ICE_Norway.Rmd

---
title: "Cross-cultural validation of the ICE: Norway"
author: "Michalina Marczak"
date: "Last compiled on `r format(Sys.time(), '%d %B, %Y')`"
output: 
  html_document:
    toc: true
    toc_depth: 3
    toc_float: false
    fig_width: 8
    fig_height: 8
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
```

```{r, include=F}
#set the global options
options(max.print=999999)  #allows printing out large outputs
options(scipen = 999)       #disables scientific notation (uses decimal instead)      
set.seed(9999) # set seed for replicability


####### load the libraries
library(tidyverse)
library(lavaan)
library(psych)
library(mvnormalTest)
library(astatur)
library(ggcorrplot)

# load the custom functions
condisc <- function(x){
  std.loadings<- inspect(x, what="std")$lambda
  #std.loadings
  std.loadings[std.loadings==0] <- NA
  #std.loadings
  std.loadings <- std.loadings^2
  #std.loadings
  ave <- colMeans(std.loadings, na.rm=TRUE)
  #ave
  #factor correlation matrix
  fcor <- lavInspect(x, "cor.lv")
  #fcor
  sqfcor <- fcor^2
  #sqfcor
  list(Squared_Factor_Correlation=round(sqfcor, digits=3),
       Average_Variance_Extracted=round(ave, digits=3))
}


### I'm using the custom correlation_matrix and save_correlation_matrix functions from Paul van der Laken
### https://paulvanderlaken.com/2020/07/28/publication-ready-correlation-matrix-significance-r/#correlation_matrix

#' correlation_matrix
#' Creates a publication-ready / formatted correlation matrix, using `Hmisc::rcorr` in the backend.
#'
#' @param df dataframe; containing numeric and/or logical columns to calculate correlations for
#' @param type character; specifies the type of correlations to compute; gets passed to `Hmisc::rcorr`; options are `"pearson"` or `"spearman"`; defaults to `"pearson"`
#' @param digits integer/double; number of decimals to show in the correlation matrix; gets passed to `formatC`; defaults to `3`
#' @param decimal.mark character; which decimal.mark to use; gets passed to `formatC`; defaults to `.`
#' @param use character; which part of the correlation matrix to display; options are `"all"`, `"upper"`, `"lower"`; defaults to `"all"`
#' @param show_significance boolean; whether to add `*` to represent the significance levels for the correlations; defaults to `TRUE`
#' @param replace_diagonal boolean; whether to replace the correlations on the diagonal; defaults to `FALSE`
#' @param replacement character; what to replace the diagonal and/or upper/lower triangles with; defaults to `""` (empty string)
#'
#' @return a correlation matrix
#' @export
#'
#' @examples
#' `correlation_matrix(iris)`
#' `correlation_matrix(mtcars)`
correlation_matrix <- function(df, 
                               type = "pearson",
                               digits = 3, 
                               decimal.mark = ".",
                               use = "all", 
                               show_significance = TRUE, 
                               replace_diagonal = FALSE, 
                               replacement = ""){
  
  # check arguments
  stopifnot({
    is.numeric(digits)
    digits >= 0
    use %in% c("all", "upper", "lower")
    is.logical(replace_diagonal)
    is.logical(show_significance)
    is.character(replacement)
  })
  # we need the Hmisc package for this
  require(Hmisc)
  
  # retain only numeric and boolean columns
  isNumericOrBoolean = vapply(df, function(x) is.numeric(x) | is.logical(x), logical(1))
  if (sum(!isNumericOrBoolean) > 0) {
    cat('Dropping non-numeric/-boolean column(s):', paste(names(isNumericOrBoolean)[!isNumericOrBoolean], collapse = ', '), '\n\n')
  }
  df = df[isNumericOrBoolean]
  
  # transform input data frame to matrix
  x <- as.matrix(df)
  
  # run correlation analysis using Hmisc package
  correlation_matrix <- Hmisc::rcorr(x, type = type)
  R <- correlation_matrix$r # Matrix of correlation coeficients
  p <- correlation_matrix$P # Matrix of p-value 
  
  # transform correlations to specific character format
  Rformatted = formatC(R, format = 'f', digits = digits, decimal.mark = decimal.mark)
  
  # if there are any negative numbers, we want to put a space before the positives to align all
  if (sum(!is.na(R) & R < 0) > 0) {
    Rformatted = ifelse(!is.na(R) & R > 0, paste0(" ", Rformatted), Rformatted)
  }
  
