Statistical modeling and inference for social science
著者
書誌事項
Statistical modeling and inference for social science
（Analytical methods for social research）
Cambridge University Press, 2014
- : hardback
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注記
Includes bibliographical references (p. 361-366) and index
内容説明・目次
内容説明
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.
目次
- 1. Introduction
- 2. Descriptive statistics: data and information
- 3. Observable data and data-generating processes
- 4. Probability theory: basic properties of data-generating processes
- 5. Expectation and moments: summaries of data-generating processes
- 6. Probability and models: linking positive theories and data-generating processes
- 7. Sampling distributions: linking data-generating processes and observable data
- 8. Hypothesis testing: assessing claims about the data-generating process
- 9. Estimation: recovering properties of the data-generating process
- 10. Causal inference: inferring causation from correlation
- Afterword: statistical methods and empirical research.
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