A6R3

NULL HYPOTHESIS (H0) There is no difference between the Before scores and After scores.

ALTERNATE HYPOTHESIS (H1) There is a difference between the Before scores and After scores.

 library(readxl)

A6R3 <- read_excel("C:\\Users\\kuppi\\OneDrive\\Desktop\\A6R3.xlsx")

Before <- A6R3$PreTraining
After <- A6R3$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?

ANSWER: The histogram is symmetrical

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

ANSWER: The histogram has a proper bell curve

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:P-value is 0.21. it is greater than .05 so the data is normal

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?

1. No dots.

QUESTION 2: Where are the dots in your boxplot?

1. 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. If there are one or two dots and they are CLOSE to the whiskers, the data is normal If there are many dots (more than one or two) and they are FAR AWAY from the whiskers, this means data is NOT normal. Switch to a Wilcoxon Sign Rank. Anything else could be normal or abnormal. Check if there is a big difference between the median and the mean. If there is a big difference, the data is not normal. If there is a small difference, the data is normal.

Answer: 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

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]

QUESTIONS

QUESTION 1: What is the size of the effect? The effect means how big or small was the difference between the group averages. ± 0.00 to 0.19 = ignore ± 0.20 to 0.49 = small ± 0.50 to 0.79 = moderate ± 0.80 to 1.29 = large ± 1.30 to + = very large Examples: A Cohen’s D of 0.10 indicates the difference between the group averages was not truly meaningful. There was no effect. A Cohen’s D of 0.22 indicates the difference between the group averages was small.

YOUR ANSWER: The effect size is very large

QUESTION 2: Which group had the higher average score? With the way we calculated differences (After minus Before), if it is NEGATIVE, it means the After scores were higher. If it is POSITIVE, it means the Before scores were higher. You can also easily look at the means and tell which scores were higher.

YOUR ANSWER: Because the difference is negative, the After scores were higher.

SUMMARY

A dependent t-test was conducted to compare employees’ communication skill scores before and after the professional training program among 150 participants. 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 < 2.2e-16. The effect size was Cohen’s d = –1.90, indicating a very large effect. These results suggest that the communication training program resulted in a strong and meaningful increase in employee communication skills.