Covid Media Manuscript Analyses for Study 1 (US vs. UK)

TO DO: - add in models with LIWC.max scores for trust in experts and vaxx intentions - re organize this document - double check work again in the prep RMD

0. import data sets

~~ DESCRIPTIVE STATS ~~

1. demographics

a. sample size

b. age

c. gender

d. party identity

e. education

2. variables of interest

a. media exposure

Not included here, media exposure asks participants to “Consider each of the media sources below. In general, how much do you get news about COVID-19 from each source?” on a scale from 1 (Not at all) to 3 (Somewhat) to 5 (A great deal).

b. average analytical media consumption (AAMC)

The first step to creating the average analytic media consumption is multiplying individual analytic thinking scores for each media outlet by participant rated exposure to that outlet, Then taking the sum of each of these products and dividing this sum by the total amount of media a participant is exposed to

fox_index = (fox participant exposure x fox analytic score) (fox_index + cnn_index + msnbc_index + …) / sum of media exposure

c. symbolic ideology

symbolic ideology is an average of three items. They ask “How liberal/conservative…” (1) in general, (2) on social issues, and (3) on economic issues. Participants answered on a scale from -3 (Very liberal) to 0 (Moderate) to +3 (Very conservative).

d. trust in experts

trust in experts is an average of 3-items asking about how much participants trust experts, medicine, economics, public health, and science. The trust response scale ranges from -3 (strongly distrust) to 0 (Neither trust nor distrust) to +3 (Strongly trust)

3. LIWC media measures

a. Analytical thinking scales

Media outlet LIWC analytic thinking scores captures the degree to which people use words that suggest formal, logical, and hierarchical thinking patterns.

i. US

iii. UK

iv. pairwise correlation for LIWC variables

- US

- UK

- both countries

4. outcome measures

Participants were asked “would you get a Covid-19 vaccine?” and answered on a scale from -3 (Definitely would not get it) to 0 (Undecided) to +3 (Definitely would get it).

5. pairwise correlation table for IVs, DVs, and covariates

a. US

b. UK

~~ ANALYSES ~~

6. Analyses for Study 1 US + UK (July-August)

a. vaxx intentions models

i. vaxxIntent ~ exp.sum * (ideology + country + age + edu)

summary(m.intent1 <- lm(vaxxIntentions ~ exp.sum.c * (USvUK + ideology.c + age.c + education.c), data = d))

## 
## Call:
## lm(formula = vaxxIntentions ~ exp.sum.c * (USvUK + ideology.c + 
##     age.c + education.c), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3709 -1.3222  0.4151  1.5270  4.8337 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.0013334  0.0312150  32.079  < 2e-16 ***
## exp.sum.c              0.0371863  0.0049488   7.514 6.88e-14 ***
## USvUK                  0.7564259  0.0623192  12.138  < 2e-16 ***
## ideology.c            -0.2338374  0.0198588 -11.775  < 2e-16 ***
## age.c                  0.0211358  0.0020119  10.505  < 2e-16 ***
## education.c            0.0596152  0.0112303   5.308 1.16e-07 ***
## exp.sum.c:USvUK       -0.0008067  0.0098026  -0.082   0.9344    
## exp.sum.c:ideology.c   0.0094591  0.0019394   4.877 1.11e-06 ***
## exp.sum.c:age.c       -0.0005223  0.0002228  -2.344   0.0191 *  
## exp.sum.c:education.c -0.0026001  0.0010096  -2.575   0.0100 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.948 on 4459 degrees of freedom
##   (100 observations deleted due to missingness)
## Multiple R-squared:  0.1245, Adjusted R-squared:  0.1227 
## F-statistic: 70.42 on 9 and 4459 DF,  p-value: < 2.2e-16

- plot exp.sum * ideology

- across country

## 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.

- US plot

## [1] -1.51398

## [1] 1.679717

## 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.

- UK plot

## [1] -1.963628

## [1] 0.7460446

## 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.

ii. vaxxIntent ~ analytic.max * (ideology + country + age + edu)

summary(m.intent2 <- lm(vaxxIntentions ~ analytic.max.c * (USvUK + ideology.c + age.c + education.c), data = d))

## 
## Call:
## lm(formula = vaxxIntentions ~ analytic.max.c * (USvUK + ideology.c + 
##     age.c + education.c), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2308 -1.2316  0.4392  1.5653  3.8144 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.0499277  0.0329277  31.886  < 2e-16 ***
## analytic.max.c              0.0058345  0.0056311   1.036 0.300209    
## USvUK                       0.7569826  0.0651917  11.612  < 2e-16 ***
## ideology.c                 -0.2258536  0.0209117 -10.800  < 2e-16 ***
## age.c                       0.0189892  0.0020943   9.067  < 2e-16 ***
## education.c                 0.0431561  0.0116315   3.710 0.000210 ***
## analytic.max.c:USvUK       -0.0410463  0.0112307  -3.655 0.000261 ***
## analytic.max.c:ideology.c   0.0004638  0.0027138   0.171 0.864314    
## analytic.max.c:age.c       -0.0006315  0.0002726  -2.316 0.020600 *  
## analytic.max.c:education.c -0.0012022  0.0015043  -0.799 0.424240    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.945 on 4091 degrees of freedom
##   (468 observations deleted due to missingness)
## Multiple R-squared:  0.08886,    Adjusted R-squared:  0.08685 
## F-statistic: 44.33 on 9 and 4091 DF,  p-value: < 2.2e-16

iii. vaxxIntent ~ AAMC * (ideology + country + age + edu)

summary(m.intent3 <- lm(vaxxIntentions ~ AAMC.c * (USvUK + ideology.c + age.c + education.c), data = d))

## 
## Call:
## lm(formula = vaxxIntentions ~ AAMC.c * (USvUK + ideology.c + 
##     age.c + education.c), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2604 -1.2285  0.4271  1.5643  4.3599 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         1.0384686  0.0330778  31.395  < 2e-16 ***
## AAMC.c              0.0167468  0.0076492   2.189 0.028627 *  
## USvUK               0.7700580  0.0649488  11.856  < 2e-16 ***
## ideology.c         -0.2167702  0.0209511 -10.346  < 2e-16 ***
## age.c               0.0195958  0.0020977   9.342  < 2e-16 ***
## education.c         0.0427614  0.0116636   3.666 0.000249 ***
## AAMC.c:USvUK       -0.0754037  0.0149618  -5.040 4.86e-07 ***
## AAMC.c:ideology.c  -0.0053693  0.0039380  -1.363 0.172814    
## AAMC.c:age.c       -0.0008045  0.0003978  -2.023 0.043189 *  
## AAMC.c:education.c -0.0024316  0.0024871  -0.978 0.328291    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.94 on 4091 degrees of freedom
##   (468 observations deleted due to missingness)
## Multiple R-squared:  0.09309,    Adjusted R-squared:  0.09109 
## F-statistic: 46.66 on 9 and 4091 DF,  p-value: < 2.2e-16

iv. comparing exposure sum, analytic max, and AAMC models

tab_model(m.intent1, m.intent2, m.intent3,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 3)

	vaxxIntentions						vaxxIntentions						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.001	0.031	0.940 – 1.063	32.079	<0.001	4459.000	1.050	0.033	0.985 – 1.114	31.886	<0.001	4091.000	1.038	0.033	0.974 – 1.103	31.395	<0.001	4091.000
exp sum c	0.037	0.005	0.027 – 0.047	7.514	<0.001	4459.000
USvUK	0.756	0.062	0.634 – 0.879	12.138	<0.001	4459.000	0.757	0.065	0.629 – 0.885	11.612	<0.001	4091.000	0.770	0.065	0.643 – 0.897	11.856	<0.001	4091.000
ideology c	-0.234	0.020	-0.273 – -0.195	-11.775	<0.001	4459.000	-0.226	0.021	-0.267 – -0.185	-10.800	<0.001	4091.000	-0.217	0.021	-0.258 – -0.176	-10.346	<0.001	4091.000
age c	0.021	0.002	0.017 – 0.025	10.505	<0.001	4459.000	0.019	0.002	0.015 – 0.023	9.067	<0.001	4091.000	0.020	0.002	0.015 – 0.024	9.342	<0.001	4091.000
education c	0.060	0.011	0.038 – 0.082	5.308	<0.001	4459.000	0.043	0.012	0.020 – 0.066	3.710	<0.001	4091.000	0.043	0.012	0.020 – 0.066	3.666	<0.001	4091.000
exp sum c × USvUK	-0.001	0.010	-0.020 – 0.018	-0.082	0.934	4459.000
exp sum c × ideology c	0.009	0.002	0.006 – 0.013	4.877	<0.001	4459.000
exp sum c × age c	-0.001	0.000	-0.001 – -0.000	-2.344	0.019	4459.000
exp sum c × education c	-0.003	0.001	-0.005 – -0.001	-2.575	0.010	4459.000
analytic max c							0.006	0.006	-0.005 – 0.017	1.036	0.300	4091.000
analytic max c × USvUK							-0.041	0.011	-0.063 – -0.019	-3.655	<0.001	4091.000
analytic max c × ideology c							0.000	0.003	-0.005 – 0.006	0.171	0.864	4091.000
analytic max c × age c							-0.001	0.000	-0.001 – -0.000	-2.316	0.021	4091.000
analytic max c × education c							-0.001	0.002	-0.004 – 0.002	-0.799	0.424	4091.000
AAMC c													0.017	0.008	0.002 – 0.032	2.189	0.029	4091.000
AAMC c × USvUK													-0.075	0.015	-0.105 – -0.046	-5.040	<0.001	4091.000
AAMC c × ideology c													-0.005	0.004	-0.013 – 0.002	-1.363	0.173	4091.000
AAMC c × age c													-0.001	0.000	-0.002 – -0.000	-2.023	0.043	4091.000
AAMC c × education c													-0.002	0.002	-0.007 – 0.002	-0.978	0.328	4091.000
Observations	4469						4101						4101
R2 / R2 adjusted	0.124 / 0.123						0.089 / 0.087						0.093 / 0.091

