University of California-Berkeley, Berkeley, CA, USA.
University of Notre Dame, Notre Dame, IN, USA.
J Health Soc Behav. 2022 Sep;63(3):392-409. doi: 10.1177/00221465211072310. Epub 2022 Feb 14.
Prior research documents associations between personal network characteristics and health, but establishing causation has been a long-standing research priority. To evaluate approaches to causal inference in egocentric network data, this article uses three waves from the University of California Berkeley Social Networks Study (N = 1,159) to investigate connections between nine network variables and two global health outcomes. We compare three modeling strategies: cross-sectional ordinary least squares regression, regression with lagged dependent variables (LDVs), and hybrid fixed and random effects models. Results suggest that cross-sectional and LDV models may overestimate the causal effects of networks on health because hybrid models show that network-health associations operate primarily between individuals, as opposed to network changes causing within-individual changes in health. These findings demonstrate uses of panel data that may advance scholarship on networks and health and suggest that causal effects of network support on health may be more limited than previously thought.
先前的研究记录了个人网络特征与健康之间的关联，但确定因果关系一直是一个长期的研究重点。为了评估在中心度网络数据中进行因果推断的方法，本文使用了加州大学伯克利分校社会网络研究（N=1159）的三个波次的数据，调查了九个网络变量与两个全球健康结果之间的联系。我们比较了三种建模策略：横截面普通最小二乘法回归、带滞后因变量的回归（LDV）和混合固定和随机效应模型。结果表明，横截面和 LDV 模型可能高估了网络对健康的因果影响，因为混合模型表明网络与健康之间的关联主要发生在个体之间，而不是网络变化导致个体内部健康的变化。这些发现展示了面板数据的用途，可能会推动网络与健康方面的研究，并表明网络支持对健康的因果影响可能比之前认为的更为有限。
