Publication:
Positively Skewed Data: Revisiting the Box-Cox Power Transformation
Positively Skewed Data: Revisiting the Box-Cox Power Transformation
dc.contributor.author | Olivier, Jake | en_US |
dc.contributor.author | Norberg, Melissa | en_US |
dc.date.accessioned | 2021-11-25T12:24:07Z | |
dc.date.available | 2021-11-25T12:24:07Z | |
dc.date.issued | 2010 | en_US |
dc.description.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. | en_US |
dc.identifier.issn | 2011-2079 | en_US |
dc.identifier.uri | http://hdl.handle.net/1959.4/51439 | |
dc.language | English | |
dc.language.iso | EN | en_US |
dc.rights | CC BY-NC-ND 3.0 | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/au/ | en_US |
dc.source | Legacy MARC | en_US |
dc.subject.other | ex-Gaussian distribution | en_US |
dc.subject.other | Logarithmic transformations | en_US |
dc.subject.other | geometric mean analysis | en_US |
dc.subject.other | log-normal distribution | en_US |
dc.title | Positively Skewed Data: Revisiting the Box-Cox Power Transformation | en_US |
dc.type | Journal Article | en |
dcterms.accessRights | open access | |
dspace.entity.type | Publication | en_US |
unsw.accessRights.uri | https://purl.org/coar/access_right/c_abf2 | |
unsw.description.notePublic | Original inactive link: http://mvint.usbmed.edu.co:8002/ojs/index.php/web/article/view/461/444 | en_US |
unsw.relation.faculty | Science | |
unsw.relation.faculty | Medicine & Health | |
unsw.relation.ispartofissue | 1 | en_US |
unsw.relation.ispartofjournal | International Journal of Psychological Research | en_US |
unsw.relation.ispartofpagefrompageto | 69-78 | en_US |
unsw.relation.ispartofvolume | 3 | en_US |
unsw.relation.originalPublicationAffiliation | Olivier, Jake, Mathematics & Statistics, Faculty of Science, UNSW | en_US |
unsw.relation.originalPublicationAffiliation | Norberg, Melissa, National Drug & Alcohol Research Centre, Faculty of Medicine, UNSW | en_US |
unsw.relation.school | School of Mathematics & Statistics | * |
unsw.relation.school | NDARC | * |
unsw.subject.fieldofresearchcode | 010401 | en_US |
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