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The last decade and a half has witnessed an explosion of research in ecological inference, the process of trying to infer individual behavior from aggregate data. This book, first published in 2004, brings together a diverse group of scholars to survey methodological strategies for solving ecological inference problems in various fields.

Introduces foundational statistical models for network data, augmenting theoretical discussion with applications across the social sciences implemented in the R language. An introductory text or reference for researchers, graduate students, and advanced undergraduate students across the social, mathematical, computational and physical sciences.

This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data analytics is applied to study social behavior.

Written specifically for graduate students and practitioners beginning social science research, this textbook introduces the essential statistical tools, models and theories that make up the social scientist's toolkit. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard demonstrates how social scientists assess relationships between variables.

Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.

Political Game Theory is a self-contained introduction to game theory and its applications to political science. The methods employed have many applications in various disciplines including comparative politics, international relations and American politics. A large number of exercises are also provided to practice the skills and techniques discussed.

Presents a geometric voting model for analyzing parliamentary roll call data. The model can be used to study various topics related to legislative voting, including how political parties evolve over time, the existence of sophisticated voting, the representation of ethnic minorities in the legislature, and constituency interests and legislative behavior.

This 2004 book provides a guide to event history analysis for researchers and advanced students in the social sciences. The authors explain the foundational principles of event-history analysis, and analyse numerous examples. They also discuss common problems encountered with time-to-event data, along with suggestions for implementing duration modeling methods.

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages.

The last decade and a half has witnessed an explosion of research in ecological inference, the process of trying to infer individual behavior from aggregate data. This book, first published in 2004, brings together a diverse group of scholars to survey methodological strategies for solving ecological inference problems in various fields.

A new advanced text in applied statistics and methodology in the social sciences, aimed at Ph.D. students in political science, sociology, and related disciplines. The authors take an applied perspective here, emphasizing core statistical concepts, computation in R, and the tools for evaluating and interpreting statistical models.

Spatial Analysis for the Social Sciences is for researchers across the social sciences who want to model and explain spatial interactions between the actors they study. The book is very user-friendly and demonstrates a variety of analytical approaches that applied researchers can use in their work.

This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data analytics is applied to study social behavior.

Essential Mathematics for Political and Social Research addresses an educational deficiency in the social and behavioral sciences. This 2006 book was the first of its kind to specifically address the comprehensive introduction to the mathematical principles needed by modern social scientists. The material introduces basic mathematical principles necessary to do analytical work in the social sciences, starting from first principles, but without unnecessary complexity. The core purpose is to present fundamental notions in standard notation and standard language with a clear, unified framework throughout. Through examples and exercises, this book is intended to not only motivate specific mathematical principles and practices, but also introduce the way that social science researchers use these tools. The intended emphasis is on conceptual understanding of key principles and their subsequent application.

Real statistical problems are complex and subtle. This text is about using regression to solve real problems of comparison, estimation, prediction, and causal inference, based on real stories from the authors' experience. It offers practical advice for understanding assumptions and implementing methods through graphics and computing in R and Stan.

Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.