- Thread starter Highhopes!
- Start date

If you have a model that is significant, but no variables that are then Multicolinearity is a likely issue. I think if controls are thought to be theoretically significant then you should include them in your model. I don't understand "...My question is do I have to include or control for age and sex variables as data is already nonparametric, e.g. sample sizes are not same, not normality distributed so age, sex etc are irrelevant anyway"

whether a variable should be in the model has nothing to do with normality (I am not sure why sample size would change).

At this point I like to step back and try to understand what you are working with. What is your sample size? What percentage of the sample has the outcome of interest? How many predictors are you examining and are and categorical with greater than 2 groups?

P.S., What does "Another" reference"? What was the other strange result?

There are different theories of when its legitimate or not to remove a variable and I am a practitioner not a theorist. I would not exclude controls that are statistically significant unless you have a good theoretical reason to do so. If you don't think age is theoretical important you can run it in the model and not in and since one model is nested inside the other you can test if adding the variable helped your prediction (I forget what the test is but you should be able to find it, it might be a deviance test). I would report the results with and without it and comment on what you find.

Your last question depends on why you doing this. As a practitioner I only report what seems useful. If this is for research than your colleagues or journal you are interested in should provide guidance to what to report. I don't find confidence intervals useful, I would report odds ratios and significance tests.