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Anomalous responses on Amazon Mechanical Turk: An Indian perspective
Author(s) -
William O’Brochta,
Sunita Parikh
Publication year - 2021
Publication title -
research and politics
Language(s) - English
Resource type - Journals
ISSN - 2053-1680
DOI - 10.1177/20531680211016971
Subject(s) - perspective (graphical) , amazon rainforest , quality (philosophy) , survey data collection , data quality , survey research , computer science , data science , psychology , business , marketing , applied psychology , artificial intelligence , statistics , mathematics , physics , ecology , metric (unit) , quantum mechanics , biology
What can researchers do to address anomalous survey and experimental responses on Amazon Mechanical Turk (MTurk)? Much of the anomalous response problem has been traced to India, and several survey and technological techniques have been developed to detect foreign workers accessing US-specific surveys. We survey Indian MTurkers and find that 26% pass survey questions used to detect foreign workers, and 3% claim to be located in the United States. We show that restricting respondents to Master Workers and removing the US location requirement encourages Indian MTurkers to correctly self-report their location, helping to reduce anomalous responses among US respondents and to improve data quality. Based on these results, we outline key considerations for researchers seeking to maximize data quality while keeping costs low.

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