Counterfactuals and Causal Inference
- 499 Seiten
- 18 Lesestunden
This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.
Analytische Methoden für die Sozialforschung Reihe
Diese Reihe befasst sich mit empirischen und formalen Methoden, die für die Sozialforschung unerlässlich sind. Sie bietet tiefe Einblicke in die theoretischen Grundlagen analytischer Techniken und deren praktische Anwendung in der Forschung. Die Bände decken ein breites Spektrum fachübergreifender Themen sowie spezifische methodische Anwendungen in Bereichen wie Politikwissenschaft, Soziologie und Public Health ab. Sie richtet sich an Studierende und Forschende, die fortgeschrittenes Wissen in den Sozialwissenschaften und der Statistik suchen.
Computational Social Science
- 337 Seiten
- 12 Lesestunden
This book provides an overview of cutting-edge approaches to computational social science.
Spatial Analysis for the Social Sciences
- 259 Seiten
- 10 Lesestunden
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
- 298 Seiten
- 11 Lesestunden
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.
Essential Mathematics for Political and Social Research
- 482 Seiten
- 17 Lesestunden
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/
