Rationale

The idea of priming states that violent media can influence habits and behaviors for a short but not extended period of time. Even absorbing something subconsciously in that fashion can lead to this kind of influence.

With that in mind, if children are exposed to a kind of violent or stressful media content, it could be argued that this could influence their short-term thoughts, feelings, and behaviors to some degree.

Hypothesis

Having children take part in video games with larger amounts of violent or stressful content will make them more likely to lash out and have short-term violent behavior.

Variables & method

We are using the One-Way ANOVA method, which will help us to determine whether or not our hypothesis is true through three examples of games that children will react to. 

The Dependent Variable was the Value or amount of time that children spent playing these particular video games. Our Independent Variable was the video games themsevles, a group of 3 which included Minecraft, Sims, and a Cooking Game.

Results & discussion

The Box plot shows that Minecraft has the most amount of kids with violent behavior after playing it, and with it being the video game with the most amount of violence, this helps confirm our hypothesis about the relationship between the short-term behavior of children and the games that they played.

Tukey HSD Post-hoc Results
Comparison diff lwr upr p adj
Minecraft-Cooking game 11.64 10.60 12.68 < .001
Sims-Cooking game -2.95 -3.99 -1.91 < .001
Sims-Minecraft -14.59 -15.63 -13.55 < .001

The Tukey Table corresponds to this with the more violent content game Minecraft differing greatly from the both the Cooking Game and Sims on the level of violent response and the Sims and Cooking Games being much closer to each other in that level.

Code:

# Read the data from the web
FetchedData <- read.csv("https://github.com/drkblake/Data/raw/refs/heads/main/PrimingANOVA_inclass.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