The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis
Open accessAbstract
The chronic widespread misuse of statistics is usually inadvertent, not intentional. We find cautionary examples in a series of recent papers by Christakis and Fowler that advance statistical arguments for the transmission via social networks of various personal characteristics, including obesity, smoking cessation, happiness, and loneliness. Those papers also assert that such influence extends to three degrees of separation in social networks. We shall show that these conclusions do not follow from Christakis and Fowler's statistical analyses. In fact, their studies even provide some evidence against the existence of such transmission. The errors that we expose arose, in part, because the assumptions behind the statistical procedures used were insufficiently examined, not only by the authors, but also by the reviewers. Our examples are instructive because the practitioners are highly reputed, their results have received enormous popular attention, and the journals that published their studies are among the most respected in the world. An educational bonus emerges from the difficulty we report in getting our critique published. We discuss the relevance of this episode to understanding statistical literacy and the role of scientific review, as well as to reforming statistics education.
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“network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Lyons, 2011
Citations
Cites
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · CHANCE · doi:10.1080/09332480.2005.10722754 · Cited by 4,702
The collective dynamics of smoking in a large social network
Christakis, N. A., & Fowler, J. H. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0706154 · Cited by 2,390
Dummy Endogenous Variables in a Simultaneous Equation System
Heckman, J. J. (1978) · Econometrica · doi:10.2307/1909757 · Cited by 1,845
Peer Effects with Random Assignment: Results for Dartmouth Roommates
Sacerdote, B. (2001) · The Quarterly Journal of Economics · doi:10.1162/00335530151144131 · Cited by 1,841
Contradicted and Initially Stronger Effects in Highly Cited Clinical Research
Ioannidis, J. P. A. (2005) · JAMA · doi:10.1001/jama.294.2.218 · Cited by 1,465
Homophily and contagion are generically confounded in observational social network studies
Shalizi, C. R., & Thomas, A. C. (2011) · Sociological Methods & Research · doi:10.1177/0049124111404820 · Cited by 994
Alone in the crowd: The structure and spread of loneliness in a large social network.
Cacioppo, J. T., Fowler, J. H., & Christakis, N. A. (2009) · Journal of Personality and Social Psychology · doi:10.1037/a0016076 · Cited by 908
Gigerenzer, G. (2004) · The Journal of Socio-Economics · doi:10.1016/j.socec.2004.09.033 · Cited by 890
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Cited by
Sorted by citations
Social contagion theory: examining dynamic social networks and human behavior
Christakis, N. A., & Fowler, J. H. (2013) · Statistics in Medicine · doi:10.1002/sim.5408 · Cited by 1,303
Identifying influential and susceptible members of social networks
Aral, S., & Walker, D. (2012) · Science · doi:10.1126/science.1215842 · Cited by 1,047
Homophily and contagion are generically confounded in observational social network studies
Shalizi, C. R., & Thomas, A. C. (2011) · Sociological Methods & Research · doi:10.1177/0049124111404820 · Cited by 994
“Treated at face value, this can produce an incoherent probability distribution for the evolution of the system (Lyons, 2010), as well as implying a scarcely-comprehensible notion of simultaneous causation (rather than coupled behavior or feedback); this can be somewhat salvaged by considering it as…”
BackgroundSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“For instance, network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”
BackgroundInfluentialSystems Science Methods in Public Health: Dynamics, Networks, and Agents
Luke, D. A., & Stamatakis, K. A. (2012) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031210-101222 · Cited by 740
Metadata
- Authors
- Lyons, R.
- Corresponding author
- Lyons, Russell
- ORCID
- Lyons, Russell
- Venue
- Statistics, Politics, and Policy
- Publisher
- Walter de Gruyter GmbH
- Published
- 2011-01-18
- Volume
- 2
- Issue
- 1
- ISSN
- 2151-7509 (ISSN-L)
- OpenAlex
- W2152314940
- Cited by
- 182
- References
- 25
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 7 · 2020: 7 · 2019: 10 · 2018: 22 · 2017: 14 · 2016: 13 · 2015: 19 · 2014: 13 · 2013: 32 · 2012: 16
- FWCI
- 20.49
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Statistics Politics and Policy (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- social network analysis, evidence-based medicine, smoking cessation, happiness, loneliness, statistical methodology critique, statistical literacy, peer review, statistics education, misuse of statistics
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