The Resource Statistical modeling and inference for social science, Sean Gailmard, University of California, Berkeley
Statistical modeling and inference for social science, Sean Gailmard, University of California, Berkeley
Resource Information
The item Statistical modeling and inference for social science, Sean Gailmard, University of California, Berkeley represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Statistical modeling and inference for social science, Sean Gailmard, University of California, Berkeley represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
- Summary
- "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"--
- Language
- eng
- Extent
- xviii, 373 pages
- Contents
-
- 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
- Isbn
- 9781107003149
- Label
- Statistical modeling and inference for social science
- Title
- Statistical modeling and inference for social science
- Statement of responsibility
- Sean Gailmard, University of California, Berkeley
- Language
- eng
- Summary
- "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"--
- Assigning source
- Provided by publisher
- Cataloging source
- DLC
- http://library.link/vocab/creatorName
- Gailmard, Sean
- Illustrations
- illustrations
- Index
- index present
- Literary form
- non fiction
- Nature of contents
- bibliography
- Series statement
- Analytical methods for social research
- http://library.link/vocab/subjectName
- Social sciences
- Label
- Statistical modeling and inference for social science, Sean Gailmard, University of California, Berkeley
- Bibliography note
- Includes bibliographical references (pages 361-366) and index
- Carrier category
- volume
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type MARC source
- rdacontent.
- Contents
- 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
- Control code
- FIEb17646728
- Dimensions
- 24 cm.
- Extent
- xviii, 373 pages
- Isbn
- 9781107003149
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Other physical details
- illustrations
- System control number
- (OCoLC)869065407
- Label
- Statistical modeling and inference for social science, Sean Gailmard, University of California, Berkeley
- Bibliography note
- Includes bibliographical references (pages 361-366) and index
- Carrier category
- volume
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type MARC source
- rdacontent.
- Contents
- 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
- Control code
- FIEb17646728
- Dimensions
- 24 cm.
- Extent
- xviii, 373 pages
- Isbn
- 9781107003149
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Other physical details
- illustrations
- System control number
- (OCoLC)869065407
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Statistical-modeling-and-inference-for-social/DYr9VK-J8Hc/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Statistical-modeling-and-inference-for-social/DYr9VK-J8Hc/">Statistical modeling and inference for social science, Sean Gailmard, University of California, Berkeley</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>