Priming

Rationale

Priming theory states that exposure to media content can influence later, unrelated attitudes and behavior, but usually not for long.

For this experiment, I am measuring the effects of priming theory on three different groups of students who participated in three different types of academic activities prior to going outside for recess. I seek to measure the effects of exposure to an academic activity which includes violence and whether it has any effect on the students’ behavior at recess, immediately following the activity.

Hypothesis

After being exposed to violent or aggressive behavior, students will exhibit aggressive behavior, at least in the short term, in unrelated activities.

Variables & method

For this experiment, 135 fifth-graders at a large suburban elementary school were randomly divided into three groups of 45 students each. About 30 minutes before recess, teachers assigned one group to complete a math lesson that involved a computer game called “Exponent Fighter.” The game involved presenting the player with a series of exponent math problems. Each time the player gave the correct answer to a problem, the player’s on-screen character got the chance to land a punch on a computer-controlled opponent. Each time the player gave an incorrect answer, however, the opposite happened: The computer-controlled opponent got the chance to land a punch on the player’s on-screen character. The player could win the game by landing more punches than the computer-controlled opponent.

During the same 30 minutes, a second group of 45 students played a similar game called “Exponent Builder.” In this game, each time the player gave the correct answer to an exponent problem, the player’s on-screen character got a chance to add a block to a structure that would allow the character to reach an otherwise-unreachable platform. The player could win the game by acquiring enough blocks to build the structure needed.

Also during the same 30 minutes, a third group of 45 students completed an art lesson that involved using a color wheel to select a color scheme for an abstract design produced using paint and pre-cut stencils.

At the end of the 30 minutes, students were dismissed to the school playground for a 30-minute recess. There, a team of trained researchers watched the students play for 30 minutes and recorded the amount of time, in minutes, each student spent engaged in any kind of aggressive act. An “aggressive act” could be either verbal (taunting, threatening, etc.) or physical (pushing, grabbing, etc.). Of course, behaviors severe enough to violate school policy were not allowed, and instances of such behavior were dealt with in accordance with school policy.

Results & discussion

For this experiment, an Analysis of Variance (ANOVA) test was used because the independent variable had more than two categories. In this case, the independent variable had three categories: Those exposed to the violent learning game, those exposed to the nonviolent learning game, and those who took part in the art project. The dependent variable measured the number of minutes the students displayed violent behaviors while at recess.

The test resulted in a box plot and four relevant tables of data.

BOX PLOT

The box plot provides a visual representation of how the three independent variables correlated with the dependent variable. The plot shows that the mean and medians for all three categories are close to equal, which shows the results were not skewed significantly by any outliers in the data. In addition, the plot shows those students who took part in the violent learning game exhibited nearly twice as many minutes of aggressive behavior than those who took part in the nonviolent learning game. It also illustrates the violent game group showed more than twice the number of minutes of aggressive behavior compared to the art project group. The nonviolent group also resulted in more minutes of aggression than the art project group, but to a much lesser degree. Specifically, the art project group resulted in a mean of 4.96 minutes of aggressive behavior, compared to a mean of 5.86 minutes of aggressive behavior with the nonviolent game group, and a mean of 10.75 minutes of aggressive behavior with the violent game group.

DESCRIPTIVE STATISTICS BY GROUP TABLE

This table verifies the numerical data for each categorical group, including the count of each group (45), the mean, the standard deviation, and the minimum and maximum of the number of aggressive behavior minutes observed in the experiment.

SHAPIRO-WILK NORMALITY TEST BY GROUP TABLE

The most important result displayed in this table is the p-value, which determines whether the results are distributed normally within each category of the independent variable, which is an important check for validity when using an ANOVA test. All three categories resulted in a p-value that proves the results are significant, because each of the resulting values is above .05.

ANOVA TEST RESULTS TABLE

The ANOVA test results in a p-value of less than .05, which means the results of the experiment, overall, are statistically significant and did not occur randomly.

TUKEY HSD POST-HOC RESULTS

Likewise, the results from this table show the adjusted p-values for each category of the independent variable compared to each other. Because all three of these values are less than .05, all three categorical relationships show statistically significant results which did not occur randomly.

The results of this experiment and analysis support the hypothesis. Those students who participate in a learning activity that includes depictions of violence or aggression are likely to show aggressive behavior during recess for a longer period of time. It would be interesting to measure the amount of aggressive behavior the next day during recess to see if the priming effects have subsided after a 24-hour period.

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

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