revision_analyses

1. descriptives

cor.test(as.numeric(b$bias), b$reliability) #page 5: R = -0.35

## Warning in cor.test(as.numeric(b$bias), b$reliability): NAs introduced by
## coercion

## 
##  Pearson's product-moment correlation
## 
## data:  as.numeric(b$bias) and b$reliability
## t = -2.1345, df = 32, p-value = 0.04056
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6174788 -0.0168784
## sample estimates:
##        cor 
## -0.3530291

mean(d$education, na.rm = T) #page 6

## [1] 14.63202

sd(d$education, na.rm = T) #page 6

## [1] 2.622589

mean(d$CRT, na.rm = T) #page 6

## [1] 0.404966

sd(d$CRT, na.rm = T) #page 6

## [1] 0.3141821

corr.test(af$bias.af[af$country == "US"], af$bias.as[af$country == "US"]) #page 7

## Call:corr.test(x = af$bias.af[af$country == "US"], y = af$bias.as[af$country == 
##     "US"])
## Correlation matrix 
## [1] 0.97
## Sample Size 
## [1] 18
## These are the unadjusted probability values.
##   The probability values  adjusted for multiple tests are in the p.adj object. 
## [1] 0
## 
##  To see confidence intervals of the correlations, print with the short=FALSE option

cor.test(af$bias.af[af$country == "US"], af$reliability[af$country == "US"])

## 
##  Pearson's product-moment correlation
## 
## data:  af$bias.af[af$country == "US"] and af$reliability[af$country == "US"]
## t = -3.1192, df = 17, p-value = 0.006243
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8300629 -0.2053931
## sample estimates:
##       cor 
## -0.603318

2. Spring 2022 Correlation Table (Table 1)

library(apaTables)

corr <- data.frame(d$vaxxIntent.w1w2.z)
colnames(corr)[colnames(corr) == "d.vaxxIntent.w1w2.z"] <- "vaxxIntentions"
corr$trustSci <- d$trustSci.z

corr$Age <- d$age.z
corr$Education <- d$education.z
corr$Ethnicity <- d$white_1
corr$Ideology <- d$ideology.z
corr$CRT <- d$crt.z

corr$CMC.2020 <- d$bias.mean.1.15.z
corr$CMC.2022 <- d$bias.mean.3.20.z

corr$DMC.2020 <- d$bias.sd.w1w2.15.z
corr$DMC.2022 <- d$bias.sd.3.20.z

# create APA table
apa.cor.table(
  corr,
  filename = "Table1.doc",
  table.number = 1,
  show.conf.interval = FALSE,
  show.sig.stars = TRUE,
  landscape = TRUE
)

## The ability to suppress reporting of reporting confidence intervals has been deprecated in this version.
## The function argument show.conf.interval will be removed in a later version.

## 
## 
## Table 1 
## 
## Means, standard deviations, and correlations with confidence intervals
##  
## 
##   Variable          M     SD   1            2            3           
##   1. vaxxIntentions -0.00 1.00                                       
##                                                                      
##   2. trustSci       0.00  1.00 .40**                                 
##                                [.34, .45]                            
##                                                                      
##   3. Age            0.27  0.93 .17**        .06                      
##                                [.10, .23]   [-.00, .13]              
##                                                                      
##   4. Education      0.12  0.91 .07*         .13**        -.00        
##                                [.01, .13]   [.07, .19]   [-.07, .06] 
##                                                                      
##   5. Ideology       0.07  1.07 -.18**       -.45**       .13**       
##                                [-.24, -.11] [-.50, -.40] [.07, .19]  
##                                                                      
##   6. CRT            0.00  1.00 .10**        .14**        .03         
##                                [.04, .16]   [.08, .20]   [-.03, .10] 
##                                                                      
##   7. CMC.2020       0.00  1.00 -.21**       -.32**       .13**       
##                                [-.27, -.14] [-.38, -.26] [.07, .20]  
##                                                                      
##   8. CMC.2022       0.00  1.00 -.20**       -.51**       -.03        
##                                [-.26, -.13] [-.56, -.46] [-.09, .04] 
##                                                                      
##   9. DMC.2020       0.00  1.00 .23**        .09**        -.03        
##                                [.17, .29]   [.03, .15]   [-.09, .04] 
##                                                                      
##   10. DMC.2022      -0.00 1.00 .19**        -.03         -.10**      
##                                [.13, .25]   [-.09, .03]  [-.17, -.04]
##                                                                      
##   4            5            6            7            8            9         
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##   -.06                                                                       
##   [-.12, .00]                                                                
##                                                                              
##   .21**        .01                                                           
##   [.15, .27]   [-.06, .07]                                                   
##                                                                              
##   -.02         .49**        .14**                                            
##   [-.08, .04]  [.44, .54]   [.08, .21]                                       
##                                                                              
##   -.07*        .53**        .03          .66**                               
##   [-.13, -.00] [.48, .57]   [-.03, .09]  [.62, .69]                          
##                                                                              
##   -.02         -.08*        -.17**       -.48**       -.22**                 
##   [-.08, .04]  [-.14, -.02] [-.23, -.11] [-.52, -.43] [-.28, -.16]           
##                                                                              
##   -.04         -.10**       -.24**       -.42**       -.17**       .61**     
##   [-.10, .03]  [-.16, -.04] [-.30, -.18] [-.47, -.37] [-.23, -.11] [.56, .64]
##                                                                              
## 
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations 
## that could have caused the sample correlation (Cumming, 2014).
##  * indicates p < .05. ** indicates p < .01.
##

3. Vaccination Behavior (Table 2A)

m.vaxx <- polr(vaxxBehavior ~ bias.mean.3.20.z * bias.sd.3.20.z + 
                 (bias.mean.3.20.z + bias.sd.3.20.z) * 
                 (crt.z + ideology.z + white_.5 + age.z + education.z), data = d, Hess = TRUE)

tab_model(m.vaxx,
          string.est = "OR",
          dv.labels = "Standardized Vaccination Behavior",
          pred.labels = c("1|2", 
                          "2|3", 
                          "Conservative Media Consumption (CMC)", 
                          "Diversity ofMedia Consumption (DMC)",
                          "Cognitive Reflection (CRT)", 
                          "Ideology",
                          "Ethnicity",
                          "Age",
                          "Education",
                          "CMC x DMC",
                          "CMC x CRT",
                          "CMC x Ideology",
                          "CMC x Ethnicity",
                          "CMC x Age",
                          "CMC x Education",
                          "DMC x CRT",
                          "DMC x Ideology",
                          "DMC x Ethnicity",
                          "DMC x Age",
                          "DMC x Education"),
          show.stat = T,
          show.se = T,
          string.se = "SE",
          string.stat = "t",
          digits = 2)

