Department of Demography, University of California, Berkeley, Berkeley, CA, USA.
Facebook, Inc., 1 Hacker Way, Menlo Park, CA, 94025, USA.
Demography. 2019 Dec;56(6):2377-2392. doi: 10.1007/s13524-019-00840-z.
Online data sources offer tremendous promise to demography and other social sciences, but researchers worry that the group of people who are represented in online data sets can be different from the general population. We show that by sampling and anonymously interviewing people who are online, researchers can learn about both people who are online and people who are offline. Our approach is based on the insight that people everywhere are connected through in-person social networks, such as kin, friendship, and contact networks. We illustrate how this insight can be used to derive an estimator for tracking the digital divide in access to the Internet, an increasingly important dimension of population inequality in the modern world. We conducted a large-scale empirical test of our approach, using an online sample to estimate Internet adoption in five countries (n ≈ 15,000). Our test embedded a randomized experiment whose results can help design future studies. Our approach could be adapted to many other settings, offering one way to overcome some of the major challenges facing demographers in the information age.
在线数据源为人口学和其他社会科学带来了巨大的前景，但研究人员担心，在线数据集所代表的人群与一般人口不同。我们表明，通过对在线人群进行抽样和匿名访谈，研究人员可以了解在线人群和离线人群。我们的方法基于这样一种观点，即各地的人都通过面对面的社交网络（如亲属、友谊和联系网络）联系在一起。我们说明了如何利用这一观点来推导出一个跟踪互联网接入数字鸿沟的估计值，这是现代世界人口不平等的一个日益重要的维度。我们对我们的方法进行了大规模的实证检验，使用在线样本估计了五个国家（n ≈ 15,000）的互联网使用率。我们的测试中嵌入了一个随机实验，其结果可以帮助设计未来的研究。我们的方法可以应用于许多其他场景，为人口学家在信息时代面临的一些主要挑战提供了一种解决方案。
