Trust Typologies
2023-06-14
Data Wrangling (not printed here)
Type 1 - Confident Trust
The Confident Trust Variables
Selecting the variables needed
conf <- tga %>%
select(
c01_positive_fa,
b06_internal_fa,
b04,
b06_external_fa,
c04_governmental_fa,
b05_turnout_frq,
c06_participation_fa,
pol_knowledge_ave,
d03_free_fa,
d01_conventional_fa,
d07_pro_science_fa,
d06_global_cons_fa_rev,
d06_national_cons_fa_rev,
d07_pro_science_fa,
d07_anti_science_fa_rev)Standardize all variables
conf_s <- data.frame(scale(conf, center=T, scale=T))Calculating dimension scores of politics media and science for Type confident
conf_s <- conf_s %>%
mutate(conf_politics = (
c01_positive_fa+
b06_internal_fa+
b04+
b06_external_fa+
c04_governmental_fa+
b05_turnout_frq+
c06_participation_fa)/7 )
#######################
conf_s <- conf_s %>%
mutate(conf_media = (
pol_knowledge_ave+
d03_free_fa+
d01_conventional_fa)/3)
######################
conf_s <- conf_s %>%
mutate(conf_science = (
d07_pro_science_fa+
d06_global_cons_fa_rev+
d06_national_cons_fa_rev+
d07_pro_science_fa+
d07_anti_science_fa_rev)/5)combine conf_s dataframe with original tga dataframe
conf_ss <- conf_s %>% select(conf_politics, conf_media, conf_science)
tga <- cbind(tga, conf_ss)Confident Trust
Graphs
dim_names_conf <- c(
`conf_media` = "Trust in Media",
`conf_politics` = "Trust in Politics",
`conf_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("conf_"),
names_to = "conf_dimension",
values_to = "conf_score"
) %>%
ggplot(aes(conf_score)) +
facet_wrap(~conf_dimension, labeller = as_labeller(dim_names_conf)) +
geom_histogram(aes(y = ..density..), alpha = .2, bins = 50)+
geom_density(color = "red") +
labs(title =" Confident Trust According to Sub-Dimensions",
x= "Confidence Score") +
firatheme::theme_fira()## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 872 rows containing non-finite values (`stat_bin()`).## Warning: Removed 872 rows containing non-finite values (`stat_density()`).
Confident Trust According to Partisanship
dim_names_conf <- c(
`conf_media` = "Trust in Media",
`conf_politics` = "Trust in Politics",
`conf_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("conf_"),
names_to = "conf_dimension",
values_to = "conf_score"
) %>%
filter( c08_f == "chp_partisan" |
c08_f =="hdp_partisan" | c08_f == "iyi_partisan" |
c08_f == "mhp_partisan" | c08_f == "akp_partisan") %>%
ggplot(aes(conf_score, c08_f)) +
geom_boxplot() +
geom_vline(xintercept = 0, color ="red") +
xlim(-2,2) +
facet_wrap(~conf_dimension, labeller = as_labeller(dim_names_conf)) +
labs(title = "Confident Trust According to Partisanship", y="", x="Confident Trust Score") +
firatheme::theme_fira()## Warning: Removed 663 rows containing non-finite values (`stat_boxplot()`).
Confident Trust According to Ideology
dim_names_conf <- c(
`conf_media` = "Trust in Media",
`conf_politics` = "Trust in Politics",
`conf_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("conf_"),
names_to = "conf_dimension",
values_to = "conf_score"
) %>%
drop_na(ideology_f) %>%
ggplot(aes(conf_score, ideology_f)) +
geom_boxplot() +
facet_wrap(~conf_dimension, labeller = as_labeller(dim_names_conf)) +
labs(title = "Confident Trust According to Ideology", y="") +
firatheme::theme_fira()## Warning: Removed 814 rows containing non-finite values (`stat_boxplot()`).
Type 2 - Gullible Trust
Selecting the variables needed
library(tidyverse)
gul <- tga %>%
select(b01_num_r ,
b06_internal_fa_rev,
b04_rev,
b06_external_fa ,
a0406, c04_governmental_fa,
b05_turnout_frq_rev,
d01_conventional_fa,
d03_not_free_fa,
d01_news_frequency,
d0702,
d0701,
d0704,
d0703_rev
)Standardize all variables
gul_s <- data.frame(scale(gul, center=T, scale=T))Calculating dimension scores of politics media and science for Type Gullible
gul_s <- gul_s %>%
mutate(
gul_politics = (
b01_num_r +
b04_rev +
b06_external_fa +
a0406 +
c04_governmental_fa +
b05_turnout_frq_rev +
b06_internal_fa_rev
) / 7
)
gul_s <- gul_s %>%
mutate (gul_media = (d01_conventional_fa +
d03_not_free_fa +
d01_news_frequency) / 3)
gul_s <- gul_s %>%
mutate (gul_science = (d0702 +
d0701 +
d0704 +
d0703_rev) / 4)combine gul_s dataframe with original tga dataframe
gul_ss <- gul_s %>% select(gul_politics, gul_media, gul_science)
tga <- cbind(tga, gul_ss)Gullible Trust
Graphs
dim_names_gul <- c(
`gul_media` = "Trust in Media",
`gul_politics` = "Trust in Politics",
`gul_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("gul_"),
names_to = "gul_dimension",
values_to = "gul_score"
) %>%
ggplot(aes(gul_score)) +
facet_wrap(~gul_dimension, labeller = as_labeller(dim_names_gul)) +
geom_histogram(aes(y = ..density..), alpha = .2, bins = 50)+
geom_density(color = "red") +
labs(title =" Gullible Trust According to Sub-Dimensions",
x= "Gullibility Score") +
firatheme::theme_fira()## Warning: Removed 579 rows containing non-finite values (`stat_bin()`).## Warning: Removed 579 rows containing non-finite values (`stat_density()`).
