Statistical modeling and inference for social science
Gailmard Sean
Statistical modeling and inference for social science - New York: Cambridge University Press, 2014. - xviii,373p.
"This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph. D. students. Focusing on 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. Gailmard explains how social scientists express and test substantive theoretical arguments in various models. Chapter exercises require application of concepts to actual data and extend students' grasp of core theoretical concepts. Students will complete the book with the ability to read and critique statistical applications in their fields of interest"
9781107003149 (Hardback)
Social sciences -Statistical methods
Descriptive statistics: data and information
Observable data and data-generating processes
Probability theory: basic properties of data-generating processes
Expectation and moments: summaries of data-generating processes
Probability and models: linking positive theories and data-generating processes
Sampling distributions: linking data-generating processes and observable data
Hypothesis testing: assessing claims about the data-generating process
stimation: recovering properties of the data-generating process
Causal inference: inferring causation from correlation
001.891:519.22 / GAI
Statistical modeling and inference for social science - New York: Cambridge University Press, 2014. - xviii,373p.
"This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph. D. students. Focusing on 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. Gailmard explains how social scientists express and test substantive theoretical arguments in various models. Chapter exercises require application of concepts to actual data and extend students' grasp of core theoretical concepts. Students will complete the book with the ability to read and critique statistical applications in their fields of interest"
9781107003149 (Hardback)
Social sciences -Statistical methods
Descriptive statistics: data and information
Observable data and data-generating processes
Probability theory: basic properties of data-generating processes
Expectation and moments: summaries of data-generating processes
Probability and models: linking positive theories and data-generating processes
Sampling distributions: linking data-generating processes and observable data
Hypothesis testing: assessing claims about the data-generating process
stimation: recovering properties of the data-generating process
Causal inference: inferring causation from correlation
001.891:519.22 / GAI