With explorable multiverse analysis reports, readers of research papers can explore alternative analysis options by interacting with the paper itself. This new approach to statistical reporting draws from two recent ideas: 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 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.
This is the companion website for the paper:
Dragicevic, Jansen, Sarma, Kay, and Chevalier. 2019. Increasing the Transparency of Research Papers with Explorable Multiverse Analyses. In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK. ACM, New York, NY, USA, 15 pages.
Useful tips before you try the examples below:
This example is a reanalysis of a CHI study evaluating physical visualizations. It is meant to illustrate a few basic multiverse analysis ideas for a classic frequentist analysis with confidence intervals.
This example is a reanalysis of a recent InfoVis study on the effect of charts on comprehension and persuasion. It is meant to provide an example of multiple alternative analyses that differ a lot in their methodology.
This example is a reanalysis of a study which examines the effect of incidental power poses on risk taking behavior. It is meant to illustrate interactive setting of a prior distribution in a Bayesian analysis.
The is example is a reanalysis of a previous InfoVis study on the perception of correlations. It is meant to illustrate the use of simulated dataverses to convey inferential information that can be missing from plots.