score Analysis

    Wei Pan, Haiyan Bai

    This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analy...

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“Haiyan Bai and M.H. Clark have delivered a readable and easily applicable guide for eager researchers with data-in-hand, chomping at the bit to determine whether and how their empirical challenges might be addressed through the careful application of propensity score methods.”

“This volume provides a thorough introduction to propensity score methods while taking care to not overwhelm the reader with dense mathematics. Simple examples, straightforward language, and a catalog of software options make this a fine primer for researchers seeking to incorporate propensity score methods into their own research plans, and an excellent desk reference.”

“This book contains excellent descriptions of propensity score matching with practical examples and clear guides using different software programs.”

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Övrig information

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.

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.


Chapter 1: Basic Concepts of Propensity Score Methods Causal Inference Propensity Scores Assumptions Summary of the Chapter Chapter 2: Covariate Selection and Propensity Score Estimation Covariate Selection Propensity Score Estimation Summary of the Chapter An Example Chapter 3: Propensity Score Adjustment Methods Propensity Score Matching Other PS Adjustment Methods Summary of the Chapter An Example Chapter 4: Covariate Evaluation and Causal Effect Estimation Evaluating the Balance of Covariate Distributions Causal Effect Estimation Sensitivity Analysis Summary of the Chapter An Example Chapter 5: Conclusion Limitations of the PS Methods and How to Address Them Summary of PS Procedures Final Comments