Installing the Packages install.packages(“readxl”) install.packages(“ggpubr”) install.packages(“effectsize”) install.packages(“rstatix”)

Loading the 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 Dataset:

Dataset6.3 <- read_excel("C:/Users/datta/Downloads/Dataset6.3.xlsx")

Seperate the Data by Condition:

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

Calculating 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

Creating a Histogram of the Difference Scores:

hist(Differences,
     main = "Histogram of Difference Scores",
     xlab = "Value",
     ylab = "Frequency",
     col = "red",
     border = "black",
     breaks = 20)

Creating a Boxplot of the Difference Scores:

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

Shapiro-Wilk Test of Normality:

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

The p-value is 0.1745 (>.05). The data is normal, we need to continue with Dependent T-Test to conduct Inferentinal Test.

Conduct Inferential Test: Dependent T-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

Since the results are significant (p-value <.05), we have to calculate the effect size.

Calculating the Effect Size: Cohen’s D for Dependent T-Test

t_result <- t.test(Before, After, paired = TRUE)
effectsize::cohens_d(t_result)
## For paired samples, 'repeated_measures_d()' provides more options.
## Cohen's d |       95% CI
## ------------------------
## 0.66      | [0.29, 1.03]

The effect size is 0.66, which is medium in magnitude.

Reporting Results: There was a significant difference in stress levels between Before joining mindfulness training program (M = 65.869, SD = 9.496) and After joining mindfulness training program (M = 57.907, SD = 10.171), t(34) = 3.9286, p < .001. The effect size was medium (Cohen’s d = 0.66).