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Examining the S factor in US states

  • Ulster Institute for Social Research

Abstract and Figures

I analyzed the S factor in US states by compiling a dataset of 25 diverse socioeconomic indicators. Results show that Washington DC is a strong outlier, but if it is excluded, then the S factor correlated strongly with state IQ at .75. Ethnoracial demographics of the states are related to the state's IQ and S in the expected order (White>Hispanic>Black).
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The Winnower
Published March 4th, 2015
Examining the S factor in US states
Emil O. W. Kirkegaard1
A dataset of 25 diverse socioeconomic indicators for US states was compiled and subjected to factor
analysis. Results showed that Washington DC was a strong outlier, but if it is excluded, then the S
factor correlated strongly with state IQ (based on NAEP) at .75.
Ethnoracial demographics of the states were related to the state’s IQ and S in the expected order
Key words: USA, United States, states, social inequality, S factor, general socioeconomic factor, IQ,
intelligence, cognitive ability, NAEP, cognitive sociology
1. Introduction and data sources
In two previous studies, I analyzed the S factors in 33 Indian states (Kirkegaard, 2015a) and 31 Chinese
regions (Kirkegaard, 2015b). Both studies found strongish S factors and they both correlated positively
with cognitive estimates (IQ or G). The purpose of this study was to examine the S factor in the US.
2. Data sources
State IQ data from McDaniel (2006) were used. He gave two sets of estimated IQs based on SAT-ACT
and on NAEP. Unfortunately, they only correlated .58, so at least one of them is not a very accurate
estimate of general intelligence.
McDaniel reports a few correlations between his IQs and socioeconomic variables: Gross State Product
per capita, median income and percent poverty. However, data for these variables is not given in the
article, so I could not copy them.
An analysis of US states should be a strong test of the S factor model because plenty of high quality
data are readily available and the number of cases is decent (50 or 51, depending on whether the capital
is included). Factor analysis requires a case to variable ratio of at least 2:1 to deliver reliable results
(Zhao, 2009). So, this means that one can do an S factor analysis with about 25 variables.
1 University of Aarhus, Denmark. Email:
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A dataset of 25 diverse socioeconomic variables was compiled. There are two reasons to gather a very
diverse sample of variables. First, for method of correlated vectors to work (Jensen, 1998), there must
be variation in the indicators’ loading on the factor. Lack of variation causes restriction of range
problems. Second, lack of diversity in the indicators of a latent variable leads to psychometric sampling
error (Jensen & Weng, 1994).
The primary source was The 2012 Statistical Abstract website. I simply searched for “state” and picked
a diverse set of variables. An attempt was made to pick variables that weren’t strongly dependent on
geography. To increase reliability, I generally used all data for the last 10 years and averaged them.
Curious readers can read the datafile for details.
The following variables were chosen:
1. Murder rate per 100k, 10 years
2. Proportion with high school or more education, 4 years
3. Proportion with bachelor or more education, 4 years
4. Proportion with advanced degree or more, 4 years
5. Voter turnout, presidential elections, 3 years
6. Voter turnout, house of representatives, 6 years
7. Percent below poverty, 10 years
8. Personal income per capita, 1 year
9. Percent unemployed, 11 years
10.Internet usage, 1 year
11.Percent smokers, male, 1 year
12.Percent smokers, female, 1 year
13.Physicians per capita, 1 year
14.Nurses per capita, 1 year
15.Percent with health care insurance, 1 year
16.Percent in ‘Medicaid Managed Care Enrollment’, 1 year
17.Proportion of population urban, 1 year
18.Abortion rate, 5 years
19.Marriage rate, 6 years
20.Divorce rate, 6 years
21.Incarceration rate, 2 years
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22.Gini coefficient, 10 years
23.Top 1%, proportion of total income, 10 years
24.Obesity rate, 1 year
Most of these are self-explanatory. For the economic inequality measures, I found 6 different measures
(here). Because I wanted diversity, I chose the GINI and the top 1% because these correlated the least
and are both well-known.
Additonally, racial demographical data were downloaded.
3. Analyses
3.1. Missing data
Figure 1 shows a matrixplot of the missing data.
We see that there aren’t many missing values. The missing data were imputed using irmi from the VIM
package (Templ, Alfons, Kowarik, & Prantner, 2015).
