Chi-Square Test
Diego Blattler
2026-09-21
Rationale
The Agenda-Setting theory illustrates how media influences the significance of certain topics and how it shapes the public agenda.
As stated by the theory’s founder Max McCombs “Elements that are prominent in the media frequently become prominent in the public mind. That’s it.”
Considering this, what the media covers and reports on could influence the thoughts and personal agenda of the viewers. Specifically this influence could be dependent upon what news or media outlet a person consumes.
Hypothesis
The amount of people who view immigration as a “Top Issue” compared to the amount who view it as a “Not Top Issue” will be dependent upon which news outlet they watch.
Variables & method
600 frequent Fox News or CNN viewers pulled for a larger national survey conducted in September of 2023, subjects from both groups were asked if they perceived immigration as a “Top Issue” or a “Not Top Issue”.
A chi-square test of independence was conducted to examine whether the association between news outlet preference and perceived significance of immigration was statistically significant.
Results & discussion
The graph and crosstabulation table below summarize the association between the dependent and independent variables. The chi-square results are shown as well.
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$DV <- mydata$PreferredNetwork #Edit
mydata$IV <- mydata$Immigration #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