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Six Approaches to Calculating Standardized Logistic Regression Coefficients
The American Statistician
Vol. 58, No. 3 (Aug., 2004), pp. 218-223
Stable URL: http://www.jstor.org/stable/27643560
Page Count: 6
You can always find the topics here!Topics: Coefficients, Regression coefficients, Variable coefficients, Logistic regression, Mathematical dependent variables, Information standards, Modeling, Standard deviation, Statistical variance, Statistics
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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.
The American Statistician © 2004 American Statistical Association