The Turker Blues: Hidden Factors Behind Increased Depression Rates Among Amazon’s Mechanical Turkers

By Yaakov Ophir, Itay Sisso, Christa S. C. Asterhan, Refael Tikochinski, Roi Reichart. Clinical psychological science : a journal of the Association for Psychological Science, 2020

110 citations recorded by the source corpus.

Data collection from online platforms, such as Amazon’s Mechanical Turk (MTurk), has become popular in clinical research. However, there are also concerns about the representativeness and the quality of these data for clinical studies. The present work explores these issues in the specific case of major depression. Analyses of two large data sets gathered from MTurk (Sample 1: N = 2,692; Sample 2: N = 2,354) revealed two major findings: First, failing to screen for inattentive and fake respondents inflates the rates of major depression artificially and significantly (by 18.5%–27.5%). Second, after cleaning the data sets, depression in MTurk is still 1.6 to 3.6 times higher than general population estimates. Approximately half of this difference can be attributed to differences in the composition of MTurk samples and the general population (i.e., sociodemographics, health, and physical activity lifestyle). Several explanations for the other half are proposed, and practical data-quality tools are provided.

Subfields: Applied Psychology, Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/beea2cab47af70c6996e6450cbffb28e3313b34a

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Citations in the corpus

The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, methodology): “We then trained DNN models to predict these labels from the users’ (independent) social-media texts (Ophir, Tikochinski, et al., 2020).”