Research Scenario 3

Research Scenario 3

Employee Training and Skill Development 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?

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Hypotheses

Null Hypothesis (H₀)

There is no difference in the mean scores between the Before and After conditions.

Alternate Hypothesis (H₁)

There is a difference in the mean scores between the Before and After conditions.

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Results Paragraph

A dependent t-test was conducted to compare scores before and after the intervention among 150 participants. Results showed that post-intervention scores (After) were significantly lower than pre-intervention scores (Before), t(149) = -23.29, p < .001. The mean difference was -9.51, with a 95% confidence interval ranging from -10.31 to -8.70. The effect size was Cohen’s d = -1.90, indicating a very large effect. These results suggest that the intervention led to a substantial decrease in scores from the Before to After condition.

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R Code

# INSTALL REQUIRED PACKAGE
# install.packages("readxl")

# LOAD THE PACKAGE
library(readxl)

# IMPORT EXCEL FILE INTO R STUDIO
dataset <- read_excel("C:/Users/Nithin Kumar Adki/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: 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 (sharp peak, high kurtosis).

# 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.-  p-value = 0.21 (> 0.05), so the differences follow a normal distribution.


# 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: No dots.

# QUESTION 2: Where are the dots in your boxplot?
#ANSWER: There are no dots.

# QUESTION 3: Based on the dots and there location, is the data normal?
#ANSWER: Data is normal (based on outlier check only).i.e Dependent t-test.


# 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

# INSTALL REQUIRED PACKAGE
# install.packages("effectsize")

# 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 1.09 indicates the difference between the group averages was large. There was a meaningful and substantial effect 

# QUESTION 2: Which group had the higher average score?
# ANSWER: The After group had the higher average score (Mean = 69.24 vs. Mean = 59.73).
# Since After > Before, the training improved scores.