Consensus-based guidance for conducting and reporting multi-analyst studies.
Balazs AczelBarnabas SzasziGustav NilsonneOlmo R van den AkkerCasper J AlbersMarcel Alm van AssenJojanneke A BastiaansenDaniel J BenjaminUdo BoehmRotem Botvinik-NezerLaura F BringmannNiko A BuschEmmanuel CaruyerAndrea M CataldoNelson CowanAndrew DeliosNoah N N van DongenChris DonkinJohnny B van DoornAnna DreberGilles DutilhGary F EganMorton Ann GernsbacherRink HoekstraSabine HoffmannFelix HolzmeisterJuergen HuberMagnus JohannessonKai J JonasAlexander T KindelMichael KirchlerYoram K KunkelsD Stephen LindsayJean-François ManginDora MatzkeMarcus R MunafòBen R NewellBrian A NosekRussell A PoldrackDon van RavenzwaaijJörg RieskampMatthew J SalganikAlexandra SarafoglouTom SchonbergMartin SchweinsbergDavid R ShanksRaphael SilberzahnDaniel J SimonsBarbara A SpellmanSamuel St-JeanJeffrey J StarnsEric Luis UhlmannJelte M WichertsEric-Jan WagenmakersPublished in: eLife (2021)
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.