Schneider Daniel, Harknett Kristen
University of California, Berkeley.
University of California, San Francisco.
Sociol Methods Res. 2022 Feb;51(1):108-140. doi: 10.1177/0049124119882477. Epub 2019 Nov 14.
In this paper, we explore the use of Facebook targeted advertisements for the collection of survey data. We illustrate the potential of survey sampling and recruitment on Facebook through the example of building a large employee-employer linked dataset as part of The Shift Project. We describe the workflow process of targeting, creating, and purchasing survey recruitment advertisements on Facebook. We address concerns about sample selectivity, and apply post-stratification weighting techniques to adjust for differences between our sample and that of "gold-standard" data sources. We then compare univariate and multi-variate relationships in the Shift data against the Current Population Survey and the National Longitudinal Survey of Youth-1997. Finally, we provide an example of the utility of the firm-level nature of the data by showing how firm-level gender composition is related to wages. We conclude by discussing some important remaining limitations of the Facebook approach, as well as highlighting some unique strengths of the Facebook targeting advertisement approach, including the ability for rapid data collection in response to research opportunities, rich and flexible sample targeting capabilities, and low cost, and we suggest broader applications of this technique.
在本文中,我们探讨了利用脸书定向广告来收集调查数据的方法。我们通过构建一个大型员工-雇主关联数据集(作为“转变项目”的一部分)的例子,阐述了在脸书上进行调查抽样和招募的潜力。我们描述了在脸书上定向、创建和购买调查招募广告的工作流程。我们解决了对样本选择性的担忧,并应用事后分层加权技术来调整我们的样本与“黄金标准”数据源之间的差异。然后,我们将“转变项目”数据中的单变量和多变量关系与当前人口调查以及1997年全国青年纵向调查进行比较。最后,我们通过展示企业层面的性别构成与工资之间的关系,举例说明了数据的企业层面性质的效用。我们在结论中讨论了脸书方法一些重要的剩余局限性,同时强调了脸书定向广告方法的一些独特优势,包括能够响应研究机会快速收集数据、丰富且灵活的样本定向能力以及低成本,并建议更广泛地应用这种技术。