Tutorial 8 (222)
Sebastien Parker
2023-11-29
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
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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")df <- germany_datadf$weight <- df$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(
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$gen)##
## Interwar Baby Boomers Gen X Millennials
## 6373 11474 7648 6704# The dataframe is further updated to recode trust and political interest # note: you could have done it one, integrating above, but I broke it down
df <- df %>%
mutate(
# Recoding the 'ppltrst' variable, which seems to measure trust on a scale from 0 to 10.
# Special codes like 77, 88, 99 are probably used for missing data and are set to NA.
# Trust levels are categorized as 'Low', 'Mid', and 'High'.
Capital = case_when(
ppltrst %in% c(77, 88, 99) ~ NA_character_,
ppltrst >= 0 & ppltrst <= 3 ~ "Low",
ppltrst >= 4 & ppltrst <= 6 ~ "Mid",
ppltrst >= 7 & ppltrst <= 10 ~ "High",
TRUE ~ as.character(ppltrst)
),
# Recoding 'polintr' variable, which seems to measure political interest.
# Values of 1 and 2 indicate 'Interested', 3 and 4 'Not Interested'.
# Special codes like 7, 8, 9 (likely missing data) are set to NA.
Politically = case_when(
polintr %in% c(1, 2) ~ "Interested",
polintr %in% c(3, 4) ~ "Not Interested",
polintr %in% c(7, 8, 9) ~ NA_character_
)
)df <- df %>%
mutate(behave = ipbhprp,
secure = impsafe,
safety = ipstrgv,
tradition = imptrad,
rules = ipfrule) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"),
~ na_if(.x, 7) %>% na_if(8) %>% na_if(9))) %>%
# Apply the reverse coding
mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))
# Now you can calculate 'schwartzauth' after the NA recoding
df$auth <- scales::rescale(df$behave +
df$secure +
df$safety +
df$tradition +
df$rules, to=c(0,100), na.rm=TRUE)
df <- df %>% filter(!is.na(auth))df <- df %>%
mutate(
polID = case_when(
lrscale %in% 0:3 ~ "Left",
lrscale %in% 7:10 ~ "Right",
lrscale %in% 4:6 ~ "Moderate",
lrscale %in% c(77, 88, 99) ~ NA_character_
),
religious = case_when(
rlgdgr %in% c(77, 88, 99) ~ NA_real_,
TRUE ~ rlgdgr
)
)df <- df %>%
mutate(religion = case_when(
rlgblg == 2 ~ "No",
rlgblg == 1 ~ "Yes",
rlgblg %in% c(7, 8, 9) ~ NA_character_,
TRUE ~ as.character(rlgblg)
))
# check
table(df$religion)##
## No Yes
## 11088 13750model3 <- 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 |
polIDModerate (4.6): This coefficient suggests that, accounting for the holding constant and comparing individuals with the same religiousness, the average outcome for individuals identifying as politically moderate are 4.6 units higher than those who identify as being on the left.
polIDRight (7.7): This coefficient suggests that, comparing individuals with the same religiousness, individuals identifying as politically right are expected to be 7.7 units higher than than those who identify as being on the left, on the authoritarian values scale.
religious (0.9): For the “religious” predictor, a coefficient of 0.9 means that, assuming the same ideological categorization, every one-unit increase in religiousness has an associated expected average increase of 0.9 units on the authoritarian values 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")
The authoritarian attitudes of people on the left increase as their
religiousness increases. There is a positive correlation between the
two. Both of them start off as 0 then increase 10 for religiousness and
around 63 for authoritarian attitudes.
People who identify as moderate also have a positive correlation between religiousness and authoritarian attitudes. However, it is steeper than the left’s, with higher authoritarian attitudes at around 70. The same applies for people on the right, with an even steeper line, with authoritarian attitudes at 75.
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$pweight
survey_design <- svydesign(ids = ~1, data = df, weights = ~weight)# Setting values greater than 10 to NA
df$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$gen)##
## Interwar Baby Boomers Gen X Millennials
## 3967 6359 4684 2803# The dataframe is further updated to recode trust and political interest # note: you could have done it one, integrating above, but I broke it down
df <- df %>%
mutate(
# Recoding the 'ppltrst' variable, which seems to measure trust on a scale from 0 to 10.
# Special codes like 77, 88, 99 are probably used for missing data and are set to NA.
# Trust levels are categorized as 'Low', 'Mid', and 'High'.
Capital = case_when(
ppltrst %in% c(77, 88, 99) ~ NA_character_,
ppltrst >= 0 & ppltrst <= 3 ~ "Low",
ppltrst >= 4 & ppltrst <= 6 ~ "Mid",
ppltrst >= 7 & ppltrst <= 10 ~ "High",
TRUE ~ as.character(ppltrst)
