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")
# 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()`).

answer: The populist scale’s distribution for Sweden is depicted on the histogram.The histogram’s distribution looks to be slightly biased to the right, indicating that populism is more common at lower levels and less common at higher ones.

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.

sweden_data <- sweden_data %>%
  mutate(
    educ.ba = case_when(
      essround < 5 & edulvla == 5 ~ "BA or more",
      essround >= 5 & edulvlb > 600 ~ "BA or more",
      TRUE ~ "No BA"
    ),
    
    edulvla = ifelse(edulvla %in% c(77, 88, 99), NA_integer_, edulvla),
    edulvlb = ifelse(edulvlb %in% c(5555, 7777, 8888), NA_integer_, edulvlb),
    
    educ.ba = factor(educ.ba, levels = c("No BA", "BA or more"))
  )
sweden_pop_model <- lm(populist ~ educ.ba, data = sweden_data)
coefficients_sweden <- coef(sweden_pop_model)
print(coefficients_sweden)
##       (Intercept) educ.baBA or more 
##         51.358680         -7.868121
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tidy(sweden_pop_model)
## # A tibble: 2 × 5
##   term              estimate std.error statistic   p.value
##   <chr>                <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)          51.4      0.181     283.  0        
## 2 educ.baBA or more    -7.87     0.350     -22.5 2.58e-110
tidy(sweden_pop_model) |>
knitr::kable(digits = 3)
term estimate std.error statistic p.value
(Intercept) 51.359 0.181 283.363 0
educ.baBA or more -7.868 0.350 -22.494 0 
summary(sweden_pop_model)
## 
## Call:
## lm(formula = populist ~ educ.ba, data = sweden_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.359 -14.692  -1.359  11.975  56.509 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        51.3587     0.1812  283.36   <2e-16 ***
## educ.baBA or more  -7.8681     0.3498  -22.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.43 on 15711 degrees of freedom
##   (2503 observations deleted due to missingness)
## Multiple R-squared:  0.0312, Adjusted R-squared:  0.03114 
## F-statistic:   506 on 1 and 15711 DF,  p-value: < 2.2e-16

awnswer: The intercept value of 51.4 shows that the predicted populist scale is 51.4 when the education level (educ.ba) is zero. A coefficient of -7.9 indicates that people who have completed a “BA or more” degree of education experience an estimated 7.9 reduction in their populist scale when compared to people who have completed less education.

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.

italy_data <- read.fst("italy_data.fst")
df <- read.fst("italy_data.fst")
## Creates an authoritarian values scale based on human modules items (can also do libertarian value scale)
df <- df %>%
  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
df$auth <- scales::rescale(df$behave + 
                      df$secure + 
                      df$safety + 
                      df$tradition + 
                      df$rules, to=c(0,100), na.rm=TRUE)


df <- df %>% filter(!is.na(auth))
table(df$secure)
## 
##    1    2    3    4    5    6 
##   54  139  626 1856 2876 2881
table(df$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
ggplot(df, aes(x = auth)) +
  geom_histogram(bins = 30, fill = "blue", color = "black") +
  theme_minimal() +
  labs(title = "Distribution of Authoritarian Scale for italy",
       x = "Authoritarian Scale",
       y = "Count")

df$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
  df$year[df$essround == i] <- replacements[i]
}
auth_avg <- df %>%
  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)

answer:From 2012 to 2020, the visualization shows a steady stability in the authoritarian scale with no notable swings.

Task 4

Now run a linear regression model, using the modelsummary package, with authoritarian values as the outcome, and generations as the predictor/potential explanatory variable, and using the Italian data again. Rename the coefficients using the coef_rename function. Interpret the coefficients, whether they are statistically significant, and the adjusted R-squared.

model_data <- df %>%
  mutate(
    # Recoding gender
    gndr = case_when(
      gndr == 1 ~ "Male",
      gndr == 2 ~ "Female",
      gndr == 9 ~ NA_character_,
      TRUE ~ as.character(gndr)
    ),

 # 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")),

    # Recoding age, setting 999 to NA
    age = ifelse(agea == 999, NA, agea),

    # Recoding cohort variable, setting years before 1930 and after 2000 to NA
    cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),

    # Recoding generational cohorts based on year of birth
    gen = case_when(
      yrbrn %in% 1900:1945 ~ "1",
      yrbrn %in% 1946:1964 ~ "2",
      yrbrn %in% 1965:1979 ~ "3",
      yrbrn %in% 1980:1996 ~ "4",
      TRUE ~ as.character(cohort)  # Keeping other values as character if they do not fit the ranges
    ),

    # Converting cohort to a factor with labels
    gen = factor(gen,
                 levels = c("1", "2", "3", "4"),
                 labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
  )
table(model_data$gen)
## 
##     Interwar Baby Boomers        Gen X  Millennials 
##         1026         2580         2214         1811
model1 <- lm(auth ~ gen, data = model_data)
models <- list(
   "model 1" = lm(auth ~ gen, data = model_data))
modelsummary(models, fmt = 1,
  estimate  = c("{estimate} ({std.error}){stars}"),
  statistic = NULL,
  coef_rename = c("genBaby Boomers" = "Baby Boomers", "genGen X" = "Gen X", "genMillennials" = "Millennials"),
  title = 'Table 2. Regression models for authoritarian attitudes')
Table 2. Regression models for authoritarian attitudes
model 1
(Intercept) 75.3 (0.5)***
Baby Boomers −1.1 (0.6)*
Gen X −2.1 (0.6)***
Millennials −3.6 (0.6)***
Num.Obs. 7631
R2 0.006
R2 Adj. 0.006
AIC 62934.0
BIC 62968.7
Log.Lik. −31462.009
RMSE 14.94

answer:Baby Boomers show lower levels of authoritarianism compared to the interwar generation, as indicated by the negative coefficient. Similarly, Gen X and Millennials also demonstrate declines in authoritarian ideals relative to the interwar generation. However, the “gen” variable only partially explains the variation in the Italian authoritarian value scale, suggesting other factors beyond generational differences may play a role.

