Dr. Janet Box- Steffensmeier, Professor, Department of Political Science
The analysis of networks, both biological and social, has become increasingly important in the health and social sciences in recent years. Inferential and predictive statistical models that analyze networks have been put to use in such areas as epidemiology (Reis, Kohane, and Mandl 2007; Gardy et al. 2011), public health (Luke and Harris 2007; Brownstein, Freifeld, and Madoff 2009), molecular biology (Wu et al. 2008; Bowman, Huang, and Pande 2010), and the social sciences (Hoff and Ward 2004; Box-Steffensmeier and Christenson 2012). Yet, while network analysis continues to attract a great deal of attention from scholars and the broader public, the quantitative study of networks remains in the early stages of development. Only recently have the statistical theory and computational techniques been developed to rigorously analyze many types of networks, but there remain significant gaps in the statistical tools that limits their usefulness to scientists.
Our objective is to improve the well-known and widely used Exponential Random Graph Model, or ERGM, by adding a frailty term to account for heterogeneity when modeling network formation. We will evaluate the model with Monte Carlo experiments and then apply the model to health behavior data. The significance of the project is that it will help fill a gap in the ERGM literature, allowing researchers to draw correct inferences in a broader set of circumstances.
Publications resulting from this seed grant