DESCRIPTIVE STATS

1. demographics

a. sample size

## [1] 3860

b. age

c. gender

## 
## custom female   male 
##      6   1681   1655
## 
## custom female   male 
##  0.002  0.503  0.495

d. ethnicity

## 
##   asian   black   latin natAmer   other  pacIsl   white 
##     293     446     288      27      47      16    2224
## 
##   asian   black   latin natAmer   other  pacIsl   white 
##   0.088   0.133   0.086   0.008   0.014   0.005   0.666

e. party identity

## 
##    Democrat Independent  Republican 
##        1533         674        1238
## 
##    Democrat Independent  Republican 
##       0.445       0.196       0.359

f. education

2. individual difference measures

a. media exposure

Not included here, but asked in all 3 waves, 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. analytical media index

i. wave 1

Collected in wave 1 (July - August 2020), the first step to creating the analytical media index is multiplying individual analytic thinking scores for each media outlet by participant rated exposure to that outlet, Then taking the proportion of each of these products (i.e., dividing by the 12 possible US outlets to be exposed to)

foxAnalyticalIndex = (foxNewsExposure x foxAnalyticalScore) (foxAnalyticalIndex + cnnAnalyticalIndex + msnbcAnalyticalIndex + …) / 12 total outlets

ii. wave 2

Collected in wave 2 (November 2020), the creation of this index follows the same path as above with the exception of using wave 2 updated media analytical thinking scores and participant media exposure ratings.

iii. wave 3

Collected in wave 3 (March 2022), the creation of this index follows the same path as above using updated participant media exposure ratings, but with the exception of using the average of wave 1 and wave 2 media analytical thinking scores.

c. symbolic ideology

Asked during our wave 1 survey (July-August 2020), 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).

## 
## Reliability analysis   
## Call: psych::alpha(x = data.frame(d$symbolic_beliefs_1, d$symbolic_beliefs_2, 
##     d$symbolic_beliefs_3), cumulative = F, na.rm = T, delete = T)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.95      0.95    0.93      0.85  17 0.0016 0.11 1.6     0.86
## 
##  lower alpha upper     95% confidence boundaries
## 0.94 0.95 0.95 
## 
##  Reliability if an item is dropped:
##                      raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r
## d.symbolic_beliefs_1      0.89      0.89    0.80      0.80  8.2   0.0035    NA
## d.symbolic_beliefs_2      0.92      0.92    0.86      0.86 12.1   0.0025    NA
## d.symbolic_beliefs_3      0.94      0.94    0.89      0.89 17.0   0.0018    NA
##                      med.r
## d.symbolic_beliefs_1  0.80
## d.symbolic_beliefs_2  0.86
## d.symbolic_beliefs_3  0.89
## 
##  Item statistics 
##                         n raw.r std.r r.cor r.drop   mean  sd
## d.symbolic_beliefs_1 3462  0.97  0.97  0.95   0.92  0.081 1.7
## d.symbolic_beliefs_2 3463  0.95  0.95  0.91   0.88 -0.041 1.7
## d.symbolic_beliefs_3 3463  0.93  0.93  0.88   0.85  0.292 1.7
## 
## Non missing response frequency for each item
##                        -3   -2   -1    0    1    2    3 miss
## d.symbolic_beliefs_1 0.07 0.13 0.08 0.38 0.10 0.13 0.10  0.1
## d.symbolic_beliefs_2 0.09 0.14 0.12 0.33 0.10 0.13 0.09  0.1
## d.symbolic_beliefs_3 0.06 0.11 0.09 0.35 0.13 0.16 0.11  0.1

d. trust in science

Asked during our wave 3 survey (March 2022), trust in science is an average of 15-items. Examples range from trust in the scientific method (e.g., “We cannot trust science because it moves too slowly”) to trust in scientists themselves (e.g., “We should trust that scientists are being ethical in their work”). Participants answered on a scale from 1 (strongly disagree) to 3 (neutral) to 5 (strongly agree).

## 
## Reliability analysis   
## Call: psych::alpha(x = data.frame(d$sciTrust_1, d$sciTrust_2, d$sciTrust_3, 
##     d$sciTrust_4, d$sciTrust_5, d$sciTrust_6, d$sciTrust_7, d$sciTrust_8, 
##     d$sciTrust_9, d$sciTrust_10, d$sciTrust_11, d$sciTrust_12, 
##     d$sciTrust_13, d$sciTrust_14, d$sciTrust_15), cumulative = F, 
##     na.rm = T, delete = T)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.94      0.94    0.95       0.5  15 0.0015  3.5 0.82      0.5
## 
##  lower alpha upper     95% confidence boundaries
## 0.94 0.94 0.94 
## 
##  Reliability if an item is dropped:
##               raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## d.sciTrust_1       0.94      0.94    0.95      0.51  15   0.0015 0.021  0.51
## d.sciTrust_2       0.93      0.93    0.95      0.49  14   0.0016 0.022  0.49
## d.sciTrust_3       0.93      0.93    0.95      0.50  14   0.0016 0.022  0.49
## d.sciTrust_4       0.93      0.93    0.95      0.50  14   0.0016 0.022  0.49
## d.sciTrust_5       0.93      0.93    0.95      0.49  14   0.0016 0.022  0.49
## d.sciTrust_6       0.94      0.93    0.95      0.51  14   0.0015 0.021  0.53
## d.sciTrust_7       0.93      0.93    0.95      0.50  14   0.0015 0.021  0.51
## d.sciTrust_8       0.94      0.94    0.95      0.51  15   0.0015 0.020  0.53
## d.sciTrust_9       0.94      0.94    0.95      0.51  15   0.0015 0.019  0.53
## d.sciTrust_10      0.94      0.93    0.95      0.51  14   0.0015 0.021  0.53
## d.sciTrust_11      0.93      0.93    0.95      0.49  13   0.0016 0.023  0.48
## d.sciTrust_12      0.93      0.93    0.95      0.50  14   0.0016 0.022  0.49
## d.sciTrust_13      0.93      0.93    0.95      0.49  13   0.0016 0.021  0.49
## d.sciTrust_14      0.93      0.93    0.95      0.50  14   0.0016 0.022  0.49
## d.sciTrust_15      0.93      0.93    0.95      0.51  14   0.0016 0.022  0.49
## 
##  Item statistics 
##                  n raw.r std.r r.cor r.drop mean   sd
## d.sciTrust_1  1077  0.65  0.64  0.60   0.59  3.3 1.16
## d.sciTrust_2  1077  0.81  0.80  0.79   0.78  3.2 1.23
## d.sciTrust_3  1075  0.78  0.77  0.75   0.74  3.6 1.11
## d.sciTrust_4  1075  0.72  0.70  0.68   0.66  3.2 1.19
## d.sciTrust_5  1075  0.81  0.80  0.79   0.77  3.5 1.19
## d.sciTrust_6  1077  0.68  0.70  0.68   0.63  3.7 1.00
## d.sciTrust_7  1071  0.72  0.74  0.73   0.68  3.6 1.01
## d.sciTrust_8  1073  0.64  0.66  0.64   0.58  3.6 1.06
## d.sciTrust_9  1076  0.62  0.64  0.62   0.57  3.5 0.96
## d.sciTrust_10 1077  0.68  0.69  0.68   0.63  3.5 1.06
## d.sciTrust_11 1075  0.83  0.83  0.81   0.80  3.5 1.19
## d.sciTrust_12 1075  0.76  0.75  0.73   0.71  3.2 1.19
## d.sciTrust_13 1075  0.84  0.83  0.83   0.81  3.3 1.17
## d.sciTrust_14 1072  0.73  0.72  0.69   0.68  3.2 1.19
## d.sciTrust_15 1073  0.70  0.70  0.68   0.66  3.8 1.04
## 
## Non missing response frequency for each item
##                  1    2    3    4    5 miss
## d.sciTrust_1  0.08 0.16 0.30 0.30 0.16 0.72
## d.sciTrust_2  0.11 0.17 0.27 0.28 0.16 0.72
## d.sciTrust_3  0.05 0.10 0.28 0.32 0.24 0.72
## d.sciTrust_4  0.09 0.18 0.32 0.24 0.18 0.72
## d.sciTrust_5  0.07 0.12 0.27 0.29 0.25 0.72
## d.sciTrust_6  0.04 0.07 0.26 0.41 0.22 0.72
## d.sciTrust_7  0.04 0.07 0.29 0.40 0.19 0.72
## d.sciTrust_8  0.06 0.08 0.27 0.41 0.18 0.72
## d.sciTrust_9  0.04 0.06 0.39 0.36 0.14 0.72
## d.sciTrust_10 0.05 0.09 0.30 0.38 0.18 0.72
## d.sciTrust_11 0.07 0.13 0.25 0.31 0.23 0.72
## d.sciTrust_12 0.09 0.17 0.31 0.26 0.16 0.72
## d.sciTrust_13 0.08 0.16 0.31 0.27 0.18 0.72
## d.sciTrust_14 0.09 0.18 0.31 0.25 0.17 0.72
## d.sciTrust_15 0.04 0.05 0.26 0.33 0.32 0.72

