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Speaking sociologically with big data: symphonic social science and the future for big data research

Author

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  • Halford, Susan
  • Savage, Mike

Abstract

Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Piketty, Putnam and Wilkinson and Pickett that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future

Suggested Citation

  • Halford, Susan & Savage, Mike, 2017. "Speaking sociologically with big data: symphonic social science and the future for big data research," LSE Research Online Documents on Economics 87236, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:87236
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    File URL: http://eprints.lse.ac.uk/87236/
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    Cited by:

    1. McDaid, Emma & Andon, Paul & Free, Clinton, 2023. "Algorithmic management and the politics of demand: Control and resistance at Uber," Accounting, Organizations and Society, Elsevier, vol. 109(C).
    2. Mark Livingston & Francesca Pannullo & Adrian W. Bowman & E. Marian Scott & Nick Bailey, 2021. "Exploiting new forms of data to study the private rented sector: Strengths and limitations of a database of rental listings," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 663-682, April.
    3. Mariangela Vespa & Timo Kortsch & Jan Hildebrand & Petra Schweizer-Ries & Sara Alida Volkmer, 2022. "Not All Places Are Equal: Using Instagram to Understand Cognitions and Affect towards Renewable Energy Infrastructures," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    4. Advani, Arun, 2021. "Missing Incomes in the UK : Evidence and Policy Implications," The Warwick Economics Research Paper Series (TWERPS) 1364, University of Warwick, Department of Economics.
    5. Stefania Capogna, 2023. "Sociology between big data and research frontiers, a challenge for educational policies and skills," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 193-212, February.
    6. Teresa Duarte Martinho, 2018. "Researching Culture through Big Data: Computational Engineering and the Human and Social Sciences," Social Sciences, MDPI, vol. 7(12), pages 1-17, December.
    7. Neeru Gupta & Samuel R. Cookson, 2023. "Evaluation of Survey Nonresponse in Measuring Cardiometabolic Health Risk Factors and Outcomes among Sexual Minority Populations: A National Data Linkage Analysis," IJERPH, MDPI, vol. 20(7), pages 1-11, March.
    8. Alnoor Bhimani, 2020. "Digital data and management accounting: why we need to rethink research methods," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 9-23, April.
    9. Emma Davidson & Rosalind Edwards & Lynn Jamieson & Susie Weller, 2019. "Big data, qualitative style: a breadth-and-depth method for working with large amounts of secondary qualitative data," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 363-376, January.
    10. Anita Lavorgna & Leslie Carr, 2021. "Tweets and Quacks: Network and Content Analyses of Providers of Non-Science-Based Anticancer Treatments and Their Supporters on Twitter," SAGE Open, , vol. 11(1), pages 21582440211, March.

    More about this item

    Keywords

    big data; computational methods; sociology; symphonic social science; visualisation;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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