Quote:1) "correlation of subtests' g-loading"

g-loadingS

Fixed.

Quote:2) "Dutch students"

Specify, "university students"

Fixed.

Quote:3) "Perhaps this is because it is a student dataset with an above average level of g. According to the ability differentiation hypothesis, the higher the level of g, the weaker the g factor."

If the university students are selected based on g, there's also range restriction which reduces g variance.

Added: "Alternatively, one may think of it as range restriction of g, so that it is relatively smaller compared to the other sources of variance in the cognitive data."

Quote: 4) "A conceptually similar measure is the g minus unit-mean metric (g advantage). This value is positive when the person has his highest scores on the more g-loaded subtests, and lower than the opposite is the case."

Rephrase the "lower than the opposite is the case." Also, with an increasing number of tests, the correlation between equally weighted and g-weighted scores approaches 1 because only the g variance tends to cumulate into composite scores regardless of weights used. See p. 103 in Jensen's g factor book. Accordingly, the correlation between g scores and unweighted scores in your data is 0.99, and the g advantage has no validity independently of g scores.

Changed to "This value is positive when the person has his highest scores on the more g-loaded subtests, and lower

when the opposite is the case."

I agree regarding the comment. The g factor scores had no incremental validity above unit-weighted scores in accordance with statistical theory.

Quote:5) "They do not seem to have any unique predictive power for GPA beyond their association with g. Multiple regression gave a similar result (results not shown)."

What's the point of using MR here? It's superfluous with the partial correlation.

I have found that sometimes MR and partial correlations give markedly different results. For this reason I often test both to make sure it isn't some strange statistical fuck-up.

Quote: 6) "Verbal analogies has a p value of .04 (N=289, two-tailed)"

The other p-values were >0.05, right? You should mention that.

Yes. Changed to "All the partial correlations were weak. Three were in the wrong direction. Only verbal analogies has a p-value below .05 (.04) (N=289, two-tailed) but since I tested 7 subtests and there is no adjustment made for multiple comparisons, it may be a fluke."

Quote: 7) "I ran the partial correlations with GPA and g partialled out"

Rephrase. You ran correlations between GPA and subtests, with g scores partialled out.

Changed to: "Do the Jensen coefficient and g adv. explain unique parts of the variance of GPA? To test this, I ran the partial correlations between both measures and GPA controlling for g scores."

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New revision can be found at OSF.

https://osf.io/gb3cy/ Revision #7, dated 24th Oct.

Additionally, I reran all the analyses from my laptop. All analyses produced the same results as before (analytic reproducibility).