Most Downloaded Articles
- The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis by Lyons, Russell
- Why and When "Flawed" Social Network Analyses Still Yield Valid Tests of no Contagion by VanderWeele, Tyler J./ Ogburn, Elizabeth L. and Tchetgen Tchetgen, Eric J.
- Improving Statistical Inference with Clustered Data by Harden, Jeffrey J.
- A New Method for Deriving Global Estimates of Maternal Mortality by Wilmoth, John R./ Mizoguchi, Nobuko/ Oestergaard, Mikkel Z./ Say, Lale/ Mathers, Colin D./ Zureick-Brown, Sarah/ Inoue, Mie and Chou, Doris
- Comprehension of Graphs and Tables Depend on the Task: Empirical Evidence from Two Web-Based Studies by Schonlau, Matthias and Peters, Ellen
Why and When "Flawed" Social Network Analyses Still Yield Valid Tests of no Contagion
Citation Information: Statistics, Politics, and Policy. Volume 3, Issue 1, ISSN (Online) 2151-7509, DOI: 10.1515/2151-7509.1050, February 2012
- Published Online:
Lyons (2011) offered several critiques of the social network analyses of Christakis and Fowler, including issues of confounding, model inconsistency, and statistical dependence in networks. Here we show that in some settings, social network analyses of the type employed by Christakis and Fowler will still yield valid tests of the null of no social contagion, even though estimates and confidence intervals may not be valid. In particular, we show that if the alter's state is lagged by an additional period, then under the null of no contagion, the problems of model inconsistency and statistical dependence effectively disappear which allow for testing for contagion. Our results clarify the setting in which even "flawed" social network analyses are still useful for assessing social contagion and social influence.