Statistical Modeling and Inference for Social Science (Analytical Methods for Social Research)
Cambridge University Press, 6/9/2014
EAN 9781107003149, ISBN10: 1107003148
Hardcover, 388 pages, 22.8 x 15.2 x 2.5 cm
Language: English
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.
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.