Statistical Modeling And Inference For Social Science
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Statistical Models and Causal Inference
Author: David A. Freedman
language: en
Publisher: Cambridge University Press
Release Date: 2010
David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.
Statistical Modeling and Inference for Social Science
Author: Sean Gailmard
language: en
Publisher: Cambridge University Press
Release Date: 2014-06-09
Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes 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 - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.
Handbook of Statistical Modeling for the Social and Behavioral Sciences
Author: G. Arminger
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-29
Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.