title: Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method with Application to Add Health.
abstract: Recent developments have made model-based imputation of network data feasible in principle, but the extant literature provides few practical examples of its use. In this paper we consider 14 schools from the widely used In-School Survey of Add Health (Harris et al., 2009), applying an ERGM-based estimation and simulation approach to impute the network missing data for each school. Add Health's complex study design leads to multiple types of missingness, and we introduce practical techniques for handing each. We also develop a cross-validation based method - Held-Out Predictive Evaluation (HOPE) - for assessing this approach. Our results suggest that ERGM-based imputation of edge variables is a viable approach to the analysis of complex studies such as Add Health, provided that care is used in understanding and accounting for the study design.
authors: Department of Sociology, University of Notre Dame.
authors: Cheng
authors: C
authors: Wang
authors: Departments of Sociology and Statistics, University of California, Irvine.
authors: Carter T
authors: CT
authors: Butts
authors: Departments of Criminology, Law and Society and Sociology, University of California, Irvine.
authors: John R
authors: JR
authors: Hipp
authors: Department of Psychology and Social Behavior, University of California, Irvine.
authors: Rupa
authors: R
authors: Jose
authors: Program in Public Health, University of California, Irvine.
authors: Cynthia M
authors: CM
authors: Lakon