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The S factor in China

  • Ulster Institute for Social Research

Abstract and Figures

I analyze the S factor in Chinese states using data obtained from Lynn and Cheng as well as new data obtained from the Chinese statistical agency. I find that S correlates .42 with IQ and .48 with ethnic Han%.
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The Winnower
Published March 5th, 2015. Revised March 23rd 2017.
The S factor in China
Emil O. W. Kirkegaard1
I analyze the S factor in Chinese province-level units using data obtained from Lynn and Cheng (2013) as well
as new data obtained from the Chinese statistical agency. I find that S correlates .42 with IQ, Han% with IQ at .
59, and Han% with S at .48.
Key words: intelligence, IQ, cognitive ability, S factor general socioeconomic factor, inequality, China, Chinese
1. Introduction
Richard Lynn has published a number of papers on IQ in subnational units along with various socioeconomic
correlates. However, usually his and co-authors’ analysis is limited to reporting the correlation matrix. This is a
pity, because the data allow for a more interesting analysis with the S factor (Kirkegaard, 2014b). Previously
(Kirkegaard, 2015), I reanalyzed Lynn and Yadav (2015) and found both general cognitive (G) and general
socioeconomic (S) factors (Rindermann, 2007). In this paper I reanalyze data published by Lynn and Cheng
(2013) as well as additional data downloaded from the Chinese statistical agency.2
2. Data
In their paper, they report 6 variables:
1. IQ
2. Sample size for IQ measurement
3. % of population Ethnic Han
4. Years of education
5. Percent with higher education
6. GDP per capita.
This only includes 3 socioeconomic variables – the bare minimum for S factor analyses – so I examine the
Chinese statistical agency’s database and added additional variables:
1 Ulster Institute for Social Research. Email:
Page 1 of 6.
Higher education per capita for 6 years
Medical technical personnel for 5 years
Life expectancy for 1 year
Proportion of population illiterate for 9 years
Internet users per capita for 10 years
Invention patents per capita for 10 years
Proportion of population urban for 9 years
Scientific personnel for 8 years
I used all available data for the last 10 years in all cases. This was done to increase reliability of the measurement
and reduce transient effects. For the variables with data for multiple years, I calculated the average yearly
intercorrelation to see how reliable the measure were. In all but one case, the average intercorrelation was >=.94
and the last case it was .86. There would be little to gain from factor analyzing these data and using the scores
instead of just averaging the years, the latter of which preserves the original scale. Thus, I averaged the variables
for each year to produce one variable. This left me with a total of 11 socioeconomic variables.
3. Analyses
The 11 socioeconomic variables were factor analyzed and a strong general factor, S, emerged. The scores were
correlated with the IQ data, as shown in Figure 1.
Page 2 of 6.
The IQ scores were clearly related to the S scores, but Beijing was an outlier. If it is removed, the correlation
changes to .37. The method of correlated vectors (Jensen, 1998) was used to examine the relationship could
plausibly be ascribed to the latent S factor. This relationship is shown in Figure 2.
The MCV correlation was very strong,
but it was almost entirely due to a single
variable with a negative loading. The
remaining variables had little variation in
their S loadings and thus were not useful
for MCV analysis.
Figures 3 and 4 show the correlation
between Han%, and IQ and S,
Page 3 of 6.
Figure 1: Scatterplot of IQ and S for 31 Chinese provinces.
Figure 2: Method of correlated vectors applied to the S x IQ
Page 4 of 6.
Figure 3: Scatterplot of IQ and percent Han for 31 Chinese provinces.
4. Discussion and conclusion
In general, correlations were of moderate strength, r = .42 (IQ x S), .59 (IQ x Han%), and r = .48 (Han% x S).
The IQ x S correlation is lower than those found in the analyses of Danish and Norwegian immigrant groups (r’s
≈ .55; Kirkegaard, 2014a), much lower than that found between countries (r = .86; Kirkegaard, 2014b), and
lower than that found in India (r = .61; Kirkegaard, 2015). The result is mostly due to the two large cities areas of
Beijing and Shanghai, and thus are not particularly convincing. However, they are consistent previously reported
Han% was a more reasonable predictor in this dataset. It may not be solely due to their higher general
