Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

An important question in social network analysis is whether observed association in individual characteristics between connected individuals in a network is a consequence of social influences (i.e., induction) or whether individuals who are similar (dissimilar) are more likely to form (break) ties (i.e., homophily). In this talk we focus on methods for estimating homophily using longitudinal data from a large social network using for illustration the effect of health traits on the status of peoples' close friendships. The health traits considered are both mutable (body mass index [BMI], smoking, blood pressure, body proportion, muscularity, and depression) and, for comparison, basically immutable (height, birth-order, personality type, only-child, and handedness); and the traits have varying degrees of observability. We test the hypotheses that existing ties are more likely to dissolve between people with dissimilar (mutable and observable) health traits while new ties are more likely to form between those with similar (mutable and observable) traits while controlling for persons' age, gender, and - in sensitivity analyses - also controlling for geographic separation and education. Novel mixed effects models containing random effects for the individual naming their close friend and the person being named are used to account for the fact that people were involved in multiple relationships and were observed at multiple exams.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.