A reviewer of a paper suggested that (Pearson product-moment) correlations cannot be used in input (predictors) in a regression model because they are "not interval levels of measurement" -- does anyone know how to interpret this statement?
Important: It is clear that correlations are not normally distributed, and it is also clear that they are bounded (by +/- 1, obviously) both of which require transforming them before using them in a regression model. Please note that we are not asking what to do with correlations to make them fit a regression model. Nor are we denying that they should not be used in regressions without some transformation. We fully understand the use of Fisher's Z transformation as shown here. Our question here is strictly conceptual.
Explicitly we would like to know what the level of measurement correlations are --- a la nominal, ordinal, interval, ratio; or the expanded range such as found in Chrisman (1998) --- or if the question is meaningless. The question seems somewhat nonsensical to me, but I have been unable to resolve the matter to my own satisfaction.
Chrisman, N. R. (1998). Rethinking Levels of Measurement for Cartography. Cartography and Geographic Information Systems, 25(4), 231–242. (Sorry paywall blocks the link!)