Scenario 6.1

Scenario 6.1 Peer Tutoring Program

Step 1 Install packages (run once)

install.packages(“readxl”) install.packages(“ggpubr”) install.packages(“dplyr”) install.packages(“effectsize”) install.packages(“effsize”)

Step 2 Load packages

library(readxl)
library(ggpubr)

## Loading required package: ggplot2

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

library(effectsize)
library(effsize)

Step 3 Import dataset

Dataset6_1 <- read_excel("C:/Users/Mrlaz/Downloads/Dataset6.1.xlsx")

Step 4 Descriptive stats

Dataset6_1 %>%
  group_by(Group) %>%
  summarise(
    Mean = mean(Exam_Score, na.rm=TRUE),
    Median = median(Exam_Score, na.rm=TRUE),
    SD = sd(Exam_Score, na.rm=TRUE),
    N = n()
  )

## # A tibble: 2 × 5
##   Group        Mean Median    SD     N
##   <chr>       <dbl>  <dbl> <dbl> <int>
## 1 No Tutoring  71.9   71.5  7.68    40
## 2 Tutoring     78.4   78.7  7.18    40

Shows average exam scores for tutoring vs no tutoring

Step 5 Histograms

hist(Dataset6_1$Exam_Score[Dataset6_1$Group=="Tutoring"],
     main="Tutoring Scores", col="lightblue", border="black")

hist(Dataset6_1$Exam_Score[Dataset6_1$Group=="No Tutoring"],
     main="No Tutoring Scores", col="lightgreen", border="black")

Step 6 Boxplot

ggboxplot(Dataset6_1, x="Group", y="Exam_Score",
          color="Group", palette="jco", add="jitter")

Step 7 Shapiro

shapiro.test(Dataset6_1$Exam_Score[Dataset6_1$Group=="Tutoring"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Dataset6_1$Exam_Score[Dataset6_1$Group == "Tutoring"]
## W = 0.98859, p-value = 0.953

shapiro.test(Dataset6_1$Exam_Score[Dataset6_1$Group=="No Tutoring"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Dataset6_1$Exam_Score[Dataset6_1$Group == "No Tutoring"]
## W = 0.98791, p-value = 0.9398

If both normal → Independent t-test

t.test(Exam_Score ~ Group, data=Dataset6_1, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Exam_Score by Group
## t = -3.8593, df = 78, p-value = 0.000233
## alternative hypothesis: true difference in means between group No Tutoring and group Tutoring is not equal to 0
## 95 percent confidence interval:
##  -9.724543 -3.105845
## sample estimates:
## mean in group No Tutoring    mean in group Tutoring 
##                  71.94627                  78.36147

If not normal use: wilcox.test(Exam_Score ~ Group, data=Dataset6_1)

Effect size

cohens_d(Exam_Score ~ Group, data=Dataset6_1, pooled_sd=TRUE)

## Cohen's d |         95% CI
## --------------------------
## -0.86     | [-1.32, -0.40]
## 
## - Estimated using pooled SD.

Students who attended tutoring (M = 78.36, SD = 7.18) scored significantly higher than students who did not attend tutoring (M = 71.95, SD = 7.68), t(78) = 3.86, p < .001

Step: Normality Tutoring p = 0.953 → normal No Tutoring p = 0.940 → normal Both normal → Independent t-test Descriptives Tutoring: M = 78.36, SD = 7.18 No tutoring: M = 71.95, SD = 7.68 Test result t(78) = 3.86 p < .001 → significant