The Burden for High-Quality Online Data Collection Lies With Researchers, Not Recruitment Platforms

Abstract

A recent article in Perspectives on Psychological Science (Webb & Tangney, 2022) reported a study in which just 2.6% of participants recruited on Amazon’s Mechanical Turk (MTurk) were deemed “valid.” The authors highlighted some well-established limitations of MTurk, but their central claims—that MTurk is “too good to be true” and that it captured “only 14 human beings . . . [out of] N = 529”—are radically misleading, yet have been repeated widely. This commentary aims to (a) correct the record (i.e., by showing that Webb and Tangney’s approach to data collection led to unusually low data quality) and (b) offer a shift in perspective for running high-quality studies online. Negative attitudes toward MTurk sometimes reflect a fundamental misunderstanding of what the platform offers and how it should be used in research. Beyond pointing to research that details strategies for effective design and recruitment on MTurk, we stress that MTurk is not suitable for every study. Effective use requires specific expertise and design considerations. Like all tools used in research—from advanced hardware to specialist software—the tool itself places constraints on what one should use it for. Ultimately, high-quality data is the responsibility of the researcher, not the crowdsourcing platform.