Untapped Potential: Designed Digital Trace Data in Online Survey Experiments

被引:0
|
作者
Macke, Erin [1 ]
Daviss, Claire [1 ]
Williams-Baron, Emma [1 ]
机构
[1] Stanford Univ, Dept Sociol, Stanford, CA 94305 USA
基金
美国国家科学基金会;
关键词
digital trace data; survey design; computational social science; paradata; online survey experiments; DIFFUSION DECISION-MODEL; BIG DATA; INFORMED-CONSENT; SELF-REPORT; INFORMATION; CHALLENGES; PARADATA; BEHAVIOR; ISSUES; DISCRIMINATION;
D O I
10.1177/00491241241268770
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
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.
引用
收藏
页数:41
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