RS 3

Research Scenario 6.3: Mindfulness Training and Stress

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

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

Pre_Test <- Dataset6.3$Stress_Pre
Post_Test <- Dataset6.3$Stress_Post

Differences <- Post_Test - Pre_Test
print(Differences)

##  [1] -22.903638 -22.142247  12.137753  -7.396717  -5.477719 -25.048371
##  [7] -13.486837  -6.827396  -7.950267   6.516529  -7.062918 -19.258741
## [13]   0.655531 -20.068451  -5.308485  -8.049830 -20.451612 -13.696728
## [19]  -9.567336 -18.117255 -26.261721  -2.517369 -12.999376  21.424030
## [25]   2.706774   6.670166  16.860869   1.306624  -5.777144   9.455990
## [31] -16.661701 -15.088606 -14.976417 -12.220246 -17.077168

STEP 4: DESCRIPTIVE STATISTICS

mean(Pre_Test, na.rm = TRUE)

## [1] 65.86954

median(Pre_Test, na.rm = TRUE)

## [1] 67.33135

sd(Pre_Test, na.rm = TRUE)

## [1] 9.496524

mean(Post_Test, na.rm = TRUE)

## [1] 57.90782

median(Post_Test, na.rm = TRUE)

## [1] 59.14539

sd(Post_Test, na.rm = TRUE)

## [1] 10.1712

STEP 5 & 6: VISUALIZATIONS (Difference Score)

hist(Differences,
     main = "Histogram of Difference Scores (Post - Pre)",
     xlab = "Change in Stress Levels",
     ylab = "Frequency",
     col = "purple",
     border = "darkblue",
     breaks = 15)

Skewness: Positively skewed ,“,”Kurtosis: Bell Curve. So based on Reports we would not use the Dependent T-test

Boxplot

boxplot(Differences,
        main = "Distribution of Score Differences (Post Stress - Pre Stress)",
        ylab = "Change in Stress Levels",
        col = "lightblue",
        border = "darkblue",
        horizontal = FALSE)

The box plot looks normal and the data appears to be within whiskers, Based on Reports we use the Dependent T-test

Method 3: Shapiro-Wilk

shapiro.test(Differences)

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

The value of p > 0.05, so the data is normal, so based on Reports we use the Dependent T-test

Interpretation: After conducting all three normality tests, it is clear that we must use a Dependent T-test.

Dependent T-Test

t.test(Pre_Test, Post_Test, paired = TRUE)

## 
##  Paired t-test
## 
## data:  Pre_Test and Post_Test
## 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

As the value of p < 0.05 (p = 0.0003972), this means the results were SIGNIFICANT.

Calculate the Effect Size

cohens_d <- effectsize::cohens_d(Pre_Test, Post_Test, paired = TRUE)

## For paired samples, 'repeated_measures_d()' provides more options.

print(cohens_d)

## Cohen's d |       95% CI
## ------------------------
## 0.66      | [0.29, 1.03]

As the size of the effect is (0.60), this indicates the effect is ‘Medium’.

Report the Results

There was a significant difference in stress between Pre-Stress Group (M = 65.86, SD = 9.49) and Post-Stress Group (M = 57.90, SD = 10.17), t(34) = 3.92, p < .0003972 The effect size was Medium (Cohen’s d = 66).