I am working on a problem where i want to see if a measure (test) is a good predictor of the outcome variable (performance). Performance is a bounded variable between 0-100. I am only thinking of the methodology for now and not working with the data yet.
I am aware that there are different models and methods that deal with bounded dependent variables, but from my understanding these are useful if one is interested in predictions?
I am interested in how much variance of the dependent variable (performance)is explained by my measure (test). I am not interested in predicting specific outcomes.
Is it OK to just use normal regression? Do i need to account for the bounded dependent variable somehow?
You can scale your dependent variable in the [0, 1] interval and run a logistic regression, that shrinks every input value into that range.
If you can, you can use fractional logit models, typically used to predict continuous outputs in the [0, 1] interval.
Alternatively, if you are into Machine Learning, you can implement a Neural Network regressor with one output note with a sigmoid activation function.