Positively skewed data: revisiting the box-cox power transformation.
Jake Olivier
University of New South Wales
Melissa M. Norberg
University of New South Wales
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Keywords

Logarithmic transformations
geometric mean analysis
ex-Gaussian distribution
log-normal distribution

How to Cite

Olivier, J., & M. Norberg, M. (2010). Positively skewed data: revisiting the box-cox power transformation. International Journal of Psychological Research, 3(1), 68-77. https://doi.org/10.21500/20112084.846
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Abstract

Although the normal probability distribution is the cornerstone of applying statistical methodology; data do not always meet the necessary normal distribution assumptions. In these cases, researchers often transform non-normal data to a distribution that is approximately normal. Power transformations constitute a family of transformations, which include logarithmic and fractional exponent transforms. The Box-Cox method offers a simple method for choosing the most appropriate power transformation. Another option for data that is positively skewed, often used when measuring reaction times, is the Ex-Gaussian distribution which is a combination of the exponential and normal distributions. In this paper, the Box-Cox power transformation and Ex-Gaussian distribution will be discussed and compared in the context of positively skewed data. This discussion will demonstrate that the Box-Cox power transformation is simpler to apply and easier to interpret than the Ex-Gaussian distribution.
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References

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