Evaluating mobile-based data collection for crowdsourcing behavioral research

1 citations

Abstract

Online crowdsourcing platforms such as MTurk and Prolific have revolutionized how researchers recruit human participants. However, since these platforms primarily recruit computer-based respondents, they risk not reaching respondents who may have exclusive access or spend more time on mobile devices that are more widely available. Additionally, there have been concerns that respondents who heavily utilize such platforms with the incentive to earn an income provide lower-quality responses. Therefore, we conducted two studies by collecting data from the popular MTurk and Prolific platforms, Pollfish, a self-proclaimed mobile-first crowdsourcing platform, and the Qualtrics audience panel. By distributing the same study across these platforms, we examine data quality and factors that may affect it. In contrast to MTurk and Prolific, most Pollfish and Qualtrics respondents were mobile-based. Using an attentiveness composite score we constructed, we find mobile-based responses comparable with computer-based responses, demonstrating that mobile devices are suitable for crowdsourcing behavioral research. However, platforms differ significantly in attentiveness, which is also affected by factors such as the respondents’ incentive for completing the survey, their activity before engaging, environmental distractions, and having recently completed a similar study. Further, we find that a stronger system 1 thinking is associated with lower levels of attentiveness and acts as a mediator between some of the factors explored, including the device used and attentiveness. In addition, we raise a concern that most MTurk users can pass frequently used attention checks but fail less utilized measures, such as the infrequency scale.

1
Citations
Evaluation
Paper Only

Study specs

Conducted two studies distributing the same survey across MTurk, Prolific, Pollfish, and Qualtrics panels to compare data quality and analyze attentiveness scores.

Study Type
Evaluation Study
Year
2025
Human Data Platform
Prolific

Measured Outcomes

Attentiveness, device usage (mobile vs. computer), and factors influencing data quality such as incentives, respondent activity, distractions, and survey familiarity.

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

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