ANOVA
Diego Blattler
2026-10-12
Rationale
The media priming theory suggests that media can activate certain ideas in a person’s mind, making it more likely to influence how they respond to later situations. For example, exposure to violent content may make aggressive thoughts more accessible and increase the likelihood of aggressive behavior.
Considering this, playing a violent video game could activate thoughts related to violence and aggression, posibbly influencing how students behave during recess.
Hypothesis
The amount of time spent engaging in aggressive behavior during recess differs across students who played the violent video game, played the nonviolent video game, and completed the art project.
Variables & method
135 5th grade students were randomly divided into three groups of 45. Each of the groups completed either a violent video game, a nonviolent video game, or an art project for 30 minutes before participating in a 30 minute recess. Researchers observed the students during recess and recorded the amount of time each student spent engaging in aggressive behavior.
A one way ANOVA test was conducted to determine whether the mean time spent engaging in aggressive behavior differed significantly across the three lesson groups.
Results & discussion
The graph and one way ANOVA results below summarize the differences in aggressive behavior among students who played the violent video game, played the nonviolent video game, or completed the art project.
These results support the hypothesis. The
results show that the amount of aggressive behavior differs across the
three lesson groups. Students who played the violent game displayed the
most aggressive behavior during recess. These findings support the media
priming theory’s claim that exposure to violent media can temporarily
activate aggressive thoughts and influence subsequent behavior.
Code:
# Read the data from the web
FetchedData <- read.csv("https://github.com/drkblake/Data/raw/refs/heads/main/Priming.csv")
# Save the data on your computer
write.csv(FetchedData, "Priming.csv", row.names=FALSE)
# remove the data from the environment
rm (FetchedData)
# ============================================================
# Setup: Install and Load Required Packages
# ============================================================
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("gt")) install.packages("gt")
if (!require("gtExtras")) install.packages("gtExtras")
if (!require("FSA")) install.packages("FSA")
if (!require("plotly")) install.packages("plotly")
library(tidyverse)
library(gt)
library(gtExtras)
library(FSA)
library(plotly)
options(scipen = 999) # suppress scientific notation
# ============================================================
# Step 1: Load Data
# ============================================================
mydata <- read.csv("Priming.csv") # <-- Edit YOURFILENAME.csv
# Specify DV and IV (edit column names here)
mydata$DV <- mydata$Value
mydata$IV <- mydata$Group
# ============================================================
# Step 2: Visualize Group Distributions (Interactive)
# ============================================================
# Compute group means
group_means <- mydata %>%
group_by(IV) %>%
summarise(mean_value = mean(DV), .groups = "drop")
# Interactive plot (boxplot + group means)
box_plot <- plot_ly() %>%
# Boxplot trace
add_trace(
data = mydata,
x = ~IV, y = ~DV,
type = "box",
boxpoints = "outliers", # only applies here
marker = list(color = "red", size = 4), # outlier style
line = list(color = "black"),
fillcolor = "royalblue",
name = ""
) %>%
# Group means (diamonds)
add_trace(
data = group_means,
x = ~IV, y = ~mean_value,
type = "scatter", mode = "markers",
marker = list(
symbol = "diamond", size = 9,
color = "black", line = list(color = "white", width = 1)
),
text = ~paste0("Mean = ", round(mean_value, 2)),
hoverinfo = "text",
name = "Group Mean"
) %>%
layout(
title = "Interactive Group Distributions with Means",
xaxis = list(title = "Independent Variable (IV)"),
yaxis = list(title = "Dependent Variable (DV)"),
showlegend = FALSE
)
# ============================================================
# Step 3: Descriptive Statistics by Group
# ============================================================
desc_stats <- mydata %>%
group_by(IV) %>%
summarise(
count = n(),
mean = mean(DV, na.rm = TRUE),
sd = sd(DV, na.rm = TRUE),
min = min(DV, na.rm = TRUE),
max = max(DV, na.rm = TRUE)
)
desc_table <- desc_stats %>%
mutate(across(where(is.numeric), ~round(.x, 2))) %>%
gt() %>%
gt_theme_538() %>%
tab_header(title = "Descriptive Statistics by Group")
# ============================================================
# Step 4: Test Normality (Shapiro-Wilk)
# ============================================================
shapiro_results <- mydata %>%
group_by(IV) %>%
summarise(
W_statistic = shapiro.test(DV)$statistic,
p_value = shapiro.test(DV)$p.value
)
shapiro_table <- shapiro_results %>%
mutate(
W_statistic = round(W_statistic, 2),
p_value = ifelse(p_value < .001, "< .001", sprintf("%.3f", p_value))
) %>%
gt() %>%
gt_theme_538() %>%
tab_header(title = "Shapiro-Wilk Normality Test by Group") %>%
tab_source_note(
source_note = "Note. If any p-value figures are 0.05 or less, if one or more group distributions appear non-normal, and any group sizes are less than 40, consider using the Kruskal-Wallis and Post-hoc Dunn’s Test results instead of the ANOVA and Tukey HSD Post-hoc results."
)
# ============================================================
# Step 5a: Non-Parametric Test (Kruskal-Wallis + Dunn)
# ============================================================
kruskal_res <- kruskal.test(DV ~ IV, data = mydata)
kruskal_table <- data.frame(
Statistic = round(kruskal_res$statistic, 2),
df = kruskal_res$parameter,
p_value = ifelse(kruskal_res$p.value < .001, "< .001",
sprintf("%.3f", kruskal_res$p.value))
) %>%
gt() %>%
gt_theme_538() %>%
tab_header(title = "Kruskal-Wallis Test Results")
dunn_res <- dunnTest(DV ~ IV, data = mydata, method = "bonferroni")$res
dunn_table <- dunn_res %>%
mutate(
Z = round(Z, 2),
P.unadj = ifelse(P.unadj < .001, "< .001", sprintf("%.3f", P.unadj)),
P.adj = ifelse(P.adj < .001, "< .001", sprintf("%.3f", P.adj))
) %>%
gt() %>%
gt_theme_538() %>%
tab_header(title = "Post-hoc Dunn’s Test Results")
# ============================================================
# Step 5b: Parametric Test (ANOVA + Tukey)
# ============================================================
anova_res <- oneway.test(DV ~ IV, data = mydata, var.equal = FALSE)
anova_table <- data.frame(
Statistic = round(anova_res$statistic, 2),
df = anova_res$parameter[1],
df_resid = anova_res$parameter[2],
p_value = ifelse(anova_res$p.value < .001, "< .001",
sprintf("%.3f", anova_res$p.value))
) %>%
gt() %>%
gt_theme_538() %>%
tab_header(title = "ANOVA Test Results")
anova_model <- aov(DV ~ IV, data = mydata)
tukey_res <- TukeyHSD(anova_model)$IV %>% as.data.frame()
tukey_table <- tukey_res %>%
rownames_to_column("Comparison") %>%
mutate(
diff = round(diff, 2),
lwr = round(lwr, 2),
upr = round(upr, 2),
`p adj` = ifelse(`p adj` < .001, "< .001", sprintf("%.3f", `p adj`))
) %>%
gt() %>%
gt_theme_538() %>%
tab_header(title = "Tukey HSD Post-hoc Results")
# ============================================================
# Step 6: Display Key Results
# ============================================================
# Interactive box plot
box_plot
# Tables
desc_table
shapiro_table
anova_table
tukey_table
kruskal_table
dunn_table