Task 5

Now visualize, using the modelplot() function from the modelsummary package, coefficient estimates and 95% confidence intervals for the following three models:

Model 1 = populist attitudes scale as the outcome; left/right as the single predictor (omitting moderates as we did in the tutorial), and using data for Greece.

Model 2 = populist attitudes scale as the outcome; gender as the single predictor, and using data for Greece.

Model 3 = populist attitudes scale as the outcome; generations as the single predictor (using the four generational category recode we did in the tutorial), and using data for Greece. Interpret.

greece_data <- read.fst("greece_data.fst")
greece_data$trstplt <- ifelse(greece_data$trstplt > 10, NA, greece_data$trstplt)
greece_data$trstprl <- ifelse(greece_data$trstprl > 10, NA, greece_data$trstprl)
greece_data$trstprt <- ifelse(greece_data$trstprt > 10, NA, greece_data$trstprt)

greece_data$trust <- scales::rescale(greece_data$trstplt + greece_data$trstprl + greece_data$trstprt, na.rm = TRUE, to = c(0, 100))

greece_data$populist <- scales::rescale(greece_data$trust, na.rm = TRUE, to=c(100,0))
model_data <- greece_data %>%
  mutate(
    # Recoding gender
    gndr = case_when(
      gndr == 1 ~ "Male",
      gndr == 2 ~ "Female",
      gndr == 9 ~ NA_character_,
      TRUE ~ as.character(gndr)
    ),

    lrscale = case_when(
      lrscale %in% 0:4 ~ "Left",                     # This time: Values 0 to 3 in 'lrscale' are categorized as "Left"
      lrscale %in% 6:10 ~ "Right",                   # This time: Values 6 to 10 in 'lrscale' are categorized as "Right"
      lrscale %in% c(77, 88, 99) ~ NA_character_,    # Values 77, 88, 99 in 'lrscale' are set as NA
      TRUE ~ NA_character_                           # Any other values not explicitly matched above
    ),

    # Recoding cohort variable, setting years before 1930 and after 2000 to NA
    cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),

    # Recoding generational cohorts based on year of birth
    gen = case_when(
      yrbrn %in% 1900:1945 ~ "1",
      yrbrn %in% 1946:1964 ~ "2",
      yrbrn %in% 1965:1979 ~ "3",
      yrbrn %in% 1980:1996 ~ "4",
      TRUE ~ as.character(cohort)  # Keeping other values as character if they do not fit the ranges
    ),

    # Converting cohort to a factor with labels
    gen = factor(gen,
                 levels = c("1", "2", "3", "4"),
                 labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
  )
table(model_data$gen)
## 
##     Interwar Baby Boomers        Gen X  Millennials 
##         2978         3484         3498         2330
model1 <- lm(populist ~ lrscale, data = model_data)
model2 <- lm(populist ~ gndr, data = model_data)
model3 <- lm(populist ~ gen, data = model_data)
models <- list(
   "Model 1" = lm(populist ~ lrscale, data = model_data),
   "Model 2" = lm(populist ~ gndr, data = model_data),
   "Model 3" = lm(populist ~ gen, data = model_data))
modelsummary(models, fmt = 1,
  estimate  = c("{estimate} ({std.error}){stars}",
                "{estimate} ({std.error}){stars}",
                "{estimate} ({std.error}){stars}"),
  statistic = NULL,

coef_rename = c("lrscale" = "Right", "gndrMale" = "Male", "genBaby Boomers" = "Baby Boomers", "genGen X" = "Gen X", "genMillennials" = "Millennials"),
  title = 'Table 3. Regression models for populist attitudes')
Table 3. Regression models for populist attitudes
Model 1  Model 2  Model 3
(Intercept) 72.0 (0.5)*** 70.0 (0.3)*** 65.8 (0.5)***
lrscaleRight −8.8 (0.6)***
Male −1.0 (0.5)*
Baby Boomers 3.5 (0.7)***
Gen X 5.6 (0.7)***
Millennials 5.8 (0.7)***
Num.Obs. 4936 9823 9562
R2 0.037 0.000 0.009
R2 Adj. 0.036 0.000 0.009
AIC 44723.8 88873.2 86473.9
BIC 44743.3 88894.8 86509.7
Log.Lik. −22358.894 −44433.590 −43231.944
RMSE 22.44 22.30 22.25 
modelplot(models, coef_omit = 'Interc')

answer: Women in Greece tend to have stronger populist attitudes than men, with Baby Boomers, Gen X, and Millennials showing progressively higher levels of populism compared to the Interwar generation. However, the “gen” variable explains only about 0.9% of the variance in authoritarian beliefs among Greeks, suggesting that the model captures just a fraction of the factors influencing such attitudes.