afana_xena_homework_5
Xena Afana
2024-03-25
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packages <- c("tidyverse", "infer", "fst", "modelsummary", "effects", "survey", "MASS", "aod", "interactions", "kableExtra", "flextable", "scales") # 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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## [31] "methods" "base"##task 1
## Loading dataset
germany_data <- read_fst("germany_data.fst")#renaming
df <- germany_datadf$weight <- df$dweight * df$pweight
survey_design <- svydesign(ids = ~1, data = df, weights = ~weight)
# Setting values greater than 10 to NA
df$trstplt <- ifelse(df$trstplt > 10, NA, df$trstplt)
df$trstprl <- ifelse(df$trstprl > 10, NA, df$trstprl)
df$trstprt <- ifelse(df$trstprt > 10, NA, df$trstprt)
# Creating and rescaling the trust variable
df$trust <- scales::rescale(df$trstplt + df$trstprl + df$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
df$populist <- scales::rescale(df$trust, na.rm = TRUE, to=c(100,0))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))df <- df %>%
mutate(
polID = case_when(
lrscale %in% 0:3 ~ "Left",
lrscale %in% 7:10 ~ "Right",
lrscale %in% 4:6 ~ "Moderate",
lrscale %in% c(77, 88, 99) ~ NA_character_
),
religious = case_when(
rlgdgr %in% c(77, 88, 99) ~ NA_real_,
TRUE ~ rlgdgr
)
)model3 <- lm(auth ~ polID + religious, data = df, weights = weight)
model4 <- lm(auth ~ polID + religious + polID*religious, data = df, weights = weight)
modelsummary(
list(model3, model4),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}"),
statistic = NULL)| (1) | (2) | |
|---|---|---|
| (Intercept) | 55.3 (0.3)*** | 56.4 (0.3)*** |
| polIDModerate | 4.6 (0.3)*** | 3.4 (0.4)*** |
| polIDRight | 7.7 (0.4)*** | 4.2 (0.7)*** |
| religious | 0.9 (0.0)*** | 0.6 (0.1)*** |
| polIDModerate × religious | 0.3 (0.1)*** | |
| polIDRight × religious | 0.8 (0.1)*** | |
| Num.Obs. | 23373 | 23373 |
| R2 | 0.048 | 0.050 |
| R2 Adj. | 0.048 | 0.050 |
| AIC | 200594.1 | 200558.6 |
| BIC | 200634.4 | 200615.0 |
| Log.Lik. | −100292.049 | −100272.298 |
| RMSE | 17.27 | 17.24 |
Interpretation of coefficients for MLR without the interaction term (model3):
on the ‘left’ with a religiousness score of 0, with high statistical significance.
compared to non-religious individuals, holding political identification constant.
IDRight (3.9 ***): Identifying as politically right is associated with a 3.9 unit increase in the
Interpretation of model fit metrics:
Num.Obs.: The model was fitted using 1176 observations.
R2 Adj. (0.093): After adjusting for the number of predictors, the model explains 9.3% of the variance.
AIC (9760.7 vs. 9762.6) and BIC (9780.9 vs. 9788.0): The AIC and BIC are slightly higher for model4,
suggesting that the interaction term does not substantially improve the model fit.
Log.Lik. (−4876.334): The log-likelihood is nearly identical between model3 and model4, indicating similar fit.
RMSE (15.28): The root mean square error is the same for both models, indicating similar predictive accuracy.
##task two
interaction_plot <- effect("polID*religious", model4, na.rm=TRUE)
# Plotting the interaction effect
plot(interaction_plot,
main="Interaction effect",
xlab="Religiousness",
ylab="Authoritarian attitudes scale")
interaction_plot##
## polID*religious effect
## religious
## polID 0 2 5 8 10
## Left 56.40706 57.56775 59.30879 61.04983 62.21053
## Moderate 59.85063 61.66176 64.37844 67.09513 68.90626
## Right 60.60237 63.35032 67.47224 71.59416 74.34211#Examining the model 4 interaction plot: # The interaction effect plot illustrates how political affiliation (Left, Moderate, Right) differentiates the association between religiousness and authoritarian attitudes. The slopes of the lines indicate that the authoritarian views scale rises with religiousness, with a stronger correlation for politically right-leaning persons. In particular, the ‘Right’ category’s sharpest slope suggests a higher correlation between religiousness # and authoritarian views for this demographic. ‘Left’ or ‘Moderate’ identity holders, on the other hand, have a less pronounced but still beneficial association. # The lines’ non-parallel character suggests that political affiliation influences the association between religiousness and authoritarian views, supporting the existence of an interaction effect.
