A multisample demonstration of using the prolific platform for repeated assessment and psychometric substance use research.

111 citations

Abstract

The Prolific platform offers a potentially useful and efficient crowdsourcing option for repeated assessment substance use research, including for psychometric research requiring large samples. We present both (a) a series of practical recommendations for using Prolific and (b) data from multiple samples demonstrating Prolific’s potential for efficiently collected repeated measures data. First, we present data from a 5-day daily diary protocol. We recruited a large sample (N = 321 at Day 1) screened for a history of self-identified mental health issues and weekly alcohol use. Participant adherence was good (82%) even without in-person contact. Alcohol use patterns conformed to theoretical expectations: Participants were more likely to drink on Fridays and Saturdays than other days, men drank more than women, and higher Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993) scores were associated with an increased likelihood of use and more overall drinking on a given day. Second, we present data from 429 Prolific participants screened for a history of mental health issues who completed assessments 2 weeks apart with strong retention (N = 377; 88%). We compare these data with the data from undergraduates (N = 529) to demonstrate Prolific’s utility for conducting psychometrically oriented substance use research. Internal consistency estimates for measures from the Prolific data matched or exceeded those from the undergraduate data. Furthermore, measure scores showed strong temporal stability, and factor structures (e.g., AUDIT item-level structures) conformed to theoretical expectations. Collectively, these findings indicate that Prolific can be used successfully for repeated measures data collection. (PsycInfo Database Record (c) 2025 APA, all rights reserved)

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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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