Propensity Score Methods and Applications

Regular price €45.99
A01=Haiyan Bai
A01=M. H. Clark
Age Group_Uncategorized
Age Group_Uncategorized
Author_Haiyan Bai
Author_M. H. Clark
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Category1=Non-Fiction
Category=GPS
Category=JHBC
COP=United States
Delivery_Delivery within 10-20 working days
eq_isMigrated=2
eq_non-fiction
eq_society-politics
Evaluation
Language_English
PA=Available
Price_€20 to €50
Propensity Score Matching
PS=Active
PSM
Quantitative Methods
SN=Quantitative Applications in the Social Sciences
softlaunch
Statistical Research

Product details

  • ISBN 9781506378053
  • Weight: 180g
  • Dimensions: 139 x 215mm
  • Publication Date: 29 Dec 2018
  • Publisher: SAGE Publications Inc
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
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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.
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.