R Notebook: The S factor in China (2015)
Emil O. W. Kirkegaard
About
This is an R notebook for the study:
- Kirkegaard, E. O. W. (2015). The S factor in China. The Winnower.
The paper was edited in March 2017 to improve readability.
Initialize
Load packages and data.
options(digits = 2)
library(pacman)
p_load(kirkegaard)
data = read.csv("data.csv", row.names=1)
colnames(data)## [1] "Number.of.participants" "IQ"
## [3] "Years.of.edu" "Higher.edu.pct."
## [5] "GDP.US.per.capita" "Ethnic.Han.pct"
## [7] "High.ed.per.100k.2013" "High.ed.per.100k.2012"
## [9] "High.ed.per.100k.2011" "High.ed.per.100k.2010"
## [11] "High.ed.per.100k.2009" "High.ed.per.100k.2008"
## [13] "Med.tech.pers.per.10k.2011" "Med.tech.pers.per.10k.2010"
## [15] "Med.tech.pers.per.10k.2009" "Med.tech.pers.per.10k.2008"
## [17] "Med.tech.pers.per.10k.2004" "life.expect.2010"
## [19] "Pop.soc.sur.13" "Pop.soc.sur.12"
## [21] "Pop.soc.sur.11" "Pop.soc.sur.09"
## [23] "Pop.soc.sur.08" "Pop.soc.sur.07"
## [25] "Pop.soc.sur.06" "Pop.soc.sur.05"
## [27] "Pop.soc.sur.04" "Pop.soc.sur.illit.13"
## [29] "Pop.soc.sur.illit.12" "Pop.soc.sur.illit.11"
## [31] "Pop.soc.sur.illit.09" "Pop.soc.sur.illit.08"
## [33] "Pop.soc.sur.illit.07" "Pop.soc.sur.illit.06"
## [35] "Pop.soc.sur.illit.05" "Pop.soc.sur.illit.04"
## [37] "Pop.size.13" "Pop.size.12"
## [39] "Pop.size.11" "Pop.size.10"
## [41] "Pop.size.09" "Pop.size.08"
## [43] "Pop.size.07" "Pop.size.06"
## [45] "Pop.size.05" "Pop.size.04"
## [47] "Pop.size.urban.13" "Pop.size.urban.12"
## [49] "Pop.size.urban.11" "Pop.size.urban.10"
## [51] "Pop.size.urban.09" "Pop.size.urban.08"
## [53] "Pop.size.urban.07" "Pop.size.urban.06"
## [55] "Pop.size.urban.05" "Pop.size.rural.13"
## [57] "Pop.size.rural.12" "Pop.size.rural.11"
## [59] "Pop.size.rural.10" "Pop.size.rural.09"
## [61] "Pop.size.rural.08" "Pop.size.rural.07"
## [63] "Pop.size.rural.06" "Pop.size.rural.05"
## [65] "Internet.users.13" "Internet.users.12"
## [67] "Internet.users.11" "Internet.users.10"
## [69] "Internet.users.09" "Internet.users.08"
## [71] "Internet.users.07" "Internet.users.06"
## [73] "Internet.users.05" "Internet.users.04"
## [75] "Invent.patent.13" "Invent.patent.12"
## [77] "Invent.patent.11" "Invent.patent.10"
## [79] "Invent.patent.09" "Invent.patent.08"
## [81] "Invent.patent.07" "Invent.patent.06"
## [83] "Invent.patent.05" "Invent.patent.04"
## [85] "Sci.personnel.11" "Sci.personnel.10"
## [87] "Sci.personnel.09" "Sci.personnel.08"
## [89] "Sci.personnel.07" "Sci.personnel.06"
## [91] "Sci.personnel.05" "Sci.personnel.04"Recode data.
##Population data
Pop.size = data[,grep("Pop.size.\\d",colnames(data))]
Pop.size.survey = data[,grep("Pop.soc.sur.\\d",colnames(data))]
Pop.size.urban = data[,grep("Pop.size.urban.\\d",colnames(data))]
Pop.size.rural = data[,grep("Pop.size.rural.\\d",colnames(data))]
Pop.size.pct.urban = Pop.size.urban/Pop.size[-10]
