Get the latest tech news

G, a Statistical Myth (2007)

Attention conservation notice: About 11,000 words on the triviality of finding that positively correlated variables are all correlated with a linear combination of each other, and why this becomes no more profound when the variables are scores on intelligence tests. Unlikely to change the opinion of anyone who's read enough about the area to have one, but also unlikely to give enough information about the underlying statistical techniques to clarify them to novices.

: It's worth noting a subtle point here: even if the two-factor theory is true, g cannot, under any circumstances, be calculated directly from observed test scores, so the idea that it gives us an operational and objective definition of general intelligence is, in a word, wrong. [4] I recommend Loehlin's Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis to students who need to know more, since it's clear, practical, decent on the strengths and limitations of the methods, sound on the need for statistical power, and written in an actual human voice. Thomson's original paper ("A Hierarchy without a General Factor", British Journal of Psychology 8(1916): 271--281), reporting results he obtained in 1914, does not seem to be available electronically, but a follow-up ("On the Cause of Hierarchical Order among the Correlation Coefficients of a Number of Variates Taken in Pairs", Proceedings of the Royal Society of London A 95(1919): 400--408) is in JSTOR, and worth reading.

Get the Android app