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[ODP] Immigrant GPA in Danish primary school is predictable from country-level variab

#51
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.

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".
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#52
Thanks for taking the time to review again.

Quote: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.

You are right. I have changed it to GCA. I have also changed the "general intelligence" to "general cognitive ability", and added some more keywords.

Quote: 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.

I have fixed the GI to GPA. I have added the minus signs to the table.

Quote: 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.

Calling something an independent/predictor has to do with what role it is used in. Correlations are the same as regressions when the variables are standardized. I will keep the wording.

With "predictor analysis" I meant the ones where I tried to predict the GPA differences, as opposed to the, i.e. those in section two (title = "Predictive analyses"). I should of course have written that, so I have changed it now.

Quote: 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.

You are right. I have removed the "decrease".

Quote: 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.

Sure, but with small datasets such as these, I'm more interested in the general tendencies. Outlier analysis is dangerous when datasets are small.

Quote: seem with an "s".

Not sure what you mean. The "seem" is correct without an 's' here.

---

I have uploaded a new draft. I will give MH a chance to respond to this altho I have 3 approvals now.

I made some changes to formulations here and there and added two more references (concerning language bias in testing).

Version 9: https://osf.io/p9d5z/
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#53
(2015-May-28, 10:09:59)Emil Wrote: I will give MH a chance to respond to this altho I have 3 approvals now.


There is no need for this. If I approved, it's because whether or not these objections were addressed, that won't change my mind. And I don't want to argue indefinitely about these points.
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#54
(2015-May-28, 08:32:30)Meng Hu Wrote: 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.


That strikes me as being a non-trivial error. I didn't catch it because I usually don't check the tables. Could the author double check all of his numbers? Thanks.
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#55
The error is trivial. It just has to do with whether or not I reverse coded the numbers.
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#56
(2015-May-28, 23:25:35)Emil Wrote: The error is trivial. It just has to do with whether or not I reverse coded the numbers.


If you are satisfied with the paper, then publish it at your convenience.
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#57
I have uploaded a publication version (no longer marked as a draft). https://osf.io/p9d5z/

I will publish it later today.
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#58
Paper published.

http://openpsych.net/ODP/2015/06/immigra...variables/

Moving thread...
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