LIWC media analyses
Dani Grant
12/23/2021
- 0. graphs
- 1. descriptive stats
- WAVE 1
- 1. AFFECT
- 2. COGNITIVE PROCESSING
- 3. ANALYTIC THINKING
- 4. POSITIVE EMOTIONS
- 5. NEGATIVE EMOTIONS
- 6. NEGATIVE VS. POSITIVE EMOTIONS
- 7. AFFECT VS. ANALYTICAL THINKING
- WAVE 2
- 1. AFFECT
- 2. COGNITIVE PROCESSING
- 3. ANALYTIC THINKING
- 4. POSITIVE EMOTIONS
- 5. NEGATIVE EMOTIONS
- 6. NEGATIVE VS. POSITIVE EMOTIONS
- 7. AFFECT VS. ANALYTICAL THINKING
- AVERAGE OF W1 & W2
- 1. Analytic + Affect
- 2. Positive emotion
- 3. Negative emotion
- LONGITUDINAL WITH AVG(LIWC)
- LONGITUDINAL W1-W2
- W2VaxxAttitude ~ party * (W1VaxxAttitudes + W1Analytic + W1Affect + W2Analytic + W2Affect)
- W2risk3 ~ party * (W1risk3 + W1Analytic + W1Affect + W2Analytic + W2Affect)
- W2risk4 ~ party * (W1risk4 + W1Analytic + W1Affect + W2Analytic + W2Affect)
- W2risk5 ~ party * (W1risk5 + W1Analytic + W1Affect + W2Analytic + W2Affect)
- W2riskSeverity ~ party * (W1riskSeverity + W1Analytic + W1Affect + W2Analytic + W2Affect)
- WAVE 1 VAXX POLICY
- WAVE 2 VAXX POLICIES
0. graphs
DV: If a Covid-19 vaccine were available today, would you get it? (Definitely would not get it = -3; Undecided = 0; Definitely would get it = +3)
wave 1: analytical x party x vaxxAttitudes
m1 <- lmer(vaxxAttitudes ~ index_ANexp.c * (party_factor) + (1|media), data = dl1)
plot_model(m1, type = "pred", terms = c("index_ANexp.c", "party_factor"), color = c("blue", "red", "purple")) +
ggtitle("wave 1") +
xlab("media analytic thinking index") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(-100, 350) +
theme_minimal()+
labs(color ='partisan identity') ## Warning: Removed 3 row(s) containing missing values (geom_path).
wave 2: analytical x party x vaxxAttitudes
m2 <- lmer(vaxxAttitudes ~ analytic * party_factor + (1|media), data = dl2)## boundary (singular) fit: see ?isSingularplot_model(m2, type = "pred", terms = c("analytic", "party_factor"), color = c("blue", "red", "purple")) +
ggtitle("wave 2") +
xlab("media analytic thinking index") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(-100, 350) +
theme_minimal()+
labs(color ='partisan identity') 
avg(w1 + w2): analytical x party x vaxxAttitudes
# make dataset long by media outlet
dANy <- dm %>% pivot_longer(c(index_ANexp_NYT.y,
index_ANexp_WSJ.y,
index_ANexp_USAT.y,
index_ANexp_Fox.y,
index_ANexp_CNN.y,
index_ANexp_MSNBC.y,
index_ANexp_NPR.y,
index_ANexp_ABC.y,
index_ANexp_NBC.y,
index_ANexp_CBS.y,
index_ANexp_PBS.y,
index_ANexp_AOL.y),
names_to = "media",
values_to = "ANexp.y")
# make dataset long by media outlet
dANx <- dm %>% pivot_longer(c(index_ANexp_NYT.x,
index_ANexp_WSJ.x,
index_ANexp_USAT.x,
index_ANexp_Fox.x,
index_ANexp_CNN.x,
index_ANexp_MSNBC.x,
index_ANexp_NPR.x,
index_ANexp_ABC.x,
index_ANexp_NBC.x,
index_ANexp_CBS.x,
index_ANexp_PBS.x,
index_ANexp_AOL.x),
names_to = "media",
values_to = "ANexp.x")
#put together long data set
dl <- data.frame(dANy$s3)
colnames(dl)[colnames(dl)=="dANy.s3"] <- "s3"
dl$media <- dANx$media
dl$vaxxAttitudes <- (dANx$vaxxAttitudes.x + dANy$vaxxAttitudes.y)/2
dl$ANexp <- (dANx$ANexp.x + dANy$ANexp.y)/2
dl$ANexp.c <- dl$ANexp - mean(dl$ANexp, na.rm = T)
dl$party_factor <- dANy$party_factor.y
m2 <- lm(vaxxAttitudes ~ ANexp.c * party_factor, data = dl)
p <- plot_model(m2, type = "pred", terms = c("ANexp.c", "party_factor"), color = c("blue", "red", "purple")) +
ggtitle("average of wave 1 and wave 2") +
xlab("media analytic thinking index") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(-150, 350) +
theme_minimal()+
labs(color ='partisan identity') 1. descriptive stats
A. LIWC means
wave 1
## mediaOutlet affect cogproc analytic posemo negemo
## 1 ABC 3.920000 7.787500 79.10750 2.635000 1.232500
## 2 AOL 3.504762 6.856667 95.42000 2.103810 1.355238
## 3 CBS 3.890000 8.275000 78.76000 2.500000 1.330000
## 4 CNN 3.800000 9.780000 73.61000 2.280000 1.460000
## 5 Fox 4.390000 10.480000 60.43000 2.690000 1.650000
## 6 MSNBC 3.880000 9.930000 70.52000 2.360000 1.450000
## 7 NBC 6.590000 6.870000 80.54500 2.730000 3.810000
## 8 NPR 3.425000 9.825000 71.83000 2.125000 1.240000
## 9 NYT 3.537222 7.912222 93.49444 1.941111 1.535556
## 10 PBS 3.830000 8.970000 78.86000 2.320000 1.460000
## 11 USAToday 3.653333 8.883333 91.28000 2.206667 1.390000
## 12 WSJ 1.846000 4.416000 96.59800 1.014000 0.800000wave 2
## mediaOutlet affect cogproc analytic posemo negemo
## 1 ABC 3.870000 8.015000 75.97750 2.750000 1.077500
## 2 AOL 3.715455 7.033182 95.29773 2.212727 1.451364
## 3 CBS 3.760000 7.620000 84.05500 2.325000 1.395000
## 4 CNN 3.678000 9.964000 64.72000 2.404000 1.226000
## 5 Fox 4.135000 10.445000 61.09500 2.775000 1.315000
## 6 MSNBC 3.660000 9.900000 69.56000 2.380000 1.220000
## 7 NBC 6.190000 7.150000 78.12000 2.665000 3.490000
## 8 NPR 3.595000 9.865000 70.81500 2.385000 1.145000
## 9 NYT 3.607895 7.792105 93.36368 2.074737 1.466842
## 10 PBS 3.680000 9.080000 77.06000 2.410000 1.230000
## 11 USAToday 3.830000 8.490000 91.85500 2.390000 1.390000
## 12 WSJ 1.628000 4.020000 96.43200 1.002000 0.582000B. media & affect
wave 1
wave 2
C. media & cognitive processing
wave 1
wave 2
D. media & analytic thinking
wave 1
wave 2
E. media & positive emotions
wave 1
wave 2
F. media & negative emotions
wave 1
wave 2
G. exposure sum indexes
will be 11x larger than LIWC2015 example ratings because summed
wave 1
## index_AFexp index_CPexp index_ANexp index_PEexp index_NEexp
## M 99.65 212.03 -0.05 57.71 40.57
## SD 41.68 88.89 866.73 24.06 17.09
## Min 46.27 99.99 -1053.31 26.91 18.71
## Max 231.33 499.93 2828.51 134.53 93.57wave 2
## index_AFexp index_CPexp index_ANexp index_PEexp index_NEexp
## M 92.83 201.50 -0.82 56.66 35.00
## SD 39.97 86.70 839.01 24.29 15.24
## Min 45.35 99.37 -948.70 27.77 16.99
## Max 226.75 496.87 2884.71 138.87 84.94WAVE 1
1. AFFECT
A. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116697.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4067 -0.6570 -0.0235 0.6680 2.7905
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01215 0.1102
## Residual 1.46015 1.2084
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.298e+00 3.253e-02 1.083e+01 132.123 < 2e-16 ***
## index_AFexp.c 3.792e-02 1.307e-03 2.521e+04 29.004 < 2e-16 ***
## pDem_Rep -7.394e-01 1.451e-02 3.625e+04 -50.976 < 2e-16 ***
## pInd_Not -7.788e-02 1.614e-02 3.624e+04 -4.827 1.39e-06 ***
## index_AFexp.c:pDem_Rep 2.379e-02 2.411e-03 3.618e+04 9.865 < 2e-16 ***
## index_AFexp.c:pInd_Not -5.886e-03 2.799e-03 3.624e+04 -2.103 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDm_Rp pInd_N i_AF.:D
## indx_AFxp.c 0.015
## pDem_Rep 0.018 0.143
## pInd_Not -0.064 -0.048 0.052
## ind_AF.:D_R 0.026 0.168 0.049 0.077
## ind_AF.:I_N -0.009 -0.322 0.071 0.083 0.117## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116697.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4067 -0.6570 -0.0235 0.6680 2.7905
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01215 0.1102
## Residual 1.46015 1.2084
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.642e+00 3.323e-02 1.179e+01 139.686 < 2e-16 ***
## index_AFexp.c 2.408e-02 1.564e-03 2.987e+04 15.392 < 2e-16 ***
## pDemR -7.394e-01 1.451e-02 3.625e+04 -50.976 < 2e-16 ***
## pDemI -2.918e-01 1.734e-02 3.625e+04 -16.827 < 2e-16 ***
## index_AFexp.c:pDemR 2.379e-02 2.411e-03 3.618e+04 9.865 < 2e-16 ***
## index_AFexp.c:pDemI 1.778e-02 2.915e-03 3.624e+04 6.100 1.07e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDemR pDemI i_AF.:DR
## indx_AFxp.c -0.047
## pDemR -0.192 0.124
## pDemI -0.160 0.098 0.370
## indx_AF.:DR 0.027 -0.561 0.049 -0.051
## indx_AF.:DI 0.022 -0.454 -0.048 0.024 0.3012. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116697.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4067 -0.6570 -0.0235 0.6680 2.7905
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01215 0.1102
## Residual 1.46015 1.2084
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.903e+00 3.361e-02 1.233e+01 116.118 <2e-16 ***
## index_AFexp.c 4.787e-02 2.007e-03 3.328e+04 23.853 <2e-16 ***
## pRepD 7.394e-01 1.451e-02 3.625e+04 50.976 <2e-16 ***
## pRepI 4.476e-01 1.803e-02 3.624e+04 24.821 <2e-16 ***
## index_AFexp.c:pRepD -2.379e-02 2.411e-03 3.618e+04 -9.865 <2e-16 ***
## index_AFexp.c:pRepI -6.008e-03 3.175e-03 3.625e+04 -1.892 0.0584 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pRepD pRepI i_AF.:RD
## indx_AFxp.c 0.062
## pRepD -0.242 -0.155
## pRepI -0.193 -0.110 0.449
## indx_AF.:RD -0.047 -0.764 0.049 0.088
## indx_AF.:RI -0.036 -0.573 0.081 0.124 0.4833. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116697.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4067 -0.6570 -0.0235 0.6680 2.7905
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01215 0.1102
## Residual 1.46015 1.2084
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.350e+00 3.493e-02 1.439e+01 124.534 < 2e-16 ***
## index_AFexp.c 4.186e-02 2.609e-03 3.533e+04 16.045 < 2e-16 ***
## pIndD 2.918e-01 1.734e-02 3.625e+04 16.827 < 2e-16 ***
## pIndR -4.476e-01 1.803e-02 3.624e+04 -24.821 < 2e-16 ***
## index_AFexp.c:pIndD -1.778e-02 2.915e-03 3.624e+04 -6.100 1.07e-09 ***
## index_AFexp.c:pIndR 6.008e-03 3.175e-03 3.625e+04 1.892 0.0584 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pIndD pIndR i_AF.:ID
## indx_AFxp.c 0.039
## pIndD -0.344 -0.086
## pIndR -0.330 -0.066 0.664
## indx_AF.:ID -0.033 -0.845 0.024 0.062
## indx_AF.:IR -0.030 -0.776 0.061 0.124 0.690B. worst of Covid is behind/ahead
worstBorA ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129498.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84605 -0.69093 0.00754 0.71394 2.23055
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.002271 0.04766
## Residual 2.026676 1.42361
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.693e-01 1.590e-02 1.014e+01 35.803 5.16e-12 ***
## index_AFexp.c 1.916e-02 1.493e-03 2.177e+03 12.831 < 2e-16 ***
## pDem_Rep -9.498e-01 1.702e-02 3.608e+04 -55.800 < 2e-16 ***
## pInd_Not -1.637e-01 1.893e-02 3.651e+04 -8.646 < 2e-16 ***
## index_AFexp.c:pDem_Rep 3.795e-02 2.825e-03 3.232e+04 13.436 < 2e-16 ***
## index_AFexp.c:pInd_Not 4.854e-03 3.286e-03 3.651e+04 1.477 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDm_Rp pInd_N i_AF.:D
## indx_AFxp.c 0.035
## pDem_Rep 0.044 0.138
## pInd_Not -0.154 -0.046 0.054
## ind_AF.:D_R 0.062 0.174 0.049 0.077
## ind_AF.:I_N -0.021 -0.330 0.072 0.082 0.118## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129498.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84605 -0.69093 0.00754 0.71394 2.23055
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.002271 0.04766