e. science literacy (might cut)

Asked during out wave 3 survey (March 2022), science literacy is a proportion of correct/incorrect answers pertaining to 6-items identifying basic scientific understanding (e.g., “The center of the earth is very hot”, “Electrons are smaller than atoms”).

f. cognitive reflection test

Asked during our wave 3 survey (March 2022), the CRT is a proportion of correct/incorrect answers that leans into system 1 intuitive answer (wrong) over deliberate system 2 answer (correct answers; e.g., “A farmer had 15 sheep and all but 8 died. How many are left?”).

3. LIWC media measures

a. Analytical thinking wave 1

Collected during wave 1 (July - August 2020), media outlet LIWC analytic thinking scores captures the degree to which people use words that suggest formal, logical, and hierarchical thinking patterns. Column two is raw scores, and column three is standardized scores.

b. Analytical thinking wave 2

Collected during wave 2 (November 2020), media outlet LIWC analytic thinking scores captures the degree to which people use words that suggest formal, logical, and hierarchical thinking patterns. Column two is raw scores, and column three is standardized scores.

4. outcome measures

a. wave 1 vaccine attitudes

Collected during wave 1 (July - August 2020), 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).

b. wave 2 vaccine attitudes

Collected during wave 2 (November 2020), participants were asked “If a Covid-19 vaccine were available today, would you get it?” and answered on a scale from -3 (Definitely would not get it) to 0 (Completely undecided) to +3 (Definitely would get it).

c. average of wave 1 + wave 2 vaccine attitudes

d. correlation between wave 1 and wave 2 vaccine attitudes

## 
##  Pearson's product-moment correlation
## 
## data:  d$vaxxAttitudes_w1 and d$vaxxAttitudes_w2
## t = 53.555, df = 2493, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7126403 0.7491665
## sample estimates:
##       cor 
## 0.7314276

e. vaccine behavior

Collected during wave 3 (March 2022), vaccine behavior is a collective measure of branched questions given to participants.

1 = not vaccinated

2 = partially vaccinated (e.g., 1 Moderna shot, but not the 2nd)

3 = fully vaccinated (e.g., 1 Johnson & Johnson shot, or 2 Modern/Pfizer shots)

4 = fully vaccinated and boosted

describe(d$vaxxBehavior)

e. risk perceptions (might cut)

i. wave 1

Collected during wave 1 (July-August 2020), risk perceptions is an average of 3-items. The scale ranges from 1 (Not severe at all) to 4 (Moderately severe) to 7 (Catastrophic).

1. How severe do you think the health consequences to American society will be of the Covid-19 pandemic (e.g., number of deaths, hospitalizations)?

2. How severe do you think the economic consequences to American society will be of the Covid-19 pandemic (e.g., job loss, businesses shutting down, and falling stock markets)?

3. How severe have the economic consequences of the Covid-19 pandemic been to you personally (e.g., job loss, businesses shutting down, and falling stock markets)?

## 
## Reliability analysis   
## Call: psych::alpha(x = data.frame(d$risk3_w1, d$risk4_w1, d$risk5_w1), 
##     cumulative = F, na.rm = T, delete = T)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean  sd median_r
##       0.64      0.65    0.57      0.38 1.9 0.01  4.3 1.3     0.33
## 
##  lower alpha upper     95% confidence boundaries
## 0.62 0.64 0.66 
## 
##  Reliability if an item is dropped:
##            raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
## d.risk3_w1      0.48      0.50    0.33      0.33 0.99    0.016    NA  0.33
## d.risk4_w1      0.48      0.48    0.31      0.31 0.92    0.017    NA  0.31
## d.risk5_w1      0.67      0.67    0.51      0.51 2.06    0.011    NA  0.51
## 
##  Item statistics 
##               n raw.r std.r r.cor r.drop mean  sd
## d.risk3_w1 3469  0.78  0.79  0.63   0.48  4.5 1.7
## d.risk4_w1 3459  0.76  0.80  0.65   0.51  5.1 1.4
## d.risk5_w1 3455  0.76  0.71  0.45   0.37  3.3 1.9
## 
## Non missing response frequency for each item
##               1    2    3    4    5    6    7 miss
## d.risk3_w1 0.05 0.10 0.12 0.17 0.22 0.21 0.13  0.1
## d.risk4_w1 0.02 0.04 0.08 0.15 0.26 0.29 0.17  0.1
## d.risk5_w1 0.24 0.20 0.12 0.16 0.13 0.09 0.06  0.1

ii. wave 2

Collected during wave 2 (November 2020), participants were asked the same questions as listed above.

## Number of categories should be increased  in order to count frequencies.
## 
## Reliability analysis   
## Call: psych::alpha(x = data.frame(d$risk3_w2, d$risk4_w2, d$risk5_w2), 
##     cumulative = F, na.rm = T, delete = T)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean  sd median_r
##       0.64      0.66    0.58      0.39 1.9 0.01  4.2 1.2     0.36
## 
##  lower alpha upper     95% confidence boundaries
## 0.62 0.64 0.66 
## 
##  Reliability if an item is dropped:
##            raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
## d.risk3_w2      0.52      0.53    0.36      0.36 1.13    0.015    NA  0.36
## d.risk4_w2      0.46      0.46    0.30      0.30 0.86    0.017    NA  0.30
## d.risk5_w2      0.67      0.68    0.51      0.51 2.10    0.010    NA  0.51
## 
##  Item statistics 
##               n raw.r std.r r.cor r.drop mean  sd
## d.risk3_w2 2720  0.78  0.78  0.62   0.47  4.6 1.7
## d.risk4_w2 2718  0.77  0.81  0.67   0.54  5.0 1.4
## d.risk5_w2 2721  0.75  0.72  0.46   0.38  3.0 1.8

iii. wave 3

Collected during wave 3 (March 2020), participants were asked the same questions as listed above.

