1. What are the correlations between CRT (wave 3), scientific literacy, attitudes toward science, vaccine behavior (wave 3), vaccine attitudes (waves 1 and 2), socioeconomic status (income and education), trust in experts and scientists (waves 1 and 2), risk perceptions (waves 1 and 2), and analytical media consumption?

notes: 1. there is no income variable in any of the waves. 2. sciLitSum = sum of correct answers for science literacy 5-item scale 3. sciTrustAvg = average for trust in science 15-item scale; high score = more trust in science 4. crtSum = sum of correct answers for CRT 6-item scale

i. all DVs

x <- cbind.data.frame(
  d$crtSum.c, 
  d$sciTrustAvg.c, 
  d$sciLitSum.c, 
  d$avgSymbBelief.c,
  d$vaxxBehavior.c, 
  d$vaxxAttitudes_w1.c, 
  d$vaxxAttitudes_w2.c,
  d$avgVaxxAttitudes.c,
  d$SESladder.c, 
  d$education.c, 
  d$trustExpert_w1.c, 
  d$trustExpert_w2.c, 
  d$avgTrustExpert.c,
  d$index_ANexp_w1.c, 
  d$index_ANexp_w2.c, 
  d$index_ANexp_w3.c)

cor2 <- cor(x, use = "complete.obs")
ggcorrplot(cor2, type = "lower",
   lab = TRUE, title = "general correlations", show.legend = F, insig = "blank", digits = 2)

ii. CRT vs. media exposure wave 1

x <- cbind.data.frame(
  d$crtSum.c, 
  d$ABC_exp_w1, 
  d$CBS_exp_w1, 
  d$CNN_exp_w1, 
  d$Fox_exp_w1,
  d$MSNBC_exp_w1,
  d$NBC_exp_w1, 
  d$NPR_exp_w1, 
  d$NYT_exp_w1, 
  d$PBS_exp_w1, 
  d$USAT_exp_w1,
  d$WSJ_exp_w1, 
  d$AOL_exp_w1,
  d$prop.media.exp_w1)

cor2 <- cor(x, use = "complete.obs")
ggcorrplot(cor2, type = "lower",
   lab = TRUE, title = "wave 1 correlations", show.legend = F, insig = "blank", digits = 2)

iii. CRT vs. media exposure wave 2

x <- cbind.data.frame(
  d$crtSum.c, 
  d$ABC_exp_w2, 
  d$CBS_exp_w2, 
  d$CNN_exp_w2, 
  d$Fox_exp_w2,
  d$MSNBC_exp_w2,
  d$NBC_exp_w2, 
  d$NPR_exp_w2, 
  d$NYT_exp_w2, 
  d$PBS_exp_w2, 
  d$USAT_exp_w2,
  d$WSJ_exp_w2, 
  d$AOL_exp_w2,
  d$prop.media.exp_w2)

cor2 <- cor(x, use = "complete.obs")
ggcorrplot(cor2, type = "lower",
   lab = TRUE, title = "wave 2 correlations", show.legend = F, insig = "blank", digits = 2)

iv. CRT vs. media exposure wave 3

x <- cbind.data.frame(
  d$crtSum.c, 
  d$ABC_exp_w3, 
  d$CBS_exp_w3, 
  d$CNN_exp_w3, 
  d$Fox_exp_w3,
  d$MSNBC_exp_w3,
  d$NBC_exp_w3, 
  d$NPR_exp_w3, 
  d$NYT_exp_w3, 
  d$PBS_exp_w3, 
  d$USAT_exp_w3,
  d$WSJ_exp_w3, 
  d$AOL_exp_w3,
  d$prop.media.exp_w3)

cor2 <- cor(x, use = "complete.obs")
ggcorrplot(cor2, type = "lower",
   lab = TRUE, title = "wave 3 correlations", show.legend = F, insig = "blank", digits = 2)

v. correlation tests

cor.test(d$crtSum.c, d$prop.media.exp_w1)
## 
##  Pearson's product-moment correlation
## 
## data:  d$crtSum.c and d$prop.media.exp_w1
## t = -3.4963, df = 1577, p-value = 0.0004848
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.13644373 -0.03853938
## sample estimates:
##         cor 
## -0.08770335
cor.test(d$crtSum.c, d$prop.media.exp_w2)
## 
##  Pearson's product-moment correlation
## 
## data:  d$crtSum.c and d$prop.media.exp_w2
## t = -3.3174, df = 1402, p-value = 0.0009319
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.13992208 -0.03610331
## sample estimates:
##         cor 
## -0.08825236
cor.test(d$crtSum.c, d$prop.media.exp_w3)
## 
##  Pearson's product-moment correlation
## 
## data:  d$crtSum.c and d$prop.media.exp_w3
## t = 6.2934, df = 1653, p-value = 3.97e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.105563 0.199682
## sample estimates:
##       cor 
## 0.1529693

2. Does analytical media consumption moderate any of the above correlations with CRT?

a. scienceTrust_w3 ~ media index * CRT sum

i. wave 1

ma.w1 <- lm(sciTrustAvg ~ index_ANexp_w1 * crtSum.c, data = d)
summary(ma.w1)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.35445 -0.53228 -0.00506  0.57755  1.75092 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              3.2535402  0.0427955  76.025  < 2e-16 ***
## index_ANexp_w1           0.0019748  0.0003557   5.551 3.62e-08 ***
## crtSum.c                -0.0103223  0.0204934  -0.504    0.615    
## index_ANexp_w1:crtSum.c  0.0011339  0.0001947   5.825 7.68e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7853 on 1011 degrees of freedom
##   (12482 observations deleted due to missingness)
## Multiple R-squared:  0.09307,    Adjusted R-squared:  0.09037 
## F-statistic: 34.58 on 3 and 1011 DF,  p-value: < 2.2e-16

ii. wave 2

ma.w2 <- lm(sciTrustAvg ~ index_ANexp_w2 * crtSum.c, data = d)
summary(ma.w2)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w2 * crtSum.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.32504 -0.51466  0.02866  0.56947  1.75012 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              3.2569108  0.0433835  75.073  < 2e-16 ***
## index_ANexp_w2           0.0021281  0.0003792   5.613 2.62e-08 ***
## crtSum.c                -0.0162854  0.0206106  -0.790     0.43    
## index_ANexp_w2:crtSum.c  0.0013704  0.0002117   6.472 1.56e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7803 on 940 degrees of freedom
##   (12553 observations deleted due to missingness)
## Multiple R-squared:  0.1064, Adjusted R-squared:  0.1036 
## F-statistic: 37.32 on 3 and 940 DF,  p-value: < 2.2e-16

iii. wave 3

ma.w3 <- lm(sciTrustAvg ~ index_ANexp_w3 * crtSum.c, data = d)
summary(ma.w3)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w3 * crtSum.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.30043 -0.48835  0.00157  0.56281  1.73875 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             3.2895470  0.0390916  84.150  < 2e-16 ***
## index_ANexp_w3          0.0018353  0.0003491   5.257 1.77e-07 ***
## crtSum.c                0.0005103  0.0184388   0.028    0.978    
## index_ANexp_w3:crtSum.c 0.0013002  0.0001979   6.571 7.78e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.782 on 1068 degrees of freedom
##   (12425 observations deleted due to missingness)
## Multiple R-squared:  0.09109,    Adjusted R-squared:  0.08854 
## F-statistic: 35.68 on 3 and 1068 DF,  p-value: < 2.2e-16

b. scienceLiteracy_w3 ~ index media * CRT

i. wave 1

mb.w1 <- lm(sciLitSum ~ index_ANexp_w1 * crtSum.c, data = d)
summary(mb.w1)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4760 -0.8671 -0.2277  0.7632  2.1965 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.4558348  0.0403383  36.091   <2e-16 ***
## index_ANexp_w1           0.0007933  0.0003728   2.128   0.0335 *  
## crtSum.c                 0.4160340  0.0209994  19.812   <2e-16 ***
## index_ANexp_w1:crtSum.c -0.0002859  0.0002143  -1.334   0.1824    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.007 on 1499 degrees of freedom
##   (11994 observations deleted due to missingness)
## Multiple R-squared:  0.3613, Adjusted R-squared:  0.3601 
## F-statistic: 282.7 on 3 and 1499 DF,  p-value: < 2.2e-16

