Dean Danielle O, Bauer Daniel J, Prinstein Mitchell J
a Data Group, Microsoft.
b Quantitative Psychology, Department of Psychology and Neuroscience , University of North Carolina at Chapel Hill.
Multivariate Behav Res. 2017 May-Jun;52(3):271-289. doi: 10.1080/00273171.2016.1267605. Epub 2017 May 2.
A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common-as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed.
社会网络视角能够为塑造人类行为的过程带来重要见解。纵向社会网络数据用于衡量个体之间随时间变化的关系,如今已越来越普遍,分析此类数据的方法亦是如此。本文开发并应用了一种友谊持续时间模型,该模型采用具有多重成员随机效应结构的离散时间多层次生存分析,以研究在更大的社会网络中导致无向友谊解体的过程。虽然建模框架是从理解友谊解体的角度引入的,但它更广泛地可用于理解社会网络的微观层面动态。在将数据转换为配对时期数据集后,这些模型可以用标准的广义线性混合模型软件进行拟合。一个实证例子突出了该模型如何应用于理解导致高中生友谊解体的过程,并且使用模拟研究来测试该建模框架在社会网络数据中常见的代表性条件下的使用情况。文中强调了该建模框架的优点,并讨论了潜在的局限性和未来方向。