Statistical Modeling and Inference for Social Science

This textbook is an introduction to probability theory, statistical inference and statistical modeling for graduate students and practitioners beginning social science research.

Sean Gailmard (Author)

9781107003149, Cambridge University Press

Hardback, published 9 June 2014

388 pages, 18 b/w illus. 18 tables
23.5 x 15.7 x 2.5 cm, 0.65 kg

'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

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.

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.

Subject Areas: Economic statistics [KCHS], Econometrics [KCH], Politics & government [JP], Sociology [JHB], Research methods: general [GPS]