Statistical Modeling and Inference for Social Science
Statistical Modeling and Inference for Social Science by Sean Gailmard, published by Cambridge University Press on April 6, 2017, is a comprehensive resource designed for social science researchers and Ph.D. students. This reprint edition spans 391 pages and is presented in English. The book introduces probability theory, statistical inference, and statistical modeling, emphasizing the relationship between statistical methods and social science theory.
Readers will find that Gailmard develops essential statistical theory as a toolkit for modeling and assessing relationships between variables, which is crucial for social scientists. The text includes chapter exercises that encourage the application of concepts to real data, enhancing students’ understanding of theoretical principles. By the end of the book, students will be equipped to read and critique statistical applications relevant to their fields, making it a valuable addition to the study of political science, sociology, and related disciplines.
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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.
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