Nepomuceno, A.; Ghosal, A.; Sandoval Lentisco, A.; Ioannidis, J. P. A.

Empirical conclusions can depend on the many individual choices researchers make when analyzing data. Multiverse-style analyses address this by computing results across a set of specifications rather than a single one, but it is unclear how widely they are being used and how they are being implemented. We searched the Web of Science Core Collection (May 2026) for articles citing six foundational papers on different variants of multiverse-style methods and classified each of the citing articles as implementing such methods or only discussing them. For implementations, we recorded the framework used, the number of specifications, which of four decision nodes (measurement, data processing, modeling, and estimation) were varied, and other aspects such as how results were visualized and interpreted. Of the 1545 classifiable articles, 613 (39.7%) implemented a multiverse-style analysis (primarily multiverse n = 336, specification curve n = 175, vibration of effects n = 20, multimodel n = 59, and multi-analyst/many-analyst n = 23). Uptake spanned many disciplines, most often psychology (39.8%), the social sciences (20.7%), and medicine (19.2%). The number of specifications ranged from fewer than ten to more than ten thousand (median 144, IQR 24 -1248). Modeling (75%) and data-processing (62%) choices were included most often. Interpretation was predominantly descriptive, whereas formal inference (10.5%), preregistration (8.6%), and explicit attention to the defensibility of specifications (3.9%) were uncommon. Multiverse-style analyses are increasingly becoming established across the quantitative sciences, but some implementation practices can be strengthened so that these analyses become more fully transparent and more genuinely informative about the robustness of research findings.