Pop.size.pct.urban.mean = data.frame(Pop.size.pct.urban.mean=apply(Pop.size.pct.urban,1,mean))
#illiteracy
Pop.size.survey.illit = data[,grep("Pop.soc.sur.illit.\\d",colnames(data))]
Illit.per.cap = Pop.size.survey.illit/Pop.size.survey
fa(Illit.per.cap)## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.## Factor Analysis using method = minres
## Call: fa(r = Illit.per.cap)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## Pop.soc.sur.illit.13 0.96 0.93 0.0749 1
## Pop.soc.sur.illit.12 0.98 0.97 0.0344 1
## Pop.soc.sur.illit.11 0.99 0.98 0.0197 1
## Pop.soc.sur.illit.09 1.00 0.99 0.0083 1
## Pop.soc.sur.illit.08 0.99 0.99 0.0122 1
## Pop.soc.sur.illit.07 0.98 0.96 0.0372 1
## Pop.soc.sur.illit.06 0.99 0.99 0.0112 1
## Pop.soc.sur.illit.05 0.98 0.96 0.0353 1
## Pop.soc.sur.illit.04 0.98 0.96 0.0392 1
##
## MR1
## SS loadings 8.73
## Proportion Var 0.97
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 34 with Chi Square of 883
## The degrees of freedom for the model are 27 and the objective function was 6.4
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 31 with the empirical chi square 0.47 with prob < 1
## The total number of observations was 31 with Likelihood Chi Square = 163 with prob < 2.4e-21
##
## Tucker Lewis Index of factoring reliability = 0.78
## RMSEA index = 0.45 and the 90 % confidence intervals are 0.35 0.47
## BIC = 70
## Fit based upon off diagonal values = 1Illit.g = as.numeric(fa(Illit.per.cap)$scores)## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.Illit.per.cap.mean = data.frame(Illit.per.cap.mean=apply(Illit.per.cap,1,mean))
#high ed per 100k
High.ed.per.100k = data[,grep("High.ed.per.100k.\\d",colnames(data))]
High.ed.per.100k.mean = data.frame(High.ed.per.100k.mean=apply(High.ed.per.100k,1,mean))
#med tech personnel per 10k
Med.tech.pers.per10kcap = data[,grep("Med.tech.pers.per.10k.\\d",colnames(data))]
Med.tech.pers.per10kcap.mean = data.frame(Med.tech.pers.per10kcap.mean=apply(Med.tech.pers.per10kcap,1,mean))
#internet users
Internet.users = data[,grep("Internet.users.\\d",colnames(data))]
Internet.users.per.capita = Internet.users/Pop.size
Internet.users.per.capita.mean = data.frame(Internet.users.per.capita.mean=apply(Internet.users.per.capita,1,mean))
#Invention patents
Invention.patents = data[,grep("Invent.patent.\\d",colnames(data))]
Invention.patents.per.cap = Invention.patents/Pop.size
Invention.patents.per.cap.mean = data.frame(Invention.patents.per.cap.mean=apply(Invention.patents.per.cap,1,mean))
#scientific personenel
Scientific.personnel = data[,grep("Sci.personnel.\\d",colnames(data))]
Scientific.personnel.per.cap = Scientific.personnel/Pop.size[-c(1:2)]
Scientific.personnel.per.cap.mean = data.frame(Scientific.personnel.per.cap.mean=apply(Scientific.personnel.per.cap,1,mean))
#Yearly intercorrelations
alpha(Pop.size.pct.urban)##
