SETTING UP

packages <- c("tidyverse", "infer", "fst", "modelsummary", "effects", "survey", "MASS", "aod", "interactions", "kableExtra", "flextable", "scales", "ggeffects", "marginaleffects", "viridis", "knitr", "rmarkdown", "ggridges", "questionr") 

new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)

lapply(packages, library, character.only = TRUE)
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ess <- read.fst("All-ESS-Data.fst")
croatia_data <- ess %>%
  filter(cntry == "HR")
croatia_data$weight <- croatia_data$dweight * croatia_data$pweight
survey_design <- svydesign(ids = ~1, data = croatia_data, weights = ~weight)

DESCRIPTIVE STATISTICS

croatia_descriptive <- croatia_data %>%
  mutate(
    impenv = case_when (
      impenv == 1 ~ "Very Much Like Me",
      impenv == 2 ~ "Like Me",
      impenv == 3 ~ "Somewhat Like Me",
      impenv == 4 ~ "A Little Like Me",
      impenv == 5 ~ "Not Like Me",
      impenv == 6 ~ "Not Like Me At All",
      impenv %in% c(7,8,9) ~ NA_character_,
      TRUE ~ as.character(impenv)
  )) %>%
  filter (!is.na(impenv))
croatia_descriptive <- croatia_data %>%
  mutate(
       impenv = ifelse(impenv %in% c(7, 8, 9), NA, impenv)
  ) %>%
    filter(!is.na(impenv))
croatia_descriptive <- croatia_descriptive %>%
 mutate(
   lrscale = ifelse(lrscale %in% c(77,88,99), NA, lrscale)) %>%
  filter(!is.na(lrscale))
croatia_descriptive <- croatia_descriptive %>%
  mutate(
    gndr = ifelse(gndr %in% c(9), NA, gndr)
  ) %>%
  filter(!is.na(gndr))
croatia_descriptive <- croatia_descriptive %>%
  mutate(
    Environment = impenv,
    PoliticalID = lrscale,
    Gender = gndr
  )
croat_table1 <- datasummary_skim(croatia_descriptive %>% dplyr::select(Environment, PoliticalID, Gender), title = "Table 1. Descriptive statistics of environmental attitudes, political ideology, and gender", output = "flextable")
## Warning: The histogram argument is only supported for (a) output types "default",
##   "html", "kableExtra", or "gt"; (b) writing to file paths with extensions
##   ".html", ".jpg", or ".png"; and (c) Rmarkdown, knitr or Quarto documents
##   compiled to PDF (via kableExtra)  or HTML (via kableExtra or gt). Use
##   `histogram=FALSE` to silence this warning.
croat_table1
Table 1. Descriptive statistics of environmental attitudes, political ideology, and gender

Unique (#)

Missing (%)

Mean

SD

Min

Median

Max

Environment

6

0

2.1

1.1

1.0

2.0

6.0

PoliticalID

11

0

5.0

2.5

0.0

5.0

10.0

Gender

2

0

1.5

0.5

1.0

2.0

2.0

flextable::save_as_docx(croat_table1, path = "Croat_Table1.docx", width = 7.0, height = 9.0)

NULL DISTRIBUTION

croatia_null <- croatia_data %>%
  mutate(
    impenv = case_when (
      impenv == 1 ~ "Very Much Like Me",
      impenv == 2 ~ "Like Me",
      impenv == 3 ~ "Somewhat Like Me",
      impenv == 4 ~ "A Little Like Me",
      impenv == 5 ~ "Not Like Me",
      impenv == 6 ~ "Not Like Me At All",
      impenv %in% c(7,8,9) ~ NA_character_,
      TRUE ~ as.character(impenv)
  )) %>%
  filter (!is.na(impenv))
croatia_null <- croatia_null %>%
  mutate(
    lrscale = case_when(
      lrscale %in% 0:4 ~ "Left",
      lrscale %in% 6:10 ~ "Right",
      lrscale %in% c(5, 77, 88, 99) ~ NA_character_)
    ) %>%
  filter(!is.na(lrscale))
croatia_null <- croatia_null %>%
  mutate (gndr = case_when (
    gndr == 1 ~ "Male",
    gndr == 2 ~ "Female",
    gndr %in% c(9) ~ NA_character_,
  )) %>%
  filter(!is.na(gndr))
test_stat1 <- croatia_null %>%
  specify(explanatory = lrscale,
          response = impenv) %>%
  hypothesize (null = "independence") %>%
  calculate (stat = "Chisq")

print(test_stat1$stat)
## X-squared 
##  13.63082

A chi-square test was used in this case for the test statistic because impenv, the response variable, is a categorical variable with six categories and lrscale, the explanatory variable, is a categorical variable with only two categories As a result, the other test statistics would not fit for these types of categorical variables for the test statistic since there are no numeric variables and both variables do not have only two categories.