b. trust in expert models

i. trustExpert ~ exp.sum * (ideology + country + age + edu)

summary(m.expert1 <- lm(trustExpert ~ exp.sum.c * (USvUK +ideology.c + age.c + education.c), data = d))

## 
## Call:
## lm(formula = trustExpert ~ exp.sum.c * (USvUK + ideology.c + 
##     age.c + education.c), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4196 -0.6175  0.2629  0.9390  3.2608 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.7131363  0.0217717  78.686  < 2e-16 ***
## exp.sum.c              0.0174254  0.0034517   5.048 4.63e-07 ***
## USvUK                  0.5780894  0.0434661  13.300  < 2e-16 ***
## ideology.c            -0.1936516  0.0138510 -13.981  < 2e-16 ***
## age.c                  0.0093749  0.0014033   6.681 2.67e-11 ***
## education.c            0.0531146  0.0078329   6.781 1.35e-11 ***
## exp.sum.c:USvUK       -0.0151759  0.0068371  -2.220   0.0265 *  
## exp.sum.c:ideology.c   0.0060783  0.0013527   4.494 7.18e-06 ***
## exp.sum.c:age.c        0.0002504  0.0001554   1.611   0.1072    
## exp.sum.c:education.c -0.0002598  0.0007042  -0.369   0.7122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.359 on 4459 degrees of freedom
##   (100 observations deleted due to missingness)
## Multiple R-squared:  0.1298, Adjusted R-squared:  0.128 
## F-statistic: 73.89 on 9 and 4459 DF,  p-value: < 2.2e-16

- plot exp.sum * ideology

- across country

## 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.

- US plot

## [1] -1.51398

## [1] 1.679717

## 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.

- UK plot

## [1] -1.963628

## [1] 0.7460446

## 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.

ii. trustExpert ~ analytic.max * (ideology + country + age + edu)

summary(m.expert2 <- lm(trustExpert ~ analytic.max.c * (USvUK +ideology.c + age.c + education.c), data = d))

## 
## Call:
## lm(formula = trustExpert ~ analytic.max.c * (USvUK + ideology.c + 
##     age.c + education.c), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3571 -0.5518  0.2827  0.9277  2.8098 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.7538031  0.0226105  77.566  < 2e-16 ***
## analytic.max.c             -0.0035568  0.0038667  -0.920  0.35771    
## USvUK                       0.5393841  0.0447653  12.049  < 2e-16 ***
## ideology.c                 -0.2060372  0.0143595 -14.348  < 2e-16 ***
## age.c                       0.0082072  0.0014381   5.707 1.23e-08 ***
## education.c                 0.0459347  0.0079870   5.751 9.51e-09 ***
## analytic.max.c:USvUK       -0.0265921  0.0077118  -3.448  0.00057 ***
## analytic.max.c:ideology.c  -0.0002878  0.0018635  -0.154  0.87728    
## analytic.max.c:age.c        0.0005239  0.0001872   2.798  0.00516 ** 
## analytic.max.c:education.c  0.0002213  0.0010330   0.214  0.83038    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.335 on 4091 degrees of freedom
##   (468 observations deleted due to missingness)
## Multiple R-squared:  0.1055, Adjusted R-squared:  0.1035 
## F-statistic:  53.6 on 9 and 4091 DF,  p-value: < 2.2e-16

iii. trustExpert ~ AAMC * (ideology + country + age + edu)

summary(m.expert3 <- lm(trustExpert ~ AAMC.c * (USvUK +ideology.c + age.c + education.c), data = d))

## 
## Call:
## lm(formula = trustExpert ~ AAMC.c * (USvUK + ideology.c + age.c + 
##     education.c), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4103 -0.5759  0.2897  0.9222  2.8223 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         1.7452730  0.0227359  76.763  < 2e-16 ***
## AAMC.c              0.0043972  0.0052576   0.836   0.4030    
## USvUK               0.5502446  0.0446423  12.326  < 2e-16 ***
## ideology.c         -0.1988518  0.0144007 -13.809  < 2e-16 ***
## age.c               0.0085194  0.0014418   5.909 3.73e-09 ***
## education.c         0.0434790  0.0080169   5.423 6.19e-08 ***
## AAMC.c:USvUK       -0.0428984  0.0102839  -4.171 3.09e-05 ***
## AAMC.c:ideology.c  -0.0044641  0.0027068  -1.649   0.0992 .  
## AAMC.c:age.c        0.0005604  0.0002734   2.050   0.0404 *  
## AAMC.c:education.c  0.0019831  0.0017095   1.160   0.2461    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.333 on 4091 degrees of freedom
##   (468 observations deleted due to missingness)
## Multiple R-squared:  0.1079, Adjusted R-squared:  0.1059 
## F-statistic: 54.97 on 9 and 4091 DF,  p-value: < 2.2e-16

iv. comparing exposure sum, analytic max, and AAMC models

tab_model(m.expert1, m.expert2, m.expert3,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	trustExpert						trustExpert						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.7131	0.0218	1.6705 – 1.7558	78.6863	<0.001	4459.0000	1.7538	0.0226	1.7095 – 1.7981	77.5658	<0.001	4091.0000	1.7453	0.0227	1.7007 – 1.7898	76.7629	<0.001	4091.0000
exp sum c	0.0174	0.0035	0.0107 – 0.0242	5.0484	<0.001	4459.0000
USvUK	0.5781	0.0435	0.4929 – 0.6633	13.2998	<0.001	4459.0000	0.5394	0.0448	0.4516 – 0.6271	12.0492	<0.001	4091.0000	0.5502	0.0446	0.4627 – 0.6378	12.3256	<0.001	4091.0000
ideology c	-0.1937	0.0139	-0.2208 – -0.1665	-13.9810	<0.001	4459.0000	-0.2060	0.0144	-0.2342 – -0.1779	-14.3485	<0.001	4091.0000	-0.1989	0.0144	-0.2271 – -0.1706	-13.8085	<0.001	4091.0000
age c	0.0094	0.0014	0.0066 – 0.0121	6.6808	<0.001	4459.0000	0.0082	0.0014	0.0054 – 0.0110	5.7071	<0.001	4091.0000	0.0085	0.0014	0.0057 – 0.0113	5.9087	<0.001	4091.0000
education c	0.0531	0.0078	0.0378 – 0.0685	6.7810	<0.001	4459.0000	0.0459	0.0080	0.0303 – 0.0616	5.7512	<0.001	4091.0000	0.0435	0.0080	0.0278 – 0.0592	5.4234	<0.001	4091.0000
exp sum c × USvUK	-0.0152	0.0068	-0.0286 – -0.0018	-2.2197	0.026	4459.0000
exp sum c × ideology c	0.0061	0.0014	0.0034 – 0.0087	4.4936	<0.001	4459.0000
exp sum c × age c	0.0003	0.0002	-0.0001 – 0.0006	1.6113	0.107	4459.0000
exp sum c × education c	-0.0003	0.0007	-0.0016 – 0.0011	-0.3689	0.712	4459.0000
analytic max c							-0.0036	0.0039	-0.0111 – 0.0040	-0.9198	0.358	4091.0000
analytic max c × USvUK							-0.0266	0.0077	-0.0417 – -0.0115	-3.4482	0.001	4091.0000
analytic max c × ideology c							-0.0003	0.0019	-0.0039 – 0.0034	-0.1544	0.877	4091.0000
analytic max c × age c							0.0005	0.0002	0.0002 – 0.0009	2.7982	0.005	4091.0000
analytic max c × education c							0.0002	0.0010	-0.0018 – 0.0022	0.2142	0.830	4091.0000
AAMC c													0.0044	0.0053	-0.0059 – 0.0147	0.8364	0.403	4091.0000
AAMC c × USvUK													-0.0429	0.0103	-0.0631 – -0.0227	-4.1714	<0.001	4091.0000
AAMC c × ideology c													-0.0045	0.0027	-0.0098 – 0.0008	-1.6492	0.099	4091.0000
AAMC c × age c													0.0006	0.0003	0.0000 – 0.0011	2.0499	0.040	4091.0000
AAMC c × education c													0.0020	0.0017	-0.0014 – 0.0053	1.1601	0.246	4091.0000
Observations	4469						4101						4101
R2 / R2 adjusted	0.130 / 0.128						0.105 / 0.104						0.108 / 0.106

