Hi Dutton/Barleymow

Thank you for submitting a paper to our journal.

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Generally the paper needs some improvement but it has publication potential.

These draw upon very large and representative samples and are strongly (around 0.8) correlated with IQ.
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Taking an IQ estimate from many years of PISA data is more reliable. PISA score correlates with IQ score at 0.82 (Rindermann, 2008)
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PISA only correlates at 80% with IQ.

Three claims on the same number, three different numbers, two different formats. Correlations as percentages is nonsense. Worse, it could be confused with the https://en.wikipedia.org/wiki/Coefficient_of_determination (R^2) measure.

Further, I could not find the actual .82 number in the source cited. Some care must be taken here because Lynn and Vanhanen's 2012 numbers (“FINAL-IQ” are based on combined scholastic + IQ data. They cannot be used to measure the correlation between IQ tests and PISA scores.

One has to use the “Measured IQ” column from their Table 2.1 (page 19ff). These are found in my megadataset.

In fact, I ran the analysis for you.

R code is:

read = read.csv("Megadataset_v1.3.csv")

DF = cbind(read["LV2012measuredIQ"],
read["PISA09READ_Native"],
read["PISA12MATH_Native"],
read["PISA12CPS_Native"],
read["PISA00"],
read["PISA03"],
read["PISA06"],
read["PISA09"],
read["PISA12"])

DF.cor = rcorr(as.matrix(DF)) #create correlation matrix with pairwise miss data deleted

I ran the correlations both for natives only and for everybody. Results:

LV2012measuredIQ PISA09READ_Native PISA12MATH_Native PISA12CPS_Native PISA00 PISA03 PISA06 PISA09 PISA12
LV2012measuredIQ 1.00 0.93 0.94 0.89 0.94 0.96 0.95 0.95 0.92
PISA09READ_Native 0.93 1.00 0.93 0.90 0.93 0.94 0.96 0.97 0.94
PISA12MATH_Native 0.94 0.93 1.00 0.92 0.90 0.94 0.96 0.96 0.98
PISA12CPS_Native 0.89 0.90 0.92 1.00 0.86 0.86 0.89 0.92 0.91
PISA00 0.94 0.93 0.90 0.86 1.00 0.95 0.94 0.95 0.94
PISA03 0.96 0.94 0.94 0.86 0.95 1.00 0.99 0.98 0.96
PISA06 0.95 0.96 0.96 0.89 0.94 0.99 1.00 0.98 0.97
PISA09 0.95 0.97 0.96 0.92 0.95 0.98 0.98 1.00 0.98
PISA12 0.92 0.94 0.98 0.91 0.94 0.96 0.97 0.98 1.00

So the PISA x IQ correlations are around .944 (natives+immi.) or .92 (natives only).

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We compared Scandinavia, Finland, and the USA (Wechsler, 2008) on the matrices sub-test. We used this sub-test because it is highly g-loaded and thus gives us the best picture of the population's general intelligence.

Matrix tests are highly g-loaded (or at least used to be), but they are not better measurements of g than is the combined results of multiple subtests. See: Johnson, W., Nijenhuis, J. T., & Bouchard Jr, T. J. (2008). Still just 1< i> g</i>: Consistent results from five test batteries. Intelligence, 36(1), 81-95.

You should use all of them if possible. The best method is to use an extracted g factor from all available subtests based on their loadings

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I note from the tables that in the Scand. data there is a clear correlation between mean age of the subsample and the IQ score.

I have put the datasets here. The corrrelation in the Scand. sample is .73. This indicates either one of the following: 1) selective recruiting, 2) old people in Scand. are relatively smarter, 3) norms are off, 4) a late acting developmental effect, 5) something else.

The same correlation in Finnish sample is .01.

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I note that you didn't use the weighted mean, but the unweighted mean. They give similar results, but you should probably use the weighted mean. It can be found in my calculations.

The two means are the same for FI sample (103.1), but slightly different for SC sample (105.1 vs. 105.3, respectively weighted and unweighted).

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From the tables. I don't understand why -1sd is apparently 7 standard points, but +1sd is 13 standard points. Are the raw data super skewed?

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In this article, we further test Dutton et al.'s theory with...
...
We compared Scandinavia
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However, we do not regard these results as in conflict with Dutton et al. (2014) for two related reasons.

There is only one author, yet you write "we". That sounds silly to me.

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Firstly, it can be seen the samples in each age group are relatively small and accordingly there is room for sampling error. Indeed, precisely this kind of error may explain the oddly low IQ score of Scandinavian 35-39 year-olds (98 on Greenwich norms).

You are arguing in words what should be argued with math. The sample sizes of individual age groups are not so important when we are interested in the total group vs. total group comparisons, which we are.

Use a t test to compare the means. I like R so I want to use R to test this. However, apparently no one wrote a t test that takes in summary statistics instead of raw data. How very silly.

So I found someone who wrote a function.

