Written specifically for graduate students and practitioners beginning social science research, this textbook introduces the essential statistical tools, models and theories that make up the social scientist's toolkit. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard demonstrates how social scientists assess relationships between variables.
Written specifically for graduate students and practitioners beginning social science research, this textbook introduces the essential statistical tools, models and theories that make up the social scientist's toolkit. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard demonstrates how social scientists assess relationships between variables.
Sean Gailmard is Associate Professor of Political Science at the University of California, Berkeley. Formerly an Assistant Professor at Northwestern University and at the University of Chicago, Gailmard earned his PhD in Social Science (economics and political science) from the California Institute of Technology. He is the author of Learning While Governing: Institutions and Accountability in the Executive Branch (2013), winner of the 2013 American Political Science Association's William H. Riker Prize for best book on political economy. His articles have been published in a variety of journals, including American Political Science Review, American Journal of Political Science and Journal of Politics. He currently serves as an associate editor for the Journal of Experimental Political Science and on the editorial boards for Political Science Research and Methods and Journal of Public Policy.
Inhaltsangabe
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.
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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