Concerns and recommendations for using Amazon MTurk for eating disorder research.
Concerns and recommendations for using Amazon MTurk for eating disorder research.
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DOI：
10.1002/eat.23614
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Calderwood L
中科院分区：
文献类型：
--
作者：
Burnette CB;Luzier JL;Bennett BL;Weisenmuller CM;Kerr P;Martin S;Keener J;Calderwood L
我们最初的目的是验证和规范美国跨性别成年人中大量代表性社区样本中常见的饮食失调（ED）症状测量。我们通过亚马逊土耳其机器人（MTurk）进行招聘，这是一个在饮食失调（ED）领域内外都很受欢迎的在线招聘和数据收集平台。我们将概述我们使用MTurk的经验。招聘于2020年春季开始；我们最初的目标N是2250名跨性别成年人，平均分布在美国各地。测量方法包括人口统计问卷、饮食失调检查问卷（ed - q）和饮食态度测试-26 （EAT-26）。与当前文献建议一致，我们实施了一套全面的注意力和有效性措施，以减少和识别机器人响应、数据种植和参与者失实陈述。建议的有效性和注意力检查未能识别大多数可能无效的回答。我们收集了两种相似的ED测量方法，全面的体重史评估和性别认同经验，使我们能够检查反应一致性并识别不可能和不可能的反应，从而揭示出明显的差异和无效的数据。此外，定性数据（例如，从MTurk工人收到的电子邮件）引起了对MTurk工人面临的经济状况的担忧，这可能会迫使虚假陈述。我们的结果强烈表明，我们的大部分数据是无效的，并对最近发表的MTurk研究结果提出质疑。我们认为，在使用MTurk作为ED研究的招募工具时，必须谨慎和严格，并为ED研究人员提供了一些建议，以减轻和识别无效数据。
Our original aim was to validate and norm common eating disorder (ED) symptom measures in a large, representative community sample of transgender adults in the US. We recruited via Amazon Mechanical Turk (MTurk), a popular online recruitment and data collection platform both within and outside of the eating disorder (ED) field. We present an overview of our experience using MTurk. Recruitment began in Spring 2020; our original target N was 2,250 transgender adults stratified evenly across the US. Measures included a demographics questionnaire, the Eating Disorder Examination-Questionnaire (EDE-Q), and the Eating Attitudes Test-26 (EAT-26). Consistent with current literature recommendations, we implemented a comprehensive set of attention and validity measures to reduce and identify bot responding, data farming, and participant misrepresentation. Recommended validity and attention checks failed to identify the majority of likely invalid responses. Our collection of two similar ED measures, thorough weight history assessment, and gender identity experiences allowed us to examine response concordance and identify impossible and improbable responses, which revealed glaring discrepancies and invalid data. Further, qualitative data (e.g., emails received from MTurk workers) raised concerns about economic conditions facing MTurk workers that could compel misrepresentation. Our results strongly suggest most of our data were invalid, and call into question results of recently-published MTurk studies. We assert that caution and rigor must be applied when using MTurk as a recruitment tool for ED research, and offer several suggestions for ED researchers to mitigate and identify invalid data.
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影响因子：
5.7
作者：
Chmielewski, Michael;Kucker, Sarah C.
通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1007/s40519-019-00651-6
发表时间：
2019-12-01
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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