Summer 2020

Spring 2022

1. For all measures, we want to scale [0, 1], as above. Then standardize (within country) for the purposes of analysis. (done)

2. Correlations of AMC, AAMC, CRT, Trust in science/experts, separate for US and UK, as appropriate (done)

A. Summer 2020 US

corr <- data.frame(d1$AAMC.r[d1$country_factor == "US"])
colnames(corr)[colnames(corr)=="d1.AAMC.r.d1.country_factor.....US.."] <- "AAMC.r"
corr$AMC.r <- d1$index_ANexp.r[d1$country_factor == "US"]
corr$sum.exp.r <- d1$sum.exp.r[d1$country_factor == "US"]
corr$expertTrust <- d1$trustExpert[d1$country_factor == "US"]

corr2 <- cor(corr, use = "pairwise")

ggcorrplot(corr2, type = "lower",
   lab = TRUE, title = "pairwise correlations for US in Summer 2020")

B. Summer 2020 UK

corr <- data.frame(d1$AAMC.r[d1$country_factor == "UK"])
colnames(corr)[colnames(corr)=="d1.AAMC.r.d1.country_factor.....UK.."] <- "AAMC.r"
corr$AMC.r<- d1$index_ANexp.r[d1$country_factor == "UK"]
corr$sum.exp.r <- d1$sum.exp.r[d1$country_factor == "UK"]
corr$expertTrust <- d1$trustExpert[d1$country_factor == "UK"]

corr2 <- cor(corr, use = "pairwise")

ggcorrplot(corr2, type = "lower",
   lab = TRUE, title = "pairwise correlations for UK in Summer 2020")

C. Spring 2022 US

corr <- data.frame(d$AAMC.w3)
colnames(corr)[colnames(corr)=="d.AAMC.w3"] <- "AAMC.w3"
corr$AAMC.w3.r <- d$AAMC.w3.r
corr$AMC.w3 <- d$AMC.w3
corr$AMC.w3.r <- d$AMC.w3.r
corr$sum.exp.w3 <- d$sum.exp.w3
corr$sum.exp.w3.r <- d$sum.exp.w3.r
corr$CRT <- d$CRT
corr$trustScience <- d$trustSci

corr2 <- cor(corr, use = "pairwise")

ggcorrplot(corr2, type = "lower",
   lab = TRUE, title = "pairwise correlations for US Spring 2022")

3. mediation models

A. Summer 2020

i. AMC.r.z –> trust in experts –> vaxx intentions

m1 <- lm(vaxxIntentions.z ~ AMC.r.z * (ideology.z + USvUK + age.z + education.z), data = d1)
m2 <- lm(trustExpert.z ~ AMC.r.z * (ideology.z + USvUK + age.z + education.z), data = d1)
m3 <- lm(vaxxIntentions.z ~ (AMC.r.z + trustExpert.z) * (ideology.z + USvUK + age.z + education.z), data = d1)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					trustExpert.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.08	0.02	0.05 – 0.11	5.14	<0.001	0.10	0.02	0.07 – 0.13	6.44	<0.001	0.02	0.02	-0.01 – 0.05	1.56	0.119
AMC.r.z	0.13	0.02	0.10 – 0.16	8.00	<0.001	0.10	0.02	0.07 – 0.13	6.43	<0.001	0.09	0.01	0.06 – 0.12	5.99	<0.001
ideology.z	-0.17	0.01	-0.20 – -0.14	-11.49	<0.001	-0.20	0.01	-0.23 – -0.17	-13.82	<0.001	-0.09	0.01	-0.12 – -0.07	-6.59	<0.001
USvUK	0.41	0.03	0.34 – 0.47	12.36	<0.001	0.37	0.03	0.31 – 0.43	11.38	<0.001	0.25	0.03	0.19 – 0.31	7.94	<0.001
age.z	0.17	0.02	0.13 – 0.20	10.61	<0.001	0.10	0.02	0.07 – 0.13	6.80	<0.001	0.13	0.01	0.10 – 0.16	8.96	<0.001
education.z	0.08	0.01	0.05 – 0.11	5.22	<0.001	0.10	0.01	0.07 – 0.12	6.62	<0.001	0.05	0.01	0.02 – 0.07	3.32	0.001
AMC.r.z * ideology.z	0.05	0.01	0.03 – 0.08	3.88	<0.001	0.05	0.01	0.02 – 0.08	3.61	<0.001	0.04	0.01	0.01 – 0.06	2.82	0.005
AMC.r.z * USvUK	-0.14	0.03	-0.20 – -0.07	-4.15	<0.001	-0.12	0.03	-0.18 – -0.05	-3.51	<0.001	-0.11	0.03	-0.17 – -0.04	-3.40	0.001
AMC.r.z * age.z	-0.04	0.02	-0.08 – -0.01	-2.68	0.007	0.02	0.02	-0.01 – 0.06	1.48	0.140	-0.06	0.02	-0.09 – -0.03	-3.74	<0.001
AMC.r.z * education.z	-0.03	0.01	-0.06 – -0.01	-2.43	0.015	0.00	0.01	-0.02 – 0.02	0.00	1.000	-0.03	0.01	-0.06 – -0.01	-2.80	0.005
trustExpert.z											0.42	0.02	0.38 – 0.45	24.81	<0.001
trustExpert.z * ideology.z											-0.00	0.01	-0.03 – 0.02	-0.21	0.833
trustExpert.z * USvUK											0.13	0.03	0.07 – 0.20	3.90	<0.001
trustExpert.z * age.z											0.02	0.02	-0.01 – 0.05	1.19	0.234
trustExpert.z * education.z											0.02	0.01	-0.00 – 0.05	1.73	0.084
Observations	4469					4469					4469
R² / R² adjusted	0.121 / 0.120					0.126 / 0.124					0.246 / 0.243

- M1 simple effects

m1.us <- lm(vaxxIntentions.z ~ AMC.r.z * (ideology.z + US_0 + age.z + education.z), data = d1)
m1.uk <- lm(vaxxIntentions.z ~ AMC.r.z * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us, m1.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.12	0.02	-0.16 – -0.09	-6.92	<0.001	0.28	0.03	0.23 – 0.33	10.78	<0.001
AMC.r.z	0.20	0.02	0.16 – 0.23	11.05	<0.001	0.06	0.03	0.00 – 0.11	2.12	0.034
ideology.z	-0.17	0.01	-0.20 – -0.14	-11.49	<0.001	-0.17	0.01	-0.20 – -0.14	-11.49	<0.001
US_0	0.41	0.03	0.34 – 0.47	12.36	<0.001
age.z	0.17	0.02	0.13 – 0.20	10.61	<0.001	0.17	0.02	0.13 – 0.20	10.61	<0.001
education.z	0.08	0.01	0.05 – 0.11	5.22	<0.001	0.08	0.01	0.05 – 0.11	5.22	<0.001
AMC.r.z * ideology.z	0.05	0.01	0.03 – 0.08	3.88	<0.001	0.05	0.01	0.03 – 0.08	3.88	<0.001
AMC.r.z * US_0	-0.14	0.03	-0.20 – -0.07	-4.15	<0.001
AMC.r.z * age.z	-0.04	0.02	-0.08 – -0.01	-2.68	0.007	-0.04	0.02	-0.08 – -0.01	-2.68	0.007
AMC.r.z * education.z	-0.03	0.01	-0.06 – -0.01	-2.43	0.015	-0.03	0.01	-0.06 – -0.01	-2.43	0.015
UK_0						-0.41	0.03	-0.47 – -0.34	-12.36	<0.001
AMC.r.z * UK_0						0.14	0.03	0.07 – 0.20	4.15	<0.001
Observations	4469					4469
R² / R² adjusted	0.121 / 0.120					0.121 / 0.120

- M2 simple effects

m2.us <- lm(trustExpert.z ~ AMC.r.z * (ideology.z + US_0 + age.z + education.z), data = d1)
m2.uk <- lm(trustExpert.z ~ AMC.r.z * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m2.us, m2.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	trustExpert.z					trustExpert.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.09	0.02	-0.12 – -0.05	-4.92	<0.001	0.28	0.03	0.23 – 0.33	10.92	<0.001
AMC.r.z	0.16	0.02	0.12 – 0.19	9.05	<0.001	0.04	0.03	-0.01 – 0.10	1.60	0.109
ideology.z	-0.20	0.01	-0.23 – -0.17	-13.82	<0.001	-0.20	0.01	-0.23 – -0.17	-13.82	<0.001
US_0	0.37	0.03	0.31 – 0.43	11.38	<0.001
age.z	0.10	0.02	0.07 – 0.13	6.80	<0.001	0.10	0.02	0.07 – 0.13	6.80	<0.001
education.z	0.10	0.01	0.07 – 0.12	6.62	<0.001	0.10	0.01	0.07 – 0.12	6.62	<0.001
AMC.r.z * ideology.z	0.05	0.01	0.02 – 0.08	3.61	<0.001	0.05	0.01	0.02 – 0.08	3.61	<0.001
AMC.r.z * US_0	-0.12	0.03	-0.18 – -0.05	-3.51	<0.001
AMC.r.z * age.z	0.02	0.02	-0.01 – 0.06	1.48	0.140	0.02	0.02	-0.01 – 0.06	1.48	0.140
AMC.r.z * education.z	0.00	0.01	-0.02 – 0.02	0.00	1.000	0.00	0.01	-0.02 – 0.02	0.00	1.000
UK_0						-0.37	0.03	-0.43 – -0.31	-11.38	<0.001
AMC.r.z * UK_0						0.12	0.03	0.05 – 0.18	3.51	<0.001
Observations	4469					4469
R² / R² adjusted	0.126 / 0.124					0.126 / 0.124

- M3 simple effects

m3.us <- lm(vaxxIntentions.z ~ (AMC.r.z + trustExpert.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m3.uk <- lm(vaxxIntentions.z ~ (AMC.r.z + trustExpert.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m3.us, m3.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.10	0.02	-0.14 – -0.07	-5.92	<0.001	0.15	0.03	0.10 – 0.20	5.81	<0.001
AMC.r.z	0.14	0.02	0.11 – 0.18	8.49	<0.001	0.04	0.03	-0.01 – 0.09	1.43	0.153
trustExpert.z	0.35	0.02	0.32 – 0.38	21.01	<0.001	0.48	0.03	0.42 – 0.54	16.34	<0.001
ideology.z	-0.09	0.01	-0.12 – -0.07	-6.59	<0.001	-0.09	0.01	-0.12 – -0.07	-6.59	<0.001
US_0	0.25	0.03	0.19 – 0.31	7.94	<0.001
age.z	0.13	0.01	0.10 – 0.16	8.96	<0.001	0.13	0.01	0.10 – 0.16	8.96	<0.001
education.z	0.05	0.01	0.02 – 0.07	3.32	0.001	0.05	0.01	0.02 – 0.07	3.32	0.001
AMC.r.z * ideology.z	0.04	0.01	0.01 – 0.06	2.82	0.005	0.04	0.01	0.01 – 0.06	2.82	0.005
AMC.r.z * US_0	-0.11	0.03	-0.17 – -0.04	-3.40	0.001
AMC.r.z * age.z	-0.06	0.02	-0.09 – -0.03	-3.74	<0.001	-0.06	0.02	-0.09 – -0.03	-3.74	<0.001
AMC.r.z * education.z	-0.03	0.01	-0.06 – -0.01	-2.80	0.005	-0.03	0.01	-0.06 – -0.01	-2.80	0.005
trustExpert.z * ideology.z	-0.00	0.01	-0.03 – 0.02	-0.21	0.833	-0.00	0.01	-0.03 – 0.02	-0.21	0.833
trustExpert.z * US_0	0.13	0.03	0.07 – 0.20	3.90	<0.001
trustExpert.z * age.z	0.02	0.02	-0.01 – 0.05	1.19	0.234	0.02	0.02	-0.01 – 0.05	1.19	0.234
trustExpert.z * education.z	0.02	0.01	-0.00 – 0.05	1.73	0.084	0.02	0.01	-0.00 – 0.05	1.73	0.084
UK_0						-0.25	0.03	-0.31 – -0.19	-7.94	<0.001
AMC.r.z * UK_0						0.11	0.03	0.04 – 0.17	3.40	0.001
trustExpert.z * UK_0						-0.13	0.03	-0.20 – -0.07	-3.90	<0.001
Observations	4469					4469
R² / R² adjusted	0.246 / 0.243					0.246 / 0.243

ii. AAMC.r.z –> trust in experts –> vaxx intentions

m1 <- lm(vaxxIntentions.z ~ (AAMC.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m2 <- lm(trustExpert.z ~ (AAMC.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m3 <- lm(vaxxIntentions.z ~ (AAMC.r.z + trustExpert.z) * (ideology.z + USvUK + age.z + education.z), data = d1)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					trustExpert.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.10	0.02	0.07 – 0.13	6.15	<0.001	0.12	0.02	0.09 – 0.15	7.48	<0.001	0.04	0.02	0.01 – 0.07	2.67	0.008
AAMC.r.z	0.05	0.02	0.02 – 0.08	2.88	0.004	0.03	0.02	-0.00 – 0.06	1.71	0.087	0.04	0.02	0.01 – 0.07	2.47	0.013
ideology.z	-0.16	0.02	-0.19 – -0.13	-10.34	<0.001	-0.21	0.02	-0.24 – -0.18	-13.81	<0.001	-0.08	0.02	-0.11 – -0.05	-5.39	<0.001
USvUK	0.41	0.03	0.34 – 0.47	11.74	<0.001	0.34	0.03	0.27 – 0.40	10.04	<0.001	0.27	0.03	0.20 – 0.33	7.96	<0.001
age.z	0.15	0.02	0.12 – 0.18	9.33	<0.001	0.09	0.02	0.06 – 0.12	5.90	<0.001	0.12	0.02	0.09 – 0.15	7.72	<0.001
education.z	0.06	0.02	0.03 – 0.09	3.68	<0.001	0.08	0.01	0.05 – 0.11	5.44	<0.001	0.03	0.01	0.00 – 0.06	2.00	0.046
AAMC.r.z * ideology.z	-0.02	0.02	-0.05 – 0.01	-1.44	0.150	-0.03	0.02	-0.06 – 0.00	-1.74	0.082	-0.01	0.01	-0.04 – 0.02	-0.76	0.450
AAMC.r.z * USvUK	-0.22	0.04	-0.29 – -0.15	-5.99	<0.001	-0.14	0.04	-0.21 – -0.07	-3.91	<0.001	-0.17	0.03	-0.23 – -0.10	-4.89	<0.001
AAMC.r.z * age.z	-0.03	0.02	-0.06 – -0.00	-1.96	0.050	0.03	0.02	-0.00 – 0.06	1.87	0.062	-0.04	0.02	-0.07 – -0.01	-2.90	0.004
AAMC.r.z * education.z	-0.02	0.02	-0.05 – 0.02	-1.06	0.290	0.02	0.02	-0.01 – 0.05	1.19	0.235	-0.03	0.02	-0.06 – 0.01	-1.63	0.104
trustExpert.z											0.40	0.02	0.36 – 0.44	22.12	<0.001
trustExpert.z * ideology.z											-0.00	0.01	-0.03 – 0.03	-0.16	0.870
trustExpert.z * USvUK											0.08	0.04	0.01 – 0.16	2.21	0.027
trustExpert.z * age.z											0.01	0.02	-0.02 – 0.04	0.70	0.483
trustExpert.z * education.z											0.02	0.01	-0.00 – 0.05	1.72	0.086
Observations	4101					4101					4101
R² / R² adjusted	0.093 / 0.091					0.108 / 0.106					0.214 / 0.211