c. AAMC +/* exp.sum models

i. vaxx intentions

m.intent1 <- lm(vaxxIntentions ~ (AAMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)
m.intent2 <- lm(vaxxIntentions ~ AAMC.c * exp.sum.c * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent1, m.intent2,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0019	0.0330	0.9371 – 1.0666	30.3377	<0.001	4086.0000	1.0405	0.0422	0.9578 – 1.1232	24.6610	<0.001	4081.0000
AAMC c	-0.0139	0.0086	-0.0308 – 0.0030	-1.6117	0.107	4086.0000	-0.0338	0.0136	-0.0604 – -0.0072	-2.4950	0.013	4081.0000
exp sum c	0.0437	0.0061	0.0318 – 0.0557	7.1725	<0.001	4086.0000	0.0558	0.0101	0.0360 – 0.0757	5.5148	<0.001	4081.0000
USvUK	0.7523	0.0648	0.6252 – 0.8793	11.6053	<0.001	4086.0000	0.7931	0.0836	0.6292 – 0.9570	9.4855	<0.001	4081.0000
ideology c	-0.2277	0.0212	-0.2692 – -0.1861	-10.7508	<0.001	4086.0000	-0.1949	0.0236	-0.2412 – -0.1485	-8.2469	<0.001	4081.0000
age c	0.0218	0.0021	0.0177 – 0.0259	10.4332	<0.001	4086.0000	0.0229	0.0024	0.0182 – 0.0275	9.6421	<0.001	4081.0000
education c	0.0529	0.0120	0.0293 – 0.0765	4.3945	<0.001	4086.0000	0.0525	0.0137	0.0258 – 0.0793	3.8478	<0.001	4081.0000
AAMC c × USvUK	-0.1001	0.0171	-0.1335 – -0.0667	-5.8697	<0.001	4086.0000	-0.1131	0.0268	-0.1655 – -0.0606	-4.2225	<0.001	4081.0000
AAMC c × ideology c	-0.0142	0.0042	-0.0224 – -0.0059	-3.3716	0.001	4086.0000	-0.0304	0.0066	-0.0433 – -0.0175	-4.6170	<0.001	4081.0000
AAMC c × age c	-0.0004	0.0004	-0.0012 – 0.0004	-0.9667	0.334	4086.0000	-0.0011	0.0007	-0.0024 – 0.0002	-1.6620	0.097	4081.0000
AAMC c × education c	0.0001	0.0027	-0.0052 – 0.0053	0.0196	0.984	4086.0000	0.0006	0.0038	-0.0069 – 0.0080	0.1481	0.882	4081.0000
exp sum c × USvUK	0.0287	0.0122	0.0049 – 0.0526	2.3643	0.018	4086.0000	0.0510	0.0202	0.0113 – 0.0906	2.5212	0.012	4081.0000
exp sum c × ideology c	0.0110	0.0022	0.0066 – 0.0154	4.9309	<0.001	4086.0000	0.0168	0.0030	0.0110 – 0.0226	5.6811	<0.001	4081.0000
exp sum c × age c	-0.0004	0.0003	-0.0009 – 0.0001	-1.6272	0.104	4086.0000	-0.0002	0.0003	-0.0008 – 0.0004	-0.6451	0.519	4081.0000
exp sum c × education c	-0.0020	0.0012	-0.0044 – 0.0003	-1.7146	0.086	4086.0000	-0.0024	0.0017	-0.0057 – 0.0010	-1.3885	0.165	4081.0000
AAMC c × exp sum c							-0.0040	0.0026	-0.0091 – 0.0011	-1.5405	0.124	4081.0000
(AAMC c × exp sum c) × USvUK							-0.0066	0.0052	-0.0167 – 0.0036	-1.2712	0.204	4081.0000
(AAMC c × exp sum c) × ideology c							-0.0021	0.0007	-0.0035 – -0.0007	-3.0018	0.003	4081.0000
(AAMC c × exp sum c) × age c							-0.0001	0.0001	-0.0002 – 0.0001	-1.2478	0.212	4081.0000
(AAMC c × exp sum c) × education c							0.0001	0.0004	-0.0007 – 0.0010	0.2895	0.772	4081.0000
Observations	4101						4101
R2 / R2 adjusted	0.117 / 0.114						0.120 / 0.116

ii. trust in experts

m.trust1 <- lm(trustExpert ~ (AAMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)
m.trust2 <- lm(trustExpert ~ AAMC.c * exp.sum.c * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.trust1, m.trust2,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	trustExpert						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.7353	0.0228	1.6906 – 1.7800	76.1080	<0.001	4086.0000	1.7764	0.0291	1.7193 – 1.8334	61.0242	<0.001	4081.0000
AAMC c	-0.0081	0.0060	-0.0197 – 0.0036	-1.3528	0.176	4086.0000	-0.0287	0.0094	-0.0471 – -0.0104	-3.0718	0.002	4081.0000
exp sum c	0.0182	0.0042	0.0100 – 0.0265	4.3348	<0.001	4086.0000	0.0295	0.0070	0.0158 – 0.0432	4.2192	<0.001	4081.0000
USvUK	0.5374	0.0448	0.4496 – 0.6251	12.0067	<0.001	4086.0000	0.5397	0.0577	0.4266 – 0.6528	9.3567	<0.001	4081.0000
ideology c	-0.2049	0.0146	-0.2336 – -0.1763	-14.0158	<0.001	4086.0000	-0.1868	0.0163	-0.2188 – -0.1549	-11.4612	<0.001	4081.0000
age c	0.0095	0.0014	0.0066 – 0.0123	6.5578	<0.001	4086.0000	0.0103	0.0016	0.0071 – 0.0135	6.3011	<0.001	4081.0000
education c	0.0451	0.0083	0.0288 – 0.0614	5.4256	<0.001	4086.0000	0.0448	0.0094	0.0264 – 0.0633	4.7600	<0.001	4081.0000
AAMC c × USvUK	-0.0475	0.0118	-0.0706 – -0.0244	-4.0346	<0.001	4086.0000	-0.0444	0.0185	-0.0806 – -0.0081	-2.4015	0.016	4081.0000
AAMC c × ideology c	-0.0110	0.0029	-0.0167 – -0.0053	-3.7863	<0.001	4086.0000	-0.0209	0.0045	-0.0298 – -0.0120	-4.6057	<0.001	4081.0000
AAMC c × age c	0.0005	0.0003	-0.0000 – 0.0011	1.8346	0.067	4086.0000	-0.0001	0.0005	-0.0010 – 0.0008	-0.2456	0.806	4081.0000
AAMC c × education c	0.0023	0.0018	-0.0013 – 0.0059	1.2548	0.210	4086.0000	0.0028	0.0026	-0.0023 – 0.0080	1.0836	0.279	4081.0000
exp sum c × USvUK	0.0029	0.0084	-0.0136 – 0.0193	0.3414	0.733	4086.0000	0.0135	0.0139	-0.0139 – 0.0408	0.9652	0.334	4081.0000
exp sum c × ideology c	0.0090	0.0015	0.0059 – 0.0120	5.8063	<0.001	4086.0000	0.0115	0.0020	0.0075 – 0.0155	5.6472	<0.001	4081.0000
exp sum c × age c	0.0002	0.0002	-0.0001 – 0.0006	1.1773	0.239	4086.0000	0.0004	0.0002	-0.0000 – 0.0008	1.8358	0.066	4081.0000
exp sum c × education c	-0.0001	0.0008	-0.0017 – 0.0015	-0.0920	0.927	4086.0000	-0.0004	0.0012	-0.0027 – 0.0019	-0.3386	0.735	4081.0000
AAMC c × exp sum c							-0.0039	0.0018	-0.0074 – -0.0004	-2.1907	0.029	4081.0000
(AAMC c × exp sum c) × USvUK							-0.0027	0.0036	-0.0097 – 0.0043	-0.7669	0.443	4081.0000
(AAMC c × exp sum c) × ideology c							-0.0010	0.0005	-0.0019 – -0.0000	-2.0231	0.043	4081.0000
(AAMC c × exp sum c) × age c							-0.0001	0.0001	-0.0002 – 0.0000	-1.8652	0.062	4081.0000
(AAMC c × exp sum c) × education c							0.0001	0.0003	-0.0005 – 0.0007	0.3868	0.699	4081.0000
Observations	4101						4101
R2 / R2 adjusted	0.124 / 0.121						0.128 / 0.124

