title: On the efficacy of procedures to normalize Ex-Gaussian distributions.
abstract: Reaction time (RT) is one of the most common types of measure used in experimental psychology. Its distribution is not normal (Gaussian) but resembles a convolution of normal and exponential distributions (Ex-Gaussian). One of the major assumptions in parametric tests (such as ANOVAs) is that variables are normally distributed. Hence, it is acknowledged by many that the normality assumption is not met. This paper presents different procedures to normalize data sampled from an Ex-Gaussian distribution in such a way that they are suitable for parametric tests based on the normality assumption. Using simulation studies, various outlier elimination and transformation procedures were tested against the level of normality they provide. The results suggest that the transformation methods are better than elimination methods in normalizing positively skewed data and the more skewed the distribution then the transformation methods are more effective in normalizing such data. Specifically, transformation with parameter lambda -1 leads to the best results.
authors.affiliation: Gösta Ekman Laboratory, Department of Psychology, Stockholm University Stockholm, Sweden.
authors.forename: Fernando
authors.initials: F
authors.lastname: Marmolejo-Ramos
authors.affiliation: School of Psychology, University of Ottawa Ottawa, ON, Canada.
authors.forename: Denis
authors.initials: D
authors.lastname: Cousineau
authors.affiliation: Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo São Paulo, Brazil.
authors.forename: Luis
authors.initials: L
authors.lastname: Benites
authors.affiliation: Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo São Paulo, Brazil.
authors.forename: Rocío
authors.initials: R
authors.lastname: Maehara
keywords: Ex-Gaussian; normality tests; outliers; reaction times