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 
 
[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

Comments

Post a Comment

Popular posts from this blog

Charting Correlation Matrices in R

-
Image
I noticed this very simple, very powerful article by James Marquez, Seven Easy Graphs to Visualize Correlation Matrices in R , in the Google+ community, R Programming for Data Analysis , so thought to give it a try, since I started some of my current analyses a decade ago by generating correlation matrices in Excel, which I've sometimes redone and improved in R. Some of these packages are only designed for display, or as extensions to ggplot2: corrplot: Visualization of a Correlation Matrix GGally: Extension to 'ggplot2' ggcorrplot: Visualization of a Correlation Matrix using 'ggplot2' These two are focused on more complex analysis: PerformanceAnalytics: Econometric tools for performance and risk analysis psych: Procedures for Psychological, Psychometric, and Personality Research As for data, I used Hofstede's culture dimensions, limited to developed countries. Using a broader and larger set of of countries would significantly reduce the correlation
Read more

Developers in New York City by Zip Code

-
Image
Photo by Lukas Kloeppel from Pexels In 2016, after reading a Dice Insight article, I downloaded data that had technology professional numbers by zip code, along with density. A recent NY Times article How Big Tech Is Turning New York Into a Silicon Valley Rival prompted me to resurrect the data, decorate it a bit with neighborhood names, and then import it into Google Maps , which was surprisingly easy. The original data is available in Excel .
Read more

Cultural Dimensions and Coffee Consumption

-
Image
Photo by Viktoria Alipatova from Pexels Responding to a Treehugger article, Why Americans will never love tea as much as coffee , I initially wrote my personal preferences for tea and coffee , ending with, BTW, this has just given me an idea for comparing Hofstede's cultural dimensions and coffee and tea consumption. Afterward, I did some analysis in Excel, then ran the same processes in R using Visual Studio, then converted that to a Jupyter Notebook on Microsoft's Azure Notebooks . Although this analysis is limited to 45 countries that have Hofstede's Cultural Dimensions , as well as per capita consumption for both coffee and tea, it would seem that coffee consumption correlates with power distance, individuality, and masculinity. Tea had small correlations with the dimensions and sometimes in the same direction as coffee. A fuller analysis is available on Microsoft's Azure Notebook , but some quick findings: Higher power distance, lower coffee consumption
Read more