Positively skewed data: revisiting the box-cox power transformation.
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 transformation...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN ES |
| Publicado: |
Universidad de San Buenaventura
2010
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ff8ff67de3c34cf1a06984deb4592c13 |
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| Sumario: | 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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