以自我为中心网络中的随机误差。
Random errors in egocentric networks.
作者信息
Almquist Zack W
机构信息
Department of Sociology, University of California, Irvine, 3151 Social Science Plaza A, Irvine, CA 92697-5100, United States.
出版信息
Soc Networks. 2012 Oct;34(4):493-505. doi: 10.1016/j.socnet.2012.03.002.
The systematic errors that are induced by a combination of human memory limitations and common survey design and implementation have long been studied in the context of egocentric networks. Despite this, little if any work exists in the area of random error analysis on these same networks; this paper offers a perspective on the effects of random errors on egonet analysis, as well as the effects of using egonet measures as independent predictors in linear models. We explore the effects of false-positive and false-negative error in egocentric networks on both standard network measures and on linear models through simulation analysis on a ground truth egocentric network sample based on facebook-friendships. Results show that 5-20% error rates, which are consistent with error rates known to occur in ego network data, can cause serious misestimation of network properties and regression parameters.
由人类记忆限制以及常见的调查设计与实施相结合所引发的系统误差，长期以来一直在以自我中心网络为背景进行研究。尽管如此，在这些相同网络的随机误差分析领域，即便有相关工作也极少；本文提供了一个关于随机误差对自我中心网络分析的影响的视角，以及在线性模型中将自我中心网络度量用作独立预测变量的影响的视角。我们通过对基于脸书友谊关系的真实自我中心网络样本进行模拟分析，探究自我中心网络中假阳性和假阴性误差对标准网络度量以及线性模型的影响。结果表明，5% - 20%的误差率与已知在自我中心网络数据中出现的误差率一致，可能会导致对网络属性和回归参数的严重错误估计。
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