03_HA.R

# Laden der Pakete ####
library(report) # Einfaches Erstellen von statistischen Berichten
library(tidyverse) # Datenmanagement und Visualisierung: https://www.tidyverse.org/

# Laden und aufbereiten des Datensatzes ####
# Kopiert aus Skript zur Vorlesung ####
d <- haven::read_stata(here::here("data/Vanerkel_Vanaelst_2021.dta")) |>
  rename(
    Political_knowledge = PK,
    Personalized_news = personalized_news,
    Radio = News_channels_w4_1,
    Television = News_channels_w4_2,
    Newspapers = News_channels_w4_3,
    Online_news_sites = News_channels_w4_4,
    Twitter = News_channels_w4_5,
    Facebook = News_channels_w4_6
  ) |>
  mutate(
    Gender = as_factor(Gender),
    Education = as_factor(Education),
    trad = factor(trad, labels = c(
      "traditional news diet: no",
      "traditional news diet: yes"
    ))
  )

# T-Test zum Testen der Hypothese ####
# Gruppenvergleich
d |>
  select(Political_knowledge, trad) |>
  report_sample(by = "trad")

## # Descriptive Statistics
## 
## Variable                      | traditional news diet: no (n=572)
## -----------------------------------------------------------------
## Mean Political_knowledge (SD) |                       2.74 (1.39)
## 
## Variable                      | traditional news diet: yes (n=421) | Total (n=993)
## ----------------------------------------------------------------------------------
## Mean Political_knowledge (SD) |                        3.46 (1.20) |   3.04 (1.36)

# Tabelle
t.test(Political_knowledge ~ trad, data = d) |>
  report_table(data = d)

## Welch Two Sample t-test
## 
## Parameter           | Group | Mean_Group1 | Mean_Group2 | Difference
## --------------------------------------------------------------------
## Political_knowledge |  trad |        2.74 |        3.46 |      -0.73
## 
## Parameter           |         95% CI | t(966.68) |      p | Cohen's d |  Cohen's d  CI
## --------------------------------------------------------------------------------------
## Political_knowledge | [-0.89, -0.57] |     -8.81 | < .001 |     -0.56 | [-0.69, -0.43]
## 
## Alternative hypothesis: two.sided

# Text 
t.test(Political_knowledge ~ trad, data = d) |>
  report(data = d)

## Effect sizes were labelled following Cohen's (1988) recommendations.
## 
## The Welch Two Sample t-test testing the difference of Political_knowledge by
## trad (mean in group traditional news diet: no = 2.74, mean in group traditional
## news diet: yes = 3.46) suggests that the effect is negative, statistically
## significant, and medium (difference = -0.73, 95% CI [-0.89, -0.57], t(966.68) =
## -8.81, p < .001; Cohen's d = -0.56, 95% CI [-0.69, -0.43])