	Standardized Vaccination Behavior
Predictors	OR	SE	CI	t	p
1\|2	0.20	0.02	0.15 – 0.25	-13.76	<0.001
2\|3	0.55	0.06	0.45 – 0.68	-5.63	<0.001
Conservative Media Consumption (CMC)	0.52	0.07	0.40 – 0.66	-5.16	<0.001
Diversity ofMedia Consumption (DMC)	1.53	0.16	1.26 – 1.88	4.19	<0.001
Cognitive Reflection (CRT)	1.22	0.11	1.03 – 1.45	2.28	0.023
Ideology	0.77	0.07	0.65 – 0.91	-3.03	0.003
Ethnicity	0.91	0.16	0.64 – 1.29	-0.50	0.617
Age	1.74	0.15	1.47 – 2.07	6.32	<0.001
Education	1.35	0.12	1.14 – 1.61	3.43	0.001
CMC x DMC	1.47	0.20	1.13 – 1.92	2.91	0.004
CMC x CRT	1.01	0.09	0.85 – 1.19	0.07	0.944
CMC x Ideology	0.93	0.08	0.79 – 1.10	-0.83	0.405
CMC x Ethnicity	0.82	0.17	0.55 – 1.22	-0.99	0.325
CMC x Age	0.92	0.09	0.76 – 1.12	-0.79	0.427
CMC x Education	0.95	0.10	0.78 – 1.16	-0.54	0.586
DMC x CRT	1.05	0.10	0.88 – 1.27	0.57	0.571
DMC x Ideology	1.10	0.09	0.94 – 1.29	1.15	0.249
DMC x Ethnicity	0.82	0.14	0.58 – 1.14	-1.18	0.237
DMC x Age	1.03	0.10	0.86 – 1.25	0.35	0.727
DMC x Education	0.93	0.07	0.80 – 1.07	-0.94	0.346
Observations	909
R² Nagelkerke	0.416

- CMC x DMC

d$hi.diversity <- d$bias.sd.3.20.z - 1
d$low.diversity <- d$bias.sd.3.20.z + 1

m.low <- polr(vaxxBehavior ~ (bias.mean.3.20.z * low.diversity)  +
               (bias.mean.3.20.z + low.diversity) * 
               (crt.z + ideology.z + white_.5 + age.z + education.z), data = d, Hess = TRUE)


m.hi <- polr(vaxxBehavior ~ (bias.mean.3.20.z * hi.diversity)  +
               (bias.mean.3.20.z + hi.diversity) * 
               (crt.z + ideology.z + white_.5 + age.z + education.z), data = d, Hess = TRUE)


tab_model(m.low, m.vaxx, m.hi,
          string.est = "OR",
          title = "Outcome: Standardized Vaccination Behavior",
          dv.labels = c("Low diversity","Mean", "High diversity"),
          pred.labels = c("1|2", 
                          "2|3", 
                          "Conservative Media Consumption (CMC)", 
                          "Diversity of Media Consumption (DMC)",
                          "Cognitive Reflection (CRT)", 
                          "Ideology",
                          "Ethnicity",
                          "Age",
                          "Education",
                          "CMC x DMC",
                          "CMC x CRT",
                          "CMC x Ideology",
                          "CMC x Ethnicity",
                          "CMC x Age",
                          "CMC x Education",
                          "DMC x CRT",
                          "DMC x Ideology",
                          "DMC x Ethnicity",
                          "DMC x Age",
                          "DMC x Education"),
          show.stat = T,
          show.se = T,
          string.se = "SE",
          string.stat = "t",
          digits = 2)

## Length of `pred.labels` does not equal number of predictors, no labelling applied.

Outcome: Standardized Vaccination Behavior
	Low diversity					Mean					High diversity
Predictors	OR	SE	CI	t	p	OR	SE	CI	t	p	OR	SE	CI	t	p
1\|2	0.30	0.04	0.23 – 0.39	-8.58	<0.001	0.20	0.02	0.15 – 0.25	-13.76	<0.001	0.13	0.02	0.09 – 0.18	-12.09	<0.001
2\|3	0.85	0.11	0.65 – 1.10	-1.25	0.212	0.55	0.06	0.45 – 0.68	-5.63	<0.001	0.36	0.06	0.27 – 0.49	-6.46	<0.001
bias.mean.3.20.z	0.35	0.08	0.22 – 0.54	-4.65	<0.001	0.52	0.07	0.40 – 0.66	-5.16	<0.001	0.76	0.10	0.59 – 0.99	-2.04	0.042
low.diversity	1.53	0.16	1.26 – 1.88	4.19	<0.001
crt.z	1.16	0.13	0.93 – 1.44	1.33	0.183	1.22	0.11	1.03 – 1.45	2.28	0.023	1.29	0.18	0.98 – 1.71	1.76	0.079
ideology.z	0.70	0.08	0.56 – 0.87	-3.14	0.002	0.77	0.07	0.65 – 0.91	-3.03	0.003	0.85	0.10	0.66 – 1.08	-1.35	0.178
white_.5	1.12	0.27	0.69 – 1.81	0.47	0.640	0.91	0.16	0.64 – 1.29	-0.50	0.617	0.75	0.19	0.45 – 1.21	-1.17	0.242
age.z	1.68	0.19	1.35 – 2.11	4.52	<0.001	1.74	0.15	1.47 – 2.07	6.32	<0.001	1.80	0.25	1.37 – 2.38	4.17	<0.001
education.z	1.45	0.18	1.14 – 1.87	2.93	0.004	1.35	0.12	1.14 – 1.61	3.43	0.001	1.26	0.13	1.02 – 1.53	2.27	0.023
bias.mean.3.20.z:low.diversity	1.47	0.20	1.13 – 1.92	2.91	0.004
bias.mean.3.20.z:crt.z	1.01	0.09	0.85 – 1.19	0.07	0.944	1.01	0.09	0.85 – 1.19	0.07	0.944	1.01	0.09	0.85 – 1.19	0.07	0.944
bias.mean.3.20.z:ideology.z	0.93	0.08	0.79 – 1.10	-0.83	0.405	0.93	0.08	0.79 – 1.10	-0.83	0.405	0.93	0.08	0.79 – 1.10	-0.83	0.405
bias.mean.3.20.z:white_.5	0.82	0.17	0.55 – 1.22	-0.99	0.325	0.82	0.17	0.55 – 1.22	-0.99	0.325	0.82	0.17	0.55 – 1.22	-0.99	0.325
bias.mean.3.20.z:age.z	0.92	0.09	0.76 – 1.12	-0.79	0.427	0.92	0.09	0.76 – 1.12	-0.79	0.427	0.92	0.09	0.76 – 1.12	-0.79	0.427
bias.mean.3.20.z:education.z	0.95	0.10	0.78 – 1.16	-0.54	0.586	0.95	0.10	0.78 – 1.16	-0.54	0.586	0.95	0.10	0.78 – 1.16	-0.54	0.586
low.diversity:crt.z	1.05	0.10	0.88 – 1.27	0.57	0.571
low.diversity:ideology.z	1.10	0.09	0.94 – 1.29	1.15	0.249
low.diversity:white_.5	0.82	0.14	0.58 – 1.14	-1.18	0.237
low.diversity:age.z	1.03	0.10	0.86 – 1.25	0.35	0.727
low.diversity:education.z	0.93	0.07	0.80 – 1.07	-0.94	0.346
bias.sd.3.20.z						1.53	0.16	1.26 – 1.88	4.19	<0.001
bias.mean.3.20.z:bias.sd.3.20.z						1.47	0.20	1.13 – 1.92	2.91	0.004
bias.sd.3.20.z:crt.z						1.05	0.10	0.88 – 1.27	0.57	0.571
bias.sd.3.20.z:ideology.z						1.10	0.09	0.94 – 1.29	1.15	0.249
bias.sd.3.20.z:white_.5						0.82	0.14	0.58 – 1.14	-1.18	0.237
bias.sd.3.20.z:age.z						1.03	0.10	0.86 – 1.25	0.35	0.727
bias.sd.3.20.z:education.z						0.93	0.07	0.80 – 1.07	-0.94	0.346
hi.diversity											1.53	0.16	1.26 – 1.88	4.19	<0.001
bias.mean.3.20.z:hi.diversity											1.47	0.20	1.13 – 1.92	2.91	0.004
hi.diversity:crt.z											1.05	0.10	0.88 – 1.27	0.57	0.571
hi.diversity:ideology.z											1.10	0.09	0.94 – 1.29	1.15	0.249
hi.diversity:white_.5											0.82	0.14	0.58 – 1.14	-1.18	0.237
hi.diversity:age.z											1.03	0.10	0.86 – 1.25	0.35	0.727
hi.diversity:education.z											0.93	0.07	0.80 – 1.07	-0.94	0.346
Observations	909					909					909
R² Nagelkerke	0.416					0.416					0.416

