rm(list=ls()); gc()
##          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 526795 28.2    1170420 62.6         NA   669417 35.8
## Vcells 970375  7.5    8388608 64.0      16384  1851733 14.2

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# 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
# 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(
    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 
##         6373        11474         7648         6704
# 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(
    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(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))
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) 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

Answer: Without interaction - The coefficients for polID are positive, which means that there is an increase in the variable “auth”, thus showing theres a stronger authoritarian attitude, holding relgiousness constant. Religiousness on the other hand, is also positive, meaning that higher levels are associated with higher scores of auth. This indicates a stronger authoritarian attitude, holding political idiology constant. Both Models - Because the R-squared is higher in this case, it indicates a better fit. The same can be said about the adjusted r-square.

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 74.31689

Answer: based on the interaction plot, for each level of polID, there is a change in the predicted scores of the Authoritarian attitudes scale as religious changes. Religious level 0, the predicted scores are lowest for the Left political ideology, higher for Moderate, and highest for Right.This trend continues as religious increases, with Right consistently having the highest predicted scores, followed by Moderate and then Left.

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 <- 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(
    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(religion = case_when(
    rlgblg == 2 ~ "No",
    rlgblg == 1 ~ "Yes",
    rlgblg %in% c(7, 8, 9) ~ NA_character_,
    TRUE ~ as.character(rlgblg)
  ))

# check
table(df$religion)
## 
##    No   Yes 
## 10913  6750
df <- df %>%
  mutate(ID = case_when(
    lrscale >= 0 & lrscale <= 4 ~ "Left",
    lrscale >= 6 & lrscale <= 10 ~ "Right",
    lrscale > 10 ~ NA_character_,  # Set values above 10 as NA
    TRUE ~ NA_character_  # Ensure value 5 and any other unexpected values are set as NA
  ))
table(df$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 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

Answer: Interpreting Coefficients for Model5 (MLR without Interaction)…The variable religion is positive, meaning that individuals who report being religious will have higher scores on the dependent variable “auth”. The variable ID is also positive, suggesting that those who are on the Right side of the scale will tend to have higher scores on the dependent variable “auth” than those categorized as Left, holding other variables constant. For the variable Gen, they are all negative, which indicates that the variable “auth” is not for the respective generational cohort. As for the interpretation of both models, the R squared has higher values, meaning it is a better fit. The adjusted r-squared in this case is also positive, thus indicating it is a better fit.

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 <- 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
    )
  )
model7 <- lm(auth ~ + cohort + polID + cohort*polID, data = df, weights = weight)
interact_plot(model7, pred = cohort, modx = polID, jnplot = TRUE)

Answer: Over the past 100 years (1900-2000), as the years progress the PolID has decreased for people on the Left, Moderate, and Right. Those especially who identify on the right side, have had the greatest decrease overall, compared to those on the left or moderate.

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 = df, weights = weight)
interact_plot(model8, pred = religious, modx = ID, jnplot = TRUE)

Answer: Considering this scale, both those who identify on the left or right show that an increase is evident in religion. This means that the effect of religion on the dependent variable is positive regardless of the level of “ID.