Metodi Analitici per la Ricerca Sociale Serie

This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.

This book provides an overview of cutting-edge approaches to computational social science.

This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.

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.

Intended for students in political science and economics who have already taken a course in game theory, this text provides a unified and accessible survey of canonical and important new formal models of domestic politics.

The book offers a thorough introduction to the mathematical principles essential for contemporary social scientists. It emphasizes the application of mathematical concepts to social science research, providing tools and techniques that enhance analytical skills. By bridging the gap between mathematics and social science, it equips readers with the necessary knowledge to effectively interpret data and engage in quantitative analysis.

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http: // www.stat.columbia.edu/ gelman/arm/