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

Attractive Confusion Matrices in R Plotted with fourfoldplot

-
Image
As part of writing analyses using neural networks I thought of displaying a confusion matrix, but went looking for something more. What I found was either ugly and simple, or attractive but complicated. The following code demonstrates using fourfoldplot, with original insight gained from fourfoldplot: A prettier confusion matrix in base R , and a great documentation page, R Documentation's page for fourfoldplot . The code is below, as is the related graph. Source data can be found here . ############################################### # Load and clean data ################################################ Politics.df <- read.csv("BigFiveScoresByState.csv", na.strings = c("", "NA")) Politics.df <- na.omit(Politics.df) ################################################ # Neural Net with nnet and caret ################################################ # load packages library(nnet) library(caret) # set
Read more

Calculating Value at Risk (VaR) with Python or R

-
Image
The following modules linked below are based on a Pluralsight course, Understanding and Applying Financial Risk Modeling Techniques , and while the code itself is nearly verbatim, this is mostly for my own development, working through the peculiarities of Value at Risk (VaR) in both R and Python, and adding commentary as needed. The general outline of this process is as follows: Load and clean Data Calculate returns Calculate historical variance Calculate systemic, idiosyncratic, and total variance Develop a range of stress variants, e.g. scenario-based possibilities Calculate VaR as the worst case loss in a period for a particular probability The modules: In R: Financial Risk - Calculating Value At Risk (VaR) with R In Python: Financial Risk - Calculating Value At Risk (VaR) with Python
Read more