  # add significance levels if desired
  if (show_significance) {
    # define notions for significance levels; spacing is important.
    stars <- ifelse(is.na(p), "", ifelse(p < .001, "***", ifelse(p < .01, "**", ifelse(p < .05, "*", ""))))
    Rformatted = paste0(Rformatted, stars)
  }
  
  # make all character strings equally long
  max_length = max(nchar(Rformatted))
  Rformatted = vapply(Rformatted, function(x) {
    current_length = nchar(x)
    difference = max_length - current_length
    return(paste0(x, paste(rep(" ", difference), collapse = ''), sep = ''))
  }, FUN.VALUE = character(1))
  
  # build a new matrix that includes the formatted correlations and their significance stars
  Rnew <- matrix(Rformatted, ncol = ncol(x))
  rownames(Rnew) <- colnames(Rnew) <- colnames(x)
  
  # replace undesired values
  if (use == 'upper') {
    Rnew[lower.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (use == 'lower') {
    Rnew[upper.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (replace_diagonal) {
    diag(Rnew) <- replacement
  }
  
  return(Rnew)
}


#' save_correlation_matrix
#' Creates and save to file a fully formatted correlation matrix, using `correlation_matrix` and `Hmisc::rcorr` in the backend
#' @param df dataframe; passed to `correlation_matrix`
#' @param filename either a character string naming a file or a connection open for writing. "" indicates output to the console; passed to `write.csv`
#' @param ... any other arguments passed to `correlation_matrix`
#'
#' @return NULL
#'
#' @examples
#' `save_correlation_matrix(df = iris, filename = 'iris-correlation-matrix.csv')`
#' `save_correlation_matrix(df = mtcars, filename = 'mtcars-correlation-matrix.csv', digits = 3, use = 'lower')`
save_correlation_matrix = function(df, filename, ...) {
  return(write.csv2(correlation_matrix(df, ...), file = filename))
}

condisc <- function(x){
  std.loadings<- inspect(x, what="std")$lambda
  #std.loadings
  std.loadings[std.loadings==0] <- NA
  #std.loadings
  std.loadings <- std.loadings^2
  #std.loadings
  ave <- colMeans(std.loadings, na.rm=TRUE)
  #ave
  #factor correlation matrix
  fcor <- lavInspect(x, "cor.lv")
  #fcor
  sqfcor <- fcor^2
  #sqfcor
  list(Squared_Factor_Correlation=round(sqfcor, digits=3),
       Average_Variance_Extracted=round(ave, digits=3))
}
```


```{r, include=F}
### Load the data
load(Sys.readlink("./NO/dataset-Norway.RData"))
```

## Inspection of the climate emotions data

```{r, include=F}
##### subset the data for the CFA

#subset the data frame to work with for now
data.for.CFA <- dplyr::select(qdata, starts_with("ICE-60-no"))
ICE_data <- as.data.frame(data.for.CFA)

#change the variables type from character to numeric (keeping them in the data frame format)
ICE_data <- as.data.frame(lapply(ICE_data, as.numeric))

####explore & inspect the ICE data for consistency
#is there any missing data?
sum(colSums(is.na(ICE_data)))  # no missing data

#a categorical representation of the ICE data to check for inconsistent values
lapply(ICE_data, table)

# For the descriptives, the response format should be 1-5 not 0-4, hence we add 1 to each value in the data frame
ICE_data <- ICE_data + 1

# overview of the data
psych::describe(ICE_data)
```

### Multi- & uni-variate normality check

In the first step, let's check whether the data meets the assumptions of multi- and univariate normality using Mardia’s test for multivariate skewness and kurtosis and Shapiro-Wilk test for univariate normality

```{r, include=T}
mvnormalTest::mardia(ICE_data, std = TRUE)
# the values imply that we should reject the null hypothesis of uni & multivariate normality
```


```{r, include=F}
##### Visual inspection
theme_set(
  theme_minimal() +
    theme(legend.position = "top")
)

ICE.gathered <- ICE_data %>%
  as_tibble() %>%
  select_if(is.numeric) %>%
  gather(key = "variable", value = "value")

ggplot(ICE.gathered, aes(value)) +
  geom_density(fill = "lightgrey") +
  facet_wrap(~variable)
```

## Confirmatory Factor Analysis

We conduct the CFA with the Satorra-Bentler MLM because the data deviates significantly from normal distribution. Yet, there is no need to use MLR because we have complete data.

### Model specification

First, we specify the model based on the results from Marczak et al., 2022

