[ODP] The personal Jensen coefficient does not predict grades beyond its association
Cannot you use the difference between g score and IQ score? (g-IQ)?I guess it's very similar to the MCV but may produce slightly different results.
Correl. between iq-g and gpa should be negative.
If my memory is correct, "unit-weighted average" is just a simple average such as (a+b+c+d)/4. In general, I hate to read these terms. I know a lot of examples where the authors use different names to say the same thing. It's very confusing. By the same token, I'm not sure I will recommend you to use the term "Jensen coefficient". Even I, I don't appreciate "Jensen effect". As I said, adding more and more new terms can be very exhausting for those who read.

However, it's correlation with GPA is weak.

I think it should be "its correlation", no ?

Your syntax may have a problem, because at some point, it's said that the Omega function needs the package GPA rotation, which you didn't have in your list.

More important. Can you explain me this code ? I don't understand it (but I see that DFcompleteZ is the "subset" of the data).

Jensen.coef = as.vector(rep(0, nrow(DF.complete.Z)))for (case in 1:nrow(DF.complete.Z)){ cor = cor(as.numeric(DF.paf\$loadings), as.numeric(DF.complete.Z[case,1:7])) Jensen.coef[case] = cor}
I want to be sure. The Jensen coefficient is calculated as follows : you take each person's z-score on all subtests (7) and the g-loading for these subtests as calculated for the entire group, and you correlate the z-score and g-loading for each person to get the Jensen coefficient. Am I correct ?

I don't see anything wrong in the article, and I want to approve, but I need to be sure about the above question.
It's ok. I approve.

EDIT: it's really optional, but i think the article will gain much by explaining the implication of a positive correlation between Jensen coeff and other variables (g scores, GPA, full IQ, etc) and how it differs with g-loading. Most people do not know what is the Jensen coeff. And it's also new to me.
I approve
New revision (#6) with the above results added in a new section. Nothing else changed.

https://osf.io/gb3cy/

"The personal Jensen coefficient correlates moderately with both g factor scores (.35) and the unit-mean (.23)."

Given SLDR, shouldn't the personal Jensen coefficient negatively correlate with g-scores? Or doesn't SLDR work this way? If it does, then your sample is, on this account, problematic - and you should note this.
g score is higher than full scale IQ score if the Jensen effect (cor with g-loadings) is positive, but g score is lower than full scale IQ if Jensen effect is negative. Now, what SLODR says is that the strength of the (sub)tests intercorrelation is lower at higher IQ levels. I don't remember it says that the correlation with g-loadings becomes negative. But perhaps it's possible that g-loadings' correlation is lower when IQ levels go up.
I don't see why SLDR should predict that. The correlation between personal Jensen coef. and g scores and unit-mean simply shows that the smarter people tend to get their higher scores on the more g-loaded tests. The measure Piffer came up with (g score minus unit-mean) shows the same behavior.

It would depend on how one conceptualized the mechanism behind SLDR. I was imaging that the population level correlations were lower at higher IQs, because on the individual level g was less potent. If this was the case, I would expect an individual level anti-Jensen Effect at higher IQs. I thought Armstrong suggested this. But I agree that this need not be the mechanism.

I like this paper. It was well written, simple and to the point. An interesting idea was explored.

I approve.

2) "Dutch students"

Specify, "university students"

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.

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.

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.

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.

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.

8) As a general point, the g factor is a between-individuals variable whereas your personal Jensen coefficient is a within-individual variable. You cannot easily generalize from individual differences processes to within-person processes, so your entire analysis is a bit suspect. Peter Molenaar has written about this a lot.
"A Jensen coefficient is the correlation between a subtests’ g-loadings and a vector of interest."

"I hypothesized that the personal Jensen coefficient from a subjects’ subtest scores will predict grade point average beyond g."

Rewrite: "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" What is "it" referencing?

Was there no way to correct for subtest reliability? Could you mention that you were unable to do this despite this being standard methodology.