3.2. Extreme values
A useful feature of the matrixplot is that it shows in grey-tone the relative outliers for each variable.
Some outlying datapoints can be seen and these were inspected for possible data error.
The outlier in the two university degree variables is DC, surely because it’s the seat of the government,
and there is a large lobbyist center. For the marriage rate, the outlier is Nevada. Many people go there
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Figure 1: Matrixplot of socioeconomic data before imputation.
and get married. Physician and nurse rates are also DC.
Figure shows the matrixplot after data imputation.
It looks much the same as before. This is good because it means the imputation did not radically
change the data.
3.3. Factor analysis
The data were factor analyzed using fa from psych (Revelle, 2015). The loadings are shown in Figure
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Figure 2: Matrixplot of socioeconomic data after imputation.
We see a wide spread of variable loadings. All but two of them load in the expected direction —
positive are socially valued outcomes, negative the opposite — showing the existence of the S factor.
The exceptions are: abortion rate loading +.60, but often considered as a negative thing. It is however
open to discussion. Maybe higher abortion rates can be interpreted as less backward religiousness or
more freedom for women (both good in my view). The other is marriage rate at -.19 (weak loading).
I’m not sure how to interpret that. In any case, both of these are debatable which way the proper
desirable direction is.
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Figure 3: S factor loadings. DC included.
3.4. Correlations with cognitive measures
Because have two sets of IQ estimates, we will plot both to see if we can see which is superior. Figures
4 and 5 show the relationships between the IQ measures and S.
First, the SAT-ACT estimates are pretty strange for three states: California, Arizona and Nevada. I note
that these are three adjacent states, so it is quite possibly some kind of regional testing practice that’s
throwing off the estimates. Second, DC is a strong outlier in S, as we may have expected from our short
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Figure 4: ACT-SAT based IQ estimates and S. DC included.
Figure 5: NAEP based IQ estimates and S. DC included.
discussion of extreme values above. It’s the only state that’s almost entirely a city.
3.5. Dealing with outliers – Spearman’s correlation
There are various ways to deal with outliers. One simple way is to convert the data into ranked data,
and then analyze like normal. Pearson’s correlations assume that the data are normally distributed,
which is often not the case with higher-level data (states, countries). Figures 6 and 7 show the
relationships between ACT-SAT, NAEP and S with ranked data.
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Figure 6: ACT-SAT based IQ estimates and S. DC included. Rank data.
Figure 7: NAEP based IQ estimates and S. DC included. Rank data.
The rank order correlations are stronger as expected.
3.6. Results without DC
An alternative approach is excluding DC before carrying out the factor analysis. A parallel dataset was
created without DC. Figure shows the factor loadings.
These are very similar to before, excluding DC did not substantially change results. The factor size
increased from 30% to 36% indicating that DC was distoring the general factor. The reason this
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Figure 8: S factor loadings. DC excluded.
happens is that DC is an odd case, scoring very high in some indicators (e.g. education) and very
poorly in others (e.g. murder rate). Figures 9 and 10 show the IQ x S correlations again, but based on
the dataset without DC.
Not surprisingly, we see an increase in the effect sizes from before: .14 to .31 and .43 to .69.
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Figure 9: ACT-SAT based IQ estimates and S. DC excluded.
Figure 10: NAEP based IQ estimates and S. DC excluded.
3.6.1. Without DC and rank-order
Still, one may wonder what the results would be with rank-order and DC removed. These are shown in
Figures .
Compared to before, effect size increased for the SAT-ACT IQ and decreased slightly for the NAEP IQ.
One could also do regression with weights based on some metric of the state population and this may
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Figure 11: ACT-SAT based IQ estimates and S. DC excluded. Rank data.
Figure 12: NAEP based IQ estimates and S. DC excluded. Rank data.
further change results, but it seems safe to say that the cognitive measures correlate in the expected
direction and with the removal of one odd case, the better measure performs at about the expected level
with or without using rank-order correlations.
3.7. Method of correlated vectors
The MCV (Jensen, 1998) can be used to test whether a specific latent variable underlying some data is
responsible for the observed correlation between the factor score (or factor score approximation such as
IQ — an unweighted sum) and some criteria variable. Although originally invented for use on cognitive
test data and the general intelligence factor, I have previously used it in other areas (e.g. Kirkegaard,
2014, 2015a).
