Time-of-Day and Day-of-Week Variations in Amazon Mechanical Turk Survey Responses

C. Binder
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引用次数: 2

Abstract

Social science research studies are frequently conducted on Amazon Mechanical Turk (MTurk). I use data from four previous economics studies conducted on Amazon Mechanical Turk, with a total of 2780 observations, to study how participant characteristics and behaviors depend on the day of the week and time of day of participation. Most notably, Saturday participants are older, less educated, and more likely to have low income compared to other participants. Controlling for demographics, Saturday participants are more likely to answer objective knowledge questions correctly and to provide reasonable inflation forecasts, less likely to provide "don't know" responses, and less likely to use social media as a primary source of economic news. Night participants are less likely to get economic news from print sources such as newspapers. Standard data cleaning procedures typically neither reduce nor exacerbate these patterns. Implications of these findings are especially important for researchers designing high-frequency surveys of macroeconomic expectations intended to enable identification of the effects of monetary policy announcements or other events of interest via a high frequency approach. Systematic day-of-week variation in respondents' knowledge and reported expectations, if not properly accounted for, could threaten such an identification scheme.
亚马逊土耳其机器人调查反应的时间和星期变化
社会科学研究经常在亚马逊土耳其机器人(MTurk)上进行。我使用了之前在Amazon Mechanical Turk上进行的四项经济学研究的数据,总共有2780个观察结果,来研究参与者的特征和行为如何依赖于一周中的哪一天和参与的时间。最值得注意的是,与其他参与者相比,周六的参与者年龄较大,受教育程度较低,收入较低。在人口统计因素的控制下,周六的参与者更有可能正确回答客观知识问题,并提供合理的通胀预测,不太可能提供“不知道”的回答,也不太可能将社交媒体作为经济新闻的主要来源。夜猫子不太可能从报纸等纸媒上获得经济新闻。标准的数据清理过程通常既不会减少也不会加剧这些模式。这些发现的含义对于设计宏观经济预期高频调查的研究人员尤其重要,这些调查旨在通过高频方法识别货币政策公告或其他感兴趣的事件的影响。被调查者的知识和报告的期望在一周中的系统变化,如果没有得到适当的解释,可能会威胁到这种识别方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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