## Reliability analysis
## Call: alpha(x = Pop.size.pct.urban)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 1 1 1 0.99 1289 0.00029 0.5 0.15
##
## lower alpha upper 95% confidence boundaries
## 1 1 1
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Pop.size.urban.13 1 1 1 0.99 1353 0.00026
## Pop.size.urban.12 1 1 1 0.99 1241 0.00029
## Pop.size.urban.11 1 1 1 0.99 1094 0.00033
## Pop.size.urban.10 1 1 1 0.99 1050 0.00035
## Pop.size.urban.09 1 1 1 0.99 1056 0.00036
## Pop.size.urban.08 1 1 1 0.99 1058 0.00035
## Pop.size.urban.07 1 1 1 0.99 1091 0.00034
## Pop.size.urban.06 1 1 1 0.99 1162 0.00032
## Pop.size.urban.05 1 1 1 0.99 1288 0.00028
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Pop.size.urban.13 31 0.99 0.99 0.99 0.99 0.54 0.14
## Pop.size.urban.12 31 1.00 1.00 1.00 0.99 0.53 0.14
## Pop.size.urban.11 31 1.00 1.00 1.00 1.00 0.52 0.14
## Pop.size.urban.10 31 1.00 1.00 1.00 1.00 0.51 0.15
## Pop.size.urban.09 31 1.00 1.00 1.00 1.00 0.49 0.15
## Pop.size.urban.08 31 1.00 1.00 1.00 1.00 0.48 0.15
## Pop.size.urban.07 31 1.00 1.00 1.00 1.00 0.47 0.15
## Pop.size.urban.06 31 1.00 1.00 1.00 1.00 0.46 0.15
## Pop.size.urban.05 31 0.99 0.99 0.99 0.99 0.45 0.16alpha(Illit.per.cap)##
## Reliability analysis
## Call: alpha(x = Illit.per.cap)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.99 1 1 0.97 287 0.0012 0.088 0.069
##
## lower alpha upper 95% confidence boundaries
## 0.99 0.99 1
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Pop.soc.sur.illit.13 0.99 1 1 0.98 316 0.0010
## Pop.soc.sur.illit.12 0.99 1 1 0.97 259 0.0012
## Pop.soc.sur.illit.11 0.99 1 1 0.97 244 0.0013
## Pop.soc.sur.illit.09 0.99 1 1 0.97 234 0.0014
## Pop.soc.sur.illit.08 0.99 1 1 0.97 237 0.0014
## Pop.soc.sur.illit.07 0.99 1 1 0.97 263 0.0013
## Pop.soc.sur.illit.06 0.99 1 1 0.97 236 0.0014
## Pop.soc.sur.illit.05 0.99 1 1 0.97 261 0.0013
## Pop.soc.sur.illit.04 0.99 1 1 0.97 265 0.0013
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Pop.soc.sur.illit.13 31 0.97 0.97 0.97 0.96 0.060 0.071
## Pop.soc.sur.illit.12 31 0.98 0.98 0.99 0.98 0.060 0.060
## Pop.soc.sur.illit.11 31 0.99 0.99 0.99 0.99 0.060 0.051
## Pop.soc.sur.illit.09 31 0.99 1.00 1.00 0.99 0.083 0.069
## Pop.soc.sur.illit.08 31 0.99 0.99 0.99 0.99 0.089 0.067
## Pop.soc.sur.illit.07 31 0.98 0.98 0.98 0.98 0.096 0.069
## Pop.soc.sur.illit.06 31 0.99 0.99 0.99 0.99 0.108 0.081
## Pop.soc.sur.illit.05 31 0.99 0.98 0.98 0.98 0.124 0.082
## Pop.soc.sur.illit.04 31 0.98 0.98 0.98 0.98 0.115 0.077alpha(High.ed.per.100k)##
## Reliability analysis
## Call: alpha(x = High.ed.per.100k)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.99 1 1 0.98 367 0.0013 2346 987
##
## lower alpha upper 95% confidence boundaries
## 0.99 0.99 1
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## High.ed.per.100k.2013 0.99 1 1 0.99 381 0.0012
## High.ed.per.100k.2012 0.99 1 1 0.98 301 0.0015
## High.ed.per.100k.2011 0.99 1 1 0.98 262 0.0018
## High.ed.per.100k.2010 0.99 1 1 0.98 272 0.0019
## High.ed.per.100k.2009 0.99 1 1 0.98 301 0.0017
## High.ed.per.100k.2008 0.99 1 1 0.99 346 0.0014
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## High.ed.per.100k.2013 31 0.99 0.99 0.99 0.98 2493 856
