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The S factor in China

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  • 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
Abstract
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
provinces
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: emil@emilkirkegaard.dk
2http://data.stats.gov.cn/english/index.htm
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,
respectively.
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
relationship.
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
results.
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 https://osf.io/mu3tj/.
References
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
http://openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-among-
immigrant-groups-in-norway-and-finland/
Kirkegaard, E. O. W. (2014b). The international general socioeconomic factor: Factor analyzing international
rankings. Open Differential Psychology. Retrieved from http://openpsych.net/ODP/2014/09/the-
international-general-socioeconomic-factor-factor-analyzing-international-rankings/
Kirkegaard, E. O. W. (2015). Indian states: G and S factors. The Winnower. Retrieved from
https://thewinnower.com/papers/indian-states-g-and-s-factors
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. https://doi.org/10.1016/j.intell.2013.07.009
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. https://doi.org/10.1016/j.intell.2015.01.009
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.
https://doi.org/10.1002/per.634
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