5 edition of **Statistical Modeling and Inference for Social Science** found in the catalog.

- 19 Want to read
- 25 Currently reading

Published
**2014**
by Cambridge University Press in New York, USA
.

Written in English

"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"-- Provided by publisher.

**Edition Notes**

Series | (Analytical Methods for Social Research) |

Classifications | |
---|---|

LC Classifications | HA29 .G136 2014 |

The Physical Object | |

Format | Paperback; Hardcover |

Pagination | xviii, 373 pages : illustrations ; 24 cm. |

Number of Pages | 391 |

ID Numbers | |

Open Library | OL26129331M |

ISBN 10 | 1107003148, 1316622223 |

ISBN 10 | 9781107003149, 9781316622223 |

LC Control Number | 2013050034 |

OCLC/WorldCa | 1003588130 |

It’s a common thing with social science types, point out their data don’t obey the assumptions of their statistical model and there’s always a standard *correction* they make. There’s not usually any willingness to drill down into the issue any more than that. This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of Author: Kelvyn Jones.

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology/5. David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences. Counterfactuals and Causal Inference Stephen L. Morgan,Christopher Winship — Mathematics.

Statistical modeling and inference for social science. [Sean Gailmard] -- "This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph. D. students. Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives presents an overview with examples of these key topics suitable for researchers in all areas of statistics. It adopts a practical approach suitable for applied statisticians working in social and political sciences, biological and medical sciences, and physical /5(3).

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"In Statistical Modeling and Inference for Social Science, Gailmard provides a complete and well-written review of statistical modeling from the modern perspective of causal inference. It provides all the material necessary for an introduction to quantitative methods for social science students."Cited by: 8.

'In Statistical Modeling and Inference for Social Science, Gailmard provides a complete and well-written review of statistical modeling from the modern perspective of causal inference.

It provides all the material necessary for an introduction to quantitative methods for social science students.'Cited by: 8. Statistical Models and Causal Inference: A Dialogue with the Social Sciences 1st Edition. Statistical Models and Causal Inference: A Dialogue with the Social Sciences.

1st Edition. by David A. Freedman (Author) out of 5 stars 5 ratings. ISBN Cited by: This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D. s: 1.

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 Range: $ - $ 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. Statistical Modeling and Inference for Social Science 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 proce-dures and social science theory, Sean Gailmard develops core statistical. Statistical Modeling, Causal Inference, and Social Science.

Skip to content. Home; Books; Blogroll; Sponsors; Authors; Books. I listened to a book on the epidemic. It was huge – at least 20 discs – and badly written and poorly read.

But the information was good if you could deal with the lame drama. There was a long section on the biology of viruses the immune system that was probably the best part of the book.

Sensitivity to the model is apparent in Bayesian inference, but it would arise with any other statistical method as well. For example, Bendavid et al.

(a) used an (incorrectly applied) delta method to propagate uncertainty, but this is problematic when sample size is low and probabilities are near 0 or 1.

Statistical Modeling and Inference for Social Science: Sean Gailmard: Books - Back in I went to a conference of Bayesians and I was disappointed that the vast majority seem to not be interested in checking their statistical models. The attitude seemed to be, first, that model checking was not possible in a Bayesian context, and, second, that model checking was illegitimate because models were subjective.

Statistical models and causal inference: a dialogue with the social sciences / David A. Freedman ; edited by David Collier, Jasjeet Sekhon, Philip B. Stark. Includes bibliographical references and index. ISBN 1. Social sciences – Statistical methods.

Linear models (Statistics) 3. Causation. Collier, David, Buy Statistical Modeling and Inference for Social Science (Analytical Methods for Social Research) by Sean Gailmard (ISBN: ) from Amazon's Book Store.

Everyday low prices and free delivery on eligible orders. "Statistical models: theory and practice is lucid, helpful, insightful and a joy to read.

It focuses on the most common tools of applied statistics with a clear and simple presentation. This revised edition organizes the chapters differently, making reading much easier.

Moreover, it includes many new examples and by: An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists. Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on Cited by: 2.

James N. Druckman, Payson S. Wild Professor of Political Science, Northwestern University, Illinois 'In Statistical Modeling and Inference for Social Science, Gailmard provides a complete and well-written review of statistical modeling from the modern perspective of causal : Sean Gailmard.

The book serves as a model for social scientific research. The authors first develop a probabilistic behavioral social choice theory that generalizes deterministic social choice theory, which has dominated thinking in the field.

This alone represents a major contribution. Regenwetter et al. then creatively use survey data to test their by: "This book will revolutionize how applied statistics is taught in statistics and the social and biomedical sciences.

The authors present a unified vision of causal inference that covers both experimental and observational by: Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies.

The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation. Statistical Models and Causal Inference: A Dialogue with the Social Sciences | Freedman David A.

| download | B–OK. Download books for free. Find books.Written notably for graduate school college students and practitioners beginning social science evaluation, Statistical Modeling and Inference for Social Science covers the necessary statistical tools, fashions and theories that make up the social scientist's toolkit.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.