Hays Ron D, Liu Honghu, Kapteyn Arie
Division of General Internal Medicine & Health Services Research, University of California, 911 Broxton Avenue, Los Angeles, 90024, CA, USA,
Behav Res Methods. 2015 Sep;47(3):685-90. doi: 10.3758/s13428-015-0617-9.
The use of Internet panels to collect survey data is increasing because it is cost-effective, enables access to large and diverse samples quickly, takes less time than traditional methods to obtain data for analysis, and the standardization of the data collection process makes studies easy to replicate. A variety of probability-based panels have been created, including Telepanel/CentERpanel, Knowledge Networks (now GFK KnowledgePanel), the American Life Panel, the Longitudinal Internet Studies for the Social Sciences panel, and the Understanding America Study panel. Despite the advantage of having a known denominator (sampling frame), the probability-based Internet panels often have low recruitment participation rates, and some have argued that there is little practical difference between opting out of a probability sample and opting into a nonprobability (convenience) Internet panel. This article provides an overview of both probability-based and convenience panels, discussing potential benefits and cautions for each method, and summarizing the approaches used to weight panel respondents in order to better represent the underlying population. Challenges of using Internet panel data are discussed, including false answers, careless responses, giving the same answer repeatedly, getting multiple surveys from the same respondent, and panelists being members of multiple panels. More is to be learned about Internet panels generally and about Web-based data collection, as well as how to evaluate data collected using mobile devices and social-media platforms.
利用互联网样本库来收集调查数据的做法日益普遍,因为它具有成本效益,能够迅速获取大规模且多样的样本,获取用于分析的数据所需时间比传统方法更少,而且数据收集过程的标准化使得研究易于复制。现已创建了各种基于概率的样本库,包括Telepanel/CentERpanel、知识网络(现称GFK知识样本库)、美国生活样本库、社会科学纵向互联网研究样本库以及美国社会调查样本库。尽管基于概率的互联网样本库具有已知分母(抽样框)这一优势,但此类样本库的招募参与率往往较低,而且一些人认为,选择退出概率样本与选择加入非概率(便利)互联网样本库之间实际上没有太大差别。本文概述了基于概率的样本库和便利样本库,讨论了每种方法的潜在益处及注意事项,并总结了为使样本库受访者更能代表总体而对其进行加权的方法。文中还讨论了使用互联网样本库数据所面临的挑战,包括虚假回答、随意作答、重复给出相同答案、同一位受访者收到多份调查问卷,以及样本库成员同时属于多个样本库等问题。关于互联网样本库以及基于网络的数据收集,还有如何评估使用移动设备和社交媒体平台收集的数据,仍有许多有待了解之处。