## Residual 2.026676 1.42361
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.901e-01 1.778e-02 1.584e+01 55.690 < 2e-16 ***
## index_AFexp.c 1.785e-03 1.799e-03 4.212e+03 0.992 0.321
## pDemR -9.498e-01 1.702e-02 3.608e+04 -55.800 < 2e-16 ***
## pDemI -3.112e-01 2.034e-02 3.647e+04 -15.302 < 2e-16 ***
## index_AFexp.c:pDemR 3.795e-02 2.825e-03 3.232e+04 13.436 < 2e-16 ***
## index_AFexp.c:pDemI 1.412e-02 3.420e-03 3.650e+04 4.129 3.65e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDemR pDemI i_AF.:DR
## indx_AFxp.c -0.103
## pDemR -0.420 0.119
## pDemI -0.351 0.096 0.369
## indx_AF.:DR 0.059 -0.570 0.049 -0.051
## indx_AF.:DI 0.048 -0.462 -0.049 0.023 0.3002. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129498.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84605 -0.69093 0.00754 0.71394 2.23055
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.002271 0.04766
## Residual 2.026676 1.42361
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.035e-02 1.875e-02 1.958e+01 2.152 0.0441 *
## index_AFexp.c 3.973e-02 2.329e-03 8.576e+03 17.062 < 2e-16 ***
## pRepD 9.498e-01 1.702e-02 3.608e+04 55.800 < 2e-16 ***
## pRepI 6.386e-01 2.117e-02 3.651e+04 30.167 < 2e-16 ***
## index_AFexp.c:pRepD -3.795e-02 2.825e-03 3.232e+04 -13.436 < 2e-16 ***
## index_AFexp.c:pRepI -2.383e-02 3.727e-03 3.629e+04 -6.394 1.63e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pRepD pRepI i_AF.:RD
## indx_AFxp.c 0.129
## pRepD -0.509 -0.151
## pRepI -0.408 -0.110 0.450
## indx_AF.:RD -0.100 -0.773 0.049 0.089
## indx_AF.:RI -0.075 -0.580 0.082 0.124 0.4833. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129498.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84605 -0.69093 0.00754 0.71394 2.23055
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.002271 0.04766
## Residual 2.026676 1.42361
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.789e-01 2.181e-02 3.589e+01 31.131 < 2e-16 ***
## index_AFexp.c 1.591e-02 3.041e-03 1.835e+04 5.230 1.71e-07 ***
## pIndD 3.112e-01 2.034e-02 3.647e+04 15.302 < 2e-16 ***
## pIndR -6.386e-01 2.117e-02 3.651e+04 -30.167 < 2e-16 ***
## index_AFexp.c:pIndD -1.412e-02 3.420e-03 3.650e+04 -4.129 3.65e-05 ***
## index_AFexp.c:pIndR 2.383e-02 3.727e-03 3.629e+04 6.394 1.63e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pIndD pIndR i_AF.:ID
## indx_AFxp.c 0.071
## pIndD -0.646 -0.083
## pIndR -0.620 -0.067 0.664
## indx_AF.:ID -0.061 -0.851 0.023 0.062
## indx_AF.:IR -0.056 -0.781 0.061 0.124 0.691C. vaccine attitudes
vaxAttitudes ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 157160.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2234 -0.6998 0.0548 0.8911 1.5926
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01932 0.139
## Residual 4.33993 2.083
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.854e-01 4.179e-02 1.069e+01 11.614 2.13e-07 ***
## index_AFexp.c 4.943e-02 2.237e-03 1.484e+04 22.100 < 2e-16 ***
## pDem_Rep -8.675e-01 2.494e-02 3.646e+04 -34.784 < 2e-16 ***
## pInd_Not 5.777e-01 2.771e-02 3.647e+04 20.850 < 2e-16 ***
## index_AFexp.c:pDem_Rep 4.375e-02 4.144e-03 3.617e+04 10.558 < 2e-16 ***
## index_AFexp.c:pInd_Not -5.207e-03 4.809e-03 3.647e+04 -1.083 0.279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDm_Rp pInd_N i_AF.:D
## indx_AFxp.c 0.019
## pDem_Rep 0.025 0.142
## pInd_Not -0.085 -0.047 0.053
## ind_AF.:D_R 0.034 0.170 0.048 0.077
## ind_AF.:I_N -0.012 -0.323 0.071 0.082 0.118## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157160.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2234 -0.6998 0.0548 0.8911 1.5926
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01932 0.139
## Residual 4.33993 2.083
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.110e+00 4.339e-02 1.242e+01 25.579 4.06e-12 ***
## index_AFexp.c 2.583e-02 2.677e-03 2.126e+04 9.651 < 2e-16 ***
## pDemR -8.675e-01 2.494e-02 3.646e+04 -34.784 < 2e-16 ***
## pDemI -1.011e+00 2.977e-02 3.648e+04 -33.973 < 2e-16 ***
## index_AFexp.c:pDemR 4.375e-02 4.144e-03 3.617e+04 10.558 < 2e-16 ***
## index_AFexp.c:pDemI 2.708e-02 5.006e-03 3.648e+04 5.410 6.35e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDemR pDemI i_AF.:DR
## indx_AFxp.c -0.063
## pDemR -0.252 0.124
## pDemI -0.211 0.099 0.369
## indx_AF.:DR 0.035 -0.562 0.048 -0.051
## indx_AF.:DI 0.029 -0.455 -0.049 0.024 0.3002. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157160.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2234 -0.6998 0.0548 0.8911 1.5926
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01932 0.139
## Residual 4.33993 2.083
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.422e-01 4.425e-02 1.344e+01 5.474 9.48e-05 ***
## index_AFexp.c 6.958e-02 3.446e-03 2.801e+04 20.194 < 2e-16 ***
## pRepD 8.675e-01 2.494e-02 3.646e+04 34.784 < 2e-16 ***
## pRepI -1.440e-01 3.099e-02 3.647e+04 -4.646 3.40e-06 ***
## index_AFexp.c:pRepD -4.375e-02 4.144e-03 3.617e+04 -10.558 < 2e-16 ***
## index_AFexp.c:pRepI -1.667e-02 5.457e-03 3.647e+04 -3.054 0.00226 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pRepD pRepI i_AF.:RD
## indx_AFxp.c 0.081
## pRepD -0.316 -0.154
## pRepI -0.253 -0.110 0.450
## indx_AF.:RD -0.062 -0.766 0.048 0.088
## indx_AF.:RI -0.046 -0.575 0.081 0.124 0.4843. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157160.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2234 -0.6998 0.0548 0.8911 1.5926
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01932 0.139
## Residual 4.33993 2.083
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.828e-02 4.716e-02 1.733e+01 2.084 0.05224 .
## index_AFexp.c 5.292e-02 4.477e-03 3.338e+04 11.820 < 2e-16 ***
## pIndD 1.011e+00 2.977e-02 3.648e+04 33.973 < 2e-16 ***
## pIndR 1.440e-01 3.099e-02 3.647e+04 4.646 3.40e-06 ***
## index_AFexp.c:pIndD -2.708e-02 5.006e-03 3.648e+04 -5.410 6.35e-08 ***
## index_AFexp.c:pIndR 1.667e-02 5.457e-03 3.647e+04 3.054 0.00226 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pIndD pIndR i_AF.:ID
## indx_AFxp.c 0.049
## pIndD -0.437 -0.085
## pIndR -0.419 -0.066 0.664
## indx_AF.:ID -0.041 -0.846 0.024 0.062
## indx_AF.:IR -0.038 -0.777 0.061 0.124 0.6892. COGNITIVE PROCESSING
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134711.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91051 -0.77924 -0.05241 0.62888 2.32507
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001474 0.03839
## Residual 2.339729 1.52962
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.500e+00 1.403e-02 1.181e+01 320.840 < 2e-16 ***
## index_ANexp.c 2.587e-03 8.528e-05 2.191e+04 30.334 < 2e-16 ***
## pDem_Rep -1.364e+00 1.837e-02 3.650e+04 -74.255 < 2e-16 ***
## pInd_Not 1.448e-01 2.038e-02 3.650e+04 7.107 1.21e-12 ***
## index_ANexp.c:pDem_Rep 3.289e-03 1.760e-04 3.155e+04 18.686 < 2e-16 ***
## index_ANexp.c:pInd_Not -8.931e-04 2.029e-04 3.650e+04 -4.401 1.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.049
## pDem_Rep 0.056 0.145
## pInd_Not -0.184 -0.044 0.057
## ind_AN.:D_R 0.090 0.192 0.062 0.093
## ind_AN.:I_N -0.026 -0.345 0.088 0.091 0.125## Warning: Removed 60 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134711.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91051 -0.77924 -0.05241 0.62888 2.32507
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001474 0.03839
## Residual 2.339729 1.52962
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.230e+00 1.644e-02 2.227e+01 318.14 < 2e-16 ***
## index_ANexp.c 6.477e-04 1.058e-04 1.913e+04 6.12 9.53e-10 ***
## pDemR -1.364e+00 1.837e-02 3.650e+04 -74.25 < 2e-16 ***
## pDemI -8.270e-01 2.187e-02 3.650e+04 -37.81 < 2e-16 ***
## index_ANexp.c:pDemR 3.289e-03 1.760e-04 3.155e+04 18.69 < 2e-16 ***
## index_ANexp.c:pDemI 2.537e-03 2.108e-04 3.647e+04 12.04 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.134
## pDemR -0.488 0.121
## pDemI -0.410 0.102 0.367
## indx_AN.:DR 0.080 -0.597 0.062 -0.061
## indx_AN.:DI 0.066 -0.491 -0.059 0.021 0.2972. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134711.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91051 -0.77924 -0.05241 0.62888 2.32507
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001474 0.03839
## Residual 2.339729 1.52962
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.865e+00 1.769e-02 2.987e+01 218.53 < 2e-16 ***
## index_ANexp.c 3.936e-03 1.412e-04 3.280e+04 27.88 < 2e-16 ***
## pRepD 1.364e+00 1.837e-02 3.650e+04 74.25 < 2e-16 ***
## pRepI 5.373e-01 2.283e-02 3.650e+04 23.54 < 2e-16 ***
## index_ANexp.c:pRepD -3.289e-03 1.760e-04 3.155e+04 -18.69 < 2e-16 ***
## index_ANexp.c:pRepI -7.511e-04 2.311e-04 3.622e+04 -3.25 0.00115 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.174
## pRepD -0.585 -0.168
## pRepI -0.471 -0.135 0.453
## indx_AN.:RD -0.139 -0.799 0.062 0.108
## indx_AN.:RI -0.106 -0.607 0.101 0.144 0.4913. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134711.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91051 -0.77924 -0.05241 0.62888 2.32507
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001474 0.03839
## Residual 2.339729 1.52962
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.403e+00 2.130e-02 6.282e+01 206.69 < 2e-16 ***
## index_ANexp.c 3.185e-03 1.837e-04 3.518e+04 17.34 < 2e-16 ***
## pIndD 8.270e-01 2.187e-02 3.650e+04 37.81 < 2e-16 ***
## pIndR -5.373e-01 2.283e-02 3.650e+04 -23.54 < 2e-16 ***
## index_ANexp.c:pIndD -2.537e-03 2.108e-04 3.647e+04 -12.04 < 2e-16 ***
## index_ANexp.c:pIndR 7.511e-04 2.311e-04 3.622e+04 3.25 0.00115 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.084
## pIndD -0.710 -0.083
## pIndR -0.681 -0.077 0.663
## indx_AN.:ID -0.072 -0.865 0.021 0.067
## indx_AN.:IR -0.066 -0.792 0.065 0.144 0.686B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127484.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1963 -0.6262 0.0913 0.7309 1.5414
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.115e+00 7.838e-03 3.641e+04 652.543 < 2e-16 ***
