Data from a pre-publication independent replication initiative examining ten moral judgement effects

Warren Tierney, Martin Schweinsberg, Jennifer Jordan, Deanna M Kennedy, Israr Qureshi, S Amy Sommer, Nico Thornley, Nikhil Madan, Michelangelo Vianello, Eli Awtrey, Luke Lei Zhu, Daniel Diermeier, Justin E Heinze, Malavika Srinivasan, David Tannenbaum, Eliza Bivolaru, Jason Dana, Clintin P Davis-Stober, Christilene du Plessis, Quentin F GronauAndrew C Hafenbrack, Eko Yi Liao, Alexander Ly, Maarten Marsman, Toshio Murase, Michael Schaerer, Christina M Tworek, Eric-Jan Wagenmakers, Lynn Wong, Tabitha Anderson, Christopher W Bauman, Wendy L Bedwell, Victoria Brescoll, Andrew Canavan, Jesse J Chandler, Erik Cheries, Sapna Cheryan, Felix Cheung, Andrei Cimpian, Mark A Clark, Diana Cordon, Fiery Cushman, Peter H Ditto, Alice Amell, Sarah E Frick, Monica Gamez-Djokic, Rebecca Hofstein Grady, Jesse Graham, Jun Gu, Adam Hahn, Brittany E Hanson, Nicole J Hartwich, Kristie Hein, Yoel Inbar, Lily Jiang, Tehlyr Kellogg, Nicole Legate, Timo P Luoma, Heidi Maibeucher, Peter Meindl, Jennifer Miles, Alexandra Mislin, Daniel C Molden, Matt Motyl, George Newman, Hoai Huong Ngo, Harvey Packham, P Scott Ramsay, Jennifer L Ray, Aaron M Sackett, Anne-Laure Sellier, Tatiana Sokolova, Walter Sowden, Daniel Storage, Xiaomin Sun, Jay J Van Bavel, Anthony N Washburn, Cong Wei, Erik Wetter, Carlos T Wilson, Sophie-Charlotte Darroux, Eric Luis Uhlmann

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science.

Original languageEnglish
Pages (from-to)160082
JournalScientific Data
Volume3
DOIs
Publication statusPublished - 11 Oct 2016

Keywords

  • Humans
  • Morals
  • Reproducibility of Results

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