Cosma Shalizi (2007): g, a Statistical Myth: "Factor analysis is handy for summarizing data, but can't tell us where the correlations came from; it always says that there is a general factor whenever there are only positive correlations. The appearance of g is a trivial reflection of that correlation structure. A clear example, known since 1916, shows that factor analysis can give the appearance of a general factor when there are actually many thousands of completely independent and equally strong causes at work...

...I'm going to show you some cases where you can see that the data don't have a single dominant cause, because I made them up randomly, but they nonetheless give that appearance when viewed through the lens of factor analysis.... It is not an automatic consequence of the algebra that the apparent general factor describes a lot of the variance in the scores. Nonetheless, while less trivial, it is still trivial. Recall that factor analysis works only with the correlations among the measured variables. If I take an arbitrary set of positive correlations, provided there are not too many variables and the individual correlations are not too weak, then the apparent general factor will, typically, seem to describe a large chunk of the variance in the individual scores. To support that statement, I want to show you some evidence from what happens with random, artificial patterns of correlation, where we know where the data came from (my computer), and can repeat the experiment many times to see what is, indeed, typical....

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...

#shouldread