## just a model to help make sense of the CMC x DMC interaction!
d$vaxx.num <- as.numeric(d$vaxxBehavior)

m.test <- lm(vaxx.num ~ (bias.mean.3.20.z * bias.sd.3.20.z)  +
               (bias.mean.3.20.z + bias.sd.3.20.z) * 
               (crt.z + ideology.z + white_.5 + age.z + education.z), data = d)

interact_plot(m.test, bias.mean.3.20.z, bias.sd.3.20.z)

- Figure 1A

(Fig1A <- ggpredict(m.vaxx, terms = "bias.mean.3.20.z[-3, -2.5, -2, -1.5, -1, -.5, 0, .5, 1, 1.5, 2, 2.5, 3]") %>%
  ggplot(mapping = aes(x = x, y = predicted, colour = response.level, fill = response.level)) +
  geom_line(size = 1) + 
  scale_x_continuous(limits = c(-2, 2), breaks = c(-3:3)) +
  scale_y_continuous(limits = c(0, 1), breaks = c(0, .1,.2,.3,.4,.5, .6, .7, .8, .9, 1)) +
  labs(title = "Predicted Probabilities for Vaccination Behaviors",
       x = "Standardized Conservative Media Consumption",
       y = "Predicted Probability for Vaccination Behavior Choice") +
  theme_minimal() +
  theme(plot.title = element_text(size = 12)) + labs(colour = "Vaccination Behavior") +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.2, colour = NA))

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Removed 12 rows containing missing values or values outside the scale range
## (`geom_line()`).

- Figure 1B

(Fig1B <- ggpredict(m.vaxx, terms = "bias.sd.3.20.z[-3, -2.5, -2, -1.5, -1, -.5, 0, .5, 1, 1.5, 2, 2.5, 3]") %>%
  ggplot(mapping = aes(x = x, y = predicted, colour = response.level, fill = response.level)) +
  geom_line(size = 1) + 
  scale_x_continuous(limits = c(-2, 2), breaks = c(-3:3)) +
  scale_y_continuous(limits = c(0, 1), breaks = c(0, .1,.2,.3,.4,.5, .6, .7, .8, .9, 1)) +
  labs(title = "Predicted Probabilities for Vaccination Behaviors",
       x = "Standardized Diversity of Media Consumption",
       y = "Predicted Probability for Vaccination Behavior Choice") +
  theme_minimal() +
  theme(plot.title = element_text(size = 12)) + labs(colour = "Vaccination Behavior") +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.2, colour = NA))

## Warning: Removed 12 rows containing missing values or values outside the scale range
## (`geom_line()`).

4. Trust in Science (Table 2B)

m2.med <- lm(trustSci.z ~ (bias.mean.3.20.z * bias.sd.3.20.z)  +
               (bias.mean.3.20.z + bias.sd.3.20.z) * 
               (crt.z + ideology.z + white_.5 + age.z + education.z), data = d)

tab_model(m2.med,
          string.est = "Est",
          dv.labels = "Standardized Trust in Science",
          pred.labels = c("Intercept", 
                          "Conservative Media Consumption (CMC)", 
                          "Diversity of Media Consumption (DMC)",
                          "Cognitive Reflection (CRT)", 
                          "Ideology",
                          "Ethnicity",
                          "Age",
                          "Education",
                          
                          "CMC x DMC",
                          "CMC x CRT",
                          "CMC x Ideology",
                          "CMC x Ethnicity",
                          "CMC x Age",
                          "CMC x Education",
                          
                          "DMC x CRT",
                          "DMC x Ideology",
                          "DMC x Ethnicity",
                          "DMC x Age",
                          "DMC x Education"),
          show.stat = T,
          show.se = T,
          string.se = "SE",
          string.stat = "t",
          digits = 2)

	Standardized Trust in Science
Predictors	Est	SE	CI	t	p
Intercept	0.02	0.04	-0.05 – 0.09	0.64	0.521
Conservative Media Consumption (CMC)	-0.53	0.04	-0.62 – -0.44	-11.94	<0.001
Diversity of Media Consumption (DMC)	-0.07	0.03	-0.14 – -0.01	-2.15	0.032
Cognitive Reflection (CRT)	0.10	0.03	0.04 – 0.16	3.40	0.001
Ideology	-0.23	0.03	-0.28 – -0.17	-7.49	<0.001
Ethnicity	0.19	0.06	0.07 – 0.32	3.05	0.002
Age	0.07	0.03	0.01 – 0.13	2.33	0.020
Education	0.06	0.03	-0.00 – 0.12	1.89	0.059
CMC x DMC	0.21	0.04	0.12 – 0.30	4.76	<0.001
CMC x CRT	-0.01	0.03	-0.07 – 0.04	-0.46	0.646
CMC x Ideology	-0.01	0.03	-0.06 – 0.04	-0.43	0.668
CMC x Ethnicity	-0.08	0.06	-0.20 – 0.05	-1.23	0.218
CMC x Age	0.06	0.03	-0.00 – 0.12	1.93	0.054
CMC x Education	0.02	0.04	-0.05 – 0.09	0.66	0.508
DMC x CRT	0.03	0.03	-0.04 – 0.09	0.85	0.396
DMC x Ideology	0.05	0.03	-0.00 – 0.11	1.81	0.070
DMC x Ethnicity	-0.08	0.06	-0.20 – 0.04	-1.33	0.182
DMC x Age	0.10	0.03	0.04 – 0.16	3.17	0.002
DMC x Education	-0.04	0.02	-0.09 – 0.00	-1.77	0.077
Observations	908
R² / R² adjusted	0.396 / 0.383

round(etaSquared(m2.med),3)

##                                 eta.sq eta.sq.part
## bias.mean.3.20.z                 0.112       0.156
## bias.sd.3.20.z                   0.003       0.005
## crt.z                            0.007       0.012
## ideology.z                       0.038       0.060
## white_.5                         0.007       0.011
## age.z                            0.003       0.005
## education.z                      0.002       0.003
## bias.mean.3.20.z:bias.sd.3.20.z  0.015       0.025
## bias.mean.3.20.z:crt.z           0.000       0.000
## bias.mean.3.20.z:ideology.z      0.000       0.000
## bias.mean.3.20.z:white_.5        0.001       0.002
## bias.mean.3.20.z:age.z           0.003       0.004
## bias.mean.3.20.z:education.z     0.000       0.000
## bias.sd.3.20.z:crt.z             0.000       0.001
## bias.sd.3.20.z:ideology.z        0.002       0.004
## bias.sd.3.20.z:white_.5          0.001       0.002
## bias.sd.3.20.z:age.z             0.007       0.011
## bias.sd.3.20.z:education.z       0.002       0.004