d. analytic.max */+ exp.sum

m.intent1 <- lm(vaxxIntentions ~ (analytic.max.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)
m.intent2 <- lm(vaxxIntentions ~ analytic.max.c * exp.sum.c * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent1, m.intent2,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0176	0.0329	0.9531 – 1.0821	30.9295	<0.001	4086.0000	1.0120	0.0340	0.9452 – 1.0788	29.7217	<0.001	4081.0000
analytic max c	-0.0063	0.0058	-0.0177 – 0.0051	-1.0828	0.279	4086.0000	-0.0083	0.0059	-0.0199 – 0.0033	-1.4037	0.160	4081.0000
exp sum c	0.0360	0.0055	0.0252 – 0.0468	6.5388	<0.001	4086.0000	0.0330	0.0059	0.0214 – 0.0446	5.5708	<0.001	4081.0000
USvUK	0.7589	0.0650	0.6314 – 0.8864	11.6692	<0.001	4086.0000	0.7081	0.0674	0.5761 – 0.8402	10.5107	<0.001	4081.0000
ideology c	-0.2258	0.0212	-0.2673 – -0.1843	-10.6626	<0.001	4086.0000	-0.2147	0.0219	-0.2577 – -0.1717	-9.7835	<0.001	4081.0000
age c	0.0216	0.0021	0.0175 – 0.0257	10.3507	<0.001	4086.0000	0.0214	0.0022	0.0172 – 0.0257	9.9295	<0.001	4081.0000
education c	0.0522	0.0120	0.0286 – 0.0758	4.3360	<0.001	4086.0000	0.0538	0.0124	0.0295 – 0.0781	4.3415	<0.001	4081.0000
analytic max c × USvUK	-0.0464	0.0116	-0.0691 – -0.0236	-3.9925	<0.001	4086.0000	-0.0427	0.0117	-0.0657 – -0.0197	-3.6447	<0.001	4081.0000
analytic max c × ideology c	-0.0048	0.0028	-0.0103 – 0.0007	-1.7140	0.087	4086.0000	-0.0072	0.0030	-0.0131 – -0.0014	-2.4180	0.016	4081.0000
analytic max c × age c	-0.0005	0.0003	-0.0011 – 0.0000	-1.9394	0.053	4086.0000	-0.0006	0.0003	-0.0012 – 0.0000	-1.9126	0.056	4081.0000
analytic max c × education c	0.0004	0.0016	-0.0027 – 0.0034	0.2413	0.809	4086.0000	0.0003	0.0016	-0.0028 – 0.0034	0.1945	0.846	4081.0000
exp sum c × USvUK	0.0091	0.0109	-0.0123 – 0.0305	0.8368	0.403	4086.0000	-0.0001	0.0117	-0.0231 – 0.0228	-0.0110	0.991	4081.0000
exp sum c × ideology c	0.0097	0.0021	0.0055 – 0.0139	4.5323	<0.001	4086.0000	0.0106	0.0023	0.0060 – 0.0151	4.5659	<0.001	4081.0000
exp sum c × age c	-0.0004	0.0002	-0.0009 – 0.0001	-1.5896	0.112	4086.0000	-0.0004	0.0003	-0.0009 – 0.0001	-1.6892	0.091	4081.0000
exp sum c × education c	-0.0021	0.0011	-0.0043 – 0.0002	-1.8003	0.072	4086.0000	-0.0018	0.0012	-0.0043 – 0.0006	-1.4710	0.141	4081.0000
analytic max c × exp sum c							0.0015	0.0011	-0.0006 – 0.0037	1.4123	0.158	4081.0000
(analytic max c × exp sum c) × USvUK							0.0050	0.0021	0.0008 – 0.0091	2.3134	0.021	4081.0000
(analytic max c × exp sum c) × ideology c							-0.0005	0.0003	-0.0012 – 0.0002	-1.4862	0.137	4081.0000
(analytic max c × exp sum c) × age c							0.0000	0.0000	-0.0001 – 0.0001	0.2728	0.785	4081.0000
(analytic max c × exp sum c) × education c							-0.0001	0.0002	-0.0004 – 0.0003	-0.4346	0.664	4081.0000
Observations	4101						4101
R2 / R2 adjusted	0.113 / 0.110						0.116 / 0.111

tab_model(m.trust1, m.trust2,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	trustExpert						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.7353	0.0228	1.6906 – 1.7800	76.1080	<0.001	4086.0000	1.7764	0.0291	1.7193 – 1.8334	61.0242	<0.001	4081.0000
AAMC c	-0.0081	0.0060	-0.0197 – 0.0036	-1.3528	0.176	4086.0000	-0.0287	0.0094	-0.0471 – -0.0104	-3.0718	0.002	4081.0000
exp sum c	0.0182	0.0042	0.0100 – 0.0265	4.3348	<0.001	4086.0000	0.0295	0.0070	0.0158 – 0.0432	4.2192	<0.001	4081.0000
USvUK	0.5374	0.0448	0.4496 – 0.6251	12.0067	<0.001	4086.0000	0.5397	0.0577	0.4266 – 0.6528	9.3567	<0.001	4081.0000
ideology c	-0.2049	0.0146	-0.2336 – -0.1763	-14.0158	<0.001	4086.0000	-0.1868	0.0163	-0.2188 – -0.1549	-11.4612	<0.001	4081.0000
age c	0.0095	0.0014	0.0066 – 0.0123	6.5578	<0.001	4086.0000	0.0103	0.0016	0.0071 – 0.0135	6.3011	<0.001	4081.0000
education c	0.0451	0.0083	0.0288 – 0.0614	5.4256	<0.001	4086.0000	0.0448	0.0094	0.0264 – 0.0633	4.7600	<0.001	4081.0000
AAMC c × USvUK	-0.0475	0.0118	-0.0706 – -0.0244	-4.0346	<0.001	4086.0000	-0.0444	0.0185	-0.0806 – -0.0081	-2.4015	0.016	4081.0000
AAMC c × ideology c	-0.0110	0.0029	-0.0167 – -0.0053	-3.7863	<0.001	4086.0000	-0.0209	0.0045	-0.0298 – -0.0120	-4.6057	<0.001	4081.0000
AAMC c × age c	0.0005	0.0003	-0.0000 – 0.0011	1.8346	0.067	4086.0000	-0.0001	0.0005	-0.0010 – 0.0008	-0.2456	0.806	4081.0000
AAMC c × education c	0.0023	0.0018	-0.0013 – 0.0059	1.2548	0.210	4086.0000	0.0028	0.0026	-0.0023 – 0.0080	1.0836	0.279	4081.0000
exp sum c × USvUK	0.0029	0.0084	-0.0136 – 0.0193	0.3414	0.733	4086.0000	0.0135	0.0139	-0.0139 – 0.0408	0.9652	0.334	4081.0000
exp sum c × ideology c	0.0090	0.0015	0.0059 – 0.0120	5.8063	<0.001	4086.0000	0.0115	0.0020	0.0075 – 0.0155	5.6472	<0.001	4081.0000
exp sum c × age c	0.0002	0.0002	-0.0001 – 0.0006	1.1773	0.239	4086.0000	0.0004	0.0002	-0.0000 – 0.0008	1.8358	0.066	4081.0000
exp sum c × education c	-0.0001	0.0008	-0.0017 – 0.0015	-0.0920	0.927	4086.0000	-0.0004	0.0012	-0.0027 – 0.0019	-0.3386	0.735	4081.0000
AAMC c × exp sum c							-0.0039	0.0018	-0.0074 – -0.0004	-2.1907	0.029	4081.0000
(AAMC c × exp sum c) × USvUK							-0.0027	0.0036	-0.0097 – 0.0043	-0.7669	0.443	4081.0000
(AAMC c × exp sum c) × ideology c							-0.0010	0.0005	-0.0019 – -0.0000	-2.0231	0.043	4081.0000
(AAMC c × exp sum c) × age c							-0.0001	0.0001	-0.0002 – 0.0000	-1.8652	0.062	4081.0000
(AAMC c × exp sum c) × education c							0.0001	0.0003	-0.0005 – 0.0007	0.3868	0.699	4081.0000
Observations	4101						4101
R2 / R2 adjusted	0.124 / 0.121						0.128 / 0.124

