Linked Ego Networks: Improving estimate reliability and validity with respondent-driven sampling
Linked Ego Networks: Improving estimate reliability and validity with respondent-driven sampling
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DOI：
10.1016/j.socnet.2013.10.001
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发表时间：
2013-10-01
期刊：
影响因子：
3.1
通讯作者：
Lu, Xin
中科院分区：
文献类型：
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作者：
Lu, Xin
被调查者驱动抽样(RDS)目前被广泛用于HIV/AIDS相关高危人群的研究。然而，最近的研究表明，传统的RDS方法可能会产生很大的方差，并且可能存在严重的偏差，因为RDS背后的假设在现实生活中很少被完全满足。为了改进RDS研究中的估计，我们提出了一种新的方法来使用EGO网络数据来生成估计，该数据是通过询问受访者的个人网络的组成来收集的，例如“你的朋友中有多大比例已婚？”通过在提取的男同性恋者的真实社会网络以及具有不同结构属性的人工网络上的仿真，我们表明，对人群特征的估计精度得到了极大的提高。与传统的RDS估计器相比，该估计器表现出更好的优势，最重要的是，该方法对RDS实践中常见的应答者的招聘偏好和学位报告误差表现出很强的稳健性，并且可能会对传统的RDS估计器产生较大的估计偏差和误差。从那时起，积极的结果鼓励研究人员通过RDS收集感兴趣变量的自我网络数据，无论是对于难以获得的人群还是在随机抽样不适用的一般人群。(C)2013爱思唯尔B.V.保留所有权利。
Respondent-driven sampling (RDS) is currently widely used for the study of HIV/AIDS-related high risk populations. However, recent studies have shown that traditional RDS methods are likely to generate large variances and may be severely biased since the assumptions behind RDS are seldom fully met in real life. To improve estimation in RDS studies, we propose a new method to generate estimates with ego network data, which is collected by asking respondents about the composition of their personal networks, such as "what proportion of your friends are married?". By simulations on an extracted real-world social network of gay men as well as on artificial networks with varying structural properties, we show that the precision of estimates for population characteristics is greatly improved. The proposed estimator shows superior advantages over traditional RDS estimators, and most importantly, the method exhibits strong robustness to the recruitment preference of respondents and degree reporting error, which commonly happen in RDS practice and may generate large estimate biases and errors for traditional RDS estimators. The positive results henceforth encourage researchers to collect ego network data for variables of interests by RDS, for both hard-to-access populations and general populations when random sampling is not applicable. (C) 2013 Elsevier B.V. All rights reserved.