Using the dataset without DC, the MCV result for NAEP is shown in Figure 13.
We see that MCV can reach high r’s when there is a large number of diverse variables. But note that the
value can be considered inflated because of the negative loadings of some variables. It is debatable
whether one should reverse them.
3.8. Racial proportions of states and S and IQ
A last question is whether the states’ racial proportions predict their S and IQ. There are many problems
with this approach. First, the actual genomic proportions within these racial groups vary by state (Bryc,
Durand, Macpherson, Reich, & Mountain, 2015). Second, within ‘pure-breed’ groups, general
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Figure 13: Method of correlated vectors applied to the S x NAEP relationship. DC excluded.
intelligence varies by state too (this was shown in the testing of draftees in the US in WW1). Third,
there is an ‘other’ group that varies from state to state, presumably different kinds of Asians (Japanese,
Chinese, Indians, other SE Asia). Fourth, it is unclear how one should combine these proportions into
an estimate used for correlation analysis or model them. Standard multiple regression is unsuited for
handling data like these. This is because there is a perfect linear dependency among the proportions, i.e.
the total proportion must add up to 1 (100%). Given the four problems above, one will not expect near-
perfect results, but one would probably expect most going in the right direction with moderate effect
Perhaps the simplest way of analyzing the data is the correlations. These are susceptible to confounds
e.g. if White% correlates differentially with the other racial proportions. However, they should get the
basic directions correct if not the effect size order too.
3.8.1. Racial proportions, NAEP IQ and S
For this analysis I use only the NAEP IQs and without DC, as I believe this is the best subdataset to
rely on. I correlate this with the S factor and each racial proportion. The results are:
group NAEP IQ S
White 0.69 0.18
Black -0.50 -0.42
Hispanic -0.38 -0.08
Other -0.26 0.20
For NAEP IQ, depending on what one thinks of the ‘other’ category, these have either exactly or
roughly the order one expects: W>O>H>B. If one thinks “other” is mostly East Asian (Japanese,
Chinese, Korean) with higher cognitive ability than Europeans, one would expect O>W>H>B. For S,
however, the order is O>W>H>B and the effect sizes are much weaker. In general, given the limitations
above, these are perhaps reasonable if somewhat on the weak side.
3.8.2. Estimating state IQ from racial proportions using racial IQs
One way to utilize all the four variables (White, Black, Hispanic and Other) without having MR assign
them weights is to assign them weights based on known group IQs and then calculate a weighted mean
estimated IQ for each state.
Depending on which estimates for group IQs one accepts, one might use something like the following:
State IQ est. = White*100 + Other*100 + Black*85 + Hispanic*90
Or if one thinks Other is somewhat higher than Whites (this is not entirely unreasonable, but recall that
the NAEP includes reading tests which foreigners and Asians perform less well on), one might want to
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use 105 for the other group (#2). Or one might want to raise Black and Hispanic IQs a bit if one thinks
the group differences have narrowed, say, to 88 and 93 (#3). Or do both (#4). All the variations are
shown in Table .
Variable Race.IQ Race.IQ2 Race.IQ3 Race.IQ4
Race.IQ 1 0.96 1 0.93
Race.IQ2 0.96 1 0.96 0.99
Race.IQ3 1.00 0.96 1 0.94
Race.IQ4 0.93 0.99 0.94 1
NAEP IQ 0.67 0.56 0.67 0.51
S0.41 0.44 0.42 0.45
Table 1: Intercorrelations of state IQ estimates, NAEP and S.
As far as I can tell, there is no strong reason to pick any of these over the others. However, what we
learn is that the racial IQ estimate and NAEP IQ estimate is somewhere between .51 and .67, and the
racial IQ estimate and S is somewhere between .41 and .45. These are reasonable results given the
problems of this analysis described above.
3.9. Added March 11: New NAEP data
Shortly after publication of this study, I came across a series of posts by science blogger The Audacious
Epigone, who had also estimated IQs based on NAEP data. He has done this three times (for 2013,
2009 and 2005 data), so along with McDaniel’s estimates, this gives us 4 non-identical estimates. The
intercorrelations of these new variables is shown in Table 2. NAEP.1 is a factor score extracted from
the base NAEP variables.