R code:
#t test from summary stats
# m1, m2: the sample means
# s1, s2: the sample standard deviations
# n1, n2: the same sizes
# m0: the null value for the difference in means to be tested for. Default is 0.
# equal.variance: whether or not to assume equal variance. Default is FALSE.
t.test2 <- function(m1,m2,s1,s2,n1,n2,m0=0,equal.variance=FALSE)
{
if( equal.variance==FALSE )
{
se <- sqrt( (s1^2/n1) + (s2^2/n2) )
# welch-satterthwaite df
df <- ( (s1^2/n1 + s2^2/n2)^2 )/( (s1^2/n1)^2/(n1-1) + (s2^2/n2)^2/(n2-1) )
} else
{
# pooled standard deviation, scaled by the sample sizes
se <- sqrt( (1/n1 + 1/n2) * ((n1-1)*s1^2 + (n2-1)*s2^2)/(n1+n2-2) )
df <- n1+n2-2
}
t <- (m1-m2-m0)/se
dat <- c(m1-m2, se, t, 2*pt(-abs(t),df))
names(dat) <- c("Difference of means", "Std Error", "t", "p-value")
return(dat)
}

#input numbers
mean.fi = 17.96583333
sd.fi = 3.8275
n.fi = 600
mean.sc = 18.15192308
sd.sc = 4.320705128
n.sc = 780

t.test2(mean.fi,mean.sc,sd.fi,sd.sc,n.fi,n.sc)


Result:
Difference of means Std Error t p-value
-0.186 0.220 -0.846 0.398

Difference in raw scores isn't statistically certain. Likely a fluke.

Note that for the above I used the weighted means which reduced the FI sample since the last age group was not used.

Note also that I used the Welch test because the variances seemed unlikely to be identical (3.83 vs. 4.32). The Levene test also does not work for summary statistics so I didn't actually calculate this.

In short, the difference between the samples is not statistically certain according to Welch test.

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we have large, representative samples from each country for 15 year olds and we have 5 such samples between 1998 and 2012.

There are 6, no? PISA00, PISA03, PISA06, PISA09, PISA12, PISA12CPS.

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The most recent sample, on the subtest closest to WAIS IV matrices, can be seen in Table 3 and Finns score higher than the Scandinavian countries on this.

This is the CPS, right? Unclear.

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Secondly, the exclusion criteria for the WAIS IV would seem to explain the discrepancy between the WAIS IV and PISA results to a considerable degree. The Scandinavian WAIS IV specifically excludes 'persons with mother tongue other than Swedish (or Danish or Norwegian).' The Finnish WAIS IV likewise excludes those whose mother tongue is not Finnish. However, the PISA exclusion criteria are far less strict. A student who is a non-native speaker can only be excluded if he has been resident in the country for less than one year and even in this case a maximum of 5% of students may be excluded (OECD, 2013). Despite this, Sweden (5.4%), Denmark (6.18%), and Norway (6.11%) could not reach this standard and excluded over 5%, because so many of its school children had been born abroad and had lived in the country for less than a year. Finland was able to reach this standard.
Apart from this 5%, all other children whose native-language is not the language of instruction must be included in PISA. The Scandinavian countries have much larger non-European immigrant populations than Finland and these are typically from countries with significantly lower average IQs than Europe, such as those in North Africa and the Middle East (see Lynn & Vanhanen, 2012). In 2010, Norway was 13.1% non-European, Denmark was 11.4% non-European, and Sweden was 14.3% non-European. Finland was 1.8% non-European, as most of its immigrants (4.8% of the population are immigrant) are from Russia or Estonia, countries that typically have a similar average IQ to Europe (see Dutton & Lynn, 2013). PISA does not exclude on these grounds. As such, pronounced differences in the immigrant percentage of the population, in addition to sampling errors, may help to explain the discrepancy between PISA subtest and the WAIS IV matrices.

All this, while correct, is unnecessary as the author can use the Native only scores instead. They should result in the samples looking a lot more like each other with respect to first language / immigrant status.

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In genetic terms, this minority are more similar

Change to "Genetically, this..."

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Conscientiousness, in particular, is associated with performing well in tests and as PISA is a low-stakes test if Dutton et al are correct - and Finns are especially high in Conscientiousness for genetic reasons, due to adopting an extreme K-strategy - then this could partly explain superior Finnish performance.

Rewrite. Needs cite for claim about big C and scholastic tests. Furthermore, the CPS is not a scholastic test, so big C has little relevance.

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5. Conclusion

An examination of the WAIS IV matrices presents us with a Scandinavian Greenwich IQ of 102.8 in contrast to a Finnish one of 101.9. This is inconsistent with Nordic scores on the problem solving subtest in PISA, which give Finland a higher IQ than the Scandinavian countries, and with Nordic scores on PISA overall. However, this discrepancy can be explained by a number of factors. (1) PISA is a larger and more reliable sample. (2) WAIS IV excluded non-native speakers of the test language while PISA does not. Thus, the significantly high percentages of low IQ immigrants in Scandinavia compared to Finland will help to explain the difference between the two sets of results. Accordingly, we conclude that Dutton et al.'s (2014) contention that the Finns are the most intelligent population in Europe stands up against this potential counter-evidence.

Why do you need a conclusion that basically just repeats earlier stuff? The abstract is for summarizing the content of an article.

We are working on a new revision here.

https://docs.google.com/document/d/1QqVsnF8D_Pys6EnS8YCYOR5xzqvS7ar-y0XGeVAhfRE/edit#

A note on terminology. LV's books summarize other studies and report findings, but generally do not meta-analyze studies. I don't know if V has published meta-analyses before, but Lynn has done a number (e.g. sex diffs in RPM).

Some grammatical errors:
"Although Finland has a higher fluid g than Scandinavia but its CPS native score is only slightly higher than the rest of north­western Europe, at 526 points, giving a Greenwich fluid g estimate of 101.4."
"This is possible, however a meta­analysis..." This should read, "possible; however, a meta-analysis...".
"the presence of significant Northeast Asian admixture in the Finnish population" Citation, please.

I prefer the term "fluid intelligence" (or, even better "fluid skills") to "fluid g". This is quibbling, however. (Also, g should be italicized.)

E.g. http://www.eupedia.com/europe/autosomal_maps_dodecad.shtml