The Data

library(readxl)
tga<- read_excel("C:/Users/emretoros/OneDrive/Projeler/2019_1001/Trust/2022_rr/kabul_sonrasi/anket/sahasonuc/tga_v3.xlsx")

Selecting the variables for gullible trust, all arranged as the theoritcal setup, high values high Gullible Trust

library(tidyverse)
gul <- tga %>% 
  select(b01_num_r , #political interest
         b06_internal_fa_rev, #internal efficacy
         b04_rev, #importance of living under democratic administration
         b06_external_fa , #external efficacy
         a0406, #trust in government
         c04_governmental_fa,#trust in state/bureaucratic organisations 
         b05_turnout_frq_rev, #turnout
  )

Standardize all variables

gul_s <- data.frame(scale(gul, center=T, scale=T))

Create Gullible Trust index

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
  )

Join the Gullible Trust Variable with original data

gul_ss <- gul_s %>% select(gul_politics)
tga <- cbind(tga, gul_ss)

Graphs for Ideology and Religiosity as modertor of Gullible Trust for Democratic Satisfaction

### gulliable trust

# religiosity moderator
gul_rel <- tga %>% 
  drop_na(religiosity_f) %>% 
  ggplot(aes(gul_politics, b03)) +
  geom_jitter(alpha = 0.1) +
  geom_point(aes(color = religiosity_f), alpha = 0.1) +
  geom_smooth(aes(color = religiosity_f), method = "lm", formula = "y ~ x") +
  #geom_smooth(method = "lm", formula = "y ~ x", color = "black") +
  labs(
    x = "Gullible Trust in Politics",
    y = "Democratic Satisfaction",
    color = "Religiosity"
  ) +
  theme_minimal() +
  scale_y_continuous(limits=c(0,10)) +
  #scale_color_brewer(palette = "Set1") +
  theme(legend.position = "bottom") 

#ideology moderator
gul_ide <- tga %>% 
  drop_na(ideology_f) %>% 
  ggplot(aes(gul_politics, b03)) +
  geom_jitter(alpha = 0.1) +
  geom_point(aes(color = ideology_f), alpha = 0.1) +
  geom_smooth(aes(color = ideology_f), method = "lm", formula = "y ~ x") +
  #geom_smooth(method = "lm", formula = "y ~ x", color = "black") +
  labs(
    x = "Gullible Trust in Politics",
    y = "Democratic Satisfaction",
    color = "Ideology"
  ) +
  theme_minimal() +
  scale_y_continuous(limits=c(0,10)) +
  scale_color_brewer(palette = "Set2") +
  theme(legend.position = "bottom") 

cowplot::plot_grid(gul_rel, gul_ide)

Models

Variable explanations
The DV
b03 Democratic Satisfaction, ordinal 0-10, high levels represents high satisfaction

The IVs
c13 refers to ideology, ordinal 0-10, 0 left 10 right
d02_conventional_fa refers to trust in conventional media sources (tv, newspapers, radio, journals)
z12 refers to income
religiosity_num refers to religiosity, 0 low 1 high
gender, 0 Male, 1 Female

tab_model(gt_b, gt_rel, gt_ide,
           show.se = T,
          collapse.se = T, 
          show.ci = F,
          p.style = "stars")
  b 03 b 03 b 03
Predictors Estimates Estimates Estimates
(Intercept) 0.02
(0.44) 		0.17
(0.44) 		0.05
(0.43)
gul politics 3.11 ***
(0.29) 		2.31 ***
(0.44) 		2.05 ***
(0.53)
religiosity num 0.67 ***
(0.20) 		0.71 ***
(0.20) 		0.58 **
(0.20)
c13 0.40 ***
(0.03) 		0.39 ***
(0.03) 		0.41 ***
(0.03)
d02 conventional fa 0.10
(0.43) 		-0.03
(0.43) 		0.04
(0.42)
age 0.02 **
(0.01) 		0.02 **
(0.01) 		0.02 **
(0.01)
gender -0.04
(0.18) 		-0.03
(0.18) 		-0.03
(0.18)
z12 0.09 *
(0.04) 		0.08 *
(0.04) 		0.08 *
(0.04)
gul politics ×
religiosity num 		1.31 *
(0.54)
gul politics × c13 0.18 *
(0.08)
Observations 654 654 654
R2 / R2 adjusted 0.528 / 0.523 0.532 / 0.526 0.532 / 0.526
  • p<0.05   ** p<0.01   *** p<0.001

Predicted Values

ggpredict(gt_rel, terms = c("gul_politics", "religiosity_num")) %>%  
  plot(colors = "bw") + 
  labs(title = "Predicted Values for Satisfaction of Democracy", 
       y="Democratic Satisfaction", x= "Gullible Trust", linetype = "Religiosity") +
  scale_linetype_manual(values = 1:2, labels=c("Low", "High"))

ggpredict(gt_ide, terms = c("gul_politics", "c13")) %>%  
  plot(colors = "bw") + 
  labs(title = "Predicted Values for Satisfaction of Democracy", 
       y="Democratic Satisfaction", x= "Gullible Trust", linetype = "c13") +
  scale_linetype_manual(values = 1:3, labels=c("Left", "Center", "Right"))