Petsche_Lauren_Homework_5
2024-03-19
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library(remotes)install_version("estimability", version = "1.3", repos = "http://cran.us.r-project.org")## Downloading package from url: http://cran.us.r-project.org/src/contrib/Archive/estimability/estimability_1.3.tar.gzlibrary(estimability)germany_data <- read_fst("germany_data.fst")df <- germany_dataTask 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.
df$weight <- df$dweight * df$pweight
survey_design <- svydesign(ids = ~1, data = df, weights = ~weight)df$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)
df$trust <- scales::rescale(df$trstplt + df$trstprl + df$trstprt, na.rm = TRUE, to = c(0, 100))
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))) %>%
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))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)## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtools| Â (1) | Â Â (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 |
The intercept at 55.3 represents the expected value of the outcome variable when all the predictor variables are set to their reference levels
All three coefficients have ***, indicating a p < 0.001. So the null can be incorreclty rejected under frequentist assumptions.
polIDModerate (4.6): the average outcome for individuals identifying as politically moderate are 4.6 units higher compared to the reference category.
polIDRight (7.7): indicates that individuals with the same religiousness that identify as politically right are expected to be, on average, 7.7 units higher than the reference category on the authoritarian values scale.
religious (0.9): with every one-unit increase in religiousness, there is an associated expected average increase of 0.9 units on the authoritarian values scale
model fit metric: There is not a significant change of the intercept between both models. All values of the model fit metric are higher in the first model than the second model. polIDRight has the most significant change from 7.7 to 4.2 indicating that there is a lower amount of individuals with the same religiousness that identify as politically right.
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="polID",
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 74.34211All slopes appear to have parallel trends. This could indicate that the relationship between the variables is relatively consistent and stable as eachother increase. So there is no interaction effect between the variables.
Task 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")dn <- Netherlands_datadn$weight <- dn$dweight * dn$pweight
survey_design2 <- svydesign(ids = ~1, data = dn, weights = ~weight)dn$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)
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(
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$gen)##
## Interwar Baby Boomers Gen X Millennials
## 3967 6359 4684 2803dn <- dn %>%
mutate(religion = case_when(
rlgblg == 2 ~ "No",
rlgblg == 1 ~ "Yes",
rlgblg %in% c(7, 8, 9) ~ NA_character_,
TRUE ~ as.character(rlgblg)
))
table(dn$religion)##
## No Yes
## 11289 7008dn <- dn %>%
mutate(ID = case_when(
lrscale >= 0 & lrscale <= 4 ~ "Left",
lrscale >= 6 & lrscale <= 10 ~ "Right",
lrscale > 10 ~ NA_character_,
TRUE ~ NA_character_
))
table(dn$ID)##
## Left Right
## 5605 7247dn <- dn %>%
mutate(behave = ipbhprp,
secure = impsafe,
safety = ipstrgv,
tradition = imptrad,
rules = ipfrule) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"),
~ na_if(.x, 7) %>% na_if(8) %>% na_if(9))) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))
dn$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))model5 <- 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")| Â (1) | Â Â (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 |
All three coefficients have ***, indicating a p < 0.001. So the null can be incorreclty rejected under frequentist assumptions.
ReligionYes (7.2) is the average outcome for individuals identifying as relgigous are 7.2 units higher compared to the reference category
IDright (4.2): the average outcome for individuals identifying as right are 4.2 units compared to the reference category.
model fit metric: There is a significant change between both models. from genBaby boomer, genGen x, and genMillennials there is a a increase in the decreasing values from the first model to the second model. This could indicate that across the generations there has been a continuous decline in people being relgious along side their authoritarian beleifs.
Task 4
Produce the model7 interaction plot from the tutorial, but again using the Netherlands data. Interpret.
dn <- 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)
This chart shows that across cohorts and political views there has been a consistent decline in authoritarian values. Right started off as having the highest authoritarian values but across the generations closer to the Millenial generation has dropped below the moderate PollD showing the largest amount of decline. Left has consistently been the lowest for authoritarian values and has showed the least amount of decline.
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 = dn, weights = weight)
interact_plot(model8, pred = religious, modx = ID, jnplot = TRUE)
Both left and right ID have shown a consistent increase in authoritarian values as religion increases.
As religion increases it is shown that for both the left and right ID there is an increase with authoritarian values. This is more significantly true to the right than the left as it shows a steeper increase relative to the Left.