The Daily Lives of Crowdsourced US Respondents: A Time Use Comparison of MTurk, Prolific, and ATUS
Abstract
Amazon’s Mechanical Turk (MTurk) and Prolific are popular online platforms for connecting academic researchers with respondents. A broad literature has sought to assess the extent to which these respondents are representative of the U.S. population in terms of their demographic background, yet no work has assessed the representativeness of their daily lives. The authors provide this analysis by collecting time diaries from 136 MTurk and 156 Prolific respondents, which they compare with diary responses from 468 contemporaneous responses to the American Time Use Survey (ATUS). Responses from MTurk and Prolific respondents include several notable differences relative to ATUS responses, including doing less housework and care work, spending less time traveling, spending more time at home, and spending more time alone. In general, MTurk respondents worked more than ATUS respondents, and Prolific respondents spent more time in leisure. These differences persist even after adjusting for demographic differences. The present findings highlight time use as a potential major source of differences across samples that go beyond demographic differences. Thus, scholars interested in these samples should consider how time use may moderate processes of interest.
Study specs
Time diaries were collected and analyzed for 136 MTurk and 156 Prolific respondents, then compared with 468 ATUS responses.
- Authors
- RG Rinderknecht,L Doan
- Institution
- RAND
- Discipline
- Artificial Intelligence
- Sample Size
- N=760
- Study Type
- Survey Research
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
Daily time use patterns including work, housework, travel, leisure, and time spent alone or at home.
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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