MTurk character misrepresentation: Assessment and solutions

Published

Journal Article

© The Author 2016. This tutorial provides evidence that character misrepresentation in survey screeners by Amazon Mechanical Turk Workers ("Turkers") can substantially and significantly distort research findings. Using five studies, we demonstrate that a large proportion of respondents in paid MTurk studies claim a false identity, ownership, or activity in order to qualify for a study. The extent of misrepresentation can be unacceptably high, and the responses to subsequent questions can have little correspondence to responses from appropriately identified participants. We recommend a number of remedies to deal with the problem, largely involving strategies to take away the economic motive to misrepresent and to make it difficult for Turkers to recognize that a particular response will gain them access to a study. The major short-run solution involves a two-survey process that first asks respondents to identify their characteristics when there is no motive to deceive, and then limits the second survey to those who have passed this screen. The long-run recommendation involves building an ongoing MTurk participant pool ("panel") that (1) continuously collects information that could be used to classify respondents, and (2) eliminates from the panel those who misrepresent themselves.

Full Text

Duke Authors

Cited Authors

  • Wessling, KS; Huber, J; Netzer, O

Published Date

  • June 1, 2017

Published In

Volume / Issue

  • 44 / 1

Start / End Page

  • 211 - 230

International Standard Serial Number (ISSN)

  • 0093-5301

Digital Object Identifier (DOI)

  • 10.1093/jcr/ucx053

Citation Source

  • Scopus