2.50
Hdl Handle:
http://hdl.handle.net/2436/66082
Title:
Scaling, normalizing, and per ratio standards: an allometric modeling approach.
Authors:
Nevill, Alan M.; Holder, Roger L.
Abstract:
The practice of scaling or normalizing physiological variables (Y) by dividing the variable by an appropriate body size variable (X) to produce what is known as a "per ratio standard" (Y/ X), has come under strong criticism from various authors. These authors propose an alternative regression standard based on the linear regression of (Y) on (X) as the predictor variable. However, if linear regression is to be used to adjust such physiological measurements (Y), the residual errors should have a constant variance and, in order to carry out parametric tests of significance, be normally distributed. Unfortunately, since neither of these assumptions appear to be satisfied for many physiological variables, e.g., maximum oxygen uptake, peak and mean power, an alternative approach is proposed of using allometric modeling where the concept of a ratio is an integral part of the model form. These allometric models naturally help to overcome the heteroscedasticity and skewness observed with per ratio variables. Furthermore, if per ratio standards are to be incorporated in regression models to predict other dependent variables, the allometric or log-linear model form is shown to be more appropriate than linear models. By using multiple regression, simply by taking logarithms of the dependent variable and entering the logarithmic transformed per ratio variables as separate independent variables, the resulting estimated log-linear multiple-regression model will automatically provide the most appropriate per ratio standard to reflect the dependent variable, based on the proposed allometric model.
Citation:
Journal of Applied Physiology, 79: 1027-1031
Publisher:
Bethesda, MD: The American Physiological Society
Journal:
Journal of Applied Physiology
Issue Date:
1995
URI:
http://hdl.handle.net/2436/66082
Additional Links:
http://jap.physiology.org/cgi/content/abstract/79/3/1027
Type:
Article
Language:
en
ISSN:
8750-7587; 1522-1601
Appears in Collections:
Sport, Exercise and Health Research Group; Learning and Teaching in Sport, Exercise and Performance

Full metadata record

DC FieldValue Language
dc.contributor.authorNevill, Alan M.-
dc.contributor.authorHolder, Roger L.-
dc.date.accessioned2009-04-23T19:41:44Z-
dc.date.available2009-04-23T19:41:44Z-
dc.date.issued1995-
dc.identifier.citationJournal of Applied Physiology, 79: 1027-1031en
dc.identifier.issn8750-7587-
dc.identifier.issn1522-1601-
dc.identifier.urihttp://hdl.handle.net/2436/66082-
dc.description.abstractThe practice of scaling or normalizing physiological variables (Y) by dividing the variable by an appropriate body size variable (X) to produce what is known as a "per ratio standard" (Y/ X), has come under strong criticism from various authors. These authors propose an alternative regression standard based on the linear regression of (Y) on (X) as the predictor variable. However, if linear regression is to be used to adjust such physiological measurements (Y), the residual errors should have a constant variance and, in order to carry out parametric tests of significance, be normally distributed. Unfortunately, since neither of these assumptions appear to be satisfied for many physiological variables, e.g., maximum oxygen uptake, peak and mean power, an alternative approach is proposed of using allometric modeling where the concept of a ratio is an integral part of the model form. These allometric models naturally help to overcome the heteroscedasticity and skewness observed with per ratio variables. Furthermore, if per ratio standards are to be incorporated in regression models to predict other dependent variables, the allometric or log-linear model form is shown to be more appropriate than linear models. By using multiple regression, simply by taking logarithms of the dependent variable and entering the logarithmic transformed per ratio variables as separate independent variables, the resulting estimated log-linear multiple-regression model will automatically provide the most appropriate per ratio standard to reflect the dependent variable, based on the proposed allometric model.en
dc.language.isoenen
dc.publisherBethesda, MD: The American Physiological Societyen
dc.relation.urlhttp://jap.physiology.org/cgi/content/abstract/79/3/1027en
dc.subjectLog-linear regression modelsen
dc.subjectLog-linear modelsen
dc.subjectHeteroscedasticityen
dc.subjectMultiple regression analysisen
dc.subjectAllometric scalingen
dc.subjectAllometric modellingen
dc.subjectRatiosen
dc.subjectAnthropometryen
dc.titleScaling, normalizing, and per ratio standards: an allometric modeling approach.en
dc.typeArticleen
dc.identifier.journalJournal of Applied Physiologyen
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