Wilson_Marina_Homework_4
2024-03-12
Homework 4:
# List of packages
packages <- c("tidyverse", "infer", "fst", "broom", "modelsummary", "effects", "survey", "interactions") # add any you need here
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new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
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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$trust, na.rm = TRUE, to=c(100,0))ggplot(sweden_data, aes(x = populist)) +
geom_histogram(bins = 30, fill = "blue", color = "black") +
theme_minimal() +
labs(title = "Distribution of Populist Scale for Sweden",
x = "Populist Scale",
y = "Count")## Warning: Removed 2503 rows containing non-finite values (`stat_bin()`).
## Interpretation: In a Norris and Ingelhart populist scale, a score of 0 indicates complete trust in parliament/politicians, whereas a score of 100 indicates complete distrust (anti-trust) of parliament/politicians. In Sweden, the distribution peaks at around 50, with higher counts in the 25-50 range than the 50-75 range. Because Norris and Ingelhart use low levels of trust (high scores) as an indicator of populism, we can say that Sweden is not a populist country. Based on the data, Swedes tend to have more trust in parliament/politicians than not, with most Swedes indicating a moderate/neutral level of trust (score of 50).Task 2
Run a linear regression with populist attitudes as the outcome, educational attainment (recoded as a binary “BA or more” and “No BA”), and using the Swedish data. Print the intercept and coefficients only and interpret. Then do a tidy model table displaying the p-value (with digits = 3) and interpret.
sweden_data <- sweden_data %>%
mutate(
educ.ba = case_when(
essround < 5 & edulvla == 5 ~ "BA or more",
essround >= 5 & edulvlb > 600 ~ "BA or more",
TRUE ~ "No BA"
),
# Handle NAs for education levels
edulvla = ifelse(edulvla %in% c(77, 88, 99), NA_integer_, edulvla),
edulvlb = ifelse(edulvlb %in% c(5555, 7777, 8888), NA_integer_, edulvlb),
# Explicitly making 'No BA' the reference category
educ.ba = factor(educ.ba, levels = c("No BA", "BA or more"))
)
table(sweden_data$educ.ba)##
## No BA BA or more
## 13477 4739sweden_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## Interpretation: The intercept value of ~51.3 (rounded) represents the average score on the populist scale for the reference group in Sweden, which is the "No BA" group. Meanwhile, the coefficient of -8.0 (rounded) associated with the "BA or more" group indicates that, on average, individuals with a BA or more have have populist scale scores that are 8.0 points lower than those with no BA. Therefore, comparing individuals with a BA or more and no BA in Sweden, we expect to see the average populist scale score to be 8.0 pointslower for the "BA or more" group. This suggests that individuals with a BA or more exhibit weaker populist attitudes than their no BA counterparts.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## Interpretation: The p-value in this case is essentially zero, indicating that it is statistically significant. The difference in populist attitudes between individuals in Sweden who have a "BA or more" vs "No BA" is highly unlikely to be due to chance. Swedish individuals with no BA tend to exhibit stronger populist attitudes than Swedish individuals with a BA or more.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$secure)##
## 1 2 3 4 5 6
## 54 139 626 1856 2876 2881table(df$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 297df$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
df$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)
## Interpretation: The authoritarian scale average for Italy has not changed or fluctuated much from 2012-2020. The average score dipped slightly from 75 in 2012 to ~71 in 2016, but slowly increased again after 2016 (~73 in 2018 & ~74 in 2020). The average score of 75 indicates that regardless of year, Italians generally exhibit attitudes/values which correspond relatively strongly to authoritarianism. 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 1811model1 <- lm(auth ~ gen, data = model_data)
model2 <- lm(auth ~ gndr, data = model_data)
model3 <- lm(auth ~ educ.ba, data = model_data)models <- list(
"Model 1" = lm(auth ~ gen, data = model_data),
"Model 2" = lm(auth ~ gndr, data = model_data),
"Model 3" = lm(auth ~ educ.ba, 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) | 75.3 (0.5)*** | 74.4 (0.2)*** | 73.3 (0.2)*** |
