Hypothesis

Null hypotheses :- There is no difference in employees’ communication skills before and after the training program.

Alternate Hypotheses :- There is a difference in employees’ communication skills before and after the training program.

Result:

A dependent t-test was conducted to compare employees’ communication skills before and after participating in a professional communication training program (N = 150). Results showed that communication scores after training (M = 69.24, MDN = 69.5, SD = 9.48) were significantly higher than scores before training (M = 59.73, MDN = 60, SD = 7.97), t(149) = -23.285, p < .001. The effect size was Cohen’s d = 1.90, indicating a very large effect. Thus alternate hypotheses is supported, that there is a difference in employees’ communication skills before and after the training program.

#install.packages("readxl")
library(readxl)
dataset <- read_excel("~/Downloads/A6R3.xlsx")
Before <- dataset$PreTraining
After <- dataset$PostTraining

Differences <- After - Before
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?

The histogram is symmetrical

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

The histogram look too flat

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?

The data is normally distributed

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?

No dots.

QUESTION 2: Where are the dots in your boxplot?

There are no dots.

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

If there are no dots, the data is normal.

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
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
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
# install.packages("effectsize")
library(effectsize)
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?

A Cohen’s D of 1.90 indicates the difference between the group averages was large.

QUESTION 2: Which group had the higher average score?

The After scores were higher than the before scores.