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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