Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144062
Title: Big data in social and psychological science : theoretical and methodological issues
Authors: Qiu, Lin
Chan, Sarah Hian May
Chan, David
Keywords: Social sciences::Psychology
Issue Date: 2017
Source: Qiu, L., Chan, S. H. M., & Chan, D. (2018). Big data in social and psychological science : theoretical and methodological issues. Journal of Computational Social Science, 1(1), 59-66. doi:10.1007/s42001-017-0013-6
Journal: Journal of Computational Social Science
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.
URI: https://hdl.handle.net/10356/144062
ISSN: 2432-2717
DOI: 10.1007/s42001-017-0013-6
Schools: School of Social Sciences 
Rights: © 2017 Springer Nature Sinagapore Pte Ltd. This is a post-peer-review, pre-copyedit version of an article published in Journal of Computational Social Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/s42001-017-0013-6.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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