- M1 simple effects for country

m1.us <- lm(vaxxIntentions.z ~ AAMC.r.z + (ideology.z + US_0 + age.z + education.z), data = d1)
m1.uk <- lm(vaxxIntentions.z ~ AAMC.r.z + (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z
Predictors	B	SE	CI	t	p
(Intercept)	-0.09	0.02	-0.13 – -0.05	-4.82	<0.001
AAMC.r.z	0.07	0.02	0.04 – 0.10	4.53	<0.001
ideology.z	-0.17	0.02	-0.20 – -0.14	-10.88	<0.001
US_0	0.39	0.03	0.32 – 0.46	11.38	<0.001
age.z	0.15	0.02	0.12 – 0.18	9.12	<0.001
education.z	0.05	0.02	0.02 – 0.08	3.44	0.001
Observations	4101
R² / R² adjusted	0.084 / 0.083

- M2 simple effects for country

m2.us <- lm(trustExpert.z ~ AAMC.r.z * (ideology.z + US_0 + age.z + education.z), data = d1)
m2.uk <- lm(trustExpert.z ~ AAMC.r.z * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m2.us, m2.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	trustExpert.z					trustExpert.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.05	0.02	-0.09 – -0.01	-2.74	0.006	0.28	0.03	0.23 – 0.34	10.79	<0.001
AAMC.r.z	0.10	0.02	0.05 – 0.14	4.51	<0.001	-0.04	0.03	-0.09 – 0.01	-1.62	0.106
ideology.z	-0.21	0.02	-0.24 – -0.18	-13.81	<0.001	-0.21	0.02	-0.24 – -0.18	-13.81	<0.001
US_0	0.34	0.03	0.27 – 0.40	10.04	<0.001
age.z	0.09	0.02	0.06 – 0.12	5.90	<0.001	0.09	0.02	0.06 – 0.12	5.90	<0.001
education.z	0.08	0.01	0.05 – 0.11	5.44	<0.001	0.08	0.01	0.05 – 0.11	5.44	<0.001
AAMC.r.z * ideology.z	-0.03	0.02	-0.06 – 0.00	-1.74	0.082	-0.03	0.02	-0.06 – 0.00	-1.74	0.082
AAMC.r.z * US_0	-0.14	0.04	-0.21 – -0.07	-3.91	<0.001
AAMC.r.z * age.z	0.03	0.02	-0.00 – 0.06	1.87	0.062	0.03	0.02	-0.00 – 0.06	1.87	0.062
AAMC.r.z * education.z	0.02	0.02	-0.01 – 0.05	1.19	0.235	0.02	0.02	-0.01 – 0.05	1.19	0.235
UK_0						-0.34	0.03	-0.40 – -0.27	-10.04	<0.001
AAMC.r.z * UK_0						0.14	0.04	0.07 – 0.21	3.91	<0.001
Observations	4101					4101
R² / R² adjusted	0.108 / 0.106					0.108 / 0.106

- M3 simple effects for country

m3.us <- lm(vaxxIntentions.z ~ (AAMC.r.z + trustExpert.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m3.uk <- lm(vaxxIntentions.z ~ (AAMC.r.z + trustExpert.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m3.us, m3.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.09	0.02	-0.13 – -0.05	-4.92	<0.001	0.18	0.03	0.12 – 0.23	6.51	<0.001
AAMC.r.z	0.12	0.02	0.08 – 0.16	5.87	<0.001	-0.05	0.03	-0.09 – 0.00	-1.81	0.070
trustExpert.z	0.36	0.02	0.32 – 0.39	20.25	<0.001	0.44	0.03	0.38 – 0.50	13.70	<0.001
ideology.z	-0.08	0.02	-0.11 – -0.05	-5.39	<0.001	-0.08	0.02	-0.11 – -0.05	-5.39	<0.001
US_0	0.27	0.03	0.20 – 0.33	7.96	<0.001
age.z	0.12	0.02	0.09 – 0.15	7.72	<0.001	0.12	0.02	0.09 – 0.15	7.72	<0.001
education.z	0.03	0.01	0.00 – 0.06	2.00	0.046	0.03	0.01	0.00 – 0.06	2.00	0.046
AAMC.r.z * ideology.z	-0.01	0.01	-0.04 – 0.02	-0.76	0.450	-0.01	0.01	-0.04 – 0.02	-0.76	0.450
AAMC.r.z * US_0	-0.17	0.03	-0.23 – -0.10	-4.89	<0.001
AAMC.r.z * age.z	-0.04	0.02	-0.07 – -0.01	-2.90	0.004	-0.04	0.02	-0.07 – -0.01	-2.90	0.004
AAMC.r.z * education.z	-0.03	0.02	-0.06 – 0.01	-1.63	0.104	-0.03	0.02	-0.06 – 0.01	-1.63	0.104
trustExpert.z * ideology.z	-0.00	0.01	-0.03 – 0.03	-0.16	0.870	-0.00	0.01	-0.03 – 0.03	-0.16	0.870
trustExpert.z * US_0	0.08	0.04	0.01 – 0.16	2.21	0.027
trustExpert.z * age.z	0.01	0.02	-0.02 – 0.04	0.70	0.483	0.01	0.02	-0.02 – 0.04	0.70	0.483
trustExpert.z * education.z	0.02	0.01	-0.00 – 0.05	1.72	0.086	0.02	0.01	-0.00 – 0.05	1.72	0.086
UK_0						-0.27	0.03	-0.33 – -0.20	-7.96	<0.001
AAMC.r.z * UK_0						0.17	0.03	0.10 – 0.23	4.89	<0.001
trustExpert.z * UK_0						-0.08	0.04	-0.16 – -0.01	-2.21	0.027
Observations	4101					4101
R² / R² adjusted	0.214 / 0.211					0.214 / 0.211

iii. AAMC.z –> trust in experts –> vaxx intentions

m1 <- lm(vaxxIntentions.z ~ (AAMC.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m2 <- lm(trustExpert.z ~ (AAMC.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m3 <- lm(vaxxIntentions.z ~ (AAMC.z + trustExpert.z) * (ideology.z + USvUK + age.z + education.z), data = d1)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					trustExpert.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.10	0.02	0.07 – 0.13	6.15	<0.001	0.12	0.02	0.09 – 0.15	7.48	<0.001	0.04	0.02	0.01 – 0.07	2.67	0.008
AAMC.z	0.05	0.02	0.02 – 0.08	2.88	0.004	0.03	0.02	-0.00 – 0.06	1.71	0.087	0.04	0.02	0.01 – 0.07	2.47	0.013
ideology.z	-0.16	0.02	-0.19 – -0.13	-10.34	<0.001	-0.21	0.02	-0.24 – -0.18	-13.81	<0.001	-0.08	0.02	-0.11 – -0.05	-5.39	<0.001
USvUK	0.41	0.03	0.34 – 0.47	11.74	<0.001	0.34	0.03	0.27 – 0.40	10.04	<0.001	0.27	0.03	0.20 – 0.33	7.96	<0.001
age.z	0.15	0.02	0.12 – 0.18	9.33	<0.001	0.09	0.02	0.06 – 0.12	5.90	<0.001	0.12	0.02	0.09 – 0.15	7.72	<0.001
education.z	0.06	0.02	0.03 – 0.09	3.68	<0.001	0.08	0.01	0.05 – 0.11	5.44	<0.001	0.03	0.01	0.00 – 0.06	2.00	0.046
AAMC.z * ideology.z	-0.02	0.02	-0.05 – 0.01	-1.44	0.150	-0.03	0.02	-0.06 – 0.00	-1.74	0.082	-0.01	0.01	-0.04 – 0.02	-0.76	0.450
AAMC.z * USvUK	-0.22	0.04	-0.29 – -0.15	-5.99	<0.001	-0.14	0.04	-0.21 – -0.07	-3.91	<0.001	-0.17	0.03	-0.23 – -0.10	-4.89	<0.001
AAMC.z * age.z	-0.03	0.02	-0.06 – -0.00	-1.96	0.050	0.03	0.02	-0.00 – 0.06	1.87	0.062	-0.04	0.02	-0.07 – -0.01	-2.90	0.004
AAMC.z * education.z	-0.02	0.02	-0.05 – 0.02	-1.06	0.290	0.02	0.02	-0.01 – 0.05	1.19	0.235	-0.03	0.02	-0.06 – 0.01	-1.63	0.104
trustExpert.z											0.40	0.02	0.36 – 0.44	22.12	<0.001
trustExpert.z * ideology.z											-0.00	0.01	-0.03 – 0.03	-0.16	0.870
trustExpert.z * USvUK											0.08	0.04	0.01 – 0.16	2.21	0.027
trustExpert.z * age.z											0.01	0.02	-0.02 – 0.04	0.70	0.483
trustExpert.z * education.z											0.02	0.01	-0.00 – 0.05	1.72	0.086
Observations	4101					4101					4101
R² / R² adjusted	0.093 / 0.091					0.108 / 0.106					0.214 / 0.211

- M1 simple effects for country

m1.us <- lm(vaxxIntentions.z ~ AAMC.z + (ideology.z + US_0 + age.z + education.z), data = d1)
m1.uk <- lm(vaxxIntentions.z ~ AAMC.z + (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z
Predictors	B	SE	CI	t	p
(Intercept)	-0.09	0.02	-0.13 – -0.05	-4.82	<0.001
AAMC.z	0.07	0.02	0.04 – 0.10	4.53	<0.001
ideology.z	-0.17	0.02	-0.20 – -0.14	-10.88	<0.001
US_0	0.39	0.03	0.32 – 0.46	11.38	<0.001
age.z	0.15	0.02	0.12 – 0.18	9.12	<0.001
education.z	0.05	0.02	0.02 – 0.08	3.44	0.001
Observations	4101
R² / R² adjusted	0.084 / 0.083

- M2 simple effects for country

m2.us <- lm(trustExpert.z ~ AAMC.z * (ideology.z + US_0 + age.z + education.z), data = d1)
m2.uk <- lm(trustExpert.z ~ AAMC.z * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m2.us, m2.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	trustExpert.z					trustExpert.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.05	0.02	-0.09 – -0.01	-2.74	0.006	0.28	0.03	0.23 – 0.34	10.79	<0.001
AAMC.z	0.10	0.02	0.05 – 0.14	4.51	<0.001	-0.04	0.03	-0.09 – 0.01	-1.62	0.106
ideology.z	-0.21	0.02	-0.24 – -0.18	-13.81	<0.001	-0.21	0.02	-0.24 – -0.18	-13.81	<0.001
US_0	0.34	0.03	0.27 – 0.40	10.04	<0.001
age.z	0.09	0.02	0.06 – 0.12	5.90	<0.001	0.09	0.02	0.06 – 0.12	5.90	<0.001
education.z	0.08	0.01	0.05 – 0.11	5.44	<0.001	0.08	0.01	0.05 – 0.11	5.44	<0.001
AAMC.z * ideology.z	-0.03	0.02	-0.06 – 0.00	-1.74	0.082	-0.03	0.02	-0.06 – 0.00	-1.74	0.082
AAMC.z * US_0	-0.14	0.04	-0.21 – -0.07	-3.91	<0.001
AAMC.z * age.z	0.03	0.02	-0.00 – 0.06	1.87	0.062	0.03	0.02	-0.00 – 0.06	1.87	0.062
AAMC.z * education.z	0.02	0.02	-0.01 – 0.05	1.19	0.235	0.02	0.02	-0.01 – 0.05	1.19	0.235
UK_0						-0.34	0.03	-0.40 – -0.27	-10.04	<0.001
AAMC.z * UK_0						0.14	0.04	0.07 – 0.21	3.91	<0.001
Observations	4101					4101
R² / R² adjusted	0.108 / 0.106					0.108 / 0.106

- M3 simple effects for country

m3.us <- lm(vaxxIntentions.z ~ (AAMC.z + trustExpert.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m3.uk <- lm(vaxxIntentions.z ~ (AAMC.z + trustExpert.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m3.us, m3.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.09	0.02	-0.13 – -0.05	-4.92	<0.001	0.18	0.03	0.12 – 0.23	6.51	<0.001
AAMC.z	0.12	0.02	0.08 – 0.16	5.87	<0.001	-0.05	0.03	-0.09 – 0.00	-1.81	0.070
trustExpert.z	0.36	0.02	0.32 – 0.39	20.25	<0.001	0.44	0.03	0.38 – 0.50	13.70	<0.001
ideology.z	-0.08	0.02	-0.11 – -0.05	-5.39	<0.001	-0.08	0.02	-0.11 – -0.05	-5.39	<0.001
US_0	0.27	0.03	0.20 – 0.33	7.96	<0.001
age.z	0.12	0.02	0.09 – 0.15	7.72	<0.001	0.12	0.02	0.09 – 0.15	7.72	<0.001
education.z	0.03	0.01	0.00 – 0.06	2.00	0.046	0.03	0.01	0.00 – 0.06	2.00	0.046
AAMC.z * ideology.z	-0.01	0.01	-0.04 – 0.02	-0.76	0.450	-0.01	0.01	-0.04 – 0.02	-0.76	0.450
AAMC.z * US_0	-0.17	0.03	-0.23 – -0.10	-4.89	<0.001
AAMC.z * age.z	-0.04	0.02	-0.07 – -0.01	-2.90	0.004	-0.04	0.02	-0.07 – -0.01	-2.90	0.004
AAMC.z * education.z	-0.03	0.02	-0.06 – 0.01	-1.63	0.104	-0.03	0.02	-0.06 – 0.01	-1.63	0.104
trustExpert.z * ideology.z	-0.00	0.01	-0.03 – 0.03	-0.16	0.870	-0.00	0.01	-0.03 – 0.03	-0.16	0.870
trustExpert.z * US_0	0.08	0.04	0.01 – 0.16	2.21	0.027
trustExpert.z * age.z	0.01	0.02	-0.02 – 0.04	0.70	0.483	0.01	0.02	-0.02 – 0.04	0.70	0.483
trustExpert.z * education.z	0.02	0.01	-0.00 – 0.05	1.72	0.086	0.02	0.01	-0.00 – 0.05	1.72	0.086
UK_0						-0.27	0.03	-0.33 – -0.20	-7.96	<0.001
AAMC.z * UK_0						0.17	0.03	0.10 – 0.23	4.89	<0.001
trustExpert.z * UK_0						-0.08	0.04	-0.16 – -0.01	-2.21	0.027
Observations	4101					4101
R² / R² adjusted	0.214 / 0.211					0.214 / 0.211

