RQ3 Pre vs Post Stress

#install.packages("readxl")
#install.packages("ggpubr")
#install.packages("effectsize")
#install.packages("rstatix")


#Load Librabries

library(readxl)
library(ggpubr)

## Loading required package: ggplot2

library(effectsize)





Dataset6.3 <- read_excel("/Users/ha113ab/Desktop/datasets/Research Assignment 6/Dataset6.3.xlsx")


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

Differences <- After - Before

#Descriptive Statistics

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

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

#histogram is symmetrical and the kurosis is als0 bell shaped seems to be normally distributed and 

#Method 2 Boxplot

boxplot(Dataset6.3$Stress_Post - Dataset6.3$Stress_Pre, 
        main = "Distribution of Difference Scores (Post - Pre)",
        ylab = "Difference in Scores",
        col = "green",
        border = "black")

# Normal Distribution there are no outliers


# since the data is  normal
# we'll use Dependet T-test

shapiro.test(Differences)

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

#p-value = 0.1745 Greater than 0.5 proceed with dependent t-test

#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

#p-value = 0.001

#Since the P value is less than 0.5 we will proceed with calculating the Effect Size with cohens D

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]

#0.66 it was a medium effect size.

"There is a significant change in stress levels between students who participated in the mindfulness 
training program before and after. Stress_Pre (M = 65.86, SD = 9.49) and Stress_Post (M =57.91, SD = 10.17), 
t(34) = 3.9286, p < .001 The effect size was medium (Cohen’s d = 0.66).)."

## [1] "There is a significant change in stress levels between students who participated in the mindfulness \ntraining program before and after. Stress_Pre (M = 65.86, SD = 9.49) and Stress_Post (M =57.91, SD = 10.17), \nt(34) = 3.9286, p < .001 The effect size was medium (Cohen’s d = 0.66).)."