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Social network analysis facilitates a deeper of understanding of underlying relationships and structures. 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.

Speaker: Krista Gile, University of Massachusetts, Amherst

Contact Info

Nicholas Horton
(413) 542-5655
Please call the college operator at 413-542-2000 or e-mail info@amherst.edu if you require contact info @amherst.edu

Directions

Seeley Mudd Hall is located at the southwest corner of the first year Quadrangle (31 Quadrangle Drive). Paid parking is available at the Amherst Town Common and Boltwood Drive (approximately 8 minute walk). PVTA Bus Service is available from the Converse Hall stop.