# 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)

# 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

Model 3 polModerate - looking at the coefficient for this section it shows that comparing individuals with the same religiousness has an outcome of an average 4.6 units higher than the category.

polRight - looking at the coefficient for this section it shows that comparing individuals with the same religiousness has an outcome of an average 7.7 units higher than the category.

Religiousness - the data present shows that with every one unit increase of religiouness there needs to be an equal 0.9 increase at the same time.

Model 4 polModerate - looking at the coefficient for this section it shows that comparing individuals with the same religiousness has an outcome of an average 3.4 units higher than the category.

polRight - looking at the coefficient for this section it shows that comparing individuals with the same religiousness has an outcome of an average 4.2 units higher than the category.

Religiousness - the data present shows that with every one unit increase of religiouness there needs to be an equal 0.6 increase at the same time.

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

Looking at the graph it seems that the lines coefficients are somewhat parallel meaning that there no interaction between the left, right and moderate. Although if moderate and left were to go further there possibly would be an overlap at some point, judging on the trajectory of the lines.

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")

df <- netherlands_data
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))

# The dataframe is further updated to recode trust and political interest # note: you could have done it one, integrating above, but I broke it down
df <- df %>%
  mutate(
    # Recoding the 'ppltrst' variable, which seems to measure trust on a scale from 0 to 10.
    # Special codes like 77, 88, 99 are probably used for missing data and are set to NA.
    # Trust levels are categorized as 'Low', 'Mid', and 'High'.
    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)
    ),
    # Recoding 'polintr' variable, which seems to measure political interest.
    # Values of 1 and 2 indicate 'Interested', 3 and 4 'Not Interested'.
    # Special codes like 7, 8, 9 (likely missing data) are set to NA.
    Politically = case_when(
      polintr %in% c(1, 2) ~ "Interested",
      polintr %in% c(3, 4) ~ "Not Interested",
      polintr %in% c(7, 8, 9) ~ NA_character_
    )
  )
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(religion = case_when(
    rlgblg == 2 ~ "No",
    rlgblg == 1 ~ "Yes",
    rlgblg %in% c(7, 8, 9) ~ NA_character_,
    TRUE ~ as.character(rlgblg)
  ))

# check
table(df$religion)
## 
##    No   Yes 
## 10913  6750
df <- df %>%
  mutate(
    cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
    # Recoding generational cohorts based on the year of birth (yrbrn).
    # The year of birth is categorized into different generational cohorts.
    # Interwar (1900-1945), Baby Boomers (1946-1964), Gen X (1965-1979), Millennials (1980-1996).
    # The 'TRUE' line is a catch-all that keeps the original year of birth for those not in these ranges.
    gen = case_when(
      yrbrn %in% 1900:1945 ~ "1",
      yrbrn %in% 1946:1964 ~ "2",
      yrbrn %in% 1965:1979 ~ "3",
      yrbrn %in% 1980:1996 ~ "4",
      TRUE ~ as.character(yrbrn)  
    ),
    # After recoding, the gen variable is converted into a factor with labels for clearer interpretation.
    # Factors are used in R to handle categorical variables.
    gen = factor(gen,
                 levels = c("1", "2", "3", "4"),
                 labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
  )
table(df$gen)
## 
##     Interwar Baby Boomers        Gen X  Millennials 
##         3771         6147         4548         2730
df <- df %>%
  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(df$ID)
## 
##  Left Right 
##  5449  7010
model5 <- lm(auth ~ religion + ID + gen, data = df, weights = weight)
model6 <- lm(auth ~ religion + ID + gen + religion*gen, data = df, 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

Model 5 ReligiousYes - looking at the coefficient for this section it shows that comparing individuals with the same religiousness and left wing opinion has an outcome of an average 7.2 units higher than the category.

ReligiousRight - looking at the coefficient for this section it shows that comparing individuals with the same religiousness and right wing opinion has an outcome of an average 4.2 units higher than the category.

Generation - Looking at the data for each generations shows that consistently the data is below the category level no matter what the generation is for baby boomers they are 5.2 under the minimum level. For Gen X they are 7.7 units under and for millennials they are 8.4 units under the category.

Model 6 ReligiousYes - looking at the coefficient for this section it shows that comparing individuals with the same religiousness and left wing opinion has an outcome of an average 5.7 units higher than the category.

ReligiousRight - looking at the coefficient for this section it shows that comparing individuals with the same religiousness and right wong opinion has an outcome of an average 4.3 units higher than the category.