## index_ANexp.c 4.990e-04 7.672e-05 3.641e+04 6.504 7.93e-11 ***
## pDem_Rep -4.498e-01 1.672e-02 3.641e+04 -26.904 < 2e-16 ***
## pInd_Not 7.818e-02 1.860e-02 3.641e+04 4.203 2.64e-05 ***
## index_ANexp.c:pDem_Rep 2.980e-04 1.591e-04 3.641e+04 1.873 0.0611 .
## index_ANexp.c:pInd_Not -2.498e-04 1.852e-04 3.641e+04 -1.349 0.1773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.081
## pDem_Rep 0.089 0.141
## pInd_Not -0.304 -0.044 0.056
## ind_AN.:D_R 0.145 0.199 0.063 0.091
## ind_AN.:I_N -0.043 -0.351 0.087 0.092 0.123
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular## Warning: Removed 60 row(s) containing missing values (geom_path).
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127484.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1963 -0.6262 0.0913 0.7309 1.5414
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.365e+00 1.106e-02 3.641e+04 485.219 < 2e-16 ***
## index_ANexp.c 2.675e-04 9.506e-05 3.641e+04 2.815 0.00489 **
## pDemR -4.498e-01 1.672e-02 3.641e+04 -26.904 < 2e-16 ***
## pDemI -3.031e-01 1.996e-02 3.641e+04 -15.186 < 2e-16 ***
## index_ANexp.c:pDemR 2.980e-04 1.591e-04 3.641e+04 1.873 0.06110 .
## index_ANexp.c:pDemI 3.988e-04 1.923e-04 3.641e+04 2.074 0.03811 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.178
## pDemR -0.661 0.118
## pDemI -0.554 0.098 0.366
## indx_AN.:DR 0.106 -0.597 0.063 -0.059
## indx_AN.:DI 0.088 -0.494 -0.058 0.023 0.295
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127484.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1963 -0.6262 0.0913 0.7309 1.5414
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.915e+00 1.254e-02 3.641e+04 391.958 < 2e-16 ***
## index_ANexp.c 5.656e-04 1.276e-04 3.641e+04 4.431 9.40e-06 ***
## pRepD 4.498e-01 1.672e-02 3.641e+04 26.904 < 2e-16 ***
## pRepI 1.467e-01 2.082e-02 3.641e+04 7.049 1.84e-12 ***
## index_ANexp.c:pRepD -2.980e-04 1.591e-04 3.641e+04 -1.873 0.0611 .
## index_ANexp.c:pRepI 1.008e-04 2.103e-04 3.641e+04 0.479 0.6318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.221
## pRepD -0.750 -0.166
## pRepI -0.602 -0.133 0.452
## indx_AN.:RD -0.177 -0.802 0.063 0.107
## indx_AN.:RI -0.134 -0.607 0.101 0.144 0.487
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127484.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1963 -0.6262 0.0913 0.7309 1.5414
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.062e+00 1.661e-02 3.641e+04 304.682 < 2e-16 ***
## index_ANexp.c 6.664e-04 1.672e-04 3.641e+04 3.986 6.75e-05 ***
## pIndD 3.031e-01 1.996e-02 3.641e+04 15.186 < 2e-16 ***
## pIndR -1.467e-01 2.082e-02 3.641e+04 -7.049 1.84e-12 ***
## index_ANexp.c:pIndD -3.988e-04 1.923e-04 3.641e+04 -2.074 0.0381 *
## index_ANexp.c:pIndR -1.008e-04 2.103e-04 3.641e+04 -0.479 0.6318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.099
## pIndD -0.832 -0.083
## pIndR -0.798 -0.079 0.664
## indx_AN.:ID -0.086 -0.869 0.023 0.069
## indx_AN.:IR -0.079 -0.795 0.066 0.144 0.691
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148304.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8829 -0.8749 -0.1762 0.7573 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003372 0.05807
## Residual 3.450072 1.85744
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.302e+00 1.976e-02 1.152e+01 167.140 < 2e-16 ***
## index_ANexp.c 2.912e-03 1.038e-04 2.673e+04 28.046 < 2e-16 ***
## pDem_Rep -3.852e-01 2.235e-02 3.636e+04 -17.235 < 2e-16 ***
## pInd_Not -4.638e-01 2.477e-02 3.635e+04 -18.723 < 2e-16 ***
## index_ANexp.c:pDem_Rep 1.322e-03 2.142e-04 3.359e+04 6.172 6.84e-10 ***
## index_ANexp.c:pInd_Not 2.821e-04 2.466e-04 3.636e+04 1.144 0.253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.042
## pDem_Rep 0.047 0.145
## pInd_Not -0.159 -0.043 0.056
## ind_AN.:D_R 0.077 0.188 0.061 0.093
## ind_AN.:I_N -0.022 -0.343 0.087 0.090 0.124## Warning: Removed 60 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148304.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8829 -0.8749 -0.1762 0.7573 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003372 0.05807
## Residual 3.450072 1.85744
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.341e+00 2.235e-02 1.888e+01 149.472 < 2e-16 ***
## index_ANexp.c 2.344e-03 1.293e-04 2.445e+04 18.130 < 2e-16 ***
## pDemR -3.852e-01 2.235e-02 3.636e+04 -17.235 < 2e-16 ***
## pDemI 2.712e-01 2.660e-02 3.636e+04 10.195 < 2e-16 ***
## index_ANexp.c:pDemR 1.322e-03 2.142e-04 3.359e+04 6.172 6.84e-10 ***
## index_ANexp.c:pDemI 3.787e-04 2.564e-04 3.635e+04 1.477 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.120
## pDemR -0.438 0.121
## pDemI -0.368 0.102 0.368
## indx_AN.:DR 0.072 -0.599 0.061 -0.060
## indx_AN.:DI 0.059 -0.492 -0.058 0.020 0.2982. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148304.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8829 -0.8749 -0.1762 0.7573 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003372 0.05807
## Residual 3.450072 1.85744
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.956e+00 2.370e-02 2.386e+01 124.733 < 2e-16 ***
## index_ANexp.c 3.666e-03 1.715e-04 3.432e+04 21.375 < 2e-16 ***
## pRepD 3.852e-01 2.235e-02 3.636e+04 17.235 < 2e-16 ***
## pRepI 6.564e-01 2.774e-02 3.635e+04 23.665 < 2e-16 ***
## index_ANexp.c:pRepD -1.322e-03 2.142e-04 3.359e+04 -6.172 6.84e-10 ***
## index_ANexp.c:pRepI -9.430e-04 2.807e-04 3.622e+04 -3.359 0.000783 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.157
## pRepD -0.530 -0.168
## pRepI -0.427 -0.134 0.453
## indx_AN.:RD -0.125 -0.797 0.061 0.107
## indx_AN.:RI -0.095 -0.606 0.100 0.143 0.4903. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148304.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8829 -0.8749 -0.1762 0.7573 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003372 0.05807
## Residual 3.450072 1.85744
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.613e+00 2.774e-02 4.481e+01 130.208 < 2e-16 ***
## index_ANexp.c 2.723e-03 2.233e-04 3.567e+04 12.196 < 2e-16 ***
## pIndD -2.712e-01 2.660e-02 3.636e+04 -10.195 < 2e-16 ***
## pIndR -6.564e-01 2.774e-02 3.635e+04 -23.665 < 2e-16 ***
## index_ANexp.c:pIndD -3.787e-04 2.564e-04 3.635e+04 -1.477 0.139577
## index_ANexp.c:pIndR 9.430e-04 2.807e-04 3.622e+04 3.359 0.000783 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.077
## pIndD -0.662 -0.082
## pIndR -0.635 -0.077 0.662
## indx_AN.:ID -0.067 -0.864 0.020 0.067
## indx_AN.:IR -0.061 -0.792 0.065 0.143 0.685D. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3056 -0.6493 -0.0220 0.6772 2.7994
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005599 0.07483
## Residual 1.461175 1.20879
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.298e+00 2.265e-02 1.096e+01 189.809 < 2e-16 ***
## index_CPexp.c 1.778e-02 6.222e-04 2.552e+04 28.584 < 2e-16 ***
## pDem_Rep -7.439e-01 1.450e-02 3.625e+04 -51.301 < 2e-16 ***
## pInd_Not -7.851e-02 1.615e-02 3.624e+04 -4.861 1.17e-06 ***
## index_CPexp.c:pDem_Rep 1.163e-02 1.218e-03 3.532e+04 9.551 < 2e-16 ***
## index_CPexp.c:pInd_Not -4.322e-03 1.425e-03 3.624e+04 -3.033 0.00242 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.022
## pDem_Rep 0.026 0.134
## pInd_Not -0.092 -0.050 0.053
## ind_CP.:D_R 0.038 0.160 0.046 0.080
## ind_CP.:I_N -0.014 -0.352 0.074 0.087 0.101
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3056 -0.6493 -0.0220 0.6772 2.7994
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005599 0.07483
## Residual 1.461175 1.20879
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.644e+00 2.364e-02 1.303e+01 196.440 < 2e-16 ***
## index_CPexp.c 1.054e-02 7.706e-04 3.064e+04 13.682 < 2e-16 ***
## pDemR -7.439e-01 1.450e-02 3.625e+04 -51.301 < 2e-16 ***
## pDemI -2.934e-01 1.735e-02 3.625e+04 -16.912 < 2e-16 ***
## index_CPexp.c:pDemR 1.163e-02 1.218e-03 3.532e+04 9.551 < 2e-16 ***
## index_CPexp.c:pDemI 1.014e-02 1.492e-03 3.625e+04 6.794 1.11e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.068
## pDemR -0.270 0.117
## pDemI -0.225 0.096 0.369
## indx_CP.:DR 0.041 -0.599 0.046 -0.055
## indx_CP.:DI 0.033 -0.480 -0.052 0.026 0.3122. simple effects for reps
| Â | riskSeverity | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.90 | 3.85 – 3.95 | <0.001 |
| index_CPexp.c | 0.02 | 0.02 – 0.02 | <0.001 |
| pRepD | 0.74 | 0.72 – 0.77 | <0.001 |
| pRepI | 0.45 | 0.42 – 0.49 | <0.001 |
| index_CPexp.c * pRepD | -0.01 | -0.01 – -0.01 | <0.001 |
| index_CPexp.c * pRepI | -0.00 | -0.00 – 0.00 | 0.352 |
| Random Effects | |||
| σ2 | 1.46 | ||
| Ï„00 media | 0.01 | ||
| ICC | 0.00 | ||
| N media | 12 | ||
| Observations | 36256 | ||
| Marginal R2 / Conditional R2 | 0.105 / 0.109 | ||
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3056 -0.6493 -0.0220 0.6772 2.7994
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005599 0.07483
## Residual 1.461175 1.20879
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.900e+00 2.417e-02 1.423e+01 161.380 <2e-16 ***
## index_CPexp.c 2.217e-02 9.759e-04 3.153e+04 22.720 <2e-16 ***
## pRepD 7.439e-01 1.450e-02 3.625e+04 51.301 <2e-16 ***
## pRepI 4.504e-01 1.805e-02 3.624e+04 24.955 <2e-16 ***
## index_CPexp.c:pRepD -1.163e-02 1.218e-03 3.532e+04 -9.551 <2e-16 ***
## index_CPexp.c:pRepI -1.493e-03 1.605e-03 3.623e+04 -0.930 0.352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.087
## pRepD -0.336 -0.149
## pRepI -0.269 -0.113 0.449
## indx_CP.:RD -0.067 -0.775 0.046 0.090
## indx_CP.:RI -0.050 -0.578 0.083 0.129 0.4693. simple effects for indep
| Â | riskSeverity | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.35 | 4.30 – 4.40 | <0.001 |
