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"This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D. students. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists. Gailmard explains how social scientists express and test substantive theoretical arguments in various models. Chapter exercises require application of concepts to actual data and extend students' grasp of core theoretical concepts. Students will complete the book with the ability to read and critique statistical applications in their fields of interest"-- Provided by publisher.
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Subjects
Probability Theory, Statistics, Statistical Methods, Statistical Modeling, Statistical Inference, Causal Inference, Empirical Research, Data Generting Process, Social sciences, statistical methods, Social sciences, Statistical methods, POLITICAL SCIENCE / General, Methode, Sozialwissenschaften, StatistikShowing 3 featured editions. View all 3 editions?
Edition | Availability |
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1
Statistical Modeling and Inference for Social Science
2018, Cambridge University Press
in English
1139047442 9781139047449
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2
Statistical Modeling and Inference for Social Science
2014, Cambridge University Press
Paperback; Hardcover
in English
1107003148 9781107003149
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3
Statistical Modeling and Inference for Social Science
2014, Cambridge University Press
in English
1139989448 9781139989442
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zzzz
Libraries near you:
WorldCat
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Book Details
Table of Contents
Edition Notes
Includes bibliographical references (pages 361-366) and index.
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Work Description
With careful consideration for both rigor and intuition, Gailmard fills a large void in the social science literature. Those seeking clear mathematical exposition will not be disappointed. Those hoping for substantive applications to illuminate the data analysis will also be pleased. This book strikes a nearly perfect balance.' Wendy K. Tam Cho, National Center for Supercomputing Applications and University of Illinois, Urbana-Champaign 'This is the single best book on modeling in social science - it goes beyond any extant book and will without a doubt become the standard text in methods courses throughout the social sciences.' James N. Druckman, Payson S. Wild Professor of Political Science, Northwestern University, Illinois 'In Statistical Modeling and Inference for Social Science, Gailmard provides a complete and well-written review of statistical modeling from the modern perspective of causal inference. It provides all the material necessary for an introduction to quantitative methods for social science students.' Jonathan N. Katz, Kay Sugahara Professor of Social Sciences and Statistics, and Chair, Division of the Humanities and Social Sciences, California Institute of Technology "With careful consideration for both rigor and intuition, Gailmard fills a large void in the social science literature. Those seeking clear mathematical exposition will not be disappointed. Those hoping for substantive applications to illuminate the data analysis will also be pleased. This book strikes a nearly perfect balance." Wendy K. Tam Cho, National Center for Supercomputing Applications and University of Illinois, Urbana-Champaign "This is the single best book on modeling in social science - it goes beyond any extant book and will without a doubt become the standard text in methods courses throughout the social sciences." James N. Druckman, Payson S. Wild Professor of Political Science, Northwestern University, Illinois "In Statistical Modeling and Inference for Social Science, Gailmard provides a complete and well-written review of statistical modeling from the modern perspective of causal inference. It provides all the material necessary for an introduction to quantitative methods for social science students.
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Feedback?December 22, 2022 | Edited by MARC Bot | import existing book |
November 14, 2020 | Edited by MARC Bot | import existing book |
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November 15, 2018 | Created by Kaustubh Chakraborty | Edited information |