Statistical power of the social network autocorrelation model

• The network size requirements to identify a significant network effect are low.
• The statistical power is primarily driven by a nonlinear function of ρ and N.
• Network density and structure have little overall impact on statistical power.
• Practical advice on network size for designing high power studies is offered.
• The network autocorrelation model well controls Type I error rates.

The network autocorrelation model has become an increasingly popular tool for conducting social network analysis. More and more researchers, however, have documented evidence of a systematic negative bias in the estimation of the network effect (ρ). In this paper, we take a different approach to the problem by investigating conditions under which, despite the underestimation bias, a network effect can still be detected by the network autocorrelation model. Using simulations, we find that moderately-sized network effects (e.g., ρ = .3) are still often detectable in modest-sized networks (i.e., 40 or more nodes). Analyses reveal that statistical power is primarily a nonlinear function of network effect size (ρ) and network size (N), although both of these factors can interact with network density and network structure to impair power under certain rare conditions. We conclude by discussing implications of these findings and guidelines for users of the autocorrelation model.