I have multiple logistic regression models with all of the same IVs/controls and a variety of DVs (all health outcomes from the same sample). The primary IV is the sum of types of childhood abuse (emotional, physical or sexual). I made dummy variables that represent any one type of experience, any two types of experiences, or all three types of experiences (so each is mutually exclusive). This is the same type of model the CDC uses for their ACEs study which is where I borrowed the method from.
Question 1: Can I compare the one experience dummy to the two experience dummy within the same model? That is, talk about the odds ratios in comparison to one another without standardizing the coefficients? My sense is yes and I've seen it done all over the place but I recently was given a dissenting opinion saying that since I am only comparing each IV to the dummy referent of 0 experiences, I can't compare them to one another without standardizing first.
Question 2: What's the best method to make comparisons across models (with all the same IVs)? I'm testing the dummy IVs against a variety of physical and mental health outcomes and I'd like to compare the odds ratios for each DV based on any one type of experience, two experiences or three experiences. It would be nice to say, one experience increases the odds of this outcome by 3.2 times, this outcome by 2.1 times, etc. Therefore, I can say that one type of abuse increases the risk of depression more than anxiety disorder or two types of abuse increases the risk of PTSD over depression etc (assuming no overlap in confidence intervals).
I've read Menard's 2011 piece on standardized LR coefficients and that makes sense as to what mechanism to use within a single model (as I would apply in question 1 if necessary), but I can't tell if this can be applied across DV models if I'm using all the same IVs/controls from the same sample. If I standardize each IV coefficient, then are they comparable across models? It's a random sample and each model has the same number of valid cases (1073) with no missing data.