Complementary Social Science? Quali-Quantitative Experiments in a Big Data World

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The rise of Big Data in the social realm poses significant questions at the intersection of science, technology, and society, including in terms of how new large-scale social databases are currently changing the methods, epistemologies, and politics of social science. In this commentary, we address such epochal (“large-scale”) questions by way of a (situated) experiment: at the Danish Technical University in Copenhagen, an interdisciplinary group of computer scientists, physicists, economists, sociologists, and anthropologists (including the authors) is setting up a large-scale data infrastructure, meant to continually record the digital traces of social relations among an entire freshman class of students (N > 1000). At the same time, fieldwork is carried out on friendship (and other) relations amongst the same group of students. On this basis, the question we pose is the following: what kind of knowledge is obtained on this social micro-cosmos via the Big (computational, quantitative) and Small (embodied, qualitative) Data, respectively? How do the two relate? Invoking Bohr’s principle of complementarity as analogy, we hypothesize that social relations, as objects of knowledge, depend crucially on the type of measurement device deployed. At the same time, however, we also expect new interferences and polyphonies to arise at the intersection of Big and Small Data, provided that these are, so to speak, mixed with care. These questions, we stress, are important not only for the future of social science methods but also for the type of societal (self-)knowledge that may be expected from new large-scale social databases.
Original languageEnglish
JournalBig Data & Society
Issue number2
Pages (from-to)1-6
Number of pages6
Publication statusPublished - 2014

    Research areas

  • Faculty of Social Sciences - Principle of complementarity , method devices , quali-quantitative methods , social science experiments , computational social science , Big Data critique

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