Why and When "Flawed" Social Network Analyses Still Yield Valid Tests of no Contagion

In: Statistics, Politics, and Policy, Band 3, Heft 1

Abstract

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.