Missing behavior data in longitudinal network studies: the impact of treatment methods on estimated effect parameters in stochastic actor oriented models

Abstract

Research into missing network data is growing, with a focus on the impact of missing ties on network statistics or network model parameters. Longitudinal network studies using stochastic actor-oriented models (SAOMs) focus on the co-evolution of network structure and behavior/attributes to disentangle influence and selection mechanisms. Still little is known about the impact of missing behavior data on estimated effect parameters in SAOMs. This paper examines seven different methods that are currently available to deal with missing behavior data: complete cases, three single imputation procedures (imputing the mean, random hot deck, nearest neighbor hot deck), one multiple imputation procedure (based on predictive mean matching), and two methods available in the RSIENA software to estimate SAOMs (default method based on imputation and available cases, and a method based on dummy variables). In a simulation study based on four real-life data sets, the impact of these methods on estimated parameters of SAOMS was investigated. Missing behavior data were created under different conditions (proportions, mechanisms), and the missing data methods were used to estimate SAOMs on the incomplete data. The effect of the missing data methods was inspected using three criteria: model convergence, parameter bias, and parameter coverage. The results show that, in general, the default method available in the RSIENA software gives the best outcomes for all three criteria. The dummy-based method generally performed worse than the default method, as did the imputation procedures. The multiple imputation procedure sometimes outperformed the single imputations and the three single imputation methods often gave the same results. The effects of missing data mechanism and data set were small.