Six Approaches to Calculating Standardized Logistic Regression Coefficients

Six Approaches to Calculating Standardized Logistic Regression Coefficients

This article reviews six alternative approaches to constructing standardized logistic regression coefficients. The least attractive of the options is the one currently most readily available in logistic regression software, the unstandardized coefficient divided by its standard error (which is actua...

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Veröffentlicht in:The American Statistician. - American Statistical Association, 1947. - 58(2004), 3, Seite 218-223
1. Verfasser: Menard, Scott (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2004
Zugriff auf das übergeordnete Werk:The American Statistician
Schlagworte:Information theory Logit model Mathematics Information science Applied sciences
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520 |a This article reviews six alternative approaches to constructing standardized logistic regression coefficients. The least attractive of the options is the one currently most readily available in logistic regression software, the unstandardized coefficient divided by its standard error (which is actually the normal distribution version of the Wald statistic). One alternative has the advantage of simplicity, while a slightly more complex alternative most closely parallels the standardized coefficient in ordinary least squares regression, in the sense of being based on variance in the dependent variable and the predictors. The sixth alternative, based on information theory, may be the best from a conceptual standpoint, but unless and until appropriate algorithms are constructed to simplify its calculation, its use is limited to relatively simple logistic regression models in practical application. 
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