  Longitudinal social network studies can easily suffer from insufficient
statistical power. Studies that simultaneously investigate change of network
ties and change of nodal attributes (selection and influence studies) are
particularly at risk because the number of nodal observations is typically much
lower than the number of observed tie variables. This paper presents a
simulation-based procedure to evaluate statistical power of longitudinal social
network studies in which stochastic actor-oriented models (SAOMs) are to be
applied. Two detailed case studies illustrate how statistical power is strongly
affected by network size, number of data collection waves, effect sizes,
missing data, and participant turnover. These issues should thus be explored in
the design phase of longitudinal social network studies.
