Increasing the transparency of research papers with explorable multiverse analyses

Pierre Dragicevic, Yvonne Jansen, Abhraneel Sarma, Matthew Kay, Fanny Chevalier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

We present explorable multiverse analysis reports, a new approach to statistical reporting where readers of research papers can explore alternative analysis options by interacting with the paper itself. This approach draws from two recent ideas: i) multiverse analysis, a philosophy of statistical reporting where paper authors report the outcomes of many different statistical analyses in order to show how fragile or robust their findings are; and ii) explorable explanations, narratives that can be read as normal explanations but where the reader can also become active by dynamically changing some elements of the explanation. Based on five examples and a design space analysis, we show how combining those two ideas can complement existing reporting approaches and constitute a step towards more transparent research papers.

Original languageEnglish (US)
Title of host publicationCHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450359702
DOIs
StatePublished - May 2 2019
Externally publishedYes
Event2019 CHI Conference on Human Factors in Computing Systems, CHI 2019 - Glasgow, United Kingdom
Duration: May 4 2019May 9 2019

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2019 CHI Conference on Human Factors in Computing Systems, CHI 2019
Country/TerritoryUnited Kingdom
CityGlasgow
Period5/4/195/9/19

Keywords

  • Explorable explanation
  • Interactive documents
  • Multiverse analysis
  • Statistics
  • Transparent reporting

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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