Kaur_Gurjyot_Homework_4
Gurjyot Kaur
2024-03-18
Set-up:
# List of packages
packages <- c("tidyverse", "broom", "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] "datasets" "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")# Populist scale (Norris/Inglehart use low levels of trust in parliaments, politicians and parties as an indicator of populism)
# 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$trstprt, na.rm = TRUE, to = c(0, 100)) # new column, adds up the values (10 ones) onto the 0-100 scale
# Here's how you would "flip it" because populist is conceived by N + I as 'anti-trust'
# As Schafer explains there are some issues with that conceptualization, but N + I is also widely applied
sweden_data$populist <- scales::rescale(sweden_data$trust, na.rm = TRUE, to=c(100,0)) #rescale to 100 to 0 to invert itDistribution:
ggplot(sweden_data, aes(x = populist)) +
geom_histogram(bins = 30, fill = "pink", color = "black") +
theme_minimal() +
labs(title = "Distribution of Populist Scale for Sweden",
x = "Populist Scale",
y = "Count")## Warning: Removed 2503 rows containing non-finite values (`stat_bin()`).
Interpretation:
In the histogram, greater values are to the left of the populist scale (generally, on the left, the values are increasing with regard to count), indicating that most people are concentrated between 25 and 50 on the populist scale. Hence, most individuals, on average, seem to have moderate trust and not be populististic. This can also be further supported as the most common answer was around 50 for the populist scale.
Task 2
Run a linear regression with populist attitudes as the outcome, educational attainment (recoded as a binary “BA or more” and “No BA”), and using the Swedish data. Print the intercept and coefficients only and interpret. Then do a tidy model table displaying the p-value (with digits = 3) and interpret.
sweden_data <- read.fst("sweden_data.fst")# Populist scale (Norris/Inglehart use low levels of trust in parliaments, politicians and parties as an indicator of populism)
# Setting values greater than 10 to NA
sweden_data$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)) # new column, adds up the values (10 ones) onto the 0-100 scale
# 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)) #rescale to 100 to 0 to invert itsweden_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" # anything else is NO BA
)
) %>%
# Ensure all NAs are handled uniformly
mutate(
edulvla = ifelse(edulvla %in% c(77, 88), NA_integer_, edulvla),
edulvlb = ifelse(edulvlb %in% c(5555, 7777, 8888), NA_integer_, edulvlb),
educ.ba = as.character(educ.ba) # Force educ.ba to be treated as character
)table(sweden_data$educ.ba) # checking if there are two categories##
## BA or more No BA
## 4739 13477sweden_pop_model <- lm(populist ~ educ.ba, data = sweden_data) # y (dependent variable) ~ x (independent variable)coefficients_sweden <- coef(sweden_pop_model)
print(coefficients_sweden) # b is intercept, m is IDright***## (Intercept) educ.baNo BA
## 43.490559 7.868121Interpretation:
Intercept (43.5):
This value represents the average score on the populist scale for the BA group (reference group that is omitted)- its expected average populist scale score is 43.5.
educ.baNo BA Coefficient (7.87):
The positive coefficient for the “No BA” group indicates that, on average, individuals who have no BAs in Sweden have populist scale scores that are 7.87 points higher than those who have BAs.
Therefore, comparing individuals with no BA to BA in Sweden, the average populist scale score would be 7.87 points higher for the no BA group- suggesting that individuals with BAs exhibit lower populist attitudes than individuals with no BAs.
tidy(sweden_pop_model) |>
knitr::kable(digits = 3)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 43.491 | 0.299 | 145.376 | 0 |
| educ.baNo BA | 7.868 | 0.350 | 22.494 | 0 |
Interpretation:
P-value indicates the relationship between one’s education and position on the populist scale is statistically significant because it is 0, suggesting there is sufficient evidence to reject the null hypothesis.