Gullible Trust According to Partisanship
dim_names <- c(
`gul_media` = "Trust in Media",
`gul_politics` = "Trust in Politics",
`gul_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("gul_"),
names_to = "dimension",
values_to = "score"
) %>%
filter( c08_f == "chp_partisan" |
c08_f =="hdp_partisan" | c08_f == "iyi_partisan" |
c08_f == "mhp_partisan" | c08_f == "akp_partisan") %>%
ggplot(aes(score, c08_f)) +
geom_boxplot() +
geom_vline(xintercept = 0, color ="red") +
facet_wrap(~dimension, labeller = as_labeller(dim_names)) +
xlim(-2,2) +
labs(title = "Gullible Trust According to Partisanship", y="", x="Gullible Trust Score") +
firatheme::theme_fira()## Warning: Removed 434 rows containing non-finite values (`stat_boxplot()`).
dim_names <- c(
`gul_media` = "Trust in Media",
`gul_politics` = "Trust in Politics",
`gul_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("gul_"),
names_to = "dimension",
values_to = "score"
) %>%
drop_na(ideology_f) %>%
ggplot(aes(score, ideology_f)) +
geom_boxplot() +
facet_wrap(~dimension, labeller = as_labeller(dim_names)) +
labs(title = "Gullible Trust According to Ideology", y="") +
firatheme::theme_fira()## Warning: Removed 536 rows containing non-finite values (`stat_boxplot()`).
Type 3 - Skeptic Distrust
Selecting the variables needed
library(tidyverse)
ske <- tga %>%
select(c01_anxiety_fa,
b06_internal_fa,
c01_negative_fa,
b04,
b06_external_fa,
b05_turnout_frq,
c06_participation_frq,
c14_partisanship_degree_fa_rev,
pol_knowledge,
d03_free_fa,
d01_news_frequency,
d07_pro_science_fa,
d06_global_cons_fa_rev,
d06_national_cons_fa_rev,
d07_anti_science_fa,
d0704,
d0703
)Standardize all variables
ske_s <- data.frame(scale(ske, center=T, scale=T))Calculating dimension scores of politics media and science for Type Skeptic
ske_s <- ske_s %>%
mutate(
ske_politics = (
c01_anxiety_fa +
b06_internal_fa +
c01_negative_fa+
b04+
b06_external_fa+
b05_turnout_frq+
c06_participation_frq+
c14_partisanship_degree_fa_rev) / 8)
ske_s <- ske_s %>%
mutate (ske_media = (pol_knowledge+
d03_free_fa+
d01_news_frequency) / 3)
ske_s <- ske_s %>%
mutate (ske_science = (d07_pro_science_fa+
d06_global_cons_fa_rev+
d06_national_cons_fa_rev+
d07_anti_science_fa+
d0704+
d0703) / 6)combine ske_s dataframe with original tga dataframe
ske_ss <- ske_s %>% select(ske_politics, ske_media, ske_science)
tga <- cbind(tga, ske_ss)Skeptic Distrust
Graphs
dim_names_ske <- c(
`ske_media` = "Trust in Media",
`ske_politics` = "Trust in Politics",
`ske_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("ske_"),
names_to = "ske_dimension",
values_to = "ske_score"
) %>%
ggplot(aes(ske_score)) +
facet_wrap(~ske_dimension, labeller = as_labeller(dim_names_ske)) +
geom_histogram(aes(y = ..density..), alpha = .2, bins = 50)+
geom_density(color = "red") +
labs(title =" Skeptic Distrust According to Sub-Dimensions",
x= "Skeptic Distrust Score") +
firatheme::theme_fira()## Warning: Removed 774 rows containing non-finite values (`stat_bin()`).## Warning: Removed 774 rows containing non-finite values (`stat_density()`).
Skeptic Distrust According to Partisanship
dim_names_ske <- c(
`ske_media` = "Trust in Media",
`ske_politics` = "Trust in Politics",
`ske_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("ske_"),
names_to = "dimension",
values_to = "score"
) %>%
filter( c08_f == "chp_partisan" |
c08_f =="hdp_partisan" | c08_f == "iyi_partisan" |
c08_f == "mhp_partisan" | c08_f == "akp_partisan") %>%
ggplot(aes(score, c08_f)) +
geom_boxplot() +
geom_vline(xintercept = 0, color ="red") +
facet_wrap(~dimension, labeller = as_labeller(dim_names_ske)) +
xlim(-2,2) +
labs(title = "Skeptic Distrust According to Partisanship", y="", x="Skeptic Distrust Score") +
firatheme::theme_fira()## Warning: Removed 590 rows containing non-finite values (`stat_boxplot()`).
Skeptic Distrust According to Ideology
dim_names_ske <- c(
`ske_media` = "Trust in Media",
`ske_politics` = "Trust in Politics",
`ske_science` = "Trust in Science"
)
tga %>%
pivot_longer(
cols = starts_with("ske_"),
names_to = "dimension",
values_to = "score"
) %>%
drop_na(ideology_f) %>%
ggplot(aes(score, ideology_f)) +
geom_boxplot() +
facet_wrap(~dimension, labeller = as_labeller(dim_names_ske)) +
labs(title = "Skeptic Distrust According to Ideology", y="") +
firatheme::theme_fira()## Warning: Removed 718 rows containing non-finite values (`stat_boxplot()`).