CFE 2019: Start Registration
View Submission - CMStatistics
B0516
Title: Social network dependence, the replication crisis, and (in)valid inference Authors:  Youjin Lee - University of Pennsylvania (United States)
Elizabeth Ogburn - Johns Hopkins University (United States) [presenting]
Abstract: It will be shown that social network structure can result in a new kind of structural confounding (confounding by network structure), potentially contributing to replication crises across the health and social sciences. Researchers in these fields frequently sample subjects from one or a small number of communities, schools, hospitals, etc., and while many of the limitations of such convenience samples are well-known, the issue of statistical dependence due to social network ties has not previously been addressed. A paradigmatic example of this is the Framingham Heart Study (FHS). Using a statistic that we adapted to measure network dependence, we test for network dependence and for possible confounding by network structure in several of the thousands of influential papers published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may be biased (away from the null) and anticonservative due to unacknowledged network structure.