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")
## 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 # Looking at distribution before we proceed:
ggplot(df, aes(x = auth)) +
geom_histogram(bins = 30, fill = "pink", color = "black") +
theme_minimal() +
labs(title = "Distribution of Authoritarian Scale for Italy",
x = "Authoritarian Scale",
y = "Count")
Year-by-year average:
df$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
df$year[df$essround == i] <- replacements[i]
}
auth_avg <- df %>% # avg of auth by year
group_by(year) %>%
summarize(auth_avg = mean(auth, na.rm = TRUE))
plot_auth <- ggplot(auth_avg, aes(x = year, y = auth_avg)) +
geom_point(aes(color = auth_avg), alpha = 1.0) +
geom_line(aes(group = 1), color = "purple", linetype = "dashed") +
labs(title = "Authoritarian Scale Average by Year (Italy)",
x = "Survey Year",
y = "Authoritarian Scale Average") +
theme_minimal() +
theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 100))
print(plot_auth)
Interpretation:
Overall, the graph shows little to no fluctuations with regard to survey year and authoritarian scale values for Italy. There is, however, a very slight decrease beginning from 2014.
Task 4
Now run a linear regression model, using the modelsummary package, with authoritarian values as the outcome, and generations as the predictor/potential explanatory variable, and using the Italian data again. Rename the coefficients using the coef_rename function. Interpret the coefficients, whether they are statistically significant, and the adjusted R-squared.
## Let's run our linear regression models
# We will consider if there is a relationship to generations. So let's set-up the variables we need:
model_data <- df %>%
mutate(
# Recoding age, setting 999 to NA
age = ifelse(agea == 999, NA, agea),
# Recoding cohort variable, setting years before 1930 and after 2000 to NA
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
# Recoding generational cohorts based on year of birth
gen = case_when(
yrbrn %in% 1900:1945 ~ "1",
yrbrn %in% 1946:1964 ~ "2",
yrbrn %in% 1965:1979 ~ "3",
yrbrn %in% 1980:1996 ~ "4",
TRUE ~ as.character(cohort) # Keeping other values as character if they do not fit the ranges
),
# Converting cohort to a factor with labels
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(model_data$gen)##
## Interwar Baby Boomers Gen X Millennials
## 1026 2580 2214 1811model1 <- lm(auth ~ gen, data = model_data) # y ~ mmodelsummary(
list(model1),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_omit = "Intercept")| (1) | |
|---|---|
| genBaby Boomers | −1.1 (0.6)* |
| genGen X | −2.1 (0.6)*** |
| genMillennials | −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 |
Renaming:
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", "gndrMale" = "Male", "educ.baBA or more" = "BA or More"),
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 |
Coefficients Interpretation:
Baby Boomers (-1.1): Compared to the Interwar generation (omitted reference category), Baby Boomers have scores that are, on average, 1.1 points lower on the authoritarian values scale. The one asterisk denotes statistical significance at a high p-value (under our alpha threshold of 0.05).
Gen X (-2.1): Generation X shows a slight increase, with scores 2.1 points lower than the Interwar generation on the authoritarian values scale. The triple asterisks denote statistical significance at a very low p-value (under our alpha threshold of 0.05).
Millennials (-3.6): Millennials show the most considerable decrease in authoritarian values, with their scores being 3.6 points lower than those of the Interwar generation- Millennials diverge most strongly from the Interwar generation in comparing their authoritarian values.
Adjusted R-Squared (Adj. R² = 0.006):
The adjusted R-squared value of 0.006 means that the generation variable explains approximately 0.6% of the variance in the authoritarian values scale for Italy, suggesting that the model explains only a small portion of the variability in authoritarian values; there might be other factors that explain and shape each generation’s attitude.
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.
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)
# 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$trstprt, na.rm = TRUE, to = c(0, 100)) # new column, adds up the values (10 ones) onto the 0-100 scale
# Here's how you would "flip it" because populist is conceived by N + I as 'anti-trust'
# As Schafer explains there are some issues with that conceptualization, but N + I is also widely applied
greece_data$populist <- scales::rescale(greece_data$trust, na.rm = TRUE, to=c(100,0)) #rescale to 100 to 0 to invert itgreece_data <- greece_data %>%
mutate(
ID = case_when(
lrscale %in% 0:4 ~ "Left", # This time: Values 0 to 3 in 'lrscale' are categorized as "Left"
lrscale %in% 6:10 ~ "Right", # This time: Values 6 to 10 in 'lrscale' are categorized as "Right"
lrscale %in% c(77, 88, 99) ~ NA_character_, # Values 77, 88, 99 in 'lrscale' are set as NA
TRUE ~ NA_character_ # Any other values not explicitly matched above
)
)
table(greece_data$ID)##
## Left Right
## 2688 3522model2 <- lm(populist ~ ID, data = greece_data)modelsummary(
list(model2),
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 |
modelplot(model2, coef_omit = 'Interc')
Interpretation:
The coefficient is around -8.8. Compared to politically left-leaning individuals (omitted reference category), individuals leaning to the right have scores that are, on average, 8.8 points lower on the populist scale in Greece.
