2015-May-28, 08:32:30

I read the new version (v8) and I don't disagree with anything in the entire text. I re-approve. But even if I disagreed with any of the modified portions, due to reviews, I don't think I will disapprove because it means I have to discuss the matter with the reviewer(s) in question. I don't think it's reasonable to go so far (and it's very complicated for obvious reasons I don't need to tell). For such modifications (and only for this kind), then, the authors don't need my re-approval.

In fact, I only have some quibbles :

The use of GI (General Intelligence) while most authors would have written GCA (General Cognitive Abilities). They mean just the same thing. Why not using GCA ? Because using another, new term is not very practical (and in fact it's very irritating in my opinion) to use different terms for saying just the same thing. Say GCA and everyone in the field understands what you say. But say GI, and no one could answer that without asking the meaning of it.

1) The second GI should be GPA. 2) It can even if more than one variable affects GPA, since a variable that would have decreased the correlation IQ-GPA could have been masked (offset) by another variable that would have increased the correlation IQ-GPA.

And in page 6, you wrote "The GPA gaps were -1.7 and -1.3, -.50 and -.38 d respectively" while in your table 4 the gaps are 1.7, 1.3, -0.50 and -0.38.

It's overoptimistic. I don't call a difference of 0.8 "even a bit larger". There's no change. That's all.

Tables 2 & 5 : same problem as before. Predictor should be used for indepedent vars inserted into the regression equation in a regression analysis, as its definition implies. There is no "predictor" in a correlation analysis because both variables are treated the same. And at page 7, you wrote "predictor analyses". I know what is a regression analysis and a correlation analysis but I doubt anyone knows what is a predictor analysis.

In table 2 by the way, some numbers have 2 digits after comma, some have 1 digit, and another has zero digit. I think it's preferable to have all numbers with the same number of digit. Even for the zero correlation.

The way you write it is confusing because it implies that improved environment can either increase or decrease IQ. If a good environment has an effect on IQ, it's through IQ gain.

But outliers are sometimes useful. The more you have outliers, and the more likely you would have missed an important information, i.e., a confounding variable. And even a single outlier can be of some interest, sometimes, if you know/understand the reason for this behavior.

seem with an "s".

In fact, I only have some quibbles :

The use of GI (General Intelligence) while most authors would have written GCA (General Cognitive Abilities). They mean just the same thing. Why not using GCA ? Because using another, new term is not very practical (and in fact it's very irritating in my opinion) to use different terms for saying just the same thing. Say GCA and everyone in the field understands what you say. But say GI, and no one could answer that without asking the meaning of it.

Quote:Since GI is not the only factor that causes differences in GI, one would not expect a GI difference of 1.0 d to be associated with a 1.0 d difference in GPA.

1) The second GI should be GPA. 2) It can even if more than one variable affects GPA, since a variable that would have decreased the correlation IQ-GPA could have been masked (offset) by another variable that would have increased the correlation IQ-GPA.

And in page 6, you wrote "The GPA gaps were -1.7 and -1.3, -.50 and -.38 d respectively" while in your table 4 the gaps are 1.7, 1.3, -0.50 and -0.38.

Quote:The value for the second generation is smaller because this group does better in school while the GI gap is very similar, even a bit larger.

It's overoptimistic. I don't call a difference of 0.8 "even a bit larger". There's no change. That's all.

Tables 2 & 5 : same problem as before. Predictor should be used for indepedent vars inserted into the regression equation in a regression analysis, as its definition implies. There is no "predictor" in a correlation analysis because both variables are treated the same. And at page 7, you wrote "predictor analyses". I know what is a regression analysis and a correlation analysis but I doubt anyone knows what is a predictor analysis.

In table 2 by the way, some numbers have 2 digits after comma, some have 1 digit, and another has zero digit. I think it's preferable to have all numbers with the same number of digit. Even for the zero correlation.

Quote:or that the particular group has increased/decreased its GI in Denmark due to improved environment.

The way you write it is confusing because it implies that improved environment can either increase or decrease IQ. If a good environment has an effect on IQ, it's through IQ gain.

Quote:I examined this for all countries in both datasets and found the median value (to avoid effects of outliers).

But outliers are sometimes useful. The more you have outliers, and the more likely you would have missed an important information, i.e., a confounding variable. And even a single outlier can be of some interest, sometimes, if you know/understand the reason for this behavior.

Quote:thereby reducing the GPA gap in normal schools and making it seem smaller than it really is for the

seem with an "s".