Affiliation:
1. School of Education, The Hebrew University of Jerusalem
2. Industrial Engineering and Management, Technion–Israel Institute of Technology
3. Cognitive Science, The Hebrew University of Jerusalem
Abstract
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.
Funder
Israel Innovation Authority
Cited by
89 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献