week5work

Rationale

The theory used within this study is the cultivation theory, a theory based within sociological communications to discuss and examine the effects of media, with a more specific basis in television.

Within this experiment, the percentage questions that were asked are dependant on the length of hours monitored watching television. Cultivation theory would suggest that those who have watched more television would respond with more severity to prompted questions in comparison to those who haven’t consumed as much television.

Hypothesis

Percentage in answers will correlate positively with specific time spent consuming television media.

Variables & Method

Within this study, it is interpreted that the percentage within answers to our prompted questions surrounding the amount of law, medical, or emergency response professions represented in the real world would be the dependent variable, relying on the calculation of the specific amount of hours consuming television media that has been monitored within a household. Both of these variables are considered continuous. Within the code itself, “pct” is labeled our DV and “video” is labeled our IV.

Results

According to the experiment, regression supports the hypothesis due to the percentage within the question answers increasing with those who were observed to watch more television (F (1, 93) = 2716, p < .05, R² = 0.838). No outliers were detected within the data, and the data results show a positive relationship.

Code and output

# Read the data from the web
FetchedData <- read.csv("https://drkblake.com/wp-content/uploads/2023/09/Cultivation.csv")
# Save the data on your computer
write.csv(FetchedData, "Cultivation.csv", row.names=FALSE)
# remove the data from the environment
rm (FetchedData)

# Installing required packages
if (!require("dplyr"))
  install.packages("dplyr")
if (!require("tidyverse"))
  install.packages("tidyverse")
library(dplyr)
library(ggplot2)

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

# Specify the DV and IV
mydata$DV <- mydata$pct #Edit YOURDVNAME
mydata$IV <- mydata$video #Edit YOURIVNAME

# Look at the DV and IV
ggplot(mydata, aes(x = DV)) + geom_histogram(color = "black", fill = "#1f78b4")

ggplot(mydata, aes(x = IV)) + geom_histogram(color = "black", fill = "#1f78b4")

# Creating and summarizing an initial regression model called myreg, and checking for bivariate outliers.
options(scipen = 999)
myreg <- lm(DV ~ IV,
            data = mydata)
summary(myreg)

## 
## Call:
## lm(formula = DV ~ IV, data = mydata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.8960 -2.4820 -0.0835  2.3286  8.9264 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 0.846473   0.949889   0.891               0.373    
## IV          0.196273   0.003766  52.113 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.453 on 524 degrees of freedom
## Multiple R-squared:  0.8383, Adjusted R-squared:  0.838 
## F-statistic:  2716 on 1 and 524 DF,  p-value: < 0.00000000000000022

plot(mydata$IV, mydata$DV)
abline(lm(mydata$DV ~ mydata$IV))