NAEP.IQ.09 0.96
NAEP.IQ.05 0.83 0.89
NAEP M. 0.88 0.93 0.96
NAEP.1 0.95 0.99 0.95 0.97
S0.81 0.76 0.64 0.69 0.75
Table 2: Intercorrelations between NAEP variables and S. NAEP M = McDaniel’s IQs.
We see that intercorrelations between NAEP estimates are not that high, they average only .86. Still,
this should result in improved results due to measurement error being removed, and it does, NAEP IQ x
S is now .75, up from .69.
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4. Discussion
Washington DC was found to be a strong outlier that caused problems with the data analysis. Future
studies should be careful about capital districts for this reason.
The correlations between IQ and S were strong, as expected from previous studies. The relationships
between demographic variables and IQ were strong, while those for S only weak to moderate.
Supplementary material
Data files and R source code available at
Bryc, K., Durand, E. Y., Macpherson, J. M., Reich, D., & Mountain, J. L. (2015). The genetic ancestry
of African Americans, Latinos, and European Americans across the United States. American
Journal of Human Genetics, 96(1), 37–53.
Jensen, A. R. (1998). The g factor: the science of mental ability. Westport, Conn.: Praeger.
Jensen, A. R., & Weng, L.-J. (1994). What is a good g? Intelligence, 18(3), 231–258.
Kirkegaard, E. O. W. (2014). The international general socioeconomic factor: Factor analyzing
international rankings. Open Differential Psychology. Retrieved from
Kirkegaard, E. O. W. (2015a). Indian states: G and S factors. The Winnower. Retrieved from
Kirkegaard, E. O. W. (2015b). The S factor in China. The Winnower. Retrieved from
McDaniel, M. A. (2006). State preferences for the ACT versus SAT complicates inferences about SAT-
derived state IQ estimates: A comment on Kanazawa (2006). Intelligence, 34(6), 601–606.
Revelle, W. (2015). psych: Procedures for Psychological, Psychometric, and Personality Research
(Version 1.5.4). Retrieved from
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Templ, M., Alfons, A., Kowarik, A., & Prantner, B. (2015, February 19). VIM: Visualization and
Imputation of Missing Values. CRAN. Retrieved from http://cran.r-
Zhao, N. (2009, March 23). The Minimum Sample Size in Factor Analysis. Retrieved November 16,
2016, from
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... Secondarily, since U.S. state-level IQ scores vary strongly with other important variables (e.g., income, health), I am also interested in whether blue states have higher levels of average "well-being." Pesta, McDaniel, and Bertsch (2010; see also Kirkegaard, 2015) showed that at the level of the U.S. state, almost all measures (i.e., "subdomains") of well-being are intrinsically intercorrelated. Specifically, they found that the subdomains of intelligence, religiosity, crime, education, health, and income covaried so strongly that a general factor of state "well-being" could be derived. ...
... Pesta et al. (2010) labelled the supposed common cause for these effects "well-being." Kirkegaard (2015) called it the "S" (i.e., socioeconomic) factor. ...
... While it is not set on the same scale as the HDI values and thus is not very useful for international comparisons, we nonetheless included the 2010 AHDI scores in our US analysis. Additionally, one of us undertook an S factor study of the US (Kirkegaard, 2015e) and found an S factor using 24 diverse indicators. We excluded Washington DC in line with Kirkegaard (2015e) and the Mexican analysis in Section 3. Due to some facts which will be discussed later, one of us undertook a new and larger S factor analysis for the US (81 indicators based on 2010 data, Kirkegaard, 2015b). ...
... Additionally, one of us undertook an S factor study of the US (Kirkegaard, 2015e) and found an S factor using 24 diverse indicators. We excluded Washington DC in line with Kirkegaard (2015e) and the Mexican analysis in Section 3. Due to some facts which will be discussed later, one of us undertook a new and larger S factor analysis for the US (81 indicators based on 2010 data, Kirkegaard, 2015b). Between the datasets, the S factor scores correlated .961. ...
