I am aware that there are special models that deal with bounded variables (binomial, logistic, beta regression) and all have different benefits and are used for different data types.
However, I read on the SAS website that if most of my bounded data are within the range between 0.3 - 0.7, classical multiple linear regression can be applied.
Proportion data typically exhibit a sigmoidal, or S-shaped, curve with asymptotes at the limits of zero and one when plotted against a predictor. In general, ordinary regression does not capture this relationship, but it might be reasonable if all of the data fall in the middle of the range (between about 0.3 and 0.7) where the curve is approximately linear. However, there are other approaches that can be used.
I found this argument on numerous websites, but cannot find it in any literature: here, here
Any literature that supports this argument?
