The problem is that achievement tests and grades are a measure of IQ. Imagine I have a direct relationship, y = g. Now I measure compute a noisy data set, (x,y

The problem is that achievement tests and grades are a measure of IQ. Imagine I have a direct relationship, y = g. Now I measure compute a noisy data set, (x,y) with x = g + noise. Depending on the size of the noise I'll discover that y is correlated with g with some R^2. My regression coefficient will come out to be approximately y = 1x. Now imagine I made a new noisy data set, (x1, x2, x3, y). Here x1 = g + noise, x2 = g + noise, x3 = g + noise. Here x1 might be IQ, x2 might be SAT or other such g-loaded test, x3 grades, etc. My new regression coefficients are y = (1/3)x1 + (1/3)x2 + (1/3)x3. Suddenly x1 has lost 2/3 of it's predictive power! The problem is that my measurements are correlated, that's all. And as you note, table 2 shows a pretty solid correlation between IQ, personality, achievement and grades. If you actually want to draw the conclusion that the article is trying to draw, you need to decouple IQ from the other measurements.