Great if someone could help...

- Thread starter zxc
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Great if someone could help...

If you have moderate multicollinearity, you don't really have a problem. You just interpret the odds ratio of the interesting variable as the effect of that variable with the effect of the control removed (just what you're looking for).

A correlation between two IVs has to be really high (depending on sample size) before multicollinearity becomes severe enough to cause instability in the regression coefficients. If you're concerned about severe multicollinearity, there are better ways to check it than a correlation. I would start with running condition indices and running the model on split sampes (if your n is big enough).

Karen

I hope I haven't mis-understood anything...

ok, then this is your call if you want to do this, but if the interaction is significant i suggest you do not remove it.

Certainly by not including an interaction term, you are making an assumption that there is not an interaction. But that is no different with two categorical predictors than it is with any two predictors. Any multiple regression, for example, without any interactions is making that assumption. You could argue that all interactions should be tested, but you could end up with a mess.

If the interaction is not of interest, and there's no reason to suspect there might be one, I'm not so sure it needs to be tested.

I know SPSS puts in interactions between two categorical variables by default (SAS doesn't). But theoretically, this isn't a special situation.

Karen

You can check for an interaction. But if you are not hypothesizing one, you are not obligated to. An interaction will not test the research question you are asking.

Karen