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Emily Muskal
2024-03-19
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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
Use data for Germany and the variable coding for model3 and model4 from the tutorial (i.e., both for the cleaning & recode, as well as the linear model formulas). Do a modelsummary table displaying both model outputs. Interpret the coefficients for the MLR without the interaction (i.e., the first displayed model), and interpret model fit metrics for both models.
Germany_data <- read.fst("Germany_data.fst")Gf <- Germany_dataGf$trstplt <- ifelse(Gf$trstplt > 10, NA, Gf$trstplt)
Gf$trstprl <- ifelse(Gf$trstprl > 10, NA, Gf$trstprl)
Gf$trstprt <- ifelse(Gf$trstprt > 10, NA, Gf$trstprt)
# Creating and rescaling the trust variable
Gf$trust <- scales::rescale(Gf$trstplt + Gf$trstprl + Gf$trstprt, na.rm = TRUE, to = c(0, 100))
# Here's how you would "flip it" because populist is conceived by N + I as 'anti-trust'
# As Schafer explains there are some issues with that conceptualization, but N + I is also widely applied
Gf$populist <- scales::rescale(Gf$trust, na.rm = TRUE, to=c(100,0))Gf <- Gf %>%
mutate(behave = ipbhprp,
secure = impsafe,
safety = ipstrgv,
tradition = imptrad,
rules = ipfrule) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"),
~ na_if(.x, 7) %>% na_if(8) %>% na_if(9))) %>%
# Apply the reverse coding
mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))
# Now you can calculate 'schwartzauth' after the NA recoding
Gf$auth <- scales::rescale(Gf$behave +
Gf$secure +
Gf$safety +
Gf$tradition +
Gf$rules, to=c(0,100), na.rm=TRUE)
Gf <- Gf %>% filter(!is.na(auth))Gf <- Gf %>%
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 = Gf, weights = weight)
model4 <- lm(auth ~ polID + religious + polID*religious, data = Gf, weights = weight)modelsummary(
list(model3, model4),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}"),
statistic = NULL)| (1) | (2) | |
|---|---|---|
| (Intercept) | 57.5 (0.7)*** | 59.6 (0.9)*** |
| polIDModerate | 5.3 (0.7)*** | 2.6 (1.1)* |
| polIDRight | 9.3 (1.1)*** | 3.4 (1.9)+ |
| religious | 0.3 (0.1)** | −0.3 (0.2)+ |
| polIDModerate × religious | 0.8 (0.2)** | |
| polIDRight × religious | 1.5 (0.4)*** | |
| Num.Obs. | 2855 | 2855 |
| R2 | 0.035 | 0.041 |
| R2 Adj. | 0.034 | 0.039 |
| AIC | 24680.0 | 24665.5 |
| BIC | 24709.7 | 24707.2 |
| Log.Lik. | −12334.978 | −12325.764 |
| RMSE | 17.13 | 17.13 |
#the intercept for the first group is 57.5 with a standard error of 0.7, and for the second group, it’s 59.6 with a standard error of 0.9. The asterisks denote the level of statistical significance, with three asterisks indicating a highly significant result. the polIDModerate variable represents the effect of being identified as “Moderate” on the dependent variable. In the first group, being identified as “Moderate” increases the dependent variable by 5.3 units, with a standard error of 0.7. In the second group, the effect is 2.6 units with a standard error of 1.1. Both effects are statistically significant (denoted by three asterisks for the first group and one asterisk for the second group). the polIDRight variable represents the effect of being identified as “Right” on the dependent variable. In the first group, being identified as “Right” increases the dependent variable by 9.3 units, with a standard error of 1.1. In the second group, the effect is 3.4 units with a standard error of 1.9. Both effects are statistically significant (three asterisks for the first group and a plus sign for the second group, indicating marginal significance). the religious variable represents the effect of religiosity on the dependent variable. In the first group, an increase in religiosity by one unit leads to a 0.3 unit increase in the dependent variable, with a standard error of 0.1. In the second group, the effect is a decrease of 0.3 units, with a standard error of 0.2. The first effect is statistically significant (two asterisks), while the second effect is not. polIDModerate × religious is the interaction term which represents the effect of the interaction between being identified as “Moderate” and religiosity on the dependent variable. The coefficient is 0.8 with a standard error of 0.2, indicating that the effect of being identified as “Moderate” varies depending on religiosity. The effect is statistically significant (two asterisks).
Task 2
Now generate the model4 interaction plot that we did in the tutorial, but again using the German data instead of the French. Interpret.
interaction_plot <- effect("polID*religious", model4, na.rm=TRUE)
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 59.61010 58.93398 57.91980 56.90562 56.22950
## Moderate 62.21299 63.14317 64.53843 65.93370 66.86388
## Right 63.00456 65.26702 68.66072 72.05442 74.31689Task 3
Use data for the Netherlands and the variable coding for model5 and model6 from the tutorial (i.e., both for the cleaning & recode, as well as the linear model formulas). Do a modelsummary table displaying both model outputs. Interpret the coefficients for the MLR without the interaction (i.e., the first displayed model), and interpret model fit metrics for both models.
