9781452235004-1452235007-Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences)

Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences)

ISBN-13: 9781452235004
ISBN-10: 1452235007
Edition: Second
Author: Shenyang Guo, Mark W. Fraser
Publication date: 2014
Publisher: SAGE Publications, Inc
Format: Hardcover 448 pages
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Book details

ISBN-13: 9781452235004
ISBN-10: 1452235007
Edition: Second
Author: Shenyang Guo, Mark W. Fraser
Publication date: 2014
Publisher: SAGE Publications, Inc
Format: Hardcover 448 pages

Summary

Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences) (ISBN-13: 9781452235004 and ISBN-10: 1452235007), written by authors Shenyang Guo, Mark W. Fraser, was published by SAGE Publications, Inc in 2014. With an overall rating of 4.5 stars, it's a notable title among other Education & Reference (Research, Social Sciences) books. You can easily purchase or rent Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences) (Hardcover, Used) from BooksRun, along with many other new and used Education & Reference books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $17.55.

Description

Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.

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