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Computational social scientist beware: Simpson’s paradox in behavioral data

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  • Kristina Lerman

    (USC Information Sciences Institute)

Abstract

Observational data about human behavior are often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson’s paradox, whereby the trends observed in data that have been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson’s paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson’s paradox is affecting results of analysis. The presence of Simpson’s paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies’ findings.

Suggested Citation

  • Kristina Lerman, 2018. "Computational social scientist beware: Simpson’s paradox in behavioral data," Journal of Computational Social Science, Springer, vol. 1(1), pages 49-58, January.
    • Handle: RePEc:spr:jcsosc:v:1:y:2018:i:1:d:10.1007_s42001-017-0007-4
    DOI: 10.1007/s42001-017-0007-4
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    References listed on IDEAS

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    1. Philipp Singer & Emilio Ferrara & Farshad Kooti & Markus Strohmaier & Kristina Lerman, 2016. "Evidence of Online Performance Deterioration in User Sessions on Reddit," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
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    Cited by:

    1. Minda Hu & Ashwin Rao & Mayank Kejriwal & Kristina Lerman, 2021. "Socioeconomic Correlates of Anti-Science Attitudes in the US," Future Internet, MDPI, vol. 13(6), pages 1-14, June.

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