Covariance is one keyword. If the data is linear but not maximal dimensional, then you get covariance. This is to be expected in situations like this, where you convert a scale to a bunch of booleans. ETA: and even if one did not expect adjacent values to be correlated, that the total number of ratings is about the same is a reduction of dimension.

But if the data is not linear, many more things can go wrong. I don't know names for them.

Matt Simpson: I suppose that could solve the problem of covariance, but that's not what I'm talking about.

It would be inte... (read more)