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

7. robustness checks

- affect

a. vaxxIntentions ~ AAFMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (AAFMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

b. trustExpert ~ AAFMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (AAFMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0459	0.0327	0.9819 – 1.1100	32.0042	<0.001	4091.0000	1.7434	0.0224	1.6994 – 1.7873	77.7829	<0.001	4091.0000
AAFMC c	-0.9809	0.3017	-1.5725 – -0.3893	-3.2508	0.001	4091.0000	-0.5805	0.2069	-0.9863 – -0.1748	-2.8053	0.005	4091.0000
USvUK	0.7663	0.0651	0.6386 – 0.8939	11.7692	<0.001	4091.0000	0.5521	0.0447	0.4646 – 0.6397	12.3650	<0.001	4091.0000
ideology c	-0.2393	0.0204	-0.2794 – -0.1993	-11.7174	<0.001	4091.0000	-0.2131	0.0140	-0.2405 – -0.1856	-15.2107	<0.001	4091.0000
age c	0.0185	0.0021	0.0143 – 0.0226	8.7206	<0.001	4091.0000	0.0078	0.0015	0.0050 – 0.0107	5.3880	<0.001	4091.0000
education c	0.0406	0.0117	0.0176 – 0.0636	3.4648	0.001	4091.0000	0.0432	0.0080	0.0274 – 0.0589	5.3707	<0.001	4091.0000
AAFMC c × USvUK	-1.6357	0.6019	-2.8158 – -0.4557	-2.7177	0.007	4091.0000	-1.1347	0.4128	-1.9440 – -0.3255	-2.7491	0.006	4091.0000
AAFMC c × ideology c	0.0753	0.0520	-0.0267 – 0.1772	1.4472	0.148	4091.0000	0.0742	0.0357	0.0043 – 0.1441	2.0807	0.038	4091.0000
AAFMC c × age c	0.0106	0.0053	0.0001 – 0.0210	1.9872	0.047	4091.0000	0.0000	0.0036	-0.0071 – 0.0072	0.0120	0.990	4091.0000
AAFMC c × education c	0.0357	0.0314	-0.0259 – 0.0973	1.1374	0.255	4091.0000	-0.0254	0.0215	-0.0676 – 0.0168	-1.1791	0.238	4091.0000
Observations	4101						4101
R2 / R2 adjusted	0.084 / 0.082						0.103 / 0.101

c. vaxxIntentions ~ (AAFMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (AAFMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

d. trustExpert ~ (AAFMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (AAFMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0210	0.0327	0.9569 – 1.0850	31.2681	<0.001	4086.0000	1.7447	0.0225	1.7005 – 1.7888	77.5231	<0.001	4086.0000
AAFMC c	-1.2203	0.3051	-1.8184 – -0.6222	-4.0000	<0.001	4086.0000	-0.6335	0.2103	-1.0458 – -0.2213	-3.0129	0.003	4086.0000
exp sum c	0.0372	0.0054	0.0266 – 0.0478	6.9096	<0.001	4086.0000	0.0150	0.0037	0.0077 – 0.0223	4.0471	<0.001	4086.0000
USvUK	0.7555	0.0650	0.6281 – 0.8829	11.6236	<0.001	4086.0000	0.5379	0.0448	0.4501 – 0.6257	12.0070	<0.001	4086.0000
ideology c	-0.2212	0.0207	-0.2618 – -0.1806	-10.6802	<0.001	4086.0000	-0.2004	0.0143	-0.2284 – -0.1724	-14.0399	<0.001	4086.0000
age c	0.0212	0.0021	0.0170 – 0.0253	10.0350	<0.001	4086.0000	0.0089	0.0015	0.0061 – 0.0118	6.1453	<0.001	4086.0000
education c	0.0504	0.0121	0.0266 – 0.0741	4.1569	<0.001	4086.0000	0.0446	0.0084	0.0282 – 0.0610	5.3393	<0.001	4086.0000
AAFMC c × USvUK	-2.4397	0.6086	-3.6329 – -1.2466	-4.0088	<0.001	4086.0000	-1.4147	0.4195	-2.2371 – -0.5923	-3.3727	0.001	4086.0000
AAFMC c × ideology c	0.1314	0.0521	0.0292 – 0.2335	2.5209	0.012	4086.0000	0.1140	0.0359	0.0436 – 0.1844	3.1745	0.002	4086.0000
AAFMC c × age c	0.0083	0.0053	-0.0021 – 0.0188	1.5600	0.119	4086.0000	0.0006	0.0037	-0.0066 – 0.0078	0.1562	0.876	4086.0000
AAFMC c × education c	0.0037	0.0320	-0.0591 – 0.0664	0.1148	0.909	4086.0000	-0.0339	0.0221	-0.0772 – 0.0093	-1.5377	0.124	4086.0000
exp sum c × USvUK	0.0058	0.0107	-0.0151 – 0.0267	0.5457	0.585	4086.0000	-0.0090	0.0074	-0.0234 – 0.0054	-1.2215	0.222	4086.0000
exp sum c × ideology c	0.0100	0.0021	0.0060 – 0.0141	4.8502	<0.001	4086.0000	0.0081	0.0014	0.0053 – 0.0109	5.6736	<0.001	4086.0000
exp sum c × age c	-0.0005	0.0002	-0.0009 – 0.0000	-1.8646	0.062	4086.0000	0.0003	0.0002	-0.0000 – 0.0006	1.8597	0.063	4086.0000
exp sum c × education c	-0.0020	0.0011	-0.0042 – 0.0003	-1.7305	0.084	4086.0000	-0.0000	0.0008	-0.0015 – 0.0015	-0.0237	0.981	4086.0000
Observations	4101						4101
R2 / R2 adjusted	0.114 / 0.111						0.123 / 0.120

- authenticity

a. vaxxIntentions ~ AAUTHMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (AAUTHMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

b. trustExpert ~ AAUTHMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (AAUTHMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0546	0.0326	0.9908 – 1.1185	32.3877	<0.001	4091.0000	1.7487	0.0223	1.7050 – 1.7924	78.4130	<0.001	4091.0000
AAUTHMC c	0.0183	0.0087	0.0012 – 0.0355	2.0969	0.036	4091.0000	-0.0005	0.0060	-0.0122 – 0.0112	-0.0839	0.933	4091.0000
USvUK	0.7458	0.0655	0.6173 – 0.8743	11.3784	<0.001	4091.0000	0.5473	0.0449	0.4593 – 0.6353	12.1930	<0.001	4091.0000
ideology c	-0.2529	0.0204	-0.2929 – -0.2129	-12.3954	<0.001	4091.0000	-0.2163	0.0140	-0.2437 – -0.1889	-15.4753	<0.001	4091.0000
age c	0.0182	0.0021	0.0141 – 0.0224	8.5473	<0.001	4091.0000	0.0072	0.0015	0.0043 – 0.0101	4.9335	<0.001	4091.0000
education c	0.0445	0.0117	0.0216 – 0.0675	3.8001	<0.001	4091.0000	0.0486	0.0080	0.0329 – 0.0643	6.0540	<0.001	4091.0000
AAUTHMC c × USvUK	0.0064	0.0176	-0.0282 – 0.0410	0.3640	0.716	4091.0000	0.0361	0.0121	0.0124 – 0.0597	2.9844	0.003	4091.0000
AAUTHMC c × ideology c	-0.0020	0.0056	-0.0131 – 0.0090	-0.3570	0.721	4091.0000	-0.0006	0.0039	-0.0082 – 0.0069	-0.1668	0.868	4091.0000
AAUTHMC c × age c	-0.0002	0.0006	-0.0014 – 0.0010	-0.3875	0.698	4091.0000	-0.0002	0.0004	-0.0010 – 0.0006	-0.4712	0.638	4091.0000
AAUTHMC c × education c	-0.0053	0.0036	-0.0122 – 0.0017	-1.4841	0.138	4091.0000	-0.0019	0.0024	-0.0067 – 0.0029	-0.7774	0.437	4091.0000
Observations	4101						4101
R2 / R2 adjusted	0.081 / 0.079						0.103 / 0.101

c. vaxxIntentions ~ (AAUTHMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (AAUTHMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

