Setting up environment

# List of packages
packages <- c("tidyverse", "infer", "fst", "modelsummary", "effects", "survey", "interactions") # add any you need here

# Install packages if they aren't installed already
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)

# Load the packages
lapply(packages, library, character.only = TRUE)
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Task 1

Graph the histogram distribution using the populist scale (as we did in the tutorial) for the country of Sweden. What do you note?

sweden_data <- read.fst("sweden_data.fst")

# Populist scale (Norris/Inglehart use low levels of trust in parliaments, politicians and parties as an indicator of populism)

# Note: Schafer does it 2 ways, we will focus now on the most straightforward

# Setting values greater than 10 to NA
sweden_data$trstplt <- ifelse(sweden_data$trstplt > 10, NA, sweden_data$trstplt)
sweden_data$trstprl <- ifelse(sweden_data$trstprl > 10, NA, sweden_data$trstprl)
sweden_data$trstprt <- ifelse(sweden_data$trstprt > 10, NA, sweden_data$trstprt)

# Creating and rescaling the trust variable
sweden_data$trust <- scales::rescale(sweden_data$trstplt + sweden_data$trstprl + sweden_data$trstprt, na.rm = TRUE, to = c(0, 100))

# Here's how you would "flip it" because populist is conceived by N + I as 'anti-trust'
# As Schafer explains there are some issues with that conceptualization, but N + I is also widely applied
sweden_data$populist <- scales::rescale(sweden_data$trust, na.rm = TRUE, to=c(100,0))
ggplot(sweden_data, aes(x = populist)) +
  geom_histogram(bins = 30, fill = "blue", color = "black") +
  theme_minimal() +
  labs(title = "Distribution of Populist Scale for Sweden",
       x = "Populist Scale",
       y = "Count")
## Warning: Removed 2503 rows containing non-finite values (`stat_bin()`).

#### Interpretation This histogram’s distribution is rather symmetrical and central, not skewing too much to the left or the right. There is a slight lean/skew to the left, but it is overall still quite bell-shaped. This is implies that more the most part, most people in sweden have a middling level of populism.

Task 2

Run a linear regression with populist attitudes as the outcome, educational attainment (recoded as a binary “BA or more” and “No BA”), and using the Swedish data. Print the intercept and coefficients only and interpret. Then do a tidy model table displaying the p-value (with digits = 3) and interpret.

model_data <- sweden_data %>%
  mutate(
    # Recoding populist
    populist = case_when(
      populist & trstplt > 0  ~ "Populist",
      populist & trstprl > 0  ~ "Populist",
      populist & trstprt > 0  ~ "Populist",
    ),

 
    
    
 # Recoding education
    educ.ba = case_when(
      essround < 5 & edulvla == 5 ~ "BA or more",
      essround >= 5 & edulvlb > 600 ~ "BA or more",
      TRUE ~ "No BA"
    ),
    
    # Handle NAs for education levels
    edulvla = ifelse(edulvla %in% c(77, 88, 99), NA_integer_, edulvla),
    edulvlb = ifelse(edulvlb %in% c(5555, 7777, 8888), NA_integer_, edulvlb),
    
    # Explicitly making 'No BA' the reference category
    educ.ba = factor(educ.ba, levels = c("No BA", "BA or more")),

    # Converting cohort to a factor with labels
    populist = factor(populist,
                 levels = c("1", "2"),
                 labels = c("Populist attidudes", "Ba"))
  )
table(model_data$populist)
## 
## Populist attidudes                 Ba 
##                  0                  0

Task 3

Now using the Italian dataset and the authoritarian values scale (as we did in the tutorial), graph the average by survey year. Interpret.

daf <- read.fst("italy_data.fst")
## Creates an authoritarian values scale based on human modules items (can also do libertarian value scale)
daf <- daf %>%
  mutate(behave = ipbhprp,
         secure = impsafe,
         safety = ipstrgv,
         tradition = imptrad,
         rules = ipfrule) %>%
  mutate(across(c("behave", "secure", "safety", "tradition", "rules"),
                ~ na_if(.x, 7) %>% na_if(8) %>% na_if(9))) %>%
  # Apply the reverse coding
  mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))

# Now you can calculate 'schwartzauth' after the NA recoding
daf$auth <- scales::rescale(daf$behave + 
                      daf$secure + 
                      daf$safety + 
                      daf$tradition + 
                      daf$rules, to=c(0,100), na.rm=TRUE)


daf <- daf %>% filter(!is.na(auth))
table(daf$secure)
## 
##    1    2    3    4    5    6 
##   54  139  626 1856 2876 2881
table(daf$auth)
## 
##   0  12  16  20  24  28  32  36  40  44  48  52  56  60  64  68  72  76  80  84 
##   7   4   4   9  13  20  27  57 113 155 163 323 367 596 647 789 823 897 992 733 
##  88  92  96 100 
## 632 465 299 297
daf$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
  daf$year[daf$essround == i] <- replacements[i]
}

auth_avg <- daf %>%
  group_by(year) %>%
  summarize(auth_avg = mean(auth, na.rm = TRUE))

plot_auth <- ggplot(auth_avg, aes(x = year, y = auth_avg)) +
  geom_point(aes(color = auth_avg), alpha = 1.0) + 
  geom_line(aes(group = 1), color = "blue", linetype = "dashed") +  
  labs(title = "Authoritarian Scale Average by Year (Italy)",
       x = "Survey Year",
       y = "Authoritarian Scale Average") +
  theme_minimal() +
  theme(legend.position = "none") +
  scale_y_continuous(limits = c(0, 100))

print(plot_auth)

#### Interpretation The average per year does not change significantly between 2012, and 2020, the years shown. This indicates that the average degree of authoritarianism per year in Italy is around the same, at ~75, with only slight fluctuations.