DEPENDENT T-TEST & WILCOXON SIGN RANK

NULL HYPOTHESIS (H0)
There is no difference between the communication skills before training and after training of employees.
ALTERNATE HYPOTHESIS (H1)
There is a difference between the communication skills before training and after training of employees.

LOAD THE PACKAGE

library(readxl)

IMPORT EXCEL FILE INTO R STUDIO

dataset <- read_excel("C:\\Users\\burug\\Downloads\\A6R3.xlsx")

CALCULATE THE DIFFERENCE SCORES

Before <- dataset$PreTraining
After <- dataset$PostTraining


Differences <- After - Before

CREATE THE HISTOGRAMS

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

QUESTION 1: Is the histograms symmetrical, positively skewed, or negatively skewed?
ANSWER: Negatively Skewed

QUESTION 2: Did the histogram look too flat, too tall, or did it have a proper bell curve?
ANSWER: Too Tall

SHAPIRO-WILK TEST

shapiro.test(Differences)
## 
##  Shapiro-Wilk normality test
## 
## data:  Differences
## W = 0.98773, p-value = 0.21

QUESTION 1: Was the data normally distributed or abnormally distributed?

ANSWER: Normally Distributed because p value is greater than 0.05

BOXPLOT

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

QUESTION 1: How many dots are in your boxplot?

ANSWER: One dot

QUESTION 2: Where are the dots in your boxplot?

ANSWER: One dot far from whiskers

QUESTION 3: Based on the dots and there location, is the data normal?

ANSWER: Data Is normal

DESCRIPTIVES FOR BEFORE SCORES

mean(Before, na.rm = TRUE)
## [1] 59.73333
median(Before, na.rm = TRUE)
## [1] 60
sd(Before, na.rm = TRUE)
## [1] 7.966091
length(Before)
## [1] 150

DESCRIPTIVES FOR AFTER SCORES

mean(After, na.rm = TRUE)
## [1] 69.24
median(After, na.rm = TRUE)
## [1] 69.5
sd(After, na.rm = TRUE)
## [1] 9.481653
length(After)
## [1] 150

DEPENDENT T-TEST

t.test(Before, After, paired = TRUE)
## 
##  Paired t-test
## 
## data:  Before and After
## t = -23.285, df = 149, p-value < 2.2e-16
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -10.313424  -8.699909
## sample estimates:
## mean difference 
##       -9.506667

LOAD THE PACKAGE

library(effectsize)

CALCULATE COHEN’S D

cohens_d(Before, After, paired = TRUE)
## For paired samples, 'repeated_measures_d()' provides more options.
## Cohen's d |         95% CI
## --------------------------
## -1.90     | [-2.17, -1.63]

QUESTION 1: What is the size of the effect?
ANSWER: A Cohen’s D of 1.90 indicates the difference between the group averages was very large.

QUESTION 2: Which group had the higher average score?
ANSWER: After training employees had more communication skills

REPORT
A dependent t-test was conducted to improve employee communication skills by participating in a training program. among 150 participants. Results showed that after training (M = 69.24, SD = 9.49) were significantly higher than before training (M = 59.74, SD = 7.97), t= -23.285, p < .05. The effect size was Cohen’s d = 1.90, indicating a large effect. These results suggest that the communication skills of employees improved after training.