Improving Your Data Transformations: Applying the Box-Cox Transformation

Autor/in	Osborne, Jason W.
Titel	Improving Your Data Transformations: Applying the Box-Cox Transformation
Quelle	In: Practical Assessment, Research & Evaluation, 15 (2010) 12, (9 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1531-7714
Schlagwörter	Social Sciences; Regression (Statistics); Nonparametric Statistics; Data; Mathematical Concepts; Correlation; Effect Size; Transformations (Mathematics) + Suchen Sie Ihr Suchwort? Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Regression; Regressionsanalyse; Daten; Korrelation; Transformationstheorie
Abstract	Many of us in the social sciences deal with data that do not conform to assumptions of normality and/or homoscedasticity/homogeneity of variance. Some research has shown that parametric tests (e.g., multiple regression, ANOVA) can be robust to modest violations of these assumptions. Yet the reality is that almost all analyses (even nonparametric tests) benefit from improved the normality of variables, particularly where substantial non-normality is present. While many are familiar with select traditional transformations (e.g., square root, log, inverse) for improving normality, the Box-Cox transformation (Box & Cox, 1964) represents a family of power transformations that incorporates and extends the traditional options to help researchers easily find the optimal normalizing transformation for each variable. As such, Box-Cox represents a potential best practice where normalizing data or equalizing variance is desired. This paper briefly presents an overview of traditional normalizing transformations and how Box-Cox incorporates, extends, and improves on these traditional approaches to normalizing data. Examples of applications are presented, and details of how to automate and use this technique in SPSS and SAS are included. (Contains 7 figures and 3 footnotes.) (As Provided).
Anmerkungen	Dr. Lawrence M. Rudner. e-mail: editor@pareonline.net; Web site: http://pareonline.net
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10