## High.ed.per.100k.2012 31 0.99 0.99 0.99 0.99 2420 882
## High.ed.per.100k.2011 31 1.00 1.00 1.00 1.00 2338 913
## High.ed.per.100k.2010 31 1.00 1.00 1.00 1.00 2328 1046
## High.ed.per.100k.2009 31 1.00 0.99 0.99 0.99 2280 1097
## High.ed.per.100k.2008 31 0.99 0.99 0.99 0.99 2214 1167alpha(Med.tech.pers.per10kcap)##
## Reliability analysis
## Call: alpha(x = Med.tech.pers.per10kcap)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.99 1 1 0.99 364 0.0011 46 20
##
## lower alpha upper 95% confidence boundaries
## 0.99 0.99 1
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N
## Med.tech.pers.per.10k.2011 0.99 1 1 0.99 288
## Med.tech.pers.per.10k.2010 0.99 1 1 0.98 245
## Med.tech.pers.per.10k.2009 0.99 1 1 0.98 237
## Med.tech.pers.per.10k.2008 0.99 1 1 0.98 244
## Med.tech.pers.per.10k.2004 1.00 1 1 0.99 760
## alpha se
## Med.tech.pers.per.10k.2011 0.00140
## Med.tech.pers.per.10k.2010 0.00166
## Med.tech.pers.per.10k.2009 0.00175
## Med.tech.pers.per.10k.2008 0.00169
## Med.tech.pers.per.10k.2004 0.00047
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Med.tech.pers.per.10k.2011 31 1.00 0.99 1.00 0.99 51 22
## Med.tech.pers.per.10k.2010 31 1.00 1.00 1.00 1.00 49 21
## Med.tech.pers.per.10k.2009 31 1.00 1.00 1.00 1.00 47 21
## Med.tech.pers.per.10k.2008 31 1.00 1.00 1.00 1.00 43 20
## Med.tech.pers.per.10k.2004 31 0.98 0.98 0.98 0.98 39 16alpha(Internet.users.per.capita)##
## Reliability analysis
## Call: alpha(x = Internet.users.per.capita)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.99 0.99 1 0.94 154 0.0021 0.26 0.095
##
## lower alpha upper 95% confidence boundaries
## 0.98 0.99 0.99
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Internet.users.13 0.98 0.99 1 0.94 138 0.0025
## Internet.users.12 0.98 0.99 1 0.94 134 0.0025
## Internet.users.11 0.98 0.99 1 0.94 130 0.0026
## Internet.users.10 0.98 0.99 1 0.94 134 0.0026
## Internet.users.09 0.98 0.99 1 0.93 126 0.0027
## Internet.users.08 0.98 0.99 1 0.93 129 0.0027
## Internet.users.07 0.98 0.99 1 0.94 137 0.0024
## Internet.users.06 0.99 0.99 1 0.94 140 0.0020
## Internet.users.05 0.99 0.99 1 0.94 152 0.0018
## Internet.users.04 0.99 0.99 1 0.95 172 0.0017
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Internet.users.13 31 0.98 0.97 0.97 0.98 0.461 0.114
## Internet.users.12 31 0.99 0.98 0.98 0.98 0.423 0.119
## Internet.users.11 31 0.99 0.99 0.99 0.99 0.386 0.123
## Internet.users.10 31 0.99 0.98 0.98 0.98 0.347 0.108
## Internet.users.09 31 0.99 0.99 0.99 0.99 0.294 0.114
## Internet.users.08 31 0.99 0.99 0.98 0.99 0.237 0.121
## Internet.users.07 31 0.97 0.97 0.97 0.97 0.165 0.094
## Internet.users.06 31 0.96 0.97 0.97 0.95 0.110 0.064
## Internet.users.05 31 0.94 0.95 0.95 0.93 0.090 0.061
## Internet.users.04 31 0.91 0.93 0.93 0.90 0.078 0.058alpha(Scientific.personnel.per.cap)##
## Reliability analysis
## Call: alpha(x = Scientific.personnel.per.cap)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.98 0.98 0.99 0.86 50 0.0057 1.2 0.66
##
## lower alpha upper 95% confidence boundaries
## 0.97 0.98 0.99
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Sci.personnel.11 0.98 0.98 0.99 0.89 56 0.0051
