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Canadian IQ on WAIS IV

#31
(2014-Aug-18, 12:35:54)Duxide Wrote:
(2014-Aug-18, 12:33:12)Barleymow Wrote: It was made quite clear to me that being sent that information was a kind of favour. They're not prepared to give out information that they haven't published. Accordingly, I have made the small changes which Emil said he would be satisfied with. Indeed, they were there in the first place but removed by my co-author in the edit.


Can you also state that the higher Canadian vs US IQ is probably due to more blacks living in the US than in Canada?


I thought I kind of said that. I can make it more explicit if you like.
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#32
I converted the data to a useful file. See here.

I found some of the g-loadings for the tests in this paper: http://www.plosone.org/article/info:doi/...ne.0074980

Then I used MCV to calculate the Jensen effect. It is 0.83. P is 1e-04. This probably isn't a fluke. Scatterplot attached.

Where do you want to go from here? Presumably you want to include this analysis. It is a good finding. Jensen effects between two white populations.

The R code used to do this analysis is:
Code:
library(car)

CANUS.d = c(0.24,0.23,0.31,0.2,0.26,0.29,0.11,0.22,0.14,0.17,0.23,0.21,0.21,0.06,0.03) #input d data
CANUS.g = c(0.6474,0.7044,0.6859,0.7158,0.6796,0.7704,0.4853,0.6571,0.6487,0.5367,0.6656,0.778,0.6985,0.3472,0.4562) #input g loadings

rcorr(CANUS.d,CANUS.g) #correlation results

DF = as.data.frame(cbind(CANUS.d,CANUS.g))
scatterplot(CANUS.d ~ CANUS.g, DF,smoother = F)



Attached Files Thumbnail(s)
   
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#33
(2014-Aug-18, 13:49:56)Emil Wrote: I converted the data to a useful file. See here.

I found some of the g-loadings for the tests in this paper: http://www.plosone.org/article/info:doi/...ne.0074980

Then I used MCV to calculate the Jensen effect. It is 0.83. P is 1e-04. This probably isn't a fluke. Scatterplot attached.

Where do you want to go from here? Presumably you want to include this analysis. It is a good finding. Jensen effects between two white populations.

The R code used to do this analysis is:
Code:
library(car)

CANUS.d = c(0.24,0.23,0.31,0.2,0.26,0.29,0.11,0.22,0.14,0.17,0.23,0.21,0.21,0.06,0.03) #input d data
CANUS.g = c(0.6474,0.7044,0.6859,0.7158,0.6796,0.7704,0.4853,0.6571,0.6487,0.5367,0.6656,0.778,0.6985,0.3472,0.4562) #input g loadings

rcorr(CANUS.d,CANUS.g) #correlation results

DF = as.data.frame(cbind(CANUS.d,CANUS.g))
scatterplot(CANUS.d ~ CANUS.g, DF,smoother = F)



Oh, I see. I thought you'd found this on the internet! I will rewrite again and include this
 Reply
#34
(2014-Aug-18, 13:38:12)Barleymow Wrote:
(2014-Aug-18, 12:35:54)Duxide Wrote:
(2014-Aug-18, 12:33:12)Barleymow Wrote: It was made quite clear to me that being sent that information was a kind of favour. They're not prepared to give out information that they haven't published. Accordingly, I have made the small changes which Emil said he would be satisfied with. Indeed, they were there in the first place but removed by my co-author in the edit.


Can you also state that the higher Canadian vs US IQ is probably due to more blacks living in the US than in Canada?


I thought I kind of said that. I can make it more explicit if you like.


You wrote "This reduction is expected based on racial composition data because Northeast Asians have a significantly higher average IQ than African Americans or Hispanics". But you didn't say that the US has more Hispanics and African Americans (you left this implicit). It should be made explicit with figures from census data. After that I'll approve.
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#35
(2014-Aug-18, 14:03:07)Barleymow Wrote:
(2014-Aug-18, 13:49:56)Emil Wrote: I converted the data to a useful file. See here.