| genBaby Boomers | −1.1 (0.6)* | ||
| genGen X | −2.1 (0.6)*** | ||
| genMillennials | −3.6 (0.6)*** | ||
| gndrMale | −2.6 (0.3)*** | ||
| educ.baBA or more | −1.1 (0.4)* | ||
| Num.Obs. | 7631 | 8432 | 8432 |
| R2 | 0.006 | 0.007 | 0.001 |
| R2 Adj. | 0.006 | 0.007 | 0.001 |
| AIC | 62934.0 | 69523.1 | 69579.5 |
| BIC | 62968.7 | 69544.3 | 69600.6 |
| Log.Lik. | −31462.009 | −34758.566 | −34786.739 |
| RMSE | 14.94 | 14.93 | 14.98 |
## Interpretation: Compared to the interwar generation (which is omitted), baby boomers exhibit scores that are, on average, 1.1 points lower on the authoritarian scale (0-100). The single asterisk indicates a p-value of less than 0.05, which is statistically significant. On average, gen x exhibits scores that are 2.1 points lower than the interwar generation, and millennial exhibit scores that are 3.6 points lower (both gens have three asterisks, indicating a p-value of less than 0.001 and thus high statistical significance). The adjusted R-squared value is 0.006, meaning the "gen" variable explains about 0.6% of the variance in the authoritarian values scale for Italy, after accounting for the number of predictors. This suggests that the model explains only an extremely 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
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")dff <- read.fst("greece_data.fst")
## Creates an authoritarian values scale based on human modules items (can also do libertarian value scale)
dff <- dff %>%
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
dff$auth <- scales::rescale(dff$behave +
dff$secure +
dff$safety +
dff$tradition +
dff$rules, to=c(0,100), na.rm=TRUE)
dff <- dff %>% filter(!is.na(auth))
table(dff$secure)##
## 1 2 3 4 5 6
## 44 216 548 1345 3922 6228model_dataa <- dff %>%
mutate(
# Recoding gender
gndr = case_when(
gndr == 1 ~ "Male",
gndr == 2 ~ "Female",
gndr == 9 ~ NA_character_,
TRUE ~ as.character(gndr)
),
lrscale = case_when(
lrscale %in% 0:4 ~ "Left", # Values 0 to 4 in 'lrscale' are categorized as "Left"
lrscale %in% 6:10 ~ "Right", # Values 6 to 10 in 'lrscale' are categorized as "Right"
lrscale %in% c(77, 88, 99) ~ NA_character_, # Values 77, 88, 99 in 'lrscale' are set as NA
TRUE ~ NA_character_ # Any other values not explicitly matched above
),
lrscale = factor(lrscale, levels = c("Left", "Right")),
# Recoding age, setting 999 to NA
age = ifelse(agea == 999, NA, agea),
# Recoding cohort variable, setting years before 1930 and after 2000 to NA
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
# Recoding generational cohorts based on year of birth
gen = case_when(
yrbrn %in% 1900:1945 ~ "1",
yrbrn %in% 1946:1964 ~ "2",
yrbrn %in% 1965:1979 ~ "3",
yrbrn %in% 1980:1996 ~ "4",
TRUE ~ as.character(cohort) # Keeping other values as character if they do not fit the ranges
),
# Converting cohort to a factor with labels
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(model_dataa$gen)##
## Interwar Baby Boomers Gen X Millennials
## 2909 3420 3434 2279modell1 <- lm(auth ~ lrscale, data = model_dataa)
modell2 <- lm(auth ~ gndr, data = model_dataa)
modell3 <- lm(auth ~ gen, data = model_dataa)modelplot(modell1, coef_omit = 'Interc')
modelplot(modell2, coef_omit = 'Interc')
modelplot(modell3, coef_omit = 'Interc')
## Interpretation: Model 1, showing populist attitude scale as the outcome and left/right as the single predictor, shows a coefficient of 4.75. Since "left" has been set to the reference category, this indicates that right-leaning Greeks score, on average, 4.75 points higher on the authoritarian scale than left-leaning Greeks. Model 2, showing populist attitude scale as the outcome and gender as the single predictor, shows a coefficient of -2.00. Since "female" has been set to the reference category, this indicates that male Greeks, on average, score 2.0 points lower on the authoritarian scale than female Greeks. Finally, Model 3 shows populist attitude scale as the outcome and generations as the single predictor. On average, compared to the Greek interwar generation (reference category), Greek baby boomers score around ~3.9 points lower on the authoritarian scale, Greek gen x's score around ~5.9 points lower on the authoritarian scale, and Greek millennials score around ~10.5 points lower on the authoritarian scale. This suggests that the popularity of authoritarian values/attitudes in Greece has declined over the past 4 generations.