ii. wave 2

mb.w2 <- lm(sciLitSum ~ index_ANexp_w2 * crtSum.c, data = d)
summary(mb.w2)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w2 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4960 -0.8872 -0.1781  0.7704  2.1646 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.4709049  0.0415060  35.438   <2e-16 ***
## index_ANexp_w2           0.0008562  0.0004074   2.102   0.0357 *  
## crtSum.c                 0.4053223  0.0214703  18.878   <2e-16 ***
## index_ANexp_w2:crtSum.c -0.0002336  0.0002375  -0.984   0.3255    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.01 on 1378 degrees of freedom
##   (12115 observations deleted due to missingness)
## Multiple R-squared:  0.3566, Adjusted R-squared:  0.3552 
## F-statistic: 254.5 on 3 and 1378 DF,  p-value: < 2.2e-16

iii. wave 3

mb.w3 <- lm(sciLitSum ~ index_ANexp_w3 * crtSum.c, data = d)
summary(mb.w3)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w3 * crtSum.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.46822 -0.37611 -0.03865  0.65527  1.11800 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              2.244e+00  3.917e-02  57.289  < 2e-16 ***
## index_ANexp_w3          -5.774e-04  3.493e-04  -1.653   0.0986 .  
## crtSum.c                 9.211e-02  1.849e-02   4.981 7.36e-07 ***
## index_ANexp_w3:crtSum.c  6.651e-05  1.981e-04   0.336   0.7371    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7876 on 1074 degrees of freedom
##   (12419 observations deleted due to missingness)
## Multiple R-squared:  0.05869,    Adjusted R-squared:  0.05606 
## F-statistic: 22.32 on 3 and 1074 DF,  p-value: 5.034e-14

c. vaxxAttitude_w1 ~ index media * CRT

i. wave 1

mc.w1 <- lm(vaxxAttitudes_w1 ~ index_ANexp_w1 * crtSum.c, data = d)
summary(mc.w1)
## 
## Call:
## lm(formula = vaxxAttitudes_w1 ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1343 -1.4143  0.3882  1.7581  2.9551 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.1129074  0.0825845   1.367    0.172    
## index_ANexp_w1          0.0076259  0.0007633   9.990   <2e-16 ***
## crtSum.c                0.0433480  0.0429921   1.008    0.313    
## index_ANexp_w1:crtSum.c 0.0007052  0.0004388   1.607    0.108    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.061 on 1499 degrees of freedom
##   (11994 observations deleted due to missingness)
## Multiple R-squared:  0.06602,    Adjusted R-squared:  0.06415 
## F-statistic: 35.32 on 3 and 1499 DF,  p-value: < 2.2e-16

d. vaxxAttitude_w2 ~ index_media * CRT

i. wave 1

md.w1 <- lm(vaxxAttitudes_w2 ~ index_ANexp_w1 * crtSum.c, data = d)
summary(md.w1)
## 
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4350 -1.2536 -0.0914  1.8317  3.0213 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.0850795  0.0857533   0.992   0.3213    
## index_ANexp_w1          0.0045923  0.0007856   5.845 6.32e-09 ***
## crtSum.c                0.0678162  0.0441514   1.536   0.1248    
## index_ANexp_w1:crtSum.c 0.0007616  0.0004507   1.690   0.0913 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.036 on 1356 degrees of freedom
##   (12137 observations deleted due to missingness)
## Multiple R-squared:  0.03512,    Adjusted R-squared:  0.03298 
## F-statistic: 16.45 on 3 and 1356 DF,  p-value: 1.67e-10

ii. wave 2

md.w2 <- lm(vaxxAttitudes_w2 ~ index_ANexp_w2 * crtSum.c, data = d)
summary(md.w2)
## 
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0759 -1.2346 -0.0151  1.7433  3.1075 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -0.008619   0.082863  -0.104   0.9172    
## index_ANexp_w2           0.006218   0.000813   7.648 3.81e-14 ***
## crtSum.c                 0.063072   0.042850   1.472   0.1413    
## index_ANexp_w2:crtSum.c  0.000986   0.000474   2.080   0.0377 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.014 on 1377 degrees of freedom
##   (12116 observations deleted due to missingness)
## Multiple R-squared:  0.05146,    Adjusted R-squared:  0.0494 
## F-statistic:  24.9 on 3 and 1377 DF,  p-value: 1.083e-15

e. vaxxBehavior ~ index media * CRT

i. wave 1

me.w1 <- lm(vaxxBehavior ~ index_ANexp_w1 * crtSum.c, data = d)
summary(me.w1)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9797 -0.3303  0.4996  0.8501  1.1660 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             2.8687327  0.0602367  47.624  < 2e-16 ***
## index_ANexp_w1          0.0038203  0.0005068   7.539 1.03e-13 ***
## crtSum.c                0.0221617  0.0290230   0.764   0.4453    
## index_ANexp_w1:crtSum.c 0.0004638  0.0002784   1.666   0.0959 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.138 on 1040 degrees of freedom
##   (12453 observations deleted due to missingness)
## Multiple R-squared:  0.06176,    Adjusted R-squared:  0.05905 
## F-statistic: 22.82 on 3 and 1040 DF,  p-value: 2.602e-14

ii. wave 2

me.w2 <- lm(vaxxBehavior ~ index_ANexp_w2 * crtSum.c, data = d)
summary(me.w2)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w2 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0625 -0.3214  0.4647  0.8340  1.1076 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             2.8941733  0.0605260  47.817  < 2e-16 ***
## index_ANexp_w2          0.0040367  0.0005356   7.536 1.11e-13 ***
## crtSum.c                0.0011282  0.0289462   0.039  0.96892    
## index_ANexp_w2:crtSum.c 0.0009049  0.0003000   3.016  0.00263 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.121 on 968 degrees of freedom
##   (12525 observations deleted due to missingness)
## Multiple R-squared:  0.07449,    Adjusted R-squared:  0.07162 
## F-statistic: 25.97 on 3 and 968 DF,  p-value: 3.678e-16

iii. wave 3

me.w3 <- lm(vaxxBehavior ~ index_ANexp_w3 * crtSum.c, data = d)
summary(me.w3)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2070 -0.3237  0.4242  0.8408  1.2896 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             2.8061207  0.0559949  50.114   <2e-16 ***
## index_ANexp_w3          0.0050163  0.0005006  10.021   <2e-16 ***
## crtSum.c                0.0610337  0.0264132   2.311    0.021 *  
## index_ANexp_w3:crtSum.c 0.0003096  0.0002836   1.092    0.275    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.123 on 1072 degrees of freedom
##   (12421 observations deleted due to missingness)
## Multiple R-squared:  0.09203,    Adjusted R-squared:  0.08949 
## F-statistic: 36.22 on 3 and 1072 DF,  p-value: < 2.2e-16

f. avgVaxxAttitudes ~ index media * CRT

i. wave 1

mf.w1 <- lm(avgVaxxAttitudes ~ index_ANexp_w1 * crtSum.c, data = d)
summary(mf.w1)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7898 -1.3378  0.1786  1.6185  3.0040 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.0729296  0.0769186   0.948   0.3432    
## index_ANexp_w1          0.0063802  0.0007109   8.974   <2e-16 ***
## crtSum.c                0.0490937  0.0400425   1.226   0.2204    
## index_ANexp_w1:crtSum.c 0.0007741  0.0004087   1.894   0.0584 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.919 on 1499 degrees of freedom
##   (11994 observations deleted due to missingness)
## Multiple R-squared:  0.0575, Adjusted R-squared:  0.05561 
## F-statistic: 30.48 on 3 and 1499 DF,  p-value: < 2.2e-16

