Lie for a Dime
Lie for a Dime
复制标题
为了一毛钱而撒谎
DOI：
--
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Gabriele Paolacci
中科院分区：
文献类型：
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作者：
Jesse J. Chandler;Gabriele Paolacci
互联网使得能够招募具有具体特点的大量样本。然而，当研究人员依靠参与者的自我报告来确定资格时，数据质量取决于参与者的诚实程度。在对亚马逊土耳其机器人的四项研究中，我们发现大量参与者歪曲了理论上相关的特征（例如，人口统计学、产品所有权），以满足研究中明确的合格标准，通过先前从研究中排除或从先前类似研究的经验中推断。当招募稀有人群时，很大一部分回答可能是冒名顶替者。我们提供了关于如何确保排除不合格参与者的建议，这些建议适用于依赖自我报告的各种数据收集工作。
The Internet has enabled recruitment of large samples with specific characteristics. However, when researchers rely on participant self-report to determine eligibility, data quality depends on participant honesty. Across four studies on Amazon Mechanical Turk, we show that a substantial number of participants misrepresent theoretically relevant characteristics (e.g., demographics, product ownership) to meet eligibility criteria explicit in the studies, inferred by a previous exclusion from the study or inferred in previous experiences with similar studies. When recruiting rare populations, a large proportion of responses can be impostors. We provide recommendations about how to ensure that ineligible participants are excluded that are applicable to a wide variety of data collection efforts, which rely on self-report.
奖励延迟的机会成本以及假设金钱和香烟的折扣。
影响因子：
2.7
作者：
Johnson, Patrick S.;Herrmann, Evan S.;Johnson, Matthew W.
通讯作者：
Johnson, Matthew W.