Step 1: Install the Required Packages

install.packages(“readxl”) install.packages(“ggpubr”) install.packages(“effectsize”) install.packages(“rstatix”)

Step 2: Open the Installed Packages

library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(effectsize)
library(rstatix)
## 
## Attaching package: 'rstatix'
## The following objects are masked from 'package:effectsize':
## 
##     cohens_d, eta_squared
## The following object is masked from 'package:stats':
## 
##     filter

Step 3: Import and Name Dataset

Dataset6_3 <- read_excel("C:/Users/pavan/Desktop/Assignments/Assignment 6/Dataset6.3.xlsx")

Step 4: Seperate the Data by Condition

Before <- Dataset6_3$Stress_Pre
After  <- Dataset6_3$Stress_Post
Differences <- After - Before

Step 5: Calculate Descriptive Statistics for Each Group

mean(Before, na.rm = TRUE)
## [1] 65.86954
median(Before, na.rm = TRUE)
## [1] 67.33135
sd(Before, na.rm = TRUE)
## [1] 9.496524
mean(After, na.rm = TRUE)
## [1] 57.90782
median(After, na.rm = TRUE)
## [1] 59.14539
sd(After, na.rm = TRUE)
## [1] 10.1712

Step 6: Create a Histogram of the Difference Scores

hist(Differences,
     main = "Histogram of Difference Scores",
     xlab = "Stress Score Difference (After - Before)",
     ylab = "Frequency",
     col = "blue",
     border = "black",
     breaks = 20)

Step 7: Create a Boxplot of the Difference Scores

boxplot(Differences,
        main = "Distribution of Score Differences (After - Before)",
        ylab = "Difference in Stress Scores",
        col = "blue",
        border = "darkblue")

Step 8: Shapiro-Wilk Test of Normality

shapiro.test(Differences)
## 
##  Shapiro-Wilk normality test
## 
## data:  Differences
## W = 0.95612, p-value = 0.1745

Step 9: Conduct Inferential Test

t.test(Before, After, paired=TRUE)
## 
##  Paired t-test
## 
## data:  Before and After
## t = 3.9286, df = 34, p-value = 0.0003972
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##   3.843113 12.080317
## sample estimates:
## mean difference 
##        7.961715
effectsize::cohens_d(Before, After, paired = TRUE)
## For paired samples, 'repeated_measures_d()' provides more options.
## Cohen's d |       95% CI
## ------------------------
## 0.66      | [0.29, 1.03]

Step 10: Calculate the Effect Size

df_long <- data.frame(
  id = rep(1:length(Before), 2),
  time = rep(c("Before","After"), each=length(Before)),
  score = c(Before, After)
)

Step 11: Report the Results

There was a significant difference in stress levels between Before (M = 65.87, SD = 9.50) and After (M = 57.91, SD = 10.17), t(34) = 3.93, p < .001. The effect size was medium (Cohen’s d = 0.66).