ii. wave 2

mf.w2 <- lm(avgVaxxAttitudes ~ index_ANexp_w2 * crtSum.c, data = d)
summary(mf.w2)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w2 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7062 -1.3440  0.2427  1.5153  2.9978 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.0683479  0.0777947   0.879   0.3798    
## index_ANexp_w2          0.0072540  0.0007635   9.501   <2e-16 ***
## crtSum.c                0.0421917  0.0402418   1.048   0.2946    
## index_ANexp_w2:crtSum.c 0.0010621  0.0004452   2.386   0.0172 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.892 on 1378 degrees of freedom
##   (12115 observations deleted due to missingness)
## Multiple R-squared:  0.06954,    Adjusted R-squared:  0.06751 
## F-statistic: 34.33 on 3 and 1378 DF,  p-value: < 2.2e-16

iii. wave 3

mf.w3 <- lm(avgVaxxAttitudes ~ index_ANexp_w3 * crtSum.c, data = d)
summary(mf.w3)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w3 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5614 -1.3769  0.2136  1.6054  3.0903 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.0015467  0.0979766   0.016  0.98741    
## index_ANexp_w3          0.0070463  0.0008679   8.118 1.34e-15 ***
## crtSum.c                0.1237560  0.0462186   2.678  0.00753 ** 
## index_ANexp_w3:crtSum.c 0.0006545  0.0004918   1.331  0.18354    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.918 on 1026 degrees of freedom
##   (12467 observations deleted due to missingness)
## Multiple R-squared:  0.07286,    Adjusted R-squared:  0.07015 
## F-statistic: 26.88 on 3 and 1026 DF,  p-value: < 2.2e-16

g. expertTrust_w1 ~ index_media * CRT

i. wave 1

mg.w1 <- lm(d$trustExpert_w1 ~ index_ANexp_w1 * crtSum.c, data = d)
summary(mg.w1)
## 
## Call:
## lm(formula = d$trustExpert_w1 ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4608 -0.8097  0.3477  0.9718  2.0494 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.9880561  0.0577582  17.107   <2e-16 ***
## index_ANexp_w1          0.0058255  0.0005338  10.912   <2e-16 ***
## crtSum.c                0.0238962  0.0300679   0.795   0.4269    
## index_ANexp_w1:crtSum.c 0.0007682  0.0003069   2.503   0.0124 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.441 on 1499 degrees of freedom
##   (11994 observations deleted due to missingness)
## Multiple R-squared:  0.07952,    Adjusted R-squared:  0.07768 
## F-statistic: 43.17 on 3 and 1499 DF,  p-value: < 2.2e-16

h. expertTrust_w2 ~ index media * CRT

i. wave 1

mh.w1 <- lm(trustExpert_w2 ~ index_ANexp_w1 * crtSum.c, data = d)
summary(mh.w1)
## 
## Call:
## lm(formula = trustExpert_w2 ~ index_ANexp_w1 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5415 -0.8647  0.4547  0.9571  1.9488 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.0742837  0.0601378  17.864  < 2e-16 ***
## index_ANexp_w1          0.0057643  0.0005510  10.462  < 2e-16 ***
## crtSum.c                0.0147296  0.0309809   0.475  0.63455    
## index_ANexp_w1:crtSum.c 0.0008306  0.0003162   2.627  0.00871 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.437 on 1372 degrees of freedom
##   (12121 observations deleted due to missingness)
## Multiple R-squared:  0.07938,    Adjusted R-squared:  0.07737 
## F-statistic: 39.44 on 3 and 1372 DF,  p-value: < 2.2e-16

ii. wave 2

mh.w2 <- lm(trustExpert_w2 ~ index_ANexp_w2 * crtSum.c, data = d)
summary(mh.w2)
## 
## Call:
## lm(formula = trustExpert_w2 ~ index_ANexp_w2 * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0808 -0.8240  0.4548  0.9284  1.9209 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.0819079  0.0586731  18.440  < 2e-16 ***
## index_ANexp_w2          0.0062587  0.0005758  10.869  < 2e-16 ***
## crtSum.c                0.0017828  0.0303505   0.059  0.95317    
## index_ANexp_w2:crtSum.c 0.0010857  0.0003358   3.233  0.00125 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.427 on 1378 degrees of freedom
##   (12115 observations deleted due to missingness)
## Multiple R-squared:  0.08518,    Adjusted R-squared:  0.08319 
## F-statistic: 42.77 on 3 and 1378 DF,  p-value: < 2.2e-16

I. avgExpertTrust ~ avg index media * CRT

mh.w2 <- lm(avgTrustExpert ~ avgIndexANexp.c * crtSum.c, data = d)
summary(mh.w2)
## 
## Call:
## lm(formula = avgTrustExpert ~ avgIndexANexp.c * crtSum.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9230 -0.7378  0.3133  0.9599  2.0557 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.5866537  0.0348740  45.497  < 2e-16 ***
## avgIndexANexp.c          0.0068026  0.0005340  12.740  < 2e-16 ***
## crtSum.c                 0.1140364  0.0190182   5.996 2.52e-09 ***
## avgIndexANexp.c:crtSum.c 0.0011823  0.0003092   3.824 0.000137 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.317 on 1523 degrees of freedom
##   (11970 observations deleted due to missingness)
## Multiple R-squared:  0.1047, Adjusted R-squared:  0.1029 
## F-statistic: 59.36 on 3 and 1523 DF,  p-value: < 2.2e-16

3. Does education moderate the above effects?

a. scienceTrust_w3 ~ media index * education

i. wave 1

m3a.w1 <- lm(sciTrustAvg ~ index_ANexp_w1 * education.c, data = d)
summary(m3a.w1)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w1 * education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.52535 -0.52940  0.03215  0.55727  1.74257 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                3.286e+00  3.946e-02  83.262  < 2e-16 ***
## index_ANexp_w1             2.026e-03  3.550e-04   5.706 1.52e-08 ***
## education.c                3.388e-02  1.540e-02   2.199   0.0281 *  
## index_ANexp_w1:education.c 3.537e-05  1.058e-04   0.334   0.7381    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8061 on 1010 degrees of freedom
##   (12483 observations deleted due to missingness)
## Multiple R-squared:  0.04461,    Adjusted R-squared:  0.04177 
## F-statistic: 15.72 on 3 and 1010 DF,  p-value: 5.363e-10

ii. wave 2

ma.w2 <- lm(sciTrustAvg ~ index_ANexp_w2 * education.c, data = d)
summary(ma.w2)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w2 * education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.52189 -0.55015  0.02632  0.56954  1.76135 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 3.2882449  0.0404783  81.235  < 2e-16 ***
## index_ANexp_w2              0.0021084  0.0003835   5.497 4.99e-08 ***
## education.c                 0.0632226  0.0170202   3.715 0.000216 ***
## index_ANexp_w2:education.c -0.0002063  0.0001234  -1.671 0.095011 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8067 on 925 degrees of freedom
##   (12568 observations deleted due to missingness)
## Multiple R-squared:  0.04966,    Adjusted R-squared:  0.04657 
## F-statistic: 16.11 on 3 and 925 DF,  p-value: 3.25e-10

iii. wave 3

ma.w3 <- lm(sciTrustAvg ~ index_ANexp_w3 * education.c, data = d)
summary(ma.w3)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w3 * education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.47699 -0.56615  0.03983  0.57739  1.83153 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 3.337e+00  3.683e-02  90.602  < 2e-16 ***
## index_ANexp_w3              1.648e-03  3.591e-04   4.590 4.99e-06 ***
## education.c                 4.830e-02  1.486e-02   3.251  0.00119 ** 
## index_ANexp_w3:education.c -9.582e-05  1.125e-04  -0.852  0.39453    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8095 on 1005 degrees of freedom
##   (12488 observations deleted due to missingness)
## Multiple R-squared:  0.03526,    Adjusted R-squared:  0.03238 
## F-statistic: 12.24 on 3 and 1005 DF,  p-value: 7.163e-08

b. scienceLiteracy_w3 ~ index media * education

i. wave 1

mb.w1 <- lm(sciLitSum ~ index_ANexp_w1 * education.c, data = d)
summary(mb.w1)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w1 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7225 -1.4736  0.3702  1.3397  2.2531 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.5631561  0.0500158  31.253  < 2e-16 ***
## index_ANexp_w1             -0.0002526  0.0004570  -0.553  0.58054    
## education.c                 0.0609707  0.0198652   3.069  0.00218 ** 
## index_ANexp_w1:education.c -0.0001209  0.0001435  -0.843  0.39934    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.253 on 1497 degrees of freedom
##   (11996 observations deleted due to missingness)
## Multiple R-squared:  0.01043,    Adjusted R-squared:  0.008449 
## F-statistic: 5.261 on 3 and 1497 DF,  p-value: 0.001303