B. Spring 2022

i. AMC.r.z –> trust in science –> vaccinations

m1 <- lm(vaxxBehavior.z ~ AMC.w3.r.z * (ideology.z + age.z + education.z + white_.5), data = d)
m2 <- lm(trustSci.z ~ AMC.w3.r.z * (ideology.z + age.z + education.z + white_.5), data = d)
m3 <- lm(vaxxBehavior.z ~ (AMC.w3.r.z + trustSci.z) * (ideology.z + age.z + education.z + white_.5), data = d)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxBehavior.z					trustSci.z					vaxxBehavior.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.02	0.03	-0.09 – 0.04	-0.70	0.484	-0.04	0.03	-0.10 – 0.03	-1.09	0.274	-0.01	0.04	-0.08 – 0.06	-0.23	0.820
AMC.w3.r.z	0.24	0.03	0.18 – 0.30	7.68	<0.001	0.06	0.03	0.00 – 0.12	1.97	0.050	0.21	0.03	0.15 – 0.27	7.01	<0.001
ideology.z	-0.21	0.03	-0.26 – -0.15	-7.15	<0.001	-0.43	0.03	-0.48 – -0.37	-15.36	<0.001	-0.08	0.03	-0.14 – -0.02	-2.51	0.012
age.z	0.22	0.03	0.16 – 0.28	7.08	<0.001	0.13	0.03	0.07 – 0.19	4.30	<0.001	0.19	0.03	0.13 – 0.25	6.34	<0.001
education.z	0.15	0.03	0.08 – 0.21	4.41	<0.001	0.10	0.03	0.03 – 0.16	3.02	0.003	0.12	0.03	0.06 – 0.18	3.74	<0.001
white_.5	-0.02	0.07	-0.16 – 0.11	-0.35	0.729	0.27	0.07	0.14 – 0.40	4.15	<0.001	-0.11	0.07	-0.24 – 0.01	-1.74	0.083
AMC.w3.r.z * ideology.z	0.09	0.03	0.04 – 0.14	3.37	0.001	0.06	0.03	0.01 – 0.11	2.18	0.030	0.06	0.03	0.01 – 0.11	2.39	0.017
AMC.w3.r.z * age.z	-0.03	0.03	-0.10 – 0.03	-1.06	0.289	0.09	0.03	0.03 – 0.15	3.02	0.003	-0.04	0.03	-0.10 – 0.02	-1.35	0.177
AMC.w3.r.z * education.z	-0.05	0.03	-0.10 – -0.00	-2.02	0.043	-0.01	0.02	-0.06 – 0.04	-0.51	0.610	-0.05	0.02	-0.10 – -0.00	-1.97	0.050
AMC.w3.r.z * white_.5	-0.02	0.06	-0.14 – 0.11	-0.24	0.810	0.05	0.06	-0.07 – 0.17	0.88	0.377	-0.07	0.06	-0.19 – 0.05	-1.12	0.262
trustSci.z											0.27	0.04	0.20 – 0.34	7.41	<0.001
trustSci.z * ideology.z											0.04	0.03	-0.01 – 0.10	1.67	0.094
trustSci.z * age.z											-0.02	0.03	-0.08 – 0.04	-0.69	0.489
trustSci.z * education.z											0.04	0.03	-0.02 – 0.10	1.35	0.176
trustSci.z * white_.5											0.16	0.07	0.03 – 0.29	2.44	0.015
Observations	997					993					991
R² / R² adjusted	0.177 / 0.170					0.259 / 0.253					0.260 / 0.249

ii. AAMC.r.z –> trust in science –> vaccinations

m1 <- lm(vaxxBehavior.z ~ AAMC.w3.r.z * (ideology.z + age.z + education.z + white_.5), data = d)
m2 <- lm(trustSci.z ~ AAMC.w3.r.z * (ideology.z + age.z + education.z + white_.5), data = d)
m3 <- lm(vaxxBehavior.z ~ (AAMC.w3.r.z + trustSci.z) * (ideology.z + age.z + education.z + white_.5), data = d)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxBehavior.z					trustSci.z					vaxxBehavior.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.06	0.04	-0.01 – 0.13	1.81	0.070	-0.03	0.03	-0.09 – 0.04	-0.74	0.462	0.07	0.04	-0.00 – 0.14	1.85	0.065
AAMC.w3.r.z	0.15	0.05	0.06 – 0.24	3.25	0.001	0.10	0.04	0.01 – 0.18	2.22	0.027	0.12	0.04	0.04 – 0.21	2.79	0.005
ideology.z	-0.19	0.03	-0.25 – -0.13	-6.20	<0.001	-0.40	0.03	-0.46 – -0.35	-13.80	<0.001	-0.08	0.03	-0.14 – -0.01	-2.37	0.018
age.z	0.20	0.03	0.13 – 0.26	6.08	<0.001	0.14	0.03	0.08 – 0.20	4.43	<0.001	0.18	0.03	0.11 – 0.24	5.43	<0.001
education.z	0.11	0.03	0.05 – 0.18	3.51	<0.001	0.08	0.03	0.02 – 0.14	2.62	0.009	0.09	0.03	0.03 – 0.15	2.89	0.004
white_.5	-0.14	0.07	-0.28 – -0.01	-2.05	0.041	0.30	0.07	0.17 – 0.43	4.49	<0.001	-0.24	0.07	-0.38 – -0.11	-3.61	<0.001
AAMC.w3.r.z * ideology.z	0.04	0.03	-0.02 – 0.11	1.39	0.165	-0.02	0.03	-0.08 – 0.04	-0.65	0.518	0.05	0.03	-0.01 – 0.11	1.48	0.138
AAMC.w3.r.z * age.z	-0.02	0.03	-0.08 – 0.05	-0.50	0.619	0.08	0.03	0.01 – 0.14	2.35	0.019	-0.04	0.03	-0.10 – 0.03	-1.13	0.260
AAMC.w3.r.z * education.z	-0.05	0.04	-0.12 – 0.03	-1.26	0.207	0.08	0.04	0.01 – 0.15	2.11	0.035	-0.08	0.04	-0.15 – -0.01	-2.11	0.035
AAMC.w3.r.z * white_.5	-0.07	0.09	-0.24 – 0.10	-0.77	0.442	0.01	0.08	-0.15 – 0.17	0.11	0.911	-0.11	0.08	-0.28 – 0.05	-1.35	0.177
trustSci.z											0.23	0.04	0.16 – 0.31	6.07	<0.001
trustSci.z * ideology.z											0.03	0.03	-0.02 – 0.09	1.12	0.261
trustSci.z * age.z											-0.02	0.03	-0.09 – 0.05	-0.56	0.576
trustSci.z * education.z											0.05	0.03	-0.01 – 0.12	1.54	0.125
trustSci.z * white_.5											0.23	0.07	0.09 – 0.37	3.24	0.001
Observations	886					884					882
R² / R² adjusted	0.133 / 0.124					0.266 / 0.258					0.216 / 0.203

iii. AAMC.z –> trust in science –> vaccinations

m1 <- lm(vaxxBehavior.z ~ AAMC.w3.z * (ideology.z + age.z + education.z + white_.5), data = d)
m2 <- lm(trustSci.z ~ AAMC.w3.z * (ideology.z + age.z + education.z + white_.5), data = d)
m3 <- lm(vaxxBehavior.z ~ (AAMC.w3.z + trustSci.z) * (ideology.z + age.z + education.z + white_.5), data = d)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxBehavior.z					trustSci.z					vaxxBehavior.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.06	0.04	-0.01 – 0.13	1.81	0.071	-0.03	0.03	-0.09 – 0.04	-0.74	0.461	0.07	0.04	-0.00 – 0.14	1.85	0.065
AAMC.w3.z	0.15	0.05	0.06 – 0.24	3.25	0.001	0.10	0.04	0.01 – 0.18	2.23	0.026	0.12	0.04	0.04 – 0.21	2.79	0.005
ideology.z	-0.19	0.03	-0.25 – -0.13	-6.20	<0.001	-0.40	0.03	-0.46 – -0.35	-13.80	<0.001	-0.08	0.03	-0.14 – -0.01	-2.37	0.018
age.z	0.20	0.03	0.13 – 0.26	6.08	<0.001	0.14	0.03	0.08 – 0.20	4.43	<0.001	0.18	0.03	0.11 – 0.24	5.44	<0.001
education.z	0.11	0.03	0.05 – 0.18	3.51	<0.001	0.08	0.03	0.02 – 0.14	2.62	0.009	0.09	0.03	0.03 – 0.15	2.90	0.004
white_.5	-0.14	0.07	-0.28 – -0.01	-2.05	0.041	0.30	0.07	0.17 – 0.43	4.49	<0.001	-0.24	0.07	-0.38 – -0.11	-3.61	<0.001
AAMC.w3.z * ideology.z	0.04	0.03	-0.02 – 0.11	1.39	0.165	-0.02	0.03	-0.08 – 0.04	-0.65	0.514	0.05	0.03	-0.01 – 0.11	1.48	0.138
AAMC.w3.z * age.z	-0.02	0.03	-0.08 – 0.05	-0.49	0.621	0.08	0.03	0.01 – 0.14	2.36	0.019	-0.04	0.03	-0.10 – 0.03	-1.12	0.261
AAMC.w3.z * education.z	-0.05	0.04	-0.12 – 0.03	-1.26	0.207	0.08	0.04	0.01 – 0.15	2.11	0.035	-0.08	0.04	-0.15 – -0.01	-2.11	0.035
AAMC.w3.z * white_.5	-0.07	0.09	-0.24 – 0.10	-0.77	0.441	0.01	0.08	-0.15 – 0.17	0.12	0.908	-0.11	0.08	-0.28 – 0.05	-1.35	0.176
trustSci.z											0.23	0.04	0.16 – 0.31	6.07	<0.001
trustSci.z * ideology.z											0.03	0.03	-0.02 – 0.09	1.12	0.262
trustSci.z * age.z											-0.02	0.03	-0.09 – 0.05	-0.56	0.576
trustSci.z * education.z											0.05	0.03	-0.01 – 0.12	1.54	0.125
trustSci.z * white_.5											0.23	0.07	0.09 – 0.37	3.24	0.001
Observations	886					884					882
R² / R² adjusted	0.133 / 0.124					0.266 / 0.258					0.216 / 0.203

4. AAMC & Sum consumption

A. Summer 2020

i. (+) AAMC.z –> trust in experts –> vaxx intentions

m1 <- lm(vaxxIntentions.z ~ (AAMC.z + sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m2 <- lm(trustExpert.z ~ (AAMC.z + sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m3 <- lm(vaxxIntentions.z ~ (AAMC.z + sum.exp.r.z + trustExpert.z) * (ideology.z + USvUK + age.z + education.z), data = d1)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					trustExpert.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.07	0.02	0.04 – 0.10	4.44	<0.001	0.10	0.02	0.07 – 0.13	6.45	<0.001	0.02	0.02	-0.01 – 0.05	1.18	0.240
AAMC.z	-0.02	0.02	-0.06 – 0.01	-1.16	0.247	-0.02	0.02	-0.05 – 0.02	-0.96	0.339	-0.01	0.02	-0.05 – 0.02	-0.80	0.423
sum.exp.r.z	0.16	0.02	0.12 – 0.20	8.22	<0.001	0.11	0.02	0.08 – 0.15	6.01	<0.001	0.12	0.02	0.08 – 0.15	6.46	<0.001
ideology.z	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001	-0.22	0.02	-0.25 – -0.19	-14.25	<0.001	-0.09	0.02	-0.12 – -0.06	-5.98	<0.001
USvUK	0.41	0.03	0.34 – 0.48	11.88	<0.001	0.34	0.03	0.27 – 0.41	10.00	<0.001	0.28	0.03	0.21 – 0.34	8.25	<0.001
age.z	0.17	0.02	0.14 – 0.20	10.37	<0.001	0.10	0.02	0.07 – 0.13	6.27	<0.001	0.14	0.02	0.11 – 0.17	8.77	<0.001
education.z	0.07	0.02	0.04 – 0.10	4.43	<0.001	0.08	0.02	0.05 – 0.11	5.29	<0.001	0.04	0.02	0.01 – 0.07	2.91	0.004
AAMC.z * ideology.z	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001	-0.07	0.02	-0.10 – -0.03	-4.04	<0.001	-0.04	0.02	-0.07 – -0.00	-2.27	0.023
AAMC.z * USvUK	-0.27	0.04	-0.35 – -0.19	-6.62	<0.001	-0.17	0.04	-0.25 – -0.09	-4.33	<0.001	-0.21	0.04	-0.28 – -0.13	-5.38	<0.001
AAMC.z * age.z	-0.02	0.02	-0.05 – 0.02	-0.97	0.333	0.02	0.02	-0.01 – 0.06	1.45	0.148	-0.03	0.02	-0.06 – 0.01	-1.59	0.111
AAMC.z * education.z	0.00	0.02	-0.04 – 0.04	0.07	0.946	0.02	0.02	-0.01 – 0.06	1.32	0.186	-0.01	0.02	-0.04 – 0.03	-0.43	0.670
sum.exp.r.z * ideology.z	0.08	0.02	0.05 – 0.12	5.18	<0.001	0.09	0.02	0.06 – 0.12	5.81	<0.001	0.05	0.02	0.02 – 0.08	3.45	0.001
sum.exp.r.z * USvUK	0.02	0.04	-0.06 – 0.10	0.57	0.569	0.02	0.04	-0.06 – 0.10	0.51	0.609	0.01	0.04	-0.07 – 0.08	0.23	0.819
sum.exp.r.z * age.z	-0.02	0.02	-0.06 – 0.02	-1.17	0.241	0.03	0.02	-0.01 – 0.07	1.61	0.108	-0.04	0.02	-0.07 – -0.00	-2.01	0.045
sum.exp.r.z * education.z	-0.03	0.02	-0.06 – 0.00	-1.77	0.076	-0.00	0.02	-0.03 – 0.03	-0.11	0.911	-0.03	0.01	-0.06 – 0.00	-1.94	0.052
trustExpert.z											0.39	0.02	0.35 – 0.42	21.53	<0.001
trustExpert.z * ideology.z											-0.02	0.01	-0.04 – 0.01	-1.10	0.271
trustExpert.z * USvUK											0.09	0.04	0.02 – 0.16	2.37	0.018
trustExpert.z * age.z											0.01	0.02	-0.02 – 0.04	0.79	0.429
trustExpert.z * education.z											0.02	0.01	-0.00 – 0.05	1.59	0.111
Observations	4101					4101					4101
R² / R² adjusted	0.118 / 0.115					0.124 / 0.121					0.228 / 0.225