| index_CPexp.c | 0.02 | 0.02 – 0.02 | <0.001 |
| pIndD | 0.29 | 0.26 – 0.33 | <0.001 |
| pIndR | -0.45 | -0.49 – -0.42 | <0.001 |
| index_CPexp.c * pIndD | -0.01 | -0.01 – -0.01 | <0.001 |
| index_CPexp.c * pIndR | 0.00 | -0.00 – 0.00 | 0.352 |
| Random Effects | |||
| σ2 | 1.46 | ||
| Ï„00 media | 0.01 | ||
| ICC | 0.00 | ||
| N media | 12 | ||
| Observations | 36256 | ||
| Marginal R2 / Conditional R2 | 0.105 / 0.109 | ||
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3056 -0.6493 -0.0220 0.6772 2.7994
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005599 0.07483
## Residual 1.461175 1.20879
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.351e+00 2.598e-02 1.900e+01 167.463 < 2e-16 ***
## index_CPexp.c 2.068e-02 1.310e-03 3.560e+04 15.785 < 2e-16 ***
## pIndD 2.934e-01 1.735e-02 3.625e+04 16.912 < 2e-16 ***
## pIndR -4.504e-01 1.805e-02 3.624e+04 -24.955 < 2e-16 ***
## index_CPexp.c:pIndD -1.014e-02 1.492e-03 3.625e+04 -6.794 1.11e-11 ***
## index_CPexp.c:pIndR 1.493e-03 1.605e-03 3.623e+04 0.930 0.352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.055
## pIndD -0.463 -0.085
## pIndR -0.444 -0.074 0.665
## indx_CP.:ID -0.047 -0.857 0.026 0.066
## indx_CP.:IR -0.043 -0.794 0.066 0.129 0.693E. worst of Covid is behind/ahead
worstBorA ~ index * party + (1 |media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129506.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.83476 -0.69079 0.01103 0.71389 2.22704
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001022 0.03197
## Residual 2.027245 1.42381
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.699e-01 1.220e-02 1.104e+01 46.715 4.87e-14 ***
## index_CPexp.c 8.693e-03 7.147e-04 3.167e+03 12.164 < 2e-16 ***
## pDem_Rep -9.533e-01 1.702e-02 3.624e+04 -56.026 < 2e-16 ***
## pInd_Not -1.628e-01 1.895e-02 3.651e+04 -8.591 < 2e-16 ***
## index_CPexp.c:pDem_Rep 1.929e-02 1.421e-03 2.009e+04 13.571 < 2e-16 ***
## index_CPexp.c:pInd_Not 1.564e-03 1.673e-03 3.651e+04 0.935 0.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.047
## pDem_Rep 0.057 0.130
## pInd_Not -0.200 -0.050 0.054
## ind_CP.:D_R 0.083 0.163 0.045 0.080
## ind_CP.:I_N -0.031 -0.359 0.074 0.086 0.101
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129506.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.83476 -0.69079 0.01103 0.71389 2.22704
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001022 0.03197
## Residual 2.027245 1.42381
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.929e-01 1.457e-02 2.240e+01 68.157 < 2e-16 ***
## index_CPexp.c -4.356e-04 8.901e-04 6.421e+03 -0.489 0.625
## pDemR -9.533e-01 1.702e-02 3.624e+04 -56.026 < 2e-16 ***
## pDemI -3.139e-01 2.034e-02 3.650e+04 -15.430 < 2e-16 ***
## index_CPexp.c:pDemR 1.929e-02 1.421e-03 2.009e+04 13.571 < 2e-16 ***
## index_CPexp.c:pDemI 8.081e-03 1.750e-03 3.631e+04 4.618 3.89e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.129
## pDemR -0.513 0.114
## pDemI -0.429 0.094 0.368
## indx_CP.:DR 0.078 -0.605 0.045 -0.056
## indx_CP.:DI 0.063 -0.485 -0.053 0.025 0.3092. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129506.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.83476 -0.69079 0.01103 0.71389 2.22704
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001022 0.03197
## Residual 2.027245 1.42381
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.954e-02 1.573e-02 3.043e+01 2.514 0.0175 *
## index_CPexp.c 1.885e-02 1.132e-03 7.517e+03 16.652 < 2e-16 ***
## pRepD 9.533e-01 1.702e-02 3.624e+04 56.026 < 2e-16 ***
## pRepI 6.394e-01 2.118e-02 3.651e+04 30.184 < 2e-16 ***
## index_CPexp.c:pRepD -1.929e-02 1.421e-03 2.009e+04 -13.571 < 2e-16 ***
## index_CPexp.c:pRepI -1.121e-02 1.883e-03 3.516e+04 -5.953 2.65e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.155
## pRepD -0.607 -0.146
## pRepI -0.487 -0.113 0.450
## indx_CP.:RD -0.121 -0.780 0.045 0.090
## indx_CP.:RI -0.090 -0.583 0.083 0.129 0.4683. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129506.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.83476 -0.69079 0.01103 0.71389 2.22704
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001022 0.03197
## Residual 2.027245 1.42381
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.790e-01 1.928e-02 6.885e+01 35.209 < 2e-16 ***
## index_CPexp.c 7.645e-03 1.530e-03 2.435e+04 4.995 5.92e-07 ***
## pIndD 3.139e-01 2.034e-02 3.650e+04 15.430 < 2e-16 ***
## pIndR -6.394e-01 2.118e-02 3.651e+04 -30.184 < 2e-16 ***
## index_CPexp.c:pIndD -8.081e-03 1.750e-03 3.631e+04 -4.618 3.89e-06 ***
## index_CPexp.c:pIndR 1.121e-02 1.883e-03 3.516e+04 5.953 2.65e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.085
## pIndD -0.731 -0.083
## pIndR -0.702 -0.075 0.665
## indx_CP.:ID -0.073 -0.861 0.025 0.066
## indx_CP.:IR -0.068 -0.799 0.065 0.129 0.696F. vaccine attitudes
vaxAttitudes ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 157195
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13867 -0.69937 0.05088 0.88896 1.55529
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008481 0.09209
## Residual 4.344499 2.08435
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.849e-01 2.904e-02 1.094e+01 16.698 3.94e-09 ***
## index_CPexp.c 2.256e-02 1.065e-03 1.506e+04 21.184 < 2e-16 ***
## pDem_Rep -8.741e-01 2.494e-02 3.646e+04 -35.050 < 2e-16 ***
## pInd_Not 5.782e-01 2.774e-02 3.647e+04 20.842 < 2e-16 ***
## index_CPexp.c:pDem_Rep 2.093e-02 2.092e-03 3.322e+04 10.008 < 2e-16 ***
## index_CPexp.c:pInd_Not -3.058e-03 2.449e-03 3.647e+04 -1.249 0.212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.029
## pDem_Rep 0.035 0.133
## pInd_Not -0.123 -0.050 0.054
## ind_CP.:D_R 0.051 0.163 0.045 0.080
## ind_CP.:I_N -0.019 -0.353 0.074 0.086 0.102
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157195
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13867 -0.69937 0.05088 0.88896 1.55529
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008481 0.09209
## Residual 4.344499 2.08435
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.113e+00 3.130e-02 1.475e+01 35.556 1.04e-15 ***
## index_CPexp.c 1.108e-02 1.319e-03 2.241e+04 8.403 < 2e-16 ***
## pDemR -8.741e-01 2.494e-02 3.646e+04 -35.050 < 2e-16 ***
## pDemI -1.015e+00 2.979e-02 3.648e+04 -34.076 < 2e-16 ***
## index_CPexp.c:pDemR 2.093e-02 2.092e-03 3.322e+04 10.008 < 2e-16 ***
## index_CPexp.c:pDemI 1.353e-02 2.563e-03 3.647e+04 5.276 1.32e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.089
## pDemR -0.350 0.117
## pDemI -0.293 0.096 0.368
## indx_CP.:DR 0.053 -0.599 0.045 -0.056
## indx_CP.:DI 0.043 -0.481 -0.052 0.025 0.3112. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157195
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13867 -0.69937 0.05088 0.88896 1.55529
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008481 0.09209
## Residual 4.344499 2.08435
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.387e-01 3.248e-02 1.712e+01 7.347 1.09e-06 ***
## index_CPexp.c 3.201e-02 1.676e-03 2.410e+04 19.104 < 2e-16 ***
## pRepD 8.741e-01 2.494e-02 3.646e+04 35.050 < 2e-16 ***
## pRepI -1.411e-01 3.102e-02 3.647e+04 -4.550 5.39e-06 ***
## index_CPexp.c:pRepD -2.093e-02 2.092e-03 3.322e+04 -10.008 < 2e-16 ***
## index_CPexp.c:pRepI -7.408e-03 2.760e-03 3.635e+04 -2.685 0.00727 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.110
## pRepD -0.431 -0.148
## pRepI -0.346 -0.113 0.450
## indx_CP.:RD -0.086 -0.777 0.045 0.090
## indx_CP.:RI -0.064 -0.580 0.083 0.129 0.4703. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157195
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13867 -0.69937 0.05088 0.88896 1.55529
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008481 0.09209
## Residual 4.344499 2.08435
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.754e-02 3.635e-02 2.685e+01 2.683 0.01232 *
## index_CPexp.c 2.460e-02 2.249e-03 3.419e+04 10.939 < 2e-16 ***
## pIndD 1.015e+00 2.979e-02 3.648e+04 34.076 < 2e-16 ***
## pIndR 1.411e-01 3.102e-02 3.647e+04 4.550 5.39e-06 ***
## index_CPexp.c:pIndD -1.353e-02 2.563e-03 3.647e+04 -5.276 1.32e-07 ***
## index_CPexp.c:pIndR 7.408e-03 2.760e-03 3.635e+04 2.685 0.00727 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.066
## pIndD -0.568 -0.084
## pIndR -0.545 -0.074 0.664
## indx_CP.:ID -0.057 -0.858 0.025 0.066
## indx_CP.:IR -0.053 -0.795 0.065 0.129 0.6933. ANALYTIC THINKING
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134841.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91607 -0.76870 0.00131 0.68094 2.32163
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008355 0.0914
## Residual 2.347901 1.5323
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.493e+00 2.775e-02 1.097e+01 161.933 < 2e-16 ***
## index_CPexp.c 2.274e-02 7.861e-04 2.465e+04 28.921 < 2e-16 ***
## pDem_Rep -1.380e+00 1.833e-02 3.650e+04 -75.266 < 2e-16 ***
## pInd_Not 1.348e-01 2.039e-02 3.649e+04 6.609 3.91e-11 ***
## index_CPexp.c:pDem_Rep 2.444e-02 1.539e-03 3.542e+04 15.877 < 2e-16 ***
## index_CPexp.c:pInd_Not -9.535e-03 1.801e-03 3.650e+04 -5.295 1.19e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.022
## pDem_Rep 0.027 0.135
## pInd_Not -0.094 -0.050 0.054
## ind_CP.:D_R 0.040 0.163 0.046 0.080
## ind_CP.:I_N -0.015 -0.351 0.074 0.086 0.102## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134841.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91607 -0.76870 0.00131 0.68094 2.32163
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008355 0.0914