##task 3
netherlands_data <- read_fst("netherlands_data.fst")
dn <- netherlands_datadn$weight <- dn$dweight * dn$pweight
survey_design <- svydesign(ids = ~1, data = dn, weights = ~weight)
# Setting values greater than 10 to NA
dn$trstplt <- ifelse(dn$trstplt > 10, NA, dn$trstplt)
dn$trstprl <- ifelse(dn$trstprl > 10, NA, dn$trstprl)
dn$trstprt <- ifelse(dn$trstprt > 10, NA, dn$trstprt)
# Creating and rescaling the trust variable
dn$trust <- scales::rescale(dn$trstplt + dn$trstprl + dn$trstprt, na.rm = TRUE, to = c(0, 100))
dn$populist <- scales::rescale(dn$trust, na.rm = TRUE, to=c(100,0))dn <- dn %>%
mutate(
Capital = case_when(
ppltrst %in% c(77, 88, 99) ~ NA_character_,
ppltrst >= 0 & ppltrst <= 3 ~ "Low",
ppltrst >= 4 & ppltrst <= 6 ~ "Mid",
ppltrst >= 7 & ppltrst <= 10 ~ "High",
TRUE ~ as.character(ppltrst)
),
Politically = case_when(
polintr %in% c(1, 2) ~ "Interested",
polintr %in% c(3, 4) ~ "Not Interested",
polintr %in% c(7, 8, 9) ~ NA_character_
)
)dn <- dn %>%
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 ))
dn$auth <- scales::rescale(dn$behave +
dn$secure +
dn$safety +
dn$tradition +
dn$rules, to=c(0,100), na.rm=TRUE)
dn <- dn %>% filter(!is.na(auth))dn <- dn %>%
mutate(religion = case_when(
rlgblg == 2 ~ "No",
rlgblg == 1 ~ "Yes",
rlgblg %in% c(7, 8, 9) ~ NA_character_,
TRUE ~ as.character(rlgblg)
))
table(dn$religion)##
## No Yes
## 10913 6750dn <- dn %>%
mutate(ID = case_when(
lrscale >= 0 & lrscale <= 4 ~ "Left",
lrscale >= 6 & lrscale <= 10 ~ "Right",
lrscale > 10 ~ NA_character_,
TRUE ~ NA_character_
))
table(dn$ID)##
## Left Right
## 5449 7010dn <- dn %>%
mutate(
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(yrbrn)
),
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(dn$gen)##
## Interwar Baby Boomers Gen X Millennials
## 3771 6147 4548 2730model5 <- lm(auth ~ religion + ID + gen, data = dn, weights = weight)
model6 <- lm(auth ~ religion + ID + gen + religion*gen, data = dn, weights = weight)modelsummary(
list(model5, model6),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_omit = "Intercept")| (1) | (2) | |
|---|---|---|
| religionYes | 7.2 (0.3)*** | 5.7 (0.7)*** |
| IDRight | 4.2 (0.3)*** | 4.3 (0.3)*** |
| genBaby Boomers | −5.2 (0.4)*** | −5.9 (0.6)*** |
| genGen X | −7.7 (0.4)*** | −8.8 (0.6)*** |
| genMillennials | −8.4 (0.5)*** | −9.7 (0.6)*** |
| religionYes × genBaby Boomers | 1.1 (0.8) | |
| religionYes × genGen X | 2.3 (0.9)** | |
| religionYes × genMillennials | 2.9 (1.0)** | |
| Num.Obs. | 12116 | 12116 |
| R2 | 0.124 | 0.125 |
| R2 Adj. | 0.124 | 0.125 |
| AIC | 101219.3 | 101213.5 |
| BIC | 101271.2 | 101287.5 |
| Log.Lik. | −50602.672 | −50596.757 |
| RMSE | 15.27 | 15.26 |
#Coefficient interpretation for MLR without interaction (model 5): # religionYes (7.2 ): Keeping political identification and generational category constant, there is a 7.2 unit increase in authoritarian views when a person is religious. This relationship has significant significance. #IDRight (4.2 ): Keeping other variables equal, political identification on the right is highly significant in relation to a 4.2 unit increase in authoritarian views as compared to the left. # genBaby Boomers (-5.2 ): Among other things being equal, there is a strong significant correlation between being a Baby Boomer with a 5.2 unit reduction in authoritarian views relative to the Interwar generation. # genGen X (-7.7 ): Among strong