```{r, include=T, echo = T}
model <- 'climate_anger =~ ICE.60.no.ANG14 + ICE.60.no.ANG13 + ICE.60.no.ANG10 + ICE.60.no.ANG3
          climate_contempt =~ ICE.60.no.DIS5 + ICE.60.no.DIS7 + ICE.60.no.IND2 + ICE.60.no.IND13
          climate_enthusiasm =~ ICE.60.no.EMP12 + ICE.60.no.HOPF9 + ICE.60.no.HOPF8 + ICE.60.no.EMP7
          climate_powerlessness =~ ICE.60.no.POWL11 + ICE.60.no.POWL7 + ICE.60.no.POWL2 + ICE.60.no.POWL13
          climate_guilt =~ ICE.60.no.GUI11 + ICE.60.no.GUI6 + ICE.60.no.GUI8 + ICE.60.no.GUI12
          climate_isolation =~ ICE.60.no.ISO4 + ICE.60.no.ISO5 + ICE.60.no.ISO8 + ICE.60.no.ISO12
          climate_anxiety =~ ICE.60.no.APP7 + ICE.60.no.HOPL5 + ICE.60.no.HOPL11 + ICE.60.no.APP14
          climate_sorrow =~ ICE.60.no.SOR13 + ICE.60.no.SOR6 + ICE.60.no.SOR4 + ICE.60.no.SOR14'
```

In the next step, we fit the model with the MLM estimator

```{r, include=T, echo = T}
model.fit <- cfa(model, data = ICE_data, estimator = "MLM")
```

### Fit indices

We inspect the standard indices for model fit

```{r, include=T}
fitmeasures(model.fit, fit.measures = c("chisq.scaled", "pvalue.scaled", "df.scaled", "cfi.scaled", "tli.scaled", "rmsea.scaled", "rmsea.ci.lower.scaled", "rmsea.ci.upper.scaled", "srmr"))
```

Reasonably good fit is established by a model when the (in our case scaled) CFI and TLI values are close to .95 or greater, RMSEA values are close to .06 or below, and SRMR values are close to or below .08 (Hu & Bentler, 1999). 

We can see that our model presents reasonably good fit.

### Model characteristics {.tabset}

#### Facor loadings, variances etc

```{r, include=T}
lavaan::summary(model.fit, fit.measures = T, standardized = T)
```

We observe good values here too. Importantly, all factor loadings are > .4.

#### CFA: convergent validity
Convergent validity is established when the average variance extracted (AVE) by the latent variables is greater than .5

```{r, include=T}
knitr::kable(condisc(model.fit)$Average_Variance_Extracted, col.names = "AVE", "simple")
```

Climate powerlessness has AVE < the standard cutoff .5

#### CFA: discriminant validity

```{r, include=F}
#to keep it clean, create a new data frame for the selected indicators
ICE_select <- c(
  "ICE.60.no.ANG14", "ICE.60.no.ANG13", "ICE.60.no.ANG10", "ICE.60.no.ANG3",
  "ICE.60.no.DIS5", "ICE.60.no.DIS7", "ICE.60.no.IND2", "ICE.60.no.IND13",
  "ICE.60.no.EMP12", "ICE.60.no.HOPF9", "ICE.60.no.HOPF8", "ICE.60.no.EMP7",
  "ICE.60.no.POWL11", "ICE.60.no.POWL7", "ICE.60.no.POWL2", "ICE.60.no.POWL13",
  "ICE.60.no.GUI11", "ICE.60.no.GUI6", "ICE.60.no.GUI8", "ICE.60.no.GUI12",
  "ICE.60.no.ISO4", "ICE.60.no.ISO5", "ICE.60.no.ISO8", "ICE.60.no.ISO12",
  "ICE.60.no.APP7", "ICE.60.no.HOPL5", "ICE.60.no.HOPL11", "ICE.60.no.APP14",
  "ICE.60.no.SOR13", "ICE.60.no.SOR6", "ICE.60.no.SOR4", "ICE.60.no.SOR14"
)

ICE_data_short <- ICE_data[ICE_select]