ii. wave 2

mb.w2 <- lm(sciLitSum ~ index_ANexp_w2 * education.c, data = d)
summary(mb.w2)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w2 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7378 -1.5088  0.3581  1.3294  2.2134 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.5824455  0.0516303  30.650  < 2e-16 ***
## index_ANexp_w2             -0.0001403  0.0005021  -0.279  0.77992    
## education.c                 0.0594496  0.0219276   2.711  0.00679 ** 
## index_ANexp_w2:education.c -0.0002160  0.0001701  -1.270  0.20426    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.252 on 1352 degrees of freedom
##   (12141 observations deleted due to missingness)
## Multiple R-squared:  0.006887,   Adjusted R-squared:  0.004683 
## F-statistic: 3.125 on 3 and 1352 DF,  p-value: 0.02502

iii. wave 3

mb.w3 <- lm(sciLitSum ~ index_ANexp_w3 * education.c, data = d)
summary(mb.w3)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w3 * education.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.41737 -0.37896 -0.08089  0.65870  1.44789 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.3560369  0.0361298  65.210  < 2e-16 ***
## index_ANexp_w3             -0.0010992  0.0003511  -3.131  0.00179 ** 
## education.c                 0.0380343  0.0146085   2.604  0.00936 ** 
## index_ANexp_w3:education.c  0.0000836  0.0001106   0.756  0.44968    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7965 on 1011 degrees of freedom
##   (12482 observations deleted due to missingness)
## Multiple R-squared:  0.03349,    Adjusted R-squared:  0.03063 
## F-statistic: 11.68 on 3 and 1011 DF,  p-value: 1.585e-07

c. vaxxAttitude_w1 ~ index media * education

i. wave 1

mc.w1 <- lm(vaxxAttitudes_w1 ~ index_ANexp_w1 * education.c, data = d)
summary(mc.w1)
## 
## Call:
## lm(formula = vaxxAttitudes_w1 ~ index_ANexp_w1 * education.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7525 -1.1216  0.5175  1.7155  3.4234 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 0.7165656  0.0297149  24.115  < 2e-16 ***
## index_ANexp_w1              0.0031082  0.0002476  12.555  < 2e-16 ***
## education.c                 0.0990795  0.0117502   8.432  < 2e-16 ***
## index_ANexp_w1:education.c -0.0003242  0.0000873  -3.713 0.000206 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.933 on 10757 degrees of freedom
##   (2736 observations deleted due to missingness)
## Multiple R-squared:  0.02294,    Adjusted R-squared:  0.02267 
## F-statistic: 84.19 on 3 and 10757 DF,  p-value: < 2.2e-16

d. vaxxAttitude_w2 ~ index_media * education

i. wave 1

md.w1 <- lm(vaxxAttitudes_w2 ~ index_ANexp_w1 * education.c, data = d)
summary(md.w1)
## 
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w1 * education.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5010 -1.4187  0.0336  1.7749  4.4763 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                -0.0344276  0.0641767  -0.536   0.5917    
## index_ANexp_w1              0.0046715  0.0005735   8.145 5.93e-16 ***
## education.c                 0.1077020  0.0252728   4.262 2.11e-05 ***
## index_ANexp_w1:education.c -0.0003644  0.0001838  -1.983   0.0475 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.062 on 2486 degrees of freedom
##   (11007 observations deleted due to missingness)
## Multiple R-squared:  0.03523,    Adjusted R-squared:  0.03407 
## F-statistic: 30.26 on 3 and 2486 DF,  p-value: < 2.2e-16

ii. wave 2

md.w2 <- lm(vaxxAttitudes_w2 ~ index_ANexp_w2 * education.c, data = d)
summary(md.w2)
## 
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 * education.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6687 -1.4168  0.0257  1.7266  4.2962 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                -0.0832680  0.0621226  -1.340 0.180245    
## index_ANexp_w2              0.0056466  0.0005880   9.603  < 2e-16 ***
## education.c                 0.0906031  0.0246641   3.673 0.000244 ***
## index_ANexp_w2:education.c -0.0002156  0.0001907  -1.131 0.258373    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.051 on 2479 degrees of freedom
##   (11014 observations deleted due to missingness)
## Multiple R-squared:  0.04413,    Adjusted R-squared:  0.04297 
## F-statistic: 38.15 on 3 and 2479 DF,  p-value: < 2.2e-16

e. vaxxBehavior ~ index media * education

i. wave 1

me.w1 <- lm(vaxxBehavior ~ index_ANexp_w1 * education.c, data = d)
summary(me.w1)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w1 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9637 -0.3264  0.4808  0.7983  2.2267 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.9037212  0.0546991  53.085  < 2e-16 ***
## index_ANexp_w1              0.0036486  0.0004933   7.397 2.87e-13 ***
## education.c                 0.0844410  0.0211899   3.985 7.22e-05 ***
## index_ANexp_w1:education.c -0.0002210  0.0001477  -1.496    0.135    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.133 on 1039 degrees of freedom
##   (12454 observations deleted due to missingness)
## Multiple R-squared:  0.07021,    Adjusted R-squared:  0.06752 
## F-statistic: 26.15 on 3 and 1039 DF,  p-value: 2.608e-16

ii. wave 2

me.w2 <- lm(vaxxBehavior ~ index_ANexp_w2 * education.c, data = d)
summary(me.w2)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w2 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2670 -0.3157  0.4790  0.7965  2.0373 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.918e+00  5.572e-02  52.365  < 2e-16 ***
## index_ANexp_w2              3.899e-03  5.291e-04   7.368 3.75e-13 ***
## education.c                 7.135e-02  2.329e-02   3.063  0.00225 ** 
## index_ANexp_w2:education.c -7.641e-05  1.709e-04  -0.447  0.65485    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.126 on 952 degrees of freedom
##   (12541 observations deleted due to missingness)
## Multiple R-squared:  0.07242,    Adjusted R-squared:  0.0695 
## F-statistic: 24.78 on 3 and 952 DF,  p-value: 1.931e-15

iii. wave 3

me.w3 <- lm(vaxxBehavior ~ index_ANexp_w3 * education.c, data = d)
summary(me.w3)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2450 -0.3334  0.4251  0.7825  2.3787 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.8818951  0.0505569  57.003  < 2e-16 ***
## index_ANexp_w3              0.0045851  0.0004927   9.306  < 2e-16 ***
## education.c                 0.0941596  0.0204146   4.612 4.49e-06 ***
## index_ANexp_w3:education.c -0.0003106  0.0001547  -2.008   0.0449 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.112 on 1009 degrees of freedom
##   (12484 observations deleted due to missingness)
## Multiple R-squared:  0.09875,    Adjusted R-squared:  0.09607 
## F-statistic: 36.85 on 3 and 1009 DF,  p-value: < 2.2e-16

f. avgVaxxAttitudes ~ index media * education

i. wave 1

mf.w1 <- lm(avgVaxxAttitudes ~ index_ANexp_w1 * education.c, data = d)
summary(mf.w1)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w1 * education.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6805 -1.1145  0.3834  1.7062  3.3806 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 6.953e-01  2.920e-02  23.814  < 2e-16 ***
## index_ANexp_w1              2.955e-03  2.433e-04  12.147  < 2e-16 ***
## education.c                 9.359e-02  1.155e-02   8.106 5.78e-16 ***
## index_ANexp_w1:education.c -3.103e-04  8.578e-05  -3.618 0.000298 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.9 on 10757 degrees of freedom
##   (2736 observations deleted due to missingness)
## Multiple R-squared:  0.02135,    Adjusted R-squared:  0.02107 
## F-statistic: 78.21 on 3 and 10757 DF,  p-value: < 2.2e-16

