The science of psychology is facing a so-called replication and reproduction crisis. This crisis calls for increased transparency in the way that statistics are reported, in order to make findings...Show moreThe science of psychology is facing a so-called replication and reproduction crisis. This crisis calls for increased transparency in the way that statistics are reported, in order to make findings in psychology more reliable, more likely to be interpreted correctly, and easier to verify and replicate. Undisclosed flexibility in data collection and analysis can severely inflate the false positive rate in psychology (and other disciplines). This issue can be tackled by performing a multiverse analysis. Recent literature proposes that this method guarantees an unprecedent level of transparency for research papers. Building on this stream of research, the present thesis examines the value of using a multiverse analysis to examine the robustness of a non-significant effect. Multiverse analysis refers to a method which involves performing the analysis of interest across the complete set of data sets that arise from several defensible data processing and analysis choices. The demonstration of the multiverse focuses on data collected by Dieleman et al. (2020). The reported results were first reproduced using the R software environment, and then, alternative analysis pathways were examined. The results proved to be reproducible, yet alternative pathways revealed some fluctuation. This thesis contributes to a better understanding of how a multiverse analysis can improve the transparency of psychological science.Show less
