Consensus-based guidance for conducting and reporting multi-analyst studies

Balazs Aczel, Barnabas Szaszi, Gustav Nilsonne, Olmo R. Van Den Akker, Casper J. Albers, Marcel Alm Van Assen, Jojanneke A. Bastiaansen, Daniel Benjamin, Udo Boehm, Rotem Botvinik-Nezer, Laura F. Bringmann, Niko A. Busch, Emmanuel Caruyer, Andrea M. Cataldo, Nelson Cowan, Andrew Delios, Noah Nn Van Dongen, Chris Donkin, Johnny B. Van Doorn, Anna DreberGilles Dutilh, Gary F. Egan, Morton Ann Gernsbacher, Rink Hoekstra, Sabine Hoffmann, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Kai J. Jonas, Alexander T. Kindel, Michael Kirchler, Yoram K. Kunkels, D. Stephen Lindsay, Jean Francois Mangin, Dora Matzke, Marcus R. Munafò, Ben R. Newell, Brian A. Nosek, Russell A. Poldrack, Don Van Ravenzwaaij, Jörg Rieskamp, Matthew J. Salganik, Alexandra Sarafoglou, Tom Schonberg, Martin Schweinsberg, David Shanks, Raphael Silberzahn, Daniel J. Simons, Barbara A. Spellman, Samuel St-Jean, Jeffrey J. Starns, Eric Luis Uhlmann, Jelte Wicherts, Eric Jan Wagenmakers

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere72185
JournaleLife
Volume10
DOIs
StatePublished - Nov 2021

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

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