ii. wave 2

mf.w2 <- lm(avgVaxxAttitudes ~ index_ANexp_w2 * education.c, data = d)
summary(mf.w2)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w2 * education.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5284 -1.3737  0.2362  1.5537  4.0373 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 0.0246976  0.0579437   0.426   0.6700    
## index_ANexp_w2              0.0062007  0.0005486  11.304  < 2e-16 ***
## education.c                 0.1126115  0.0230055   4.895 1.05e-06 ***
## index_ANexp_w2:education.c -0.0002968  0.0001779  -1.668   0.0954 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.913 on 2480 degrees of freedom
##   (11013 observations deleted due to missingness)
## Multiple R-squared:  0.0625, Adjusted R-squared:  0.06137 
## F-statistic: 55.11 on 3 and 2480 DF,  p-value: < 2.2e-16

iii. wave 3

mf.w3 <- lm(avgVaxxAttitudes ~ index_ANexp_w3 * education.c, data = d)
summary(mf.w3)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w3 * education.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9398 -1.3658  0.2604  1.6416  3.5271 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 0.1586125  0.0879496   1.803   0.0716 .  
## index_ANexp_w3              0.0062296  0.0008547   7.289 6.29e-13 ***
## education.c                 0.0885659  0.0355610   2.491   0.0129 *  
## index_ANexp_w3:education.c -0.0002556  0.0002691  -0.950   0.3424    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.939 on 1011 degrees of freedom
##   (12482 observations deleted due to missingness)
## Multiple R-squared:  0.05629,    Adjusted R-squared:  0.05349 
## F-statistic:  20.1 on 3 and 1011 DF,  p-value: 1.169e-12

g. expertTrust_w1 ~ index_media * education

i. wave 1

mg.w1 <- lm(d$trustExpert_w1 ~ index_ANexp_w1 * education.c, data = d)
summary(mg.w1)
## 
## Call:
## lm(formula = d$trustExpert_w1 ~ index_ANexp_w1 * education.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0556 -0.6361  0.4329  1.1314  2.7808 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.249e+00  2.260e-02  55.269   <2e-16 ***
## index_ANexp_w1              2.041e-03  1.883e-04  10.837   <2e-16 ***
## education.c                 8.754e-02  8.939e-03   9.793   <2e-16 ***
## index_ANexp_w1:education.c -1.152e-04  6.641e-05  -1.735   0.0828 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.47 on 10748 degrees of freedom
##   (2745 observations deleted due to missingness)
## Multiple R-squared:  0.02794,    Adjusted R-squared:  0.02767 
## F-statistic:   103 on 3 and 10748 DF,  p-value: < 2.2e-16

h. expertTrust_w2 ~ index media * education

i. wave 1

mh.w1 <- lm(trustExpert_w2 ~ index_ANexp_w1 * education.c, data = d)
summary(mh.w1)
## 
## Call:
## lm(formula = trustExpert_w2 ~ index_ANexp_w1 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4691 -0.8458  0.4224  1.0427  2.4769 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                1.106e+00  4.429e-02  24.985  < 2e-16 ***
## index_ANexp_w1             4.909e-03  3.957e-04  12.403  < 2e-16 ***
## education.c                5.616e-02  1.730e-02   3.247  0.00118 ** 
## index_ANexp_w1:education.c 3.776e-05  1.262e-04   0.299  0.76470    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.431 on 2514 degrees of freedom
##   (10979 observations deleted due to missingness)
## Multiple R-squared:  0.06851,    Adjusted R-squared:  0.0674 
## F-statistic: 61.64 on 3 and 2514 DF,  p-value: < 2.2e-16

ii. wave 2

mh.w2 <- lm(trustExpert_w2 ~ index_ANexp_w2 * education.c, data = d)
summary(mh.w2)
## 
## Call:
## lm(formula = trustExpert_w2 ~ index_ANexp_w2 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7043 -0.7873  0.4037  0.9906  2.2322 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                1.1183995  0.0431904  25.895   <2e-16 ***
## index_ANexp_w2             0.0052468  0.0004089  12.832   <2e-16 ***
## education.c                0.0337567  0.0171480   1.969   0.0491 *  
## index_ANexp_w2:education.c 0.0002655  0.0001326   2.002   0.0454 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.426 on 2480 degrees of freedom
##   (11013 observations deleted due to missingness)
## Multiple R-squared:  0.07381,    Adjusted R-squared:  0.07269 
## F-statistic: 65.88 on 3 and 2480 DF,  p-value: < 2.2e-16

i. wave 1

mi.w1 <- lm(avgTrustExpert ~ index_ANexp_w1 * education.c, data = d)
summary(mi.w1)
## 
## Call:
## lm(formula = avgTrustExpert ~ index_ANexp_w1 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0603 -0.6447  0.4154  1.0613  2.7438 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.258e+00  2.210e-02  56.928   <2e-16 ***
## index_ANexp_w1              2.038e-03  1.842e-04  11.065   <2e-16 ***
## education.c                 8.531e-02  8.741e-03   9.761   <2e-16 ***
## index_ANexp_w1:education.c -1.123e-04  6.494e-05  -1.729   0.0839 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.437 on 10748 degrees of freedom
##   (2745 observations deleted due to missingness)
## Multiple R-squared:  0.02826,    Adjusted R-squared:  0.02799 
## F-statistic: 104.2 on 3 and 10748 DF,  p-value: < 2.2e-16

ii. wave 2

mi.w2 <- lm(avgTrustExpert ~ index_ANexp_w2 * education.c, data = d)
summary(mi.w2)
## 
## Call:
## lm(formula = avgTrustExpert ~ index_ANexp_w2 * education.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3778 -0.7282  0.2958  0.9559  2.3733 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                1.0999780  0.0392663  28.013   <2e-16 ***
## index_ANexp_w2             0.0049803  0.0003717  13.397   <2e-16 ***
## education.c                0.0455647  0.0155899   2.923   0.0035 ** 
## index_ANexp_w2:education.c 0.0002436  0.0001206   2.021   0.0434 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.297 on 2480 degrees of freedom
##   (11013 observations deleted due to missingness)
## Multiple R-squared:  0.08546,    Adjusted R-squared:  0.08435 
## F-statistic: 77.25 on 3 and 2480 DF,  p-value: < 2.2e-16

j. media index ~ education.c + media exposure + symbolic beliefs

i. wave 1

mj.w1 <- lm(index_ANexp_w1 ~ education.c + prop.media.exp_w1 + avgSymbBelief.c, data = d)
summary(mj.w1)
## 
## Call:
## lm(formula = index_ANexp_w1 ~ education.c + prop.media.exp_w1 + 
##     avgSymbBelief.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -82.954 -21.207  -3.839  21.908 240.976 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        14.84165    0.47929  30.966   <2e-16 ***
## education.c        -0.01965    0.11948  -0.164   0.8694    
## prop.media.exp_w1 389.90559    1.80115 216.476   <2e-16 ***
## avgSymbBelief.c    -0.49665    0.20688  -2.401   0.0164 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.43 on 10752 degrees of freedom
##   (2741 observations deleted due to missingness)
## Multiple R-squared:  0.815,  Adjusted R-squared:  0.8149 
## F-statistic: 1.579e+04 on 3 and 10752 DF,  p-value: < 2.2e-16

i. wave 1

mj.w1 <- lm(index_ANexp_w1 ~ education.c * avgSymbBelief.c + prop.media.exp_w1, data = d)
summary(mj.w1)
## 
## Call:
## lm(formula = index_ANexp_w1 ~ education.c * avgSymbBelief.c + 
##     prop.media.exp_w1, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -82.779 -21.241  -3.828  21.924 240.968 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  14.83679    0.48035  30.888   <2e-16 ***
## education.c                  -0.01996    0.11950  -0.167   0.8673    
## avgSymbBelief.c              -0.49670    0.20689  -2.401   0.0164 *  
## prop.media.exp_w1           389.91323    1.80191 216.389   <2e-16 ***
## education.c:avgSymbBelief.c  -0.01213    0.07850  -0.154   0.8772    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.43 on 10751 degrees of freedom
##   (2741 observations deleted due to missingness)
## Multiple R-squared:  0.815,  Adjusted R-squared:  0.8149 
## F-statistic: 1.184e+04 on 4 and 10751 DF,  p-value: < 2.2e-16

