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:
Olivier, Jake
Norberg, Melissa M
Tipo de recurso:
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
Rights
License
Atribución-NoComercial-SinDerivadas 2.5 Colombia
Description
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.