000			01878nam a22002537a 4500
020			_a9781107003149 (Hardback)
082			_a001.891:519.22 _bGAI
100			_aGailmard Sean
245			_aStatistical modeling and inference for social science
260			_aNew York: _bCambridge University Press, _c2014.
300			_axviii,373p.
520			_a"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"
650			_aSocial sciences -Statistical methods
650			_aDescriptive statistics: data and information
650			_aObservable data and data-generating processes
650			_aProbability theory: basic properties of data-generating processes
650			_aExpectation and moments: summaries of data-generating processes
650			_aProbability and models: linking positive theories and data-generating processes
650			_aSampling distributions: linking data-generating processes and observable data
650			_aHypothesis testing: assessing claims about the data-generating process
650			_astimation: recovering properties of the data-generating process
650			_aCausal inference: inferring causation from correlation
942			_cBK
999			_c348372 _d348372