Model 2 = populist attitudes scale as the outcome; gender as the single predictor, and using data for Greece.
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)
# 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$trstprt, na.rm = TRUE, to = c(0, 100)) # new column, adds up the values (10 ones) onto the 0-100 scale
# Here's how you would "flip it" because populist is conceived by N + I as 'anti-trust'
# As Schafer explains there are some issues with that conceptualization, but N + I is also widely applied
greece_data$populist <- scales::rescale(greece_data$trust, na.rm = TRUE, to=c(100,0)) #rescale to 100 to 0 to invert itgreece_data <- greece_data %>%
mutate(
# Recoding gender
gndr = case_when(
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
gndr == 9 ~ NA_character_,
TRUE ~ as.character(gndr)
))table(greece_data$gndr)##
## Female Male
## 6929 5629model3 <- lm(populist ~ gndr, data = greece_data)modelsummary(
list(model3),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_omit = "Intercept")| (1) | |
|---|---|
| gndrMale | −1.0 (0.5)* |
| Num.Obs. | 9823 |
| R2 | 0.000 |
| R2 Adj. | 0.000 |
| AIC | 88873.2 |
| BIC | 88894.8 |
| Log.Lik. | −44433.590 |
| RMSE | 22.30 |
modelplot(model3, coef_omit = 'Interc')
Interpretation:
The coefficient is around -0.9 to -1. Compared to females (omitted reference category), males exhibit scores that are, on average, 0.9 or 1 point(s) lower on the populist scale in 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")# Populist scale (Norris/Inglehart use low levels of trust in parliaments, politicians and parties as an indicator of populism)
# 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$trstprt, na.rm = TRUE, to = c(0, 100)) # new column, adds up the values (10 ones) onto the 0-100 scale
# Here's how you would "flip it" because populist is conceived by N + I as 'anti-trust'
# As Schafer explains there are some issues with that conceptualization, but N + I is also widely applied
greece_data$populist <- scales::rescale(greece_data$trust, na.rm = TRUE, to=c(100,0)) #rescale to 100 to 0 to invert itgreece_data <- greece_data %>%
mutate(
# Recoding age, setting 999 to NA
age = ifelse(agea == 999, NA, agea),
# Recoding cohort variable, setting years before 1930 and after 2000 to NA
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
# Recoding generational cohorts based on year of birth
gen = case_when(
yrbrn %in% 1900:1945 ~ "1",
yrbrn %in% 1946:1964 ~ "2",
yrbrn %in% 1965:1979 ~ "3",
yrbrn %in% 1980:1996 ~ "4",
TRUE ~ as.character(cohort) # Keeping other values as character if they do not fit the ranges
),
# Converting cohort to a factor with labels
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(greece_data$gen)##
## Interwar Baby Boomers Gen X Millennials
## 2978 3484 3498 2330model4 <- lm(populist ~ gen, data = greece_data)modelsummary(
list(model4),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_omit = "Intercept")| (1) | |
|---|---|
| genBaby Boomers | 3.5 (0.7)*** |
| genGen X | 5.6 (0.7)*** |
| genMillennials | 5.8 (0.7)*** |
| Num.Obs. | 9562 |
| R2 | 0.009 |
| R2 Adj. | 0.009 |
| AIC | 86473.9 |
| BIC | 86509.7 |
| Log.Lik. | −43231.944 |
| RMSE | 22.25 |
modelplot(model4, coef_omit = 'Interc')
Interpretation:
For Millennials, the coefficient is around 5.8. Compared to the Interwar generation (omitted reference category), Millennials exhibit scores that are, on average, 5.8 higher on the populist scale in Greece.
For Gen X, the coefficient is around 5.6. Compared to the Interwar generation (omitted reference category), Gen X exhibit scores that are, on average, 5.6 higher on the populist scale in Greece, slighter higher than Millennials.
For Baby Boomers, the coefficient is around 3.5. Compared to the Interwar generation (omitted reference category), Baby Boomers exhibit scores that are, on average, 3.5 higher on the populist scale in Greece. This is the closest generation to the Interwar generation in terms of the populist scale as Millennials and Gen X scores are greater.