significance, belonging to Gen X is linked to a 7.7 unit decline in authoritarian views # as compared to the Interwar generation. #Model fit measures for both models are interpreted as follows: # Num.Obs.: A total of 12116 observations were used to fit both models. # R2 (0.124): The models modestly account for 12.4% of the variation in authoritarian views. # R2 Adj. (0.124 vs. 0.125): Adding the interaction terms almost completely preserves the adjusted R-squared, indicating that the interaction terms do not significantly increase the model’s explanatory power. #despite its complexity, the model with interactions (model 6) shows a little lower AIC (101219.3 vs. 101213.5) #showing a significantly better fit to the data.
##task 4
dn <- dn %>%
mutate(
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(yrbrn)
),
# After recoding, the gen variable is converted into a factor with labels for clearer interpretation.
# Factors are used in R to handle categorical variables.
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(dn$gen)##
## Interwar Baby Boomers Gen X Millennials
## 3771 6147 4548 2730dn <- dn %>%
mutate(
polID = case_when(
lrscale %in% 0:3 ~ "Left",
lrscale %in% 7:10 ~ "Right",
lrscale %in% 4:6 ~ "Moderate",
lrscale %in% c(77, 88, 99) ~ NA_character_
),
religious = case_when(
rlgdgr %in% c(77, 88, 99) ~ NA_real_,
TRUE ~ rlgdgr
)
)model7 <- lm(auth ~ cohort + polID + cohort*polID, data = dn, weights = weight)
interact_plot(model7, pred = cohort, modx = polID, jnplot = TRUE)
# The trend lines show that authoritarian attitudes tend to decline with
more recent birth cohorts for all political identifications (Left,
Moderate, Right). When it comes to views towards authority, younger
generations are less so than older generations. # Nevertheless, the
lines’ slopes vary according to political identity. The group labelled
as ‘Left’ exhibits the shallowest slope, # suggesting a less abrupt
decrease in authoritarian sentiments among different cohorts. # Whereas
the ‘Moderate’ and ‘Right’ groups have steeper slopes, younger members
of these political groupings exhibit a more marked decline in
#authoritarian views. # The ‘Right’ and ‘Moderate’ lines crossing
indicates that older cohorts tend to have more authoritarian attitudes
than the ‘Right,’ but this tendency reverses with younger cohorts, when
the ‘Moderate’ shows a minor increase in comparison to the ‘Right’.
##task 5
model8 <- lm(auth ~ religious + ID + religious*ID, data = dn, weights = weight)
interact_plot(model8, pred = religious, modx = ID, jnplot = TRUE)
# differs depending on one’s political affiliation (Left or Right). The
positive slopes for both groups #indicate that the authoritarian
attitude score rises in tandem with religiousness. #The ‘Right’s’
steeper slope suggests that those who identify as politically
right-leaning see a more marked rise in authoritarian tendencies along
with religiousness. # Although there is still a positive association,
the influence of religiousness on authoritarian tendencies is weaker for
individuals on the ‘Left’. This implies that the association between
religiosity and authoritarian beliefs may be moderated by political
identity. #The lack of crossing lines suggests that, throughout the
observed range of religiousness, the relative influence of religiousness
on authoritarian views does not reverse between the political
identifications of the left and right.