Rationale

Priming theory is defined as the influence of media affects on the individual on their short-term behaviors and attitudes and it depends upon mental- or even subconscious- processing attitudinal and behavioral cues embedded in media content. This specifically focuses on the external response of an individual. Due to priming theory the more children exposed to violent media the more actions they will have that are violent, even though it is short term.

In this experiment, the variables “Actions” and “NextDay” are the dependent of the “Group” that they were subsequently categorized in. Using Priming Theory it is expected for a certain amount of stimulus to affect an individual dependent of what group they were in, but the affect it had on then will diminish over time since Priming Theory is based in short-term results.

Hypothesis

Among the “Violent Game” group distribution over the two testing days, it is predicted that the violent acts will decrease from the original testing day to the next day initial readings.

Variables & Method

Both the “Action”, the variable meaning the amount of violent actions that took place after their initial stimulus, and “NextDay” variable , the variable following the amount of violent acts of the same group after a 24 hour time period between the initial exposure, are dependent on the “Group” category, specifically focusing with the “Violent Games” group and the resulting violent acts from their stimulus. Therefore, a paired-sample t-test would be used to test the data from the experiment.

Results

This analysis hypothesized that the violent acts will decrease from the original testing day to the next day initial readings. The analysis found that over a 24 hour period, the average of violent acts from the violent game group decreased by approximatly 4.8 acts over the course of the day. A Shapiro-Wilk test indicated these differences were normally distributed, rendering a paired-sample t-test suitable (W = 0.98, p > .05). Therefore, the test found that the difference to be statistically significant (t (-13.416) = 39, p < .05).

Code and output

# Read the data from the web
FetchedData <- read.csv("https://drkblake.com/wp-content/uploads/2023/10/Priming2day.csv")
# Save the data on your computer
write.csv(FetchedData, "Priming2day.csv", row.names=FALSE)
# remove the data from the environment
rm (FetchedData)

# Installing required packages
if (!require("dplyr"))
  install.packages("dplyr")
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
if (!require("tidyverse"))
  install.packages("tidyverse")
## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.4
## ✔ ggplot2   3.4.3     ✔ stringr   1.5.0
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(ggplot2)
options(scipen = 999)

# Read the data
mydata <- read.csv("Priming2day.csv") #Edit YOURFILENAME.csv

# Specify the two variables involved
mydata$V1 <- mydata$Actions
mydata$V2 <- mydata$NextDay

# Look at the distribution of the pair differences
mydata$PairDifferences <- mydata$V2 - mydata$V1

ggplot(mydata, aes(x = PairDifferences)) +
  geom_histogram(color = "black", fill = "#1f78b4") +
  geom_vline(aes(xintercept = mean(PairDifferences)))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Get descriptive statistics for pair differences
mydata %>%
  select(PairDifferences) %>%
  summarise(
    count = n(),
    mean = mean(PairDifferences, na.rm = TRUE),
    sd = sd(PairDifferences, na.rm = TRUE),
    min = min(PairDifferences, na.rm = TRUE),
    max = max(PairDifferences, na.rm = TRUE)
  )
##   count   mean       sd min max
## 1    40 -4.825 2.274581 -10   0
# If pair differences look non-normal, you can use a Shapiro-Wilk test to check
# whether their distribution differs significantly from normal. If the
# Shapiro-Wilk test p-value is less than 0.05, #use a Wilcoxon signed rank test
# instead of a paired-samples t-test.

# Shapiro-Wilk test
options(scipen = 999)
shapiro.test(mydata$PairDifferences)
## 
##  Shapiro-Wilk normality test
## 
## data:  mydata$PairDifferences
## W = 0.97758, p-value = 0.6003
# If the pair distribution is non-normal, consider # using a Wilcoxon signed rank test instead of a
# paired-samples t-test.

mydata %>%
  select(V1, V2) %>%
  summarise_all(list(Mean = mean, SD = sd))
##   V1_Mean V2_Mean   V1_SD    V2_SD
## 1    14.7   9.875 1.77157 2.652406
wilcox.test(mydata$V1, mydata$V2, paired = TRUE)
## Warning in wilcox.test.default(mydata$V1, mydata$V2, paired = TRUE): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(mydata$V1, mydata$V2, paired = TRUE): cannot
## compute exact p-value with zeroes
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata$V1 and mydata$V2
## V = 780, p-value = 0.00000005065
## alternative hypothesis: true location shift is not equal to 0
# If the pair differences are normally distributed,
# though, you may use a paired-samples t-test.

mydata %>%
  select(V1, V2) %>%
  summarise_all(list(Mean = mean, SD = sd))
##   V1_Mean V2_Mean   V1_SD    V2_SD
## 1    14.7   9.875 1.77157 2.652406
options(scipen = 999)
t.test(mydata$V2, mydata$V1,
       paired = TRUE)
## 
##  Paired t-test
## 
## data:  mydata$V2 and mydata$V1
## t = -13.416, df = 39, p-value = 0.0000000000000003399
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -5.552446 -4.097554
## sample estimates:
## mean difference 
##          -4.825