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Content uploaded by Emil O. W. Kirkegaard

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All content in this area was uploaded by Emil O. W. Kirkegaard on Jun 26, 2015

Content may be subject to copyright.

Content uploaded by Emil O. W. Kirkegaard

Author content

All content in this area was uploaded by Emil O. W. Kirkegaard on Jun 26, 2015

Content may be subject to copyright.

IQ and socioeconomic development across Regions of the UK: a reanalysis

Abstract

A reanalysis of (Carl, 2015) revealed that the inclusion of London had a strong effect on the S loading

of crime and poverty variables. S factor scores from a dataset without London and redundant variables

was strongly related to IQ scores, r = .87. The Jensen coefficient for this relationship was .86.

Introduction

Carl (2015) analyzed socioeconomic inequality across 12 regions of the UK. In my reading of his

paper, I thought of several analyses that Carl had not done. I therefore asked him for the data and he

shared it with me. For a fuller description of the data sources, refer back to his article.

Redundant variables and London

Including (nearly) perfectly correlated variables can skew an extracted factor. For this reason, I created

an alternative dataset where variables that correlated above |.90| were removed. The following pairs of

strongly correlated variables were found:

1. median.weekly.earnings and log.weekly.earnings r=0.999

2. GVA.per.capita and log.GVA.per.capita r=0.997

3. R.D.workers.per.capita and log.weekly.earnings r=0.955

4. log.GVA.per.capita and log.weekly.earnings r=0.925

5. economic.inactivity and children.workless.households r=0.914

In each case, the first of the pair was removed from the dataset. However, this resulted in a dataset with

11 cases and 11 variables, which is impossible to factor analyze. For this reason, I left in the last pair.

Furthermore, because capitals are known to sometimes strongly affect results (Kirkegaard, 2015a,

2015b, 2015d), I also created two further datasets without London: one with the redundant variables,

one without. Thus, there were 4 datasets:

1. A dataset with London and redundant variables.

2. A dataset with redundant variables but without London.

3. A dataset with London but without redundant variables.

4. A dataset without London and redundant variables.

Factor analysis

Each of the four datasets was factor analyzed. Figure 1 shows the loadings.

Removing London strongly affected the loading of the crime variable, which changed from moderately

positive to moderately negative. The poverty variable also saw a large change, from slightly negative to

strongly negative. Both changes are in the direction towards a purer S factor (desirable outcomes with

positive loadings, undesirable outcomes with negative loadings). Removing the redundant variables did

not have much effect.

As a check, I investigated whether these results were stable across 30 different factor analytic

methods.1 They were, all loadings and scores correlated near 1.00. For my analysis, I used those

extracted with the combination of minimum residuals and regression.

Mixedness

Due to London's strong effect on the loadings, one should check that the two methods developed for

finding such cases can identify it (Kirkegaard, 2015c). Figure 2 shows the results from these two

methods (mean absolute residual and change in factor size):

1 There are 6 different extraction and 5 scoring methods supported by the fa() function from the psych package (Revelle,

2015). Thus, there are 6*5 combinations.

Figure 1: S factor loadings in four analyses.

As can be seen, London was identified as a far outlier using both methods.

S scores and IQ

Carl's dataset also contains IQ scores for the regions. These correlate .87 with the S factor scores from

the dataset without London and redundant variables. Figure 3 shows the scatter plot.

However, it is possible that IQ is not really related to the latent S factor, just the other variance of the

extracted S scores. For this reason I used Jensen's method (method of correlated vectors) (Jensen,

1998). Figure 4 shows the results.

Figure 2: Mixedness metrics for the complete dataset.

Figure 3: Scatter plot of S and IQ scores for regions of the UK.

Jensen's method thus supported the claim that IQ scores and the latent S factor are related.

Discussion and conclusion

My reanalysis revealed some interesting results regarding the effect of London on the loadings. This

was made possible by data sharing demonstrating the importance of this practice (Wicherts & Bakker,

2012).

Supplementary material

R source code and datasets are available at the OSF.

References

Carl, N. (2015). IQ and socioeconomic development across Regions of the UK. Journal of Biosocial

Science, 1–12. http://doi.org/10.1017/S002193201500019X

Jensen, A. R. (1998). The g factor: the science of mental ability. Westport, Conn.: Praeger.

Kirkegaard, E. O. W. (2015a). Examining the S factor in Mexican states. The Winnower. Retrieved from

https://thewinnower.com/papers/examining-the-s-factor-in-mexican-states

Kirkegaard, E. O. W. (2015b). Examining the S factor in US states. The Winnower. Retrieved from

https://thewinnower.com/papers/examining-the-s-factor-in-us-states

Kirkegaard, E. O. W. (2015c). Finding mixed cases in exploratory factor analysis. The Winnower.

Retrieved from https://thewinnower.com/papers/finding-mixed-cases-in-exploratory-factor-

analysis

Figure 4: Jensen's method for the S factor's relationship to IQ scores.

Kirkegaard, E. O. W. (2015d). The S factor in Brazilian states. The Winnower. Retrieved from

https://thewinnower.com/papers/the-s-factor-in-brazilian-states

Revelle, W. (2015). psych: Procedures for Psychological, Psychometric, and Personality Research

(Version 1.5.4). Retrieved from http://cran.r-project.org/web/packages/psych/index.html

Wicherts, J. M., & Bakker, M. (2012). Publish (your data) or (let the data) perish! Why not publish

your data too? Intelligence, 40(2), 73–76. http://doi.org/10.1016/j.intell.2012.01.004