Malahanobis distance; Median absolute deviation; Minimum covariance determinant; Outliers; Preregistration; Robust detection; Social Psychology
Résumé :
[en] Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even more frequently, researchers do not pre-specify how they plan to manage outliers. In this paper we aim to improve research practices by outlining what you need to know about outliers. We start by providing a functional definition of outliers. We then lay down an appropriate nomenclature/classification of outliers. This nomenclature is used to understand what kinds of outliers can be encountered and serves as a guideline to make appropriate decisions regarding the conservation, deletion, or recoding of outliers. These decisions might impact the validity of statistical inferences as well as the reproducibility of our experiments. To be able to make informed decisions about outliers you first need proper detection tools. We remind readers why the most common outlier detection methods are problematic and recommend the use of the median absolute deviation to detect univariate outliers, and of the Mahalanobis-MCD distance to detect multivariate outliers. An R package was created that can be used to easily perform these detection tests. Finally, we promote the use of pre-registration to avoid flexibility in data analysis when handling outliers.
Disciplines :
Mathématiques Physique, chimie, mathématiques & sciences de la terre: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
Leys, Christophe ; Université Libre de Bruxelles, Service of Analysis of the Data (SAD), Bruxelles, Belgium
Delacre, Marie; Université Libre de Bruxelles, Service of Analysis of the Data (SAD), Bruxelles, Belgium
Mora, Youri L.; Université Libre de Bruxelles, Service of Analysis of the Data (SAD), Bruxelles, Belgium
Lakens, Daniël; Eindhoven University of Technology, Human Technology Interaction Group, Eindhoven, Netherlands
LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) ; Universiteit Gent, Department of Applied Mathematics, Computer Science and Statistics, Gent, Belgium
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration
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