### Correlations among Hofstede's Cultural Values, Diversity, GINI, and IQ

Similar to the code I posted for ANOVA with Hofstede's Cultural Values and Economic Outcomes, you can perform correlations on the same data, for the same countries and for the similar vectors. The data file is linked via Google Drive at the end of this post.

Countries

Data Columns

Example Code

Example Results

Example Graph

Sample Data

Countries

```
[1] Australia Austria Belgium Canada Denmark
[6] Finland France Germany Greece Iceland
[11] Ireland Italy Japan Korea Luxembourg
[16] Netherlands New Zealand Norway Portugal Spain
[21] Sweden Switzerland United Kingdom United States
```

Data Columns

```
[1] "Country" "HofstederPowerDx"
[3] "HofstederIndividuality" "HofstederMasculinity"
[5] "HofstederUncertaintyAvoidance" "Diversity_Ethnic"
[7] "Diversity_Linguistic" "Diversity_Religious"
[9] "ReligionMatters" "Protestantism"
[11] "Religiosity" "IQ"
[13] "Gini" "Employment"
[15] "Unemployment" "EduReading"
[17] "EduScience" "TertiaryEdu"
[19] "LifeExpectancy" "InfantDeath"
[21] "Obesity" "HoursWorked"
[23] "Prison" "Carvandalism"
[25] "Cartheft" "Theftfromcar"
[27] "Motorcycletheft" "Bicycletheft"
[29] "Assaultsandthreats" "Sexualincidents"
[31] "Burglaries" "Robberies"
```

Example Code

```
oecdData <- read.table("OECD - Quality of Life.csv", header = TRUE, sep = ",")
print(names(oecdData))
# Correlations with Hofstede's cultural dimensions
#, various measures of diversity, and GINI coefficient
v1 <- oecdData$Gini
v2 <- oecdData$HofstederPowerDx
v3 <- oecdData$HofstederMasculinity
v4 <- oecdData$HofstederIndividuality
v5 <- oecdData$HofstederUncertaintyAvoidance
v6 <- oecdData$Diversity_Ethnic
v7 <- oecdData$Diversity_Linguistic
v8 <- oecdData$Diversity_Religious
v9 <- oecdData$IQ
cor.test(v1, v2)
cor.test(v1, v3)
cor.test(v1, v4)
cor.test(v1, v5)
cor.test(v1, v6)
cor.test(v1, v7)
cor.test(v1, v8)
cor.test(v1, v9)
# Correlations with Hofstede's cultural dimensions
# , various measures of diversity, and IQ
v1 <- oecdData$IQ
v2 <- oecdData$HofstederPowerDx
v3 <- oecdData$HofstederMasculinity
v4 <- oecdData$HofstederIndividuality
v5 <- oecdData$HofstederUncertaintyAvoidance
v6 <- oecdData$Diversity_Ethnic
v7 <- oecdData$Diversity_Linguistic
v8 <- oecdData$Diversity_Religious
v9 <- oecdData$Gini
cor.test(v1, v2)
cor.test(v1, v3)
cor.test(v1, v4)
cor.test(v1, v5)
cor.test(v1, v6)
cor.test(v1, v7)
cor.test(v1, v8)
cor.test(v1, v9)
```

Example Results

```
> # Correlations with Gini, Hofstede's cultural dimensions and IQ
+ cor.test(v1, v3)
+ cor.test(v1, v8)
+ cor.test(v1, v9)
Pearson's product-moment correlation
data: v1 and v3
t = 0.74784, df = 20, p-value = 0.4633
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.2758586 0.5484062
sample estimates:
cor
0.1649321
Pearson's product-moment correlation
data: v1 and v8
t = 2.2127, df = 20, p-value = 0.03871
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.02688847 0.72881171
sample estimates:
cor
0.4434696
Pearson's product-moment correlation
data: v1 and v9
t = -1.6891, df = 20, p-value = 0.1067
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.67446739 0.08022647
sample estimates:
cor
-0.3533332
>
```

Example Graph

```
```

Sample Data

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