Introducing multiverse analysis to bibliometrics: The case of team size effects on disruptive research

Abstract Although bibliometrics has become an essential tool in the evaluation of research performance, bibliometric analyses are sensitive to a range of methodological choices. Subtle choices in data selection, indicator construction, and modeling decisions can substantially alter results. Ensuring robustness—meaning that findings hold up under different reasonable scenarios—is therefore critical for credible research and research evaluation. To address this issue, this study introduces multiverse analysis to bibliometrics. Multiverse analysis is a statistical tool that enables analysts to transparently discuss modeling assumptions and thoroughly assess model robustness. Whereas standard robustness checks usually cover only a small subset of all plausible models, multiverse analysis includes all plausible models. The benefits of multiverse analysis are illustrated by assessing the robustness of the findings reported by Wu, Wang, and Evans (2019), who observed that small teams tend to produce more disruptive research than large teams. While we found robust evidence of a negative effect of team size on disruption index scores, the effect size depends substantially on the model specification. Our findings underscore the importance of assessing the multiverse robustness of bibliometric results to clarify their practical implications.