Rationale

Cultivation theory’s main idea is that heavy exposure to television content can shape a viewers perception of social reality. According to this theory, people who spend more time watching television may believe that the real world resembles what is portrayed on television. Occupations such as police officers, firefighters, and doctors are highly visible in television programming, making viewers perceive them a certain way. This analysis applies cultivation theory by examining the hours spent watching television.

Hypothesis

Participants who spend more hours watching television each week will report higher percentage estimates than participants who spend fewer hours watching television.

Variables & Method

The independent variable is video, measuring the average number of hours each participant watched television per week during the six-month study period.

The dependent variable is pct, measuring each participant’s estimate of the percentage of the U.S. population employed in the occupations included in the survey

A linear regression analysis was conducted to see the relationship between the variables. The regression model examined whether weekly television viewing predicts participants’ percentage estimates. A scatterplot with a regression line was used to visualize the relationship between the variables.

Results & Discussion

The regression plot shows a positive relationship between television viewing and participants’ percentage estimates. Participants who watched more television reported higher estimates than participants who watched less television. The upward trend in the regression line suggests that increased television viewing is associated with higher percentage estimates.

These findings are consistent with cultivation theory, which argues that television exposure can influence perceptions of reality. The results support the hypothesis that participants who watch more television tend to provide higher percentage estimates than those who watch less television.

Here is a look at the content:

Code:

##################################################
# 1. Install and load required packages
##################################################
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("gt")) install.packages("gt")
if (!require("gtExtras")) install.packages("gtExtras")

library(tidyverse)
library(gt)
library(gtExtras)


##################################################
# 2. Read in the dataset
##################################################
# Replace "YOURFILENAME.csv" with the actual filename
mydata <- read.csv("Cultivation.csv")


# ################################################
# # (Optional) 2b. Remove specific cases by row number
# ################################################
# # Example: remove rows 10 and 25
# rows_to_remove <- c(10, 25) # Edit and uncomment this line
# mydata <- mydata[-rows_to_remove, ] # Uncomment this line


##################################################
# 3. Define dependent variable (DV) and independent variable (IV)
##################################################
# Replace YOURDVNAME and YOURIVNAME with actual column names
mydata$DV <- mydata$video
mydata$IV <- mydata$pct


##################################################
# 4. Explore distributions of DV and IV
##################################################
# Make a histogram for DV
DVGraph <- ggplot(mydata, aes(x = DV)) + 
  geom_histogram(color = "black", fill = "#1f78b4")

# Make a histogram for IV
IVGraph <- ggplot(mydata, aes(x = IV)) + 
  geom_histogram(color = "black", fill = "#1f78b4")


##################################################
# 5. Fit and summarize initial regression model
##################################################
# Suppress scientific notation
options(scipen = 999)

# Fit model
myreg <- lm(DV ~ IV, data = mydata)

# Model summary
summary(myreg)


##################################################
# 6. Visualize regression and check for bivariate outliers
##################################################
# Create scatterplot with regression line as a ggplot object
RegressionPlot <- ggplot(mydata, aes(x = IV, y = DV)) +
  geom_point(color = "#1f78b4") +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Scatterplot of DV vs IV with Regression Line",
    x = "Independent Variable (IV)",
    y = "Dependent Variable (DV)"
  ) +
  theme_minimal()


##################################################
# 7. Check for potential outliers (high leverage points)
##################################################
# Calculate leverage values
hat_vals <- hatvalues(myreg)

# Rule of thumb: leverage > 2 * (number of predictors + 1) / n may be influential
threshold <- 2 * (length(coef(myreg)) / nrow(mydata))

# Create table showing 10 largest leverage values
outliers <- data.frame(
  Obs = 1:nrow(mydata),
  Leverage = hatvalues(myreg)
) %>%
  arrange(desc(Leverage)) %>%
  slice_head(n = 10)

# Format as a gt table
outliers_table <- outliers %>%
  gt() %>%
  tab_header(
    title = "Leverage estimates for 10 largest outliers"
  ) %>%
  cols_label(
    Obs = "Row #",
    Leverage = "Leverage"
  ) %>%
  fmt_number(
    columns = Leverage,
    decimals = 4
  )


##################################################
# 8. Create nicely formatted regression results tables
##################################################
# --- Coefficient-level results ---
reg_results <- as.data.frame(coef(summary(myreg))) %>%
  tibble::rownames_to_column("Term") %>%
  rename(
    Estimate = Estimate,
    `Std. Error` = `Std. Error`,
    t = `t value`,
    `p-value` = `Pr(>|t|)`
  )

reg_table <- reg_results %>%
  gt() %>%
  tab_header(
    title = "Regression Analysis Results",
    subtitle = "Coefficient Estimates"
  ) %>%
  fmt_number(
    columns = c(Estimate, `Std. Error`, t, `p-value`),
    decimals = 4
  )


# --- Model fit statistics ---
reg_summary <- summary(myreg)

fit_stats <- tibble::tibble(
  `R-squared` = reg_summary$r.squared,
  `Adj. R-squared` = reg_summary$adj.r.squared,
  `F-statistic` = reg_summary$fstatistic[1],
  `df (model)` = reg_summary$fstatistic[2],
  `df (residual)` = reg_summary$fstatistic[3],
  `Residual Std. Error` = reg_summary$sigma
)

fit_table <- fit_stats %>%
  gt() %>%
  tab_header(
    title = "Model Fit Statistics",
    subtitle = "Overall Regression Performance"
  ) %>%
  fmt_number(
    columns = everything(),
    decimals = 4
  )


##################################################
# 9. Final print of key graphics and tables
##################################################
DVGraph
IVGraph
RegressionPlot
outliers_table
reg_table
fit_table