Tutorial 7 (SOC222)
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)
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## [26] "methods" "base"New skill (scaling & rescaling)
A new skill we will introduce today is creating scales and rescaling. Coefficients are easier to interpret on a 0-100 scale as we will do.
Here, we will use Schafer’s code based on his paper reviewing claims about authoritarian populism made in the Norris and Inglehart book, Cultural Backlash (2019).
But first let’s load some data. We will first consider Hungary, since Orban is considered a quintessential right-wing populist.
hungary_data <- read.fst("hungary_data.fst")#Task 1
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00# List of packages
packages <- c("tidyverse", "infer", "fst", "modelsummary", "effects", "survey", "interactions", "broom") # 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
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## [26] "utils" "datasets" "methods" "base"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$trust, na.rm = TRUE, to=c(100,0))ggplot(sweden_data, aes(x = populist)) +
geom_histogram(bins = 30, fill = "blue", color = "black") +
theme_minimal() +
labs(title = "Distribution of Populist Scale for Sweden",
x = "Populist Scale",
y = "Count")## Warning: Removed 2503 rows containing non-finite values (`stat_bin()`).
#recoding educ.ba
linear_data <- sweden_data %>%
mutate(
# Recoding education
educ.ba = case_when(
essround < 5 & edulvla == 5 ~ "BA or more",
essround >= 5 & edulvlb > 600 ~ "BA or more",
TRUE ~ "No BA"
),
# Handle NAs for education levels
edulvla = ifelse(edulvla %in% c(77, 88, 99), NA_integer_, edulvla),
edulvlb = ifelse(edulvlb %in% c(5555, 7777, 8888), NA_integer_, edulvlb),
# Explicitly making 'No BA' the reference category
educ.ba = factor(educ.ba, levels = c("No BA", "BA or more"))
)
sweden_edu_pop <- lm(populist ~ educ.ba, data = linear_data)
coefficients_linear <- coef(sweden_edu_pop)
print(coefficients_linear)## (Intercept) educ.baBA or more
## 51.358680 -7.868121#examining the histogram, most people have a very low trust level in the goverment. Since the grpag is skwed to the left it shows there is a larger amount of people on the lft than the right.
#Task 2
sweden_data <- read.fst("sweden_data.fst")# Populist scale (Norris/Inglehart use low levels of trust in parliaments, politicians and parties as an indicator of populism)
# Note: Schafer does it 2 ways, we will focus now on the most straightforward
# Setting values greater than 10 to NA
sweden_data$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))#recoding educ.ba
linear_data <- sweden_data %>%
mutate(
# 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"))
)
sweden_edu_pop <- lm(populist ~ educ.ba, data = linear_data)
coefficients_linear <- coef(sweden_edu_pop)
print(coefficients_linear)## (Intercept) educ.baBA or more
## 51.358680 -7.868121#The intercept, 51.35, is the populist scale’s average score for the Swedish reference group of people who have “No BA.” Put more simply, for people without a BA, the projected average populist scale score would be 51.34. Higher scores imply more populist views, which denotes a particular degree of populism. However, average, -7.86 points lower than those of those who identify as having no BA, according to the coefficient-7.86 connected with the “BA or more” categories.
tidy(sweden_edu_pop)## # 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-110tidy_table <- tidy(sweden_edu_pop) %>%
mutate(p.value = format.pval(p.value, digits = 3))print(tidy_table)## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 51.4 0.181 283. <2e-16
## 2 educ.baBA or more -7.87 0.350 -22.5 <2e-16summary(sweden_edu_pop)##
## Call:
## lm(formula = populist ~ educ.ba, data = linear_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#The p-value of 2.2e-16 in this instance, concludes that there is evidence to reject the null hypothesis. Which indicates that in Sweden, there is a strong correlation between educational attainment and the propensity to embrace populist ideas
#Task 3
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$secure)##
## 1 2 3 4 5 6
## 54 139 626 1856 2876 2881table(df$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 297ggplot(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$essround == i] <- replacements[i]
}
auth_avg <- df %>%
group_by(year) %>%
summarize(auth_avg = mean(auth, na.rm = TRUE))
plot_auth <- ggplot(auth_avg, aes(x = year, y = auth_avg)) +
geom_point(aes(color = auth_avg), alpha = 1.0) +
geom_line(aes(group = 1), color = "blue", linetype = "dashed") +
labs(title = "Authoritarian Scale Average by Year (Italy)",
x = "Survey Year",
y = "Authoritarian Scale Average") +
theme_minimal() +
theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 100))
print(plot_auth)
#The graph shows the average annual authoritarian scale in Italy is a
highly stable line. From 2012 to 2025, it is rather stable; however, it
begins to decline and reaches its lowest point in 2016. It then
increases once more, reaching its starting position once more by
2020.
