de la Haye Kayla, Embree Joshua, Punkay Marc, Espelage Dorothy L, Tucker Joan S, Green Harold D
University of Southern California.
University of Florida.
Soc Networks. 2017 Jul;50:17-25. doi: 10.1016/j.socnet.2017.02.001. Epub 2017 Mar 3.
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 ( =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.
在分析完整的纵向社会网络数据时，缺失数据常常会带来问题。我们回顾了在使用基于随机行为者的模型分析纵向网络数据时处理缺失数据的方法。一种常见的做法是将分析限制在最多或所有时间点都有观测值的参与者身上，以实现模型收敛。我们提出并评估了一种替代的、更具包容性的方法来对纵向网络数据进行子集划分和分析，该方法使用了一个在四个时间点观测到的学校友谊网络的数据（n = 694）。与标准做法相比，我们的方法从部分观测到的参与者那里保留了更多信息，生成了一个更具代表性的分析样本，并且对于这个案例研究而言，导致的模型估计偏差更小。我们还讨论了纵向网络分析的意义和潜在应用。
