Statistical Modeling and Inference for Social Science
Statistical Modeling and Inference for Social Science
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Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes 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 - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.
- Integrates social science theory and statistical models
- Focuses on statistical techniques as used by social scientists
- Assumes no prior background in statistics
- Provides extensive guidance to students on introductory reading in statistical literature
Reviews & endorsements
'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
Product details
June 2014Hardback
9781107003149
388 pages
235 × 157 × 25 mm
0.65kg
18 b/w illus. 18 tables
Temporarily unavailable - available from May 2023
Table of Contents
- 1. Introduction
- 2. Descriptive statistics: data and information
- 3. Observable data and data-generating processes
- 4. Probability theory: basic properties of data-generating processes
- 5. Expectation and moments: summaries of data-generating processes
- 6. Probability and models: linking positive theories and data-generating processes
- 7. Sampling distributions: linking data-generating processes and observable data
- 8. Hypothesis testing: assessing claims about the data-generating process
- 9. Estimation: recovering properties of the data-generating process
- 10. Causal inference: inferring causation from correlation
- Afterword: statistical methods and empirical research.