- M1 simple effects

m1.us <- lm(vaxxIntentions.z ~ (AAMC.z + sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m1.uk <- lm(vaxxIntentions.z ~ (AAMC.z + sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us, m1.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.13	0.02	-0.17 – -0.10	-6.86	<0.001	0.28	0.03	0.22 – 0.33	10.23	<0.001
AAMC.z	0.11	0.02	0.07 – 0.16	4.96	<0.001	-0.16	0.03	-0.22 – -0.09	-4.97	<0.001
sum.exp.r.z	0.15	0.02	0.11 – 0.19	7.23	<0.001	0.17	0.03	0.10 – 0.24	4.98	<0.001
ideology.z	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001
US_0	0.41	0.03	0.34 – 0.48	11.88	<0.001
age.z	0.17	0.02	0.14 – 0.20	10.37	<0.001	0.17	0.02	0.14 – 0.20	10.37	<0.001
education.z	0.07	0.02	0.04 – 0.10	4.43	<0.001	0.07	0.02	0.04 – 0.10	4.43	<0.001
AAMC.z * ideology.z	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001
AAMC.z * US_0	-0.27	0.04	-0.35 – -0.19	-6.62	<0.001
AAMC.z * age.z	-0.02	0.02	-0.05 – 0.02	-0.97	0.333	-0.02	0.02	-0.05 – 0.02	-0.97	0.333
AAMC.z * education.z	0.00	0.02	-0.04 – 0.04	0.07	0.946	0.00	0.02	-0.04 – 0.04	0.07	0.946
sum.exp.r.z * ideology.z	0.08	0.02	0.05 – 0.12	5.18	<0.001	0.08	0.02	0.05 – 0.12	5.18	<0.001
sum.exp.r.z * US_0	0.02	0.04	-0.06 – 0.10	0.57	0.569
sum.exp.r.z * age.z	-0.02	0.02	-0.06 – 0.02	-1.17	0.241	-0.02	0.02	-0.06 – 0.02	-1.17	0.241
sum.exp.r.z * education.z	-0.03	0.02	-0.06 – 0.00	-1.77	0.076	-0.03	0.02	-0.06 – 0.00	-1.77	0.076
UK_0						-0.41	0.03	-0.48 – -0.34	-11.88	<0.001
AAMC.z * UK_0						0.27	0.04	0.19 – 0.35	6.62	<0.001
sum.exp.r.z * UK_0						-0.02	0.04	-0.10 – 0.06	-0.57	0.569
Observations	4101					4101
R² / R² adjusted	0.118 / 0.115					0.118 / 0.115

- M2 simple effects

m2.us <- lm(trustExpert.z ~ (AAMC.z + sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m2.uk <- lm(trustExpert.z ~ (AAMC.z + sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)


tab_model(m2.us, m2.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	trustExpert.z					trustExpert.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.07	0.02	-0.11 – -0.03	-3.55	<0.001	0.27	0.03	0.22 – 0.32	10.22	<0.001
AAMC.z	0.07	0.02	0.03 – 0.11	3.08	0.002	-0.10	0.03	-0.16 – -0.04	-3.37	0.001
sum.exp.r.z	0.10	0.02	0.06 – 0.14	5.18	<0.001	0.12	0.03	0.06 – 0.19	3.70	<0.001
ideology.z	-0.22	0.02	-0.25 – -0.19	-14.25	<0.001	-0.22	0.02	-0.25 – -0.19	-14.25	<0.001
US_0	0.34	0.03	0.27 – 0.41	10.00	<0.001
age.z	0.10	0.02	0.07 – 0.13	6.27	<0.001	0.10	0.02	0.07 – 0.13	6.27	<0.001
education.z	0.08	0.02	0.05 – 0.11	5.29	<0.001	0.08	0.02	0.05 – 0.11	5.29	<0.001
AAMC.z * ideology.z	-0.07	0.02	-0.10 – -0.03	-4.04	<0.001	-0.07	0.02	-0.10 – -0.03	-4.04	<0.001
AAMC.z * US_0	-0.17	0.04	-0.25 – -0.09	-4.33	<0.001
AAMC.z * age.z	0.02	0.02	-0.01 – 0.06	1.45	0.148	0.02	0.02	-0.01 – 0.06	1.45	0.148
AAMC.z * education.z	0.02	0.02	-0.01 – 0.06	1.32	0.186	0.02	0.02	-0.01 – 0.06	1.32	0.186
sum.exp.r.z * ideology.z	0.09	0.02	0.06 – 0.12	5.81	<0.001	0.09	0.02	0.06 – 0.12	5.81	<0.001
sum.exp.r.z * US_0	0.02	0.04	-0.06 – 0.10	0.51	0.609
sum.exp.r.z * age.z	0.03	0.02	-0.01 – 0.07	1.61	0.108	0.03	0.02	-0.01 – 0.07	1.61	0.108
sum.exp.r.z * education.z	-0.00	0.02	-0.03 – 0.03	-0.11	0.911	-0.00	0.02	-0.03 – 0.03	-0.11	0.911
UK_0						-0.34	0.03	-0.41 – -0.27	-10.00	<0.001
AAMC.z * UK_0						0.17	0.04	0.09 – 0.25	4.33	<0.001
sum.exp.r.z * UK_0						-0.02	0.04	-0.10 – 0.06	-0.51	0.609
Observations	4101					4101
R² / R² adjusted	0.124 / 0.121					0.124 / 0.121

- M3 simple effects

m3.us <- lm(vaxxIntentions.z ~ (AAMC.z + sum.exp.r.z + trustExpert.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m3.uk <- lm(vaxxIntentions.z ~ (AAMC.z + sum.exp.r.z + trustExpert.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m3.us, m3.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.12	0.02	-0.16 – -0.08	-6.37	<0.001	0.16	0.03	0.10 – 0.21	5.86	<0.001
AAMC.z	0.09	0.02	0.05 – 0.13	4.15	<0.001	-0.12	0.03	-0.17 – -0.06	-3.96	<0.001
sum.exp.r.z	0.11	0.02	0.08 – 0.15	5.90	<0.001	0.12	0.03	0.06 – 0.18	3.80	<0.001
trustExpert.z	0.34	0.02	0.31 – 0.38	19.50	<0.001	0.43	0.03	0.37 – 0.50	13.49	<0.001
ideology.z	-0.09	0.02	-0.12 – -0.06	-5.98	<0.001	-0.09	0.02	-0.12 – -0.06	-5.98	<0.001
US_0	0.28	0.03	0.21 – 0.34	8.25	<0.001
age.z	0.14	0.02	0.11 – 0.17	8.77	<0.001	0.14	0.02	0.11 – 0.17	8.77	<0.001
education.z	0.04	0.02	0.01 – 0.07	2.91	0.004	0.04	0.02	0.01 – 0.07	2.91	0.004
AAMC.z * ideology.z	-0.04	0.02	-0.07 – -0.00	-2.27	0.023	-0.04	0.02	-0.07 – -0.00	-2.27	0.023
AAMC.z * US_0	-0.21	0.04	-0.28 – -0.13	-5.38	<0.001
AAMC.z * age.z	-0.03	0.02	-0.06 – 0.01	-1.59	0.111	-0.03	0.02	-0.06 – 0.01	-1.59	0.111
AAMC.z * education.z	-0.01	0.02	-0.04 – 0.03	-0.43	0.670	-0.01	0.02	-0.04 – 0.03	-0.43	0.670
sum.exp.r.z * ideology.z	0.05	0.02	0.02 – 0.08	3.45	0.001	0.05	0.02	0.02 – 0.08	3.45	0.001
sum.exp.r.z * US_0	0.01	0.04	-0.07 – 0.08	0.23	0.819
sum.exp.r.z * age.z	-0.04	0.02	-0.07 – -0.00	-2.01	0.045	-0.04	0.02	-0.07 – -0.00	-2.01	0.045
sum.exp.r.z * education.z	-0.03	0.01	-0.06 – 0.00	-1.94	0.052	-0.03	0.01	-0.06 – 0.00	-1.94	0.052
trustExpert.z * ideology.z	-0.02	0.01	-0.04 – 0.01	-1.10	0.271	-0.02	0.01	-0.04 – 0.01	-1.10	0.271
trustExpert.z * US_0	0.09	0.04	0.02 – 0.16	2.37	0.018
trustExpert.z * age.z	0.01	0.02	-0.02 – 0.04	0.79	0.429	0.01	0.02	-0.02 – 0.04	0.79	0.429
trustExpert.z * education.z	0.02	0.01	-0.00 – 0.05	1.59	0.111	0.02	0.01	-0.00 – 0.05	1.59	0.111
UK_0						-0.28	0.03	-0.34 – -0.21	-8.25	<0.001
AAMC.z * UK_0						0.21	0.04	0.13 – 0.28	5.38	<0.001
sum.exp.r.z * UK_0						-0.01	0.04	-0.08 – 0.07	-0.23	0.819
trustExpert.z * UK_0						-0.09	0.04	-0.16 – -0.02	-2.37	0.018
Observations	4101					4101
R² / R² adjusted	0.228 / 0.225					0.228 / 0.225

ii. (int) AAMC.z –> trust in experts –> vaxx intentions

m1 <- lm(vaxxIntentions.z ~ (AAMC.z * sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m2 <- lm(trustExpert.z ~ (AAMC.z * sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m3 <- lm(vaxxIntentions.z ~ (AAMC.z * sum.exp.r.z + trustExpert.z) * (ideology.z + USvUK + age.z + education.z), data = d1)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					trustExpert.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.09	0.02	0.06 – 0.13	4.73	<0.001	0.13	0.02	0.09 – 0.17	6.70	<0.001	0.03	0.02	-0.01 – 0.07	1.57	0.117
AAMC.z	-0.08	0.03	-0.13 – -0.02	-2.69	0.007	-0.09	0.03	-0.14 – -0.04	-3.29	0.001	-0.04	0.03	-0.09 – 0.01	-1.55	0.122
sum.exp.r.z	0.20	0.03	0.14 – 0.26	6.82	<0.001	0.17	0.03	0.12 – 0.23	5.99	<0.001	0.14	0.03	0.08 – 0.19	4.95	<0.001
ideology.z	-0.15	0.02	-0.18 – -0.11	-8.34	<0.001	-0.21	0.02	-0.24 – -0.17	-11.83	<0.001	-0.07	0.02	-0.11 – -0.04	-4.17	<0.001
USvUK	0.47	0.04	0.38 – 0.55	10.79	<0.001	0.37	0.04	0.29 – 0.45	8.76	<0.001	0.32	0.04	0.24 – 0.40	7.66	<0.001
age.z	0.19	0.02	0.15 – 0.23	10.19	<0.001	0.12	0.02	0.08 – 0.15	6.49	<0.001	0.15	0.02	0.12 – 0.19	8.48	<0.001
education.z	0.07	0.02	0.04 – 0.11	4.08	<0.001	0.09	0.02	0.05 – 0.12	4.88	<0.001	0.05	0.02	0.01 – 0.08	2.68	0.007
AAMC.z * sum.exp.r.z	-0.07	0.03	-0.14 – -0.01	-2.17	0.030	-0.11	0.03	-0.18 – -0.05	-3.46	0.001	-0.03	0.03	-0.09 – 0.03	-0.96	0.335
AAMC.z * ideology.z	-0.13	0.02	-0.17 – -0.08	-5.11	<0.001	-0.11	0.02	-0.16 – -0.06	-4.64	<0.001	-0.08	0.02	-0.13 – -0.04	-3.61	<0.001
AAMC.z * USvUK	-0.37	0.06	-0.49 – -0.25	-5.98	<0.001	-0.22	0.06	-0.34 – -0.10	-3.65	<0.001	-0.28	0.06	-0.40 – -0.17	-4.89	<0.001
AAMC.z * age.z	-0.07	0.03	-0.12 – -0.02	-2.59	0.010	-0.03	0.03	-0.08 – 0.02	-1.01	0.313	-0.06	0.02	-0.11 – -0.01	-2.39	0.017
AAMC.z * education.z	-0.00	0.03	-0.05 – 0.05	-0.12	0.904	0.02	0.02	-0.03 – 0.07	0.85	0.395	-0.01	0.02	-0.06 – 0.04	-0.45	0.654
sum.exp.r.z * ideology.z	0.14	0.02	0.09 – 0.18	5.99	<0.001	0.12	0.02	0.07 – 0.16	5.29	<0.001	0.10	0.02	0.05 – 0.14	4.49	<0.001
sum.exp.r.z * USvUK	0.13	0.06	0.01 – 0.25	2.05	0.040	0.07	0.06	-0.05 – 0.19	1.18	0.239	0.09	0.06	-0.02 – 0.21	1.54	0.123
sum.exp.r.z * age.z	0.01	0.02	-0.03 – 0.06	0.57	0.568	0.07	0.02	0.02 – 0.11	2.74	0.006	-0.01	0.02	-0.06 – 0.03	-0.53	0.593
sum.exp.r.z * education.z	-0.02	0.02	-0.07 – 0.02	-1.00	0.317	0.00	0.02	-0.04 – 0.05	0.07	0.944	-0.02	0.02	-0.07 – 0.02	-1.16	0.246
(AAMC.z * sum.exp.r.z) * ideology.z	-0.09	0.03	-0.15 – -0.03	-3.20	0.001	-0.04	0.03	-0.10 – 0.01	-1.58	0.113	-0.07	0.03	-0.13 – -0.02	-2.83	0.005
(AAMC.z * sum.exp.r.z) * USvUK	-0.17	0.07	-0.31 – -0.02	-2.26	0.024	-0.07	0.07	-0.21 – 0.08	-0.89	0.371	-0.14	0.07	-0.27 – -0.00	-1.97	0.049
(AAMC.z * sum.exp.r.z) * age.z	-0.08	0.03	-0.14 – -0.02	-2.52	0.012	-0.09	0.03	-0.15 – -0.02	-2.76	0.006	-0.05	0.03	-0.11 – 0.01	-1.66	0.098
(AAMC.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.07 – 0.05	-0.26	0.798	-0.01	0.03	-0.06 – 0.05	-0.20	0.839	-0.01	0.03	-0.06 – 0.05	-0.20	0.845
trustExpert.z											0.39	0.02	0.35 – 0.42	21.31	<0.001
trustExpert.z * ideology.z											-0.02	0.01	-0.04 – 0.01	-1.18	0.239
trustExpert.z * USvUK											0.08	0.04	0.01 – 0.16	2.27	0.023
trustExpert.z * age.z											0.01	0.02	-0.02 – 0.04	0.73	0.463
trustExpert.z * education.z											0.02	0.01	-0.00 – 0.05	1.62	0.106
Observations	4101					4101					4101
R² / R² adjusted	0.122 / 0.118					0.129 / 0.125					0.230 / 0.226