## 
## Reliability analysis   
## Call: psych::alpha(x = data.frame(d$risk3_w3, d$risk4_w3, d$risk5_w3), 
##     cumulative = F, na.rm = T, delete = T)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##       0.62      0.63    0.56      0.37 1.7 0.011  3.7 1.2     0.39
## 
##  lower alpha upper     95% confidence boundaries
## 0.6 0.62 0.65 
## 
##  Reliability if an item is dropped:
##            raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
## d.risk3_w3      0.55      0.56    0.39      0.39 1.27    0.014    NA  0.39
## d.risk4_w3      0.38      0.38    0.24      0.24 0.62    0.020    NA  0.24
## d.risk5_w3      0.64      0.64    0.47      0.47 1.81    0.011    NA  0.47
## 
##  Item statistics 
##               n raw.r std.r r.cor r.drop mean  sd
## d.risk3_w3 1335  0.74  0.75  0.55   0.41  4.1 1.6
## d.risk4_w3 1336  0.80  0.82  0.69   0.54  4.3 1.5
## d.risk5_w3 1336  0.74  0.71  0.46   0.36  2.8 1.8
## 
## Non missing response frequency for each item
##               1    2    3    4    5    6    7 miss
## d.risk3_w3 0.06 0.11 0.17 0.23 0.23 0.13 0.07 0.65
## d.risk4_w3 0.04 0.08 0.17 0.25 0.24 0.15 0.06 0.65
## d.risk5_w3 0.31 0.20 0.15 0.14 0.10 0.06 0.04 0.65

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

6. ANALYSES

Study 2: US only (November 2020)

Key aims: 1) Replication, proximity vaccine, 2) stronger causal evidence w/ longitudinal data

a. vaxxAttitudes ~ analticalIndex.wave2 * ideology (replication)

summary(m.s2.a <- lm(vaxxAttitudes_w2.c ~ index_ANexp_w2.c * ideology.c + 
               age.c + education.c + white_.5, data = d))
## 
## Call:
## lm(formula = vaxxAttitudes_w2.c ~ index_ANexp_w2.c * ideology.c + 
##     age.c + education.c + white_.5, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8466 -1.4224  0.0855  1.6579  4.3979 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0841205  0.0450387  -1.868 0.061919 .  
## index_ANexp_w2.c             0.0060499  0.0006134   9.862  < 2e-16 ***
## ideology.c                  -0.0981857  0.0260865  -3.764 0.000171 ***
## age.c                        0.0243208  0.0027558   8.825  < 2e-16 ***
## education.c                  0.0497351  0.0157864   3.150 0.001650 ** 
## white_.5                     0.3811722  0.0914717   4.167 3.19e-05 ***
## index_ANexp_w2.c:ideology.c  0.0003666  0.0003359   1.091 0.275241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 2441 degrees of freedom
##   (1412 observations deleted due to missingness)
## Multiple R-squared:  0.08306,    Adjusted R-squared:  0.08081 
## F-statistic: 36.85 on 6 and 2441 DF,  p-value: < 2.2e-16
tab_model(m.s2.a)
  vaxxAttitudes_w2.c
Predictors Estimates CI p
(Intercept) -0.08 -0.17 – 0.00 0.062
index_ANexp_w2.c 0.01 0.00 – 0.01 <0.001
ideology.c -0.10 -0.15 – -0.05 <0.001
age.c 0.02 0.02 – 0.03 <0.001
education.c 0.05 0.02 – 0.08 0.002
white_.5 0.38 0.20 – 0.56 <0.001
index_ANexp_w2.c *
ideology.c 		0.00 -0.00 – 0.00 0.275
Observations 2448
R2 / R2 adjusted 0.083 / 0.081

b. Longitudinal: vaxxAttitude.wave2 ~ ideology * analyticalIndex.wave2 + analyticalIndex.wave1 + vaxxAttitude.wave1 (causal)

summary(m.s2.b <- lm(vaxxAttitudes_w2.c ~ index_ANexp_w2.c * ideology.c + 
               index_ANexp_w1 + vaxxAttitudes_w1.c +
               age.c + education.c + white_.5, data = d))
## 
## Call:
## lm(formula = vaxxAttitudes_w2.c ~ index_ANexp_w2.c * ideology.c + 
##     index_ANexp_w1 + vaxxAttitudes_w1.c + age.c + education.c + 
##     white_.5, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1857 -0.8324  0.0467  0.9249  5.4213 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0167435  0.0590299  -0.284 0.776707    
## index_ANexp_w2.c             0.0021739  0.0005881   3.697 0.000223 ***
## ideology.c                   0.0720692  0.0188143   3.831 0.000131 ***
## index_ANexp_w1              -0.0007884  0.0005655  -1.394 0.163390    
## vaxxAttitudes_w1.c           0.7085314  0.0143979  49.211  < 2e-16 ***
## age.c                        0.0057973  0.0019886   2.915 0.003586 ** 
## education.c                 -0.0057215  0.0112294  -0.510 0.610442    
## white_.5                     0.1395160  0.0649565   2.148 0.031825 *  
## index_ANexp_w2.c:ideology.c -0.0001620  0.0002387  -0.679 0.497250    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.422 on 2439 degrees of freedom
##   (1412 observations deleted due to missingness)
## Multiple R-squared:  0.5412, Adjusted R-squared:  0.5397 
## F-statistic: 359.7 on 8 and 2439 DF,  p-value: < 2.2e-16
tab_model(m.s2.b)
  vaxxAttitudes_w2.c
Predictors Estimates CI p
(Intercept) -0.02 -0.13 – 0.10 0.777
index_ANexp_w2.c 0.00 0.00 – 0.00 <0.001
ideology.c 0.07 0.04 – 0.11 <0.001
index_ANexp_w1 -0.00 -0.00 – 0.00 0.163
vaxxAttitudes_w1.c 0.71 0.68 – 0.74 <0.001
age.c 0.01 0.00 – 0.01 0.004
education.c -0.01 -0.03 – 0.02 0.610
white_.5 0.14 0.01 – 0.27 0.032
index_ANexp_w2.c *
ideology.c 		-0.00 -0.00 – 0.00 0.497
Observations 2448
R2 / R2 adjusted 0.541 / 0.540