# add the values for latent factors
ICE_data_short <- ICE_data_short %>% 
  mutate(climate.anger = rowSums(.[1:4]), 
         climate.contempt = rowSums(.[5:8]), 
         climate.enthusiasm = rowSums(.[9:12]), 
         climate.powerlessness = rowSums(.[13:16]), 
         climate.guilt = rowSums(.[17:20]),
         climate.isolation = rowSums(.[21:24]),
         climate.anxiety = rowSums(.[25:28]),
         climate.sorrow = rowSums(.[29:32]))

#compute mean correlations between latent variables and their indicators (Mehmet's recommendation for discriminant validity)

ang.m <- mean(cor(ICE_data_short[1:4], ICE_data_short$climate.anger))
cont.m <- mean(cor(ICE_data_short[5:8], ICE_data_short$climate.contempt))
enth.m <- mean(cor(ICE_data_short[9:12], ICE_data_short$climate.enthusiasm))
powl.m <- mean(cor(ICE_data_short[13:16], ICE_data_short$climate.powerlessness))
gui.m <- mean(cor(ICE_data_short[17:20], ICE_data_short$climate.guilt))
iso.m <- mean(cor(ICE_data_short[21:24], ICE_data_short$climate.isolation))
anx.m <- mean(cor(ICE_data_short[25:28], ICE_data_short$climate.anxiety))
sor.m <- mean(cor(ICE_data_short[29:32], ICE_data_short$climate.sorrow))
```

Discriminant validity is established when the average correlation between a latent variable and its indicators is higher than the squared correlation between the latent variables (Fornell & Larcker, 1981).

Here are the average correlations between latent variables and their indicators:

```{r, include=T}
data.frame(ang.m, cont.m, enth.m, powl.m, gui.m, iso.m, anx.m, sor.m)
```

And here is a table with squared factor correlations:
```{r, include=T}
knitr::kable(condisc(model.fit)$Squared_Factor_Correlation, "simple")
```

For climate sorrow the the squared correlation between it and anger is equal to the average correlation between a latent variable and its indicators. There are no problems with other variables.


### Factor internal consistencies {.tabset}

#### Raykov's rho

```{r, include=T}
raykovrho <- astatur::relicoef(model.fit)
raykov.coefficient <- round(raykovrho$RRC, 2) #rounded
clim.emotion <- c("climate_anger", "climate_contempt", "climate_enthusiasm", "climate_powerlessness", "climate_guilt", "climate_isolation", "climate_anxiety", "climate_sorrow")
knitr::kable(cbind(clim.emotion, raykov.coefficient), "simple")
```

#### Cronbach's alpha
```{r, include=T}
knitr::kable(round(semTools::reliability(model.fit, what = "alpha"), 2)[1,], col.names = "alpha", "simple")
```

Both coefficients indicate very good internal consistency across scales.


## Nomological span

```{r, include=F}
#subset the data frame to work with for now
others <- dplyr::select(qdata, starts_with(c("CCPS", "MPS", "ULS", "WTS")))
others <- as.data.frame(others)

#change the variables type from character to numeric (keeping them in the data frame format)
others <- as.data.frame(lapply(others, as.numeric))

####explore & inspect the ICE data for consistency
#is there any missing data?
sum(colSums(is.na(others)))

#a categorical representation of the data to check for inconsistent values
lapply(others, table)

# For the descriptives, the response format should be 1-5 not 0-4, hence we add 1 to each value in the data frame
others <- others + 1

#but not for climate change beliefs scale as 0 on that scale means that somebody doesn't believe in climate change
others[,c(1:5)] <- others[,c(1:5)] - 1

# overview of the data
#psych::describe(s2.general)
```

### Recoding the reverse scored items
Some items from the scales relevant for establishing concurrent & predictive validity are reversely scored. We need to recode them.

```{r, include=T, echo = TRUE}
#### CPS: 5
others$CCPS.no.4 <- 8 - others$CCPS.no.4

### ULS: 3 & 6 (2 & 5 in the numeration of this dataset)
others$ULS.no.2 <- 5 - others$ULS.no.2
others$ULS.no.5 <- 5 - others$ULS.no.5
```

### Internal consistencies of other scales

Here, let's compute Cronbach's alpha coefficient for all the relevant scales.

