6.3

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

library(effsize)

Mindfulness Program Import dataset

Dataset6.3 <- read_excel("/Users/karim/Desktop/Dataset6.3.xlsx")

Separate the Data by Condition

Before <- Dataset6.3$Stress_Pre
After  <- Dataset6.3$Stress_Post

Create difference scores

Differences <- After - Before

Descriptive Statistics

Pre-Program Stress

mean(Before, na.rm = TRUE)

## [1] 65.86954

median(Before, na.rm = TRUE)

## [1] 67.33135

sd(Before, na.rm = TRUE)

## [1] 9.496524

Post-Program Stress

mean(After, na.rm = TRUE)

## [1] 57.90782

median(After, na.rm = TRUE)

## [1] 59.14539

sd(After, na.rm = TRUE)

## [1] 10.1712

Interpretation: Report means and SDs if data is normal. Report medians if data is not normal.

Step 6: Histogram of Difference Scores

hist(Differences,
     main = "Histogram of Stress Difference Scores (Post - Pre)",
     xlab = "Difference in Stress Scores",
     ylab = "Frequency",
     col = "blue",
     border = "black",
     breaks = 20)

Interpretation: Examine symmetry (skewness) Examine shape (kurtosis) If symmetrical and bell-shaped → likely normal

Step 7: Boxplot of Difference Scores

boxplot(Differences,
        main = "Distribution of Stress Score Differences",
        ylab = "Difference in Stress Scores",
        col = "lightblue",
        border = "darkblue")

Interpretation: Check for dots (outliers) If no extreme outliers → likely normal If extreme outliers → not normal

Step 8: Shapiro-Wilk Test of Normality

shapiro.test(Differences)

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

Interpretation:

p < .05 → Data NOT normal → Use Wilcoxon Sign Rank

Wilcoxon Sign

wilcox.test(Before, After, paired = TRUE)

## 
##  Wilcoxon signed rank exact test
## 
## data:  Before and After
## V = 518, p-value = 0.0005508
## alternative hypothesis: true location shift is not equal to 0

Step 10: Calculate Effect Size

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

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

There was a significant difference in stress levels between Pre-Program (M = 42.35, SD = 8.47) and Post-Program (M = 34.39, SD = 9.12), t(29) = 3.64, p = .001. The effect size was medium (Cohen’s d = 0.66)