Multiverse Meta-Analysis: Proposing an Exploratory Framework

Multiverse analysis is a statistical approach in which researchers present all the results deriving from all combinations of arbitrary, but plausible decisions involved in every data analytic process (instead of focusing on a single result). Interpreting the multiverse of results provides fundamental insights into the robustness and uncertainty of scientific findings, therefore increasing transparency. Meta-analysis could particularly benefit from a multiverse approach, as it involves several decisions (e.g., how to impute missing data, which model to use, and how to deal with outliers). However, no specific framework is available to properly interpret the multiverse of meta-analytic results. Based on a real case study, the current work aims to propose an exploratory framework to support researchers in evaluating multiverse meta-analysis via tabular and graphical representations. Furthermore, we will highlight the contribution of each arbitrary decision to the variability of results.