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The datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry.
In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways.
Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.
|Format:||Hardback, 208 pages||Language:||English|
|Dimension:||21.84cm x 14.22cm x 2.03cm||ISBN10:||1509539271|
|Weight:||363g||ISBN13:||9781509539277||Publication Date:||07 Aug 2020||Publisher:||Polity Press|
ÂIn this elegant book, Lindgren moves beyond the frequent schizophrenia of methods debates to ask: what happens when traditional social theory and data analytics are combined smartly? The result is illuminating and useful. Highly recommended!
Â Nick Couldry, London School of Economics and Political Science ÂThis is a very interesting book with an original approach which will be useful to scholars and students.
Â Lina Dencik, Cardiff University ÂIn this provocative text, Lindgren leads us on an innovative path thatÂ should both challenge and inspireÂ researchers across the quant-qual divide.
Â A new social science methods classic for the digitalÂ media era!
Â Sarah T. Roberts, UCLA
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