Increasing the Transparency of Research Papers with Explorable Multiverse Analyses
Pierre Dragicevic, Yvonne Jansen, Abhraneel Sarma, Matthew Kay, and Fanny Chevalier
ACM Human Factors in Computing Systems (CHI) 2019 | 
BEST PAPER AWARD
An example of one explorable multiverse analysis report in which the reader can adjust modelling choices and presentation options. Interactive demo available here
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.