- DMC x CMC

m2.low <- lm(trustSci.z ~ (bias.mean.3.20.z * low.diversity)  +
               (bias.mean.3.20.z + low.diversity) * 
               (crt.z + ideology.z + white_.5 + age.z + education.z), data = d)

m2.hi <- lm(trustSci.z ~ (bias.mean.3.20.z * hi.diversity)  +
               (bias.mean.3.20.z + hi.diversity) * 
               (crt.z + ideology.z + white_.5 + age.z + education.z), data = d)

tab_model(m2.low, m2.hi,
          string.est = "Est",
          dv.labels = "Standardized Trust in Science",
          pred.labels = c("Intercept", 
                          "Conservative Media Consumption (CMC)", 
                          "Diversity of Media Consumption (DMC)",
                          "Cognitive Reflection (CRT)", 
                          "Ideology",
                          "Ethnicity",
                          "Age",
                          "Education",
                          
                          "CMC x DMC",
                          "CMC x CRT",
                          "CMC x Ideology",
                          "CMC x Ethnicity",
                          "CMC x Age",
                          "CMC x Education",
                          
                          "DMC x CRT",
                          "DMC x Ideology",
                          "DMC x Ethnicity",
                          "DMC x Age",
                          "DMC x Education"),
          show.stat = T,
          show.se = T,
          string.se = "SE",
          string.stat = "t",
          digits = 2)

## Length of `pred.labels` does not equal number of predictors, no labelling applied.

	Standardized Trust in Science
Predictors	Est	SE	CI	t	p	Est	SE	CI	t	p
(Intercept)	0.09	0.05	0.00 – 0.19	1.99	0.047	-0.05	0.05	-0.15 – 0.05	-0.96	0.339
bias.mean.3.20.z	-0.74	0.08	-0.89 – -0.59	-9.63	<0.001	-0.32	0.04	-0.41 – -0.23	-7.27	<0.001
low.diversity	-0.07	0.03	-0.14 – -0.01	-2.15	0.032
crt.z	0.07	0.04	-0.01 – 0.15	1.80	0.073	0.13	0.05	0.04 – 0.22	2.73	0.006
ideology.z	-0.28	0.04	-0.36 – -0.20	-6.76	<0.001	-0.17	0.04	-0.26 – -0.09	-4.24	<0.001
white_.5	0.27	0.09	0.10 – 0.45	3.05	0.002	0.11	0.08	-0.05 – 0.28	1.35	0.176
age.z	-0.03	0.04	-0.11 – 0.05	-0.71	0.478	0.17	0.04	0.08 – 0.26	3.74	<0.001
education.z	0.10	0.04	0.02 – 0.19	2.33	0.020	0.02	0.04	-0.05 – 0.08	0.44	0.658
bias.mean.3.20.z:low.diversity	0.21	0.04	0.12 – 0.30	4.76	<0.001
bias.mean.3.20.z:crt.z	-0.01	0.03	-0.07 – 0.04	-0.46	0.646	-0.01	0.03	-0.07 – 0.04	-0.46	0.646
bias.mean.3.20.z:ideology.z	-0.01	0.03	-0.06 – 0.04	-0.43	0.668	-0.01	0.03	-0.06 – 0.04	-0.43	0.668
bias.mean.3.20.z:white_.5	-0.08	0.06	-0.20 – 0.05	-1.23	0.218	-0.08	0.06	-0.20 – 0.05	-1.23	0.218
bias.mean.3.20.z:age.z	0.06	0.03	-0.00 – 0.12	1.93	0.054	0.06	0.03	-0.00 – 0.12	1.93	0.054
bias.mean.3.20.z:education.z	0.02	0.04	-0.05 – 0.09	0.66	0.508	0.02	0.04	-0.05 – 0.09	0.66	0.508
low.diversity:crt.z	0.03	0.03	-0.04 – 0.09	0.85	0.396
low.diversity:ideology.z	0.05	0.03	-0.00 – 0.11	1.81	0.070
low.diversity:white_.5	-0.08	0.06	-0.20 – 0.04	-1.33	0.182
low.diversity:age.z	0.10	0.03	0.04 – 0.16	3.17	0.002
low.diversity:education.z	-0.04	0.02	-0.09 – 0.00	-1.77	0.077
hi.diversity						-0.07	0.03	-0.14 – -0.01	-2.15	0.032
bias.mean.3.20.z:hi.diversity						0.21	0.04	0.12 – 0.30	4.76	<0.001
hi.diversity:crt.z						0.03	0.03	-0.04 – 0.09	0.85	0.396
hi.diversity:ideology.z						0.05	0.03	-0.00 – 0.11	1.81	0.070
hi.diversity:white_.5						-0.08	0.06	-0.20 – 0.04	-1.33	0.182
hi.diversity:age.z						0.10	0.03	0.04 – 0.16	3.17	0.002
hi.diversity:education.z						-0.04	0.02	-0.09 – 0.00	-1.77	0.077
Observations	908					908
R² / R² adjusted	0.396 / 0.383					0.396 / 0.383

- Figure 2

#create plot
(p2 <- plot_model(m2.med, type = "pred", terms = c("bias.mean.3.20.z", "bias.sd.3.20.z [-1, 0, 1]")) +
    ggtitle("") + 
    ylab("") +
    xlab("Conservative Media Consumption") +
    xlim(-2, 2) +
    ylim(-3, 2) +
    theme(panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank(),
          panel.background = element_blank(), 
          axis.line = element_line(colour = "black"),
          legend.position = c(0.8, 0.8), 
          legend.background = element_rect(fill = "white", color = "white"),
          legend.title = element_blank()) + 
  scale_color_manual( labels = c("Low (-1 SD)", "Mean", "High (+1 SD)"), 
                       values = c("blue", "purple", "red")) +

  scale_fill_manual(values = c("blue", "purple", "red")) +
  
  scale_y_continuous(breaks = c(-2.993, -1.775, -0.558, 0.660, 1.878), 
                    limits = c(-3.193, 2.2),
                    labels = c("Strongly distrust", 
                                "Moderately distrust",
                                "Neither trust nor distrust",
                                "Moderately trust",
                                "Strongly trust"))
)

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_line()`).