ii. wave 2

mj.w2 <- lm(index_ANexp_w2 ~ education.c + prop.media.exp_w2 + avgSymbBelief.c, data = d)
summary(mj.w2)
## 
## Call:
## lm(formula = index_ANexp_w2 ~ education.c + prop.media.exp_w2 + 
##     avgSymbBelief.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.0223  -2.3028   0.5035   2.0512  29.2299 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.99063    0.11191 -17.787  < 2e-16 ***
## education.c        -0.04272    0.02693  -1.587  0.11275    
## prop.media.exp_w2 319.92933    0.33365 958.868  < 2e-16 ***
## avgSymbBelief.c    -0.11911    0.04482  -2.658  0.00792 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.511 on 2480 degrees of freedom
##   (11013 observations deleted due to missingness)
## Multiple R-squared:  0.9975, Adjusted R-squared:  0.9975 
## F-statistic: 3.289e+05 on 3 and 2480 DF,  p-value: < 2.2e-16

iii. wave 3

mj.w3 <- lm(index_ANexp_w3 ~ education.c + prop.media.exp_w3 + avgSymbBelief.c, data = d)
summary(mj.w3)
## 
## Call:
## lm(formula = index_ANexp_w3 ~ education.c + prop.media.exp_w3 + 
##     avgSymbBelief.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.767  -1.767   0.450   1.637  43.607 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.614662   0.157568 -10.247   <2e-16 ***
## education.c         0.009105   0.039520   0.230    0.818    
## prop.media.exp_w3 320.474710   0.484619 661.292   <2e-16 ***
## avgSymbBelief.c    -0.052784   0.062973  -0.838    0.402    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.301 on 1011 degrees of freedom
##   (12482 observations deleted due to missingness)
## Multiple R-squared:  0.9979, Adjusted R-squared:  0.9979 
## F-statistic: 1.572e+05 on 3 and 1011 DF,  p-value: < 2.2e-16

4. Does SES ladder moderate the above effects?

a. scienceTrust_w3 ~ media index * SES

i. wave 1

m3a.w1 <- lm(sciTrustAvg ~ index_ANexp_w1 * SESladder.c, data = d)
summary(m3a.w1)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w1 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4498 -0.5369  0.0241  0.5618  1.7251 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 3.301e+00  3.956e-02  83.446  < 2e-16 ***
## index_ANexp_w1              1.932e-03  3.561e-04   5.427 7.19e-08 ***
## SESladder.c                -1.540e-02  1.982e-02  -0.777    0.437    
## index_ANexp_w1:SESladder.c -6.949e-05  1.627e-04  -0.427    0.669    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8112 on 1011 degrees of freedom
##   (12482 observations deleted due to missingness)
## Multiple R-squared:  0.03213,    Adjusted R-squared:  0.02926 
## F-statistic: 11.19 on 3 and 1011 DF,  p-value: 3.169e-07

ii. wave 2

ma.w2 <- lm(sciTrustAvg ~ index_ANexp_w2 * SESladder.c, data = d)
summary(ma.w2)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w2 * SESladder.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.40874 -0.56342  0.02857  0.58934  1.78780 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 3.304e+00  4.060e-02  81.362  < 2e-16 ***
## index_ANexp_w2              2.078e-03  3.861e-04   5.383 9.28e-08 ***
## SESladder.c                -2.619e-02  2.012e-02  -1.301    0.193    
## index_ANexp_w2:SESladder.c -5.128e-05  1.748e-04  -0.293    0.769    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8125 on 926 degrees of freedom
##   (12567 observations deleted due to missingness)
## Multiple R-squared:  0.03569,    Adjusted R-squared:  0.03257 
## F-statistic: 11.42 on 3 and 926 DF,  p-value: 2.323e-07

iii. wave 3

ma.w3 <- lm(sciTrustAvg ~ index_ANexp_w3 * SESladder.c, data = d)
summary(ma.w3)
## 
## Call:
## lm(formula = sciTrustAvg ~ index_ANexp_w3 * SESladder.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.39743 -0.56299  0.03045  0.57001  1.71074 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 3.352e+00  3.690e-02  90.849  < 2e-16 ***
## index_ANexp_w3              1.571e-03  3.612e-04   4.351 1.49e-05 ***
## SESladder.c                -1.803e-02  1.866e-02  -0.966    0.334    
## index_ANexp_w3:SESladder.c -5.593e-05  1.625e-04  -0.344    0.731    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.815 on 1006 degrees of freedom
##   (12487 observations deleted due to missingness)
## Multiple R-squared:  0.02188,    Adjusted R-squared:  0.01896 
## F-statistic:   7.5 on 3 and 1006 DF,  p-value: 5.752e-05

b. scienceLiteracy_w3 ~ index media * SESladder

i. wave 1

mb.w1 <- lm(sciLitSum ~ index_ANexp_w1 * SESladder.c, data = d)
summary(mb.w1)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w1 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7304 -1.4989  0.4083  1.3731  1.6500 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.5758256  0.0501123  31.446   <2e-16 ***
## index_ANexp_w1             -0.0002467  0.0004580  -0.539   0.5901    
## SESladder.c                -0.0502510  0.0260415  -1.930   0.0538 .  
## index_ANexp_w1:SESladder.c  0.0001215  0.0002158   0.563   0.5735    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.257 on 1497 degrees of freedom
##   (11996 observations deleted due to missingness)
## Multiple R-squared:  0.003954,   Adjusted R-squared:  0.001958 
## F-statistic: 1.981 on 3 and 1497 DF,  p-value: 0.1149

ii. wave 2

mb.w2 <- lm(sciLitSum ~ index_ANexp_w2 * SESladder.c, data = d)
summary(mb.w2)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w2 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7887 -1.5511  0.3947  1.3821  1.5983 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.5948905  0.0516472  30.880   <2e-16 ***
## index_ANexp_w2             -0.0001388  0.0005028  -0.276    0.783    
## SESladder.c                -0.0429998  0.0263450  -1.632    0.103    
## index_ANexp_w2:SESladder.c  0.0002491  0.0002333   1.067    0.286    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.254 on 1352 degrees of freedom
##   (12141 observations deleted due to missingness)
## Multiple R-squared:  0.00204,    Adjusted R-squared:  -0.0001741 
## F-statistic: 0.9214 on 3 and 1352 DF,  p-value: 0.4297

iii. wave 3

mb.w3 <- lm(sciLitSum ~ index_ANexp_w3 * SESladder.c, data = d)
summary(mb.w3)
## 
## Call:
## lm(formula = sciLitSum ~ index_ANexp_w3 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5179 -0.3552 -0.1465  0.6903  1.0229 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.373e+00  3.629e-02  65.380  < 2e-16 ***
## index_ANexp_w3             -1.174e-03  3.540e-04  -3.316 0.000945 ***
## SESladder.c                -3.414e-02  1.839e-02  -1.856 0.063721 .  
## index_ANexp_w3:SESladder.c  9.264e-05  1.599e-04   0.579 0.562523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8038 on 1012 degrees of freedom
##   (12481 observations deleted due to missingness)
## Multiple R-squared:  0.01474,    Adjusted R-squared:  0.01182 
## F-statistic: 5.045 on 3 and 1012 DF,  p-value: 0.00179

c. vaxxAttitude_w1 ~ index media * SESladder

i. wave 1

mc.w1 <- lm(vaxxAttitudes_w1 ~ index_ANexp_w1 * SESladder.c, data = d)
summary(mc.w1)
## 
## Call:
## lm(formula = vaxxAttitudes_w1 ~ index_ANexp_w1 * SESladder.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8456 -1.1316  0.4969  1.6951  2.7217 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 0.7286554  0.0292831  24.883  < 2e-16 ***
## index_ANexp_w1              0.0031629  0.0002403  13.164  < 2e-16 ***
## SESladder.c                -0.1002395  0.0158194  -6.336 2.44e-10 ***
## index_ANexp_w1:SESladder.c  0.0002455  0.0001252   1.962   0.0498 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.933 on 11171 degrees of freedom
##   (2322 observations deleted due to missingness)
## Multiple R-squared:  0.02138,    Adjusted R-squared:  0.02111 
## F-statistic: 81.34 on 3 and 11171 DF,  p-value: < 2.2e-16