## Sci.personnel.10 0.98 0.98 0.98 0.85 41 0.0070
## Sci.personnel.09 0.98 0.98 0.98 0.87 45 0.0066
## Sci.personnel.08 0.97 0.98 0.98 0.85 40 0.0073
## Sci.personnel.07 0.97 0.98 0.98 0.86 42 0.0070
## Sci.personnel.06 0.97 0.98 0.98 0.86 42 0.0070
## Sci.personnel.05 0.98 0.98 0.98 0.87 48 0.0062
## Sci.personnel.04 0.97 0.98 0.98 0.86 42 0.0069
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Sci.personnel.11 31 0.87 0.87 0.84 0.83 1.3 0.77
## Sci.personnel.10 31 0.96 0.96 0.96 0.95 1.1 0.59
## Sci.personnel.09 31 0.93 0.93 0.92 0.91 1.2 0.69
## Sci.personnel.08 31 0.97 0.97 0.97 0.96 1.2 0.68
## Sci.personnel.07 31 0.95 0.95 0.95 0.94 1.2 0.76
## Sci.personnel.06 31 0.96 0.96 0.96 0.94 1.2 0.78
## Sci.personnel.05 31 0.91 0.91 0.89 0.88 1.3 0.72
## Sci.personnel.04 31 0.95 0.95 0.95 0.93 1.2 0.68alpha(Invention.patents.per.cap)##
## Reliability analysis
## Call: alpha(x = Invention.patents.per.cap)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.97 1 1 0.96 240 0.0035 2.2 3.4
##
## lower alpha upper 95% confidence boundaries
## 0.96 0.97 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Invent.patent.13 0.97 1.00 1 0.97 258 0.0032
## Invent.patent.12 0.96 1.00 1 0.96 229 0.0044
## Invent.patent.11 0.96 1.00 1 0.96 213 0.0047
## Invent.patent.10 0.96 1.00 1 0.96 201 0.0044
## Invent.patent.09 0.96 0.99 1 0.96 197 0.0042
## Invent.patent.08 0.96 1.00 1 0.96 203 0.0040
## Invent.patent.07 0.97 1.00 1 0.96 199 0.0037
## Invent.patent.06 0.97 1.00 1 0.96 209 0.0036
## Invent.patent.05 0.97 1.00 1 0.96 219 0.0035
## Invent.patent.04 0.97 1.00 1 0.96 248 0.0034
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Invent.patent.13 31 0.98 0.96 0.96 0.96 5.10 6.8
## Invent.patent.12 31 0.99 0.97 0.97 0.98 3.91 5.5
## Invent.patent.11 31 0.99 0.98 0.98 0.99 3.12 4.7
## Invent.patent.10 31 1.00 0.99 0.99 0.99 2.27 3.6
## Invent.patent.09 31 0.99 1.00 1.00 0.99 1.89 3.2
## Invent.patent.08 31 0.98 0.99 0.99 0.98 1.67 3.2
## Invent.patent.07 31 0.98 0.99 0.99 0.98 1.31 2.4
## Invent.patent.06 31 0.97 0.99 0.99 0.97 1.08 2.0
## Invent.patent.05 31 0.96 0.98 0.98 0.96 0.91 1.8
## Invent.patent.04 31 0.94 0.96 0.96 0.94 0.67 1.3##New DF
data2 = cbind(data["IQ"],
data["Ethnic.Han.pct"]/100,
Pop.size.pct.urban.mean,
Illit.per.cap.mean,
High.ed.per.100k.mean,
Med.tech.pers.per10kcap.mean,
Internet.users.per.capita.mean,
Invention.patents.per.cap.mean,
Scientific.personnel.per.cap.mean,
data["Years.of.edu"],
data["Higher.edu.pct."],
data["GDP.US.per.capita"],
data["life.expect.2010"]
