Analytic strategies for longitudinal networks with missing data

- Missing data in longitudinal social network studies are common and problematic.
- We review approaches to longitudinal network analysis in RSiena with missing data.
- Restricting the analytic sample to actors with complete cases is common practice.
- An alternative approach can result in analytic samples that are more representative.
- Differences in the results of RSiena models using these approaches are documented.

Missing data are often problematic when analyzing complete longitudinal social network data. We review approaches for accommodating missing data when analyzing longitudinal network data with stochastic actor-based models. One common practice is to restrict analyses to participants observed at most or all time points, to achieve model convergence. We propose and evaluate an alternative, more inclusive approach to sub-setting and analyzing longitudinal network data, using data from a school friendship network observed at four waves (N = 694). Compared to standard practices, our approach retained more information from partially observed participants, generated a more representative analytic sample, and led to less biased model estimates for this case study. The implications and potential applications for longitudinal network analysis are discussed.