- M1 simple effects

m1.us <- lm(vaxxIntentions.z ~ (AAMC.z * sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m1.uk <- lm(vaxxIntentions.z ~ (AAMC.z * sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us, m1.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.14	0.02	-0.18 – -0.10	-6.33	<0.001	0.33	0.04	0.26 – 0.40	9.27	<0.001
AAMC.z	0.11	0.03	0.04 – 0.17	3.31	0.001	-0.26	0.05	-0.36 – -0.16	-5.27	<0.001
sum.exp.r.z	0.14	0.03	0.08 – 0.19	4.94	<0.001	0.27	0.05	0.16 – 0.37	4.89	<0.001
ideology.z	-0.15	0.02	-0.18 – -0.11	-8.34	<0.001	-0.15	0.02	-0.18 – -0.11	-8.34	<0.001
US_0	0.47	0.04	0.38 – 0.55	10.79	<0.001
age.z	0.19	0.02	0.15 – 0.23	10.19	<0.001	0.19	0.02	0.15 – 0.23	10.19	<0.001
education.z	0.07	0.02	0.04 – 0.11	4.08	<0.001	0.07	0.02	0.04 – 0.11	4.08	<0.001
AAMC.z * sum.exp.r.z	0.01	0.04	-0.07 – 0.09	0.29	0.772	-0.16	0.06	-0.27 – -0.04	-2.68	0.007
AAMC.z * ideology.z	-0.13	0.02	-0.17 – -0.08	-5.11	<0.001	-0.13	0.02	-0.17 – -0.08	-5.11	<0.001
AAMC.z * US_0	-0.37	0.06	-0.49 – -0.25	-5.98	<0.001
AAMC.z * age.z	-0.07	0.03	-0.12 – -0.02	-2.59	0.010	-0.07	0.03	-0.12 – -0.02	-2.59	0.010
AAMC.z * education.z	-0.00	0.03	-0.05 – 0.05	-0.12	0.904	-0.00	0.03	-0.05 – 0.05	-0.12	0.904
sum.exp.r.z * ideology.z	0.14	0.02	0.09 – 0.18	5.99	<0.001	0.14	0.02	0.09 – 0.18	5.99	<0.001
sum.exp.r.z * US_0	0.13	0.06	0.01 – 0.25	2.05	0.040
sum.exp.r.z * age.z	0.01	0.02	-0.03 – 0.06	0.57	0.568	0.01	0.02	-0.03 – 0.06	0.57	0.568
sum.exp.r.z * education.z	-0.02	0.02	-0.07 – 0.02	-1.00	0.317	-0.02	0.02	-0.07 – 0.02	-1.00	0.317
(AAMC.z * sum.exp.r.z) * ideology.z	-0.09	0.03	-0.15 – -0.03	-3.20	0.001	-0.09	0.03	-0.15 – -0.03	-3.20	0.001
(AAMC.z * sum.exp.r.z) * US_0	-0.17	0.07	-0.31 – -0.02	-2.26	0.024
(AAMC.z * sum.exp.r.z) * age.z	-0.08	0.03	-0.14 – -0.02	-2.52	0.012	-0.08	0.03	-0.14 – -0.02	-2.52	0.012
(AAMC.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.07 – 0.05	-0.26	0.798	-0.01	0.03	-0.07 – 0.05	-0.26	0.798
UK_0						-0.47	0.04	-0.55 – -0.38	-10.79	<0.001
AAMC.z * UK_0						0.37	0.06	0.25 – 0.49	5.98	<0.001
sum.exp.r.z * UK_0						-0.13	0.06	-0.25 – -0.01	-2.05	0.040
(AAMC.z * sum.exp.r.z) * UK_0						0.17	0.07	0.02 – 0.31	2.26	0.024
Observations	4101					4101
R² / R² adjusted	0.122 / 0.118					0.122 / 0.118

- M2 simple effects

m2.us <- lm(trustExpert.z ~ (AAMC.z * sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m2.uk <- lm(trustExpert.z ~ (AAMC.z * sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m2.us, m2.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	trustExpert.z					trustExpert.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.05	0.02	-0.10 – -0.01	-2.53	0.011	0.31	0.03	0.25 – 0.38	9.14	<0.001
AAMC.z	0.02	0.03	-0.04 – 0.08	0.59	0.557	-0.20	0.05	-0.29 – -0.11	-4.16	<0.001
sum.exp.r.z	0.14	0.03	0.08 – 0.19	5.05	<0.001	0.21	0.05	0.10 – 0.31	3.93	<0.001
ideology.z	-0.21	0.02	-0.24 – -0.17	-11.83	<0.001	-0.21	0.02	-0.24 – -0.17	-11.83	<0.001
US_0	0.37	0.04	0.29 – 0.45	8.76	<0.001
age.z	0.12	0.02	0.08 – 0.15	6.49	<0.001	0.12	0.02	0.08 – 0.15	6.49	<0.001
education.z	0.09	0.02	0.05 – 0.12	4.88	<0.001	0.09	0.02	0.05 – 0.12	4.88	<0.001
AAMC.z * sum.exp.r.z	-0.08	0.04	-0.16 – -0.00	-2.07	0.039	-0.15	0.06	-0.26 – -0.03	-2.55	0.011
AAMC.z * ideology.z	-0.11	0.02	-0.16 – -0.06	-4.64	<0.001	-0.11	0.02	-0.16 – -0.06	-4.64	<0.001
AAMC.z * US_0	-0.22	0.06	-0.34 – -0.10	-3.65	<0.001
AAMC.z * age.z	-0.03	0.03	-0.08 – 0.02	-1.01	0.313	-0.03	0.03	-0.08 – 0.02	-1.01	0.313
AAMC.z * education.z	0.02	0.02	-0.03 – 0.07	0.85	0.395	0.02	0.02	-0.03 – 0.07	0.85	0.395
sum.exp.r.z * ideology.z	0.12	0.02	0.07 – 0.16	5.29	<0.001	0.12	0.02	0.07 – 0.16	5.29	<0.001
sum.exp.r.z * US_0	0.07	0.06	-0.05 – 0.19	1.18	0.239
sum.exp.r.z * age.z	0.07	0.02	0.02 – 0.11	2.74	0.006	0.07	0.02	0.02 – 0.11	2.74	0.006
sum.exp.r.z * education.z	0.00	0.02	-0.04 – 0.05	0.07	0.944	0.00	0.02	-0.04 – 0.05	0.07	0.944
(AAMC.z * sum.exp.r.z) * ideology.z	-0.04	0.03	-0.10 – 0.01	-1.58	0.113	-0.04	0.03	-0.10 – 0.01	-1.58	0.113
(AAMC.z * sum.exp.r.z) * US_0	-0.07	0.07	-0.21 – 0.08	-0.89	0.371
(AAMC.z * sum.exp.r.z) * age.z	-0.09	0.03	-0.15 – -0.02	-2.76	0.006	-0.09	0.03	-0.15 – -0.02	-2.76	0.006
(AAMC.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.06 – 0.05	-0.20	0.839	-0.01	0.03	-0.06 – 0.05	-0.20	0.839
UK_0						-0.37	0.04	-0.45 – -0.29	-8.76	<0.001
AAMC.z * UK_0						0.22	0.06	0.10 – 0.34	3.65	<0.001
sum.exp.r.z * UK_0						-0.07	0.06	-0.19 – 0.05	-1.18	0.239
(AAMC.z * sum.exp.r.z) * UK_0						0.07	0.07	-0.08 – 0.21	0.89	0.371
Observations	4101					4101
R² / R² adjusted	0.129 / 0.125					0.129 / 0.125

- M3 simple effects

m3.us <- lm(vaxxIntentions.z ~ (AAMC.z * sum.exp.r.z + trustExpert.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m3.uk <- lm(vaxxIntentions.z ~ (AAMC.z * sum.exp.r.z + trustExpert.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)


tab_model(m3.us, m3.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.13	0.02	-0.17 – -0.09	-6.16	<0.001	0.19	0.03	0.12 – 0.26	5.50	<0.001
AAMC.z	0.10	0.03	0.04 – 0.16	3.28	0.001	-0.18	0.05	-0.27 – -0.09	-3.93	<0.001
sum.exp.r.z	0.09	0.03	0.04 – 0.14	3.53	<0.001	0.18	0.05	0.08 – 0.28	3.59	<0.001
trustExpert.z	0.34	0.02	0.31 – 0.38	19.39	<0.001	0.43	0.03	0.36 – 0.49	13.31	<0.001
ideology.z	-0.07	0.02	-0.11 – -0.04	-4.17	<0.001	-0.07	0.02	-0.11 – -0.04	-4.17	<0.001
US_0	0.32	0.04	0.24 – 0.40	7.66	<0.001
age.z	0.15	0.02	0.12 – 0.19	8.48	<0.001	0.15	0.02	0.12 – 0.19	8.48	<0.001
education.z	0.05	0.02	0.01 – 0.08	2.68	0.007	0.05	0.02	0.01 – 0.08	2.68	0.007
AAMC.z * sum.exp.r.z	0.04	0.04	-0.04 – 0.11	1.03	0.305	-0.10	0.05	-0.21 – 0.01	-1.81	0.071
AAMC.z * ideology.z	-0.08	0.02	-0.13 – -0.04	-3.61	<0.001	-0.08	0.02	-0.13 – -0.04	-3.61	<0.001
AAMC.z * US_0	-0.28	0.06	-0.40 – -0.17	-4.89	<0.001
AAMC.z * age.z	-0.06	0.02	-0.11 – -0.01	-2.39	0.017	-0.06	0.02	-0.11 – -0.01	-2.39	0.017
AAMC.z * education.z	-0.01	0.02	-0.06 – 0.04	-0.45	0.654	-0.01	0.02	-0.06 – 0.04	-0.45	0.654
sum.exp.r.z * ideology.z	0.10	0.02	0.05 – 0.14	4.49	<0.001	0.10	0.02	0.05 – 0.14	4.49	<0.001
sum.exp.r.z * US_0	0.09	0.06	-0.02 – 0.21	1.54	0.123
sum.exp.r.z * age.z	-0.01	0.02	-0.06 – 0.03	-0.53	0.593	-0.01	0.02	-0.06 – 0.03	-0.53	0.593
sum.exp.r.z * education.z	-0.02	0.02	-0.07 – 0.02	-1.16	0.246	-0.02	0.02	-0.07 – 0.02	-1.16	0.246
trustExpert.z * ideology.z	-0.02	0.01	-0.04 – 0.01	-1.18	0.239	-0.02	0.01	-0.04 – 0.01	-1.18	0.239
trustExpert.z * US_0	0.08	0.04	0.01 – 0.16	2.27	0.023
trustExpert.z * age.z	0.01	0.02	-0.02 – 0.04	0.73	0.463	0.01	0.02	-0.02 – 0.04	0.73	0.463
trustExpert.z * education.z	0.02	0.01	-0.00 – 0.05	1.62	0.106	0.02	0.01	-0.00 – 0.05	1.62	0.106
(AAMC.z * sum.exp.r.z) * ideology.z	-0.07	0.03	-0.13 – -0.02	-2.83	0.005	-0.07	0.03	-0.13 – -0.02	-2.83	0.005
(AAMC.z * sum.exp.r.z) * US_0	-0.14	0.07	-0.27 – -0.00	-1.97	0.049
(AAMC.z * sum.exp.r.z) * age.z	-0.05	0.03	-0.11 – 0.01	-1.66	0.098	-0.05	0.03	-0.11 – 0.01	-1.66	0.098
(AAMC.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.06 – 0.05	-0.20	0.845	-0.01	0.03	-0.06 – 0.05	-0.20	0.845
UK_0						-0.32	0.04	-0.40 – -0.24	-7.66	<0.001
AAMC.z * UK_0						0.28	0.06	0.17 – 0.40	4.89	<0.001
sum.exp.r.z * UK_0						-0.09	0.06	-0.21 – 0.02	-1.54	0.123
trustExpert.z * UK_0						-0.08	0.04	-0.16 – -0.01	-2.27	0.023
(AAMC.z * sum.exp.r.z) * UK_0						0.14	0.07	0.00 – 0.27	1.97	0.049
Observations	4101					4101
R² / R² adjusted	0.230 / 0.226					0.230 / 0.226

