Moderation with two continous predictors
Moderation means that the causal association between two variables is itself influenced by a third variable. It is tested by analysing the interaction of the supposed predictor with the supposed moderator in their effects on the dependent variable. In this example, a dataset/dataframe called
dat contains three variables, two continuous predictor called
secondIndependentVariable, and a continuous dependent variable called
Analysing an interaction in SPSS first requires creating a new variable consisting of the product of the two interacting variables (also see the section on transformation). Here this will be called
interactionTerm. Note that this often introduces collinearity, which can be ameliorated by standardizing the predictors first (also see the section on standardizing).
DESCRIPTIVES VARIABLES = independentVariable secondIndependentVariable /SAVE. COMPUTE interactionTerm = ZindependentVariable * ZsecondIndependentVariable.
The regression can then be conducted:
REGRESSION /DEPENDENT dependentVariable /METHOD ENTER independentVariable secondIndependentVariable interactionTerm /STATISTICS COEF CI(95) R ANOVA.
To standardize the variables, use
dat$independentVariable_standardized <- scale(dat$independentVariable); dat$secondIndependentVariable_standardized <- scale(dat$secondIndependentVariable);
R creates the interaction term automatically:
regr(dependentVariable ~ independentVariable_standardized * secondIndependentVariable_standardized, data=dat);
To also order a plot:
regr(dependentVariable ~ independentVariable_standardized * secondIndependentVariable_standardized, data=dat, plot=TRUE);