## Residual 2.347901 1.5323
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.228e+00 2.904e-02 1.316e+01 180.003 < 2e-16 ***
## index_CPexp.c 7.368e-03 9.719e-04 3.015e+04 7.582 3.51e-14 ***
## pDemR -1.380e+00 1.833e-02 3.650e+04 -75.266 < 2e-16 ***
## pDemI -8.246e-01 2.190e-02 3.650e+04 -37.653 < 2e-16 ***
## index_CPexp.c:pDemR 2.444e-02 1.539e-03 3.542e+04 15.877 < 2e-16 ***
## index_CPexp.c:pDemI 2.176e-02 1.884e-03 3.650e+04 11.545 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.071
## pDemR -0.277 0.118
## pDemI -0.232 0.097 0.368
## indx_CP.:DR 0.042 -0.598 0.046 -0.056
## indx_CP.:DI 0.034 -0.479 -0.052 0.025 0.3112. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134841.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91607 -0.76870 0.00131 0.68094 2.32163
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008355 0.0914
## Residual 2.347901 1.5323
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.848e+00 2.974e-02 1.448e+01 129.386 <2e-16 ***
## index_CPexp.c 3.181e-02 1.235e-03 3.117e+04 25.749 <2e-16 ***
## pRepD 1.380e+00 1.833e-02 3.650e+04 75.266 <2e-16 ***
## pRepI 5.551e-01 2.280e-02 3.649e+04 24.340 <2e-16 ***
## index_CPexp.c:pRepD -2.444e-02 1.539e-03 3.542e+04 -15.877 <2e-16 ***
## index_CPexp.c:pRepI -2.686e-03 2.029e-03 3.647e+04 -1.323 0.186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.090
## pRepD -0.346 -0.150
## pRepI -0.277 -0.114 0.450
## indx_CP.:RD -0.069 -0.776 0.046 0.090
## indx_CP.:RI -0.052 -0.579 0.083 0.129 0.4703. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134841.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91607 -0.76870 0.00131 0.68094 2.32163
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008355 0.0914
## Residual 2.347901 1.5323
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.403e+00 3.207e-02 1.957e+01 137.310 <2e-16 ***
## index_CPexp.c 2.912e-02 1.655e-03 3.574e+04 17.596 <2e-16 ***
## pIndD 8.246e-01 2.190e-02 3.650e+04 37.653 <2e-16 ***
## pIndR -5.551e-01 2.280e-02 3.649e+04 -24.340 <2e-16 ***
## index_CPexp.c:pIndD -2.176e-02 1.884e-03 3.650e+04 -11.545 <2e-16 ***
## index_CPexp.c:pIndR 2.686e-03 2.029e-03 3.647e+04 1.323 0.186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.055
## pIndD -0.473 -0.085
## pIndR -0.454 -0.074 0.664
## indx_CP.:ID -0.047 -0.857 0.025 0.065
## indx_CP.:IR -0.044 -0.794 0.065 0.129 0.693B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1981 -0.6291 0.0868 0.7410 1.5396
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.114e+00 7.816e-03 3.641e+04 654.282 < 2e-16 ***
## index_CPexp.c 4.317e-03 6.733e-04 3.641e+04 6.411 1.46e-10 ***
## pDem_Rep -4.535e-01 1.663e-02 3.641e+04 -27.269 < 2e-16 ***
## pInd_Not 7.640e-02 1.858e-02 3.641e+04 4.113 3.91e-05 ***
## index_CPexp.c:pDem_Rep 1.694e-03 1.375e-03 3.641e+04 1.232 0.2179
## index_CPexp.c:pInd_Not -2.974e-03 1.639e-03 3.641e+04 -1.814 0.0696 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.071
## pDem_Rep 0.086 0.120
## pInd_Not -0.308 -0.050 0.054
## ind_CP.:D_R 0.125 0.162 0.044 0.079
## ind_CP.:I_N -0.049 -0.375 0.074 0.087 0.099
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1981 -0.6291 0.0868 0.7410 1.5396
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.366e+00 1.103e-02 3.641e+04 486.644 < 2e-16 ***
## index_CPexp.c 2.488e-03 8.497e-04 3.641e+04 2.928 0.00341 **
## pDemR -4.535e-01 1.663e-02 3.641e+04 -27.269 < 2e-16 ***
## pDemI -3.031e-01 1.994e-02 3.641e+04 -15.204 < 2e-16 ***
## index_CPexp.c:pDemR 1.694e-03 1.375e-03 3.641e+04 1.232 0.21795
## index_CPexp.c:pDemI 3.821e-03 1.713e-03 3.641e+04 2.230 0.02575 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.161
## pDemR -0.663 0.107
## pDemI -0.553 0.089 0.367
## indx_CP.:DR 0.100 -0.618 0.044 -0.055
## indx_CP.:DI 0.080 -0.496 -0.053 0.026 0.306
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1981 -0.6291 0.0868 0.7410 1.5396
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.912e+00 1.245e-02 3.641e+04 394.599 < 2e-16 ***
## index_CPexp.c 4.182e-03 1.081e-03 3.641e+04 3.868 0.00011 ***
## pRepD 4.535e-01 1.663e-02 3.641e+04 27.269 < 2e-16 ***
## pRepI 1.503e-01 2.076e-02 3.641e+04 7.242 4.52e-13 ***
## index_CPexp.c:pRepD -1.694e-03 1.375e-03 3.641e+04 -1.232 0.21795
## index_CPexp.c:pRepI 2.127e-03 1.839e-03 3.641e+04 1.156 0.24755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.186
## pRepD -0.749 -0.140
## pRepI -0.600 -0.112 0.449
## indx_CP.:RD -0.147 -0.786 0.044 0.088
## indx_CP.:RI -0.110 -0.588 0.082 0.129 0.462
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1981 -0.6291 0.0868 0.7410 1.5396
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.062e+00 1.661e-02 3.641e+04 304.751 < 2e-16 ***
## index_CPexp.c 6.309e-03 1.488e-03 3.641e+04 4.240 2.24e-05 ***
## pIndD 3.031e-01 1.994e-02 3.641e+04 15.204 < 2e-16 ***
## pIndR -1.503e-01 2.076e-02 3.641e+04 -7.242 4.52e-13 ***
## index_CPexp.c:pIndD -3.821e-03 1.713e-03 3.641e+04 -2.230 0.0258 *
## index_CPexp.c:pIndR -2.127e-03 1.839e-03 3.641e+04 -1.156 0.2475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.097
## pIndD -0.833 -0.081
## pIndR -0.800 -0.078 0.667
## indx_CP.:ID -0.085 -0.868 0.026 0.068
## indx_CP.:IR -0.079 -0.809 0.066 0.129 0.703
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148399.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8366 -0.8773 -0.1620 0.7669 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01297 0.1139
## Residual 3.45919 1.8599
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.298e+00 3.450e-02 1.096e+01 95.609 < 2e-16 ***
## index_CPexp.c 2.566e-02 9.554e-04 2.530e+04 26.861 < 2e-16 ***
## pDem_Rep -4.047e-01 2.229e-02 3.636e+04 -18.159 < 2e-16 ***
## pInd_Not -4.709e-01 2.478e-02 3.635e+04 -19.006 < 2e-16 ***
## index_CPexp.c:pDem_Rep 8.632e-03 1.872e-03 3.539e+04 4.612 4.01e-06 ***
## index_CPexp.c:pInd_Not -2.467e-04 2.187e-03 3.635e+04 -0.113 0.91
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.022
## pDem_Rep 0.026 0.134
## pInd_Not -0.092 -0.049 0.053
## ind_CP.:D_R 0.039 0.161 0.046 0.080
## ind_CP.:I_N -0.014 -0.350 0.074 0.086 0.101## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148399.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8366 -0.8773 -0.1620 0.7669 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01298 0.1139
## Residual 3.45919 1.8599
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.345e+00 3.605e-02 1.306e+01 92.803 < 2e-16 ***
## index_CPexp.c 2.127e-02 1.184e-03 3.055e+04 17.959 < 2e-16 ***
## pDemR -4.047e-01 2.229e-02 3.636e+04 -18.159 < 2e-16 ***
## pDemI 2.685e-01 2.662e-02 3.636e+04 10.086 < 2e-16 ***
## index_CPexp.c:pDemR 8.632e-03 1.872e-03 3.539e+04 4.612 4.01e-06 ***
## index_CPexp.c:pDemI 4.562e-03 2.290e-03 3.636e+04 1.992 0.0464 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.069
## pDemR -0.272 0.117
## pDemI -0.228 0.096 0.369
## indx_CP.:DR 0.041 -0.599 0.046 -0.055
## indx_CP.:DI 0.033 -0.481 -0.052 0.024 0.3122. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148399.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8366 -0.8773 -0.1620 0.7669 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01297 0.1139
## Residual 3.45919 1.8599
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.941e+00 3.687e-02 1.429e+01 79.767 < 2e-16 ***
## index_CPexp.c 2.990e-02 1.500e-03 3.148e+04 19.929 < 2e-16 ***
## pRepD 4.047e-01 2.229e-02 3.636e+04 18.159 < 2e-16 ***
## pRepI 6.733e-01 2.770e-02 3.635e+04 24.304 < 2e-16 ***
## index_CPexp.c:pRepD -8.632e-03 1.872e-03 3.539e+04 -4.612 4.01e-06 ***
## index_CPexp.c:pRepI -4.069e-03 2.464e-03 3.633e+04 -1.651 0.0987 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.087
## pRepD -0.339 -0.149
## pRepI -0.272 -0.113 0.450
## indx_CP.:RD -0.068 -0.775 0.046 0.090
## indx_CP.:RI -0.051 -0.579 0.083 0.129 0.4703. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148399.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8366 -0.8773 -0.1620 0.7669 2.3876
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01298 0.1139
## Residual 3.45919 1.8599
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.614e+00 3.964e-02 1.910e+01 91.171 <2e-16 ***
## index_CPexp.c 2.583e-02 2.010e-03 3.567e+04 12.849 <2e-16 ***
## pIndD -2.685e-01 2.662e-02 3.636e+04 -10.086 <2e-16 ***
## pIndR -6.733e-01 2.770e-02 3.635e+04 -24.304 <2e-16 ***
## index_CPexp.c:pIndD -4.562e-03 2.290e-03 3.636e+04 -1.992 0.0464 *
## index_CPexp.c:pIndR 4.069e-03 2.464e-03 3.633e+04 1.651 0.0987 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.054
## pIndD -0.465 -0.084
## pIndR -0.446 -0.073 0.664
## indx_CP.:ID -0.046 -0.856 0.024 0.065
## indx_CP.:IR -0.043 -0.794 0.065 0.129 0.692D. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116627.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3571 -0.6639 -0.0211 0.6702 2.7900
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001196 0.03459
## Residual 1.457532 1.20728
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.302e+00 1.208e-02 1.163e+01 355.964 < 2e-16 ***
