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

Loading the libraries

Step 3: Import and Name Dataset

Dataset6.3 <- read_excel("/Users/manindra/Downloads/Dataset6.3.xlsx")

Dataset6.3 is imported

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

Descriptive Statistics before Mean 65.86954 Median 67.33135 Sd 9.496524

Descriptive Statistics after Mean 57.90782 Median 59.14539 Sd 10.1712

Step 6: Create a Histogram of the Difference Scores

hist(Differences,
     main = "Histogram of Difference Scores",
     xlab = "Value",
     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 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

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

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

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)
)

library(rstatix)

wilcox_effsize(df_long, score ~ time, paired = TRUE)
## # A tibble: 1 × 7
##   .y.   group1 group2 effsize    n1    n2 magnitude
## * <chr> <chr>  <chr>    <dbl> <int> <int> <ord>    
## 1 score After  Before   0.562    35    35 large

A tibble: 1 × 7 .y. group1 group2 effsize n1 n2 magnitude *
1 score After Before 0.562 35 35 large

Step 11:Report the results: There was a significant difference in the dependent variable between Stress Pre (M = xx.xx, SD = xx.xx) and Stress Pro (M = = xx.xx, SD = xx.x), t(df) = xx.xx, p = .xxx. The effect size was large (Cohen’s d = 0.83).