Fifth-graders who played a math game that rewarded right answers with on-screen punches spent about twice as much of their recess being aggressive as classmates who played a block-building math game or completed an art lesson, an analysis of data from a recent experiment found.
The results line up with media priming theory, which suggests that mentally processing media content can briefly shape later attitudes and behaviors, even ones that seem unrelated to the content.
At a large suburban elementary school, 135 fifth-graders were randomly assigned to one of three groups of 45 students each. During the 30 minutes before recess, one group played “Exponent Fighter,” a math game in which correct answers let the player’s character punch a computer opponent and incorrect answers let the opponent punch back. A second group played “Exponent Builder,” a similar math game in which correct answers earned blocks for building a structure. The third group completed an art lesson using a color wheel, paint, and stencils.
During the 30-minute recess that followed, trained researchers recorded the number of minutes each student spent engaged in verbal or physical aggressive acts, such as taunting, threatening, pushing, or grabbing.
A one-way analysis of variance (ANOVA) tested whether average aggression time differed by group. The research hypothesis was that students who played the violent game would spend more time being aggressive than students in either of the other two groups.
# 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")Students who played the violent game averaged 10.75 minutes of aggression during recess, compared with 5.86 minutes for students who played the nonviolent game and 4.96 minutes for students who completed the art project.
box_plotdesc_table| Descriptive Statistics by Group | |||||
| IV | count | mean | sd | min | max |
|---|---|---|---|---|---|
| Art project | 45 | 4.96 | 1.04 | 2.3 | 7.3 |
| Nonviolent game | 45 | 5.86 | 1.06 | 3.6 | 8.4 |
| Violent game | 45 | 10.75 | 1.00 | 8.5 | 12.8 |
Shapiro-Wilk tests showed that aggression times were normally distributed within all three groups, so the analysis used ANOVA rather than the nonparametric Kruskal-Wallis test.
shapiro_table| Shapiro-Wilk Normality Test by Group | ||
| IV | W_statistic | p_value |
|---|---|---|
| Art project | 0.99 | 0.923 |
| Nonviolent game | 0.98 | 0.594 |
| Violent game | 0.98 | 0.699 |
| 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. | ||
The ANOVA found a statistically significant difference among the group means, F (2, 87.95) = 420.86, p < .05.
anova_table