I am trying to interpret the SPSS output from a multiple hierarchical regression where the intercept has been eliminated because it is not significant.
I have read previous discussions about inclusion/exclusion of the intercept in this forum and I have seen that the majority of the answers were against the exclusion of the constant from the model, unless we are certain that the intercept is zero.
However, I do not know how I could be sure about that.
All I see is that the intercept is not significant and Eisenhauer (2003)$^{[1]}$ suggests to consider the intercept p value and the model standard error in order to decide whether to include the intercept. Basing on his recommendations I have excluded the intercept.
Now, I see that the R square and multicollinearity indices in this case do not have the same meaning that they have in a model that includes the constant, but what I wonder is: do the standardised and unstandardised beta coefficients have the same meaning of a model that include the intercept or not?
[1] Eisenhauer, J. G. (2003), Regression through the Origin. Teaching Statistics, 25: 76–80.
Best Answer
The only situations where you might consider excluding the constant are models where it is trivially true that the constant has to be 0. The fact that you looked at the significance level is in itself sufficient evidence that it is not trivially true that the constant is 0. So I would leave it in.