Simple Moderated Regression Model

Description

This function creates an object of class lm() specific to a moderated multiple regression involving 3 variables.

Usage

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moderate.lm(x, z, y, data, mc = FALSE)

Arguments

x

focal explanatory variable

z

moderating variable

y

outcome variable

data

data.frame containing the variables

mc

Logical specifying wheter the data are already mean centered

Details

This model takes x and z and creates the interaction term x*z. If the data are not already mean centered, then x and z are mean centered by subtracting out the means. This is necessary for interpretation and to reduce multicolinearity. The lm() is then computed thusly: Y ~ X + Z + XZ.

Value

An object of class lm(). One can use summary(), coef() or any other function useful to lm(). This model is used by other moderator tools - see below.

Warning

This is a very simplistic model. If x or z are categorical, the results will not be accurate. The function can be modified by the user to deal with complications such as covariates, non-continuous variables, etc.

Author(s)

Thomas D. Fletcher tom.fletcher.mp7e@statefarm.com

References

Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park: Sage Publications.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences, 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates.

See Also

sim.slopes, graph.mod

Examples

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data(tra)
lm.mod1 <- moderate.lm(beliefs, values, attitudes, tra)
summary(lm.mod1)