d. trustExpert ~ (AAUTHMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (AAUTHMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0243	0.0326	0.9602 – 1.0883	31.3725	<0.001	4086.0000	1.7484	0.0225	1.7042 – 1.7925	77.7105	<0.001	4086.0000
AAUTHMC c	0.0316	0.0096	0.0129 – 0.0504	3.3092	0.001	4086.0000	0.0023	0.0066	-0.0107 – 0.0152	0.3429	0.732	4086.0000
exp sum c	0.0455	0.0061	0.0334 – 0.0575	7.4079	<0.001	4086.0000	0.0188	0.0042	0.0105 – 0.0271	4.4445	<0.001	4086.0000
USvUK	0.7282	0.0656	0.5995 – 0.8568	11.0993	<0.001	4086.0000	0.5280	0.0452	0.4394 – 0.6166	11.6793	<0.001	4086.0000
ideology c	-0.2393	0.0208	-0.2800 – -0.1985	-11.5116	<0.001	4086.0000	-0.2036	0.0143	-0.2317 – -0.1755	-14.2138	<0.001	4086.0000
age c	0.0210	0.0021	0.0168 – 0.0251	9.8902	<0.001	4086.0000	0.0083	0.0015	0.0055 – 0.0112	5.6956	<0.001	4086.0000
education c	0.0567	0.0121	0.0329 – 0.0805	4.6687	<0.001	4086.0000	0.0494	0.0084	0.0330 – 0.0658	5.9019	<0.001	4086.0000
AAUTHMC c × USvUK	0.0630	0.0193	0.0253 – 0.1008	3.2721	0.001	4086.0000	0.0559	0.0133	0.0298 – 0.0819	4.2087	<0.001	4086.0000
AAUTHMC c × ideology c	0.0018	0.0056	-0.0091 – 0.0128	0.3295	0.742	4086.0000	0.0014	0.0038	-0.0061 – 0.0089	0.3603	0.719	4086.0000
AAUTHMC c × age c	-0.0004	0.0006	-0.0016 – 0.0008	-0.6531	0.514	4086.0000	-0.0002	0.0004	-0.0010 – 0.0006	-0.4429	0.658	4086.0000
AAUTHMC c × education c	-0.0029	0.0035	-0.0098 – 0.0040	-0.8337	0.404	4086.0000	-0.0009	0.0024	-0.0056 – 0.0039	-0.3656	0.715	4086.0000
exp sum c × USvUK	0.0253	0.0123	0.0013 – 0.0494	2.0663	0.039	4086.0000	-0.0003	0.0085	-0.0168 – 0.0163	-0.0320	0.974	4086.0000
exp sum c × ideology c	0.0093	0.0020	0.0053 – 0.0133	4.5848	<0.001	4086.0000	0.0071	0.0014	0.0044 – 0.0099	5.0696	<0.001	4086.0000
exp sum c × age c	-0.0005	0.0002	-0.0010 – -0.0000	-2.1519	0.031	4086.0000	0.0003	0.0002	-0.0000 – 0.0006	1.9298	0.054	4086.0000
exp sum c × education c	-0.0021	0.0011	-0.0042 – 0.0001	-1.8989	0.058	4086.0000	0.0001	0.0008	-0.0013 – 0.0016	0.1869	0.852	4086.0000
Observations	4101						4101
R2 / R2 adjusted	0.112 / 0.109						0.122 / 0.119

- clout

a. vaxxIntentions ~ ACLMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (ACLMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

b. trustExpert ~ ACLMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (ACLMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0471	0.0329	0.9826 – 1.1116	31.8398	<0.001	4091.0000	1.7432	0.0226	1.6989 – 1.7875	77.1492	<0.001	4091.0000
ACLMC c	-0.0215	0.0159	-0.0528 – 0.0098	-1.3484	0.178	4091.0000	0.0160	0.0110	-0.0055 – 0.0375	1.4596	0.144	4091.0000
USvUK	0.7595	0.0649	0.6324 – 0.8867	11.7092	<0.001	4091.0000	0.5498	0.0446	0.4624 – 0.6372	12.3361	<0.001	4091.0000
ideology c	-0.2451	0.0202	-0.2848 – -0.2054	-12.1052	<0.001	4091.0000	-0.2196	0.0139	-0.2469 – -0.1924	-15.7867	<0.001	4091.0000
age c	0.0200	0.0021	0.0158 – 0.0242	9.3476	<0.001	4091.0000	0.0078	0.0015	0.0049 – 0.0106	5.2650	<0.001	4091.0000
education c	0.0433	0.0118	0.0203 – 0.0664	3.6856	<0.001	4091.0000	0.0455	0.0081	0.0297 – 0.0613	5.6306	<0.001	4091.0000
ACLMC c × USvUK	0.1261	0.0313	0.0648 – 0.1875	4.0290	<0.001	4091.0000	0.0357	0.0215	-0.0064 – 0.0779	1.6612	0.097	4091.0000
ACLMC c × ideology c	0.0170	0.0099	-0.0025 – 0.0364	1.7116	0.087	4091.0000	0.0091	0.0068	-0.0043 – 0.0225	1.3353	0.182	4091.0000
ACLMC c × age c	0.0011	0.0010	-0.0009 – 0.0032	1.0780	0.281	4091.0000	0.0002	0.0007	-0.0012 – 0.0016	0.2369	0.813	4091.0000
ACLMC c × education c	0.0098	0.0060	-0.0019 – 0.0215	1.6396	0.101	4091.0000	-0.0042	0.0041	-0.0123 – 0.0038	-1.0299	0.303	4091.0000
Observations	4101						4101
R2 / R2 adjusted	0.086 / 0.084						0.102 / 0.100

c. vaxxIntentions ~ (ACLMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (ACLMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

d. trustExpert ~ (ACLMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (ACLMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0178	0.0328	0.9535 – 1.0822	31.0058	<0.001	4086.0000	1.7384	0.0226	1.6940 – 1.7828	76.8135	<0.001	4086.0000
ACLMC c	0.0471	0.0178	0.0122 – 0.0821	2.6462	0.008	4086.0000	0.0464	0.0123	0.0223 – 0.0705	3.7799	<0.001	4086.0000
exp sum c	0.0459	0.0061	0.0339 – 0.0579	7.4918	<0.001	4086.0000	0.0203	0.0042	0.0120 – 0.0286	4.8102	<0.001	4086.0000
USvUK	0.7255	0.0649	0.5983 – 0.8528	11.1800	<0.001	4086.0000	0.5294	0.0447	0.4417 – 0.6171	11.8320	<0.001	4086.0000
ideology c	-0.2400	0.0205	-0.2803 – -0.1998	-11.6832	<0.001	4086.0000	-0.2106	0.0142	-0.2383 – -0.1828	-14.8649	<0.001	4086.0000
age c	0.0222	0.0021	0.0180 – 0.0263	10.3918	<0.001	4086.0000	0.0085	0.0015	0.0056 – 0.0114	5.7783	<0.001	4086.0000
education c	0.0550	0.0121	0.0313 – 0.0788	4.5456	<0.001	4086.0000	0.0495	0.0083	0.0331 – 0.0658	5.9260	<0.001	4086.0000
ACLMC c × USvUK	0.1561	0.0354	0.0867 – 0.2254	4.4140	<0.001	4086.0000	0.0273	0.0244	-0.0205 – 0.0751	1.1195	0.263	4086.0000
ACLMC c × ideology c	0.0421	0.0104	0.0217 – 0.0625	4.0443	<0.001	4086.0000	0.0268	0.0072	0.0127 – 0.0408	3.7346	<0.001	4086.0000
ACLMC c × age c	0.0001	0.0011	-0.0020 – 0.0023	0.1326	0.894	4086.0000	0.0005	0.0007	-0.0010 – 0.0020	0.6695	0.503	4086.0000
ACLMC c × education c	0.0025	0.0064	-0.0100 – 0.0150	0.3961	0.692	4086.0000	-0.0072	0.0044	-0.0158 – 0.0014	-1.6322	0.103	4086.0000
exp sum c × USvUK	0.0269	0.0122	0.0029 – 0.0508	2.1977	0.028	4086.0000	-0.0015	0.0084	-0.0180 – 0.0150	-0.1778	0.859	4086.0000
exp sum c × ideology c	0.0122	0.0022	0.0080 – 0.0165	5.6514	<0.001	4086.0000	0.0094	0.0015	0.0065 – 0.0123	6.3114	<0.001	4086.0000
exp sum c × age c	-0.0005	0.0003	-0.0010 – 0.0000	-1.8833	0.060	4086.0000	0.0004	0.0002	0.0001 – 0.0007	2.3203	0.020	4086.0000
exp sum c × education c	-0.0019	0.0012	-0.0042 – 0.0004	-1.6293	0.103	4086.0000	-0.0004	0.0008	-0.0020 – 0.0012	-0.5041	0.614	4086.0000
Observations	4101						4101
R2 / R2 adjusted	0.116 / 0.113						0.125 / 0.122