croat_tab <- croatia_null

HRtab <- table(croat_tab$lrscale, croat_tab$impenv)

rsum <- rowSums(HRtab)

csums <- colSums(HRtab)

N <- sum(HRtab)

ptab <- tcrossprod(rsum/N, csums/N)
cat("Table of Expected Proportions:\n")
## Table of Expected Proportions:
print(ptab)
##            [,1]      [,2]       [,3]        [,4]       [,5]      [,6]
## [1,] 0.04123718 0.1882107 0.01419888 0.003776299 0.08322963 0.1723503
## [2,] 0.04074480 0.1859634 0.01402934 0.003731209 0.08223584 0.1702924
ftab <- N*ptab
cat("Table of Expected Frequencies:\n")
## Table of Expected Frequencies:
print(ftab)
##          [,1]     [,2]     [,3]     [,4]     [,5]     [,6]
## [1,] 137.3198 626.7417 47.28228 12.57508 277.1547 573.9264
## [2,] 135.6802 619.2583 46.71772 12.42492 273.8453 567.0736
alpha <- 0.05
c_val <- qchisq(alpha, df = 1, lower.tail = FALSE)
cat("Critical Value:", round(c_val, 3), "\n")
## Critical Value: 3.841
null_distribution <- croatia_null %>%
  specify(explanatory = lrscale,
          response = impenv) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 1000, type = "permute") %>%
  calculate(stat="Chisq")
p_val <- null_distribution %>%
  get_pvalue(obs_stat = test_stat1, direction = "two-sided")
p_val
## # A tibble: 1 × 1
##   p_value
##     <dbl>
## 1   0.046
conf_int <- null_distribution %>%
  get_confidence_interval(level = 0.95, type = "percentile")

null_distribution %>%
  visualize() +
  shade_p_value(obs_stat = test_stat1, direction = "two-sided") +
  shade_confidence_interval(endpoints = conf_int)
## Warning: Chi-Square usually corresponds to right-tailed tests. Proceed with
## caution.

null_distribution
## Response: impenv (factor)
## Explanatory: lrscale (factor)
## Null Hypothesis: independence
## # A tibble: 1,000 × 2
##    replicate  stat
##        <int> <dbl>
##  1         1  6.73
##  2         2  4.59
##  3         3  7.11
##  4         4  2.06
##  5         5  8.30
##  6         6  7.78
##  7         7  1.35
##  8         8  5.50
##  9         9  2.05
## 10        10  7.96
## # ℹ 990 more rows

REGRESSION MODEL

croatia_model1 <- croatia_data %>%
  mutate(impenv = na_if(impenv, 7) %>% na_if(8) %>% na_if(9)) %>%
  mutate(impenv = 7 - impenv) %>%
  filter(!is.na(impenv))
croatia_model1 <- croatia_model1 %>%
  mutate(
    lrscale = case_when(
      lrscale %in% 0:4 ~ "Left",
      lrscale %in% 6:10 ~ "Right",
      lrscale %in% c(5, 77, 88, 99) ~ NA_character_
    )) %>%
  filter(!is.na(lrscale))
croatia_model1 <- croatia_model1 %>%
  mutate(
    gndr = case_when(
      gndr == 1 ~ "Male",
      gndr == 2 ~ "Female",
      gndr %in% c(9) ~ NA_character_,
      TRUE ~ as.character(gndr)
  )) %>%
  filter(!is.na(gndr))
model1 <- lm(impenv ~ lrscale, data = croatia_model1, weights = weight)
modelsummary(
  list(model1),
  fmt = 3,
  estimate = c("{estimate} ({std.error}){stars}"),
  statistic = NULL
)
 (1)
(Intercept) 4.908 (0.027)***
lrscaleRight −0.047 (0.038)
Num.Obs. 3330
R2 0.000
R2 Adj. 0.000
AIC 10401.4
BIC 10419.8
Log.Lik. −5197.715
RMSE 1.10 
model2 <- lm(impenv ~ lrscale + gndr, data = croatia_model1, weights = weight)
model3 <- lm(impenv ~ lrscale + gndr + lrscale*gndr, data = croatia_model1, weights = weight)
modelsummary(
  list(model1, model2, model3),
  fmt = 3,
  estimate = c("{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}"),
  statistic = NULL,
  title = "Table 2: Regression model for Environmental Attitude, Politicla Ideology, and Gender in Croatia")
Table 2: Regression model for Environmental Attitude, Politicla Ideology, and Gender in Croatia
 (1)   (2)   (3)
(Intercept) 4.908 (0.027)*** 4.967 (0.032)*** 4.978 (0.036)***
lrscaleRight −0.047 (0.038) −0.033 (0.038) −0.057 (0.053)
gndrMale −0.135 (0.038)*** −0.160 (0.054)**
lrscaleRight × gndrMale 0.050 (0.076)
Num.Obs. 3330 3330 3330
R2 0.000 0.004 0.004
R2 Adj. 0.000 0.004 0.003
AIC 10401.4 10390.9 10392.5
BIC 10419.8 10415.4 10423.0
Log.Lik. −5197.715 −5191.458 −5191.240
RMSE 1.10 1.09 1.09

