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?

Hypotheses

H₀: There is no difference in the employees’ communication skills between scores before and after training.

H₁: There is a difference in the employees’ communication skills between scores before and after training.

Load Required Library

library(readxl)

Read dataset

A6R3 <- read_excel("C:/Users/saisa/Downloads/A6R3.xlsx")

Variables

Before <- A6R3$PreTraining

Before
##   [1] 64 42 60 61 57 56 55 59 52 66 57 52 59 56 67 54 63 55 62 54 54 65 53 63 49
##  [26] 56 58 58 67 59 57 51 65 65 54 52 49 58 55 65 59 74 73 66 70 64 35 64 65 68
##  [51] 60 48 66 59 54 65 69 54 69 70 57 66 58 79 63 57 53 57 52 66 64 68 70 57 52
##  [76] 58 72 66 63 47 75 64 52 62 49 61 63 56 48 71 56 68 53 51 39 68 62 55 64 72
## [101] 56 57 65 61 61 65 66 51 65 59 62 44 36 66 57 67 63 54 63 71 54 63 63 68 58
## [126] 65 63 60 59 62 50 64 48 62 60 44 54 65 59 84 51 45 59 65 58 54 64 70 75 55
After <- A6R3$PostTraining

After
##   [1] 71 50 71 78 63 67 63 66 62 80 72 60 65 66 83 57 65 62 80 64 57 73 64 83 65
##  [26] 62 69 57 68 72 75 61 78 68 66 70 59 65 65 74 64 79 76 84 85 79 40 74 64 76
##  [51] 54 50 70 79 62 71 78 69 71 81 66 76 72 88 72 71 68 59 61 76 71 83 76 59 60
##  [76] 70 74 83 70 58 85 81 59 78 56 72 77 69 55 75 58 81 61 57 50 85 76 66 73 86
## [101] 63 68 75 65 64 79 71 57 76 62 61 57 46 74 63 80 66 66 75 81 67 74 70 80 60
## [126] 74 76 61 74 82 63 77 48 75 69 58 62 75 69 96 57 61 69 80 69 72 68 76 90 70
Differences <- After - Before

Differences
##   [1]  7  8 11 17  6 11  8  7 10 14 15  8  6 10 16  3  2  7 18 10  3  8 11 20 16
##  [26]  6 11 -1  1 13 18 10 13  3 12 18 10  7 10  9  5  5  3 18 15 15  5 10 -1  8
##  [51] -6  2  4 20  8  6  9 15  2 11  9 10 14  9  9 14 15  2  9 10  7 15  6  2  8
##  [76] 12  2 17  7 11 10 17  7 16  7 11 14 13  7  4  2 13  8  6 11 17 14 11  9 14
## [101]  7 11 10  4  3 14  5  6 11  3 -1 13 10  8  6 13  3 12 12 10 13 11  7 12  2
## [126]  9 13  1 15 20 13 13  0 13  9 14  8 10 10 12  6 16 10 15 11 18  4  6 15 15

Descriptive Statistics

# Install and load the package

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Calculate the Descriptive Statistics

#Descriptive Statistics 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
#Descriptive Statistics 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

Histograms

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

Is the histograms symmetrical, positively skewed, or negatively skewed? Positive

Did the histogram look too flat, too tall, or did it have a proper bell curve? Proper Bell curve

Shapiro-wilk Test

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

Normality Test Results: Differences: W = 0.98773, p-value = 0.21 → normally distributed

Decision: Since it is normally distributed, we will use Dependent t test.

BOXPLOT

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

How many dots are in your boxplot? One

Where are the dots in your boxplot? close to the whiskers

Based on the dots and there location, is the data normal? The dots are CLOSE to the whiskers,so the data is normal

Determine Statistical Significance

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

p < 0.01 - Statistically significant

Effect size

# Install and load the package

library(effectsize)

Calculate the effect size

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]

What is the size of the effect? -1.90 Very Large

Which group had the higher average score? PostTraining group has the highest score

FINAL REPORT

A dependent t-test was conducted to compare communication skills before and after the professional training program among 150 employees. Results showed that post-training communication scores (M = 69.31, SD = 9.86) were significantly higher than pre-training scores (M = 59.80, SD = 7.76), t(149) = -23.29, p < .001. The effect size was Cohen’s d = -1.90, indicating a very large effect. These results suggest that the professional communication training program significantly improved employees’ communication skills.