I found some of the g-loadings for the tests in this paper: http://www.plosone.org/article/info:doi/...ne.0074980

Then I used MCV to calculate the Jensen effect. It is 0.83. P is 1e-04. This probably isn't a fluke. Scatterplot attached.

Where do you want to go from here? Presumably you want to include this analysis. It is a good finding. Jensen effects between two white populations.

The R code used to do this analysis is:
Code:
library(car)

CANUS.d = c(0.24,0.23,0.31,0.2,0.26,0.29,0.11,0.22,0.14,0.17,0.23,0.21,0.21,0.06,0.03) #input d data
CANUS.g = c(0.6474,0.7044,0.6859,0.7158,0.6796,0.7704,0.4853,0.6571,0.6487,0.5367,0.6656,0.778,0.6985,0.3472,0.4562) #input g loadings

rcorr(CANUS.d,CANUS.g) #correlation results

DF = as.data.frame(cbind(CANUS.d,CANUS.g))
scatterplot(CANUS.d ~ CANUS.g, DF,smoother = F)



Oh, I see. I thought you'd found this on the internet! I will rewrite again and include this


I understand this as meaning, correct me if i'm wrong, that the IQ of white Canadians is genuinely likely to be higher than that of white Americans. Is this so?
 Reply
#36
(2014-Aug-18, 14:19:12)Barleymow Wrote:
(2014-Aug-18, 14:03:07)Barleymow Wrote:
(2014-Aug-18, 13:49:56)Emil Wrote: I converted the data to a useful file. See here.

I found some of the g-loadings for the tests in this paper: http://www.plosone.org/article/info:doi/...ne.0074980

Then I used MCV to calculate the Jensen effect. It is 0.83. P is 1e-04. This probably isn't a fluke. Scatterplot attached.

Where do you want to go from here? Presumably you want to include this analysis. It is a good finding. Jensen effects between two white populations.

The R code used to do this analysis is:
Code:
library(car)

CANUS.d = c(0.24,0.23,0.31,0.2,0.26,0.29,0.11,0.22,0.14,0.17,0.23,0.21,0.21,0.06,0.03) #input d data
CANUS.g = c(0.6474,0.7044,0.6859,0.7158,0.6796,0.7704,0.4853,0.6571,0.6487,0.5367,0.6656,0.778,0.6985,0.3472,0.4562) #input g loadings

rcorr(CANUS.d,CANUS.g) #correlation results

DF = as.data.frame(cbind(CANUS.d,CANUS.g))
scatterplot(CANUS.d ~ CANUS.g, DF,smoother = F)



Oh, I see. I thought you'd found this on the internet! I will rewrite again and include this


I understand this as meaning, correct me if i'm wrong, that the IQ of white Canadians is genuinely likely to be higher than that of white Americans. Is this so?


Not really, it doesn't tell you anything about the racial origin of the IQ difference. It just says that this difference is based on g.
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#37
It indicates that the IQ difference is on g, not on the non-g variance (well, most of it is on g). This may be the first time I've seen a white-white country-country comparison used for a Jensen analysis.
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#38
(2014-Aug-18, 14:24:34)Emil Wrote: It indicates that the IQ difference is on g, not on the non-g variance (well, most of it is on g). This may be the first time I've seen a white-white country-country comparison used for a Jensen analysis.


92% white and 8% 'Asian.'
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#39
I submit yet another version. I have referenced the data file.


Attached Files
.doc   Canada IQ WAIS 1V.doc (Size: 91.5 KB / Downloads: 571)
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#40
There is no attached datafile. You should write something like "See supplementary material". Furthermore, you should probably add the scatterplot. Adding the scatterplot saves you from having to add the number of subtests (N=15) used for the MCV analysis. The other reason to add the scatterplot, is that a visual inspection of the data is important given that the data may be clearly non-linear. Correlations are the strength of the linear relationship.

Finally, there is no credit to the reviewer who actually suggested and did the analysis.

Otherwise, it is fine.
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