Multiple imputation for missing edge data: A predictive evaluation method with application to Add Health

• We use an ERGM-based imputation approach to handle complex network data missingness.
• We employ multiple criteria to check the ERG model convergence.
• We develop a Held-Out Predictive Evaluation (HOPE) strategy to assess this approach.
• We provide possible explanations for differences in recovery rates across schools.
• Results suggest this approach has advantages in dealing with missing data challenge.

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.