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.
Original language | English |
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Pages (from-to) | 68-75 |
Number of pages | 8 |
Journal | International Journal of Psychological Research |
Volume | 3 |
Issue number | 1 |
Publication status | Published - 2010 |
Externally published | Yes |
Keywords
- Logarithmic transformations
- geometric mean analysis
- ex-Gaussian distribution
- log-normal distribution