Are your vectors correlated?

Jensen’s method of correlated vectors is explained on page 143 of “The g factor” and also in a full Appendix B on page 589.

In an imprecise way, I always though of this as a way of estimating whether a correlation between an intelligence score and some outcome variable could be considered to show a relationship between that variable and g.

Jensen said: “If the degree to which each of the various tests is loaded on g significantly predicts the relative magnitudes of the various tests’ correlations with the external variable X, it is concluded that variable X is related to g (independently of whether or not it is related to other factors or test specificity).”

I remember reading this twice, and then nodding to myself that it seemed reasonable and wondering how one tested the match for significance. In fact,Jensen goes into this in his explanation in Appendix B, and gives his own cautionary notes.

Now along comes our Grand Visualizer, Emil Kirekgaard, to give a working example, and to warn that the Jensen method has some limitations, and can be perturbed in a repeated measures design.

http://emilkirkegaard.dk/understanding_statistics/?app=Jensens_method

Lots of other visualizations on Emil’s site, including one for the Dunning-Kruger effect.