)Analyses
Main analyses
## S factor?
S.fa = fa(data2[-c(1:2)])
data2["S"] = as.numeric(S.fa$scores)
cors = wtd.cors(data2)
S.loadings = as.numeric(S.fa$loadings)
names(S.loadings) = dimnames(S.fa$loadings)[[1]]
#MCV
fa_Jensens_method(S.fa, data2, criterion = "IQ", loading_reversing = F) +
xlim(-.7, 1.1)## Using Pearson correlations for the criterion-indicators relationships.
silence(ggsave("MCV.png"))
#Main plots
GG_scatter(data2, "IQ", "S")
silence(ggsave("IQ_S.png"))
GG_scatter(data2[-2, ], "IQ", "S")
silence(ggsave("IQ_S_noB.png"))
GG_scatter(data2, "Ethnic.Han.pct", "IQ") +
xlab("Percent Han") +
scale_x_continuous(labels = scales::percent)
silence(ggsave("Han_IQ.png"))
GG_scatter(data2[-2, ], "Ethnic.Han.pct", "IQ") +
xlab("Percent Han") +
scale_x_continuous(labels = scales::percent)
silence(ggsave("Han_IQ_noB.png"))
GG_scatter(data2, "Ethnic.Han.pct", "S") +
xlab("Percent Han") +
scale_x_continuous(labels = scales::percent)
silence(ggsave("Han_S.png"))
GG_scatter(data2[-2, ], "Ethnic.Han.pct", "S") +
xlab("Percent Han") +
scale_x_continuous(labels = scales::percent)
silence(ggsave("Han_S_noB.png"))Correlations for meta-analysis
wtd.cors(data2)## IQ Ethnic.Han.pct
## IQ 1.00 0.586
## Ethnic.Han.pct 0.59 1.000
## Pop.size.pct.urban.mean 0.43 0.504
## Illit.per.cap.mean -0.20 -0.706
## High.ed.per.100k.mean 0.50 0.472
## Med.tech.pers.per10kcap.mean 0.31 0.183
## Internet.users.per.capita.mean 0.41 0.328
## Invention.patents.per.cap.mean 0.55 0.312
## Scientific.personnel.per.cap.mean 0.21 0.025
## Years.of.edu 0.32 0.680
## Higher.edu.pct. 0.38 0.237
## GDP.US.per.capita 0.46 0.379
## life.expect.2010 0.52 0.732
## S 0.43 0.452
## Pop.size.pct.urban.mean
## IQ 0.43
## Ethnic.Han.pct 0.50
## Pop.size.pct.urban.mean 1.00
## Illit.per.cap.mean -0.59
## High.ed.per.100k.mean 0.82
## Med.tech.pers.per10kcap.mean 0.83
## Internet.users.per.capita.mean 0.90
## Invention.patents.per.cap.mean 0.82
## Scientific.personnel.per.cap.mean 0.69
## Years.of.edu 0.80
## Higher.edu.pct. 0.84
## GDP.US.per.capita 0.92
## life.expect.2010 0.86
## S 0.97
## Illit.per.cap.mean High.ed.per.100k.mean
## IQ -0.20 0.50
## Ethnic.Han.pct -0.71 0.47
## Pop.size.pct.urban.mean -0.59 0.82
## Illit.per.cap.mean 1.00 -0.45
## High.ed.per.100k.mean -0.45 1.00
## Med.tech.pers.per10kcap.mean -0.37 0.84
## Internet.users.per.capita.mean -0.40 0.73
## Invention.patents.per.cap.mean -0.31 0.88
## Scientific.personnel.per.cap.mean -0.15 0.72
## Years.of.edu -0.92 0.72
## Higher.edu.pct. -0.37 0.90
## GDP.US.per.capita -0.42 0.76
## life.expect.2010 -0.73 0.76
## S -0.54 0.91
## Med.tech.pers.per10kcap.mean
## IQ 0.31
## Ethnic.Han.pct 0.18
## Pop.size.pct.urban.mean 