Researchers have developed many uses for digital trace data, yet most online survey experiments continue to rely on attitudinal rather than behavioral measures. We argue that researchers can collect digital trace data during online survey experiments with relative ease, at modest costs, and to substantial benefit. Because digital trace data unobtrusively measure survey participants’ behaviors, they can be used to analyze digital outcomes of theoretical and empirical interest, while reducing the risk of social desirability bias. We demonstrate the feasibility and utility of collecting digital trace data during online survey experiments through two original studies. In both, participants evaluated interactive digital resumes designed to track participants’ clicks, mouse movements, and time spent on the resumes. This novel approach allowed us to better understand participants’ search for information and cognitive processing in hiring decisions. There is immense, untapped potential value in collecting digital trace data during online survey experiments and using it to address important sociological research questions.

中文翻译：

---

未开发的潜力：在线调查实验中设计的数字跟踪数据


研究人员已经开发了数字跟踪数据的许多用途，但大多数在线调查实验仍然依赖于态度而不是行为测量。我们认为，研究人员可以在在线调查实验期间相对轻松地以适度的成本收集数字跟踪数据，并获得巨大的收益。由于数字跟踪数据不引人注目地测量调查参与者的行为，因此它们可用于分析理论和经验兴趣的数字结果，同时降低社会期望偏差的风险。我们通过两项原始研究证明了在线调查实验期间收集数字跟踪数据的可行性和实用性。在这两项研究中，参与者评估了交互式数字简历，该简历旨在跟踪参与者的点击、鼠标移动以及在简历上花费的时间。这种新颖的方法使我们能够更好地了解参与者在招聘决策中对信息的搜索和认知处理。在在线调查实验期间收集数字跟踪数据并使用它来解决重要的社会学研究问题具有巨大的、未开发的潜在价值。