Data collection via online platforms: Challenges and recommendations for future research
Abstract
Online platforms such as Amazon's Mechanical Turk (MTurk) are increasingly used by researchers to collect survey and experimental data. Yet, such platforms often represent a tumultuous terrain for both researchers and reviewers. Researchers have to navigate the complexities of obtaining representative samples from online participant cohorts, ensuring data quality, ethically incentivizing participant engagement, and maintaining transparency. Reviewers, on the other hand, have to navigate the complexities of evaluating the efficacy of such data collection and execution efforts in answering important research questions. In order to provide clarity to these issues, this article provides researchers and reviewers with a series of recommendations for effectively executing and evaluating data collection via online platforms, respectively.
Study specs
- Institution
- Griffith University,Macquarie University,Australian Catholic University,University of New South Wales
- Discipline
- Applied Psychology
- Year
- 2021
- 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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