Statistical modeling and inference for social science / Sean Gailmard.

Includes bibliographical references (pages 361-366) and index.

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;
Bibliography;
Index.

"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"--