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...
| Autor Principal: | Olivier, Jake |
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| Otros Autores: | Norberg, Melissa M |
| Formato: | info:eu-repo/semantics/article |
| Idioma: | spa |
| Publicado: |
Editorial Bonaventuriana
2018
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| Materias: | |
| Acceso en línea: |
Olivier, J., & 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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| 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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