R Final Project

Introduction

The project attempts to investigate the impact of cultural tightness on values and beliefs of people in different societies. Cultural tightness, a new cultural dimension developed by Gelfand in 2006, refers to the strength of norms and degree of sanctioning within societies. Research has identified its influence on creativity, leadership, and personality, acknowledging its vital role on shaping people’s mind.

Packages Required

Following are the packages used in this project:

library(tidyverse)    #creating tidy data
library(readxl)       #reading data in excel
library(magrittr)     #pipe operator
library(ggplot2)      #visualizing data
library(dplyr)        #transforming data

Data Preparation

Data Import

  • Cultural Tightness Score: The data is collect from Gelfand’s research, which calculates the tightness score of 33 countries.
TS<-read_excel("/Users/shienhao/Desktop/R/working directory/Tightness score.XLSX")
WVS<-read_excel("/Users/shienhao/Desktop/R/working directory/World Values Survey Aggregate Data.XLSX", sheet = "Data")

Data Transformation

In order to examine the relationship between cultural tightness score and values, we first need to merge the two datasets by country names.

mergeddata <- merge(WVS,TS,by= "CNAME")

The World Values Survey Aggregate Data consists research result of wave 1-4. Compared with wave 1,2, and 4, wave 3 contains the most comprehensive result. First we need to filter the data to get the result of wave 3.

WVS3<-select(mergeddata,ends_with("3"))

Then we create varibles that is of interest to this project. They include:environment belief, gender views, passiveness, and religious devotion.

envir_belief<-select(WVS3,starts_with("ENV"))%>%rowMeans(na.rm=TRUE)
gender_belief<-select(WVS3, "TOLERA_3", "JOBS1_3","MENPOL_3", "BOYSCH_3")%>%rowMeans(na.rm=TRUE)
passiveness<-select(WVS3, "POL1_3", "POL2_3","POL3_3", "POL4_3")%>%rowMeans(na.rm=TRUE)
religious_devotion<-select(WVS3, "IMPREL_3","EVIL_3","ATTEND_3","GODIMP_3")%>%rowMeans(na.rm=TRUE)

Adding these variables to the merged dataset, we have the final dataset now.

Final<-cbind(WVS3, envir_belief=envir_belief,gender_belief=gender_belief,passiveness=passiveness)

Summary of Variables

Although there are 139 variables in the “Final” dataset, only a few are of concern to the project. Following is the summary of these variables.

Tightness_score:envir_belief:A higher scores means more emphasis for environment.

gender_belief:A higher score means lower gender equality.

passiveness: A higher score means greater passive views toward politics.

religious_devotion: A higher score means stronger devotion

Exploratory Data Analysis

ggplot(data=TS, aes(x= CNAME, y= Tightness_score))+geom_col()+theme(axis.text.x  = element_text(angle = 45, vjust = 1, hjust = 1))+ggtitle("Figure 1:Tightness Score of 33 Countries")

plot(Tightness_score, envir_belief, main = "Figure2:tightness score vs environment belief", xlab="tightness score", ylab = "environment belief", pch=19)

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plot(Tightness_score, gender_belief, main = "Figure3:tightness score vs gender equalityf", xlab="tightness score", ylab = "gender_equality", pch=19)

plot(Tightness_score, passiveness, main = "Figure4:tightness score vs passiveness", xlab="tightness score", ylab = "passiveness", pch=19)

Then we calculate the correlation between tightness score and the 4 variables.

cor(envir_belief, Tightness_score, use = "complete.obs")
cor(gender_belief, Tightness_score,use = "complete.obs")
cor(passiveness, Tightness_score, use = "complete.obs")
cor(religious_devotion, Tightness_score, use = "complete.obs")

Summary