Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology
Pepijn Obels; Daniël Lakens; Nicholas A. Coles; Jaroslav Gottfried; Seth A. Green · 2020 · Advances in Methods and Practices in Psychological Science
WASTE classifies this as Replication Failure · AI classification, approximate
A previously reported effect did not replicate here — verify it holds before you build on it.
Abstract
Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to reuse or check published research. However, these benefits will emerge only if researchers can reproduce the analyses reported in published articles and if data are annotated well enough so that it is clear what all variable and value labels mean. Because most researchers are not trained in computational reproducibility, it is important to evaluate current practices to identify those tha
Abstract by Pepijn Obels; Daniël Lakens; Nicholas A. Coles; Jaroslav Gottfried; Seth A. Green, Advances in Methods and Practices in Psychological Science (2020) — licensed CC BY 4.0.
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1177/2515245920918872