d. vaxxAttitude_w2 ~ index_media * SESladder

i. wave 1

md.w1 <- lm(vaxxAttitudes_w2 ~ index_ANexp_w1 * SESladder.c, data = d)
summary(md.w1)
## 
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w1 * SESladder.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4129 -1.3593 -0.0322  1.8143  3.4757 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                -0.0192511  0.0644377  -0.299  0.76515    
## index_ANexp_w1              0.0046397  0.0005747   8.074 1.05e-15 ***
## SESladder.c                -0.1015772  0.0333033  -3.050  0.00231 ** 
## index_ANexp_w1:SESladder.c  0.0004094  0.0002639   1.551  0.12097    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.068 on 2489 degrees of freedom
##   (11004 observations deleted due to missingness)
## Multiple R-squared:  0.03063,    Adjusted R-squared:  0.02946 
## F-statistic: 26.21 on 3 and 2489 DF,  p-value: < 2.2e-16

ii. wave 2

md.w2 <- lm(vaxxAttitudes_w2 ~ index_ANexp_w2 * SESladder.c, data = d)
summary(md.w2)
## 
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 * SESladder.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5464 -1.3598 -0.0028  1.7913  3.5544 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                -0.0652966  0.0624523  -1.046 0.295873    
## index_ANexp_w2              0.0055652  0.0005912   9.413  < 2e-16 ***
## SESladder.c                -0.1088665  0.0323332  -3.367 0.000771 ***
## index_ANexp_w2:SESladder.c  0.0004562  0.0002683   1.700 0.089276 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.055 on 2482 degrees of freedom
##   (11011 observations deleted due to missingness)
## Multiple R-squared:  0.0412, Adjusted R-squared:  0.04004 
## F-statistic: 35.55 on 3 and 2482 DF,  p-value: < 2.2e-16

e. vaxxBehavior ~ index media * SESladder

i. wave 1

me.w1 <- lm(vaxxBehavior ~ index_ANexp_w1 * SESladder.c, data = d)
summary(me.w1)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w1 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1204 -0.3000  0.4903  0.8153  1.4603 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.9241408  0.0547219  53.436  < 2e-16 ***
## index_ANexp_w1              0.0036175  0.0004932   7.334 4.47e-13 ***
## SESladder.c                -0.0855644  0.0274113  -3.122  0.00185 ** 
## index_ANexp_w1:SESladder.c  0.0002032  0.0002250   0.903  0.36673    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.137 on 1040 degrees of freedom
##   (12453 observations deleted due to missingness)
## Multiple R-squared:  0.06348,    Adjusted R-squared:  0.06077 
## F-statistic:  23.5 on 3 and 1040 DF,  p-value: 1.019e-14

ii. wave 2

me.w2 <- lm(vaxxBehavior ~ index_ANexp_w2 * SESladder.c, data = d)
summary(me.w2)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w2 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3127 -0.2858  0.4877  0.8038  1.4595 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.9395063  0.0556524  52.819  < 2e-16 ***
## index_ANexp_w2              0.0038590  0.0005298   7.284  6.8e-13 ***
## SESladder.c                -0.0887943  0.0275457  -3.224  0.00131 ** 
## index_ANexp_w2:SESladder.c  0.0001695  0.0002404   0.705  0.48099    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.128 on 953 degrees of freedom
##   (12540 observations deleted due to missingness)
## Multiple R-squared:  0.06907,    Adjusted R-squared:  0.06614 
## F-statistic: 23.57 on 3 and 953 DF,  p-value: 1.014e-14

iii. wave 3

me.w3 <- lm(vaxxBehavior ~ index_ANexp_w3 * SESladder.c, data = d)
summary(me.w3)
## 
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2164 -0.2700  0.4191  0.8160  1.4732 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.9112281  0.0506152  57.517  < 2e-16 ***
## index_ANexp_w3              0.0044695  0.0004953   9.023  < 2e-16 ***
## SESladder.c                -0.0855653  0.0256396  -3.337 0.000877 ***
## index_ANexp_w3:SESladder.c  0.0003159  0.0002228   1.418 0.156522    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.119 on 1010 degrees of freedom
##   (12483 observations deleted due to missingness)
## Multiple R-squared:  0.08715,    Adjusted R-squared:  0.08444 
## F-statistic: 32.14 on 3 and 1010 DF,  p-value: < 2.2e-16

f. avgVaxxAttitudes ~ index media * SESladder

i. wave 1

mf.w1 <- lm(avgVaxxAttitudes ~ index_ANexp_w1 * SESladder.c, data = d)
summary(mf.w1)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w1 * SESladder.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7230 -1.1324  0.4013  1.6767  2.7462 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 0.7068162  0.0287872  24.553  < 2e-16 ***
## index_ANexp_w1              0.0030323  0.0002362  12.838  < 2e-16 ***
## SESladder.c                -0.1008260  0.0155515  -6.483 9.35e-11 ***
## index_ANexp_w1:SESladder.c  0.0002875  0.0001231   2.336   0.0195 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.9 on 11171 degrees of freedom
##   (2322 observations deleted due to missingness)
## Multiple R-squared:  0.02044,    Adjusted R-squared:  0.02018 
## F-statistic:  77.7 on 3 and 11171 DF,  p-value: < 2.2e-16

ii. wave 2

mf.w2 <- lm(avgVaxxAttitudes ~ index_ANexp_w2 * SESladder.c, data = d)
summary(mf.w2)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w2 * SESladder.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4753 -1.3996  0.1915  1.5997  3.4439 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 0.0411906  0.0583479   0.706 0.480286    
## index_ANexp_w2              0.0061805  0.0005524  11.188  < 2e-16 ***
## SESladder.c                -0.1079698  0.0301822  -3.577 0.000354 ***
## index_ANexp_w2:SESladder.c  0.0002291  0.0002507   0.914 0.360835    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.92 on 2483 degrees of freedom
##   (11010 observations deleted due to missingness)
## Multiple R-squared:  0.05697,    Adjusted R-squared:  0.05583 
## F-statistic:    50 on 3 and 2483 DF,  p-value: < 2.2e-16

iii. wave 3

mf.w3 <- lm(avgVaxxAttitudes ~ index_ANexp_w3 * SESladder.c, data = d)
summary(mf.w3)
## 
## Call:
## lm(formula = avgVaxxAttitudes ~ index_ANexp_w3 * SESladder.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9421 -1.3492  0.2074  1.6526  3.2876 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 0.1896249  0.0873168   2.172  0.03011 *  
## index_ANexp_w3              0.0060543  0.0008519   7.107 2.24e-12 ***
## SESladder.c                -0.1366105  0.0442538  -3.087  0.00208 ** 
## index_ANexp_w3:SESladder.c  0.0003903  0.0003848   1.014  0.31072    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.934 on 1012 degrees of freedom
##   (12481 observations deleted due to missingness)
## Multiple R-squared:  0.05993,    Adjusted R-squared:  0.05714 
## F-statistic:  21.5 on 3 and 1012 DF,  p-value: 1.658e-13

g. expertTrust_w1 ~ index_media * education

i. wave 1

mg.w1 <- lm(d$trustExpert_w1 ~ index_ANexp_w1 * SESladder.c, data = d)
summary(mg.w1)
## 
## Call:
## lm(formula = d$trustExpert_w1 ~ index_ANexp_w1 * SESladder.c, 
##     data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4489 -0.5879  0.5041  0.9672  1.9801 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.254e+00  2.273e-02  55.183  < 2e-16 ***
## index_ANexp_w1              1.607e-03  1.866e-04   8.616  < 2e-16 ***
## SESladder.c                -5.216e-02  1.228e-02  -4.246 2.19e-05 ***
## index_ANexp_w1:SESladder.c -2.953e-04  9.719e-05  -3.038  0.00238 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.5 on 11162 degrees of freedom
##   (2331 observations deleted due to missingness)
## Multiple R-squared:  0.0179, Adjusted R-squared:  0.01764 
## F-statistic: 67.82 on 3 and 11162 DF,  p-value: < 2.2e-16