iii. (+) AAMC.r.z –> trust in experts –> vaxx intentions

m1 <- lm(vaxxIntentions.z ~ (AAMC.r.z + sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m2 <- lm(trustExpert.z ~ (AAMC.r.z + sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m3 <- lm(vaxxIntentions.z ~ (AAMC.r.z + sum.exp.r.z + trustExpert.z) * (ideology.z + USvUK + age.z + education.z), data = d1)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					trustExpert.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.07	0.02	0.04 – 0.10	4.44	<0.001	0.10	0.02	0.07 – 0.13	6.45	<0.001	0.02	0.02	-0.01 – 0.05	1.18	0.240
AAMC.r.z	-0.02	0.02	-0.06 – 0.01	-1.16	0.247	-0.02	0.02	-0.05 – 0.02	-0.96	0.339	-0.01	0.02	-0.05 – 0.02	-0.80	0.423
sum.exp.r.z	0.16	0.02	0.12 – 0.20	8.22	<0.001	0.11	0.02	0.08 – 0.15	6.01	<0.001	0.12	0.02	0.08 – 0.15	6.46	<0.001
ideology.z	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001	-0.22	0.02	-0.25 – -0.19	-14.25	<0.001	-0.09	0.02	-0.12 – -0.06	-5.98	<0.001
USvUK	0.41	0.03	0.34 – 0.48	11.88	<0.001	0.34	0.03	0.27 – 0.41	10.00	<0.001	0.28	0.03	0.21 – 0.34	8.25	<0.001
age.z	0.17	0.02	0.14 – 0.20	10.37	<0.001	0.10	0.02	0.07 – 0.13	6.27	<0.001	0.14	0.02	0.11 – 0.17	8.77	<0.001
education.z	0.07	0.02	0.04 – 0.10	4.43	<0.001	0.08	0.02	0.05 – 0.11	5.29	<0.001	0.04	0.02	0.01 – 0.07	2.91	0.004
AAMC.r.z * ideology.z	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001	-0.07	0.02	-0.10 – -0.03	-4.04	<0.001	-0.04	0.02	-0.07 – -0.00	-2.27	0.023
AAMC.r.z * USvUK	-0.27	0.04	-0.35 – -0.19	-6.62	<0.001	-0.17	0.04	-0.25 – -0.09	-4.33	<0.001	-0.21	0.04	-0.28 – -0.13	-5.38	<0.001
AAMC.r.z * age.z	-0.02	0.02	-0.05 – 0.02	-0.97	0.333	0.02	0.02	-0.01 – 0.06	1.45	0.148	-0.03	0.02	-0.06 – 0.01	-1.59	0.111
AAMC.r.z * education.z	0.00	0.02	-0.04 – 0.04	0.07	0.946	0.02	0.02	-0.01 – 0.06	1.32	0.186	-0.01	0.02	-0.04 – 0.03	-0.43	0.670
sum.exp.r.z * ideology.z	0.08	0.02	0.05 – 0.12	5.18	<0.001	0.09	0.02	0.06 – 0.12	5.81	<0.001	0.05	0.02	0.02 – 0.08	3.45	0.001
sum.exp.r.z * USvUK	0.02	0.04	-0.06 – 0.10	0.57	0.569	0.02	0.04	-0.06 – 0.10	0.51	0.609	0.01	0.04	-0.07 – 0.08	0.23	0.819
sum.exp.r.z * age.z	-0.02	0.02	-0.06 – 0.02	-1.17	0.241	0.03	0.02	-0.01 – 0.07	1.61	0.108	-0.04	0.02	-0.07 – -0.00	-2.01	0.045
sum.exp.r.z * education.z	-0.03	0.02	-0.06 – 0.00	-1.77	0.076	-0.00	0.02	-0.03 – 0.03	-0.11	0.911	-0.03	0.01	-0.06 – 0.00	-1.94	0.052
trustExpert.z											0.39	0.02	0.35 – 0.42	21.53	<0.001
trustExpert.z * ideology.z											-0.02	0.01	-0.04 – 0.01	-1.10	0.271
trustExpert.z * USvUK											0.09	0.04	0.02 – 0.16	2.37	0.018
trustExpert.z * age.z											0.01	0.02	-0.02 – 0.04	0.79	0.429
trustExpert.z * education.z											0.02	0.01	-0.00 – 0.05	1.59	0.111
Observations	4101					4101					4101
R² / R² adjusted	0.118 / 0.115					0.124 / 0.121					0.228 / 0.225

- M1 simple effects

m1.us <- lm(vaxxIntentions.z ~ (AAMC.r.z + sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m1.uk <- lm(vaxxIntentions.z ~ (AAMC.r.z + sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us, m1.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.13	0.02	-0.17 – -0.10	-6.86	<0.001	0.28	0.03	0.22 – 0.33	10.23	<0.001
AAMC.r.z	0.11	0.02	0.07 – 0.16	4.96	<0.001	-0.16	0.03	-0.22 – -0.09	-4.97	<0.001
sum.exp.r.z	0.15	0.02	0.11 – 0.19	7.23	<0.001	0.17	0.03	0.10 – 0.24	4.98	<0.001
ideology.z	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001
US_0	0.41	0.03	0.34 – 0.48	11.88	<0.001
age.z	0.17	0.02	0.14 – 0.20	10.37	<0.001	0.17	0.02	0.14 – 0.20	10.37	<0.001
education.z	0.07	0.02	0.04 – 0.10	4.43	<0.001	0.07	0.02	0.04 – 0.10	4.43	<0.001
AAMC.r.z * ideology.z	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001
AAMC.r.z * US_0	-0.27	0.04	-0.35 – -0.19	-6.62	<0.001
AAMC.r.z * age.z	-0.02	0.02	-0.05 – 0.02	-0.97	0.333	-0.02	0.02	-0.05 – 0.02	-0.97	0.333
AAMC.r.z * education.z	0.00	0.02	-0.04 – 0.04	0.07	0.946	0.00	0.02	-0.04 – 0.04	0.07	0.946
sum.exp.r.z * ideology.z	0.08	0.02	0.05 – 0.12	5.18	<0.001	0.08	0.02	0.05 – 0.12	5.18	<0.001
sum.exp.r.z * US_0	0.02	0.04	-0.06 – 0.10	0.57	0.569
sum.exp.r.z * age.z	-0.02	0.02	-0.06 – 0.02	-1.17	0.241	-0.02	0.02	-0.06 – 0.02	-1.17	0.241
sum.exp.r.z * education.z	-0.03	0.02	-0.06 – 0.00	-1.77	0.076	-0.03	0.02	-0.06 – 0.00	-1.77	0.076
UK_0						-0.41	0.03	-0.48 – -0.34	-11.88	<0.001
AAMC.r.z * UK_0						0.27	0.04	0.19 – 0.35	6.62	<0.001
sum.exp.r.z * UK_0						-0.02	0.04	-0.10 – 0.06	-0.57	0.569
Observations	4101					4101
R² / R² adjusted	0.118 / 0.115					0.118 / 0.115

- M2 simple effects

m2.us <- lm(trustExpert.z ~ (AAMC.r.z + sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m2.uk <- lm(trustExpert.z ~ (AAMC.r.z + sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us, m1.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.13	0.02	-0.17 – -0.10	-6.86	<0.001	0.28	0.03	0.22 – 0.33	10.23	<0.001
AAMC.r.z	0.11	0.02	0.07 – 0.16	4.96	<0.001	-0.16	0.03	-0.22 – -0.09	-4.97	<0.001
sum.exp.r.z	0.15	0.02	0.11 – 0.19	7.23	<0.001	0.17	0.03	0.10 – 0.24	4.98	<0.001
ideology.z	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001	-0.18	0.02	-0.21 – -0.15	-10.99	<0.001
US_0	0.41	0.03	0.34 – 0.48	11.88	<0.001
age.z	0.17	0.02	0.14 – 0.20	10.37	<0.001	0.17	0.02	0.14 – 0.20	10.37	<0.001
education.z	0.07	0.02	0.04 – 0.10	4.43	<0.001	0.07	0.02	0.04 – 0.10	4.43	<0.001
AAMC.r.z * ideology.z	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001	-0.06	0.02	-0.10 – -0.03	-3.72	<0.001
AAMC.r.z * US_0	-0.27	0.04	-0.35 – -0.19	-6.62	<0.001
AAMC.r.z * age.z	-0.02	0.02	-0.05 – 0.02	-0.97	0.333	-0.02	0.02	-0.05 – 0.02	-0.97	0.333
AAMC.r.z * education.z	0.00	0.02	-0.04 – 0.04	0.07	0.946	0.00	0.02	-0.04 – 0.04	0.07	0.946
sum.exp.r.z * ideology.z	0.08	0.02	0.05 – 0.12	5.18	<0.001	0.08	0.02	0.05 – 0.12	5.18	<0.001
sum.exp.r.z * US_0	0.02	0.04	-0.06 – 0.10	0.57	0.569
sum.exp.r.z * age.z	-0.02	0.02	-0.06 – 0.02	-1.17	0.241	-0.02	0.02	-0.06 – 0.02	-1.17	0.241
sum.exp.r.z * education.z	-0.03	0.02	-0.06 – 0.00	-1.77	0.076	-0.03	0.02	-0.06 – 0.00	-1.77	0.076
UK_0						-0.41	0.03	-0.48 – -0.34	-11.88	<0.001
AAMC.r.z * UK_0						0.27	0.04	0.19 – 0.35	6.62	<0.001
sum.exp.r.z * UK_0						-0.02	0.04	-0.10 – 0.06	-0.57	0.569
Observations	4101					4101
R² / R² adjusted	0.118 / 0.115					0.118 / 0.115

- M3 simple effects

m3.us <- lm(vaxxIntentions.z ~ (AAMC.r.z + sum.exp.r.z + trustExpert.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m3.uk <- lm(vaxxIntentions.z ~ (AAMC.r.z + sum.exp.r.z + trustExpert.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m3.us, m3.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.12	0.02	-0.16 – -0.08	-6.37	<0.001	0.16	0.03	0.10 – 0.21	5.86	<0.001
AAMC.r.z	0.09	0.02	0.05 – 0.13	4.15	<0.001	-0.12	0.03	-0.17 – -0.06	-3.96	<0.001
sum.exp.r.z	0.11	0.02	0.08 – 0.15	5.90	<0.001	0.12	0.03	0.06 – 0.18	3.80	<0.001
trustExpert.z	0.34	0.02	0.31 – 0.38	19.50	<0.001	0.43	0.03	0.37 – 0.50	13.49	<0.001
ideology.z	-0.09	0.02	-0.12 – -0.06	-5.98	<0.001	-0.09	0.02	-0.12 – -0.06	-5.98	<0.001
US_0	0.28	0.03	0.21 – 0.34	8.25	<0.001
age.z	0.14	0.02	0.11 – 0.17	8.77	<0.001	0.14	0.02	0.11 – 0.17	8.77	<0.001
education.z	0.04	0.02	0.01 – 0.07	2.91	0.004	0.04	0.02	0.01 – 0.07	2.91	0.004
AAMC.r.z * ideology.z	-0.04	0.02	-0.07 – -0.00	-2.27	0.023	-0.04	0.02	-0.07 – -0.00	-2.27	0.023
AAMC.r.z * US_0	-0.21	0.04	-0.28 – -0.13	-5.38	<0.001
AAMC.r.z * age.z	-0.03	0.02	-0.06 – 0.01	-1.59	0.111	-0.03	0.02	-0.06 – 0.01	-1.59	0.111
AAMC.r.z * education.z	-0.01	0.02	-0.04 – 0.03	-0.43	0.670	-0.01	0.02	-0.04 – 0.03	-0.43	0.670
sum.exp.r.z * ideology.z	0.05	0.02	0.02 – 0.08	3.45	0.001	0.05	0.02	0.02 – 0.08	3.45	0.001
sum.exp.r.z * US_0	0.01	0.04	-0.07 – 0.08	0.23	0.819
sum.exp.r.z * age.z	-0.04	0.02	-0.07 – -0.00	-2.01	0.045	-0.04	0.02	-0.07 – -0.00	-2.01	0.045
sum.exp.r.z * education.z	-0.03	0.01	-0.06 – 0.00	-1.94	0.052	-0.03	0.01	-0.06 – 0.00	-1.94	0.052
trustExpert.z * ideology.z	-0.02	0.01	-0.04 – 0.01	-1.10	0.271	-0.02	0.01	-0.04 – 0.01	-1.10	0.271
trustExpert.z * US_0	0.09	0.04	0.02 – 0.16	2.37	0.018
trustExpert.z * age.z	0.01	0.02	-0.02 – 0.04	0.79	0.429	0.01	0.02	-0.02 – 0.04	0.79	0.429
trustExpert.z * education.z	0.02	0.01	-0.00 – 0.05	1.59	0.111	0.02	0.01	-0.00 – 0.05	1.59	0.111
UK_0						-0.28	0.03	-0.34 – -0.21	-8.25	<0.001
AAMC.r.z * UK_0						0.21	0.04	0.13 – 0.28	5.38	<0.001
sum.exp.r.z * UK_0						-0.01	0.04	-0.08 – 0.07	-0.23	0.819
trustExpert.z * UK_0						-0.09	0.04	-0.16 – -0.02	-2.37	0.018
Observations	4101					4101
R² / R² adjusted	0.228 / 0.225					0.228 / 0.225