intelligence compared to the other ethnic groups, they may have some other traits that cause them to do well.
Perhaps higher conscientiousness or rule-conforming behavioral, which is arguably rather important in
authoritarian societies like China.
Supplementary material and acknowledgments
Supplementary materials including code, high quality figures and data can be found at
Jensen, A. R. (1998). The g factor: the science of mental ability. Westport, Conn.: Praeger.
Kirkegaard, E. O. W. (2014a). Crime, income, educational attainment and employment among immigrant groups
Page 5 of 6.
Figure 4: Scatterplot of S and percent Han for 31 Chinese provinces
in Norway and Finland. Open Differential Psychology. Retrieved from
Kirkegaard, E. O. W. (2014b). The international general socioeconomic factor: Factor analyzing international
rankings. Open Differential Psychology. Retrieved from
Kirkegaard, E. O. W. (2015). Indian states: G and S factors. The Winnower. Retrieved from
Lynn, R., & Cheng, H. (2013). Differences in intelligence across thirty-one regions of China and their economic
and demographic correlates. Intelligence, 41(5), 553–559.
Lynn, R., & Yadav, P. (2015). Differences in cognitive ability, per capita income, infant mortality, fertility and
latitude across the states of India. Intelligence, 49, 179–185.
Rindermann, H. (2007). The g-factor of international cognitive ability comparisons: the homogeneity of results
in PISA, TIMSS, PIRLS and IQ-tests across nations. European Journal of Personality, 21(5), 667–706.
Page 6 of 6.
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Full-text available
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.
Full-text available
I present new predictive analyses for crime, income, educational attainment and employment among immigrant groups in Norway and crime in Finland. Furthermore I show that the Norwegian data contains a strong general socioeconomic factor (S) which is highly predictable from country-level variables (National IQ .59, Islam prevalence -.71, international general socioeconomic factor .72, GDP .55), and correlates highly (.78) with the analogous factor among immigrant groups in Denmark. Analyses of the prediction vectors show very high correlations (generally > ±.9) between predictors which means that the same variables are relatively well or weakly predicted no matter which predictor is used. Using the method of correlated vectors shows that it is the underlying S factor that drives the associations between predictors and socioeconomic traits, not the remaining variance (all correlations near unity).
Full-text available
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.
This study reports the differences in intelligence across thirty-one regions of the People's Republic of China. It was found that regional IQs were significantly associated with the percentage of Han in the population (r = .59), GDP per capita (r = .42), the percentage of those with higher education (r = 38, p<.05), and non-significantly with years of education (r = .32). The results of the multiple regression showed that both the percentage of Han in the region and the GDP per capita were significant predictors of regional IQs, accounting for 39% of the total variance.
Regional differences in cognitive ability are presented for 33 states and union territories of India. Ability was positively correlated with GDP per capita, literacy and life expectancy and negatively correlated with infant and child mortality, fertility and the percentage of Muslims. Ability was higher in the south than in the north and in states with a coast line than with those that were landlocked.
International cognitive ability and achievement comparisons stem from different research traditions. But analyses at the interindividual data level show that they share a common positive manifold. Correlations of national ability means are even higher to very high (within student assessment studies, r = .60–.98; between different student assessment studies [PISA-sum with TIMSS-sum] r = .82–.83; student assessment sum with intelligence tests, r = .85–.86). Results of factor analyses indicate a strong g-factor of differences between nations (variance explained by the first unrotated factor: 94–95%). Causes of the high correlations are seen in the similarities of tests within studies, in the similarities of the cognitive demands for tasks from different tests, and in the common developmental factors at the individual and national levels including known environmental and unknown genetic influences. Copyright © 2007 John Wiley & Sons, Ltd.
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.