I have found a group difference (3 groups) for my dependent variable and now I am supposed to find out if possible covariates influence this finding.
What is the best way to "correct" for 5 different covariates (age, duration of illness, medication, symptom score, gender)?
Shall I run 5 separate ANCOVAs (1 for each covariate) and see if the group difference remains significant or should I better run one single ANCOVA including all covariates?
Best Answer
Welcome to the site
This is a question about model selection (see the tag with that label) but not completely.
The answer to your question depends on what you want to find out; it also depends on some things about the data.
If you add one variable at a time, you get to see how each variable affects the group differences, but you are then not controlling for other variables. If you run one model with all the variables, you have controlled for them all. However, when you have multiple variables you may run into a) Over fitting (if you don't have a large sample) b) Collinearity (if independent variables are strongly related to each other). In your case, I'd guess there might be colinearity among duration, medication and symptom variables.
However, if those issues are not problems, my inclination is to include all the variables in one model.