),
# Recoding 'polintr' variable, which seems to measure political interest.
# Values of 1 and 2 indicate 'Interested', 3 and 4 'Not Interested'.
# Special codes like 7, 8, 9 (likely missing data) are set to NA.
Politically = case_when(
polintr %in% c(1, 2) ~ "Interested",
polintr %in% c(3, 4) ~ "Not Interested",
polintr %in% c(7, 8, 9) ~ NA_character_
)
)df <- df %>%
mutate(behave = ipbhprp,
secure = impsafe,
safety = ipstrgv,
tradition = imptrad,
rules = ipfrule) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"),
~ na_if(.x, 7) %>% na_if(8) %>% na_if(9))) %>%
# Apply the reverse coding
mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))
# Now you can calculate 'schwartzauth' after the NA recoding
df$auth <- scales::rescale(df$behave +
df$secure +
df$safety +
df$tradition +
df$rules, to=c(0,100), na.rm=TRUE)
df <- df %>% filter(!is.na(auth))df <- df %>%
mutate(
polID = case_when(
lrscale %in% 0:3 ~ "Left",
lrscale %in% 7:10 ~ "Right",
lrscale %in% 4:6 ~ "Moderate",
lrscale %in% c(77, 88, 99) ~ NA_character_
),
religious = case_when(
rlgdgr %in% c(77, 88, 99) ~ NA_real_,
TRUE ~ rlgdgr
)
)df <- df %>%
mutate(religion = case_when(
rlgblg == 2 ~ "No",
rlgblg == 1 ~ "Yes",
rlgblg %in% c(7, 8, 9) ~ NA_character_,
TRUE ~ as.character(rlgblg)
))
# check
table(df$religion)##
## No Yes
## 10913 6750df <- 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 7010model5 <- 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 |
People who identify as religious have a 7.2 unit increase in the authoritarian values compared to non-religious people, assuming every other variable is the same. For every unit increase for people who identify as right-wing, their authoritarian values increase by 4.2. Baby boomers have, on average, a decrease of 5.2 units in authoritarian values, while Gen X and Millennials have a decrease of 7.7 and 8.4 units, respectively.
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 <- 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
## 3967 6359 4684 2803df <- 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
)
)model7 <- lm(auth ~ + cohort + polID + cohort*polID, data = df, weights = weight)
interact_plot(model7, pred = cohort, modx = polID, jnplot = TRUE)
Those on the left started off with lower authoritarian values than the
other two political orientations, and decreased in authoritarian values
over time, eventually reaching a low point in 2000. However, the right,
despite having started off with the highest authoritarian values prior
to 1940, also faced a decrease. This decrease was sharper than the other
two’s, and eventually became slightly lower than the left’s in 2000. The
moderates had authoritarian values higher than the left but lower than
the right, which decreased over time, and eventually ended off higher
than the other two’s. There does not appear to be an interaction effect
here, as the lines are not parallel and do not seem to be dependent on
any other variable.
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 <- 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$gen)##
## Interwar Baby Boomers Gen X Millennials
## 3967 6359 4684 2803df <- df %>%
mutate(behave = ipbhprp,
secure = impsafe,
safety = ipstrgv,
tradition = imptrad,
rules = ipfrule) %>%
mutate(across(c("behave", "secure", "safety", "tradition", "rules"),
~ na_if(.x, 7) %>% na_if(8) %>% na_if(9))) %>%
# Apply the reverse coding
mutate(across(c("behave", "secure", "safety", "tradition", "rules"), ~ 7 - .x ))
# Now you can calculate 'schwartzauth' after the NA recoding
df$auth <- scales::rescale(df$behave +
df$secure +
df$safety +
df$tradition +
df$rules, to=c(0,100), na.rm=TRUE)
df <- df %>% filter(!is.na(auth))df <- df %>%
mutate(ID = case_when(
lrscale >= 0 & lrscale <= 4 ~ "Left",
lrscale >= 6 & lrscale <= 10 ~ "Right",
lrscale > 10 ~ NA_character_, # Set values above 10 as NA
TRUE ~ NA_character_ # Ensure value 5 and any other unexpected values are set as NA
))
table(df$ID)##
## Left Right
## 5449 7010df <- 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 religiosity of those on the right as well as those on the left both
have a positive relationship with authoritarian values, although the
right has higher values for both variables. There appears to be an
interaction effect, as both lines are parallel, and the values would
differ from the current values shown if one changes up the religiosity
or the authoritarian values.
End
Appendix
If you want some examples of ESS papers and how they talk about different elements like data and findings, here are some links:
Food Comes First, Then Morals: Redistribution Preferences, Parochial Altruism, an Immigration in Western Europe https://www.journals.uchicago.edu/doi/pdf/10.1086/694201?casa_token=eUhZ0HbtlD4AAAAA:SGZ0609PlTu7pmc-8cE-ztOIaCUfD9pbkUWlMrTUlYZP-32wRLHQQfRA7sT8_WcwCp39mE6C2BnI
Cultural Backlash? How (Not) to Explain the Rise of Authoritarian Populism https://www.cambridge.org/core/services/aop-cambridge-core/content/view/FFE9742798D8CC4BF6ED325FDBAFA251/S0007123421000363a.pdf/div-class-title-cultural-backlash-how-not-to-explain-the-rise-of-authoritarian-populism-div.pdf
Party membership and closeness and the development of trust in political institutions: An analysis of the European Social Survey, 2002–2010 https://journals.sagepub.com/doi/abs/10.1177/1354068813509519?casa_token=cWvN2GK3tjYAAAAA%3A88ipxKnceH2FJ0suJsd7Q5RzK3IfsCOsqARBVonjnQnUuIj7UZR0hWDebi4fxbBdtFddpiLHFCebjw&journalCode=ppqa
Deservingness perceptions, welfare state support and vote choice in Western Europe https://www-tandfonline-com.myaccess.library.utoronto.ca/doi/full/10.1080/01402382.2020.1715704?casa_token=c0kCwK30vRAAAAAA%3AQgU2EcX6gggkMbguBnSNUdb-YeLTu89ath6GuWppbBGZfIHEGfAl7zG269mPl-aIA6FjpQ1E8FgCXg
Increasingly unequal? The economic crisis, social inequalities and trust in the European Parliament in 20 European countries https://ejpr.onlinelibrary.wiley.com/doi/pdf/10.1111/1475-6765.12126?casa_token=H3MiTpwFHA0AAAAA:yP3OQdvt26J46rojOyB_kx5Y4x4Fsg6vI56Z7P6CKm4-8APdmnmLxY5wgNcwimB97CbvnNQOP_ueUx4