## index_ANexp.c 2.005e-03 6.759e-05 2.482e+04 29.657 < 2e-16 ***
## pDem_Rep -7.317e-01 1.454e-02 3.625e+04 -50.319 < 2e-16 ***
## pInd_Not -7.211e-02 1.615e-02 3.624e+04 -4.465 8.04e-06 ***
## index_ANexp.c:pDem_Rep 1.637e-03 1.393e-04 3.272e+04 11.752 < 2e-16 ***
## index_ANexp.c:pInd_Not -2.921e-04 1.607e-04 3.625e+04 -1.817 0.0692 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.045
## pDem_Rep 0.050 0.144
## pInd_Not -0.170 -0.045 0.056
## ind_AN.:D_R 0.082 0.189 0.062 0.092
## ind_AN.:I_N -0.024 -0.345 0.087 0.092 0.124## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116627.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3571 -0.6639 -0.0211 0.6702 2.7900
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001196 0.03459
## Residual 1.457532 1.20728
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.644e+00 1.387e-02 2.015e+01 334.905 < 2e-16 ***
## index_ANexp.c 1.089e-03 8.407e-05 2.228e+04 12.959 < 2e-16 ***
## pDemR -7.317e-01 1.454e-02 3.625e+04 -50.319 < 2e-16 ***
## pDemI -2.937e-01 1.734e-02 3.625e+04 -16.941 < 2e-16 ***
## index_ANexp.c:pDemR 1.637e-03 1.393e-04 3.272e+04 11.752 < 2e-16 ***
## index_ANexp.c:pDemI 1.111e-03 1.671e-04 3.623e+04 6.648 3.02e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.125
## pDemR -0.459 0.120
## pDemI -0.385 0.101 0.368
## indx_AN.:DR 0.075 -0.599 0.062 -0.060
## indx_AN.:DI 0.061 -0.491 -0.058 0.022 0.2982. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116627.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3571 -0.6639 -0.0211 0.6702 2.7900
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001196 0.03459
## Residual 1.457532 1.20728
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.912e+00 1.478e-02 2.602e+01 264.673 <2e-16 ***
## index_ANexp.c 2.727e-03 1.116e-04 3.364e+04 24.438 <2e-16 ***
## pRepD 7.317e-01 1.454e-02 3.625e+04 50.319 <2e-16 ***
## pRepI 4.379e-01 1.808e-02 3.624e+04 24.228 <2e-16 ***
## index_ANexp.c:pRepD -1.637e-03 1.393e-04 3.272e+04 -11.752 <2e-16 ***
## index_ANexp.c:pRepI -5.265e-04 1.829e-04 3.607e+04 -2.878 0.004 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.164
## pRepD -0.553 -0.167
## pRepI -0.445 -0.134 0.452
## indx_AN.:RD -0.131 -0.797 0.062 0.107
## indx_AN.:RI -0.099 -0.605 0.100 0.144 0.4903. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116627.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3571 -0.6639 -0.0211 0.6702 2.7900
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001196 0.03459
## Residual 1.457532 1.20728
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.350e+00 1.754e-02 5.159e+01 248.012 < 2e-16 ***
## index_ANexp.c 2.200e-03 1.456e-04 3.537e+04 15.114 < 2e-16 ***
## pIndD 2.937e-01 1.734e-02 3.625e+04 16.941 < 2e-16 ***
## pIndR -4.379e-01 1.808e-02 3.624e+04 -24.228 < 2e-16 ***
## index_ANexp.c:pIndD -1.111e-03 1.671e-04 3.623e+04 -6.648 3.02e-11 ***
## index_ANexp.c:pIndR 5.265e-04 1.829e-04 3.607e+04 2.878 0.004 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.081
## pIndD -0.684 -0.084
## pIndR -0.656 -0.078 0.664
## indx_AN.:ID -0.070 -0.864 0.022 0.068
## indx_AN.:IR -0.065 -0.792 0.066 0.144 0.687E. worst of Covid is behind/ahead
worstBorA ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129420
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82054 -0.69215 0.01106 0.71760 2.26207
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.751e-01 7.990e-03 3.651e+04 71.975 < 2e-16 ***
## index_ANexp.c 1.045e-03 7.820e-05 3.651e+04 13.369 < 2e-16 ***
## pDem_Rep -9.417e-01 1.707e-02 3.651e+04 -55.172 < 2e-16 ***
## pInd_Not -1.542e-01 1.894e-02 3.651e+04 -8.138 4.15e-16 ***
## index_ANexp.c:pDem_Rep 2.644e-03 1.624e-04 3.651e+04 16.280 < 2e-16 ***
## index_ANexp.c:pInd_Not 3.915e-04 1.886e-04 3.651e+04 2.076 0.0379 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.080
## pDem_Rep 0.090 0.142
## pInd_Not -0.301 -0.043 0.057
## ind_AN.:D_R 0.146 0.201 0.063 0.092
## ind_AN.:I_N -0.042 -0.349 0.088 0.091 0.124
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129420
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82054 -0.69215 0.01106 0.71760 2.26207
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.950e-01 1.128e-02 3.651e+04 88.194 < 2e-16 ***
## index_ANexp.c -1.474e-04 9.688e-05 3.651e+04 -1.521 0.128
## pDemR -9.417e-01 1.707e-02 3.651e+04 -55.172 < 2e-16 ***
## pDemI -3.167e-01 2.033e-02 3.651e+04 -15.576 < 2e-16 ***
## index_ANexp.c:pDemR 2.644e-03 1.624e-04 3.651e+04 16.280 < 2e-16 ***
## index_ANexp.c:pDemI 9.306e-04 1.959e-04 3.651e+04 4.751 2.03e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.179
## pDemR -0.661 0.118
## pDemI -0.555 0.099 0.367
## indx_AN.:DR 0.107 -0.596 0.063 -0.059
## indx_AN.:DI 0.089 -0.495 -0.059 0.021 0.295
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129420
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82054 -0.69215 0.01106 0.71760 2.26207
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.338e-02 1.281e-02 3.651e+04 4.168 3.08e-05 ***
## index_ANexp.c 2.497e-03 1.304e-04 3.651e+04 19.152 < 2e-16 ***
## pRepD 9.417e-01 1.707e-02 3.651e+04 55.172 < 2e-16 ***
## pRepI 6.250e-01 2.121e-02 3.651e+04 29.461 < 2e-16 ***
## index_ANexp.c:pRepD -2.644e-03 1.624e-04 3.651e+04 -16.280 < 2e-16 ***
## index_ANexp.c:pRepI -1.713e-03 2.144e-04 3.651e+04 -7.992 1.36e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.222
## pRepD -0.750 -0.166
## pRepI -0.604 -0.134 0.453
## indx_AN.:RD -0.178 -0.803 0.063 0.107
## indx_AN.:RI -0.135 -0.608 0.101 0.143 0.488
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129420
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82054 -0.69215 0.01106 0.71760 2.26207
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.784e-01 1.691e-02 3.651e+04 40.110 < 2e-16 ***
## index_ANexp.c 7.832e-04 1.702e-04 3.651e+04 4.601 4.22e-06 ***
## pIndD 3.167e-01 2.033e-02 3.651e+04 15.576 < 2e-16 ***
## pIndR -6.250e-01 2.121e-02 3.651e+04 -29.461 < 2e-16 ***
## index_ANexp.c:pIndD -9.306e-04 1.959e-04 3.651e+04 -4.751 2.03e-06 ***
## index_ANexp.c:pIndR 1.713e-03 2.144e-04 3.651e+04 7.992 1.36e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.098
## pIndD -0.832 -0.081
## pIndR -0.797 -0.078 0.663
## indx_AN.:ID -0.085 -0.869 0.021 0.068
## indx_AN.:IR -0.077 -0.794 0.064 0.143 0.690
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularF. vaccine attitudes
vaxAttitudes ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 157095
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16892 -0.72030 0.06035 0.88465 1.53268
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001451 0.03809
## Residual 4.332658 2.08150
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.906e-01 1.606e-02 1.211e+01 30.556 7.81e-13 ***
## index_ANexp.c 2.660e-03 1.157e-04 1.515e+04 22.995 < 2e-16 ***
## pDem_Rep -8.538e-01 2.500e-02 3.645e+04 -34.147 < 2e-16 ***
## pInd_Not 5.861e-01 2.773e-02 3.648e+04 21.132 < 2e-16 ***
## index_ANexp.c:pDem_Rep 2.802e-03 2.390e-04 2.721e+04 11.724 < 2e-16 ***
## index_ANexp.c:pInd_Not -1.568e-04 2.761e-04 3.648e+04 -0.568 0.57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.058
## pDem_Rep 0.066 0.144
## pInd_Not -0.219 -0.044 0.057
## ind_AN.:D_R 0.107 0.194 0.061 0.092
## ind_AN.:I_N -0.031 -0.346 0.087 0.091 0.125## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157095
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16892 -0.72030 0.06035 0.88465 1.53268
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001451 0.03809
## Residual 4.332658 2.08150
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.111e+00 1.985e-02 2.825e+01 55.965 < 2e-16 ***
## index_ANexp.c 1.207e-03 1.435e-04 1.253e+04 8.407 < 2e-16 ***
## pDemR -8.538e-01 2.500e-02 3.645e+04 -34.147 < 2e-16 ***
## pDemI -1.013e+00 2.977e-02 3.648e+04 -34.029 < 2e-16 ***
## index_ANexp.c:pDemR 2.802e-03 2.390e-04 2.721e+04 11.724 < 2e-16 ***
## index_ANexp.c:pDemI 1.558e-03 2.869e-04 3.639e+04 5.431 5.62e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.151
## pDemR -0.550 0.120
## pDemI -0.462 0.101 0.367
## indx_AN.:DR 0.090 -0.597 0.061 -0.060
## indx_AN.:DI 0.074 -0.492 -0.059 0.021 0.2962. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157095
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16892 -0.72030 0.06035 0.88465 1.53268
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001451 0.03809
## Residual 4.332658 2.08150
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.571e-01 2.175e-02 4.071e+01 11.822 9.64e-15 ***
## index_ANexp.c 4.009e-03 1.917e-04 2.924e+04 20.910 < 2e-16 ***
## pRepD 8.538e-01 2.500e-02 3.645e+04 34.147 < 2e-16 ***
## pRepI -1.591e-01 3.106e-02 3.647e+04 -5.124 3.01e-07 ***
## index_ANexp.c:pRepD -2.802e-03 2.390e-04 2.721e+04 -11.724 < 2e-16 ***
## index_ANexp.c:pRepI -1.244e-03 3.143e-04 3.581e+04 -3.959 7.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.191
## pRepD -0.647 -0.167
## pRepI -0.521 -0.134 0.453
## indx_AN.:RD -0.153 -0.800 0.061 0.107
## indx_AN.:RI -0.116 -0.607 0.100 0.143 0.4903. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157095
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16892 -0.72030 0.06035 0.88465 1.53268
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001451 0.03809
## Residual 4.332658 2.08150
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.795e-02 2.709e-02 9.800e+01 3.616 0.000475 ***
