Propensity Score Analysis

FUNDAMENTALS AND DEVELOPMENTS

Edited by Wei Pan and Haiyan Bai

Published by The Guilford Press in April 2015

Home

Sample Chapters

Data and Code

About Authors

Errata

To the contributing authors

for their remarkable cooperation

and commitment to the advancement

of propensity score methodology

Sample Data and Software Code

Chapter	Data	Code
1. Propensity Score Analysis: Concepts and Issues	n.a.	n.a.
2. Overview of Implementing Propensity Score Analyses in Statistical Software	.csv	R SAS STATA
3. Propensity Score Estimation with Boosted Regression	(contact author)	(contact author)
4. Methodological Considerations in Implementing Propensity Score Matching	(contact author)	(contact author)
5. Evaluating Covariate Balance	(contact author)	(contact author)
6. Propensity Score Adjustment Methods	.sav	SPSS Excel
7. Propensity Score Analysis with Matching Weights	.csv	R (Instructions)
8. Robust Outcome Analysis for Propensity‑Matched Designs	.dat .dat	R
9. Latent Growth Modeling of Longitudinal Data with Propensity‑Score‑Matched Groups	.csv .csv	R Mplus Mplus Mplus
10. Propensity Score Matching on Multilevel Data	(contact author)	(contact author)
11. Propensity Score Analysis with Complex Survey Samples	.sav	n.a.
12. Missing Data in Propensity Scores	(contact author)	(contact author)
13. Unobserved Confounding in Propensity Score Analysis	n.a.	R R
14. Propensity Score Based Sensitivity Analysis	.sas7bdat	R
15. Prognostic Scores in Clustered Settings	(contact author)	(contact author)

(Order the book from The Guilford Press or Amazon.com)

Reviews

"Pan and Bai have assembled a comprehensive volume on all aspects of propensity score methods. Both the user and the statistician will find something to like in this book. I recommend it. "

-William R. Shadish, Ph.D. (1949-2016),

Late Distinguished Professor of Psychology, University of California, Merced

"There is no question that this book will serve as an excellent resource for those who want to add PSA to their repertoire of analytical methods. The chapters provide sufficient materials and examples to help both 'green hands' and seasoned analysts deal with the methodological and practical challenges of applying PSA in research work."

-Xitao Fan, Ph.D.,

Wei Lun Professor and Dean, Faculty of Education, The Chinese University of Hong Kong, China

"This book effectively synthesizes general principles of PSA with recent developments regarding complex issues such as estimation techniques, covariate balance, weighting, complex datasets, and sensitivity analysis. The discussion of statistical software and examples of computer code are helpful additions. This book will be useful to graduate students and applied researchers who are interested in learning about PSA for the first time or who have some knowledge and would like to learn about issues and recent developments. I recommend it as a textbook for graduate-level courses in methods of causal inference or as a reference for researchers in the social and biomedical sciences."

-Suzanne E. Graham, Ed.D.,

Associate Professor, Department of Education, University of New Hampshire

"This book is a go-to guide for designing and analyzing observational data. The editors have produced a brilliant work that addresses both methodological and practical issues in propensity score analysis. A 'must read' for all biostatisticians as well as applied researchers in the social, behavioral, and health sciences."

-Ding-Geng (Din) Chen, Ph.D.,

Professor, College of Health Solutions, Arizona State University

Questions? Phone: (919) 684-9324 or Email: wei.pan@duke.edu, haiyan.bai@ucf.edu