- positive emotion

a. vaxxIntentions ~ APEMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (APEMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

b. trustExpert ~ APEMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (APEMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0401	0.0331	0.9751 – 1.1051	31.3883	<0.001	4091.0000	1.7374	0.0228	1.6928 – 1.7821	76.3356	<0.001	4091.0000
APEMC c	-0.7178	0.3280	-1.3610 – -0.0747	-2.1884	0.029	4091.0000	-0.2874	0.2253	-0.7291 – 0.1544	-1.2755	0.202	4091.0000
USvUK	0.7730	0.0652	0.6452 – 0.9009	11.8525	<0.001	4091.0000	0.5554	0.0448	0.4676 – 0.6432	12.3982	<0.001	4091.0000
ideology c	-0.2344	0.0207	-0.2749 – -0.1939	-11.3417	<0.001	4091.0000	-0.2122	0.0142	-0.2401 – -0.1844	-14.9495	<0.001	4091.0000
age c	0.0197	0.0021	0.0155 – 0.0239	9.2640	<0.001	4091.0000	0.0083	0.0015	0.0055 – 0.0112	5.7052	<0.001	4091.0000
education c	0.0400	0.0118	0.0169 – 0.0630	3.3935	0.001	4091.0000	0.0428	0.0081	0.0270 – 0.0587	5.2937	<0.001	4091.0000
APEMC c × USvUK	0.3714	0.6480	-0.8991 – 1.6419	0.5731	0.567	4091.0000	0.0226	0.4451	-0.8501 – 0.8952	0.0507	0.960	4091.0000
APEMC c × ideology c	0.2383	0.1138	0.0152 – 0.4613	2.0941	0.036	4091.0000	0.1911	0.0781	0.0379 – 0.3443	2.4453	0.015	4091.0000
APEMC c × age c	0.0220	0.0123	-0.0021 – 0.0461	1.7920	0.073	4091.0000	-0.0009	0.0084	-0.0174 – 0.0156	-0.1067	0.915	4091.0000
APEMC c × education c	0.1139	0.0716	-0.0265 – 0.2542	1.5908	0.112	4091.0000	-0.0447	0.0492	-0.1411 – 0.0517	-0.9098	0.363	4091.0000
Observations	4101						4101
R2 / R2 adjusted	0.086 / 0.084						0.103 / 0.101

c. vaxxIntentions ~ (APEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (APEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

d. trustExpert ~ (APEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (APEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0091	0.0331	0.9442 – 1.0741	30.4817	<0.001	4086.0000	1.7331	0.0228	1.6884 – 1.7778	75.9787	<0.001	4086.0000
APEMC c	-0.2075	0.3295	-0.8536 – 0.4386	-0.6297	0.529	4086.0000	-0.0486	0.2271	-0.4937 – 0.3966	-0.2139	0.831	4086.0000
exp sum c	0.0324	0.0053	0.0219 – 0.0429	6.0641	<0.001	4086.0000	0.0128	0.0037	0.0055 – 0.0200	3.4670	0.001	4086.0000
USvUK	0.7685	0.0652	0.6407 – 0.8963	11.7905	<0.001	4086.0000	0.5490	0.0449	0.4610 – 0.6371	12.2254	<0.001	4086.0000
ideology c	-0.2303	0.0209	-0.2712 – -0.1894	-11.0339	<0.001	4086.0000	-0.2065	0.0144	-0.2347 – -0.1783	-14.3603	<0.001	4086.0000
age c	0.0217	0.0021	0.0175 – 0.0258	10.2220	<0.001	4086.0000	0.0090	0.0015	0.0061 – 0.0119	6.1482	<0.001	4086.0000
education c	0.0530	0.0122	0.0291 – 0.0769	4.3463	<0.001	4086.0000	0.0467	0.0084	0.0303 – 0.0632	5.5618	<0.001	4086.0000
APEMC c × USvUK	0.1892	0.6513	-1.0878 – 1.4661	0.2904	0.772	4086.0000	-0.2232	0.4488	-1.1030 – 0.6566	-0.4974	0.619	4086.0000
APEMC c × ideology c	0.4686	0.1173	0.2386 – 0.6986	3.9947	<0.001	4086.0000	0.3515	0.0808	0.1931 – 0.5100	4.3495	<0.001	4086.0000
APEMC c × age c	0.0182	0.0128	-0.0068 – 0.0432	1.4266	0.154	4086.0000	0.0049	0.0088	-0.0123 – 0.0222	0.5630	0.573	4086.0000
APEMC c × education c	0.0035	0.0758	-0.1451 – 0.1521	0.0463	0.963	4086.0000	-0.0869	0.0522	-0.1893 – 0.0155	-1.6641	0.096	4086.0000
exp sum c × USvUK	-0.0025	0.0106	-0.0233 – 0.0184	-0.2314	0.817	4086.0000	-0.0146	0.0073	-0.0290 – -0.0003	-1.9963	0.046	4086.0000
exp sum c × ideology c	0.0115	0.0021	0.0074 – 0.0157	5.4216	<0.001	4086.0000	0.0092	0.0015	0.0063 – 0.0121	6.2626	<0.001	4086.0000
exp sum c × age c	-0.0004	0.0002	-0.0009 – 0.0001	-1.5971	0.110	4086.0000	0.0004	0.0002	0.0000 – 0.0007	2.0913	0.037	4086.0000
exp sum c × education c	-0.0021	0.0012	-0.0044 – 0.0002	-1.7859	0.074	4086.0000	-0.0003	0.0008	-0.0018 – 0.0013	-0.3326	0.739	4086.0000
Observations	4101						4101
R2 / R2 adjusted	0.113 / 0.110						0.123 / 0.120

- negative emotion

a. vaxxIntentions ~ ANEMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (ANEMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

b. trustExpert ~ ANEMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (ANEMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0518	0.0325	0.9882 – 1.1154	32.4061	<0.001	4091.0000	1.7484	0.0222	1.7048 – 1.7920	78.6068	<0.001	4091.0000
ANEMC c	-0.9364	0.3723	-1.6664 – -0.2065	-2.5151	0.012	4091.0000	-0.5826	0.2551	-1.0828 – -0.0824	-2.2834	0.022	4091.0000
USvUK	0.7555	0.0649	0.6282 – 0.8828	11.6349	<0.001	4091.0000	0.5479	0.0445	0.4607 – 0.6352	12.3139	<0.001	4091.0000
ideology c	-0.2495	0.0202	-0.2892 – -0.2099	-12.3339	<0.001	4091.0000	-0.2183	0.0139	-0.2455 – -0.1912	-15.7490	<0.001	4091.0000
age c	0.0180	0.0021	0.0138 – 0.0221	8.5240	<0.001	4091.0000	0.0078	0.0014	0.0049 – 0.0106	5.3694	<0.001	4091.0000
education c	0.0437	0.0116	0.0209 – 0.0665	3.7521	<0.001	4091.0000	0.0457	0.0080	0.0300 – 0.0613	5.7230	<0.001	4091.0000
ANEMC c × USvUK	-1.8541	0.7442	-3.3132 – -0.3950	-2.4913	0.013	4091.0000	-1.3001	0.5100	-2.3000 – -0.3002	-2.5492	0.011	4091.0000
ANEMC c × ideology c	0.0579	0.0765	-0.0922 – 0.2079	0.7562	0.450	4091.0000	0.0758	0.0524	-0.0270 – 0.1787	1.4462	0.148	4091.0000
ANEMC c × age c	0.0099	0.0075	-0.0047 – 0.0245	1.3277	0.184	4091.0000	-0.0012	0.0051	-0.0112 – 0.0088	-0.2352	0.814	4091.0000
ANEMC c × education c	0.0363	0.0454	-0.0527 – 0.1252	0.7996	0.424	4091.0000	-0.0325	0.0311	-0.0935 – 0.0284	-1.0470	0.295	4091.0000
Observations	4101						4101
R2 / R2 adjusted	0.082 / 0.080						0.102 / 0.100

c. vaxxIntentions ~ (ANEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (ANEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

