Rationale

Media priming theory says that what we see or interact with in media can briefly bring certain ideas or behaviors to the front of our minds. Those thoughts stay more accessible for a short time and can shape how we react right after. So if someone plays a game that rewards hitting or fighting, that aggressive mindset might still be active when they do something else soon after.

In this study, that idea connects to the different activities students did before recess. Kids who played the violent math game might have had aggressive thoughts or actions “primed” by the punching part of the game. The nonviolent math game and the art project don’t include anything aggressive, so those students shouldn’t have the same mindset going into recess.

Hypothesis

Students who played the violent game (“Exponent Fighter”) will spend more time being aggressive during recess than students who played the nonviolent game (“Exponent Builder”) or did the art project.

Variables & method

The independent variable is the type of activity each student did before recess; violent game, nonviolent game, or art project. That’s a categorical variable with three groups.

The dependent variable is the total number of minutes each student spent being aggressive during recess, which is a continuous variable measured in minutes.

I used a one-way ANOVA to see if the average amount of aggressive behavior was different across the three groups. If the normality check didn’t hold up, I also ran a Kruskal-Wallis test, which doesn’t rely on normal distribution assumptions.

Results & discussion

The data showed clear differences between the groups. The students who did the art project were the least aggressive, the ones who played the nonviolent game were just a bit higher, and the violent game group spent the most time acting aggressively, a lot more, actually.

Both the ANOVA and Kruskal-Wallis tests confirmed that those differences weren’t random. All the follow-up tests agreed that each group was significantly different from the others.

So overall, the results supported the hypothesis and lined up with what priming theory would predict. The kids who played the violent game showed more aggressive behavior afterward, while the others stayed relatively calm. It’s a good example of how even short exposure to certain kinds of content can have a quick impact on how people act.

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:

# ============================================================
#  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