Intelligence, Assumptions, and the g Conundrum

Cosma Shalizi at Three-Toed Sloth has written a long (but well worth the read) article entitled g, A Statistical Myth.

This article really elucidates a lot of the issues I have with the usual attempts to quantify “intelligence” and explain what causes it.

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Complete entry

Posted by debunker  on  06/02  at  12:40 AM

That piece you linked by Cosma Shalizi 'g is a statistical myth' is incorrect.

The main thrusts of his argument is that test data do not statistically support a g-factor. Gould tried to discredit g but his argument argument was statistically incompetent (for a statistican's critique see Measuring intelligence: facts and fallacies by David J. Bartholomew, 2004). Shalizi's criticism is incredibly sophisticated, but likewise incorrect. In a nutshell, Shalizi is trying to argue around the positive correlations between test batteries. If those correlations didn't exist, his argument would be meaningful. However, these intercorrelations are one of the best documented patterns in the social sciences.

Cosma Shalizi misrepresented Spearman and his two factor model. The author tried to present Spearman as ignorant of group factors (he should have called them out as such or noted that they are from the second stratum). The fact is that Spearman gave up on the two factor model and accepted group factors. The fact beyond that is that the predictive validity of group factors typically appears in the range from (and including) zero to about 4%. In other words, the two factor model is not rigorously correct, but it captures virtually all of the practical validity of any test.

For a discussion of neurological correlates with g see this discussion by Neurologist Paul Thompson:

www.loni.ucla.edu/~thompson/PDF/nrn0604-GrayThompson.pdf

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