IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v40y2011i2p240-255.html
   My bibliography  Save this article

Sensitivity Analysis for Contagion Effects in Social Networks

Author

Listed:
  • Tyler J. VanderWeele

    (Harvard University, Boston, MA, USA, tvanderw@hsph.harvard.edu)

Abstract

Analyses of social network data have suggested that obesity, smoking, happiness, and loneliness all travel through social networks. Individuals exert ‘‘contagion effects’’ on one another through social ties and association. These analyses have come under critique because of the possibility that homophily from unmeasured factors may explain these statistical associations and because similar findings can be obtained when the same methodology is applied to height, acne, and headaches, for which the conclusion of contagion effects seems somewhat less plausible. The author uses sensitivity analysis techniques to assess the extent to which supposed contagion effects for obesity, smoking, happiness, and loneliness might be explained away by homophily or confounding and the extent to which the critique using analysis of data on height, acne, and headaches is relevant. Sensitivity analyses suggest that contagion effects for obesity and smoking cessation are reasonably robust to possible latent homophily or environmental confounding; those for happiness and loneliness are somewhat less so. Supposed effects for height, acne, and headaches are all easily explained away by latent homophily and confounding. The methodology that has been used in past studies for contagion effects in social networks, when used in conjunction with sensitivity analysis, may prove useful in establishing social influence for various behaviors and states. The sensitivity analysis approach can be used to address the critique of latent homophily as a possible explanation of associations interpreted as contagion effects.

Suggested Citation

  • Tyler J. VanderWeele, 2011. "Sensitivity Analysis for Contagion Effects in Social Networks," Sociological Methods & Research, , vol. 40(2), pages 240-255, May.
    • Handle: RePEc:sae:somere:v:40:y:2011:i:2:p:240-255
    DOI: 10.1177/0049124111404821
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124111404821
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124111404821?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fortin, Bernard & Yazbeck, Myra, 2015. "Peer effects, fast food consumption and adolescent weight gain," Journal of Health Economics, Elsevier, vol. 42(C), pages 125-138.
    2. Shalizi Cosma Rohilla, 2012. "Comment on "Why and When 'Flawed' Social Network Analyses Still Yield Valid Tests of no Contagion"," Statistics, Politics and Policy, De Gruyter, vol. 3(1), pages 1-5, February.
    3. Lincoln, James R. & Doerr, Bernadette, 2012. "Cultural Effects on Employee Loyalty in Japan and The U. S.: Individual- or Organization-Level? An Analysis of Plant and Employee Survey Data from the 80’s," Institute for Research on Labor and Employment, Working Paper Series qt8sc9k91b, Institute of Industrial Relations, UC Berkeley.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bareinboim Elias & Pearl Judea, 2013. "A General Algorithm for Deciding Transportability of Experimental Results," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 107-134, June.
    2. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    3. Maarten J. Bijlsma & Rhian M. Daniel & Fanny Janssen & Bianca L. De Stavola, 2017. "An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models," Demography, Springer;Population Association of America (PAA), vol. 54(2), pages 721-743, April.
    4. Chen, Pu & Hsiao, Chih-Ying, 2008. "What happens to Japan if China catches a cold?: A causal analysis of Chinese growth and Japanese growth," Japan and the World Economy, Elsevier, vol. 20(4), pages 622-638, December.
    5. Chen, Pu & Chihying, Hsiao, 2007. "Learning Causal Relations in Multivariate Time Series Data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 1, pages 1-43.
    6. Kaiyue Liu & Lihua Liu & Kaiming Xiao & Xuan Li & Hang Zhang & Yun Zhou & Hongbin Huang, 2024. "CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework," Mathematics, MDPI, vol. 12(17), pages 1-22, August.
    7. Hao Li & Jianjun Zhan & Haosen Wang & Zipeng Zhao, 2024. "A Novel Ensemble Method of Divide-and-Conquer Markov Boundary Discovery for Causal Feature Selection," Mathematics, MDPI, vol. 12(18), pages 1-21, September.
    8. Ruijie Tang, 2024. "Trading with Time Series Causal Discovery: An Empirical Study," Papers 2408.15846, arXiv.org, revised Aug 2024.
    9. Benjamin A Logsdon & Jason Mezey, 2010. "Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-13, December.
    10. Stimel Derek, 2009. "A Statistical Analysis of NFL Quarterback Rating Variables," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-26, May.
    11. Xingyu Liao & Xiaoping Liu, 2024. "Hidden Variable Discovery Based on Regression and Entropy," Mathematics, MDPI, vol. 12(9), pages 1-16, April.
    12. Behnam Azhdari & Jean Bonnet & Sébastien Bourdin, 2022. "Towards a Causal Model and Causal Inference of Regional Entrepreneurship Development Index, its antecedents and outcomes in European regions," Economics Working Paper Archive (University of Rennes & University of Caen) 2022-06, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    13. C Schultheiss & P Bühlmann, 2023. "Ancestor regression in linear structural equation models," Biometrika, Biometrika Trust, vol. 110(4), pages 1117-1124.
    14. Klimova, Anna & Uhler, Caroline & Rudas, Tamás, 2015. "Faithfulness and learning hypergraphs from discrete distributions," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 57-72.
    15. Pearl Judea, 2017. "Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions," Journal of Causal Inference, De Gruyter, vol. 5(2), pages 1-10, September.
    16. Stimel Derek S, 2011. "Dependence Relationships between On Field Performance, Wins, and Payroll in Major League Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-19, May.
    17. Jong-Min Kim & Chulhee Jun & Hope H. Han, 2020. "Sustainable Causal Interpretation with Board Characteristics: Caveat Emptor," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    18. Huang, Wei & Lai, Pei-Chun & Bessler, David A., 2018. "On the changing structure among Chinese equity markets: Hong Kong, Shanghai, and Shenzhen," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1020-1032.
    19. Paul Muentener & Elizabeth Bonawitz & Alexandra Horowitz & Laura Schulz, 2012. "Mind the Gap: Investigating Toddlers’ Sensitivity to Contact Relations in Predictive Events," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    20. Heinlein, Reinhold & Krolzig, Hans-Martin, 2012. "On the construction of two-country cointegrated VAR models with an application to the UK and US," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62310, Verein für Socialpolitik / German Economic Association.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:somere:v:40:y:2011:i:2:p:240-255. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.