## index_ANexp.c 2.765e-03 2.498e-04 3.372e+04 11.068 < 2e-16 ***
## pIndD 1.013e+00 2.977e-02 3.648e+04 34.029 < 2e-16 ***
## pIndR 1.591e-01 3.106e-02 3.647e+04 5.124 3.01e-07 ***
## index_ANexp.c:pIndD -1.558e-03 2.869e-04 3.639e+04 -5.431 5.62e-08 ***
## index_ANexp.c:pIndR 1.244e-03 3.143e-04 3.581e+04 3.959 7.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.089
## pIndD -0.760 -0.082
## pIndR -0.728 -0.077 0.663
## indx_AN.:ID -0.077 -0.866 0.021 0.067
## indx_AN.:IR -0.071 -0.792 0.065 0.143 0.687G. vulnerable worker
vulnWorker ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 149744.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4474 -0.7719 -0.2699 0.7617 2.5603
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0001612 0.0127
## Residual 3.5282298 1.8784
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.153e+00 1.117e-02 1.274e+01 -103.187 <2e-16 ***
## index_ANexp.c 1.361e-03 1.035e-04 6.649e+03 13.142 <2e-16 ***
## pDem_Rep 9.587e-01 2.255e-02 3.639e+04 42.521 <2e-16 ***
## pInd_Not -6.392e-02 2.502e-02 3.651e+04 -2.554 0.0106 *
## index_ANexp.c:pDem_Rep -1.747e-04 2.148e-04 1.719e+04 -0.814 0.4159
## index_ANexp.c:pInd_Not 1.367e-04 2.491e-04 3.649e+04 0.549 0.5832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.076
## pDem_Rep 0.085 0.143
## pInd_Not -0.285 -0.043 0.057
## ind_AN.:D_R 0.138 0.199 0.063 0.092
## ind_AN.:I_N -0.040 -0.348 0.088 0.091 0.124## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149744.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4474 -0.7719 -0.2699 0.7617 2.5603
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0001612 0.0127
## Residual 3.5282298 1.8784
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.653e+00 1.535e-02 4.515e+01 -107.719 <2e-16 ***
## index_ANexp.c 1.493e-03 1.283e-04 5.145e+03 11.637 <2e-16 ***
## pDemR 9.587e-01 2.255e-02 3.639e+04 42.521 <2e-16 ***
## pDemI 5.433e-01 2.686e-02 3.649e+04 20.229 <2e-16 ***
## index_ANexp.c:pDemR -1.747e-04 2.148e-04 1.719e+04 -0.814 0.416
## index_ANexp.c:pDemI -2.241e-04 2.587e-04 3.614e+04 -0.866 0.386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.175
## pDemR -0.642 0.119
## pDemI -0.539 0.100 0.367
## indx_AN.:DR 0.104 -0.597 0.063 -0.060
## indx_AN.:DI 0.086 -0.494 -0.059 0.021 0.2952. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149744.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4474 -0.7719 -0.2699 0.7617 2.5603
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0001612 0.01269
## Residual 3.5282298 1.87836
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.946e-01 1.731e-02 7.311e+01 -40.128 < 2e-16 ***
## index_ANexp.c 1.319e-03 1.724e-04 2.002e+04 7.649 2.11e-14 ***
## pRepD -9.587e-01 2.255e-02 3.639e+04 -42.521 < 2e-16 ***
## pRepI -4.154e-01 2.802e-02 3.651e+04 -14.825 < 2e-16 ***
## index_ANexp.c:pRepD 1.747e-04 2.148e-04 1.719e+04 0.814 0.416
## index_ANexp.c:pRepI -4.934e-05 2.833e-04 3.425e+04 -0.174 0.862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.217
## pRepD -0.733 -0.167
## pRepI -0.590 -0.134 0.453
## indx_AN.:RD -0.174 -0.802 0.063 0.108
## indx_AN.:RI -0.132 -0.608 0.101 0.143 0.4883. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149744.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4474 -0.7719 -0.2699 0.7617 2.5603
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0001612 0.0127
## Residual 3.5282298 1.8784
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.110e+00 2.264e-02 2.140e+02 -49.033 < 2e-16 ***
## index_ANexp.c 1.269e-03 2.250e-04 2.888e+04 5.641 1.7e-08 ***
## pIndD -5.433e-01 2.686e-02 3.649e+04 -20.229 < 2e-16 ***
## pIndR 4.154e-01 2.802e-02 3.651e+04 14.825 < 2e-16 ***
## index_ANexp.c:pIndD 2.241e-04 2.587e-04 3.614e+04 0.866 0.386
## index_ANexp.c:pIndR 4.934e-05 2.833e-04 3.425e+04 0.174 0.862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.096
## pIndD -0.821 -0.081
## pIndR -0.787 -0.078 0.663
## indx_AN.:ID -0.084 -0.868 0.021 0.068
## indx_AN.:IR -0.076 -0.794 0.065 0.143 0.6894. POSITIVE EMOTIONS
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134772.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94290 -0.77463 -0.00079 0.67903 2.32769
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01217 0.1103
## Residual 2.34372 1.5309
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.494e+00 3.299e-02 1.089e+01 136.235 < 2e-16 ***
## index_PEexp.c 8.661e-02 2.896e-03 2.482e+04 29.907 < 2e-16 ***
## pDem_Rep -1.374e+00 1.832e-02 3.650e+04 -75.013 < 2e-16 ***
## pInd_Not 1.348e-01 2.037e-02 3.649e+04 6.615 3.76e-11 ***
## index_PEexp.c:pDem_Rep 8.984e-02 5.549e-03 3.616e+04 16.191 < 2e-16 ***
## index_PEexp.c:pInd_Not -3.187e-02 6.459e-03 3.649e+04 -4.934 8.07e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.018
## pDem_Rep 0.023 0.139
## pInd_Not -0.079 -0.049 0.054
## ind_PE.:D_R 0.033 0.158 0.045 0.080
## ind_PE.:I_N -0.012 -0.338 0.074 0.085 0.104
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134772.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94290 -0.77463 -0.00079 0.67903 2.32769
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01217 0.1103
## Residual 2.34372 1.5309
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.225e+00 3.408e-02 1.241e+01 153.328 <2e-16 ***
## index_PEexp.c 3.118e-02 3.562e-03 3.015e+04 8.753 <2e-16 ***
## pDemR -1.374e+00 1.832e-02 3.650e+04 -75.013 <2e-16 ***
## pDemI -8.219e-01 2.188e-02 3.650e+04 -37.562 <2e-16 ***
## index_PEexp.c:pDemR 8.984e-02 5.549e-03 3.616e+04 16.191 <2e-16 ***
## index_PEexp.c:pDemI 7.679e-02 6.759e-03 3.650e+04 11.362 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.060
## pDemR -0.236 0.122
## pDemI -0.197 0.099 0.369
## indx_PE.:DR 0.035 -0.588 0.045 -0.055
## indx_PE.:DI 0.029 -0.473 -0.052 0.023 0.3112. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134772.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94290 -0.77463 -0.00079 0.67903 2.32769
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01217 0.1103
## Residual 2.34372 1.5309
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.851e+00 3.467e-02 1.330e+01 111.06 <2e-16 ***
## index_PEexp.c 1.210e-01 4.497e-03 3.260e+04 26.91 <2e-16 ***
## pRepD 1.374e+00 1.832e-02 3.650e+04 75.01 <2e-16 ***
## pRepI 5.524e-01 2.278e-02 3.649e+04 24.25 <2e-16 ***
## index_PEexp.c:pRepD -8.984e-02 5.549e-03 3.616e+04 -16.19 <2e-16 ***
## index_PEexp.c:pRepI -1.305e-02 7.290e-03 3.650e+04 -1.79 0.0735 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.077
## pRepD -0.296 -0.152
## pRepI -0.238 -0.113 0.450
## indx_PE.:RD -0.059 -0.768 0.045 0.089
## indx_PE.:RI -0.044 -0.576 0.082 0.127 0.4733. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134772.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94290 -0.77463 -0.00079 0.67903 2.32769
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01217 0.1103
## Residual 2.34372 1.5309
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.403e+00 3.668e-02 1.667e+01 120.03 <2e-16 ***
## index_PEexp.c 1.080e-01 5.966e-03 3.564e+04 18.10 <2e-16 ***
## pIndD 8.219e-01 2.188e-02 3.650e+04 37.56 <2e-16 ***
## pIndR -5.524e-01 2.278e-02 3.649e+04 -24.25 <2e-16 ***
## index_PEexp.c:pIndD -7.679e-02 6.759e-03 3.650e+04 -11.36 <2e-16 ***
## index_PEexp.c:pIndR 1.305e-02 7.290e-03 3.650e+04 1.79 0.0735 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.047
## pIndD -0.413 -0.085
## pIndR -0.396 -0.071 0.664
## indx_PE.:ID -0.040 -0.850 0.023 0.064
## indx_PE.:IR -0.037 -0.788 0.063 0.127 0.690B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127463.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1962 -0.6266 0.0913 0.7391 1.5437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.114e+00 7.813e-03 3.641e+04 654.481 < 2e-16 ***
## index_PEexp.c 1.580e-02 2.423e-03 3.641e+04 6.520 7.13e-11 ***
## pDem_Rep -4.527e-01 1.662e-02 3.641e+04 -27.232 < 2e-16 ***
## pInd_Not 7.699e-02 1.857e-02 3.641e+04 4.146 3.39e-05 ***
## index_PEexp.c:pDem_Rep 6.740e-03 4.970e-03 3.641e+04 1.356 0.175
## index_PEexp.c:pInd_Not -8.908e-03 5.886e-03 3.641e+04 -1.513 0.130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.070
## pDem_Rep 0.086 0.119
## pInd_Not -0.308 -0.048 0.054
## ind_PE.:D_R 0.124 0.164 0.044 0.078
## ind_PE.:I_N -0.047 -0.369 0.073 0.085 0.101
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127463.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1962 -0.6266 0.0913 0.7391 1.5437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.365e+00 1.102e-02 3.641e+04 486.794 < 2e-16 ***
## index_PEexp.c 9.490e-03 3.067e-03 3.641e+04 3.095 0.00197 **
## pDemR -4.527e-01 1.662e-02 3.641e+04 -27.232 < 2e-16 ***
## pDemI -3.033e-01 1.993e-02 3.641e+04 -15.218 < 2e-16 ***
## index_PEexp.c:pDemR 6.740e-03 4.970e-03 3.641e+04 1.356 0.17506
## index_PEexp.c:pDemI 1.228e-02 6.154e-03 3.641e+04 1.995 0.04605 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.159
## pDemR -0.663 0.106
## pDemI -0.553 0.088 0.367
## indx_PE.:DR 0.098 -0.617 0.044 -0.054
## indx_PE.:DI 0.079 -0.498 -0.053 0.025 0.307
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127463.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1962 -0.6266 0.0913 0.7391 1.5437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.913e+00 1.244e-02 3.641e+04 394.790 < 2e-16 ***