```{r, include=F}
Climate_perceptions <- dplyr::select(others, c(1:5))
Mit_policy <- dplyr::select(others, c(6:10))
Loneliness <- dplyr::select(others, c(11:18))
WillingnessToSacrifice <- dplyr::select(others, c(19:23))

s_reliabilities_list <- list(Climate_perceptions,
                             Mit_policy,
                             Loneliness, 
                             WillingnessToSacrifice)

compute.alpha.each.scale <- (lapply(s_reliabilities_list, psych::alpha, check.keys=TRUE))


s_reliabilities <- sapply(compute.alpha.each.scale, "[[", 1)[1:2,]
colnames(s_reliabilities) <- c("Climate_perceptions",
                               "Mit_policy",
                               "Loneliness", 
                               "WillingnessToSacrifice")
```

```{r, include=T}
knitr::kable(s_reliabilities, "simple")
```

### Descriptives for all the scales

```{r, include=F}
###create a data frame with global and dimensional results for each scale
## add the final ICE scales to it

glob_dim <- data.frame(climate_perceptions = rowSums(others[1:5])/5, 
                       Mit_policy = rowSums(others[, c(6:10)])/5,
                       Loneliness = rowSums(others[, c(11:18)])/8,
                       WillingnessToSacrifice = rowSums(others[, c(19:23)])/5
)


#add ICE scales to this data frame
#but first, change total score to mean
ICE_scales_means <- ICE_data_short[,33:40]/4

all <- cbind(glob_dim, ICE_scales_means)
```

```{r, include=T}
#### descriptives for all the data
colnames(all) <- c("pro-climate perceptions", "mitigation policy support", "loneliness", " willingness to sacrifice", "climate anger", "climate contempt", "climate enthusiasm", "climate powerlessness", "climate guilt", "climate isolation", "climate anxiety", "climate sorrow")

knitr::kable(psych::describe(all), "simple")
```

```{r, include=F}
## save the descriptives to copy it easily to the paper
#descriptivez <- psych::describe(all)

#write.table(descriptivez, file = "Descriptives_NOR.csv", sep = ";")
```

### Distribution of the variables

We will investigate the distribution using the Shapiro-Wilk test.

```{r, include=T}
#formal inspection with shapiro-wilk test
lapply(all, shapiro.test)
```

For all  variables p < .05 so we can say that most of our variables deviate from normal distribution

```{r, include=F}
#### Visual inspection
#scale the data so that the graphs can be displayed together
data.scaled <- scale(all)

data.gathered <- data.scaled %>%
  as_tibble() %>%
  select_if(is.numeric) %>%
  gather(key = "variable", value = "value")


ggplot(data.gathered, aes(value)) +
  geom_density(fill = "lightgrey") +
  facet_wrap(~variable)
```

### Correlational analysis 

As the data deviates from the normal distribution and because it is ordinal anyways, let's use Spearman correlation coefficients.

```{r, include=T}
###### Add demographic variables to the dataframe for additional correlations
dems_cor <- dplyr::select(qdata, starts_with("demo"))
dems_cor <- as.data.frame(dems_cor)

#rename the variables so that it's easier to navigate
colnames(dems_cor) <- c("cc_concern", "gender", "yearOfbirth", "country", "language1", "area", "education", "any_views", "any_l_r", "perceived_SES", "second_lang", "edu_other_specify", "politicalViews", "gender_other_specify")

age <- 2022 - as.numeric(dems_cor$yearOfbirth)
dems_cor <- as.data.frame(lapply(dems_cor, as.numeric))

#add dems_cor to all for the analysis
all2 <- cbind(dems_cor$cc_concern, dems_cor$gender, age, dems_cor$area, dems_cor$education, rev(dems_cor$perceived_SES), all)

colnames(all2) <- c("climate concern", "gender", "age", "residence area", "education", "perceived SES", "pro-climate perceptions", "mitigation policy support", "loneliness", " willingness to sacrifice", "climate anger", "climate contempt", "climate enthusiasm", "climate powerlessness", "climate guilt", "climate isolation", "climate anxiety", "climate sorrow")

#table(all2$gender)
# filter out one case of gender = 2 (other)
#which(all2$gender==2) #246
all2 <- all2[-c(246), ]
#table(all2$gender)

