Crowdsourcing Academic Online Research on Prolific
Abstract
Prolific is a website that offers researchers the ability to recruit and sample participants for online research. In contrast to earlier crowdsourcing platforms, such as Amazon Mechanical Turk (MTurk), it focuses primarily on academic and marketing research – typically done through online surveys and experiments. In this chapter, I aim to introduce this platform to researchers conducting online studies and to provide knowledge and practical advice on how to best use the platform for online research. The review includes explanations of how the site works, the composition of its pool of participants, the options available to researchers for sampling and recruiting participants online, how to achieve advanced abilities by connecting Prolific to research software (e.g., Qualtrics, Gorilla), and how to ensure high data quality when using Prolific. I then review the evidence on the current state of data quality on Prolific, suggesting that it can provide higher data quality than MTurk and also better than some commercial panels. I conclude with a summary of the advantages and disadvantages of using Prolific for online research and potential future developments in the platform that could promote more credible online research.
Study specs
- Authors
- Eyal Peer
- Institution
- Hebrew University,University of Cambridge
- Discipline
- Social,Behavioral Science
- Year
- 2024
- 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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