5. Long - Vaccination Behavior (Table 3)

m.vaxx.long <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.w1w2.15.z * bias.sd.w1w2.15.z) + 
                         (bias.mean.3.20.z + bias.sd.3.20.z + bias.mean.w1w2.15.z + bias.sd.w1w2.15.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z) +
                         vaxxIntent.w1w2.z, 
                     data = d, Hess = TRUE)

tab_model(m.vaxx.long,
          string.est = "OR",
          dv.labels = "Standardized Vaccination Behavior",
          pred.labels = c("1|2", 
                          "2|3", 
                          "Conservative Media Consumption 2022 (CMC 2022)", 
                          "Diversity of Media Consumption 2022 (DMC 2022)",
                          "Conservative Media Consumption 2020 (CMC 2020)",
                          "Diversity of Media Consumption 2020 (DMC 2020)",
                          
                          "Cognitive Reflection (CRT)", 
                          "Ideology",
                          "Ethnicity",
                          "Age",
                          "Education",
                          
                          "Vaccination Intention 2020",
                          
                          "CMC 2022 x DMC22",
                          "CMC 2020 x DMC20",
                          
                          "CMC 2022 x CRT",
                          "CMC 2022 x Ideology",
                          "CMC 2022 x Ethnicity",
                          "CMC 2022 x Age",
                          "CMC 2022 x Education",
                          
                          "DMC 2022 x CRT",
                          "DMC 2022 x Ideology",
                          "DMC 2022 x Ethnicity",
                          "DMC 2022 x Age",
                          "DMC 2022 x Education",
                          
                          "CMC 2020 x CRT",
                          "CMC 2020 x Ideology",
                          "CMC 2020 x Ethnicity",
                          "CMC 2020 x Age",
                          "CMC 2020 x Education",
                          
                          "DMC 2020 x CRT",
                          "DMC 2020 x Ideology",
                          "DMC 2020 x Ethnicity",
                          "DMC 2020 x Age",
                          "DMC 2020 x Education"),
          show.stat = T,
          show.se = T,
          string.se = "SE",
          string.stat = "t",
          digits = 2)

	Standardized Vaccination Behavior
Predictors	OR	SE	CI	t	p
1\|2	0.11	0.02	0.08 – 0.15	-14.28	<0.001
2\|3	0.44	0.06	0.34 – 0.56	-6.38	<0.001
Conservative Media Consumption 2022 (CMC 2022)	0.56	0.10	0.39 – 0.78	-3.34	0.001
Diversity of Media Consumption 2022 (DMC 2022)	1.52	0.21	1.16 – 2.00	3.01	0.003
Conservative Media Consumption 2020 (CMC 2020)	0.89	0.18	0.60 – 1.32	-0.59	0.552
Diversity of Media Consumption 2020 (DMC 2020)	0.78	0.11	0.59 – 1.02	-1.78	0.076
Cognitive Reflection (CRT)	1.07	0.10	0.89 – 1.30	0.74	0.460
Ideology	0.83	0.08	0.68 – 1.00	-1.97	0.049
Ethnicity	0.79	0.16	0.54 – 1.16	-1.19	0.234
Age	1.55	0.15	1.28 – 1.87	4.50	<0.001
Education	1.35	0.13	1.12 – 1.62	3.14	0.002
Vaccination Intention 2020	3.02	0.27	2.53 – 3.62	12.13	<0.001
CMC 2022 x DMC22	1.29	0.20	0.94 – 1.75	1.60	0.111
CMC 2020 x DMC20	1.39	0.15	1.13 – 1.71	3.14	0.002
CMC 2022 x CRT	0.85	0.11	0.65 – 1.10	-1.27	0.206
CMC 2022 x Ideology	0.97	0.12	0.75 – 1.25	-0.27	0.785
CMC 2022 x Ethnicity	1.60	0.48	0.89 – 2.88	1.58	0.115
CMC 2022 x Age	0.67	0.10	0.50 – 0.89	-2.76	0.006
CMC 2022 x Education	0.99	0.15	0.75 – 1.33	-0.05	0.957
DMC 2022 x CRT	1.06	0.13	0.83 – 1.36	0.46	0.647
DMC 2022 x Ideology	0.83	0.10	0.64 – 1.06	-1.51	0.130
DMC 2022 x Ethnicity	0.84	0.20	0.52 – 1.34	-0.74	0.462
DMC 2022 x Age	1.04	0.13	0.81 – 1.35	0.34	0.736
DMC 2022 x Education	0.88	0.11	0.69 – 1.11	-1.03	0.305
CMC 2020 x CRT	1.35	0.21	1.00 – 1.82	1.97	0.049
CMC 2020 x Ideology	1.02	0.15	0.77 – 1.35	0.12	0.902
CMC 2020 x Ethnicity	0.44	0.15	0.22 – 0.86	-2.39	0.017
CMC 2020 x Age	1.20	0.18	0.89 – 1.63	1.22	0.225
CMC 2020 x Education	0.90	0.14	0.66 – 1.23	-0.64	0.524
DMC 2020 x CRT	1.12	0.13	0.90 – 1.41	1.02	0.310
DMC 2020 x Ideology	1.35	0.16	1.08 – 1.71	2.57	0.010
DMC 2020 x Ethnicity	0.54	0.15	0.31 – 0.91	-2.30	0.022
DMC 2020 x Age	1.09	0.13	0.87 – 1.39	0.75	0.454
DMC 2020 x Education	0.99	0.12	0.79 – 1.25	-0.04	0.966
Observations	909
R² Nagelkerke	0.570

6. Robust check - Vaccination behavior (Supp Table 3)

m.analytic <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * analytic.mean.3.12.z) + 
                         (bias.sd.3.20.z * analytic.mean.3.12.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + analytic.mean.3.12.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

m.threat <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * threat.mean.3.12.z) + 
                         (threat.mean.3.12.z * bias.sd.3.20.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + threat.mean.3.12.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

m.posEmo <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * posEmo.mean.3.12.z) + 
                         (posEmo.mean.3.12.z * bias.sd.3.20.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + posEmo.mean.3.12.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

m.negEmo <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * negEmo.mean.3.12.z) + 
                         (negEmo.mean.3.12.z * bias.sd.3.20.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + negEmo.mean.3.12.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

m.affect <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * affect.mean.3.12.z) + 
                         (affect.mean.3.12.z * bias.sd.3.20.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + affect.mean.3.12.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

m.clout <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * clout.mean.3.12.z) + 
                         (clout.mean.3.12.z * bias.sd.3.20.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + clout.mean.3.12.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

m.authentic <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * authentic.mean.3.12.z) + 
                         (authentic.mean.3.12.z * bias.sd.3.20.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + authentic.mean.3.12.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

m.reliability <- polr(vaxxBehavior ~ (bias.mean.3.20.z * bias.sd.3.20.z) + 
                         (bias.mean.3.20.z * rel.mean.3.19.z) + 
                         (rel.mean.3.19.z * bias.sd.3.20.z) +
                         (bias.mean.3.20.z + bias.sd.3.20.z + rel.mean.3.19.z) * 
                         (crt.z + ideology.z + white_.5 + age.z + education.z), 
                     data = d, Hess = TRUE)

tab_model(m.reliability,
          show.se = T, 
          show.stat = T)

