Rationale

Considering what agenda-setting theory says, the amount of attention given to an issue in the media could influence whether viewers consider that issue to be important. Specifically, the frequency of “immigration” being mentioned in Fox News and CNN coverage might influence whether viewers identify immigration as the most important problem facing the United States.

In this analysis, whether the respondent usually watches Fox or CNN indicates their primary media exposure, while whether the respondent named immigration as the most important problem facing the United States indicates whether immigration was or was not considered the most important issue.

Hypothesis

Respondents who usually watch Fox or CNN will more likely consider immigration as the most important issue facing the United States when their preferred news source gives a greater attention to immigration.

Variables &method

The dependent variable in the analysis was a categorical measure of whether respondents identified immigration as the most important issue facing the United States right now or not. Respondents who identified immigration as the top issue wered labeled as “1 Top Issue,” while respondents who identified another issue as the most important problem were labeled as “2 Not top issue.” The categorical independent usually watched Fox or CNN.

A sample of 600 respondents was used in the analysis. Each respondent was categorized according to their preferred news network, either CNN or Fox. Respondents were also categorized based on whether they identified immigration as the top issue or did not indetify immigration as the top issue.

A chi-square test was used to test for a statistically significant relationship between respondents’ preferred news network and whether they identified immigration as the most important problem facing the United States.

Results & discussion

The charts below contrasts the distribution of the dependent variable across the two news networks. The sample included 600 respondents, with respondents categorized according to whether they usually watched Fox or CNN and whether they identified immigration as the top issue or not.

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 chi-square test indicated that the proportion of respondents who identified immigration as the top issue was higher among Fox viewers then among CNN viewers.

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