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Many analysts, one dataset: Making transparent how variations in analytical choices affect results /
Many analysts, one dataset: Making transparent how variations in analytical choices affect results
- Raphael Silberzahn
- Eric Luis Uhlmann
- Dan Martin
- Pasquale Anselmi
- Frederik Aust
- Eli C. Awtrey
- Štěpán Bahník
- Feng Bai
- Colin Bannard
- Evelina Bonnier
- Rickard Carlsson
- Felix Cheung
- Garret Christensen
- Russ Clay
- Maureen A. Craig
- Anna Dalla Rosa
- Lammertjan Dam
- Mathew H. Evans
- Ismael Flores Cervantes
- Nathan Fong
- Monica Gamez-Djokic
- Andreas Glenz
- Shauna Gordon-McKeon
- Tim Heaton
- Karin Hederos Eriksson
- Moritz Heene
- Alicia Hofelich Mohr
- Fabia Högden
- Kent Hui
- Magnus Johannesson
- Jonathan Kalodimos
- Erikson Kaszubowski
- Deanna Kennedy
- Ryan F. Lei
- Thomas Andrew Lindsay
- Silvia Liverani
- Christopher Madan
- Daniel C. Molden
- Eric Molleman
- Richard D. Morey
- Laetitia Mulder
- Bernard A. Nijstad
- Bryson Pope
- Nolan Pope
- Jason M. Prenoveau
- Floor Rink
- Egidio Robusto
- Hadiya Roderique
- Anna Sandberg
- Elmar Schlueter
- Felix S
- Martin F Sherman
- S. Amy Sommer
- Kristin Lee Sotak
- Seth M. Spain
- Christoph Spörlein
- Tom Stafford
- Luca Stefanutti
- Susanne Täuber
- Johannes Ullrich
- Michelangelo Vianello
- Eric-Jan Wagenmakers
- Maciej Witkowiak
- Sangsuk Yoon
- Brian A. Nosek
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Category: Communication
Description: Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Overall 29 different analyses used 21 unique combinations of covariates. We found that neither analysts' prior beliefs about the effect, nor their level of expertise, nor peer-reviewed quality of analysis readily explained variation in analysis outcomes. This suggests that significant variation in analysis of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective analytic choices influence research results.