Statistical Modeling and Inference for Social Science (Analytical Methods for Social Research)

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.

• A Mathematics Course for Political and Social Research
• Designing Social Inquiry: Scientific Inference in Qualitative Research
• Quantitative Social Science: An Introduction
• Field Experiments: Design, Analysis, and Interpretation
• The Craft of Research, Fourth Edition (Chicago Guides to Writing, Editing, and Publishing)
• Paradigms and Sand Castles: Theory Building and Research Design in Comparative Politics (Analytical Perspectives On Politics)
• The Logic of Political Survival (The MIT Press)
• Data Analysis Using Regression and Multilevel/Hierarchical Models
• Introductory Statistics (9th Edition)
• Counterfactuals and Causal Inference: Methods And Principles For Social Research (Analytical Methods for Social Research)