Consensus-based guidance for conducting and reporting multi-analyst studies
- Eötvös Loránd University, Hungary
- Karolinska Institutet, Sweden
- Tilburg University, Netherlands
- University of Groningen, Netherlands
- University of Southern California, United States
- University of Amsterdam, Netherlands
- Dartmouth Collge, United States
- University of Muenster, Germany
- University of Rennes, France
- McLean Hospital, United States
- University of Missouri, United States
- National University of Singapore, Singapore
- University of New South Wales, Australia
- Stockholm School of Economics, Sweden
- University Hospital Basel, Switzerland
- Monash University, Australia
- University of Wisconsin-Madison, United States
- Ludwig-Maximilians-University, Germany
- University of Innsbruck, Austria
- Maastricht University, Netherlands
- Princeton University, United States
- University of Victoria, Canada
- Université Paris-Saclay, France
- University of Bristol, United Kingdom
- Center for Open Science and University of Virginia, United States
- Stanford University, United States
- University of Basel, Switzerland
- Tel Aviv University, Israel
- ESMT Berlin, Germany
- University College London, United Kingdom
- University of Sussex, United Kingdom
- University of Illinois, United States
- University of Virginia, United States
- University of Alberta, Canada
- University of Massachusetts Amherst, United States
- INSEAD, Singapore
Abstract
Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.
Data availability
All anonymized data as well as the survey materials are publicly shared on the Open Science Framework page of the project: https://osf.io/4zvst/. Our methodology and data-analysis plan were preregistered. The preregistration document can be accessed at: https://osf.io/dgrua.
Article and author information
Author details
Jojanneke A Bastiaansen
Rotem Botvinik-Nezer
Noah N N van Dongen
Johnny B van Doorn
Morton Ann Gernsbacher
Magnus Johannesson
Jean-Francois Mangin
Russell A Poldrack
Don van Ravenzwaaij
Martin Schweinsberg
Funding
Netherlands Organisations for Scientific Research (406-17-568)
- Alexandra Sarafoglou
Natural Sciences and Engineering Research Council of Canada (BP-546283-2020)
- Samuel St-Jean
Fonds de recherche du Québec – Nature et technologies (290978)
- Samuel St-Jean
European Research Council (726361)
- Jelte Wicherts
European Research Council (726361)
- Olmo R van den Akker
European Research Council (681466)
- Yoram K Kunkels
VIDI fellowship organisation (016.Vidi.188.001)
- Don van Ravenzwaaij
VENI fellowship grant (Veni 191G.037)
- Laura F Bringmann
National Science Foundation (1760052)
- Matthew J Salganik
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Aczel et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Consensus-based guidance for conducting and reporting multi-analyst studies
eLife 10:e72185.
https://doi.org/10.7554/eLife.72185
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