Data quality of platforms and panels for online behavioral research

Eyal Peer*, David Rothschild, Andrew Gordon, Zak Evernden, Ekaterina Damer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

333 Scopus citations


We examine key aspects of data quality for online behavioral research between selected platforms (Amazon Mechanical Turk, CloudResearch, and Prolific) and panels (Qualtrics and Dynata). To identify the key aspects of data quality, we first engaged with the behavioral research community to discover which aspects are most critical to researchers and found that these include attention, comprehension, honesty, and reliability. We then explored differences in these data quality aspects in two studies (N ~ 4000), with or without data quality filters (approval ratings). We found considerable differences between the sites, especially in comprehension, attention, and dishonesty. In Study 1 (without filters), we found that only Prolific provided high data quality on all measures. In Study 2 (with filters), we found high data quality among CloudResearch and Prolific. MTurk showed alarmingly low data quality even with data quality filters. We also found that while reputation (approval rating) did not predict data quality, frequency and purpose of usage did, especially on MTurk: the lowest data quality came from MTurk participants who report using the site as their main source of income but spend few hours on it per week. We provide a framework for future investigation into the ever-changing nature of data quality in online research, and how the evolving set of platforms and panels performs on these key aspects.

Original languageAmerican English
Pages (from-to)1643-1662
Number of pages20
JournalBehavior Research Methods
Issue number4
StatePublished - Aug 2022

Bibliographical note

Publisher Copyright:
© 2021, The Psychonomic Society, Inc.


  • Amazon mechanical turk
  • Attention
  • Comprehension
  • Data quality
  • Honesty
  • Online research
  • Prolific
  • Reliability


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