This model has lower AIC and BIC scores and will be used in the report.

model1.2 <- glm(impenv ~ lrscale, data = croatia_model1, family = poisson)
model2.2 <- glm(impenv ~ lrscale + gndr, data = croatia_model1, family = poisson)
model3.2 <- glm(impenv ~ lrscale + gndr + lrscale*gndr, data = croatia_model1, family = poisson)
modelsummary(
  list(model1.2, model2.2, model3.2),
  fmt = 3,
  estimate = c("{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}",
               "{estimate} ({std.error}){stars}"),
  statistic = NULL
)
 (1)   (2)   (3)
(Intercept) 1.596 (0.011)*** 1.610 (0.013)*** 1.613 (0.015)***
lrscaleRight −0.013 (0.016) −0.011 (0.016) −0.017 (0.022)
gndrMale −0.033 (0.016)* −0.040 (0.022)+
lrscaleRight × gndrMale 0.013 (0.032)
Num.Obs. 3330 3330 3330
AIC 12345.2 12342.7 12344.5
BIC 12357.4 12361.0 12368.9
Log.Lik. −6170.578 −6168.336 −6168.247
RMSE 1.09 1.09 1.09

THIRD VISUAL

croatia_plot <- croatia_data %>%
   mutate(
    impenv = case_when(
      impenv %in% c(7,8,9) ~ NA_character_,
      TRUE ~ as.character(impenv))
  ) %>%
  filter (!is.na(impenv))
croatia_plot <- croatia_plot %>%
  mutate(
    lrscale = case_when(
      lrscale %in% 0:4 ~ "Left",
      lrscale %in% 6:10 ~ "Right",
      lrscale %in% c(5, 77, 88, 99) ~ NA_character_)
    ) %>%
  filter(!is.na(lrscale))
croatia_plot <- croatia_plot %>%
  mutate (gndr = case_when (
    gndr == 1 ~ "Male",
    gndr == 2 ~ "Female",
    gndr %in% c(9) ~ NA_character_,
  )) %>%
  filter(!is.na(gndr))
table(croatia_plot$impenv, croatia_plot$lrscale) %>%
  cprop() %>% 
  as.data.frame() %>%
  filter(Var1 != "Total",
         Var2 != "All") %>%
  ggplot(aes(x=Var1, y=Freq, fill=Var2)) +
  geom_col(position = "dodge") +
  labs(title="Importance of Caring for the Environment in Croatia by Political Ideology",
       y = "Conditional Percentage",
       x = "Very much - to - Not like me at all",
       fill = "Political Ideology")

table(croatia_plot$impenv, croatia_plot$gndr) %>%
  cprop() %>% 
  as.data.frame() %>%
  filter(Var1 != "Total",
         Var2 != "All") %>%
  ggplot(aes(x=Var1, y=Freq, fill=Var2)) +
  geom_col(position = "dodge") +
  labs(title="Importance of Caring for the Environment in Croatia by Gender",
       y = "Conditional Percentage",
       x = "Very much - to - Not like me at all",
       fill = "Gender")