Methodological considerations for behavioral studies relying on response time outcomes through online crowdsourcing platforms.

Patrick A McConnell, Christian Finetto, Kirstin-Friederike Heise

Abstract read

In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Patrick A McConnellIntegrative Neuromodulation and Recovery (iNR) Laboratory, Department of Health Sciences and Research, Medical University of South Carolina, 77 President Street, Charleston, SC, 29425, USA.

Christian FinettoIntegrative Neuromodulation and Recovery (iNR) Laboratory, Department of Health Sciences and Research, Medical University of South Carolina, 77 President Street, Charleston, SC, 29425, USA.

Kirstin-Friederike HeiseIntegrative Neuromodulation and Recovery (iNR) Laboratory, Department of Health Sciences and Research, Medical University of South Carolina, 77 President Street, Charleston, SC, 29425, USA. heisek@musc.edu.

Funding

Subproject Title: Clinical Research Education, Mentoring and Career Development CoreU54GM104941 · NIGMS · UNIVERSITY OF DELAWARE · PI Megan M Wenner · 2013 to 2026

$60.5M

NIGMS NIH HHS U54 GM104941

6 · The paper itself

Abstract

This perspective paper explores challenges associated with online crowdsourced data collection, particularly focusing on longitudinal tasks with time-sensitive outcomes like response latencies. Based on our research, we identify two significant sources of bias: technical shortcomings such as low, variable frame rates, and human factors, contributing to high attrition rates. We explored potential solutions to these problems, such as enforcing hardware acceleration and defining study-specific frame rate thresholds, as well as pre-screening participants and monitoring hardware performance and task engagement over each experimental session. With this discussion, we intend to provide recommendations on how to improve the quality and reliability of data collected via online crowdsourced platforms and emphasize the need for researchers to be cognizant of potential pitfalls in online research.

Identifiers

PMID38565854

DOI10.1038/s41598-024-58300-7

PMCPMC10987629

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.