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Association Versus Causation

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Encyclopedia of Big Data

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Scientific knowledge provides a general understanding of how the world is connected among one another. It is useful in providing a means of categorizing things (typology), a prediction of future events, an explanation of past events, and a sense of understanding about the causes of the phenomenon (causation). Association, also called correlation or covariation, is an empirical and statistical relationship between two variables such that changes in one variable are connected to changes in the other. However, association in and of itself does not necessarily imply a causal relationship between the two variables. It is only one of several necessary criteria for establishing causation. The other two criteria for causal relationships are time order and non-spurious relationships. While the advance of big data makes it possible and more effective to capture tremendous number of correlations and predictions than ever before, and statistical analyses may assess the degree of association...

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Further Readings

  • Babbie, E. (2007). The practice of social research (11th ed.). Belmont: Wadsworth.

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  • Hermida, A., Lewis, S., & Zamith, R. (2014). Sourcing the Arab spring: A case study of Andy Carvin’s sources on Twitter during the Tunisian and Egyptian revolutions. Journal of Computer-Mediated Communication, 19(3), 479.

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  • Hoffman, L., & Fang, H. (2014). Quantifying political behavior on mobile devices over time: A user evaluation study. Journal of Information Technology & Politics, 11(4), 435.

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  • Mahrt, M., & Scharkow, M. (2013). The value of big data in digital media research. Journal of Broadcasting & Electronic Media, 57(1), 20.

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  • McCombs, M. (2004). Setting the agenda: The mass media and public opinion. Cambridge, UK: Polity.

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  • Reynolds, P. (2007). A primer in theory construction. Boston: Pearson/Allyn & Bacon.

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  • Shoemaker, P., Tankard, J., & Lasorsa, D. (2004). How to build social science theories. Thousand Oaks: Sage.

    Book  Google Scholar 

  • Singleton, R., & Straits, B. (2010). Approaches to social research (5th ed.). New York: Oxford University Press.

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Author information

Authors and Affiliations

  1. College of Media and Communication, Texas Tech University, Lubbock, TX, USA

    Weiwu Zhang

  2. Department of Communication, Appalachian State University, Boone, NC, USA

    Matthew S. VanDyke

Authors
  1. Weiwu Zhang
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  2. Matthew S. VanDyke
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Correspondence to Weiwu Zhang .

Editor information

Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Connie L. McNeely

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Zhang, W., VanDyke, M.S. (2018). Association Versus Causation. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_15-1

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  • DOI: https://doi.org/10.1007/978-3-319-32001-4_15-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

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