The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines

Hoffmann, Sabine; Schönbrodt, Felix D. ; Elsas, Ralf; Wilson, Rory; Strasser, Ulrich and Boulesteix, Anne-Laure (2021): The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines. In: Royal Society Open Science, Vol. 8, No. 4

Abstract

For a given research question, there are usually a large variety of possible analysis strategies acceptable according to the scientific standards of the field, and there are concerns that this multiplicity of analysis strategies plays an important role in the non-replicability of research findings. Here, we define a general framework on common sources of uncertainty arising in computational analyses that lead to this multiplicity, and apply this framework within an overview of approaches proposed across disciplines to address the issue. Armed with this framework, and a set of recommendations derived therefrom, researchers will be able to recognize strategies applicable to their field and use them to generate findings more likely to be replicated in future studies, ultimately improving the credibility of the scientific process.

• Item Type: Journal article
• Faculties: Munich School of Management > Institute for Finance and Banking; Psychology and Education Science > Department Psychology; Mathematics, Computer Science and Statistics > Statistics; Medicine > Institute for Medical Information Processing, Biometry and Epidemiology
• Subjects: 600 Technology > 610 Medicine and health
• ISSN: 2054-5703
• Language: English