d. trustExpert ~ (ANEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (ANEMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0250	0.0325	0.9614 – 1.0887	31.5578	<0.001	4086.0000	1.7503	0.0224	1.7064 – 1.7942	78.1199	<0.001	4086.0000
ANEMC c	-1.8176	0.4092	-2.6198 – -1.0154	-4.4422	<0.001	4086.0000	-0.8443	0.2822	-1.3977 – -0.2910	-2.9916	0.003	4086.0000
exp sum c	0.0436	0.0058	0.0322 – 0.0549	7.5172	<0.001	4086.0000	0.0175	0.0040	0.0097 – 0.0254	4.3868	<0.001	4086.0000
USvUK	0.7374	0.0648	0.6103 – 0.8646	11.3722	<0.001	4086.0000	0.5281	0.0447	0.4404 – 0.6158	11.8068	<0.001	4086.0000
ideology c	-0.2323	0.0205	-0.2725 – -0.1920	-11.3119	<0.001	4086.0000	-0.2057	0.0142	-0.2335 – -0.1780	-14.5275	<0.001	4086.0000
age c	0.0212	0.0021	0.0171 – 0.0253	10.0920	<0.001	4086.0000	0.0091	0.0014	0.0062 – 0.0119	6.2696	<0.001	4086.0000
education c	0.0540	0.0120	0.0304 – 0.0777	4.4880	<0.001	4086.0000	0.0462	0.0083	0.0299 – 0.0625	5.5587	<0.001	4086.0000
ANEMC c × USvUK	-3.8447	0.8171	-5.4467 – -2.2427	-4.7051	<0.001	4086.0000	-1.9368	0.5636	-3.0418 – -0.8317	-3.4362	0.001	4086.0000
ANEMC c × ideology c	0.0771	0.0755	-0.0710 – 0.2252	1.0202	0.308	4086.0000	0.0862	0.0521	-0.0159 – 0.1884	1.6547	0.098	4086.0000
ANEMC c × age c	0.0076	0.0074	-0.0068 – 0.0221	1.0347	0.301	4086.0000	-0.0017	0.0051	-0.0117 – 0.0082	-0.3394	0.734	4086.0000
ANEMC c × education c	0.0078	0.0451	-0.0806 – 0.0962	0.1727	0.863	4086.0000	-0.0370	0.0311	-0.0980 – 0.0239	-1.1908	0.234	4086.0000
exp sum c × USvUK	0.0198	0.0115	-0.0028 – 0.0424	1.7192	0.086	4086.0000	-0.0021	0.0079	-0.0176 – 0.0135	-0.2595	0.795	4086.0000
exp sum c × ideology c	0.0093	0.0020	0.0054 – 0.0133	4.5840	<0.001	4086.0000	0.0075	0.0014	0.0047 – 0.0102	5.3000	<0.001	4086.0000
exp sum c × age c	-0.0005	0.0002	-0.0010 – -0.0000	-2.0656	0.039	4086.0000	0.0003	0.0002	-0.0000 – 0.0006	1.8039	0.071	4086.0000
exp sum c × education c	-0.0020	0.0011	-0.0042 – 0.0001	-1.8332	0.067	4086.0000	0.0001	0.0008	-0.0014 – 0.0016	0.1753	0.861	4086.0000
Observations	4101						4101
R2 / R2 adjusted	0.113 / 0.110						0.122 / 0.119

- threat

a. vaxxIntentions ~ ATRMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (ATRMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

b. trustExpert ~ ATRMC.c * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (ATRMC.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0528	0.0334	0.9873 – 1.1183	31.5080	<0.001	4091.0000	1.7479	0.0229	1.7029 – 1.7928	76.2296	<0.001	4091.0000
ATRMC c	2.0129	0.5832	0.8695 – 3.1563	3.4513	0.001	4091.0000	0.9032	0.4002	0.1185 – 1.6878	2.2567	0.024	4091.0000
USvUK	0.7594	0.0650	0.6319 – 0.8869	11.6771	<0.001	4091.0000	0.5367	0.0446	0.4492 – 0.6242	12.0259	<0.001	4091.0000
ideology c	-0.2353	0.0208	-0.2760 – -0.1946	-11.3273	<0.001	4091.0000	-0.2084	0.0143	-0.2363 – -0.1804	-14.6172	<0.001	4091.0000
age c	0.0189	0.0021	0.0148 – 0.0230	9.0109	<0.001	4091.0000	0.0082	0.0014	0.0053 – 0.0110	5.6645	<0.001	4091.0000
education c	0.0430	0.0116	0.0202 – 0.0659	3.6998	<0.001	4091.0000	0.0453	0.0080	0.0296 – 0.0609	5.6718	<0.001	4091.0000
ATRMC c × USvUK	-2.8900	1.1829	-5.2092 – -0.5708	-2.4431	0.015	4091.0000	-1.0300	0.8118	-2.6215 – 0.5615	-1.2689	0.205	4091.0000
ATRMC c × ideology c	0.1135	0.3902	-0.6516 – 0.8785	0.2908	0.771	4091.0000	-0.0182	0.2678	-0.5432 – 0.5068	-0.0680	0.946	4091.0000
ATRMC c × age c	-0.0889	0.0395	-0.1663 – -0.0115	-2.2517	0.024	4091.0000	0.0374	0.0271	-0.0157 – 0.0906	1.3810	0.167	4091.0000
ATRMC c × education c	-0.0486	0.2337	-0.5067 – 0.4095	-0.2080	0.835	4091.0000	0.2732	0.1603	-0.0412 – 0.5875	1.7036	0.089	4091.0000
Observations	4101						4101
R2 / R2 adjusted	0.085 / 0.083						0.103 / 0.101

c. vaxxIntentions ~ (ATRMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.intent <- lm(vaxxIntentions ~ (ATRMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

d. trustExpert ~ (ATRMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

m.trust <- lm(trustExpert ~ (ATRMC.c + exp.sum.c) * (USvUK +ideology.c + age.c + education.c), data = d)

tab_model(m.intent, m.trust,
          show.df = T, 
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "Est",
          digits = 4)

	vaxxIntentions						trustExpert
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.0197	0.0334	0.9542 – 1.0852	30.5413	<0.001	4086.0000	1.7428	0.0230	1.6977 – 1.7879	75.7335	<0.001	4086.0000
ATRMC c	0.7848	0.5890	-0.3701 – 1.9396	1.3323	0.183	4086.0000	0.2761	0.4060	-0.5198 – 1.0721	0.6801	0.496	4086.0000
exp sum c	0.0319	0.0053	0.0214 – 0.0424	5.9754	<0.001	4086.0000	0.0117	0.0037	0.0044 – 0.0189	3.1672	0.002	4086.0000
USvUK	0.7404	0.0652	0.6125 – 0.8682	11.3556	<0.001	4086.0000	0.5196	0.0449	0.4315 – 0.6077	11.5620	<0.001	4086.0000
ideology c	-0.2314	0.0211	-0.2728 – -0.1900	-10.9643	<0.001	4086.0000	-0.2048	0.0145	-0.2334 – -0.1763	-14.0813	<0.001	4086.0000
age c	0.0215	0.0021	0.0174 – 0.0256	10.2527	<0.001	4086.0000	0.0092	0.0014	0.0063 – 0.0120	6.3602	<0.001	4086.0000
education c	0.0538	0.0121	0.0300 – 0.0776	4.4338	<0.001	4086.0000	0.0462	0.0084	0.0298 – 0.0626	5.5194	<0.001	4086.0000
ATRMC c × USvUK	-1.8435	1.1944	-4.1851 – 0.4982	-1.5435	0.123	4086.0000	-0.2479	0.8232	-1.8618 – 1.3661	-0.3011	0.763	4086.0000
ATRMC c × ideology c	-0.5850	0.4019	-1.3729 – 0.2029	-1.4558	0.146	4086.0000	-0.5175	0.2770	-1.0605 – 0.0256	-1.8682	0.062	4086.0000
ATRMC c × age c	-0.0618	0.0401	-0.1404 – 0.0168	-1.5418	0.123	4086.0000	0.0358	0.0276	-0.0184 – 0.0900	1.2963	0.195	4086.0000
ATRMC c × education c	0.0850	0.2399	-0.3853 – 0.5552	0.3543	0.723	4086.0000	0.2554	0.1653	-0.0687 – 0.5795	1.5448	0.122	4086.0000
exp sum c × USvUK	-0.0000	0.0106	-0.0208 – 0.0208	-0.0024	0.998	4086.0000	-0.0128	0.0073	-0.0271 – 0.0016	-1.7434	0.081	4086.0000
exp sum c × ideology c	0.0097	0.0021	0.0055 – 0.0139	4.5363	<0.001	4086.0000	0.0079	0.0015	0.0050 – 0.0107	5.3294	<0.001	4086.0000
exp sum c × age c	-0.0004	0.0002	-0.0009 – 0.0000	-1.7899	0.074	4086.0000	0.0002	0.0002	-0.0001 – 0.0006	1.4818	0.138	4086.0000
exp sum c × education c	-0.0021	0.0011	-0.0044 – 0.0001	-1.8919	0.059	4086.0000	-0.0000	0.0008	-0.0015 – 0.0015	-0.0179	0.986	4086.0000
Observations	4101						4101
R2 / R2 adjusted	0.110 / 0.107						0.119 / 0.116