Order affects Regression Parameter Estimates in SPSS GLM

Stage 2I just discovered something in SPSS GLM that I never knew.

When you have an interaction in the model, the order you put terms into the Model statement affects which parameters SPSS gives you.

The default in SPSS is to automatically create interaction terms among all the categorical predictors.  But if you want fewer than all those interactions, or if you want to put in an interaction involving a continuous variable, you need to choose Model–>Custom Model.

In the specific example of an interaction between a categorical and continuous variable, to interpret this interaction you need to output Regression Coefficients. Do this by choosing  Options–>Regression Parameter Estimates.

If you put the main effects into the model first, followed by interactions, you will find the usual output–the regression coefficients (column B) for the continuous variable is the slope for the reference group.  The coefficients for the interactions in the other categories tell you the difference between the slope for that category and the slope for the reference group.  The coefficient for the reference group here in the interaction is 0.

What I was surprised to find is that if the interactions are put into the model first, you don’t get that.

Instead, the coefficients for the interaction of each category is the actual slope for that group, NOT the difference.

This is actually quite useful–it can save a bit of calculating and now you have a p-value for whether each slope is different from 0.  However, it also means you have to be cautious and make sure you realize what each parameter estimate is actually estimating.


Interpreting Linear Regression Coefficients: A Walk Through Output
Learn the approach for understanding coefficients in that regression as we walk through output of a model that includes numerical and categorical predictors and an interaction.

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