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Network Dependence Can Lead to Spurious Associations and Invalid Inference

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  • Youjin Lee
  • Elizabeth L. Ogburn

Abstract

Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example of this is the Framingham Heart Study (FHS). Many of the limitations of such samples are well-known, but the issue of statistical dependence due to social network ties has not previously been addressed. We show that, along with anticonservative variance estimation, this can result in spurious associations due to network dependence. Using a statistical test that we adapted from one developed for spatial autocorrelation, we test for network dependence in several of the thousands of influential papers that have been published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious associations, error-prone point estimates, and anticonservative inference due to unacknowledged network dependence. These issues are not unique to the FHS; as researchers in psychology, medicine, and beyond grapple with replication failures, this unacknowledged source of invalid statistical inference should be part of the conversation. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Suggested Citation

  • Youjin Lee & Elizabeth L. Ogburn, 2021. "Network Dependence Can Lead to Spurious Associations and Invalid Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1060-1074, July.
  • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:535:p:1060-1074
    DOI: 10.1080/01621459.2020.1782219
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    Cited by:

    1. Brent Simpson & Bradley Montgomery & David Melamed, 2023. "Reputations for treatment of outgroup members can prevent the emergence of political segregation in cooperative networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Jiang, Zhi-Qiang & Wang, Peng & Ma, Jun-Chao & Zhu, Peican & Han, Zhen & Podobnik, Boris & Stanley, H. Eugene & Zhou, Wei-Xing & Alfaro-Bittner, Karin & Boccaletti, Stefano, 2023. "Unraveling the effects of network, direct and indirect reciprocity in online societies," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    3. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    4. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.

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