Initial set-up

# List of packages
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
lapply(packages, library, character.only = TRUE)
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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")
df <- Germany_data
df$weight <- df$dweight * df$pweight
survey_design <- svydesign(ids = ~1, data = df, weights = ~weight)
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 ))

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

#Religous = 0.9 which means with every increase there is a estimated increaase of 0.9 units on the authoritarian scale

#PollDright = 7.7 indicates that individuals identify as politically right and individuals with the same religous belif score 7.7 higher than those on the left side on the authoritarian scale

#PollDmoderate = those who are on the moderate of the political spectrum who are also religous score 4.6 points higher on the authortiarian scale

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     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

#all three (moderate, left and right) have a upwards/positive slope. The lines aren’t exacly parrallel as well. The slopes indicate these results that individuals on the right will be more authritarian if they are religous whereas individuals on the left will be less authoritarian if they are religios which shows how religion plays a strong role in authoritarian scale.

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")
BD <- netherlands_data
BD$weight <- BD$dweight * BD$pweight
survey_design <- svydesign(ids = ~1, data = BD, weights = ~weight)
BD <- BD %>%
  mutate(
    cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
    gen = case_when(
      yrbrn %in% 1900:1945 ~ "1",
      yrbrn %in% 1946:1964 ~ "2",
      yrbrn %in% 1965:1979 ~ "3",
      yrbrn %in% 1980:1996 ~ "4",
      TRUE ~ as.character(yrbrn)  
    ),
    gen = factor(gen,
                 levels = c("1", "2", "3", "4"),
                 labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
  )
table(BD$gen)
## 
##     Interwar Baby Boomers        Gen X  Millennials 
##         3967         6359         4684         2803
BD <- BD %>%
  mutate(religion = case_when(
    rlgblg == 2 ~ "No",
    rlgblg == 1 ~ "Yes",
    rlgblg %in% c(7, 8, 9) ~ NA_character_,
    TRUE ~ as.character(rlgblg)
  ))

# check
table(BD$religion)
## 
##    No   Yes 
## 11289  7008
BD <- BD %>%
  mutate(ID = case_when(
    lrscale >= 0 & lrscale <= 4 ~ "Left",
    lrscale >= 6 & lrscale <= 10 ~ "Right",
    lrscale > 10 ~ NA_character_,  
    TRUE ~ NA_character_  
  ))
table(BD$ID)
## 
##  Left Right 
##  5605  7247
BD <- BD %>%
  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 ))

BD$auth <- scales::rescale(BD$behave + 
                      BD$secure + 
                      BD$safety + 
                      BD$tradition + 
                      BD$rules, to=c(0,100), na.rm=TRUE)


BD <- BD %>% filter(!is.na(auth))
model5 <- lm(auth ~ religion + ID + gen, data = BD, weights = weight)
model6 <- lm(auth ~ religion + ID + gen + religion*gen, data = BD, 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

#GenMillenials = -8.4 which indicates that millenials will have 8.4 score less than the interwar generation on the authoritarian scale

#GenBaby Boomers = -5.2 which indicates that GenBaby Boomers will have 5.2 score less than interwar generation on the authoritarian scale

#GenX = -7.7 which indicates that GenX will have 7.7 score less than the interwar generation on the authoritarian scale

#IDRight = 4.2 which indicates politically right individuals score 4.2 higher than the left category on the authoritarian scale

#Religous = 7.2 which indicates that people with a religous will score 7.2 points higher compared to individuals without one

Task 4

Produce the model7 interaction plot from the tutorial, but again using the Netherlands data. Interpret.

BD <- BD %>%
  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 = BD, weights = weight)
interact_plot(model7, pred = cohort, modx = polID, jnplot = TRUE)

# This indicates that there is a strong relation between right and moderate political and authoritarian values as the lines are not parrallel. Another thing you can tell is that someone that is right can have less authoritarian values compared to someone that is moderate can have higher authoritarian views.

Task 5

Produce the model8 interaction plot from the tutorial, but again using the Netherlands data. Interpret.

netherlands_data <- netherlands_data %>%
  mutate(
   religious = case_when(
      rlgdgr %in% c(77, 88, 99) ~ NA_real_,
      TRUE ~ rlgdgr
    )
  )
model8 <- lm(auth ~ religious + ID + religious*ID, data = BD, weights = weight)
interact_plot(model8, pred = religious, modx = ID, jnplot = TRUE)

# Both lines are parrallel, the graph shows as you increase religous wise individuals on the left and right increase on the authoritarian value scale.

End