Speakers
Description
Modern research in psychology is adopting several tools to face the replicability and reproducibility crisis. In this context, multi-lab research combined with the multiverse approach is a very powerful yet complex approach. The symposium aims to present a modern overview of editorial aspects, statistical methods, and data management in the era of multi-lab and multiverse studies highlighting strengths, limitations, and challenges. The first talk by Crepaldi provides an introduction to multi-lab and multiverse studies focusing on the editorial aspects. The talk by Liuzza provides an example of planning a multi-lab study from a methodological and statistical point of view. Multi-lab studies require appropriate statistical models when planning (e.g., statistical power) and analyzing data. The talk by Calignano integrated the multiverse approach into the multi-lab methodology focusing on data pre-processing showing the amount of researchers' degrees of freedom and the impact on the final results. Summarising and presenting the results of a multiverse analysis requires appropriate descriptive and inferential tools. Given the lack of proper inferential methods, the talk by Finos proposed an innovative, flexible, and powerful inferential approach to summarise the results of a multiverse analysis. Especially for multi-lab studies, adopting open science practices in terms of transparency, preregistration, and data sharing, is becoming a new standard. However, data sharing is also a controversial, delicate, and often overlooked topic. The final talk by Scandola illustrates the problem of data management and sharing considering privacy policies and modern open science practices.
| If you're submitting a symposium, or a talk that is part of a symposium, is this a junior symposium? | No |
|---|