c. mediation path: analyticalIndex –> trustInExperts –> vaxxAttitudes.wave2

- no interaction

summary(m1.xy <- lm(vaxxAttitudes_w2.c ~ index_ANexp_w2.c + 
                         ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxAttitudes_w2.c ~ index_ANexp_w2.c + ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8307 -1.4118  0.0874  1.6673  4.3363 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0936105  0.0441930  -2.118 0.034257 *  
## index_ANexp_w2.c  0.0060133  0.0006125   9.817  < 2e-16 ***
## ideology.c       -0.0975037  0.0260801  -3.739 0.000189 ***
## age.c             0.0241163  0.0027495   8.771  < 2e-16 ***
## white_.5          0.3758579  0.0913455   4.115 4.01e-05 ***
## education.c       0.0495724  0.0157863   3.140 0.001708 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 2442 degrees of freedom
##   (1412 observations deleted due to missingness)
## Multiple R-squared:  0.08261,    Adjusted R-squared:  0.08073 
## F-statistic: 43.98 on 5 and 2442 DF,  p-value: < 2.2e-16
summary(m1.xm <- lm(trustExpert_w2.c ~ index_ANexp_w2.c + 
                         ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = trustExpert_w2.c ~ index_ANexp_w2.c + ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7656 -0.6941  0.3171  0.9331  3.1169 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0373385  0.0297462  -1.255 0.209513    
## index_ANexp_w2.c  0.0043281  0.0004123  10.497  < 2e-16 ***
## ideology.c       -0.2511731  0.0175497 -14.312  < 2e-16 ***
## age.c             0.0135169  0.0018505   7.304 3.75e-13 ***
## white_.5          0.2501985  0.0614912   4.069 4.87e-05 ***
## education.c       0.0380858  0.0106249   3.585 0.000344 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.353 on 2443 degrees of freedom
##   (1411 observations deleted due to missingness)
## Multiple R-squared:  0.1578, Adjusted R-squared:  0.1561 
## F-statistic: 91.57 on 5 and 2443 DF,  p-value: < 2.2e-16
summary(m1.xmy <- lm(vaxxAttitudes_w2.c ~ trustExpert_w2.c + index_ANexp_w2.c + 
                          ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxAttitudes_w2.c ~ trustExpert_w2.c + index_ANexp_w2.c + 
##     ideology.c + age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9849 -1.3933  0.1412  1.5834  4.6127 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0808941  0.0429767  -1.882  0.05992 .  
## trustExpert_w2.c  0.3494156  0.0292435  11.948  < 2e-16 ***
## index_ANexp_w2.c  0.0045032  0.0006088   7.397 1.90e-13 ***
## ideology.c       -0.0100922  0.0263888  -0.382  0.70217    
## age.c             0.0193674  0.0027024   7.167 1.01e-12 ***
## white_.5          0.2882353  0.0891065   3.235  0.00123 ** 
## education.c       0.0364180  0.0153866   2.367  0.01802 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.954 on 2441 degrees of freedom
##   (1412 observations deleted due to missingness)
## Multiple R-squared:  0.1333, Adjusted R-squared:  0.1312 
## F-statistic: 62.57 on 6 and 2441 DF,  p-value: < 2.2e-16
tab_model(m1.xy, m1.xm, m1.xmy)
  vaxxAttitudes_w2.c trustExpert_w2.c vaxxAttitudes_w2.c
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) -0.09 -0.18 – -0.01 0.034 -0.04 -0.10 – 0.02 0.210 -0.08 -0.17 – 0.00 0.060
index_ANexp_w2.c 0.01 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001
ideology.c -0.10 -0.15 – -0.05 <0.001 -0.25 -0.29 – -0.22 <0.001 -0.01 -0.06 – 0.04 0.702
age.c 0.02 0.02 – 0.03 <0.001 0.01 0.01 – 0.02 <0.001 0.02 0.01 – 0.02 <0.001
white_.5 0.38 0.20 – 0.55 <0.001 0.25 0.13 – 0.37 <0.001 0.29 0.11 – 0.46 0.001
education.c 0.05 0.02 – 0.08 0.002 0.04 0.02 – 0.06 <0.001 0.04 0.01 – 0.07 0.018
trustExpert_w2.c 0.35 0.29 – 0.41 <0.001
Observations 2448 2449 2448
R2 / R2 adjusted 0.083 / 0.081 0.158 / 0.156 0.133 / 0.131

- interaction

summary(m2.xy <- lm(vaxxAttitudes_w2.c ~ index_ANexp_w2.c * ideology.c + 
                         age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxAttitudes_w2.c ~ index_ANexp_w2.c * ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8466 -1.4224  0.0855  1.6579  4.3979 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0841205  0.0450387  -1.868 0.061919 .  
## index_ANexp_w2.c             0.0060499  0.0006134   9.862  < 2e-16 ***
## ideology.c                  -0.0981857  0.0260865  -3.764 0.000171 ***
## age.c                        0.0243208  0.0027558   8.825  < 2e-16 ***
## white_.5                     0.3811722  0.0914717   4.167 3.19e-05 ***
## education.c                  0.0497351  0.0157864   3.150 0.001650 ** 
## index_ANexp_w2.c:ideology.c  0.0003666  0.0003359   1.091 0.275241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.01 on 2441 degrees of freedom
##   (1412 observations deleted due to missingness)
## Multiple R-squared:  0.08306,    Adjusted R-squared:  0.08081 
## F-statistic: 36.85 on 6 and 2441 DF,  p-value: < 2.2e-16
summary(m2.xm <- lm(trustExpert_w2.c ~ index_ANexp_w2.c * ideology.c + 
                         age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = trustExpert_w2.c ~ index_ANexp_w2.c * ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7001 -0.6824  0.3385  0.9194  3.2877 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0108956  0.0301984  -0.361 0.718281    
## index_ANexp_w2.c             0.0044294  0.0004113  10.769  < 2e-16 ***
## ideology.c                  -0.2530127  0.0174848 -14.470  < 2e-16 ***
## age.c                        0.0140900  0.0018475   7.626 3.44e-14 ***
## white_.5                     0.2650083  0.0613346   4.321 1.62e-05 ***
## education.c                  0.0385134  0.0105831   3.639 0.000279 ***
## index_ANexp_w2.c:ideology.c  0.0010195  0.0002252   4.527 6.26e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.348 on 2442 degrees of freedom
##   (1411 observations deleted due to missingness)
## Multiple R-squared:  0.1648, Adjusted R-squared:  0.1628 
## F-statistic: 80.33 on 6 and 2442 DF,  p-value: < 2.2e-16
summary(m2.xmy <- lm(vaxxAttitudes_w2.c ~ (trustExpert_w2.c + index_ANexp_w2.c) * ideology.c + 
                          age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxAttitudes_w2.c ~ (trustExpert_w2.c + index_ANexp_w2.c) * 
##     ideology.c + age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9811 -1.3947  0.1519  1.5914  4.7361 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0904156  0.0446772  -2.024  0.04310 *  
## trustExpert_w2.c             0.3565625  0.0300758  11.855  < 2e-16 ***
## index_ANexp_w2.c             0.0045180  0.0006106   7.400 1.87e-13 ***
## ideology.c                  -0.0068887  0.0265834  -0.259  0.79555    
## age.c                        0.0191589  0.0027186   7.047 2.37e-12 ***
## white_.5                     0.2831654  0.0894105   3.167  0.00156 ** 
## education.c                  0.0357191  0.0154039   2.319  0.02049 *  
## trustExpert_w2.c:ideology.c -0.0197259  0.0176170  -1.120  0.26295    
## index_ANexp_w2.c:ideology.c  0.0001283  0.0003434   0.374  0.70878    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.954 on 2439 degrees of freedom
##   (1412 observations deleted due to missingness)
## Multiple R-squared:  0.1337, Adjusted R-squared:  0.1309 
## F-statistic: 47.07 on 8 and 2439 DF,  p-value: < 2.2e-16
tab_model(m2.xy, m2.xm, m2.xmy)
  vaxxAttitudes_w2.c trustExpert_w2.c vaxxAttitudes_w2.c
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) -0.08 -0.17 – 0.00 0.062 -0.01 -0.07 – 0.05 0.718 -0.09 -0.18 – -0.00 0.043
index_ANexp_w2.c 0.01 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001
ideology.c -0.10 -0.15 – -0.05 <0.001 -0.25 -0.29 – -0.22 <0.001 -0.01 -0.06 – 0.05 0.796
age.c 0.02 0.02 – 0.03 <0.001 0.01 0.01 – 0.02 <0.001 0.02 0.01 – 0.02 <0.001
white_.5 0.38 0.20 – 0.56 <0.001 0.27 0.14 – 0.39 <0.001 0.28 0.11 – 0.46 0.002
education.c 0.05 0.02 – 0.08 0.002 0.04 0.02 – 0.06 <0.001 0.04 0.01 – 0.07 0.020
index_ANexp_w2.c *
ideology.c 		0.00 -0.00 – 0.00 0.275 0.00 0.00 – 0.00 <0.001 0.00 -0.00 – 0.00 0.709
trustExpert_w2.c 0.36 0.30 – 0.42 <0.001
trustExpert_w2.c *
ideology.c 		-0.02 -0.05 – 0.01 0.263
Observations 2448 2449 2448
R2 / R2 adjusted 0.083 / 0.081 0.165 / 0.163 0.134 / 0.131