Netherlands_data <- read.fst("Netherlands_data.fst")Nf <- Netherlands_dataNf$trstplt <- ifelse(Nf$trstplt > 10, NA, Nf$trstplt)
Nf$trstprl <- ifelse(Nf$trstprl > 10, NA, Nf$trstprl)
Nf$trstprt <- ifelse(Nf$trstprt > 10, NA, Nf$trstprt)
# Creating and rescaling the trust variable
Nf$trust <- scales::rescale(Nf$trstplt + Nf$trstprl + Nf$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
Nf$populist <- scales::rescale(Nf$trust, na.rm = TRUE, to=c(100,0))Nf <- Nf %>%
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
Nf$auth <- scales::rescale(Nf$behave +
Nf$secure +
Nf$safety +
Nf$tradition +
Nf$rules, to=c(0,100), na.rm=TRUE)
Nf <- Nf %>% filter(!is.na(auth))Nf <- Nf %>%
mutate(religion = case_when(
rlgblg == 2 ~ "No",
rlgblg == 1 ~ "Yes",
rlgblg %in% c(7, 8, 9) ~ NA_character_,
TRUE ~ as.character(rlgblg)
))
# check
table(Nf$religion)##
## No Yes
## 10913 6750Nf <- Nf %>%
mutate(ID = case_when(
lrscale >= 0 & lrscale <= 4 ~ "Left",
lrscale >= 6 & lrscale <= 10 ~ "Right",
lrscale > 10 ~ NA_character_, # Set values above 10 as NA
TRUE ~ NA_character_ # Ensure value 5 and any other unexpected values are set as NA
))
table(Nf$ID)##
## Left Right
## 5449 7010Nf <- Nf %>%
mutate(
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
# Recoding generational cohorts based on the year of birth (yrbrn).
# The year of birth is categorized into different generational cohorts.
# Interwar (1900-1945), Baby Boomers (1946-1964), Gen X (1965-1979), Millennials (1980-1996).
# The 'TRUE' line is a catch-all that keeps the original year of birth for those not in these ranges.
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(Nf$gen)##
## Interwar Baby Boomers Gen X Millennials
## 3771 6147 4548 2730model5 <- lm(auth ~ religion + ID + gen, data = Nf, weights = weight)
model6 <- lm(auth ~ religion + ID + gen + religion*gen, data = Nf, weights = weight)modelsummary(
list(model5, model6),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_omit = "Intercept")| (1) | (2) | |
|---|---|---|
| religionYes | 8.2 (0.9)*** | 7.5 (2.1)*** |
| IDRight | 3.4 (0.9)*** | 3.4 (0.9)*** |
| genBaby Boomers | −5.8 (1.3)*** | −6.5 (1.7)*** |
| genGen X | −7.3 (1.4)*** | −7.6 (1.8)*** |
| genMillennials | −8.6 (1.5)*** | −8.6 (1.9)*** |
| religionYes × genBaby Boomers | 1.6 (2.6) | |
| religionYes × genGen X | 0.5 (2.8) | |
| religionYes × genMillennials | −0.6 (3.2) | |
| Num.Obs. | 1147 | 1147 |
| R2 | 0.126 | 0.127 |
| R2 Adj. | 0.123 | 0.121 |
| AIC | 9479.3 | 9484.5 |
| BIC | 9514.7 | 9535.0 |
| Log.Lik. | −4732.669 | −4732.251 |
| RMSE | 14.94 | 14.94 |
Task 4
Produce the model7 interaction plot from the tutorial, but again using the Netherlands data. Interpret.
Nf <- Nf %>%
mutate(
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
# Recoding generational cohorts based on the year of birth (yrbrn).
# The year of birth is categorized into different generational cohorts.
# Interwar (1900-1945), Baby Boomers (1946-1964), Gen X (1965-1979), Millennials (1980-1996).
# The 'TRUE' line is a catch-all that keeps the original year of birth for those not in these ranges.
gen = case_when(
yrbrn %in% 1900:1945 ~ "1",
yrbrn %in% 1946:1964 ~ "2",
yrbrn %in% 1965:1979 ~ "3",
yrbrn %in% 1980:1996 ~ "4",
TRUE ~ as.character(yrbrn)
),
gen = factor(gen,
levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
)
table(Nf$gen)##
## Interwar Baby Boomers Gen X Millennials
## 3771 6147 4548 2730Nf <- Nf %>%
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 = Nf, weights = weight)
interact_plot(model7, pred = cohort, modx = polID, jnplot = TRUE)
Task 5
Produce the model8 interaction plot from the tutorial, but again using the Netherlands data. Interpret.
model8 <- lm(auth ~ religious + ID + religious*ID, data = Nf, weights = weight)
interact_plot(model8, pred = religious, modx = ID, jnplot = TRUE)