Chapman and Hall/CRC
356 pages | 60 Color Illus.
Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems.
Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation.
The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations.
For more information, including sample chapters and news, please visit the author's website.
"This book builds a nice bridge connecting social science and big data methodology. Big data such as social media and electronic health records, empowered by the advances in information technology, are an emerging phenomenon in recent years and present unprecedented opportunities for social science research. This book was written by pioneering scientists in applying big data methods to address social science problems. As shown by numerous examples in the book, social science could benefit significantly by embracing the new mode of big data and taking advantage of the technical progress in analysing such data. If you work in social science and would like to explore the power of big data, this book is clearly for you. Indeed, if you do not have previous experience in dealing with big data, you should read this book first, before implementing a big-data project.
As indicated by the title, this book acts as a practical guide and targets readers with minimum big data experience, hence it is very hands-on. … It covers all necessary steps to finish a big data project: collecting raw data, cleaning and preprocessing data, applying various modelling tools to analyze the data, evaluating results, protecting privacy, and addressing ethical problems. … All the important topics concerning big data are covered, making this book a good reference that you should always keep on your desk."
— Guoqiang Yu, Virginia Tech, in Journal of the American Statistical Association, July 2017
"…In summary, although there is a growing number of books related to social science and big data, this volume contains several non-trivial aspects which make it worth to have in the library, possibly along with other similar textbooks as a good complement to them."
—Stefano M. Iacus, University of Milan, in Journal of Statistical Software, June 2017
"This is a well-written book and showcases a good number of examples and applications to demonstrate how the methods are actually used in real life situation using real datasets. Further, topics at hand are motivated by social science data. … The chapters are nicely structured, well presented and motivated by data examples. The main strength of the book is that it still offers a good number of applications that are based on real datasets emerging from social science perspectives. The book will be useful to students, practitioners, and data analyst in the respective fields. The editors did a very good job introducing the book, it aims and goals, intendent audience, clarifying underneath concepts and phrases, a must read before moving to other chapters."
—S. Ejaz Ahmed, in Technometrics, April 2017
"Economists and Social Scientist have a lot to learn from Machine Learning, and Engineers have a lot to learn from Econometricians and Statisticians. This two way sharing is long overdue and it is time to start the conversation. This book is a tour-de-force for anyone interested in participating in such a discussion."
—Roberto Rigobon, Society of Sloan Fellows Professor of Applied Economics, MIT
"This ambitious sweep through data science techniques provides an invaluable introduction to the toolbox of big data methodologies, as applied to social science data. It provides tremendous value not only to beginners in the field, but also to experienced data scientists wishing round out their knowledge of this broad and dynamic field."
—Kenneth Benoit, Department of Methodology, London School of Economics and Political Science
"Most social scientists would agree that ‘big data’ – the term we use to encapsulate the huge amount of electronic information we generate in our everyday lives – provide the potential for path-breaking research not just into our economic, social, and political lives but also the physical environment we create and inhabit. However, few have the knowledge, or critically, the tools that equip them to realize this potential. This book provides a bridge between computer science, statistics, and the social sciences, demonstrating this new field of ‘data science’ via practical applications. The book is remarkable in many ways. It originates from classes taught by leading practitioners in this area to federal agency research staff, drawing in particular upon the example of a hugely successful project that linked federal research spending to outcomes in terms of patents, job creation, and the subsequent career development of researchers. By making these workbooks accessible, the book takes the novice on a step-by-step journey through complex areas such as database dynamics, data linkage, text analysis, networks and data visualization. The book is a treasure trove of information. It leads the field in the important task of bringing together computer science, statistics, and social science. I strongly recommend that all social scientists with an interest in ‘big data’ immerse themselves in this book."
—Peter Elias CBE, University of Warwick
"The explosive growth in big data and in new technologies to analyze these data is transforming the practice of research in a variety of fields. Foster, et al. provides a well-timed, valuable guide to the new methods and tools associated with big data that can be used to address critical research questions in the social science field. The breadth of the material is impressive, providing a comprehensive summary of the methods and tools as well as practical guidance for their use. A key feature of the guide is the use of a case study to illustrate how big data techniques can be used to address a research question from beginning to end of the project, including providing examples of computer code targeted to specific steps in the project. Any researcher will find this unique guide to be useful, and it is essential reading for any social science practitioner that wants to use the best available data to conduct influential research in the near future."
—Paul Decker, President and CEO, Mathematica Policy Research
"The typical statistics pedagogy has changed. In the past, textbooks assumed that data was hard to obtain, but neatly organized in a single file. Today, data is very easy to obtain from a number of data sources, often very messy, and analysts are now responsible for organizing it in addition to deriving useful insights. Foster, Ghani, Jarmin, Kreuter, and Lane have assembled a book that gives a pointed overview of tools to facilitate the entire digital lifespan of data in this era of analytics. Big Data and Social Science gives an evenhanded look at the myriad of ways to obtain data--whether scraping the web, web APIs, or databases--to conducting statistical analysis to doing analysis when your data cannot fit on a single computer. Meanwhile, they provide sound, diligent advice on pitfalls that still, and will always, exist. A book like this is useful for social scientists, experienced statisticians, econometricians, and computer programmers who want to see the tools available to them. It will also be a helpful text for a budding data scientist who wants a fairly technical preview of the landscape."
—Tom Schenk Jr., Chief Data Officer, City of Chicago
"In Big Data and Social Science, the authors have deftly crafted one of the very best "how-to" books on big data that researchers, enterprise analysts, and government practitioners will find equally valuable. From Nodes, to Edges, to Arcs, the book takes the reader along a near-perfect path to understanding the fundamental elements of constructing a practical and realistic model for Big Data Analysis that any organization can execute by simply following the path outlined in this book. Elegant in its simplicity, Big Data and Social Science is one of those books that every research group and data-analysis team will want to have on their reference shelf."
—Tom Herzog, Former Deputy Commissioner, NY State Department of Corrections and Community Supervision
Why this book?
Defining big data and its value
Social science, inference, and big data
Social science, data quality, and big data
New tools for new data
The book’s "use case"
The structure of the book
Capture and Curation
Working with Web Data and APIs
Scraping information from the web
New data in the research enterprise
A functional view
Programming against an API
Using the ORCID API via a wrapper
Quality, scope, and management
Integrating data from multiple sources
Working with the graph of relationships
Bringing it together: Tracking pathways to impact
Acknowledgements and copyright
Introduction to record linkage
Record linkage and data protection
DBMS: When and why
Linking DBMSs and other tools
Which database to use?
Programming with Big Data
The MapReduce programming model
Apache Hadoop MapReduce
Modeling and Analysis
What is machine learning?
The machine learning process
Problem formulation: Mapping a problem to machine learning methods
How can social scientists benefit from machine learning?
Understanding what people write
How to analyze text
Approaches and applications
Text analysis tools
Networks: The Basics
Comparing collaboration networks
Inference and Ethics
Developing effective visualizations
A data-by-tasks taxonomy
Errors and Inference
The total error paradigm
Illustrations of errors in big data
Errors in big data analytics
Some methods for mitigating, detecting, and compensating for errors
Privacy and Confidentiality
Why is access at all important?
The new challenges
Legal and ethical framework