Andrew Sullivan links briefly to the post below on whether g is a statistical myth, describing it as another expression of the “conventional left-liberal view,” and defending again his decision as editor of The New Republic to publish extracts from The Bell Curve. I would have much preferred to have seen a substantive response to the essay by Cosma Shalizi that the post linked to and summarized. I don’t see anything in Cosma’s essay that requires subscription to left-liberal views, conventional or otherwise – instead, I see a (to me entirely convincing) methodological critique of the basis for statistical claims that g, the purported general factor for intelligence, exists. To quote Cosma again:
If, after looking at your watch, you say that it’s 12 o’clock, and I point out that your watch has stopped at 12, I am not saying that it’s not 12 o’clock, just that your watch doesn’t actually give you any evidence about the time. Similarly, pointing out that factor analysis and related techniques are unreliable guides to causal structure does not establish the non-existence of a one-dimensional latent variable driving the success of almost all human mental performance. It’s possible that there is such a thing. But the major supposed evidence for it is irrelevant, and it accords very badly with what we actually know about the functioning of the brain and the mind.
If Andrew would like to take issue with something, these are the claims that he needs to be taking issue with. And there’s nothing stopping him, if he has even a moderate grasp of statistical reasoning (Shalizi’s arguments are quite comprehensible to someone with a basic minimum of statistical training, as evidenced by the fact that a gawp like me can reasonably claim to understand them). What Cosma is saying is that the entire body of research on g is demonstrably based on bad statistical reasoning. Nor is it only Cosma who says this. Nor is this a product of political druthers – it clearly flows from a set of methodological claims that are widely accepted among statisticians, and that have many applications outside this particular and highly heated debate. If Andrew wants to show how Cosma’s methodological critique is fundamentally flawed in some way because of left-liberal preconceptions, he really should do so. If not, then all of his claims about “conventional left-liberal view”s and “going to challenge many assumptions of right-thinking liberalism” are by-the-by – they don’t count for anything unless they are actually backed up by, like, methodologically sound science.