# filter out one case of education = 4 (other)
#which(all2$education==4) #2
all2 <- all2[-c(2), ]
#table(all2$education)

#merge "secondary education" and "vocational training" into one level
all2$education <- as.factor(all2$education)
levels(all2$education)[levels(all2$education)=="1"] <-"2"
#change the numbering of factors in education so that it starts from 1
all2$education <- dplyr::recode(all2$education, '0' = '1', '2' = '2', '3' = '3')
all2$education <- as.numeric(all2$education)
```


```{r, include=T}
knitr::kable(correlation_matrix(all2, type = "spearman", digits = 2, use = 'lower', replace_diagonal = T), "simple")

#save_correlation_matrix(all, filename = "correlations_NOR.csv", type = "spearman", digits = 2, use = 'lower', replace_diagonal = T)
```

Note: N =  489; Climate concern: 0 - "Not at all concerned", 1 - "Not very concerned", 2 - "Somewhat concerned", 3 - "Very concerned", 4 - "Extremely concerned"; Gender: 0 - Female, 1 - Male (the option "other" (n = 1) was not included in this correlational analysis); Residence area: 0 - "A big city", 1 - "The suburbs or outskirts of a big city", 2 - "A town or a small city", 3 - "A country village", 4 - "A farm or home in the countryside"; Education: 0 - "Primary education", 1 - "Secondary education or vocational training", 3 - "University/college degree" (the option "other" (n = 1) was not included in this correlational analysis); Perceived SES: 0 - "Finding it very difficult on present income", 1 - ""Finding it difficult on present income", 2 - "Coping on present income", 3 - "Living comfortably on present income".

#### Heatmap

A hierarchically clustered heatmap summarising the correlational analysis; the clustering was performed based on the complete-linkage method.

```{r, include=T}
###correlation heatmap 2 - clustered (the version used in the manuscript)

library(ggcorrplot)

corr <- round(cor(all2), 2)
# Compute a matrix of correlation p-values
p.mat <- cor_pmat(all2)

# Get the upper triangle clustered heatmap
ggcorrplot(corr, hc.order = TRUE, type = "upper",
           outline.col = "white", legend.title = "Spearman's ρ", p.mat = p.mat, lab = F) +
  theme(legend.position="bottom")
```

```{r, include=F}
###### Analyse the demographics for reporting in the manuscript
dems <- dplyr::select(qdata, starts_with("demo"))
dems <- as.data.frame(dems)

lapply(dems, table)

#rename the variables so that it's easier to navigate
colnames(dems) <- c("cc_concern", "gender", "yearOfbirth", "country", "language1", "area", "education", "any_views", "any_l_r", "perceived_SES", "second_lang", "edu_other_specify", "politicalViews", "gender_other_specify")

#add dems to s2.all for later analysis
all <- cbind(all, dems)

###decribe the demographics more for the paper - means etc
#Gender
table(dems$gender)
#50% women

#Age
age <- 2022 - as.numeric(dems$yearOfbirth)
psych::describe(age)

all <- cbind(all, age)
#all["age_group"] <- cut(all$age, c(0, 23, 35, 55, Inf), c("genZ", "millennial", "genX", "boomer"), include.lowest=TRUE)

prop.table(table(all$age_group))*100

# urban vs rural
round(prop.table(table(dems$area))*100, 0)

#0 - "A big city"
# 1 - The suburbs or outskirts of a big city"
# 2 - A town or a small city"
# 3 - "A country village"
# 4 - A farm or home in the countryside
# urban: big city, suburbs of a big city, small city or town; sum up

# educational attainment
round(prop.table(table(as.numeric(dems$education)))*100, 0)

# 0 - "Primary education"
# 1 -"Secondary education"
# 2 - "Vocational training"
# 3 - "University/College degree"
# 4 - ("Other" :subq ((:te "Please specify:")))))

# climate change concern
round(prop.table(table(dems$cc_concern))*100, 0)

#0 "Not at all concerned"
#1 "Not very concerned"
#2 "Somewhat concerned"
#3 "Very concerned"
#4 "Extremely concerned"

# perceived SES
round(prop.table(table(dems$perceived_SES))*100, 2)

# 0 "Living comfortably on present income"
# 1 "Coping on present income"
# 2 "Finding it difficult on present income"
# 3 "Finding it very difficult on present income"
```


## Note
This html output presents the general logic of the analysis along with some results not outlined in the main body of the manuscript. Please note that the full R code for the data cleaning and data analysis is available in the supplementary materials on the accompanying OSF website.