Use of Internet panels to conduct surveys
Use of Internet panels to conduct surveys
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DOI：
10.3758/s13428-015-0617-9
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发表时间：
2015-09-01
影响因子：
5.4
通讯作者：
Kapteyn, Arie
中科院分区：
文献类型：
--
作者：
Hays, Ron D.;Liu, Honghu;Kapteyn, Arie
使用互联网面板收集调查数据的情况越来越多，因为它具有成本效益，能够快速获取大量且多样化的样本，比传统方法花费更少的时间来获取数据进行分析，并且数据收集过程的标准化使研究易于复制。已经创建了各种基于概率的小组，包括 Telepanel/CentERpanel、知识网络（现为 GFK KnowledgePanel）、美国生活小组、社会科学纵向互联网研究小组和了解美国研究小组。尽管具有已知分母（抽样框架）的优势，但基于概率的互联网小组通常具有较低的招聘参与率，并且一些人认为选择退出概率样本和选择加入非概率（方便）互联网小组之间几乎没有实际差异。本文概述了基于概率的面板和便利面板，讨论了每种方法的潜在好处和注意事项，并总结了用于对面板受访者进行加权的方法，以便更好地代表基础人群。讨论了使用互联网小组数据的挑战，包括错误答案、粗心的回答、重复给出相同的答案、从同一受访者那里获得多次调查以及小组成员是多个小组的成员。人们需要更多地了解一般的互联网面板和基于网络的数据收集，以及如何评估使用移动设备和社交媒体平台收集的数据。
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.