	vaxxBehavior
Predictors	Odds Ratios	std. Error	CI	Statistic	p
1\|2	0.19	0.02	0.15 – 0.24	-13.21	<0.001
2\|3	0.54	0.06	0.43 – 0.67	-5.47	<0.001
bias mean 3 20 z	0.73	0.15	0.48 – 1.09	-1.55	0.122
bias sd 3 20 z	1.63	0.18	1.32 – 2.02	4.48	<0.001
rel mean 3 19 z	1.50	0.29	1.03 – 2.20	2.10	0.036
crt z	1.23	0.11	1.04 – 1.47	2.34	0.019
ideology z	0.76	0.07	0.64 – 0.91	-3.10	0.002
white 5	0.91	0.16	0.64 – 1.29	-0.54	0.590
age z	1.75	0.16	1.47 – 2.09	6.27	<0.001
education z	1.35	0.12	1.14 – 1.61	3.44	0.001
bias mean 3 20 z × bias sd 3 20 z	1.93	0.48	1.19 – 3.14	2.66	0.008
bias mean 3 20 z × rel mean 3 19 z	1.03	0.08	0.88 – 1.19	0.37	0.711
bias sd 3 20 z × rel mean 3 19 z	1.38	0.32	0.88 – 2.19	1.39	0.164
bias mean 3 20 z × crt z	1.07	0.19	0.75 – 1.50	0.37	0.714
bias mean 3 20 z × ideology z	1.06	0.17	0.77 – 1.46	0.34	0.736
bias mean 3 20 z × white 5	0.80	0.29	0.39 – 1.64	-0.61	0.540
bias mean 3 20 z × age z	1.03	0.19	0.71 – 1.49	0.18	0.859
bias mean 3 20 z × education z	1.05	0.19	0.73 – 1.51	0.27	0.789
bias sd 3 20 z × crt z	1.05	0.10	0.87 – 1.26	0.52	0.604
bias sd 3 20 z × ideology z	1.10	0.09	0.93 – 1.29	1.12	0.265
bias sd 3 20 z × white 5	0.80	0.14	0.57 – 1.13	-1.24	0.215
bias sd 3 20 z × age z	1.06	0.10	0.88 – 1.28	0.60	0.546
bias sd 3 20 z × education z	0.95	0.07	0.81 – 1.10	-0.67	0.506
rel mean 3 19 z × crt z	1.09	0.19	0.76 – 1.54	0.47	0.640
rel mean 3 19 z × ideology z	1.12	0.19	0.80 – 1.56	0.65	0.518
rel mean 3 19 z × white 5	0.90	0.34	0.43 – 1.86	-0.29	0.771
rel mean 3 19 z × age z	1.12	0.21	0.78 – 1.61	0.63	0.529
rel mean 3 19 z × education z	1.12	0.20	0.78 – 1.61	0.65	0.518
Observations	909
R² Nagelkerke	0.425

tab_model(m.affect, m.analytic, m.authentic, m.clout, m.posEmo, m.negEmo, m.threat, m.reliability,
          dv.labels = c("affect", "analytic", "authentic", "clout", 
                        "positive emotion", "negative emotion", "threat", "reliability"),
          title = "Outcome: Vaccination Behavior",
          pred.labels = c("Intercept"), 
          string.est = "Est",
          show.stat = F,
          show.se = F,
          show.ci = F,
          string.stat = "t",
          digits = 2)

## Length of `pred.labels` does not equal number of predictors, no labelling applied.

Outcome: Vaccination Behavior
	affect		analytic		authentic		clout		positive emotion		negative emotion		threat		reliability
Predictors	Est	p	Est	p	Est	p	Est	p	Est	p	Est	p	Est	p	Est	p
1\|2	0.19	<0.001	0.20	<0.001	0.19	<0.001	0.20	<0.001	0.19	<0.001	0.19	<0.001	0.20	<0.001	0.19	<0.001
2\|3	0.55	<0.001	0.57	<0.001	0.55	<0.001	0.57	<0.001	0.56	<0.001	0.55	<0.001	0.57	<0.001	0.54	<0.001
bias.mean.3.20.z	0.50	<0.001	0.51	<0.001	0.52	<0.001	0.52	<0.001	0.51	<0.001	0.50	<0.001	0.50	<0.001	0.73	0.122
bias.sd.3.20.z	1.56	<0.001	1.47	0.001	1.53	<0.001	1.54	<0.001	1.50	<0.001	1.57	<0.001	1.55	<0.001	1.63	<0.001
affect.mean.3.12.z	0.92	0.427
crt.z	1.24	0.016	1.24	0.014	1.23	0.020	1.24	0.014	1.24	0.013	1.23	0.020	1.23	0.017	1.23	0.019
ideology.z	0.77	0.003	0.76	0.002	0.76	0.001	0.76	0.002	0.77	0.003	0.76	0.002	0.77	0.002	0.76	0.002
white_.5	0.94	0.728	0.90	0.565	0.93	0.701	0.92	0.655	0.93	0.663	0.94	0.710	0.94	0.722	0.91	0.590
age.z	1.77	<0.001	1.77	<0.001	1.76	<0.001	1.77	<0.001	1.78	<0.001	1.77	<0.001	1.75	<0.001	1.75	<0.001
education.z	1.35	0.001	1.36	0.001	1.36	0.001	1.36	0.001	1.36	0.001	1.35	0.001	1.35	0.001	1.35	0.001
bias.mean.3.20.z:bias.sd.3.20.z	1.53	0.002	1.48	0.005	1.45	0.010	1.48	0.006	1.53	0.002	1.53	0.002	1.45	0.008	1.93	0.008
bias.mean.3.20.z:affect.mean.3.12.z	1.14	0.170
bias.sd.3.20.z:affect.mean.3.12.z	1.03	0.798
bias.mean.3.20.z:crt.z	1.04	0.703	1.03	0.721	1.02	0.822	1.03	0.779	1.05	0.596	1.01	0.890	1.02	0.804	1.07	0.714
bias.mean.3.20.z:ideology.z	0.90	0.253	0.90	0.249	0.88	0.187	0.93	0.416	0.90	0.221	0.91	0.311	0.92	0.324	1.06	0.736
bias.mean.3.20.z:white_.5	0.84	0.418	0.80	0.288	0.94	0.796	0.81	0.343	0.80	0.284	0.87	0.516	0.82	0.331	0.80	0.540
bias.mean.3.20.z:age.z	0.98	0.833	0.87	0.186	0.95	0.652	0.99	0.919	0.95	0.619	0.97	0.787	0.90	0.291	1.03	0.859
bias.mean.3.20.z:education.z	0.94	0.561	0.94	0.544	0.91	0.403	0.94	0.573	0.95	0.648	0.93	0.549	0.92	0.423	1.05	0.789
bias.sd.3.20.z:crt.z	1.07	0.490	1.05	0.593	1.05	0.577	1.05	0.574	1.07	0.489	1.06	0.520	1.07	0.478	1.05	0.604
bias.sd.3.20.z:ideology.z	1.08	0.339	1.08	0.328	1.07	0.389	1.08	0.323	1.08	0.342	1.09	0.316	1.08	0.365	1.10	0.265
bias.sd.3.20.z:white_.5	0.82	0.261	0.82	0.245	0.85	0.350	0.81	0.239	0.82	0.251	0.82	0.244	0.81	0.233	0.80	0.215
bias.sd.3.20.z:age.z	1.06	0.525	0.97	0.782	1.06	0.538	1.03	0.747	1.06	0.556	1.06	0.565	0.98	0.807	1.06	0.546
bias.sd.3.20.z:education.z	0.93	0.313	0.93	0.331	0.92	0.287	0.93	0.339	0.93	0.305	0.93	0.310	0.92	0.294	0.95	0.506
affect.mean.3.12.z:crt.z	0.99	0.872
affect.mean.3.12.z:ideology.z	0.89	0.177
affect.mean.3.12.z:white_.5	1.20	0.348
affect.mean.3.12.z:age.z	1.25	0.019
affect.mean.3.12.z:education.z	0.94	0.552
analytic.mean.3.12.z			1.03	0.780
bias.mean.3.20.z:analytic.mean.3.12.z			0.88	0.162
bias.sd.3.20.z:analytic.mean.3.12.z			0.98	0.868
analytic.mean.3.12.z:crt.z			1.00	0.972
analytic.mean.3.12.z:ideology.z			1.03	0.736
analytic.mean.3.12.z:white_.5			1.03	0.869
analytic.mean.3.12.z:age.z			0.76	0.005
analytic.mean.3.12.z:education.z			1.04	0.677
authentic.mean.3.12.z					0.99	0.942
bias.mean.3.20.z:authentic.mean.3.12.z					0.96	0.569
bias.sd.3.20.z:authentic.mean.3.12.z					1.07	0.564
authentic.mean.3.12.z:crt.z					0.98	0.830
authentic.mean.3.12.z:ideology.z					1.13	0.195
authentic.mean.3.12.z:white_.5					0.83	0.390
authentic.mean.3.12.z:age.z					0.94	0.581
authentic.mean.3.12.z:education.z					1.12	0.374
clout.mean.3.12.z							1.00	0.967
bias.mean.3.20.z:clout.mean.3.12.z							1.09	0.343
bias.sd.3.20.z:clout.mean.3.12.z							1.03	0.814
clout.mean.3.12.z:crt.z							0.99	0.937
clout.mean.3.12.z:ideology.z							0.96	0.663
clout.mean.3.12.z:white_.5							1.03	0.888
clout.mean.3.12.z:age.z							1.28	0.023
clout.mean.3.12.z:education.z							0.98	0.845
posEmo.mean.3.12.z									0.94	0.552
bias.mean.3.20.z:posEmo.mean.3.12.z									1.17	0.080
bias.sd.3.20.z:posEmo.mean.3.12.z									0.95	0.618
posEmo.mean.3.12.z:crt.z									1.01	0.896
posEmo.mean.3.12.z:ideology.z									0.90	0.191
posEmo.mean.3.12.z:white_.5									1.08	0.667
posEmo.mean.3.12.z:age.z									1.28	0.010
posEmo.mean.3.12.z:education.z									0.93	0.407
negEmo.mean.3.12.z											0.93	0.455
bias.mean.3.20.z:negEmo.mean.3.12.z											1.09	0.389
bias.sd.3.20.z:negEmo.mean.3.12.z											1.10	0.413
negEmo.mean.3.12.z:crt.z											0.96	0.659
negEmo.mean.3.12.z:ideology.z											0.89	0.207
negEmo.mean.3.12.z:white_.5											1.26	0.239
negEmo.mean.3.12.z:age.z											1.20	0.053
negEmo.mean.3.12.z:education.z											0.97	0.795
threat.mean.3.12.z													1.06	0.571
bias.mean.3.20.z:threat.mean.3.12.z													0.89	0.220
bias.sd.3.20.z:threat.mean.3.12.z													0.88	0.246
threat.mean.3.12.z:crt.z													1.07	0.433
threat.mean.3.12.z:ideology.z													1.02	0.834
threat.mean.3.12.z:white_.5													0.97	0.872
threat.mean.3.12.z:age.z													0.76	0.003
threat.mean.3.12.z:education.z													0.99	0.884
rel.mean.3.19.z															1.50	0.036
bias.mean.3.20.z:rel.mean.3.19.z															1.03	0.711
bias.sd.3.20.z:rel.mean.3.19.z															1.38	0.164
rel.mean.3.19.z:crt.z															1.09	0.640
rel.mean.3.19.z:ideology.z															1.12	0.518
rel.mean.3.19.z:white_.5															0.90	0.771
rel.mean.3.19.z:age.z															1.12	0.529
rel.mean.3.19.z:education.z															1.12	0.518
Observations	909		909		909		909		909		909		909		909
R² Nagelkerke	0.424		0.424		0.420		0.421		0.426		0.421		0.425		0.425