Study 3: US Vaxx Behavior (March 2022)

a. vaxxBehavior ~ analticalIndex.wave3 * ideology (replication)

summary(m.s3.a <- lm(vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
               age.c + education.c + white_.5, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
##     age.c + education.c + white_.5, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1044 -0.3664  0.3046  0.7190  2.4337 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0172195  0.0394684  -0.436  0.66272    
## index_ANexp_w3.c             0.0039663  0.0005031   7.884 8.33e-15 ***
## ideology.c                  -0.1495609  0.0207887  -7.194 1.24e-12 ***
## age.c                        0.0163019  0.0023635   6.897 9.44e-12 ***
## education.c                  0.0516708  0.0129714   3.983 7.29e-05 ***
## white_.5                    -0.0406239  0.0784844  -0.518  0.60485    
## index_ANexp_w3.c:ideology.c  0.0008704  0.0002740   3.176  0.00154 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.065 on 990 degrees of freedom
##   (2863 observations deleted due to missingness)
## Multiple R-squared:  0.1755, Adjusted R-squared:  0.1705 
## F-statistic: 35.13 on 6 and 990 DF,  p-value: < 2.2e-16
tab_model(m.s3.a)
  vaxxBehavior.c
Predictors Estimates CI p
(Intercept) -0.02 -0.09 – 0.06 0.663
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001
ideology.c -0.15 -0.19 – -0.11 <0.001
age.c 0.02 0.01 – 0.02 <0.001
education.c 0.05 0.03 – 0.08 <0.001
white_.5 -0.04 -0.19 – 0.11 0.605
index_ANexp_w3.c *
ideology.c 		0.00 0.00 – 0.00 0.002
Observations 997
R2 / R2 adjusted 0.176 / 0.171

b. Longitudinal: vaxxAtitudes ~ ideology * analyticalIndex.wave3 + analyticalIndex.wave1 + vaxxAttitude.wave1 (causal)

summary(m.s3.b <- lm(vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
                              avgANexp_w1w2.c + avgVaxxAttitudes.c + 
                              age.c + education.c + white_.5, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
##     avgANexp_w1w2.c + avgVaxxAttitudes.c + age.c + education.c + 
##     white_.5, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4559 -0.4809  0.1716  0.6222  2.1887 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0286641  0.0351613  -0.815  0.41514    
## index_ANexp_w3.c             0.0024027  0.0006104   3.936 8.86e-05 ***
## ideology.c                  -0.1123268  0.0188568  -5.957 3.57e-09 ***
## avgANexp_w1w2.c             -0.0002928  0.0006809  -0.430  0.66725    
## avgVaxxAttitudes.c           0.2602710  0.0161889  16.077  < 2e-16 ***
## age.c                        0.0092900  0.0021537   4.314 1.77e-05 ***
## education.c                  0.0380303  0.0115826   3.283  0.00106 ** 
## white_.5                    -0.1284313  0.0704800  -1.822  0.06872 .  
## index_ANexp_w3.c:ideology.c  0.0004994  0.0002456   2.033  0.04230 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9488 on 988 degrees of freedom
##   (2863 observations deleted due to missingness)
## Multiple R-squared:  0.3476, Adjusted R-squared:  0.3423 
## F-statistic: 65.79 on 8 and 988 DF,  p-value: < 2.2e-16
tab_model(m.s3.b)
  vaxxBehavior.c
Predictors Estimates CI p
(Intercept) -0.03 -0.10 – 0.04 0.415
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001
ideology.c -0.11 -0.15 – -0.08 <0.001
avgANexp_w1w2.c -0.00 -0.00 – 0.00 0.667
avgVaxxAttitudes.c 0.26 0.23 – 0.29 <0.001
age.c 0.01 0.01 – 0.01 <0.001
education.c 0.04 0.02 – 0.06 0.001
white_.5 -0.13 -0.27 – 0.01 0.069
index_ANexp_w3.c *
ideology.c 		0.00 0.00 – 0.00 0.042
Observations 997
R2 / R2 adjusted 0.348 / 0.342

c. VaxxBehavior ~ (analyticalIndex.wave3 + trustInScience) * ideology + avgAnalyticalIndex.wave1&2 + avgVaxxAttitudes.wave1&2

summary(m.s3.c <- lm(vaxxBehavior.c ~ (avgTrustSci.c + index_ANexp_w3.c) * ideology.c + 
                      avgANexp_w1w2.c + avgVaxxAttitudes.c + 
                      age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ (avgTrustSci.c + index_ANexp_w3.c) * 
##     ideology.c + avgANexp_w1w2.c + avgVaxxAttitudes.c + age.c + 
##     white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5085 -0.4229  0.1539  0.6246  2.4800 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.0113560  0.0365911   0.310  0.75636    
## avgTrustSci.c                0.2369087  0.0440297   5.381 9.29e-08 ***
## index_ANexp_w3.c             0.0023833  0.0006039   3.946 8.51e-05 ***
## ideology.c                  -0.0671123  0.0207230  -3.239  0.00124 ** 
## avgANexp_w1w2.c             -0.0004778  0.0006719  -0.711  0.47716    
## avgVaxxAttitudes.c           0.2308458  0.0168009  13.740  < 2e-16 ***
## age.c                        0.0085888  0.0021412   4.011 6.50e-05 ***
## white_.5                    -0.1803450  0.0703880  -2.562  0.01055 *  
## education.c                  0.0339963  0.0114490   2.969  0.00306 ** 
## avgTrustSci.c:ideology.c     0.0490348  0.0207701   2.361  0.01843 *  
## index_ANexp_w3.c:ideology.c  0.0003616  0.0002448   1.477  0.13992    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9328 on 980 degrees of freedom
##   (2869 observations deleted due to missingness)
## Multiple R-squared:  0.3709, Adjusted R-squared:  0.3644 
## F-statistic: 57.77 on 10 and 980 DF,  p-value: < 2.2e-16
tab_model(m.s3.c)
  vaxxBehavior.c
Predictors Estimates CI p
(Intercept) 0.01 -0.06 – 0.08 0.756
avgTrustSci.c 0.24 0.15 – 0.32 <0.001
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001
ideology.c -0.07 -0.11 – -0.03 0.001
avgANexp_w1w2.c -0.00 -0.00 – 0.00 0.477
avgVaxxAttitudes.c 0.23 0.20 – 0.26 <0.001
age.c 0.01 0.00 – 0.01 <0.001
white_.5 -0.18 -0.32 – -0.04 0.011
education.c 0.03 0.01 – 0.06 0.003
avgTrustSci.c *
ideology.c 		0.05 0.01 – 0.09 0.018
index_ANexp_w3.c *
ideology.c 		0.00 -0.00 – 0.00 0.140
Observations 991
R2 / R2 adjusted 0.371 / 0.364

d. vaxxBehavior ~ analyticalIndex.wave3 * ideology + avgAnalyticalIndex.wave1&2 + avgVaxxAttitudes.wave1&2 + CRT + science literacy