iv. (int) AAMC.r.z –> trust in experts –> vaxx intentions

m1 <- lm(vaxxIntentions.z ~ (AAMC.r.z * sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m2 <- lm(trustExpert.z ~ (AAMC.r.z * sum.exp.r.z) * (ideology.z + USvUK + age.z + education.z), data = d1)
m3 <- lm(vaxxIntentions.z ~ (AAMC.r.z * sum.exp.r.z + trustExpert.z) * (ideology.z + USvUK + age.z + education.z), data = d1)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					trustExpert.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	0.09	0.02	0.06 – 0.13	4.73	<0.001	0.13	0.02	0.09 – 0.17	6.70	<0.001	0.03	0.02	-0.01 – 0.07	1.57	0.117
AAMC.r.z	-0.08	0.03	-0.13 – -0.02	-2.69	0.007	-0.09	0.03	-0.14 – -0.04	-3.29	0.001	-0.04	0.03	-0.09 – 0.01	-1.55	0.122
sum.exp.r.z	0.20	0.03	0.14 – 0.26	6.82	<0.001	0.17	0.03	0.12 – 0.23	5.99	<0.001	0.14	0.03	0.08 – 0.19	4.95	<0.001
ideology.z	-0.15	0.02	-0.18 – -0.11	-8.34	<0.001	-0.21	0.02	-0.24 – -0.17	-11.83	<0.001	-0.07	0.02	-0.11 – -0.04	-4.17	<0.001
USvUK	0.47	0.04	0.38 – 0.55	10.79	<0.001	0.37	0.04	0.29 – 0.45	8.76	<0.001	0.32	0.04	0.24 – 0.40	7.66	<0.001
age.z	0.19	0.02	0.15 – 0.23	10.19	<0.001	0.12	0.02	0.08 – 0.15	6.49	<0.001	0.15	0.02	0.12 – 0.19	8.48	<0.001
education.z	0.07	0.02	0.04 – 0.11	4.08	<0.001	0.09	0.02	0.05 – 0.12	4.88	<0.001	0.05	0.02	0.01 – 0.08	2.68	0.007
AAMC.r.z * sum.exp.r.z	-0.07	0.03	-0.14 – -0.01	-2.17	0.030	-0.11	0.03	-0.18 – -0.05	-3.46	0.001	-0.03	0.03	-0.09 – 0.03	-0.96	0.335
AAMC.r.z * ideology.z	-0.13	0.02	-0.17 – -0.08	-5.11	<0.001	-0.11	0.02	-0.16 – -0.06	-4.64	<0.001	-0.08	0.02	-0.13 – -0.04	-3.61	<0.001
AAMC.r.z * USvUK	-0.37	0.06	-0.49 – -0.25	-5.98	<0.001	-0.22	0.06	-0.34 – -0.10	-3.65	<0.001	-0.28	0.06	-0.40 – -0.17	-4.89	<0.001
AAMC.r.z * age.z	-0.07	0.03	-0.12 – -0.02	-2.59	0.010	-0.03	0.03	-0.08 – 0.02	-1.01	0.313	-0.06	0.02	-0.11 – -0.01	-2.39	0.017
AAMC.r.z * education.z	-0.00	0.03	-0.05 – 0.05	-0.12	0.904	0.02	0.02	-0.03 – 0.07	0.85	0.395	-0.01	0.02	-0.06 – 0.04	-0.45	0.654
sum.exp.r.z * ideology.z	0.14	0.02	0.09 – 0.18	5.99	<0.001	0.12	0.02	0.07 – 0.16	5.29	<0.001	0.10	0.02	0.05 – 0.14	4.49	<0.001
sum.exp.r.z * USvUK	0.13	0.06	0.01 – 0.25	2.05	0.040	0.07	0.06	-0.05 – 0.19	1.18	0.239	0.09	0.06	-0.02 – 0.21	1.54	0.123
sum.exp.r.z * age.z	0.01	0.02	-0.03 – 0.06	0.57	0.568	0.07	0.02	0.02 – 0.11	2.74	0.006	-0.01	0.02	-0.06 – 0.03	-0.53	0.593
sum.exp.r.z * education.z	-0.02	0.02	-0.07 – 0.02	-1.00	0.317	0.00	0.02	-0.04 – 0.05	0.07	0.944	-0.02	0.02	-0.07 – 0.02	-1.16	0.246
(AAMC.r.z * sum.exp.r.z) * ideology.z	-0.09	0.03	-0.15 – -0.03	-3.20	0.001	-0.04	0.03	-0.10 – 0.01	-1.58	0.113	-0.07	0.03	-0.13 – -0.02	-2.83	0.005
(AAMC.r.z * sum.exp.r.z) * USvUK	-0.17	0.07	-0.31 – -0.02	-2.26	0.024	-0.07	0.07	-0.21 – 0.08	-0.89	0.371	-0.14	0.07	-0.27 – -0.00	-1.97	0.049
(AAMC.r.z * sum.exp.r.z) * age.z	-0.08	0.03	-0.14 – -0.02	-2.52	0.012	-0.09	0.03	-0.15 – -0.02	-2.76	0.006	-0.05	0.03	-0.11 – 0.01	-1.66	0.098
(AAMC.r.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.07 – 0.05	-0.26	0.798	-0.01	0.03	-0.06 – 0.05	-0.20	0.839	-0.01	0.03	-0.06 – 0.05	-0.20	0.845
trustExpert.z											0.39	0.02	0.35 – 0.42	21.31	<0.001
trustExpert.z * ideology.z											-0.02	0.01	-0.04 – 0.01	-1.18	0.239
trustExpert.z * USvUK											0.08	0.04	0.01 – 0.16	2.27	0.023
trustExpert.z * age.z											0.01	0.02	-0.02 – 0.04	0.73	0.463
trustExpert.z * education.z											0.02	0.01	-0.00 – 0.05	1.62	0.106
Observations	4101					4101					4101
R² / R² adjusted	0.122 / 0.118					0.129 / 0.125					0.230 / 0.226

- M1 simple effects

m1.us <- lm(vaxxIntentions.z ~ (AAMC.r.z * sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m1.uk <- lm(vaxxIntentions.z ~ (AAMC.r.z * sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m1.us, m1.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.14	0.02	-0.18 – -0.10	-6.33	<0.001	0.33	0.04	0.26 – 0.40	9.27	<0.001
AAMC.r.z	0.11	0.03	0.04 – 0.17	3.31	0.001	-0.26	0.05	-0.36 – -0.16	-5.27	<0.001
sum.exp.r.z	0.14	0.03	0.08 – 0.19	4.94	<0.001	0.27	0.05	0.16 – 0.37	4.89	<0.001
ideology.z	-0.15	0.02	-0.18 – -0.11	-8.34	<0.001	-0.15	0.02	-0.18 – -0.11	-8.34	<0.001
US_0	0.47	0.04	0.38 – 0.55	10.79	<0.001
age.z	0.19	0.02	0.15 – 0.23	10.19	<0.001	0.19	0.02	0.15 – 0.23	10.19	<0.001
education.z	0.07	0.02	0.04 – 0.11	4.08	<0.001	0.07	0.02	0.04 – 0.11	4.08	<0.001
AAMC.r.z * sum.exp.r.z	0.01	0.04	-0.07 – 0.09	0.29	0.772	-0.16	0.06	-0.27 – -0.04	-2.68	0.007
AAMC.r.z * ideology.z	-0.13	0.02	-0.17 – -0.08	-5.11	<0.001	-0.13	0.02	-0.17 – -0.08	-5.11	<0.001
AAMC.r.z * US_0	-0.37	0.06	-0.49 – -0.25	-5.98	<0.001
AAMC.r.z * age.z	-0.07	0.03	-0.12 – -0.02	-2.59	0.010	-0.07	0.03	-0.12 – -0.02	-2.59	0.010
AAMC.r.z * education.z	-0.00	0.03	-0.05 – 0.05	-0.12	0.904	-0.00	0.03	-0.05 – 0.05	-0.12	0.904
sum.exp.r.z * ideology.z	0.14	0.02	0.09 – 0.18	5.99	<0.001	0.14	0.02	0.09 – 0.18	5.99	<0.001
sum.exp.r.z * US_0	0.13	0.06	0.01 – 0.25	2.05	0.040
sum.exp.r.z * age.z	0.01	0.02	-0.03 – 0.06	0.57	0.568	0.01	0.02	-0.03 – 0.06	0.57	0.568
sum.exp.r.z * education.z	-0.02	0.02	-0.07 – 0.02	-1.00	0.317	-0.02	0.02	-0.07 – 0.02	-1.00	0.317
(AAMC.r.z * sum.exp.r.z) * ideology.z	-0.09	0.03	-0.15 – -0.03	-3.20	0.001	-0.09	0.03	-0.15 – -0.03	-3.20	0.001
(AAMC.r.z * sum.exp.r.z) * US_0	-0.17	0.07	-0.31 – -0.02	-2.26	0.024
(AAMC.r.z * sum.exp.r.z) * age.z	-0.08	0.03	-0.14 – -0.02	-2.52	0.012	-0.08	0.03	-0.14 – -0.02	-2.52	0.012
(AAMC.r.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.07 – 0.05	-0.26	0.798	-0.01	0.03	-0.07 – 0.05	-0.26	0.798
UK_0						-0.47	0.04	-0.55 – -0.38	-10.79	<0.001
AAMC.r.z * UK_0						0.37	0.06	0.25 – 0.49	5.98	<0.001
sum.exp.r.z * UK_0						-0.13	0.06	-0.25 – -0.01	-2.05	0.040
(AAMC.r.z * sum.exp.r.z) * UK_0						0.17	0.07	0.02 – 0.31	2.26	0.024
Observations	4101					4101
R² / R² adjusted	0.122 / 0.118					0.122 / 0.118

- M2 simple effects

m2.us <- lm(trustExpert.z ~ (AAMC.r.z * sum.exp.r.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m2.uk <- lm(trustExpert.z ~ (AAMC.r.z * sum.exp.r.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)


tab_model(m2.us, m2.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	trustExpert.z					trustExpert.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.05	0.02	-0.10 – -0.01	-2.53	0.011	0.31	0.03	0.25 – 0.38	9.14	<0.001
AAMC.r.z	0.02	0.03	-0.04 – 0.08	0.59	0.557	-0.20	0.05	-0.29 – -0.11	-4.16	<0.001
sum.exp.r.z	0.14	0.03	0.08 – 0.19	5.05	<0.001	0.21	0.05	0.10 – 0.31	3.93	<0.001
ideology.z	-0.21	0.02	-0.24 – -0.17	-11.83	<0.001	-0.21	0.02	-0.24 – -0.17	-11.83	<0.001
US_0	0.37	0.04	0.29 – 0.45	8.76	<0.001
age.z	0.12	0.02	0.08 – 0.15	6.49	<0.001	0.12	0.02	0.08 – 0.15	6.49	<0.001
education.z	0.09	0.02	0.05 – 0.12	4.88	<0.001	0.09	0.02	0.05 – 0.12	4.88	<0.001
AAMC.r.z * sum.exp.r.z	-0.08	0.04	-0.16 – -0.00	-2.07	0.039	-0.15	0.06	-0.26 – -0.03	-2.55	0.011
AAMC.r.z * ideology.z	-0.11	0.02	-0.16 – -0.06	-4.64	<0.001	-0.11	0.02	-0.16 – -0.06	-4.64	<0.001
AAMC.r.z * US_0	-0.22	0.06	-0.34 – -0.10	-3.65	<0.001
AAMC.r.z * age.z	-0.03	0.03	-0.08 – 0.02	-1.01	0.313	-0.03	0.03	-0.08 – 0.02	-1.01	0.313
AAMC.r.z * education.z	0.02	0.02	-0.03 – 0.07	0.85	0.395	0.02	0.02	-0.03 – 0.07	0.85	0.395
sum.exp.r.z * ideology.z	0.12	0.02	0.07 – 0.16	5.29	<0.001	0.12	0.02	0.07 – 0.16	5.29	<0.001
sum.exp.r.z * US_0	0.07	0.06	-0.05 – 0.19	1.18	0.239
sum.exp.r.z * age.z	0.07	0.02	0.02 – 0.11	2.74	0.006	0.07	0.02	0.02 – 0.11	2.74	0.006
sum.exp.r.z * education.z	0.00	0.02	-0.04 – 0.05	0.07	0.944	0.00	0.02	-0.04 – 0.05	0.07	0.944
(AAMC.r.z * sum.exp.r.z) * ideology.z	-0.04	0.03	-0.10 – 0.01	-1.58	0.113	-0.04	0.03	-0.10 – 0.01	-1.58	0.113
(AAMC.r.z * sum.exp.r.z) * US_0	-0.07	0.07	-0.21 – 0.08	-0.89	0.371
(AAMC.r.z * sum.exp.r.z) * age.z	-0.09	0.03	-0.15 – -0.02	-2.76	0.006	-0.09	0.03	-0.15 – -0.02	-2.76	0.006
(AAMC.r.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.06 – 0.05	-0.20	0.839	-0.01	0.03	-0.06 – 0.05	-0.20	0.839
UK_0						-0.37	0.04	-0.45 – -0.29	-8.76	<0.001
AAMC.r.z * UK_0						0.22	0.06	0.10 – 0.34	3.65	<0.001
sum.exp.r.z * UK_0						-0.07	0.06	-0.19 – 0.05	-1.18	0.239
(AAMC.r.z * sum.exp.r.z) * UK_0						0.07	0.07	-0.08 – 0.21	0.89	0.371
Observations	4101					4101
R² / R² adjusted	0.129 / 0.125					0.129 / 0.125

- M3 simple effects

m3.us <- lm(vaxxIntentions.z ~ (AAMC.r.z * sum.exp.r.z + trustExpert.z) * (ideology.z + US_0 + age.z + education.z), data = d1)
m3.uk <- lm(vaxxIntentions.z ~ (AAMC.r.z * sum.exp.r.z + trustExpert.z) * (ideology.z + UK_0 + age.z + education.z), data = d1)

tab_model(m3.us, m3.uk,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxIntentions.z					vaxxIntentions.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.13	0.02	-0.17 – -0.09	-6.16	<0.001	0.19	0.03	0.12 – 0.26	5.50	<0.001
AAMC.r.z	0.10	0.03	0.04 – 0.16	3.28	0.001	-0.18	0.05	-0.27 – -0.09	-3.93	<0.001
sum.exp.r.z	0.09	0.03	0.04 – 0.14	3.53	<0.001	0.18	0.05	0.08 – 0.28	3.59	<0.001
trustExpert.z	0.34	0.02	0.31 – 0.38	19.39	<0.001	0.43	0.03	0.36 – 0.49	13.31	<0.001
ideology.z	-0.07	0.02	-0.11 – -0.04	-4.17	<0.001	-0.07	0.02	-0.11 – -0.04	-4.17	<0.001
US_0	0.32	0.04	0.24 – 0.40	7.66	<0.001
age.z	0.15	0.02	0.12 – 0.19	8.48	<0.001	0.15	0.02	0.12 – 0.19	8.48	<0.001
education.z	0.05	0.02	0.01 – 0.08	2.68	0.007	0.05	0.02	0.01 – 0.08	2.68	0.007
AAMC.r.z * sum.exp.r.z	0.04	0.04	-0.04 – 0.11	1.03	0.305	-0.10	0.05	-0.21 – 0.01	-1.81	0.071
AAMC.r.z * ideology.z	-0.08	0.02	-0.13 – -0.04	-3.61	<0.001	-0.08	0.02	-0.13 – -0.04	-3.61	<0.001
AAMC.r.z * US_0	-0.28	0.06	-0.40 – -0.17	-4.89	<0.001
AAMC.r.z * age.z	-0.06	0.02	-0.11 – -0.01	-2.39	0.017	-0.06	0.02	-0.11 – -0.01	-2.39	0.017
AAMC.r.z * education.z	-0.01	0.02	-0.06 – 0.04	-0.45	0.654	-0.01	0.02	-0.06 – 0.04	-0.45	0.654
sum.exp.r.z * ideology.z	0.10	0.02	0.05 – 0.14	4.49	<0.001	0.10	0.02	0.05 – 0.14	4.49	<0.001
sum.exp.r.z * US_0	0.09	0.06	-0.02 – 0.21	1.54	0.123
sum.exp.r.z * age.z	-0.01	0.02	-0.06 – 0.03	-0.53	0.593	-0.01	0.02	-0.06 – 0.03	-0.53	0.593
sum.exp.r.z * education.z	-0.02	0.02	-0.07 – 0.02	-1.16	0.246	-0.02	0.02	-0.07 – 0.02	-1.16	0.246
trustExpert.z * ideology.z	-0.02	0.01	-0.04 – 0.01	-1.18	0.239	-0.02	0.01	-0.04 – 0.01	-1.18	0.239
trustExpert.z * US_0	0.08	0.04	0.01 – 0.16	2.27	0.023
trustExpert.z * age.z	0.01	0.02	-0.02 – 0.04	0.73	0.463	0.01	0.02	-0.02 – 0.04	0.73	0.463
trustExpert.z * education.z	0.02	0.01	-0.00 – 0.05	1.62	0.106	0.02	0.01	-0.00 – 0.05	1.62	0.106
(AAMC.r.z * sum.exp.r.z) * ideology.z	-0.07	0.03	-0.13 – -0.02	-2.83	0.005	-0.07	0.03	-0.13 – -0.02	-2.83	0.005
(AAMC.r.z * sum.exp.r.z) * US_0	-0.14	0.07	-0.27 – -0.00	-1.97	0.049
(AAMC.r.z * sum.exp.r.z) * age.z	-0.05	0.03	-0.11 – 0.01	-1.66	0.098	-0.05	0.03	-0.11 – 0.01	-1.66	0.098
(AAMC.r.z * sum.exp.r.z) * education.z	-0.01	0.03	-0.06 – 0.05	-0.20	0.845	-0.01	0.03	-0.06 – 0.05	-0.20	0.845
UK_0						-0.32	0.04	-0.40 – -0.24	-7.66	<0.001
AAMC.r.z * UK_0						0.28	0.06	0.17 – 0.40	4.89	<0.001
sum.exp.r.z * UK_0						-0.09	0.06	-0.21 – 0.02	-1.54	0.123
trustExpert.z * UK_0						-0.08	0.04	-0.16 – -0.01	-2.27	0.023
(AAMC.r.z * sum.exp.r.z) * UK_0						0.14	0.07	0.00 – 0.27	1.97	0.049
Observations	4101					4101
R² / R² adjusted	0.230 / 0.226					0.230 / 0.226

