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Updated: Jul 15, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
工资率和指令对众包研究中的消耗的影响
Carolyn M Ritchey1, Corina Jimenez-Gomez2, Christopher A Podlesnik2
1Department of Psychological Sciences, Auburn University, Auburn, AL, United States of America.
像Prolific这样的众包平台上的更高的薪酬率减少了参与者的消耗,并提高了数据的准确性. 然而,在本研究中,附加指令并没有显著减少磨损.
科学领域:
- 社会科学 社会科学 社会科学
- 人与计算机的交互
- 研究方法研究方法研究方法学
背景情况:
- 众包平台 (如亚马逊机械土耳其,Prolific) 对于社会科学研究数据收集至关重要.
- 在众包研究中,参与者的高消耗率威胁到数据的有效性.
- 之前的研究表明,更高的工资和额外的指令可以减少MTurk上的消耗.
研究的目的:
- 调查提高工资率和额外指令在减少Prolific平台上的磨损方面的有效性.
- 将MTurk的发现扩展到Prolific,考虑到Prolific更严格的薪酬要求.
主要方法:
- 一个由225名参与者组成的样本被随机分配到Prolific.的四个实验组.
- 该研究操纵了工资率 (与美国最低工资相称或两倍) 和提供额外的说明.
- 测量了磨损率和实验任务的性能.
主要成果:
- 提高工资率显著降低了参与者的消耗.
- 更高的薪水与更准确的实验任务表现相关.
- 额外的指令对减少磨损没有显著影响.
结论:
- 提高工资对减少劳动力消耗的积极影响,可以将其推广到更高的工资率和各种众包平台,如Prolific.
- 额外指令在缓解磨损方面的有效性可能不会在不同的平台,任务持续时间或任务类型上通用.
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