Research Question

The CEO of a company evaluated the communication skills (multiple-item rating scale) of all employees and found that, on average, their performance was below the company’s desired standard. To address this gap, all employees participated in a professional communication training program. The CEO now wants to determine whether the training has led to measurable improvements in employees’ communication abilities. Is there an improvement in the employees’ communication skills?

Hypothesis

Null Hypothesis (H0): There is no difference in employees communication skills between scores before and After trainings.

Alternate Hypothesis (H1): There is no difference in employees communication skills between scores before and After trainings.

# install.packages("readxl")
# Load the package
library(readxl)
# Import Excel File
A6R3 <- read_excel("C:/Users/sravz/Downloads/A6R3.xlsx")

# CALCULATE THE DIFFERENCE SCORES
Before <- A6R3$PreTraining
After <- A6R3$PostTraining

Differences <- After - Before

HISTOGRAM

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

QUESTIONS QUESTION 1: Is the histograms symmetrical, positively skewed, or negatively skewed?

ANSWER: Symmetrical, not strongly 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

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

ANSWER: Normally distributed, p-value = 0.21 > 0.05. Hence, it is Dependent t-test.

BOXPLOT

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

QUESTIONS

QUESTION 1: How many dots are in your boxplot?

ANSWER: One dot

QUESTION 2: Where are the dots in your boxplot?

ANSWER: Almost close to the whiskers (lines of the boxplot).

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

ANSWER: The dot is close to whisker. So, 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
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 required package
# install.packages("effectsize")
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]

QUESTIONS

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: Value is negative. Hence, after score is higher

FINAL REPORT

A dependent t-test was conducted to compare communication skills before and after training program among 150 participants. Results showed that post trainings scores (M = 69.24, SD = 9.48) were significantly higher than pre training scores (M = 59.73, SD = 7.96), t(149) = -23.28, p < .001. The effect size was Cohen’s d = - 1.90, indicating a very large effect. These results suggest that the communication training program significantly improved employees skills.