Recruiting older adult participants through crowdsourcing platforms: Mechanical Turk versus Prolific Academic
Abstract
Background: Recruiting older adults (OA) into research is challenging. Objective: To assess the feasibility of using two crowdsourcing platforms, Amazon’s Mechanical Turk (MTurk) and Prolific Academic (ProA), as efficient and low-cost venues for recruiting survey participants aged 65 and older. Methods: We developed an online survey to investigate and compare the demographics, technology use, and motivations for research participation of OA on MTurk and ProA. Qualitative responses, response time, word count, and recruitment costs were analyzed. Results: We recruited 97 OA survey participants on both MTurk and ProA. Participants were similar in terms ofdemographics, technology usage, and motivations for participation (topic interest and payment). Conclusion: Both crowdsourcing platforms are useful for rapid and low-cost recruitment of OA. The OA recruitment process was more efficient with ProA. Crowdsourcing platforms are potential sources of OA research participants; however, the pool is limited to generally healthy, technologically active, and well-educated older adults.
Study specs
- Authors
- AM Turner,T Engelsma,JO Taylor
- Institution
- NiH
- Discipline
- Behavioral Research Methods,Gerontology
- 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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