The reliability of replications: A study in computational reproductions

Nate Breznau*, Eike Mark Rinke, Alexander Wuttke, Muna Adem, Jule Adriaans, Esra Akdeniz, Amalia Alvarez-Benjumea, Henrik K. Andersen, Daniel Auer, Flavio Azevedo, Oke Bahnsen, Ling Bai, Dave Balzer, Paul C. Bauer, Gerrit Bauer, Markus Baumann, Sharon Baute, Verena Benoit, Julian Bernauer, Carl BerningAnna Berthold, Felix S. Bethke, Thomas Biegert, Katharina Blinzler, Johannes N. Blumenberg, Licia Bobzien, Andrea Bohman, Thijs Bol, Amie Bostic, Zuzanna Brzozowska, Katharina Burgdorf, Kaspar Burger, Kathrin Busch, Juan Carlos Castillo, Nathan Chan, Pablo Christmann, Roxanne Connelly, Christian S. Czymara, Elena Damian, Eline A. De Rooij, Alejandro Ecker, Achim Edelmann, Christina Eder, Maureen A. Eger, Simon Ellerbrock, Anna Forke, Andrea Forster, Danilo Freire, Chris Gaasendam, Konstantin Gavras, Vernon Gayle, Theresa Gessler, Timo Gnambs, Amélie Godefroidt, Max Grömping, Martin Groß, Stefan Gruber, Tobias Gummer, Andreas Hadjar, Verena Halbherr, Jan Paul Heisig, Sebastian Hellmeier, Stefanie Heyne, Magdalena Hirsch, Mikael Hjerm, Oshrat Hochman, Jan H. Höffler, Andreas Hövermann, Sophia Hunger, Christian Hunkler, Nora Huth-Stöckle, Zsófia S. Ignácz, Sabine Israel, Laura Jacobs, Jannes Jacobsen, Bastian Jaeger, Sebastian Jungkunz, Nils Jungmann, Jennifer Kanjana, Mathias Kauff, Salman Khan, Sayak Khatua, Manuel Kleinert, Julia Klinger, Jan Philipp Kolb, Marta Kołczyńska, John Kuk, Katharina Kunißen, Dafina Kurti Sinatra, Alexander Langenkamp, Robin C. Lee, Philipp M. Lersch, David Liu, Lea Maria Löbel, Philipp Lutscher, Matthias Mader, Joan E. Madia, Natalia Malancu, Luis Maldonado, Helge Marahrens, Nicole Martin, Paul Martinez, Jochen Mayerl, Oscar J. Mayorga, Robert McDonnell, Patricia McManus, Kyle McWagner, Cecil Meeusen, Daniel Meierrieks, Jonathan Mellon, Friedolin Merhout, Samuel Merk, Daniel Meyer, Leticia Micheli, Jonathan Mijs, Cristóbal Moya, Marcel Neunhoeffer, Daniel Nüst, Olav Nygård, Fabian Ochsenfeld, Gunnar Otte, Anna Pechenkina, Mark Pickup, Christopher Prosser, Louis Raes, Kevin Ralston, Miguel Ramos, Frank Reichert, Arne Roets, Jonathan Rogers, Guido Ropers, Robin Samuel, Gregor Sand, Constanza Sanhueza Petrarca, Ariela Schachter, Merlin Schaeffer, David Schieferdecker, Elmar Schlueter, Katja Schmidt, Regine Schmidt, Alexander Schmidt-Catran, Claudia Schmiedeberg, Jürgen Schneider, Martijn Schoonvelde, Julia Schulte-Cloos, Sandy Schumann, Reinhard Schunck, Julian Seuring, Henning Silber, Willem Sleegers, Nico Sonntag, Alexander Staudt, Nadia Steiber, Nils D. Steiner, Sebastian Sternberg, Dieter Stiers, Dragana Stojmenovska, Nora Storz, Erich Striessnig, Anne Kathrin Stroppe, Jordan W. Suchow, Janna Teltemann, Andrey Tibajev, Brian Tung, Giacomo Vagni, Jasper Van Assche, Meta Van Der Linden, Jolanda Van Der Noll, Arno Van Hootegem, Stefan Vogtenhuber, Bogdan Voicu, Fieke Wagemans, Nadja Wehl, Hannah Werner, Brenton M. Wiernik, Fabian Winter, Christof Wolf, Cary Wu, Yuki Yamada, Björn Zakula, Nan Zhang, Conrad Ziller, Stefan Zins, Tomasz Żółtak, Hung H.V. Nguyen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigates researcher variability in computational reproduction, an activity for which it is least expected. Eighty-five independent teams attempted numerical replication of results from an original study of policy preferences and immigration. Reproduction teams were randomly grouped into a 'transparent group' receiving original study and code or 'opaque group' receiving only a method and results description and no code. The transparent group mostly verified original results (95.7% same sign and p-value cutoff), while the opaque group had less success (89.3%). Second-decimal place exact numerical reproductions were less common (76.9 and 48.1%). Qualitative investigation of the workflows revealed many causes of error, including mistakes and procedural variations. When curating mistakes, we still find that only the transparent group was reliably successful. Our findings imply a need for transparency, but also more. Institutional checks and less subjective difficulty for researchers 'doing reproduction' would help, implying a need for better training. We also urge increased awareness of complexity in the research process and in 'push button' replications.

Original languageEnglish
Article number241038
Pages (from-to)1-23
Number of pages23
JournalRoyal Society Open Science
Volume12
Issue number3
Early online date19 Mar 2025
DOIs
Publication statusPublished - Mar 2025

Keywords / Materials (for Non-textual outputs)

  • computational reproduction
  • reliability
  • replications
  • social and behavioural sciences

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