Generation - Looking at the data for each generations shows that consistently the data is below the category level no matter what the generation is for baby boomers they are 5.9 under the minimum level. For Gen X they are 8.8 units under and for millennials they are 9.7 units under the category. ## Task 4

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

netherlands_data <- read_fst("netherlands_data.fst")

df <- netherlands_data
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))

# The dataframe is further updated to recode trust and political interest # note: you could have done it one, integrating above, but I broke it down
df <- df %>%
  mutate(
    # Recoding the 'ppltrst' variable, which seems to measure trust on a scale from 0 to 10.
    # Special codes like 77, 88, 99 are probably used for missing data and are set to NA.
    # Trust levels are categorized as 'Low', 'Mid', and 'High'.
    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)
    ),
    # Recoding 'polintr' variable, which seems to measure political interest.
    # Values of 1 and 2 indicate 'Interested', 3 and 4 'Not Interested'.
    # Special codes like 7, 8, 9 (likely missing data) are set to NA.
    Politically = case_when(
      polintr %in% c(1, 2) ~ "Interested",
      polintr %in% c(3, 4) ~ "Not Interested",
      polintr %in% c(7, 8, 9) ~ NA_character_
    )
  )
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(religion = case_when(
    rlgblg == 2 ~ "No",
    rlgblg == 1 ~ "Yes",
    rlgblg %in% c(7, 8, 9) ~ NA_character_,
    TRUE ~ as.character(rlgblg)
  ))

# check
table(df$religion)
## 
##    No   Yes 
## 10913  6750
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
    )
  )

df <- df %>%
  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(df$gen)
## 
##     Interwar Baby Boomers        Gen X  Millennials 
##         3771         6147         4548         2730
model7 <- lm(auth ~ + cohort + polID + cohort*polID, data = df, weights = weight)
interact_plot(model7, pred = cohort, modx = polID, jnplot = TRUE)

Judging from the graph present there is an interaction between the right and moderate votes for authoritarian and cohorts. This overlap happens around the year 2000, but other than that overlap the lines are parallel for the rest of the cohort years. For the left side it stays parallel to the moderate line the whole time, not overlaping with either modertae or right wing individuls or cohorts. ## Task 5

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

netherlands_data <- read_fst("netherlands_data.fst")

df <- netherlands_data
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))

# The dataframe is further updated to recode trust and political interest # note: you could have done it one, integrating above, but I broke it down
df <- df %>%
  mutate(
    # Recoding the 'ppltrst' variable, which seems to measure trust on a scale from 0 to 10.
    # Special codes like 77, 88, 99 are probably used for missing data and are set to NA.
    # Trust levels are categorized as 'Low', 'Mid', and 'High'.
    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)
    ),
    # Recoding 'polintr' variable, which seems to measure political interest.
    # Values of 1 and 2 indicate 'Interested', 3 and 4 'Not Interested'.
    # Special codes like 7, 8, 9 (likely missing data) are set to NA.
    Politically = case_when(
      polintr %in% c(1, 2) ~ "Interested",
      polintr %in% c(3, 4) ~ "Not Interested",
      polintr %in% c(7, 8, 9) ~ NA_character_
    )
  )

df <- df %>%
  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(df$ID)
## 
##  Left Right 
##  5605  7247
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(religion = case_when(
    rlgblg == 2 ~ "No",
    rlgblg == 1 ~ "Yes",
    rlgblg %in% c(7, 8, 9) ~ NA_character_,
    TRUE ~ as.character(rlgblg)
  ))

# check
table(df$religion)
## 
##    No   Yes 
## 10913  6750
df <- df %>%
  mutate(
    cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
    # Recoding generational cohorts based on the year of birth (yrbrn).
    # The year of birth is categorized into different generational cohorts.
    # Interwar (1900-1945), Baby Boomers (1946-1964), Gen X (1965-1979), Millennials (1980-1996).
    # The 'TRUE' line is a catch-all that keeps the original year of birth for those not in these ranges.
    gen = case_when(
      yrbrn %in% 1900:1945 ~ "1",
      yrbrn %in% 1946:1964 ~ "2",
      yrbrn %in% 1965:1979 ~ "3",
      yrbrn %in% 1980:1996 ~ "4",
      TRUE ~ as.character(yrbrn)  
    ),
    # After recoding, the gen variable is converted into a factor with labels for clearer interpretation.
    # Factors are used in R to handle categorical variables.
    gen = factor(gen,
                 levels = c("1", "2", "3", "4"),
                 labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials"))
  )
table(df$gen)
## 
##     Interwar Baby Boomers        Gen X  Millennials 
##         3771         6147         4548         2730
table(df$religion)
## 
##    No   Yes 
## 10913  6750
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
    )
  )
model8 <- lm(auth ~ religious + ID + religious*ID, data = df, weights = weight)
interact_plot(model8, pred = religious, modx = ID, jnplot = TRUE)

The lines present show that there is no interaction between authoritarian levels and religiosity. The lines stay parallel the whole time with no overlap happening.

End