I only skimmed enough of the article for it to convince me of the opposite of what its author intended.
He gives the example of an exercise where he discovered the “g for cars” as being the size of the car, and that all the measurements were correlated with size, mostly positively. Well, gee, size is very much like g for cars. Big cars are bigger than smaller cars, and have more capabilities but need more fuel. Kind of like big brains are smarter than small brains, but need more fuel (across species).
He is saying, “g is just this artifact of having a bunch of intelligence tests that are all positively correlated”. It’s really not an argument at all; it’s an attempt to imbue the semantics of factor analysis with negative connotations. Why are all those intelligence tests positively correlated in the first place?
For random data points in n dimensions, what fraction of their variance would we expect to be accounted for by the first axis found in factor analysis? That’s the real question. But instead of looking at random correlation matrices, he looks at “random” matrices for factors that are all positively correlated! What made them all positively correlated with each other? The chance of even a single pair being positively correlated are only 50%!
This is one of those puzzling cases where the author has a deep understanding of what he is talking about, and yet what he says … is hopeless, so far from being relevant that it’s hard to believe it isn’t deliberate deception.
This is one of those puzzling cases where the author has a deep understanding of what he is talking about, and yet what he says … is hopeless, so far from being relevant that it’s hard to believe it isn’t deliberate deception.
This is actually relatively common, the word for this is “rationalization”.
I only skimmed enough of the article for it to convince me of the opposite of what its author intended.
He gives the example of an exercise where he discovered the “g for cars” as being the size of the car, and that all the measurements were correlated with size, mostly positively. Well, gee, size is very much like g for cars. Big cars are bigger than smaller cars, and have more capabilities but need more fuel. Kind of like big brains are smarter than small brains, but need more fuel (across species).
He is saying, “g is just this artifact of having a bunch of intelligence tests that are all positively correlated”. It’s really not an argument at all; it’s an attempt to imbue the semantics of factor analysis with negative connotations. Why are all those intelligence tests positively correlated in the first place?
For random data points in n dimensions, what fraction of their variance would we expect to be accounted for by the first axis found in factor analysis? That’s the real question. But instead of looking at random correlation matrices, he looks at “random” matrices for factors that are all positively correlated! What made them all positively correlated with each other? The chance of even a single pair being positively correlated are only 50%!
This is one of those puzzling cases where the author has a deep understanding of what he is talking about, and yet what he says … is hopeless, so far from being relevant that it’s hard to believe it isn’t deliberate deception.
This is actually relatively common, the word for this is “rationalization”.