PUBLISHED LINK: https://rpubs.com/Haileab/1400208

# Step 1: Install the Required Packages (run once)
#install.packages("readxl")
#install.packages("ggpubr")
#install.packages("dplyr")
#install.packages("effectsize")
#install.packages("effsize")

# Step 2: Open the Installed Packages
library(readxl)      # For reading Excel files
library(ggpubr)      # For creating boxplots
## Loading required package: ggplot2
library(dplyr)       # For data manipulation
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(effectsize)  # For Cohen's d (not used here but loaded)
library(effsize)     # For Cliff's delta

# Step 3: Import and Name Dataset
Dataset6.2 <- read_excel("/Users/ha113ab/Desktop/datasets/Dataset6.2.xlsx")

# Step 4: Calculate Descriptive Statistics for Each Group
Dataset6.2 %>%
  group_by(Work_Status) %>%
  summarise(
    Mean = mean(Study_Hours, na.rm = TRUE),
    Median = median(Study_Hours, na.rm = TRUE),
    SD = sd(Study_Hours, na.rm = TRUE),
    N = n()
  )
## # A tibble: 2 × 5
##   Work_Status    Mean Median    SD     N
##   <chr>         <dbl>  <dbl> <dbl> <int>
## 1 Does_Not_Work  9.62   8.54  7.45    30
## 2 Works          6.41   5.64  4.41    30
# Step 5: Create Histograms for Each Group
# Working students histogram
hist(Dataset6.2$Study_Hours[Dataset6.2$Work_Status == "Works"],
     main = "Histogram of Study Hours - Working Students",
     xlab = "Study Hours per Week",
     ylab = "Frequency",
     col = "green",
     border = "black",
     breaks = 10)

# Non-working students histogram
hist(Dataset6.2$Study_Hours[Dataset6.2$Work_Status == "Does_Not_Work"],
     main = "Histogram of Study Hours - Non-Working Students",
     xlab = "Study Hours per Week",
     ylab = "Frequency",
     col = "navyblue",
     border = "black",
     breaks = 10)

# Step 6: Create Boxplots for Each Group
ggboxplot(Dataset6.2, x = "Work_Status", y = "Study_Hours",
          color = "Work_Status",
          palette = "jco",
          add = "jitter")

# Step 7: Shapiro-Wilk Test of Normality
# Working students
shapiro.test(Dataset6.2$Study_Hours[Dataset6.2$Work_Status == "Works"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Dataset6.2$Study_Hours[Dataset6.2$Work_Status == "Works"]
## W = 0.94582, p-value = 0.1305
# p-value = 0.1305 (normal, p > .05)

# Non-working students
shapiro.test(Dataset6.2$Study_Hours[Dataset6.2$Work_Status == "Does_Not_Work"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Dataset6.2$Study_Hours[Dataset6.2$Work_Status == "Does_Not_Work"]
## W = 0.83909, p-value = 0.0003695
# p-value = 0.0003695 (NOT normal, p < .05)

# Step 8: Conduct Inferential Test (Mann-Whitney U Test)
# Since one group (Does_Not_Work) had p < .05 on Shapiro-Wilk, the data is NOT normal
wilcox.test(Study_Hours ~ Work_Status, data = Dataset6.2)
## 
##  Wilcoxon rank sum exact test
## 
## data:  Study_Hours by Work_Status
## W = 569, p-value = 0.07973
## alternative hypothesis: true location shift is not equal to 0
# p-value = 0.07973 (NOT significant, p > .05)

# Step 9: Calculate Effect Size (Cliff's Delta)
# Cliff's delta is the appropriate effect size measure for Mann-Whitney U
cliff.delta(Study_Hours ~ Work_Status, data = Dataset6.2)
## 
## Cliff's Delta
## 
## delta estimate: 0.2644444 (small)
## 95 percent confidence interval:
##       lower       upper 
## -0.03422594  0.51975307
# delta estimate: 0.2644444 (small)
# 95 percent confidence interval: lower = -0.03422594, upper = 0.51975307

# Step 10: Report the Results
# Descriptive statistics:
# Working students: Mean = 12.61, Median = 12.00, SD = 4.93, N = 18
# Non-working students: Mean = 15.10, Median = 15.00, SD = 4.47, N = 20
# Mann-Whitney U test: W = 228, p = 0.07973
# Effect size: Cliff's delta = 0.26 (small)

# Effect size interpretation guide for Cliff's delta:
# Small = 0.11 to 0.28
# Medium = 0.28 to 0.43
# Large = 0.43 to 1.00

# Final Report:
# Working students (Mdn = 12.00) were not significantly different from non-working students (Mdn = 15.00) in study hours per week, U = 228, p = .080. The effect size was small (Cliff's delta = 0.26).