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We conducted novel analyses regarding the association between continental racial ancestry, cognitive ability and socioeconomic outcomes across 6 datasets: states of Mexico, states of the United States, states of Brazil, departments of Colombia, sovereign nations and all units together. We find that European ancestry is consistently and usually strongly positively correlated with cognitive ability and socioeconomic outcomes (mean r for cognitive ability = .708; for socioeconomic well-being = .643) (Sections 3-8). In most cases, including another ancestry component, in addition to European ancestry, did not increase predictive power (Section 9). At the national level, the association between European ancestry and outcomes was robust to controls for natural-environmental factors (Section 10). This was not always the case at the regional level (Section 18). It was found that genetic distance did not have predictive power independent of European ancestry (Section 10). Automatic modeling using best subset selection and lasso regression agreed in most cases that European ancestry was a non-redundant predictor (Section 11). Results were robust across 4 different ways of weighting the analyses (Section 12). It was found that the effect of European ancestry on socioeconomic outcomes was mostly mediated by cognitive ability (Section 13). We failed to find evidence of international colorism or culturalism (i.e., neither skin reflectance nor self-reported race/ethnicity showed incremental predictive ability once genomic ancestry had been taken into account) (Section 14). The association between European ancestry and cognitive outcomes was robust across a number of alternative measures of cognitive ability (Section 15). It was found that the general socioeconomic factor was not structurally different in the American sample as compared to the worldwide sample, thus justifying the use of that measure. Using Jensen's method of correlated vectors, it was found that the association between European ancestry and socioeconomic outcomes was stronger on more S factor loaded outcomes, r = .75 (Section 16). There was some evidence that tourist expenditure helped explain the relatively high socioeconomic performance of Caribbean states (Section 17).
... Many recent studies have examined within-country regional correlates of (general) cognitive ability (also known as (general) intelligence, general mental ability, g),. This has been done for the British Isles (Lynn, 1979;Kirkegaard, 2015g), France (Lynn, 1980), Italy (Lynn, 2010;Kirkegaard, 2015e), Spain (Lynn, 2012), Portugal (Almeida, Lemos, & Lynn, 2011), India (Kirkegaard, 2015d;Lynn & Yadav, 2015), China (Kirkegaard, 2015f;Lynn & Cheng, 2013), Japan (Kura, 2013), the US (Kirkegaard, 2015b;McDaniel, 2006;Templer & Rushton, 2011), Mexico (Kirkegaard, 2015a) and Turkey (Lynn, Sakar, & Cheng, 2015). This paper examines data for Brazil. ...
... racial ancestry as done by e.g. (Kirkegaard, 2015b). ...
Full-text available
Sizeable S factors were found across 3 different datasets (from years 1991, 2000 and 2010), which explained 56 to 71% of the variance. Correlations of extracted S factors with cognitive ability were strong ranging from .69 to .81 depending on which year, analysis and dataset is chosen. Method of correlated vectors supported the interpretation that the latent S factor was primarily responsible for the association (r’s .71 to .81).
... Because the S factor is an aggregate of such outcomes, it is not surprising that S scores have been found to have strong positive correlations with cognitive ability as well, e.g. (Kirkegaard, 2015b(Kirkegaard, , 2015c. ...
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Two datasets of Japanese socioeconomic data for Japanese prefectures (N=47) were obtained and merged. After quality control, there were 44 variables for use in a factor analysis. Indicator sampling reliability analysis revealed poor reliability (54% of the correlations were |r| > .50). Inspection of the factor loadings revealed no clear S factor with many indicators loading in opposite than expected directions. A cognitive ability measure was constructed from three scholastic ability measures (all loadings > .90). On first analysis, cognitive ability was not strongly related to 'S' factor scores, r = -.19 [CI95: -.45 to .19; N=47]. Jensen's method did not support the interpretation that the relationship is between latent 'S' and cognitive ability (r = -.15; N=44). Cognitive ability was nevertheless related to some socioeconomic indicators in expected ways. A reviewer suggested controlling for population size or population density. When this was done, a relatively clear S factor emerged. Using the best control method (log population density), indicator sampling reliability was high (93% |r|>.50). The scores were strongly related to cognitive ability r = .67 [CI95: .48 to .80]. Jensen's method supported the interpretation that cognitive ability was related to the S factor (r = .78) and not just to the non-general factor variance.
... Furthermore, because capitals are known to sometimes strongly affect results (Kirkegaard, 2015a(Kirkegaard, , 2015b(Kirkegaard, , 2015d, I also created two further datasets without London: one with the redundant variables, one without. Thus, there were 4 datasets: 1. ...
Full-text available
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.