7. US vs. UK correlation

cor.test(dUK$ideology.z, dUK$bias.sd.z)

## 
##  Pearson's product-moment correlation
## 
## data:  dUK$ideology.z and dUK$bias.sd.z
## t = 0.67006, df = 1500, p-value = 0.5029
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03331049  0.06781878
## sample estimates:
##        cor 
## 0.01729839

cor.test(dUK$vaxxIntentions.z, dUK$bias.sd.z)

## 
##  Pearson's product-moment correlation
## 
## data:  dUK$vaxxIntentions.z and dUK$bias.sd.z
## t = 2.0983, df = 1500, p-value = 0.03605
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.003527728 0.104391894
## sample estimates:
##        cor 
## 0.05409781

cor.test(dUK$vaxxIntentions.z, dUK$bias.mean.z)

## 
##  Pearson's product-moment correlation
## 
## data:  dUK$vaxxIntentions.z and dUK$bias.mean.z
## t = -4.7414, df = 1500, p-value = 2.324e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.17104469 -0.07137521
## sample estimates:
##        cor 
## -0.1215162

mean(as.numeric(dUK$age), na.rm = T) #35.35

## Warning in mean(as.numeric(dUK$age), na.rm = T): NAs introduced by coercion

## [1] 35.35425

sd(as.numeric(dUK$age), na.rm = T) #13.35

## Warning in is.data.frame(x): NAs introduced by coercion

## [1] 13.34589

mean(as.numeric(dUS$age), na.rm = T) #48.23

## [1] 48.22771

sd(as.numeric(dUS$age), na.rm = T) #15.55

## [1] 15.55155

8. US vs. UK Vaccine Intentions (Supp Table 4)

m1 <- lm(vaxxIntentions.z ~ 
           (bias.mean.z * bias.sd.z * USvUK) +
           (bias.mean.z + bias.sd.z + USvUK) * 
           (ideology.z + age.z + education.z), data = d2)

m1.us <- lm(vaxxIntentions.z ~ 
           (bias.mean.z * bias.sd.z * US_0) +
           (bias.mean.z + bias.sd.z + US_0) * 
           (ideology.z + age.z + education.z), data = d2)

m1.uk <- lm(vaxxIntentions.z ~ 
           (bias.mean.z * bias.sd.z * UK_0) +
           (bias.mean.z + bias.sd.z + UK_0) * 
           (ideology.z + age.z + education.z), data = d2)


tab_model(m1, m1.us, m1.uk,
          dv.labels = c("Country (US = -.5, UK = .5)", "United States (US = 0, UK = 1)", "United Kingdom (US = 1, UK = 0)"),
          title = "Outcome: Vaccine Intention",
          pred.labels = c("Intercept", 
                          "Conservative Media Consumption (CMC)", 
                          "Diversity ofMedia Consumption (DMC)",
                          "Country",
                          "Ideology",
                          "Age",
                          "Education",
                          
                          "CMC x DMC",
                          
                          "CMC x Country",
                          "DMC x Country",
                          
                          "CMC x Ideology",
                          "CMC x Age",
                          "CMC x Education",
                          
                          "DMC x Ideology",
                          "DMC x Age",
                          "DMC x Education",
                          
                          "Country x Ideology",
                          "Country x Age",
                          "Country x Education",
                          "CMC x DMC x Country",
                          
                          "US",
                          "CMC x US",
                          "DMC x US",
                          "US x Ideology",
                          "US x Age",
                          "US x Education",
                          "CMC x DMC x US",
                          
                          "UK",
                          "CMC x UK",
                          "DMC x UK",
                          "UK x Ideology",
                          "UK x Age",
                          "UK x Education",
                          "CMC x DMC x UK"
                          ),
          string.est = "Est",
          show.stat = T,
          show.se = T,
          show.ci = F,
          string.se = "SE",
          string.stat = "t",
          digits = 2)

