Increasing the Transparency of Research Papers with Explorable Multiverse Analyses

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.
Document type :
Conference papers
Complete list of metadatas
Contributor : Pierre Dragicevic <>
Submitted on : Thursday, January 10, 2019 - 2:02:40 PM
Last modification on : Wednesday, May 15, 2019 - 4:03:19 AM


Files produced by the author(s)



Pierre Dragicevic, Yvonne Jansen, Abhraneel Sarma, Matthew Kay, Fanny Chevalier. Increasing the Transparency of Research Papers with Explorable Multiverse Analyses. CHI 2019 - The ACM CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, United Kingdom. ⟨10.1145/3290605.3300295⟩. ⟨hal-01976951⟩



Record views


Files downloads