Most social network analysis assumes an objective network of shared social ties, typically measured as self-reports from research subjects. Although it is common for two parties to give discrepant reports of their shared relationship status, there is no standard way to resolve such discrepancies. We develop a Bayesian model that leverages patterns of agreement among respondents across multiple relations, using flexible priors to allow for aberrant reporting behaviors. The model allows for posterior inference for individual reporter error rates and for the underlying true network. The method is motivated by and applied to the Food, Activity, Screens, and Teens (FAST) study, an investigation of social networks and health behavior among U.S. middle school students.
This is joint work with former PhD student Dongah Kim, John Sirard, and former UW folks James Kitts, Maryclare Griffin, and David Nolin.
Bayesian Resolution of Discrepant Self-Reported Network Ties