Outcome: Vaccine Intention
	Country (US = -.5, UK = .5)				United States (US = 0, UK = 1)				United Kingdom (US = 1, UK = 0)
Predictors	Est	SE	t	p	Est	SE	t	p	Est	SE	t	p
Intercept	0.02	0.02	1.06	0.291	0.03	0.02	1.44	0.151	0.01	0.03	0.24	0.807
Conservative Media Consumption (CMC)	-0.16	0.03	-6.00	<0.001	-0.21	0.03	-7.33	<0.001	-0.11	0.04	-2.45	0.014
Diversity ofMedia Consumption (DMC)	0.06	0.02	3.14	0.002	0.04	0.02	1.96	0.050	0.07	0.03	2.49	0.013
Country	-0.02	0.03	-0.73	0.465
Ideology	-0.13	0.02	-7.84	<0.001	-0.15	0.02	-7.50	<0.001	-0.12	0.03	-4.25	<0.001
Age	0.10	0.02	6.16	<0.001	0.17	0.02	9.72	<0.001	0.02	0.03	0.75	0.454
Education	0.04	0.02	2.33	0.020	0.09	0.02	4.93	<0.001	-0.02	0.03	-0.69	0.489
CMC x DMC	0.02	0.02	0.76	0.445	0.06	0.02	3.10	0.002	-0.03	0.04	-0.67	0.504
CMC x Country	0.10	0.05	1.89	0.059
DMC x Country	0.03	0.04	0.78	0.437
CMC x Ideology	0.01	0.01	0.69	0.489	0.01	0.01	0.69	0.489	0.01	0.01	0.69	0.489
CMC x Age	0.05	0.02	3.40	0.001	0.05	0.02	3.40	0.001	0.05	0.02	3.40	0.001
CMC x Education	0.01	0.01	0.87	0.386	0.01	0.01	0.87	0.386	0.01	0.01	0.87	0.386
DMC x Ideology	0.08	0.02	4.96	<0.001	0.08	0.02	4.96	<0.001	0.08	0.02	4.96	<0.001
DMC x Age	-0.00	0.02	-0.20	0.839	-0.00	0.02	-0.20	0.839	-0.00	0.02	-0.20	0.839
DMC x Education	-0.03	0.01	-2.21	0.027	-0.03	0.01	-2.21	0.027	-0.03	0.01	-2.21	0.027
Country x Ideology	0.03	0.03	1.01	0.311
Country x Age	-0.16	0.03	-4.98	<0.001
Country x Education	-0.11	0.03	-3.44	0.001
CMC x DMC x Country	-0.08	0.04	-1.98	0.048
US					-0.02	0.03	-0.73	0.465
CMC x US					0.10	0.05	1.89	0.059
DMC x US					0.03	0.04	0.78	0.437
US x Ideology					0.03	0.03	1.01	0.311
US x Age					-0.16	0.03	-4.98	<0.001
US x Education					-0.11	0.03	-3.44	0.001
CMC x DMC x US					-0.08	0.04	-1.98	0.048
UK									0.02	0.03	0.73	0.465
CMC x UK									-0.10	0.05	-1.89	0.059
DMC x UK									-0.03	0.04	-0.78	0.437
UK x Ideology									-0.03	0.03	-1.01	0.311
UK x Age									0.16	0.03	4.98	<0.001
UK x Education									0.11	0.03	3.44	0.001
CMC x DMC x UK									0.08	0.04	1.98	0.048
Observations	4460				4460				4460
R² / R² adjusted	0.100 / 0.097				0.100 / 0.097				0.100 / 0.097

round(etaSquared(m1),3)

##                             eta.sq eta.sq.part
## bias.mean.z                  0.012       0.013
## bias.sd.z                    0.004       0.005
## USvUK                        0.000       0.000
## ideology.z                   0.014       0.016
## age.z                        0.015       0.016
## education.z                  0.002       0.002
## bias.mean.z:bias.sd.z        0.001       0.001
## bias.mean.z:USvUK            0.000       0.000
## bias.sd.z:USvUK              0.000       0.000
## bias.mean.z:ideology.z       0.000       0.000
## bias.mean.z:age.z            0.002       0.003
## bias.mean.z:education.z      0.000       0.000
## bias.sd.z:ideology.z         0.005       0.006
## bias.sd.z:age.z              0.000       0.000
## bias.sd.z:education.z        0.001       0.001
## USvUK:ideology.z             0.000       0.000
## USvUK:age.z                  0.005       0.006
## USvUK:education.z            0.002       0.003
## bias.mean.z:bias.sd.z:USvUK  0.001       0.001

9. Measures

# vaccine intentions
mean(dUS$vaxxIntentions, na.rm = T); sd(dUS$vaxxIntentions, na.rm = T)

## [1] 0.5844539

## [1] 2.156637

mean(dUK$vaxxIntentions, na.rm = T); sd(dUK$vaxxIntentions, na.rm = T)

## [1] 1.368842

## [1] 1.812618

cor.test(af$bias.af, af$bias.as)

## 
##  Pearson's product-moment correlation
## 
## data:  af$bias.af and af$bias.as
## t = 11.454, df = 21, p-value = 1.711e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8363048 0.9695822
## sample estimates:
##       cor 
## 0.9284467

# conservative media consumption
mean(d$bias.mean.1.15, na.rm = T); sd(d$bias.mean.1.15, na.rm = T)

## [1] -0.4205296

## [1] 0.531026

mean(d$bias.mean.2.15, na.rm = T); sd(d$bias.mean.2.15, na.rm = T)

## [1] -0.4084528

## [1] 0.526748

mean(d$bias.mean.3.20, na.rm = T); sd(d$bias.mean.3.20, na.rm = T)

## [1] -0.2308115

## [1] 0.4403217

mean(dUK$bias.mean, na.rm = T); sd(dUK$bias.mean, na.rm = T)

## [1] -0.01259991

## [1] 0.3303387

# ideological diversity of media consumption
mean(d$bias.sd.1.15, na.rm = T); sd(d$bias.sd.1.15, na.rm = T)

## [1] 1.094834

## [1] 0.6386927

mean(d$bias.sd.2.15, na.rm = T); sd(d$bias.sd.2.15, na.rm = T)

## [1] 1.053586

## [1] 0.6232238

mean(d$bias.sd.3.20, na.rm = T); sd(d$bias.sd.3.20, na.rm = T)

## [1] 1.415245

## [1] 1.155824

mean(dUK$bias.mean, na.rm = T); sd(dUK$bias.sd, na.rm = T)

## [1] -0.01259991

## [1] 0.7730456

# reliability
cor.test(as.numeric(b$bias.trust), b$trust)

## Warning in cor.test(as.numeric(b$bias.trust), b$trust): NAs introduced by
## coercion

## 
##  Pearson's product-moment correlation
## 
## data:  as.numeric(b$bias.trust) and b$trust
## t = -2.4344, df = 37, p-value = 0.01986
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.61498231 -0.06348817
## sample estimates:
##        cor 
## -0.3715622

revision_analyses

dani grant

2024-03-21

0. data prep

1. descriptives

2. Spring 2022 Correlation Table (Table 1)

3. Vaccination Behavior (Table 2A)

- CMC x DMC

- Figure 1A

- Figure 1B

4. Trust in Science (Table 2B)

- DMC x CMC

- Figure 2

5. Long - Vaccination Behavior (Table 3)

6. Robust check - Vaccination behavior (Supp Table 3)

7. US vs. UK correlation

8. US vs. UK Vaccine Intentions (Supp Table 4)

9. Measures