After I posted on Russ Lyons’s criticisms of the work of Nicholas Christakis and James Fowler’s work on social networks, several people emailed in with links to related articles. (Nobody wants to comment on the blog anymore; all I get is emails.)

Here they are:

Political scientists Hans Noel and Brendan Nyhan wrote a paper called “The ‘Unfriending’ Problem: The Consequences of Homophily in Friendship Retention for Causal Estimates of Social Influence” in which they argue that the Christakis-Fowler results are subject to bias because of patterns in the time course of friendships.

Statisticians Cosma Shalizi and Andrew Thomas wrote a paper called “Homophily and Contagion Are Generically Confounded in Observational Social Network Studies” arguing that analyses such as those of Christakis and Fowler cannot hope to disentangle different sorts of network effects.

And Christakis and Fowler reply to Noel and Nyhan, Shalizi and Thomas, Lyons, and others in an article that begins:

Here, we review the research we have done on social contagion. We describe the methods we have employed (and the assumptions they have entailed) in order to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a “three degrees of influence” property, and we review statistical approaches we have used to characterize inter-personal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations.

This was about what I was imagining: the work is not definitive but they have found some patterns in this particular idiosyncratic dataset and it will be interesting to see what comes next.

Finally, Lyons wrote in his paper that the Christakis-Fowler data are “are not available to others.” That’s not completely correct; at the time of the writing of his paper Lyons was unaware of this online data source. Unfortunately, confidentiality restrictions appear to limit the data in such a way that only partial replications can be done using this public dataset.