Data quality of platforms and panels for online behavioral research
Abstract
This erratum reports on a technical error that was discovered in Study 2 of Peer et al. (2021). Because of this technical error, some specific findings on participants’ proclivity for dishonesty reported in the paper have been found incorrect. We detail the error, which only affected female participants, and its impact on the findings and report on the reanalyzed findings accounting for the error. The new findings do not change the conclusions provided in the paper, and show again that participants from MTurk are more likely to engage in dishonest behavior than participants from Prolific or CloudResearch.
Study specs
- Authors
- E Peer,D Rothschild,A Gordon,E Damer
- Year
- 2022
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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