##Opening libraries.

library("readxl")
library("ggplot2")
library("rcompanion")

##Importing data

A5Q2 <- read_excel("A5Q2.xlsx")

##Contigency table

MyTable <- table(A5Q2$nationality, A5Q2$scholarship_awarded)
MyTable
##                
##                   0   1
##   Domestic       39 111
##   International 118  32

##Bar charts

barplot(MyTable, beside = TRUE,
        col = rainbow(nrow(MyTable)),
        legend = rownames(MyTable))

##Chi-Square Test of Independence

chisq.test(MyTable)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  MyTable
## X-squared = 81.297, df = 1, p-value < 2.2e-16

##Cramer’s V (Effect Size)

rcompanion::cramerV(MyTable)
## Cramer V 
##   0.5272

##Decriptive statistics

A Chi-Square Test of Independence was conducted to determine if there was an association between Variable 1 and Variable 2.

The results showed that there was an association between the two variables, χ²(1) = 81.30, p = <001

The association was strong, (Cramer’s V = .53)