summary(m.s3.d <- lm(vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c +
                              avgANexp_w1w2.c + avgVaxxAttitudes.c + 
                              age.c + white_.5 + education.c +
                              avgCRT.c + avgSciLit.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
##     avgANexp_w1w2.c + avgVaxxAttitudes.c + age.c + white_.5 + 
##     education.c + avgCRT.c + avgSciLit.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3898 -0.4961  0.1574  0.6166  2.2476 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0300971  0.0360710  -0.834  0.40427    
## index_ANexp_w3.c             0.0024694  0.0006260   3.945 8.57e-05 ***
## ideology.c                  -0.1163917  0.0191496  -6.078 1.75e-09 ***
## avgANexp_w1w2.c             -0.0003831  0.0006947  -0.551  0.58151    
## avgVaxxAttitudes.c           0.2566747  0.0165462  15.513  < 2e-16 ***
## age.c                        0.0095431  0.0021842   4.369 1.38e-05 ***
## white_.5                    -0.1417309  0.0727701  -1.948  0.05175 .  
## education.c                  0.0358247  0.0120070   2.984  0.00292 ** 
## avgCRT.c                     0.1095137  0.1059316   1.034  0.30148    
## avgSciLit.c                  0.1227634  0.2424642   0.506  0.61275    
## index_ANexp_w3.c:ideology.c  0.0004903  0.0002493   1.967  0.04947 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9518 on 968 degrees of freedom
##   (2881 observations deleted due to missingness)
## Multiple R-squared:  0.348,  Adjusted R-squared:  0.3413 
## F-statistic: 51.67 on 10 and 968 DF,  p-value: < 2.2e-16
tab_model(m.s3.d)
  vaxxBehavior.c
Predictors Estimates CI p
(Intercept) -0.03 -0.10 – 0.04 0.404
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001
ideology.c -0.12 -0.15 – -0.08 <0.001
avgANexp_w1w2.c -0.00 -0.00 – 0.00 0.582
avgVaxxAttitudes.c 0.26 0.22 – 0.29 <0.001
age.c 0.01 0.01 – 0.01 <0.001
white_.5 -0.14 -0.28 – 0.00 0.052
education.c 0.04 0.01 – 0.06 0.003
avgCRT.c 0.11 -0.10 – 0.32 0.301
avgSciLit.c 0.12 -0.35 – 0.60 0.613
index_ANexp_w3.c *
ideology.c 		0.00 0.00 – 0.00 0.049
Observations 979
R2 / R2 adjusted 0.348 / 0.341

e. VaxxBehavior ~ (analyticalIndex.wave3 + trustInScience) * ideology + avgAnalyticalIndex.wave1&2 + avgVaxxAttitudes.wave1&2 + CRT + science literacy

summary(m.s3.e <- lm(vaxxBehavior.c ~ (avgTrustSci.c + index_ANexp_w3.c) * ideology.c + 
                      avgANexp_w1w2.c + avgVaxxAttitudes.c + 
                      age.c + white_.5 + education.c +
                      avgCRT.c + avgSciLit.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ (avgTrustSci.c + index_ANexp_w3.c) * 
##     ideology.c + avgANexp_w1w2.c + avgVaxxAttitudes.c + age.c + 
##     white_.5 + education.c + avgCRT.c + avgSciLit.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4480 -0.4303  0.1580  0.6237  2.5453 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.0070833  0.0373845   0.189 0.849762    
## avgTrustSci.c                0.2384658  0.0446028   5.346 1.12e-07 ***
## index_ANexp_w3.c             0.0024151  0.0006157   3.923 9.37e-05 ***
## ideology.c                  -0.0701254  0.0208808  -3.358 0.000815 ***
## avgANexp_w1w2.c             -0.0005434  0.0006834  -0.795 0.426734    
## avgVaxxAttitudes.c           0.2286039  0.0170259  13.427  < 2e-16 ***
## age.c                        0.0088771  0.0021547   4.120 4.12e-05 ***
## white_.5                    -0.1818701  0.0720013  -2.526 0.011698 *  
## education.c                  0.0324337  0.0118240   2.743 0.006200 ** 
## avgCRT.c                     0.0711474  0.1049146   0.678 0.497842    
## avgSciLit.c                  0.1324274  0.2382683   0.556 0.578482    
## avgTrustSci.c:ideology.c     0.0513078  0.0209455   2.450 0.014478 *  
## index_ANexp_w3.c:ideology.c  0.0003413  0.0002469   1.382 0.167139    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9351 on 966 degrees of freedom
##   (2881 observations deleted due to missingness)
## Multiple R-squared:  0.3719, Adjusted R-squared:  0.3641 
## F-statistic: 47.66 on 12 and 966 DF,  p-value: < 2.2e-16
tab_model(m.s3.e)
  vaxxBehavior.c
Predictors Estimates CI p
(Intercept) 0.01 -0.07 – 0.08 0.850
avgTrustSci.c 0.24 0.15 – 0.33 <0.001
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001
ideology.c -0.07 -0.11 – -0.03 0.001
avgANexp_w1w2.c -0.00 -0.00 – 0.00 0.427
avgVaxxAttitudes.c 0.23 0.20 – 0.26 <0.001
age.c 0.01 0.00 – 0.01 <0.001
white_.5 -0.18 -0.32 – -0.04 0.012
education.c 0.03 0.01 – 0.06 0.006
avgCRT.c 0.07 -0.13 – 0.28 0.498
avgSciLit.c 0.13 -0.34 – 0.60 0.578
avgTrustSci.c *
ideology.c 		0.05 0.01 – 0.09 0.014
index_ANexp_w3.c *
ideology.c 		0.00 -0.00 – 0.00 0.167
Observations 979
R2 / R2 adjusted 0.372 / 0.364

f. mediation path: mediaIndex.wave3 –> trustInScience –> vaxxBehavior

sobel test: https://quantpsy.org/sobel/sobel.htm

- no interaction

summary(m3.xy <- lm(vaxxBehavior.c ~ index_ANexp_w3.c + 
                         ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ index_ANexp_w3.c + ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3665 -0.3807  0.3173  0.7568  2.3831 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0422926  0.0388479  -1.089    0.277    
## index_ANexp_w3.c  0.0038373  0.0005037   7.618 6.02e-14 ***
## ideology.c       -0.1512641  0.0208769  -7.246 8.65e-13 ***
## age.c             0.0157091  0.0023669   6.637 5.26e-11 ***
## white_.5         -0.0454964  0.0788284  -0.577    0.564    
## education.c       0.0539354  0.0130111   4.145 3.68e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.07 on 991 degrees of freedom
##   (2863 observations deleted due to missingness)
## Multiple R-squared:  0.1671, Adjusted R-squared:  0.1629 
## F-statistic: 39.77 on 5 and 991 DF,  p-value: < 2.2e-16
summary(m3.xm <- lm(avgTrustSci.c ~ index_ANexp_w3.c + 
                         ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = avgTrustSci.c ~ index_ANexp_w3.c + ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.37123 -0.45216  0.00732  0.51357  1.78295 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0591067  0.0261601  -2.259 0.024075 *  
## index_ANexp_w3.c  0.0007242  0.0003391   2.135 0.032971 *  
## ideology.c       -0.2268343  0.0140184 -16.181  < 2e-16 ***
## age.c             0.0065054  0.0015966   4.075 4.98e-05 ***
## white_.5          0.2391661  0.0530809   4.506 7.41e-06 ***
## education.c       0.0292658  0.0087270   3.353 0.000828 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7177 on 987 degrees of freedom
##   (2867 observations deleted due to missingness)
## Multiple R-squared:  0.2461, Adjusted R-squared:  0.2423 
## F-statistic: 64.43 on 5 and 987 DF,  p-value: < 2.2e-16
summary(m3.xmy <- lm(vaxxBehavior.c ~ avgTrustSci.c + index_ANexp_w3.c + 
                          ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ avgTrustSci.c + index_ANexp_w3.c + 
##     ideology.c + age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6413 -0.4671  0.2718  0.6970  2.1198 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0178400  0.0373609  -0.478 0.633110    
## avgTrustSci.c     0.4437393  0.0454474   9.764  < 2e-16 ***
## index_ANexp_w3.c  0.0034348  0.0004865   7.060 3.13e-12 ***
## ideology.c       -0.0528464  0.0225107  -2.348 0.019092 *  
## age.c             0.0129624  0.0022937   5.651 2.08e-08 ***
## white_.5         -0.1598884  0.0765124  -2.090 0.036901 *  
## education.c       0.0413826  0.0125052   3.309 0.000969 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.022 on 984 degrees of freedom
##   (2869 observations deleted due to missingness)
## Multiple R-squared:  0.2414, Adjusted R-squared:  0.2368 
## F-statistic: 52.19 on 6 and 984 DF,  p-value: < 2.2e-16
tab_model(m3.xy, m3.xm, m3.xmy)
  vaxxBehavior.c avgTrustSci.c vaxxBehavior.c
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) -0.04 -0.12 – 0.03 0.277 -0.06 -0.11 – -0.01 0.024 -0.02 -0.09 – 0.06 0.633
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001 0.00 0.00 – 0.00 0.033 0.00 0.00 – 0.00 <0.001
ideology.c -0.15 -0.19 – -0.11 <0.001 -0.23 -0.25 – -0.20 <0.001 -0.05 -0.10 – -0.01 0.019
age.c 0.02 0.01 – 0.02 <0.001 0.01 0.00 – 0.01 <0.001 0.01 0.01 – 0.02 <0.001
white_.5 -0.05 -0.20 – 0.11 0.564 0.24 0.14 – 0.34 <0.001 -0.16 -0.31 – -0.01 0.037
education.c 0.05 0.03 – 0.08 <0.001 0.03 0.01 – 0.05 0.001 0.04 0.02 – 0.07 0.001
avgTrustSci.c 0.44 0.35 – 0.53 <0.001
Observations 997 993 991
R2 / R2 adjusted 0.167 / 0.163 0.246 / 0.242 0.241 / 0.237

