This app is designed to guide researchers through the transparent and systematic definition of pipelines to be included in multiverse analyses. By using this tool, you can ensure your research meets recently proposed guidelines (Short et al., 2025) of transparency and rigor.
Multiverse analysis addresses critical issues in research replicability by reporting the uncertainty that arises in the reported outcome due to the multiplicity of defensible data processing and analysis decisions. By reporting a distribution of outcomes across defensible variations in data processing and analysis, researchers can:
Communicate uncertainty: Illustrate how different analytical choices influence research findings. Foster robust scientific findings: Build confidence in results by demonstrating their consistency across defensible analytic variations. Enhance transparency: Clearly document and communicate the decision-making process.
In order to achieve these benefits, the multiverse should be principled (Del Giudice & Gangestad, 2021), including only defensible pipelines that are arbitrary in terms of a specific criterion, such as validity, testing for the same effect, and estimating the effect with comparable precision. This ensures that the included pipelines align with the research goals without introducing irrelevant noise.
Implementing this principled approach requires transparency and systematicity throughout the decision-making process. This is where the Systematic Multiverse Analysis Registration Tool helps, providing researchers with a structured, user-friendly platform to define, evaluate, and refine their multiverse analyses. By facilitating these critical steps, the app ensures that researchers can achieve transparency, rigor, and replicability in their multiverse studies.
The Systematic Multiverse Analysis Registration Tool is designed as a practical solution for researchers across disciplines, providing a user-friendly interface for defining, evaluating, and refining analytic pipelines in a systematic and transparent way.
The app serves several key purposes:
Supporting rigorous decision-making: Help users identify defensible pipelines and refine them based on clear criteria, reducing the risk of bias. Efficient and systematic implementation: Save time and effort by using a structured workflow. Facilitating transparency: Generate detailed documentation of the decision-making process that can be shared as a multiverse analysis preregistration of pipelines and as supplementary information with the reported results, to make your multiverse analysis traceable and reproducible. Encouraging interdisciplinary innovation: Bridge gaps between fields by providing a standardized platform for multiverse analysis that incorporates best practices and fosters collaboration.
The app is intended for use by researchers in fields that can benefit from multiverse analysis, such as Sociology, Psychology, Neurocognitive Science, Education, Economics, Epidemiology and beyond, helping them to define and refine their multiverse analyses with greater ease. Whether you are conducting a single study or collaborating on a large-scale interdisciplinary project, this tool is designed to make multiverse analysis more accessible and impactful.
This app walks you through two key stages:
Multiverse 1.0: Define the defensible pipelines for your analysis. Multiverse 2.0: Refine these pipelines into a principled multiverse (Del Giudice and Gangestad, 2021).
Each step is designed to be intuitive and exportable. Overall this app is here to guide you through a transparent, systematic, and replicable research process.
You can watch the instructional video that demonstrates the use of app:
Del Giudice, M., & Gangestad, S. W. (2021). A traveler’s guide to the multiverse: Promises, pitfalls, and a framework for the evaluation of analytic decisions. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920954925. https://doi.org/10.1177/2515245920954925
Gollwitzer, M. (2020). DFG Priority Program SPP 2317 Proposal: A meta-scientific program to analyze and optimize replicability in the behavioral, social, and cognitive sciences (META-REP). PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.3010
Short, C. A., Breznau, N., Bruntsch, M., Burkhardt, M., Busch, N., Cesnaite, E., Frank, M., Gießing, C., Krähmer, D., Kristanto, D., Lonsdorf, T., Neuendorf, C., Nguyen, H. H. V., Rausch, M., Schmalz, X., Schneck, A., Tabakci, C., Hildebrandt, A. (2025a). Multi-curious: A multi-disciplinary guide to a multiverse analysis. MetaArXiV. https://doi.org/10.31222/osf.io/4yzeh_v1