# 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 `independentVariable`

and `secondIndependentVariable`

, and a continuous dependent variable called `dependentVariable`

.

## SPSS

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.
```

## R

To standardize the variables, use `scale`

:

```
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);
```