Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to use the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. In this article the authors ask whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, in which one uses sampled ego network data to infer the network features of each context and then uses the inferred network features as second-level predictors in a hierarchical linear model. The authors test the validity of this idea in the case of network cohesion. Using two complete data sets as a test, the authors find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, such as attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.

中文翻译：

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从 Ego 网络数据估计上下文影响

网络概念通常用于表征社会背景的特征。例如，过去的工作询问社会凝聚力更强的社区中的个人是否有更好的心理健康结果。尽管使用无处不在，但上下文研究使用网络分析方法的情况相对较少。之所以如此，部分是因为网络数据很难收集，需要所有参与者之间的所有联系的信息。在本文中，作者询问是否有可能避免如此繁重的数据收集，同时仍保留上下文网络研究的最佳特征。基本思想是将网络采样应用于上下文模型问题，其中使用采样的自我网络数据来推断每个上下文的网络特征，然后使用推断的网络特征作为分层线性模型中的二级预测器。作者在网络凝聚力的情况下测试了这个想法的有效性。作者使用两个完整的数据集作为测试，发现自我网络数据足以捕捉凝聚力与重要结果（如依恋和偏差）之间的关系。展望未来，研究人员会发现将整体网络测量纳入传统回归模型更容易。作者发现自我网络数据足以捕捉凝聚力与重要结果（如依恋和偏差）之间的关系。展望未来，研究人员会发现将整体网络测量纳入传统回归模型更容易。作者发现自我网络数据足以捕捉凝聚力与重要结果（如依恋和偏差）之间的关系。展望未来，研究人员会发现将整体网络测量纳入传统回归模型更容易。