Categorización de variables en el análisis estadístico de datos

Consecuencias sobre la interpretación de resultados

Bibliographic Data

ID14968952
AuthorsFrancisco Cumsille (corresponding author), Shrikant I Bangdiwala (0000-0002-3111-0689, Universidad del Norte)
Year2000
Volume8
Issue5
Pages348-354
Publication date2000-11-01
Open AccessYes
TypeARTICLE
VenueRevista Panamericana de Salud Pública (JOURNAL)
Journal identifiersISSN: 1020-4989 • E-ISSN: 1680-5348
PublisherFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/s1020-49892000001000005
PMID11190972
OpenAlexW1972475435
SciELOS1020-49892000001000005
LanguageES
Citations received4
References cited2

Frequently during the process of data analysis in epidemiological studies, the scale of one or more continuous variables is changed. The objective of this paper was to assess the consequences of categorizing variables during data analysis. We studied three situations with different scenarios for statistical analysis with regression models. The results show that dichotomizing continuous variables can substantially modify the relationships between dependent and independent variables. Thus, for example, in epidemiological studies trying to evaluate the effect of an exposure on a response, the magnitude and/or the direction of this effect can be biased by dichotomizing a variable. We therefore recommend avoiding, as much as possible, the categorization of variables when doing analyses

Continuous variable · Humanities · Statistics · Variables · Mathematics · Philosophy · Reliability and Agreement in Measurement · Statistical Methods and Applications

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