Rationale

Media priming theory suggests that being exposed to particular forms of media will momentarily “prime” related thoughts, feelings, or behavioral scripts in memory. When aggressive cues are primed, the person is more likely to respond with aggression in the following behavior, even if the contexts are unrelated. The theory suggests the effects are generally short-lived and rely on the availability of the related cognitive associations.

In this study, fifth-grade students were randomly assigned to one of three pre-recess activities: playing a violent math game, playing a nonviolent math game, or completing an art project. According to media priming theory, students who completed the violent game — and therefore were exposed to aggressive content — should experience a relevant increase in aggressive thoughts and behaviors in recess than the students who completed the other two conditions.

Hypothesis

Students who engage in the violent educational game (“Exponent Fighter”) will show greater levels of aggressive behavior during recess compared to students who engage in the nonviolent game (“Exponent Builder”) or complete the art project.

Variables & method

Independent Variable (IV): Group — categorical (three levels: Violent Game, Nonviolent Game, Art Project).

Dependent Variable (DV): Value — continuous (total minutes engaged in aggressive behaviors during recess).

In the analysis, a one-way ANOVA conducted was to see if the mean aggression levels differed between the groups. Assumptions for normality (Shapiro-Wilk) and homogeneity for variance (Levene’s test) were confirmed. If the assumptions were not met, a Kruskal-Wallis test and Dunn’s post-hoc test were utilized.

Results & discussion

The box plot shows that students who played the violent game showed greater times of engaging in aggressive acts than students in both the nonviolent game and art projects groups. The one-way ANOVA revealed a statistically significant difference between groups (p < .05). Following up with Tukey HSD tests, the violent game group showed a significantly greater aggression score than the nonviolent game and art projects, and there was not a statistically significant difference between the two groups, nonviolent game and art projects.

In conclusion, these findings lend support for the media priming hypothesis; exposure to violent media content led to a brief increase in aggression immediately following.

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 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.
ANOVA Test Results
Statistic df df_resid p_value
420.86 2 87.94717 < .001
Tukey HSD Post-hoc Results
Comparison diff lwr upr p adj
Nonviolent game-Art project 0.90 0.38 1.42 < .001
Violent game-Art project 5.79 5.27 6.30 < .001
Violent game-Nonviolent game 4.89 4.37 5.40 < .001
Kruskal-Wallis Test Results
Statistic df p_value
95.23 2 < .001
Post-hoc Dunn’s Test Results
Comparison Z P.unadj P.adj
Art project - Nonviolent game -2.42 0.016 0.047
Art project - Violent game -9.40 < .001 < .001
Nonviolent game - Violent game -6.98 < .001 < .001

Code:

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

mydata <- read.csv("Priming.csv") # <-- Edit YOURFILENAME.csv

# Specify DV and IV (edit column names here)
mydata$DV <- mydata$Value
mydata$IV <- mydata$Group


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
  )

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")


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."
  )


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")

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")


box_plot
desc_table
shapiro_table
anova_table
tukey_table
kruskal_table
dunn_table