Frontiers in Psychology (Jan 2015)

On the efficacy of procedures to normalise Ex-Gaussian distributions

  • Fernando eMarmolejo-Ramos,
  • Denis eCousineau,
  • Luis eBenites Sánchez,
  • Rocio eMaehara

DOI
https://doi.org/10.3389/fpsyg.2014.01548
Journal volume & issue
Vol. 5

Abstract

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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 normalise 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 normalising positively skewed data and the more skewed the distribution then the transformation methods are more effective in normalising such data. Specifically, transformation with parameter lambda -1 leads to the best results.

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