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.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Betriebswirtschaft > Institut für Finance und Banking
Psychologie und Pädagogik > Department Psychologie Mathematik, Informatik und Statistik > Statistik Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
URN: | urn:nbn:de:bvb:19-epub-76418-7 |
ISSN: | 2054-5703 |
Sprache: | Englisch |
Dokumenten ID: | 76418 |
Datum der Veröffentlichung auf Open Access LMU: | 05. Jul. 2021, 13:07 |
Letzte Änderungen: | 12. Jun. 2023, 06:18 |