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 article presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which stochastic actor-oriented models 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.

中文翻译：

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纵向网络研究中的统计功效

纵向社交网络研究很容易受到统计能力不足的影响。同时调查网络联系变化和节点属性变化的研究（选择和影响研究）特别危险，因为节点观察的数量通常远低于观察到的联系变量的数量。本文提出了一种基于模拟的程序来评估纵向社会网络研究的统计能力，其中将应用随机面向行动者的模型。两个详细的案例研究说明了统计能力如何受到网络规模、数据收集波数、效应规模、缺失数据和参与者流失率的强烈影响。因此，应在纵向社交网络研究的设计阶段探索这些问题。