Kirk Dombrowski (University of Nebraska-Lincoln)
Bilal Khan (City University of New York)
Peer influence has received considerable attention from social network analysis, and more broadly. The availability of topologies that go beyond ego networks has opened considerable debate about commutative effects, while time series network data has allowed for the separation of assortative effects from behavior change. Missing from most accounts, however, is information about—and means for incorporating into network models—peoples’ dispositions towards their own behaviors, as well as the behaviors of their peers. Someone can be both indifferent to their own smoking behavior while disapproving of the same behaviors in their close associates, and vice versa.
This presentation explores a method for formalizing network data that includes both disposition towards ego’s own behavior, and his/her disposition toward the same behaviors in those around him/her. The ability to incorporate such data into models that normally deal only with similarity or difference of behavioral state moves our models a step closer to what intuition tells us to be an important aspect of peer influence—how those around us “feel” about our habits, behaviors, or personal states. The paper describes the formalization process and applies it to social network data on alcohol co-use in a northern indigenous community in North America.