10.7910/DVN/K3D1M2Morgan, JasonJasonMorganThe Ohio State UniversityBox-Steffensmeier, Janet M.Janet M.Box-SteffensmeierThe Ohio State UniversityChristenson, Dino P.Dino P.ChristensonBoston UniversityHarvard Dataverse2017Social SciencesMorgan, JasonJasonMorganThe Ohio State UniversityPolitical Analysis2017-02-282017-04-21115500636application/zip1.0CC0 1.0In the study of social processes, the presence of unobserved heterogeneity is a regular concern. It should be particularly worrisome for the statistical analysis of networks, given the complex dependencies that shape network formation combined with the re- strictive assumptions of related models. In this paper, we demonstrate the importance of explicitly accounting for unobserved heterogeneity in exponential random graph models (ERGM) with a Monte Carlo analysis and two applications that have played an important role in the networks literature. Overall, these analyses show that failing to account for unobserved heterogeneity can have a significant impact on inferences about network formation. The proposed frailty extension to the ERGM (FERGM) generally outperforms the ERGM in these cases, and does so by relatively large mar- gins. Moreover, our novel multilevel estimation strategy has the advantage of avoiding the problem of degeneration that plagues the standard MCMC-MLE approach.