Causal Relationships between Personal Networks and Health: A Comparison of Three Modeling Strategies

Abstract

Prior research documents associations between personal network characteristics and health, but establishing causation has been a long-standing research priority. To evaluate approaches to causal inference in egocentric network data, this article uses three waves from the University of California Berkeley Social Networks Study (N = 1,159) to investigate connections between nine network variables and two global health outcomes. We compare three modeling strategies: cross-sectional ordinary least squares regression, regression with lagged dependent variables (LDVs), and hybrid fixed and random effects models. Results suggest that cross-sectional and LDV models may overestimate the causal effects of networks on health because hybrid models show that network–health associations operate primarily between individuals, as opposed to network changes causing within-individual changes in health. These findings demonstrate uses of panel data that may advance scholarship on networks and health and suggest that causal effects of network support on health may be more limited than previously thought.