Multiverse analysis for dynamic network models: Investigating the influence of plausible alternative modeling choices [Author Accepted Manuscript]

Specifying complex time series models typically allows for a wide range of plausible analysis strategies. However, researchers typically perform and report only a single, preferred analysis while ignoring alternatives that could yield different conclusions. As a remedy, we propose multiverse analysis to investigate the robustness of dynamic network analysis to arbitrary modeling choices. We focus on group iterative multiple model estimation (GIMME), a highly data-driven approach, and re-analyze two datasets (combined n=199). We vary seven modeling parameters, resulting in 3,888 fitted models. Group-level and, to a lesser extent, subgroup-level results were mostly stable. Individual-level estimates were more heterogeneous, with some decisions strongly influencing results and conclusions. The robustness of GIMME to alternative modeling choices depends on the level of analysis. For some individuals, results may differ strongly even when changing the algorithm only slightly. Multiverse analysis is a valuable tool for checking the robustness of results from time series models. reviewed acceptedVersion