# Moderation with a dichotomous and a continous predictor

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, a continuous predictor called `independentVariable`

, a dichotomous supposed moderator called `dichotomousVariable`

, 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 there is debate on whether the dichotomous predictor should be coded `0`

and `1`

or `-0.5`

and `0.5`

: this influences the interpretation of the resulting coefficients.

```
COMPUTE interactionTerm = dichotomousVariable * independentVariable.
```

The regression can then be conducted:

```
REGRESSION
/DEPENDENT dependentVariable
/METHOD ENTER independentVariable
dichotomousVariable
interactionTerm
/STATISTICS COEF CI(95) R ANOVA.
```

To order a plot:

```
GRAPH
/SCATTERPLOT=independentVariable WITH dependentVariable BY dichotomousVariable.
```

## R

R creates the interaction term automatically:

```
regr(dependentVariable ~ independentVariable * dichotomousVariable,
data=dat);
```

To also order a plot:

```
regr(dependentVariable ~ independentVariable * dichotomousVariable,
data=dat, plot=TRUE);
```