Understanding China through big data : applications of theory-oriented quantitative approaches / Yunsong Chen, Guangye He, and Fei Yan.

Author
Chen, Yunsong, 1975- [Browse]
Format
Book
Language
English
Published/​Created
  • Abingdon, Oxon ; New York, NY : Routledge, Taylor & Francis Group, 2022.
  • ©2022
Description
xiii, 257 pages : illustrations, maps ; 25 cm.

Details

Subject(s)
Author
Series
Routledge advances in sociology [More in this series]
Summary note
"Chen, He and Yan present a range of applications of multiple-source big data to core areas of contemporary sociology, demonstrating how a theory-guided approach to macrosociology can help to understand social change in China, especially where traditional approaches are limited by constrained and biased data. In each chapter of the book, the authors highlight an application of theory-guided macrosociology that has the potential to reinvigorate an ambitious, open-minded and bold approach to sociological research. These include social stratification, social networks, medical care, and online behaviours among many others. This research approach focuses on macro-level social process and phenomena by using quantitative models to statistically test for associations and causalities suggested by a clearly hypothesised social theory. By deploying theory-oriented macrosociology where it can best assure macro-level robustness and reliability, big data applications can be more relevant to and guided by social theory. An essential read for sociologists with an interest in quantitative and macro-scale research methods, which also provides fascinating insights into Chinese society as a demonstration of the utility of its methodology"-- Provided by publisher.
Bibliographic references
Includes bibliographical references and index.
ISBN
  • 9780367758264
  • 0367758261 (hardcover)
  • 9780367758257 (paperback)
  • 0367758253 (paperback)
LCCN
2021003342
OCLC
1235416618
Statement on responsible collection description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...
Other views
Staff view

Supplementary Information