#Task 4
df <- read.fst("italy_data.fst")
## Creates an authoritarian values scale based on human modules items (can also do libertarian value scale)
df <- df %>%
mutate(behave = ipbhprp,
secure = impsafe,
safety = ipstrgv,
tradition = imptrad,
rules = ipfrule) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"),
~ na_if(.x, 7) %>% na_if(8) %>% na_if(9))) %>%
# Apply the reverse coding
mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))
# Now you can calculate 'schwartzauth' after the NA recoding
df$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 2881model_data <- df %>%
mutate(
# 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 1811model1 <- lm(auth ~ gen, data = model_data)
modelsummary(
list(model1),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_rename = c("genBaby Boomers" = "Baby Boomers", "genGen X" = "Gen X", "genMillennials" = "Millennials"),
title = 'Table1. Regression model for authoritarian attitudes')| (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 |
#Baby Boomers −1.1, p<0.0001): Compared to the Interwar generation Baby Boomers exhibit scores that are, on average 1.1 points lower on the authoritarian values scale (0-100). The triple asterisks denote statistical significance at a very low p-value (under our alpha threshold of 0.05)
#Gen X (−2.1 p<0.0001): Generation X shows an even more pronounced decrease, with scores 2.1 points lower than the Interwar generation on the authoritarian values scale. Again, the triple asterisks denote statistical significance at a very low p-value (under our alpha threshold of 0.05)
#genMillennials (-3.6, p<0.001): Millennials exhibit the most considerable decrease in authoritarian values, with their scores being 3.6 points lower than those of the Interwar generation. This marked difference is statistically significant, indicating that Millennials diverge most strongly from the Interwar generation in comparing their authoritarian values.
#The adjusted R-squared value of 0.0006 means that the ‘gen’ variable (with our four coded generations) explain approximately 0.6 of the variance in the authoritarian values scale for Portugal, after accounting for the number of predictors While statistically significant, this suggests that the model explains only a small portion of the variability in authoritarian values, pointing to other factors outside generational differences that may play critical roles in shaping these attitudes.
#Task 5
greece_data <- read.fst("greece_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
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)
# Creating and rescaling the trust variable
greece_data$trust <- scales::rescale(greece_data$trstplt + greece_data$trstprl + greece_data$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
greece_data$populist <- scales::rescale(greece_data$trust, na.rm = TRUE, to=c(100,0))
ggplot(greece_data, aes(x = populist)) +
geom_histogram(bins = 30, fill = "blue", color = "black") +
theme_minimal() +
labs(title = "Distribution of Populist Scale for Greece",
x = "Populist Scale",
y = "Count")## Warning: Removed 2735 rows containing non-finite values (`stat_bin()`).
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 2330model1 <- 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)| Model 1 | Model 2 | Model 3 | |
|---|---|---|---|
| (Intercept) | 72.0 (0.5)*** | 70.0 (0.3)*** | 65.8 (0.5)*** |
| lrscaleRight | −8.8 (0.6)*** | ||
| gndrMale | −1.0 (0.5)* | ||
| genBaby Boomers | 3.5 (0.7)*** | ||
| genGen X | 5.6 (0.7)*** | ||
| genMillennials | 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')
# Generation X shows an even more pronounced decrease, with scores 5.2
points higher than the Interwar generation on the authoritarian values
scale. Again, the triple asterisks (***) denote statistical significance
at a very low p-value (under our alpha threshold of 0.05) # Millennials
exhibit the most considerable decrease in authoritarian values, with
their scores being 5.6 points higher than those of the Interwar
generation. This marked difference is statistically significant,
indicating that Millennials diverge least strongly from the Interwar
generation in comparing their authoritarian values.