- interaction

summary(m4.xy <- lm(vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
                         age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1044 -0.3664  0.3046  0.7190  2.4337 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0172195  0.0394684  -0.436  0.66272    
## index_ANexp_w3.c             0.0039663  0.0005031   7.884 8.33e-15 ***
## ideology.c                  -0.1495609  0.0207887  -7.194 1.24e-12 ***
## age.c                        0.0163019  0.0023635   6.897 9.44e-12 ***
## white_.5                    -0.0406239  0.0784844  -0.518  0.60485    
## education.c                  0.0516708  0.0129714   3.983 7.29e-05 ***
## index_ANexp_w3.c:ideology.c  0.0008704  0.0002740   3.176  0.00154 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.065 on 990 degrees of freedom
##   (2863 observations deleted due to missingness)
## Multiple R-squared:  0.1755, Adjusted R-squared:  0.1705 
## F-statistic: 35.13 on 6 and 990 DF,  p-value: < 2.2e-16
summary(m4.xm <- lm(avgTrustSci.c ~ index_ANexp_w3.c * ideology.c + 
                         age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = avgTrustSci.c ~ index_ANexp_w3.c * ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.37912 -0.47334  0.00095  0.51059  1.79660 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0439404  0.0265930  -1.652  0.09879 .  
## index_ANexp_w3.c             0.0008138  0.0003393   2.398  0.01666 *  
## ideology.c                  -0.2258625  0.0139712 -16.166  < 2e-16 ***
## age.c                        0.0069055  0.0015968   4.324 1.68e-05 ***
## white_.5                     0.2431512  0.0529049   4.596 4.87e-06 ***
## education.c                  0.0276728  0.0087127   3.176  0.00154 ** 
## index_ANexp_w3.c:ideology.c  0.0005250  0.0001826   2.875  0.00413 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7151 on 986 degrees of freedom
##   (2867 observations deleted due to missingness)
## Multiple R-squared:  0.2523, Adjusted R-squared:  0.2478 
## F-statistic: 55.47 on 6 and 986 DF,  p-value: < 2.2e-16
summary(m4.xmy <- lm(vaxxBehavior.c ~ (avgTrustSci.c + index_ANexp_w3.c) * ideology.c + 
                          age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ (avgTrustSci.c + index_ANexp_w3.c) * 
##     ideology.c + age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3584 -0.4085  0.2579  0.6639  2.2270 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.0259312  0.0398994   0.650  0.51590    
## avgTrustSci.c                0.4283296  0.0455023   9.413  < 2e-16 ***
## index_ANexp_w3.c             0.0034743  0.0004872   7.132 1.92e-12 ***
## ideology.c                  -0.0563628  0.0224622  -2.509  0.01226 *  
## age.c                        0.0137182  0.0022965   5.974 3.24e-09 ***
## white_.5                    -0.1475230  0.0762881  -1.934  0.05343 .  
## education.c                  0.0408735  0.0124782   3.276  0.00109 ** 
## avgTrustSci.c:ideology.c     0.0460980  0.0226606   2.034  0.04219 *  
## index_ANexp_w3.c:ideology.c  0.0006053  0.0002657   2.278  0.02294 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 982 degrees of freedom
##   (2869 observations deleted due to missingness)
## Multiple R-squared:  0.2494, Adjusted R-squared:  0.2433 
## F-statistic: 40.79 on 8 and 982 DF,  p-value: < 2.2e-16
tab_model(m4.xy, m4.xm, m4.xmy)
  vaxxBehavior.c avgTrustSci.c vaxxBehavior.c
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) -0.02 -0.09 – 0.06 0.663 -0.04 -0.10 – 0.01 0.099 0.03 -0.05 – 0.10 0.516
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001 0.00 0.00 – 0.00 0.017 0.00 0.00 – 0.00 <0.001
ideology.c -0.15 -0.19 – -0.11 <0.001 -0.23 -0.25 – -0.20 <0.001 -0.06 -0.10 – -0.01 0.012
age.c 0.02 0.01 – 0.02 <0.001 0.01 0.00 – 0.01 <0.001 0.01 0.01 – 0.02 <0.001
white_.5 -0.04 -0.19 – 0.11 0.605 0.24 0.14 – 0.35 <0.001 -0.15 -0.30 – 0.00 0.053
education.c 0.05 0.03 – 0.08 <0.001 0.03 0.01 – 0.04 0.002 0.04 0.02 – 0.07 0.001
index_ANexp_w3.c *
ideology.c 		0.00 0.00 – 0.00 0.002 0.00 0.00 – 0.00 0.004 0.00 0.00 – 0.00 0.023
avgTrustSci.c 0.43 0.34 – 0.52 <0.001
avgTrustSci.c *
ideology.c 		0.05 0.00 – 0.09 0.042
Observations 997 993 991
R2 / R2 adjusted 0.176 / 0.171 0.252 / 0.248 0.249 / 0.243

