Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2017
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.
Keywords
Big data, Computational social science, Psychology, Social science, Social media, Methodology
Discipline
Psychology | Social Psychology and Interaction
Research Areas
Psychology
Publication
Journal of Computational Social Science
Volume
1
Issue
1
First Page
59
Last Page
66
ISSN
2432-2717
Identifier
10.1007/s42001-017-0013-6
Citation
QIU, Lin, CHAN, Sarah Hian May, & CHAN, David.(2017). Big data in social and psychological science: Theoretical and methodological issues. Journal of Computational Social Science, 1(1), 59-66.
Available at: https://ink.library.smu.edu.sg/soss_research/2464
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s42001-017-0013-6