I am completely in over my head with logistic regression at the moment, so what follows is probably very basic and silly questions. But I would appreciate it hugely if anyone took the time to respond nevertheless!
I will be conducting a cross-sectional analysis involving data from a large cohort study. My goal is to look at the impact of one categorical (binary) predictor variable on five different categorical outcome variables, all of which can be considered different proxy measures for the same concept (hence why I am looking at all five, as no direct measure was collected in this data set – in essence, I am trying to corroborate my results by looking at the impact of the predictor variable on five different outcome variables supposedly measuring of the same/related concept(s)).
As mentioned above, all of these variables are categorical. My outcome variables have different numbers of categories, ranging from 2 to 4. The 3- and 4- category variables are ordinal. All of these variables (both outcome and predictor) are basically items on self-report questionnaires.
Does this sound way over-complicated and mad? If this sounds like a terrible plan, I could potentially get rid of some of the outcome variables and just use fewer ones instead, although ideally I would use all five. I am currently completely lost with regards to how to analyse this. The only way I can think of would be to run separate logistic regressions for all of these outcome variables.
Any advice on how to analyse data like this would be hugely appreciated!
Best Answer
If the 5 outcome variables are meant to measure the same latent concept, the questions you have to ask is:
From a research perspective, are you interested in assessing the impact of the predictor variable on this latent concept (which is measured indirectly via the 5 outcome variables)? Or are you interested in assessing the impact of the predictor variable separately on each of the five outcome variables?
How you analyze your data will depend on the answer to these questions.