... Furthermore, because capitals are known to sometimes strongly affect results (Kirkegaard, 2015a(Kirkegaard, , 2015b(Kirkegaard, , 2015d, I also created two further datasets without London. One with the redundant variables, one without. ...
Full-text available
A dataset of 127 variables concerning socioeconomic outcomes for US states was analyzed. Of these, 81 were used in a factor analysis. The analysis revealed a general socioeconomic factor. This factor correlated .961 with one from a previous analysis of socioeconomic data for US states.
... I have used datasets with 50 cases and 25 variables to avoid the excessive sampling error of small samples and to keep a realistic number of cases compared to the datasets examined in S factor studies (e.g. Kirkegaard, 2015). The matrix plot is shown in Figure 1. ...
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Two methods are presented that allow for identification of mixed cases in the extraction of general factors. Simulated data is used to illustrate them.
... To get data for the analysis, the same approach as was used in a previous publication on the S factor in US was used (Kirkegaard, 2015). Were were selected and downloaded from the Italian statistics agency, IStat ( ...
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I analyze the S factor in Italian states by reanalyzing data published by Lynn (2010) as well as new data compiled from the Italian statistics agency (7 and 10 socioeconomic variables, respectively). The S factors from the datasets are highly correlated (.92) and both are strongly correlated with a G factor from PISA scores (.93 and .88).
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A dataset of socioeconomic, demographic and geographic data for US counties (N≈3,100) was created by merging data from several sources. A suitable subset of 28 socioeconomic indicators was chosen for analysis. Factor analysis revealed a clear general socioeconomic factor (S factor) which was stable across extraction methods and different samples of indicators (absolute split-half sampling reliability = .85). Self-identified race/ethnicity (SIRE) population percentages were strongly, but non-linearly, related to cognitive ability and S. In general, the effect of White% and Asian% were positive, while those for Black%, Hispanic% and Amerindian% were negative. The effect was unclear for Other/mixed%. The best model consisted of White%, Black%, Asian% and Amerindian% and explained 41/43% of the variance in cognitive ability/S among counties. SIRE homogeneity had a non-linear relationship to S, both with and without taking into account the effects of SIRE variables. Overall, the effect was slightly negative due to low S, high White% areas. Geospatial (latitude, longitude, and elevation) and climatological (temperature, precipitation) predictors were tested in models. In linear regression, they had little incremental validity. However, there was evidence of non-linear relationships. When models were fitted that allowed for non-linear effects of the environmental predictors, they were able to add a moderate amount of incremental validity. LASSO regression, however, suggested that much of this predictive validity was due to overfitting. Furthermore, it was difficult to make causal sense of the results. Spatial patterns in the data were examined using multiple methods, all of which indicated strong spatial autocorrelation for cognitive ability, S and SIRE (k nearest spatial neighbor regression [KNSNR] correlations of .62 to .89). Model residuals were also spatially autocorrelated, and for this reason the models were re-fit controlling for spatial autocorrelation using KNSNR-based residuals and spatial local regression. The results indicated that the effects of SIREs were not due to spatially autocorrelated confounds except possibly for Black% which was about 50% weaker in the controlled analyses. Pseudo-multilevel analyses of both the factor structure of S and the SIRE predictive model showed results consistent with the main analyses. Specifically, the factor structure was similar across levels of analysis (states and counties) and within states. Furthermore, the SIRE predictors had similar betas when examined within each state compared to when analyzed across all states. It was tested whether the relationship between SIREs and S was mediated by cognitive ability. Several methods were used to examine this question and the results were mixed, but generally in line with a partial mediation model. Jensen's method (method of correlated vectors) was used to examine whether the observed relationship between cognitive ability and S scores was plausibly due to the latent S factor. This was strongly supported (r = .91, Nindicators=28). Similarly, it was examined whether the relationship between SIREs and S scores was plausibly due to the latent S factor. This did not appear to be the case.
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Two sets of socioeconomic data for 90-96 French departements were analyzed. One dataset was found in Lynn (1980) and contained four socioeconomic variables. Mixed results were found for this dataset, both with regards to the factor structure and the relationship to cognitive ability. Another dataset with 53 variables was created by compiling variables from the official French statistics bureau (Insee). This dataset contained an impure general socioeconomic (S) factor (some undesirable variables loaded positively), but after controlling for the presence of immigrants, the S factor became purer. This was especially salient for crime, unemployment and poverty variables. The two S factors correlated at r = 0.66 [CI95:0.52-0.76; N = 88]. The IQ scores from the 1950s dataset correlated at 0.33 [CI95:0.13-0.51, N = 88] with the S factor from the 2010-2015 dataset.