h. expertTrust_w2 ~ index media * education

i. wave 1

mh.w1 <- lm(trustExpert_w2 ~ index_ANexp_w1 * SESladder.c, data = d)
summary(mh.w1)
## 
## Call:
## lm(formula = trustExpert_w2 ~ index_ANexp_w1 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.3424 -0.8533  0.4524  1.0313  1.9158 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.117e+00  4.454e-02   25.08   <2e-16 ***
## index_ANexp_w1              4.896e-03  3.971e-04   12.33   <2e-16 ***
## SESladder.c                -7.345e-03  2.297e-02   -0.32    0.749    
## index_ANexp_w1:SESladder.c -2.931e-05  1.826e-04   -0.16    0.873    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.438 on 2517 degrees of freedom
##   (10976 observations deleted due to missingness)
## Multiple R-squared:  0.05743,    Adjusted R-squared:  0.0563 
## F-statistic: 51.12 on 3 and 2517 DF,  p-value: < 2.2e-16

ii. wave 2

mh.w2 <- lm(trustExpert_w2 ~ index_ANexp_w2 * SESladder.c, data = d)
summary(mh.w2)
## 
## Call:
## lm(formula = trustExpert_w2 ~ index_ANexp_w2 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7719 -0.7551  0.4443  1.0289  1.9386 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.131e+00  4.355e-02  25.977   <2e-16 ***
## index_ANexp_w2              5.223e-03  4.123e-04  12.666   <2e-16 ***
## SESladder.c                -1.556e-02  2.253e-02  -0.691    0.490    
## index_ANexp_w2:SESladder.c -9.554e-06  1.871e-04  -0.051    0.959    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.433 on 2483 degrees of freedom
##   (11010 observations deleted due to missingness)
## Multiple R-squared:  0.06185,    Adjusted R-squared:  0.06071 
## F-statistic: 54.56 on 3 and 2483 DF,  p-value: < 2.2e-16

i. wave 1

mi.w1 <- lm(avgTrustExpert ~ index_ANexp_w1 * SESladder.c, data = d)
summary(mi.w1)
## 
## Call:
## lm(formula = avgTrustExpert ~ index_ANexp_w1 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4373 -0.6120  0.4828  1.0256  1.9607 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.263e+00  2.225e-02  56.764  < 2e-16 ***
## index_ANexp_w1              1.609e-03  1.827e-04   8.808  < 2e-16 ***
## SESladder.c                -4.984e-02  1.203e-02  -4.143 3.45e-05 ***
## index_ANexp_w1:SESladder.c -2.881e-04  9.516e-05  -3.027  0.00248 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.468 on 11162 degrees of freedom
##   (2331 observations deleted due to missingness)
## Multiple R-squared:  0.01787,    Adjusted R-squared:  0.0176 
## F-statistic: 67.68 on 3 and 11162 DF,  p-value: < 2.2e-16

ii. wave 2

mi.w2 <- lm(avgTrustExpert ~ index_ANexp_w2 * SESladder.c, data = d)
summary(mi.w2)
## 
## Call:
## lm(formula = avgTrustExpert ~ index_ANexp_w2 * SESladder.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5724 -0.7316  0.3083  0.9670  1.9096 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.1075307  0.0398072  27.822   <2e-16 ***
## index_ANexp_w2              0.0049989  0.0003769  13.264   <2e-16 ***
## SESladder.c                -0.0038198  0.0205914  -0.186    0.853    
## index_ANexp_w2:SESladder.c -0.0002649  0.0001710  -1.549    0.122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.31 on 2483 degrees of freedom
##   (11010 observations deleted due to missingness)
## Multiple R-squared:  0.06793,    Adjusted R-squared:  0.0668 
## F-statistic: 60.32 on 3 and 2483 DF,  p-value: < 2.2e-16

j. media index ~ SESladder + media exposure + symbolic beliefs

i. wave 1

mj.w1 <- lm(index_ANexp_w1 ~ SESladder.c + prop.media.exp_w1 + avgSymbBelief.c, data = d)
summary(mj.w1)
## 
## Call:
## lm(formula = index_ANexp_w1 ~ SESladder.c + prop.media.exp_w1 + 
##     avgSymbBelief.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -90.232 -19.514  -2.421  21.831 240.518 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        14.4440     0.4695  30.767  < 2e-16 ***
## SESladder.c        -0.9978     0.1649  -6.052 1.48e-09 ***
## prop.media.exp_w1 394.6010     1.7483 225.702  < 2e-16 ***
## avgSymbBelief.c    -0.5345     0.2012  -2.656  0.00791 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.15 on 11166 degrees of freedom
##   (2327 observations deleted due to missingness)
## Multiple R-squared:  0.8219, Adjusted R-squared:  0.8219 
## F-statistic: 1.718e+04 on 3 and 11166 DF,  p-value: < 2.2e-16

i. wave 1

mj.w1 <- lm(index_ANexp_w1 ~ SESladder.c * avgSymbBelief.c + prop.media.exp_w1, data = d)
summary(mj.w1)
## 
## Call:
## lm(formula = index_ANexp_w1 ~ SESladder.c * avgSymbBelief.c + 
##     prop.media.exp_w1, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -90.232 -19.514  -2.421  21.831 240.518 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  1.444e+01  4.701e-01  30.726  < 2e-16 ***
## SESladder.c                 -9.978e-01  1.649e-01  -6.051 1.49e-09 ***
## avgSymbBelief.c             -5.345e-01  2.014e-01  -2.654  0.00797 ** 
## prop.media.exp_w1            3.946e+02  1.749e+00 225.638  < 2e-16 ***
## SESladder.c:avgSymbBelief.c -3.294e-05  1.054e-01   0.000  0.99975    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.15 on 11165 degrees of freedom
##   (2327 observations deleted due to missingness)
## Multiple R-squared:  0.8219, Adjusted R-squared:  0.8219 
## F-statistic: 1.288e+04 on 4 and 11165 DF,  p-value: < 2.2e-16

ii. wave 2

mj.w2 <- lm(index_ANexp_w2 ~ SESladder.c + prop.media.exp_w2 + avgSymbBelief.c, data = d)
summary(mj.w2)
## 
## Call:
## lm(formula = index_ANexp_w2 ~ SESladder.c + prop.media.exp_w2 + 
##     avgSymbBelief.c, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.9921  -2.3551   0.5093   2.0309  29.7838 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -2.00206    0.11202 -17.872   <2e-16 ***
## SESladder.c         0.05575    0.03686   1.513    0.131    
## prop.media.exp_w2 319.92095    0.33385 958.273   <2e-16 ***
## avgSymbBelief.c    -0.11048    0.04492  -2.460    0.014 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.514 on 2483 degrees of freedom
##   (11010 observations deleted due to missingness)
## Multiple R-squared:  0.9975, Adjusted R-squared:  0.9975 
## F-statistic: 3.288e+05 on 3 and 2483 DF,  p-value: < 2.2e-16

iii. wave 3

mj.w3 <- lm(index_ANexp_w3 ~ SESladder.c + prop.media.exp_w3 + avgSymbBelief.c, data = d)
summary(mj.w3)
## 
## Call:
## lm(formula = index_ANexp_w3 ~ SESladder.c + prop.media.exp_w3 + 
##     avgSymbBelief.c, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.567  -1.761   0.444   1.643  43.791 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.61890    0.15661 -10.337   <2e-16 ***
## SESladder.c         0.06890    0.05318   1.295    0.195    
## prop.media.exp_w3 320.50594    0.48380 662.475   <2e-16 ***
## avgSymbBelief.c    -0.04577    0.06306  -0.726    0.468    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.297 on 1012 degrees of freedom
##   (12481 observations deleted due to missingness)
## Multiple R-squared:  0.9979, Adjusted R-squared:  0.9979 
## F-statistic: 1.579e+05 on 3 and 1012 DF,  p-value: < 2.2e-16