Research Scenario

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 between the pre-training scores and post-training scores.

Alternative Hypothesis (H₁): There is a difference between the pre-training scores and post-training scores.

FINAL REPORT

A Dependent t-test was conducted to compare communication skills before and after participating in a professional communication training program among 150 employees. Results showed that post-training communication scores (M = 69.24,SD = 9.48) were significantly higher than pre-training scores (M = 59.73, SD = 7.97), t(149) = -23.29, p < .001. These results suggest that the professional communication training program significantly improved employee’s communication skills.

Load Package & Import Dataset

# Load required packages
library(readxl)
library(effectsize)

# Import dataset
dataset <- read_excel("C:\\Users\\rohit\\Downloads\\A6R3.xlsx")

CALCULATE THE DIFFERENCE SCORES

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

Differences <- After - Before

Histogram

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

# QUESTION 1: Is the histograms symmetrical, positively skewed, or negatively skewed?
# ANSWER:  The histogram is positively skewed.

# QUESTION 2: Did the histogram look too flat, too tall, or did it have a proper bell curve?
# ANSWER: The histogram has a bell-shaped curve.

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?
# If p > 0.05 (P-value is GREATER than .05) this means the data is NORMAL (continue with Dependent t-test).
# If p < 0.05 (P-value is LESS than .05) this means the data is NOT normal (switch to Wilcoxon Sign Rank).
# ANSWER:The data is normally distributed. The p-value is 0.21, which is greater than 0.05, so we continue with the Dependent t-test.

Boxplot

# Boxplot of Differences
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 or two dots.


# QUESTION 2: Where are the dots in your boxplot?
# ANSWER: Very close to the whiskers (lines of the boxplot).

# QUESTION 3: Based on the dots and there location, is the data normal?
# ANSWER: The data is normal. There is only one dot, and it is close to the whisker.

Descriptive Statistics

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
#The results were statistically significant (p < .001, which is less than .05). So, we caluculate the effect size.

Load Packages

library(effectsize)

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: Cohen's d = 1.90, which indicates a very large effect size.
# QUESTION 2: Which group had the higher average score?
# ANSWER: The Post-Training group had the higher average score. The mean for After was 69.24 compared to Before which was 59.73.