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)
# Load the packages
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## [26] "methods" "base"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")
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$trstprt)
sweden_data$trust <- scales::rescale(sweden_data$trstplt + sweden_data$trstprl + sweden_data$trstprt, na.rm = TRUE, to = c(0, 100))
sweden_data$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()`).
#the data of the graph is skewed to the left side although the majority of the data sits in the middle. there is also a peak at 50 which shows the average level of populism in sweden.
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.sweden_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"
),
# Handling 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_model <- lm(populist ~ educ.ba, data = model.sweden_data)coefficients_sweden <- coef(sweden_model)
print(coefficients_sweden)## (Intercept) educ.baBA or more
## 51.358680 -7.868121#the interceptions of 51.35 represents the average score on the populist scale when you do not have a BA degree. Whereas the -7.86 represents the difference of average score on the populist scale for individuals that have a BA degree which show that individuals with a BA have -7.86 points compared to the individual without a BA.
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.
df <- read.fst("italy_data.fst")
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))) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))
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 2881df$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
df$year[df$essround == i] <- replacements[i]
}
auth_avg <- df %>%
group_by(year) %>%
summarize(auth_avg = mean(auth, na.rm = TRUE))
plot_auth <- ggplot(auth_avg, aes(x = year, y = auth_avg)) +
geom_point(aes(color = auth_avg), alpha = 1.0) +
geom_line(aes(group = 1), color = "blue", linetype = "dashed") +
labs(title = "Authoritarian Scale Average by Year (Italy)",
x = "Survey Year",
y = "Authoritarian Scale Average") +
theme_minimal() +
theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 100))
print(plot_auth)
#the authoritarian scale for italy sits around and remains constant around 75 which proves there has not been any significant changes in authoritarian beliefs.
Task 4
Now run a linear regression model, using the modelsummary package, with authoritarian values as the outcome, and generations as the predictor/potential explanatory variable, and using the Italian data again. Rename the coefficients using the coef_rename function. Interpret the coefficients, whether they are statistically significant, and the adjusted R-squared.
model_data <- df %>%
mutate(
age = ifelse(agea == 999, NA, agea),
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
gen = case_when(
yrbrn %in% 1900:1945 ~ "1",
yrbrn %in% 1946:1964 ~ "2",
yrbrn %in% 1965:1979 ~ "3",
yrbrn %in% 1980:1996 ~ "4",
TRUE ~ as.character(cohort)
),
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(model_data$gen)##
## Interwar Baby Boomers Gen X Millennials
## 1026 2580 2214 1811model1 <- lm(auth ~ gen, data = model_data)modelsummary(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 = 'Table 1. Regression models 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 |
R squared value 0.6% represents the variance of italian authoritarian value scale
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$trust, na.rm = TRUE, to=c(100,0))model.greece_data <- greece_data %>%
mutate(
gndr = case_when(
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
gndr == 9 ~ NA_character_,
TRUE ~ as.character(gndr)
),
ID = case_when(
lrscale %in% 0:4 ~ "Left",
lrscale %in% 6:10 ~ "Right",
lrscale %in% c(77, 88, 99) ~ NA_character_,
TRUE ~ NA_character_
),
age = ifelse(agea == 999, NA, agea),
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
gen = case_when(
yrbrn %in% 1900:1945 ~ "1",
yrbrn %in% 1946:1964 ~ "2",
yrbrn %in% 1965:1979 ~ "3",
yrbrn %in% 1980:1996 ~ "4",
TRUE ~ as.character(cohort)
),
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(model.greece_data$gen)##
## Interwar Baby Boomers Gen X Millennials
## 2978 3484 3498 2330model1 <- lm(populist ~ ID, data = model.greece_data)
model2 <- lm(populist ~ gndr, data = model.greece_data)
model3 <- lm(populist ~ gen, data = model.greece_data)modelsummary(
list(model1),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_omit = "Intercept")| (1) | |
|---|---|
| IDRight | −8.8 (0.6)*** |
| Num.Obs. | 4936 |
| R2 | 0.037 |
| R2 Adj. | 0.036 |
| AIC | 44723.8 |
| BIC | 44743.3 |
| Log.Lik. | −22358.894 |
| RMSE | 22.44 |
models <- list(
"Model 1" = lm(populist ~ lrscale, data = model.greece_data),
"Model 2" = lm(populist ~ gndr, data = model.greece_data),
"Model 3" = lm(populist ~ gen, data = model.greece_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) | 67.5 (0.3)*** | 70.0 (0.3)*** | 65.8 (0.5)*** |
| lrscale | 0.1 (0.0)*** | ||
| gndrMale | −1.0 (0.5)* | ||
| genBaby Boomers | 3.5 (0.7)*** | ||
| genGen X | 5.6 (0.7)*** | ||
| genMillennials | 5.8 (0.7)*** | ||
| Num.Obs. | 9823 | 9823 | 9562 |
| R2 | 0.021 | 0.000 | 0.009 |
| R2 Adj. | 0.021 | 0.000 | 0.009 |
| AIC | 88667.6 | 88873.2 | 86473.9 |
| BIC | 88689.2 | 88894.8 | 86509.7 |
| Log.Lik. | −44330.802 | −44433.590 | −43231.944 |
| RMSE | 22.07 | 22.30 | 22.25 |
models <- list(
"Model 1" <- lm(populist ~ ID, data = model.greece_data),
"Model 2" <- lm(populist ~ gndr, data = model.greece_data),
"Model 3" <- lm(populist ~ gen, data = model.greece_data))
modelplot(models, coef_omit = 'Interc')
#Considering gender, on average men in Greece score 1 point less
compared to women on the populist scale which show women show stronger
populist opinions than men. When we consider generational differences,
gen x, millenials and baby boomers all have higher populist scores
compared to the interwar generation. Millenials have 5.8 points more
than the interwar generation which show how millenials have stronger
populist opions compared to other generations in Greece. Baby boomers
have 3.5 points higher than the interwar generation and Gen x with 5.6
points higher.
#THE END