RAND Corporation, Santa Monica, CA, United States.
PROVE Center, Boston, MA, United States.
Am J Health Behav. 2022 Oct 17;46(5):497-502. doi: 10.5993/AJHB.46.5.1.
In this study, we examined the impact of a range of methods to improve data quality on the demographic and health status representativeness of Amazon Mechanical Turk (MTurk) samples.
We developed and field-tested a general survey of health on MTurk in 2017 among 5755 participants and 2021 among 6752 participants. We collected information on participant demographic characteristics and health status and implemented different quality checks in 2017 and 2021.
Adding data quality checks generally improves the representativeness of the final MTurk sample, but there are persistent differences in mental health and pain conditions, age, education, and income between the MTurk population and the broader US population.
We conclude that data quality checks improve the data quality and representativeness.
本研究旨在考察一系列提高数据质量的方法对亚马逊土耳其机器人（MTurk）样本人口统计学和健康状况代表性的影响。
我们于 2017 年在 MTurk 上开发并现场测试了一项关于健康的综合调查，共有 5755 名参与者，2021 年有 6752 名参与者。我们收集了参与者人口统计学特征和健康状况的信息，并在 2017 年和 2021 年实施了不同的质量检查。
总体而言，添加数据质量检查通常会提高最终 MTurk 样本的代表性，但 MTurk 人群与更广泛的美国人群在心理健康和疼痛状况、年龄、教育程度和收入方面仍存在差异。
我们的结论是，数据质量检查可以提高数据质量和代表性。