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I reanalyze data published by Lynn and Yadav (2015) for Indian states. I find both G and S factors which correlate at .61. The statistical language R is used thruout the paper and the code is explained. The paper thus is both an analysis as a walkthru of how to conduct this type of study.
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Over the past 500 years, North America has been the site of ongoing mixing of Native Americans, European settlers, and Africans (brought largely by the trans-Atlantic slave trade), shaping the early history of what became the United States. We studied the genetic ancestry of 5,269 self-described African Americans, 8,663 Latinos, and 148,789 European Americans who are 23andMe customers and show that the legacy of these historical interactions is visible in the genetic ancestry of present-day Americans. We document pervasive mixed ancestry and asymmetrical male and female ancestry contributions in all groups studied. We show that regional ancestry differences reflect historical events, such as early Spanish colonization, waves of immigration from many regions of Europe, and forced relocation of Native Americans within the US. This study sheds light on the fine-scale differences in ancestry within and across the United States and informs our understanding of the relationship between racial and ethnic identities and genetic ancestry. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
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Many studies have examined the correlations between national IQs and various country-level indexes of well-being. The analyses have been unsystematic and not gathered in one single analysis or dataset. In this paper I gather a large sample of country-level indexes and show that there is a strong general socioeconomic factor (S factor) which is highly correlated (.86-.87) with national cognitive ability using either Lynn and Vanhanen's dataset or Altinok's. Furthermore, the method of correlated vectors shows that the correlations between variable loadings on the S factor and cognitive measurements are .99 in both datasets using both cognitive measurements, indicating that it is the S factor that drives the relationship with national cognitive measurements, not the remaining variance.
Kanazawa [Kanazawa, S. (2006). IQ and the wealth of states. Intelligence, 34, 593–600.] offered estimates of state IQ derived from SAT data. The purpose of this commentary is to argue that state preferences for the use of the ACT versus the SAT create biased estimates of SAT-derived state IQ for states where the ACT is more frequently used than the SAT. This error can be reduced by using both ACT and SAT data to estimate state IQ. An IQ estimate based on a ACT-SAT composite and a NAEP-derived state IQ estimate were compared as predictors of three wealth variables. Both IQ estimates cause one to conclude that states with higher mean IQ have larger gross state product per capita, higher median incomes, and a lower percentage of their population in poverty.
A pesar de la relativamente corta historia de la Psicología como ciencia, existen pocos constructos psicológicos que perduren 90 años después de su formulación y que, aún más, continúen plenamente vigentes en la actualidad. El factor «g» es sin duda alguna uno de esos escasos ejemplos y para contrastar su vigencia actual tan sólo hace falta comprobar su lugar de preeminencia en los modelos factoriales de la inteligencia más aceptados en la actualidad, bien como un factor de tercer orden en los modelos jerárquicos o bien identificado con un factor de segundo orden en el modelo del recientemente desaparecido R.B.Cattell.
What is a good g? Intelligence
  • A R Jensen
  • L.-J Weng
Jensen, A. R., & Weng, L.-J. (1994). What is a good g? Intelligence, 18(3), 231–258.
The Minimum Sample Size in Factor Analysis
  • N Zhao
Zhao, N. (2009, March 23). The Minimum Sample Size in Factor Analysis. Retrieved November 16, 2016, from
VIM: Visualization and Imputation of Missing Values. CRAN. Retrieved from http
  • M Templ
  • A Alfons
  • A Kowarik
  • B Prantner
Templ, M., Alfons, A., Kowarik, A., & Prantner, B. (2015, February 19). VIM: Visualization and Imputation of Missing Values. CRAN. Retrieved from
The international general socioeconomic factor: Factor analyzing international rankings. Open Differential Psychology Retrieved from analyzing-international-rankings
  • E O W Kirkegaard
Kirkegaard, E. O. W. (2014). The international general socioeconomic factor: Factor analyzing international rankings. Open Differential Psychology. Retrieved from analyzing-international-rankings/