Dataanalyse: generelt
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Thick Big Data
Doing Digital Social Sciences
Udgivet af Oxford University Press
ISBN
9780198839705
Om denne bog
The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double th…
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Bogdetaljer
Forfatter
Dariusz Jemielniak
Forlag
Oxford University Press
Kategori
Dataanalyse: generelt
ISBN-13
9780198839705
EAN
9780198839705
Format
Indbundet
Sprog
Engelsk
sider
208
Undertitel
- Doing Digital Social Sciences
Produktbeskrivelse
The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double threat of becoming marginalized or even irrelevant, both from new methods of research which require more computational skills and from increasing competition from the corporate world which gains an additional advantage based on data access. However, unlike data scientists, sociologists and anthropologists have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data therefore needs Thick Data. This book presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. It shows that Big Data can and should be supplemented and interpreted through thick data as well as cultural analysis. Thick Big Data is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and to successfully build mixed-methods approaches.
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