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Big data in social and psychological science: theoretical and methodological issues

Author

Listed:
  • Lin Qiu

    (Nanyang Technological University)

  • Sarah Hian May Chan

    (Nanyang Technological University)

  • David Chan

    (Singapore Management University)

Abstract

Big data presents unprecedented opportunities to understand human behavior on a large scale. It has been increasingly used in social and psychological research to reveal individual differences and group dynamics. There are a few theoretical and methodological challenges in big data research that require attention. In this paper, we highlight four issues, namely data-driven versus theory-driven approaches, measurement validity, multi-level longitudinal analysis, and data integration. They represent common problems that social scientists often face in using big data. We present examples of these problems and propose possible solutions.

Suggested Citation

  • Lin Qiu & Sarah Hian May Chan & David Chan, 2018. "Big data in social and psychological science: theoretical and methodological issues," Journal of Computational Social Science, Springer, vol. 1(1), pages 59-66, January.
    • Handle: RePEc:spr:jcsosc:v:1:y:2018:i:1:d:10.1007_s42001-017-0013-6
    DOI: 10.1007/s42001-017-0013-6
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    Citations

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    Cited by:

    1. Alex Luscombe & Kevin Dick & Kevin Walby, 2022. "Algorithmic thinking in the public interest: navigating technical, legal, and ethical hurdles to web scraping in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1023-1044, June.
    2. Qianjia Huang & Vivek K. Singh & Pradeep K. Atrey, 2018. "On cyberbullying incidents and underlying online social relationships," Journal of Computational Social Science, Springer, vol. 1(2), pages 241-260, September.
    3. 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.
    4. Sean M. Fitzhugh, 2024. "Towards a taxonomy of team workflow structures," Journal of Computational Social Science, Springer, vol. 7(3), pages 2871-2895, December.

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