Rationale

According to the agenda-setting theory, a media consumer’s perceptions of the importance of public issues are influenced by the amount of coverage a media outlet gives the issue. Some media outlets may cover a topic or issue more than others, making their audience more likely to believe that the topic is more important, in comparison to a media outlet that may provide less coverage.

Considering what this theory says, the more that media covers certain topics, the more people think about them. Specifically, the volume of coverage surrounding “immigration” may influence media consumer’s thinking on the importance of a topic in America.

Hypothesis

The proportion of participants who consider “immigration” their “Top issue” in the United States will vary based on if the participant prefers to watch CNN or Fox News media.

Variables & Method

A total of 600 frequent Fox News or CNN viewers were surveyed nationally in September of 2023. In the survey, viewers were asked their preferred network for consuming news, as well as “what is the most important problem facing the U.S. right now?”. Viewers had the option to choose “immigration” as the “top issue” in America, or “not (the) top issue”, and named something else as the most important problem.

The dependent variable in the analysis was a measure of whether subjects viewed “immigration” as the “top” issue in America or not. The independent variable was a measure of whether subject preferred to get their news from “CNN” or “Fox”.

A chi-square test of independence was conducted to examine whether the association between preferred news outlet and perceived immigration importance was statistically significant.

Results & Discussion

The graph and crosstabluation table below summarize the association between the dependent and independent variables. The chi-square results are shown as well.

Crosstabulation of DV by IV
Counts and (Column Percentages)
CNN Fox
1 Top issue 35 (11.7%) 115 (38.3%)
2 Not top issue 265 (88.3%) 185 (61.7%)
Chi-squared Test Results
Test of Independence between DV and IV
Test Chi-squared Statistic Degrees of Freedom p-value
Chi-squared Test of Independence 55.476 1 0.000

The results supported the hypothesis. While the majority of both CNN and Fox news viewers considered “immigration” as “not (the) top issue”, proportionally more participants among the “CNN” news viewers considered it so (88.3%) than among “Fox” news viewers (61.7%). The chi-square test found the association to be statistically significant.

Code

# ------------------------------
# Setup: Install and load packages
# ------------------------------
if (!require("tidyverse")) install.packages("tidyverse")   # Data wrangling & plotting
if (!require("gmodels")) install.packages("gmodels")       # Crosstabs
if (!require("gt")) install.packages("gt")                 # Table formatting

library(tidyverse)
library(gmodels)
library(gt)

# ------------------------------
# Load the data
# ------------------------------
# Replace "YOURFILENAME.csv" with your dataset name
mydata <- read.csv("TopIssue.csv") #Edit

# ------------------------------
# Define Dependent (DV) and Independent (IV) variables
# ------------------------------
# Replace YOURDVNAME and YOURIVNAME with actual column names in your data
mydata$DV <- mydata$Immigration #Edit
mydata$IV <- mydata$PreferredNetwork #Edit

# ------------------------------
# Visualization: Stacked bar chart of IV by DV
# ------------------------------
graph <- ggplot(mydata, aes(x = IV, fill = DV)) +
  geom_bar(colour = "black") +
  scale_fill_brewer(palette = "Paired") +
  labs(
    title = "Distribution of DV by IV",
    x = "Independent Variable",
    y = "Count",
    fill = "Dependent Variable"
  )

#Show the graph
graph

# ------------------------------
# Crosstabulation of DV by IV (DV = rows, IV = columns)
# ------------------------------

crosstab <- mydata %>%
  count(DV, IV) %>%
  group_by(IV) %>%
  mutate(RowPct = 100 * n / sum(n)) %>%
  ungroup() %>%
  mutate(Cell = paste0(n, "\n(", round(RowPct, 1), "%)")) %>%
  select(DV, IV, Cell) %>%
  pivot_wider(names_from = IV, values_from = Cell)

# Format into gt table
crosstab_table <- crosstab %>%
  gt(rowname_col = "DV") %>%
  tab_header(
    title = "Crosstabulation of DV by IV",
    subtitle = "Counts and (Column Percentages)"
  ) %>%
  cols_label(
    DV = "Dependent Variable"
  )

# Show the polished crosstab table
crosstab_table

# ------------------------------
# Chi-squared test of independence
# ------------------------------
options(scipen = 999)  # Prevents scientific notation
chitestresults <- chisq.test(mydata$DV, mydata$IV)

# ------------------------------
# Format Chi-squared test results into a table
# ------------------------------
chitest_summary <- tibble(
  Test   = "Chi-squared Test of Independence",
  Chi_sq = chitestresults$statistic,
  df     = chitestresults$parameter,
  p      = chitestresults$p.value
)

chitest_table <- chitest_summary %>%
  gt() %>%
  # Round χ² and p-value to 3 decimals, df to integer
  fmt_number(columns = c(Chi_sq, p), decimals = 3) %>%
  fmt_number(columns = df, decimals = 0) %>%
  tab_header(
    title = "Chi-squared Test Results",
    subtitle = "Test of Independence between DV and IV"
  ) %>%
  cols_label(
    Test   = "Test",
    Chi_sq = "Chi-squared Statistic",
    df     = "Degrees of Freedom",
    p      = "p-value"
  )

# Show the formatted results table
chitest_table