Computational Social Science :Discovery and Prediction - Analytical Methods for Social Research
Computational Social Science :Discovery and Prediction - Analytical Methods for Social Research
hardback
Published:
10 March, 2016
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Description
More Details
| Type | Book |
|---|---|
| ISBN13 | 9781107107885 |
| ISBN10 | 1107107881 |
| Number Of Pages | 338 |
| Item Weight | 580 g |
| Product Dimensions | 160 x 235 x 23 mm |
| Publisher / Reseller | Cambridge University Press |
| Format | hardback |
Media Reviews
'Computational social science is either the coming or just arrived tidal wave. But how the computations part fits with social science is the most important issue that needs to be settled before this wave overtakes us all. This book does a great job in laying out some of the issues in general terms but, perhaps more importantly, showing the areas where computational social science is (not so) simply good social science.' Nathaniel Beck, New York University
'Computational social science is a revolution that is sweeping us into the twenty-first century with increasingly sophisticated tools for generating insight about fundamental human behaviors, and this book reads like a Who's Who of the revolutionary vanguard. From public opinion to protest, each chapter of this superb collection of essays gives great examples of new data and new techniques for analyzing it to learn how society functions and to apply that knowledge to make our world better. This volume is a must-read for anyone who wants to understand what big data means for social scientists.' James Fowler, University of California, San Diego
'This book offers a delightful sampling of some of the key issues and challenges at the center of computational social science, an emergent field often popularly referred to as 'big data'. This collection of fascinating essays offers both a conceptual overview and more detailed explanations that can delight expert and novices alike.' danah boyd, Microsoft Research and Founder, Data and Society
'With big data analytics comes a complex relationship between computational social science and public policy. For social scientists, these essays will present exciting new ways to think about and leverage big data analytics. Data scientists will enjoy seeing their tricks of the trade being applied to interesting social and public policy issues.' Jeff Jonas, IBM Fellow
Author's Bio
R. Michael Alvarez is a Professor of Political Science at the California Institute of Technology. He is a Fellow of the Society for Political Methodology. He is the coeditor of Political Analysis and of the Cambridge University Press series, Analytical Methods for Social Science. He recently coauthored, with Lonna Rae Atkeson and Thad E. Hall, Evaluating Elections: A Handbook of Methods and Standards. He is also codirector of the Caltech/MIT Voting Technology Project.