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 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.

• Autores: Olivier, Jake; Norberg, Melissa M
• Fecha de publicación: 2010
• Institución: Universidad de San Buenaventura
• Repositorio: Repositorio USB
• Idioma: spa
• OAI Identifier: oai:bibliotecadigital.usb.edu.co:10819/6497
• Acceso en línea: http://hdl.handle.net/10819/6497
• Palabra clave: Ex-Gaussian distribution; Geometric mean analysis; Logarithmic transformations; Log-normal distribution; Análisis de la media geométrica; Distribución exponencial Gaussiana; Distribución logarítmica normal; Transformaciones logarítmicas; Estadística; Investigación
• License: Atribución-NoComercial-SinDerivadas 2.5 Colombia