B. Spring 2022

i. (+) AAMC.r.z –> trust in science –> vaccinations

m1 <- lm(vaxxBehavior.z ~ (AAMC.w3.r.z + sum.exp.w3.r.z) * (ideology.z + age.z + education.z + white_.5), data = d)
m2 <- lm(trustSci.z ~ (AAMC.w3.r.z + sum.exp.w3.r.z) * (ideology.z + age.z + education.z + white_.5), data = d)
m3 <- lm(vaxxBehavior.z ~ (AAMC.w3.r.z + trustSci.z + sum.exp.w3.r.z) * (ideology.z + age.z + education.z + white_.5), data = d)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxBehavior.z					trustSci.z					vaxxBehavior.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.04	0.04	-0.12 – 0.05	-0.91	0.361	0.00	0.04	-0.08 – 0.08	0.03	0.980	-0.03	0.04	-0.12 – 0.05	-0.81	0.420
AAMC.w3.r.z	0.07	0.05	-0.03 – 0.16	1.42	0.155	0.11	0.05	0.02 – 0.20	2.34	0.019	0.04	0.05	-0.05 – 0.13	0.90	0.368
sum.exp.w3.r.z	0.17	0.03	0.10 – 0.23	5.03	<0.001	-0.01	0.03	-0.08 – 0.05	-0.41	0.685	0.16	0.03	0.10 – 0.23	5.02	<0.001
ideology.z	-0.21	0.04	-0.28 – -0.14	-5.89	<0.001	-0.43	0.03	-0.50 – -0.37	-12.72	<0.001	-0.08	0.04	-0.16 – -0.01	-2.22	0.027
age.z	0.24	0.04	0.16 – 0.31	6.40	<0.001	0.11	0.04	0.04 – 0.18	2.96	0.003	0.21	0.04	0.14 – 0.28	5.89	<0.001
education.z	0.15	0.04	0.07 – 0.24	3.75	<0.001	0.12	0.04	0.04 – 0.20	2.94	0.003	0.12	0.04	0.04 – 0.20	2.96	0.003
white_.5	-0.08	0.08	-0.24 – 0.08	-0.94	0.349	0.26	0.08	0.10 – 0.41	3.22	0.001	-0.16	0.08	-0.32 – -0.00	-1.98	0.048
AAMC.w3.r.z * ideology.z	0.03	0.03	-0.04 – 0.10	0.86	0.391	-0.04	0.03	-0.11 – 0.02	-1.30	0.194	0.04	0.03	-0.02 – 0.11	1.23	0.220
AAMC.w3.r.z * age.z	-0.00	0.04	-0.07 – 0.07	-0.02	0.983	0.05	0.03	-0.01 – 0.12	1.53	0.125	-0.02	0.03	-0.09 – 0.05	-0.50	0.614
AAMC.w3.r.z * education.z	-0.01	0.04	-0.09 – 0.07	-0.23	0.817	0.10	0.04	0.02 – 0.18	2.52	0.012	-0.05	0.04	-0.13 – 0.03	-1.22	0.224
AAMC.w3.r.z * white_.5	-0.03	0.09	-0.21 – 0.15	-0.36	0.716	-0.02	0.09	-0.20 – 0.15	-0.24	0.812	-0.05	0.09	-0.23 – 0.12	-0.60	0.551
sum.exp.w3.r.z * ideology.z	0.06	0.03	0.00 – 0.12	1.99	0.047	0.06	0.03	0.00 – 0.11	2.10	0.036	0.03	0.03	-0.02 – 0.09	1.16	0.246
sum.exp.w3.r.z * age.z	-0.04	0.03	-0.10 – 0.03	-1.09	0.277	0.07	0.03	0.01 – 0.13	2.12	0.034	-0.04	0.03	-0.10 – 0.03	-1.13	0.259
sum.exp.w3.r.z * education.z	-0.04	0.03	-0.10 – 0.01	-1.51	0.132	-0.05	0.03	-0.10 – 0.01	-1.76	0.080	-0.02	0.03	-0.08 – 0.03	-0.87	0.387
sum.exp.w3.r.z * white_.5	0.02	0.07	-0.11 – 0.15	0.27	0.791	0.04	0.07	-0.08 – 0.17	0.67	0.501	-0.02	0.07	-0.15 – 0.11	-0.29	0.772
trustSci.z											0.25	0.04	0.18 – 0.33	6.51	<0.001
trustSci.z * ideology.z											0.03	0.03	-0.02 – 0.09	1.16	0.247
trustSci.z * age.z											-0.04	0.03	-0.11 – 0.03	-1.07	0.284
trustSci.z * education.z											0.04	0.03	-0.02 – 0.11	1.33	0.183
trustSci.z * white_.5											0.18	0.07	0.04 – 0.32	2.54	0.011
Observations	886					884					882
R² / R² adjusted	0.163 / 0.149					0.278 / 0.266					0.240 / 0.223

ii. (int) AAMC.r.z –> trust in science –> vaccinations

m1 <- lm(vaxxBehavior.z ~ (AAMC.w3.r.z * sum.exp.w3.r.z) * (ideology.z + age.z + education.z + white_.5), data = d)
m2 <- lm(trustSci.z ~ (AAMC.w3.r.z * sum.exp.w3.r.z) * (ideology.z + age.z + education.z + white_.5), data = d)
m3 <- lm(vaxxBehavior.z ~ (AAMC.w3.r.z * sum.exp.w3.r.z + trustSci.z) * (ideology.z + age.z + education.z + white_.5), data = d)

tab_model(m1, m2, m3,
          show.ci = .95,
          show.se = T,
          show.stat = T,
          string.stat = "t",
          string.se="SE",
          string.est = "B",
          digits = 2)

	vaxxBehavior.z					trustSci.z					vaxxBehavior.z
Predictors	B	SE	CI	t	p	B	SE	CI	t	p	B	SE	CI	t	p
(Intercept)	-0.04	0.04	-0.13 – 0.05	-0.90	0.367	0.02	0.04	-0.07 – 0.10	0.37	0.715	-0.04	0.04	-0.12 – 0.05	-0.91	0.364
AAMC.w3.r.z	0.04	0.05	-0.06 – 0.15	0.77	0.440	0.06	0.05	-0.04 – 0.17	1.22	0.224	0.03	0.05	-0.07 – 0.13	0.56	0.576
sum.exp.w3.r.z	0.23	0.06	0.12 – 0.34	4.11	<0.001	0.06	0.05	-0.05 – 0.16	1.07	0.286	0.20	0.05	0.09 – 0.31	3.69	<0.001
ideology.z	-0.20	0.04	-0.27 – -0.13	-5.51	<0.001	-0.42	0.03	-0.48 – -0.35	-12.28	<0.001	-0.08	0.04	-0.15 – -0.00	-2.04	0.041
age.z	0.24	0.04	0.17 – 0.32	6.36	<0.001	0.10	0.04	0.03 – 0.17	2.74	0.006	0.22	0.04	0.15 – 0.29	5.87	<0.001
education.z	0.16	0.04	0.07 – 0.24	3.75	<0.001	0.11	0.04	0.03 – 0.19	2.83	0.005	0.12	0.04	0.04 – 0.20	3.02	0.003
white_.5	-0.08	0.08	-0.24 – 0.09	-0.92	0.357	0.29	0.08	0.14 – 0.45	3.66	<0.001	-0.17	0.08	-0.33 – -0.01	-2.06	0.040
AAMC.w3.r.z * sum.exp.w3.r.z	-0.12	0.08	-0.29 – 0.04	-1.51	0.132	-0.16	0.08	-0.31 – -0.00	-2.01	0.045	-0.07	0.08	-0.23 – 0.09	-0.86	0.391
AAMC.w3.r.z * ideology.z	-0.01	0.04	-0.09 – 0.07	-0.20	0.838	-0.10	0.04	-0.18 – -0.02	-2.58	0.010	0.02	0.04	-0.06 – 0.10	0.55	0.579
AAMC.w3.r.z * age.z	-0.04	0.05	-0.13 – 0.05	-0.85	0.394	0.03	0.04	-0.06 – 0.11	0.62	0.535	-0.04	0.04	-0.13 – 0.04	-0.98	0.326
AAMC.w3.r.z * education.z	0.01	0.05	-0.09 – 0.10	0.11	0.912	0.14	0.05	0.05 – 0.23	2.90	0.004	-0.05	0.05	-0.14 – 0.05	-0.96	0.337
AAMC.w3.r.z * white_.5	-0.01	0.10	-0.21 – 0.19	-0.07	0.941	-0.13	0.10	-0.32 – 0.06	-1.33	0.184	-0.00	0.10	-0.19 – 0.19	-0.02	0.987
sum.exp.w3.r.z * ideology.z	0.08	0.05	-0.01 – 0.17	1.73	0.084	0.08	0.04	-0.01 – 0.17	1.76	0.078	0.05	0.04	-0.04 – 0.14	1.11	0.266
sum.exp.w3.r.z * age.z	0.03	0.05	-0.08 – 0.13	0.48	0.628	0.12	0.05	0.02 – 0.22	2.41	0.016	0.00	0.05	-0.10 – 0.10	0.04	0.971
sum.exp.w3.r.z * education.z	-0.08	0.06	-0.19 – 0.03	-1.40	0.160	-0.12	0.05	-0.22 – -0.01	-2.22	0.026	-0.03	0.05	-0.14 – 0.07	-0.62	0.537
sum.exp.w3.r.z * white_.5	-0.05	0.11	-0.26 – 0.17	-0.42	0.677	0.23	0.10	0.03 – 0.44	2.26	0.024	-0.12	0.10	-0.33 – 0.08	-1.18	0.238
(AAMC.w3.r.z sum.exp.w3.r.z) ideology.z	-0.05	0.07	-0.18 – 0.08	-0.71	0.480	-0.04	0.06	-0.17 – 0.08	-0.71	0.479	-0.03	0.06	-0.16 – 0.09	-0.53	0.597
(AAMC.w3.r.z sum.exp.w3.r.z) age.z	-0.13	0.08	-0.28 – 0.03	-1.63	0.104	-0.12	0.07	-0.27 – 0.03	-1.62	0.105	-0.08	0.08	-0.23 – 0.07	-1.05	0.295
(AAMC.w3.r.z sum.exp.w3.r.z) education.z	0.06	0.08	-0.10 – 0.22	0.75	0.452	0.12	0.08	-0.03 – 0.28	1.53	0.127	0.02	0.08	-0.14 – 0.17	0.20	0.841
(AAMC.w3.r.z sum.exp.w3.r.z) white_.5	0.10	0.15	-0.20 – 0.41	0.67	0.500	-0.37	0.15	-0.66 – -0.08	-2.47	0.014	0.19	0.15	-0.11 – 0.48	1.25	0.213
trustSci.z											0.25	0.04	0.18 – 0.33	6.46	<0.001
trustSci.z * ideology.z											0.03	0.03	-0.03 – 0.09	0.97	0.332
trustSci.z * age.z											-0.04	0.04	-0.11 – 0.03	-1.08	0.279
trustSci.z * education.z											0.04	0.03	-0.02 – 0.11	1.23	0.219
trustSci.z * white_.5											0.18	0.07	0.03 – 0.32	2.43	0.015
Observations	886					884					882
R² / R² adjusted	0.170 / 0.152					0.300 / 0.285					0.242 / 0.221

5. Explore the above using rescaled versions of other measures that we currently use for robustness checks. Especially affective tone and threat.

media update

Dani Grant

2023-04-11

Summer 2020

Spring 2022

1. For all measures, we want to scale [0, 1], as above. Then standardize (within country) for the purposes of analysis. (done)

2. Correlations of AMC, AAMC, CRT, Trust in science/experts, separate for US and UK, as appropriate (done)

A. Summer 2020 US

B. Summer 2020 UK

C. Spring 2022 US

3. mediation models

A. Summer 2020

i. AMC.r.z –> trust in experts –> vaxx intentions

- M1 simple effects

- M2 simple effects

- M3 simple effects

ii. AAMC.r.z –> trust in experts –> vaxx intentions

- M1 simple effects for country

- M2 simple effects for country

- M3 simple effects for country

iii. AAMC.z –> trust in experts –> vaxx intentions

- M1 simple effects for country

- M2 simple effects for country

- M3 simple effects for country

B. Spring 2022

i. AMC.r.z –> trust in science –> vaccinations

ii. AAMC.r.z –> trust in science –> vaccinations

iii. AAMC.z –> trust in science –> vaccinations

4. AAMC & Sum consumption

A. Summer 2020

i. (+) AAMC.z –> trust in experts –> vaxx intentions

- M1 simple effects

- M2 simple effects

- M3 simple effects

ii. (int) AAMC.z –> trust in experts –> vaxx intentions

- M1 simple effects

- M2 simple effects

- M3 simple effects

iii. (+) AAMC.r.z –> trust in experts –> vaxx intentions

- M1 simple effects

- M2 simple effects

- M3 simple effects

iv. (int) AAMC.r.z –> trust in experts –> vaxx intentions

- M1 simple effects

- M2 simple effects

- M3 simple effects

B. Spring 2022

i. (+) AAMC.r.z –> trust in science –> vaccinations

ii. (int) AAMC.r.z –> trust in science –> vaccinations

5. Explore the above using rescaled versions of other measures that we currently use for robustness checks. Especially affective tone and threat.

A. Summer 2020

i. affective tone

ii. threat

B. Spring 2022

i. affective tone

ii. threat