Best Practices for Using Median Splits, Artificial Categorization, and their Continuous Alternatives
Author(s) -
Jamie DeCoster,
Marcello Gallucci,
AnneMarie R. Iselin
Publication year - 2011
Publication title -
journal of experimental psychopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.711
H-Index - 10
ISSN - 2043-8087
DOI - 10.5127/jep.008310
Subject(s) - categorization , categorical variable , perspective (graphical) , computer science , continuous variable , artificial intelligence , psychology , machine learning , statistics , mathematics
Methodologists have long discussed the costs and benefits of using medians or other cut points to artificially turn continuous variables into categorical variables. The current paper attempts to provide a perspective on this literature that will be of practical use to experimental psychopathologists. After discussing the reasons that clinical researchers might use artificial categorization, we summarize the arguments both for and against this procedure. We then provide a number of specific suggestions related to the use of artificial categorization, including our thoughts on when researchers should use artificial categories, how their use can be justified, what continuous alternatives are available, and how the continuous alternatives should be used.
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