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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universidad de San Buenaventura
2010-06-01
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| Series: | International Journal of Psychological Research |
| Subjects: | |
| Online Access: | https://revistas.usb.edu.co/index.php/IJPR/article/view/846 |
| Summary: | 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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| ISSN: | 2011-2084 2011-7922 |