Missing data are common in studies that involve multiple informants to study relationships among individuals within groups. Structural equation modeling (SEM), routinely implemented, allows for incomplete data so that groups may be retained when at least one member contributes data. Missing data are assumed to be missing at random such that whether or not data are missing is independent of the missing data. Multiple imputation and a saturated correlates model that incorporate correlates of missing data into an analysis offer advantages over the standard implementation of SEM when data are not missing at random because these approaches may result in a data analysis problem for which the missingness is then ignorable. This paper considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates to assumptions about missing data, a strategy that may be easily implemented using SEM software.