g. mediation path: analyticalIndex –> scienceLiteracy –> vaxxBehavior

- no interaction

summary(m5.xy <- lm(vaxxBehavior.c ~ index_ANexp_w3.c + 
                         ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ index_ANexp_w3.c + ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3665 -0.3807  0.3173  0.7568  2.3831 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0422926  0.0388479  -1.089    0.277    
## index_ANexp_w3.c  0.0038373  0.0005037   7.618 6.02e-14 ***
## ideology.c       -0.1512641  0.0208769  -7.246 8.65e-13 ***
## age.c             0.0157091  0.0023669   6.637 5.26e-11 ***
## white_.5         -0.0454964  0.0788284  -0.577    0.564    
## education.c       0.0539354  0.0130111   4.145 3.68e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.07 on 991 degrees of freedom
##   (2863 observations deleted due to missingness)
## Multiple R-squared:  0.1671, Adjusted R-squared:  0.1629 
## F-statistic: 39.77 on 5 and 991 DF,  p-value: < 2.2e-16
summary(m5.xm <- lm(avgSciLit.c ~ index_ANexp_w3.c + 
                         ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = avgSciLit.c ~ index_ANexp_w3.c + ideology.c + age.c + 
##     white_.5 + education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.40318 -0.07185  0.00390  0.10233  0.25581 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -1.261e-02  4.723e-03  -2.670  0.00772 ** 
## index_ANexp_w3.c -8.425e-05  6.107e-05  -1.379  0.16806    
## ideology.c        3.205e-03  2.535e-03   1.264  0.20636    
## age.c             7.379e-05  2.878e-04   0.256  0.79772    
## white_.5          4.419e-02  9.576e-03   4.615 4.46e-06 ***
## education.c       7.810e-03  1.579e-03   4.948 8.82e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1299 on 990 degrees of freedom
##   (2864 observations deleted due to missingness)
## Multiple R-squared:  0.05807,    Adjusted R-squared:  0.05331 
## F-statistic: 12.21 on 5 and 990 DF,  p-value: 1.686e-11
summary(m5.xmy <- lm(vaxxBehavior.c ~ avgSciLit.c + index_ANexp_w3.c + 
                          ideology.c + age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ avgSciLit.c + index_ANexp_w3.c + 
##     ideology.c + age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3311 -0.3940  0.3155  0.7533  2.3642 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.0445562  0.0390653  -1.141    0.254    
## avgSciLit.c       0.2240026  0.2640638   0.848    0.396    
## index_ANexp_w3.c  0.0038545  0.0005059   7.620 5.95e-14 ***
## ideology.c       -0.1542812  0.0209272  -7.372 3.55e-13 ***
## age.c             0.0160169  0.0023723   6.752 2.49e-11 ***
## white_.5         -0.0527245  0.0799309  -0.660    0.510    
## education.c       0.0524037  0.0131697   3.979 7.42e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.07 on 987 degrees of freedom
##   (2866 observations deleted due to missingness)
## Multiple R-squared:  0.1699, Adjusted R-squared:  0.1648 
## F-statistic: 33.67 on 6 and 987 DF,  p-value: < 2.2e-16
tab_model(m5.xy, m5.xm, m5.xmy)
  vaxxBehavior.c avgSciLit.c vaxxBehavior.c
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) -0.04 -0.12 – 0.03 0.277 -0.01 -0.02 – -0.00 0.008 -0.04 -0.12 – 0.03 0.254
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001 -0.00 -0.00 – 0.00 0.168 0.00 0.00 – 0.00 <0.001
ideology.c -0.15 -0.19 – -0.11 <0.001 0.00 -0.00 – 0.01 0.206 -0.15 -0.20 – -0.11 <0.001
age.c 0.02 0.01 – 0.02 <0.001 0.00 -0.00 – 0.00 0.798 0.02 0.01 – 0.02 <0.001
white_.5 -0.05 -0.20 – 0.11 0.564 0.04 0.03 – 0.06 <0.001 -0.05 -0.21 – 0.10 0.510
education.c 0.05 0.03 – 0.08 <0.001 0.01 0.00 – 0.01 <0.001 0.05 0.03 – 0.08 <0.001
avgSciLit.c 0.22 -0.29 – 0.74 0.396
Observations 997 996 994
R2 / R2 adjusted 0.167 / 0.163 0.058 / 0.053 0.170 / 0.165

- interaction

summary(m6.xy <- lm(vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
                         age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ index_ANexp_w3.c * ideology.c + 
##     age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1044 -0.3664  0.3046  0.7190  2.4337 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0172195  0.0394684  -0.436  0.66272    
## index_ANexp_w3.c             0.0039663  0.0005031   7.884 8.33e-15 ***
## ideology.c                  -0.1495609  0.0207887  -7.194 1.24e-12 ***
## age.c                        0.0163019  0.0023635   6.897 9.44e-12 ***
## white_.5                    -0.0406239  0.0784844  -0.518  0.60485    
## education.c                  0.0516708  0.0129714   3.983 7.29e-05 ***
## index_ANexp_w3.c:ideology.c  0.0008704  0.0002740   3.176  0.00154 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.065 on 990 degrees of freedom
##   (2863 observations deleted due to missingness)
## Multiple R-squared:  0.1755, Adjusted R-squared:  0.1705 
## F-statistic: 35.13 on 6 and 990 DF,  p-value: < 2.2e-16
summary(m6.xm <- lm(avgSciLit.c ~ index_ANexp_w3.c * ideology.c + 
                         age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = avgSciLit.c ~ index_ANexp_w3.c * ideology.c + age.c + 
##     white_.5 + education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.40431 -0.07188  0.00414  0.10238  0.25577 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -1.257e-02  4.822e-03  -2.607  0.00928 ** 
## index_ANexp_w3.c            -8.400e-05  6.138e-05  -1.368  0.17150    
## ideology.c                   3.207e-03  2.536e-03   1.264  0.20637    
## age.c                        7.477e-05  2.889e-04   0.259  0.79585    
## white_.5                     4.420e-02  9.585e-03   4.611 4.52e-06 ***
## education.c                  7.806e-03  1.582e-03   4.933 9.48e-07 ***
## index_ANexp_w3.c:ideology.c  1.389e-06  3.303e-05   0.042  0.96647    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1299 on 989 degrees of freedom
##   (2864 observations deleted due to missingness)
## Multiple R-squared:  0.05807,    Adjusted R-squared:  0.05235 
## F-statistic: 10.16 on 6 and 989 DF,  p-value: 6.29e-11
summary(m6.xmy <- lm(vaxxBehavior.c ~ (avgSciLit.c + index_ANexp_w3.c) * ideology.c + 
                          age.c + white_.5 + education.c, data = d))
## 
## Call:
## lm(formula = vaxxBehavior.c ~ (avgSciLit.c + index_ANexp_w3.c) * 
##     ideology.c + age.c + white_.5 + education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1186 -0.3772  0.3055  0.7242  2.3752 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -0.0212611  0.0397330  -0.535 0.592702    
## avgSciLit.c                  0.2392498  0.2629107   0.910 0.363043    
## index_ANexp_w3.c             0.0039992  0.0005054   7.912 6.76e-15 ***
## ideology.c                  -0.1535708  0.0208663  -7.360 3.88e-13 ***
## age.c                        0.0166840  0.0023701   7.039 3.62e-12 ***
## white_.5                    -0.0446779  0.0796454  -0.561 0.574952    
## education.c                  0.0504533  0.0131392   3.840 0.000131 ***
## avgSciLit.c:ideology.c       0.1512509  0.1490418   1.015 0.310440    
## index_ANexp_w3.c:ideology.c  0.0009218  0.0002794   3.299 0.001004 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.065 on 985 degrees of freedom
##   (2866 observations deleted due to missingness)
## Multiple R-squared:  0.1791, Adjusted R-squared:  0.1724 
## F-statistic: 26.86 on 8 and 985 DF,  p-value: < 2.2e-16
tab_model(m6.xy, m6.xm, m6.xmy)
  vaxxBehavior.c avgSciLit.c vaxxBehavior.c
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) -0.02 -0.09 – 0.06 0.663 -0.01 -0.02 – -0.00 0.009 -0.02 -0.10 – 0.06 0.593
index_ANexp_w3.c 0.00 0.00 – 0.00 <0.001 -0.00 -0.00 – 0.00 0.172 0.00 0.00 – 0.00 <0.001
ideology.c -0.15 -0.19 – -0.11 <0.001 0.00 -0.00 – 0.01 0.206 -0.15 -0.19 – -0.11 <0.001
age.c 0.02 0.01 – 0.02 <0.001 0.00 -0.00 – 0.00 0.796 0.02 0.01 – 0.02 <0.001
white_.5 -0.04 -0.19 – 0.11 0.605 0.04 0.03 – 0.06 <0.001 -0.04 -0.20 – 0.11 0.575
education.c 0.05 0.03 – 0.08 <0.001 0.01 0.00 – 0.01 <0.001 0.05 0.02 – 0.08 <0.001
index_ANexp_w3.c *
ideology.c 		0.00 0.00 – 0.00 0.002 0.00 -0.00 – 0.00 0.966 0.00 0.00 – 0.00 0.001
avgSciLit.c 0.24 -0.28 – 0.76 0.363
avgSciLit.c * ideology.c 0.15 -0.14 – 0.44 0.310
Observations 997 996 994
R2 / R2 adjusted 0.176 / 0.171 0.058 / 0.052 0.179 / 0.172

7. Figures