Comments on Noah Carl's new study: IQ and socio-economic development across local authorities of the UK
There are a few researchers engaged in the cognitive sociology adventure. Aside from myself and John Fuerst, Noah Carl has also taken up the task. There is of course also Richard Lynn, the grand old man of these studies (publishing his first in 1979). Mostly the job of doing these analyses involves searching around to find a suitable dataset. Usually, one has to combine multiple resources. Noah has done exactly that.
Being British it is not surprising that Noah's new study is also on the UK, like his previous study. The abstract reads:
Cross-regional correlations between average IQ and socio-economic development have been reported for many different countries. This paper analyses data on average IQ and a range of socio-economic variables at the local authority level in the UK. Local authorities are administrative bodies in local government; there are over 400 in the UK, and they contain anywhere from tens of thousands to more than a million people. The paper finds that local authority IQ is positively related to indicators of health, socio-economic status and tertiary industrial activity; and is negatively related to indicators of disability, unemployment and single parenthood. A general socioeconomic factor is correlated with local authority IQ at r = .56. This correlation increases to r = .65 when correcting for measurement error in the estimates of IQ.
In general, results are in line with the by now many previous studies: sizable correlations between group-level cognitive ability and group-level S indicators, and the existence of a clear S factor (16 of 16 loadings were in the expected direction; 15 correlated with cognitive ability in the expected direction). At a personal level, I am happy to be cited in the journal because it sends some attention towards the work published in the OpenPsych journals, including the new sociology & polit. sci. journal. Noah cites the London boroughs study, of which he was a reviewer.
Some things I am happy about:
He compiled a dataset of 16 S indicators.
He reported on the S factor.
He did some light multi-level analysis (reporting the variance associated with the regions and countries, which was small).
He used weighted analyses (square root of population, as we have also done before, but other weights could be used too).
He had access to 6 cognitive measures and used factor analysis to get a general factor.
Some critical comments:
The S factor loadings were not reported. Neither were the g loadings.
The scatterplot could have been better. For instance, one could have color boded the points by their region (helpful to spot regional effects) and mapped sample size to point size (like we did in the Admixture paper coming out in MQ sometimes soonish). Naming the outlier points could have been useful. I'm sure many readers wonder about the unit with an IQ of 110 and an S score of -1, or the unit with 90 IQ and an S score of 0. And what is that unit in the top right with 112 IQ and S of 2.5?
He did not use Jensen's method to see if IQ is more strongly related to the indicators with stronger loadings, as one would expect. Because he does not report the loadings at all, no one else can do this. One could also use it on the cognitive measures, even if there are only 6 indicators. It is worth doing, like in the study of Indian states. It could be fed into a meta-analysis at some future point.
Related to the above, I did not see a mention of where one can find the data or analysis code. Is it public or hidden? I will ask. Not necessary:
"Next, average IQ was calculated for each of the 404 local authorities represented in the dataset. It is important to note that information on local authorities was obtained from the UK Data Service via a Special Licence. Therefore making these data available to other researchers is not possible. Information on local authorities are not included in the main Understanding Society dataset due to the fact that some local authorities contain relatively few respondents, which could permit identification of specific individuals."
However, summary level information should be sufficient and the S data should still publishable. Noah is looking into it.
I'd like to see more multi-level analyses, e.g. like those done in my analysis of the religious general factor among Muslims. If one analyses the authorities in each region, are the S factor loadings the same as when analyzed at the national level? They need not be and differences could be revealing.
No demographic analyses. For some indicators, one would expect age to be a confound. One should at least try regressing it out if one can find mean age. Noah tells me it wasn't (he did not report that in the paper I think). Noah didn't do any correlations with SIRE (self-identified race/ethnicity). Perhaps he couldn't find any data. Perhaps he did not want to be labelled a racist, only a classist. Perhaps one could find country of origin data for the units to try a compositional analysis like in this paper.
All in all, this study confirmed what has been found elsewhere. The findings in this field are very reproducible. There is still the odd case of Japan to wonder about, where results only are in line after ad hoc adjustment for population density, but that's the only strange result I've come across. Well, that as Chile (the analysis of which will also be reported in more detail in the upcoming issue of Mankind Quarterly.)