beta.coef: Standardized Beta Coefficients

Description

Generate standardized beta coefficients from an adjusted model. Standardized coefficients refer to how many standard deviations a dependent variable will change per standard deviation increase in the predictor variable. See details.

Usage

beta.coef(md)

Arguments

is a linear object model.

Value

a numeric value of standardized beta coefficient.

Details

Standardized beta coefficients--also known as beta coefficients--are quantities estimated from unstandardized coefficients, which are partially adjusted by the quotient of the standard deviation of an independent variable, and the standard deviation of a dependent variable. Basically, beta coefficients report the relative importance of each independent variables. The standardization is typically done after running a multiple regression model, since it aids the researcher to identify which independent variables have the greater effect. Therefore, the standardization procedure is specially useful when variables are measured in different units.

References

Baguley, T. (2009) Standardized or simple effect size: What should be reported?. British Journal of Psychology, 100(3), 603-617.

Dallal, J. Which Predictors Are More Important? http://www.jerrydallal.com/LHSP/importnt.htm

Gelman, A., & Hill, J. (2006) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.

Examples

Run this code

data(galton) #get some data

#fit a linear regression (OLS) model.

(model <- lm(child ~ parent, data = galton) )

#finally, apply the standardization onto model's coefficients.

beta.coef(model)

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