0.83
## Illit.per.cap.mean -0.37
## High.ed.per.100k.mean 0.84
## Med.tech.pers.per10kcap.mean 1.00
## Internet.users.per.capita.mean 0.83
## Invention.patents.per.cap.mean 0.89
## Scientific.personnel.per.cap.mean 0.81
## Years.of.edu 0.67
## Higher.edu.pct. 0.97
## GDP.US.per.capita 0.77
## life.expect.2010 0.64
## S 0.92
## Internet.users.per.capita.mean
## IQ 0.41
## Ethnic.Han.pct 0.33
## Pop.size.pct.urban.mean 0.90
## Illit.per.cap.mean -0.40
## High.ed.per.100k.mean 0.73
## Med.tech.pers.per10kcap.mean 0.83
## Internet.users.per.capita.mean 1.00
## Invention.patents.per.cap.mean 0.82
## Scientific.personnel.per.cap.mean 0.65
## Years.of.edu 0.62
## Higher.edu.pct. 0.80
## GDP.US.per.capita 0.88
## life.expect.2010 0.73
## S 0.88
## Invention.patents.per.cap.mean
## IQ 0.55
## Ethnic.Han.pct 0.31
## Pop.size.pct.urban.mean 0.82
## Illit.per.cap.mean -0.31
## High.ed.per.100k.mean 0.88
## Med.tech.pers.per10kcap.mean 0.89
## Internet.users.per.capita.mean 0.82
## Invention.patents.per.cap.mean 1.00
## Scientific.personnel.per.cap.mean 0.69
## Years.of.edu 0.59
## Higher.edu.pct. 0.94
## GDP.US.per.capita 0.80
## life.expect.2010 0.68
## S 0.90
## Scientific.personnel.per.cap.mean
## IQ 0.207
## Ethnic.Han.pct 0.025
## Pop.size.pct.urban.mean 0.695
## Illit.per.cap.mean -0.153
## High.ed.per.100k.mean 0.721
## Med.tech.pers.per10kcap.mean 0.814
## Internet.users.per.capita.mean 0.651
## Invention.patents.per.cap.mean 0.687
## Scientific.personnel.per.cap.mean 1.000
## Years.of.edu 0.453
## Higher.edu.pct. 0.800
## GDP.US.per.capita 0.667
## life.expect.2010 0.470
## S 0.731
## Years.of.edu Higher.edu.pct.
## IQ 0.32 0.38
## Ethnic.Han.pct 0.68 0.24
## Pop.size.pct.urban.mean 0.80 0.84
## Illit.per.cap.mean -0.92 -0.37
## High.ed.per.100k.mean 0.72 0.90
## Med.tech.pers.per10kcap.mean 0.67 0.97
## Internet.users.per.capita.mean 0.62 0.80
## Invention.patents.per.cap.mean 0.59 0.94
## Scientific.personnel.per.cap.mean 0.45 0.80
## Years.of.edu 1.00 0.67
## Higher.edu.pct. 0.67 1.00
## GDP.US.per.capita 0.63 0.79
## life.expect.2010 0.86 0.65
## S 0.80 0.94
## GDP.US.per.capita life.expect.2010 S
## IQ 0.46 0.52 0.43
## Ethnic.Han.pct 0.38 0.73 0.45
## Pop.size.pct.urban.mean 0.92 0.86 0.97
## Illit.per.cap.mean -0.42 -0.73 -0.54
## High.ed.per.100k.mean 0.76 0.76 0.91
## Med.tech.pers.per10kcap.mean 0.77 0.64 0.92
## Internet.users.per.capita.mean 0.88 0.73 0.88
## Invention.patents.per.cap.mean 0.80 0.68 0.90
## Scientific.personnel.per.cap.mean 0.67 0.47 0.73
## Years.of.edu 0.63 0.86 0.80
## Higher.edu.pct. 0.79 0.65 0.94
## GDP.US.per.capita 1.00 0.77 0.89
## life.expect.2010 0.77 1.00 0.84
## S 0.89 0.84 1.00