A Step-By-Step Guide on Preregistration and Effective Data Sharing for Psychopathology Research

[DOI OF FINAL ARTICLE: http://dx.doi.org/10.1037/abn0000424] Data analysis in psychopathology research typically entails multiple stages of data preprocessing (e.g., coding of physiological measures), statistical decisions (e.g., inclusion of covariates), and reporting (e.g., selecting which variables best answer the research questions). The complexity and lack of transparency of these procedures have resulted in two troubling trends: the central hypotheses and analytical approaches are often selected after observing the data, and the research data are often not properly indexed. These practices are particularly problematic for (experimental) psychopathology research because the data are often hard to gather due to the target populations (e.g., individuals with mental disorders), and because the standard methodological approaches are challenging and time consuming (e.g., longitudinal studies). Here, we present a workflow that covers study preregistration, data anonymization, and the easy sharing of data and experimental material with the rest of the research community. This workflow is tailored to both original studies and secondary statistical analyses of archival datasets. In order to facilitate the implementation of the described workflow, we have developed a free and open-source software program. We argue that this workflow will result in more transparent and easily shareable psychopathology research, eventually increasing and replicability reproducibility in our research field.