## index_PEexp.c 1.623e-02 3.911e-03 3.641e+04 4.149 3.34e-05 ***
## pRepD 4.527e-01 1.662e-02 3.641e+04 27.232 < 2e-16 ***
## pRepI 1.493e-01 2.075e-02 3.641e+04 7.197 6.29e-13 ***
## index_PEexp.c:pRepD -6.740e-03 4.970e-03 3.641e+04 -1.356 0.175
## index_PEexp.c:pRepI 5.537e-03 6.616e-03 3.641e+04 0.837 0.403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.185
## pRepD -0.749 -0.138
## pRepI -0.600 -0.111 0.449
## indx_PE.:RD -0.145 -0.787 0.044 0.087
## indx_PE.:RI -0.109 -0.591 0.082 0.127 0.465
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127463.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1962 -0.6266 0.0913 0.7391 1.5437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.062e+00 1.661e-02 3.641e+04 304.804 < 2e-16 ***
## index_PEexp.c 2.177e-02 5.336e-03 3.641e+04 4.080 4.52e-05 ***
## pIndD 3.033e-01 1.993e-02 3.641e+04 15.218 < 2e-16 ***
## pIndR -1.493e-01 2.075e-02 3.641e+04 -7.197 6.29e-13 ***
## index_PEexp.c:pIndD -1.228e-02 6.154e-03 3.641e+04 -1.995 0.046 *
## index_PEexp.c:pIndR -5.537e-03 6.616e-03 3.641e+04 -0.837 0.403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.095
## pIndD -0.833 -0.079
## pIndR -0.800 -0.076 0.667
## indx_PE.:ID -0.082 -0.867 0.025 0.066
## indx_PE.:IR -0.077 -0.807 0.064 0.127 0.699
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7623 -0.8800 -0.1570 0.7707 2.3990
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01867 0.1366
## Residual 3.45829 1.8596
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.298e+00 4.080e-02 1.088e+01 80.832 < 2e-16 ***
## index_PEexp.c 9.487e-02 3.522e-03 2.525e+04 26.936 < 2e-16 ***
## pDem_Rep -4.016e-01 2.229e-02 3.636e+04 -18.016 < 2e-16 ***
## pInd_Not -4.707e-01 2.477e-02 3.635e+04 -19.003 < 2e-16 ***
## index_PEexp.c:pDem_Rep 2.957e-02 6.749e-03 3.605e+04 4.381 1.18e-05 ***
## index_PEexp.c:pInd_Not 3.957e-03 7.851e-03 3.635e+04 0.504 0.614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.018
## pDem_Rep 0.022 0.138
## pInd_Not -0.078 -0.049 0.053
## ind_PE.:D_R 0.032 0.155 0.045 0.079
## ind_PE.:I_N -0.012 -0.337 0.073 0.084 0.103
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7623 -0.8800 -0.1570 0.7707 2.3990
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01867 0.1366
## Residual 3.45829 1.8596
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.343e+00 4.212e-02 1.236e+01 79.381 < 2e-16 ***
## index_PEexp.c 8.139e-02 4.343e-03 3.041e+04 18.741 < 2e-16 ***
## pDemR -4.016e-01 2.229e-02 3.636e+04 -18.016 < 2e-16 ***
## pDemI 2.698e-01 2.662e-02 3.636e+04 10.137 < 2e-16 ***
## index_PEexp.c:pDemR 2.957e-02 6.749e-03 3.605e+04 4.381 1.18e-05 ***
## index_PEexp.c:pDemI 1.083e-02 8.221e-03 3.636e+04 1.317 0.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.059
## pDemR -0.233 0.121
## pDemI -0.195 0.098 0.370
## indx_PE.:DR 0.035 -0.590 0.045 -0.055
## indx_PE.:DI 0.028 -0.474 -0.052 0.023 0.3122. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7623 -0.8800 -0.1570 0.7707 2.3990
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01867 0.1366
## Residual 3.45829 1.8596
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.942e+00 4.282e-02 1.320e+01 68.703 < 2e-16 ***
## index_PEexp.c 1.110e-01 5.462e-03 3.270e+04 20.315 < 2e-16 ***
## pRepD 4.016e-01 2.229e-02 3.636e+04 18.016 < 2e-16 ***
## pRepI 6.715e-01 2.769e-02 3.635e+04 24.250 < 2e-16 ***
## index_PEexp.c:pRepD -2.957e-02 6.749e-03 3.605e+04 -4.381 1.18e-05 ***
## index_PEexp.c:pRepI -1.874e-02 8.858e-03 3.635e+04 -2.116 0.0344 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.075
## pRepD -0.291 -0.151
## pRepI -0.234 -0.112 0.450
## indx_PE.:RD -0.057 -0.767 0.045 0.089
## indx_PE.:RI -0.043 -0.576 0.082 0.126 0.4723. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148386
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7623 -0.8800 -0.1570 0.7707 2.3990
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01867 0.1366
## Residual 3.45829 1.8596
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.613e+00 4.523e-02 1.643e+01 79.892 <2e-16 ***
## index_PEexp.c 9.222e-02 7.251e-03 3.556e+04 12.719 <2e-16 ***
## pIndD -2.698e-01 2.662e-02 3.636e+04 -10.137 <2e-16 ***
## pIndR -6.715e-01 2.769e-02 3.635e+04 -24.250 <2e-16 ***
## index_PEexp.c:pIndD -1.083e-02 8.221e-03 3.636e+04 -1.317 0.1878
## index_PEexp.c:pIndR 1.874e-02 8.858e-03 3.635e+04 2.116 0.0344 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.046
## pIndD -0.407 -0.085
## pIndR -0.391 -0.070 0.664
## indx_PE.:ID -0.039 -0.850 0.023 0.063
## indx_PE.:IR -0.037 -0.788 0.063 0.126 0.690D. risk severity
risk severity ~ indexes * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c) * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 116676.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2844 -0.6511 -0.0284 0.6710 2.8007
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008194 0.09052
## Residual 1.459607 1.20814
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.298e+00 2.700e-02 1.089e+01 159.190 < 2e-16 ***
## index_PEexp.c 6.688e-02 2.293e-03 2.577e+04 29.161 < 2e-16 ***
## pDem_Rep -7.404e-01 1.450e-02 3.625e+04 -51.073 < 2e-16 ***
## pInd_Not -7.836e-02 1.614e-02 3.624e+04 -4.856 1.21e-06 ***
## index_PEexp.c:pDem_Rep 4.221e-02 4.390e-03 3.596e+04 9.615 < 2e-16 ***
## index_PEexp.c:pInd_Not -1.259e-02 5.115e-03 3.624e+04 -2.461 0.0139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.018
## pDem_Rep 0.022 0.138
## pInd_Not -0.077 -0.050 0.052
## ind_PE.:D_R 0.032 0.155 0.045 0.079
## ind_PE.:I_N -0.012 -0.339 0.073 0.085 0.102
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116676.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2844 -0.6511 -0.0284 0.6710 2.8007
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008194 0.09052
## Residual 1.459607 1.20814
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.643e+00 2.784e-02 1.231e+01 166.747 < 2e-16 ***
## index_PEexp.c 4.162e-02 2.825e-03 3.069e+04 14.731 < 2e-16 ***
## pDemR -7.404e-01 1.450e-02 3.625e+04 -51.073 < 2e-16 ***
## pDemI -2.919e-01 1.734e-02 3.625e+04 -16.830 < 2e-16 ***
## index_PEexp.c:pDemR 4.221e-02 4.390e-03 3.596e+04 9.615 < 2e-16 ***
## index_PEexp.c:pDemI 3.369e-02 5.355e-03 3.625e+04 6.291 3.19e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.058
## pDemR -0.229 0.121
## pDemI -0.191 0.098 0.369
## indx_PE.:DR 0.034 -0.590 0.045 -0.055
## indx_PE.:DI 0.027 -0.474 -0.051 0.024 0.3122. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157153.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13927 -0.71634 0.05963 0.88731 1.55918
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01269 0.1126
## Residual 4.34000 2.0833
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.418e-01 3.749e-02 1.498e+01 6.450 1.10e-05 ***
## index_PEexp.c 1.223e-01 6.102e-03 2.683e+04 20.046 < 2e-16 ***
## pRepD 8.698e-01 2.493e-02 3.646e+04 34.886 < 2e-16 ***
## pRepI -1.434e-01 3.100e-02 3.647e+04 -4.626 3.75e-06 ***
## index_PEexp.c:pRepD -7.991e-02 7.544e-03 3.534e+04 -10.593 < 2e-16 ***
## index_PEexp.c:pRepI -2.879e-02 9.916e-03 3.645e+04 -2.903 0.00369 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.095
## pRepD -0.373 -0.150
## pRepI -0.299 -0.112 0.450
## indx_PE.:RD -0.073 -0.769 0.044 0.089
## indx_PE.:RI -0.055 -0.577 0.082 0.127 0.4723. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116676.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2844 -0.6511 -0.0284 0.6710 2.8007
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008194 0.09052
## Residual 1.459607 1.20814
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.351e+00 2.985e-02 1.627e+01 145.753 < 2e-16 ***
## index_PEexp.c 7.531e-02 4.725e-03 3.552e+04 15.938 < 2e-16 ***
## pIndD 2.919e-01 1.734e-02 3.625e+04 16.830 < 2e-16 ***
## pIndR -4.486e-01 1.804e-02 3.624e+04 -24.872 < 2e-16 ***
## index_PEexp.c:pIndD -3.369e-02 5.355e-03 3.625e+04 -6.291 3.19e-10 ***
## index_PEexp.c:pIndR 8.519e-03 5.769e-03 3.625e+04 1.477 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.047
## pIndD -0.402 -0.086
## pIndR -0.386 -0.071 0.665
## indx_PE.:ID -0.040 -0.850 0.024 0.065
## indx_PE.:IR -0.037 -0.788 0.064 0.127 0.691E. worst of Covid is behind/ahead
worstBorA ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129484.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84285 -0.68133 0.02035 0.72368 2.23007
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001513 0.0389
## Residual 2.026295 1.4235
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.699e-01 1.377e-02 1.062e+01 41.378 4.43e-13 ***
## index_PEexp.c 3.315e-02 2.628e-03 2.699e+03 12.613 < 2e-16 ***
## pDem_Rep -9.509e-01 1.702e-02 3.618e+04 -55.879 < 2e-16 ***
## pInd_Not -1.627e-01 1.894e-02 3.651e+04 -8.589 < 2e-16 ***
## index_PEexp.c:pDem_Rep 7.056e-02 5.135e-03 2.698e+04 13.741 < 2e-16 ***
## index_PEexp.c:pInd_Not 7.879e-03 6.004e-03 3.651e+04 1.312 0.189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.040
## pDem_Rep 0.051 0.133
## pInd_Not -0.177 -0.049 0.054
## ind_PE.:D_R 0.073 0.159 0.044 0.079
## ind_PE.:I_N -0.027 -0.346 0.074 0.084 0.103
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129484.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84285 -0.68133 0.02035 0.72368 2.23007
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001513 0.0389
## Residual 2.026295 1.4235
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.917e-01 1.591e-02 1.887e+01 62.334 < 2e-16 ***
## index_PEexp.c 4.667e-04 3.258e-03 5.512e+03 0.143 0.886
## pDemR -9.509e-01 1.702e-02 3.618e+04 -55.879 < 2e-16 ***
## pDemI -3.128e-01 2.034e-02 3.649e+04 -15.378 < 2e-16 ***
## index_PEexp.c:pDemR 7.056e-02 5.135e-03 2.698e+04 13.741 < 2e-16 ***
## index_PEexp.c:pDemI 2.740e-02 6.282e-03 3.645e+04 4.362 1.29e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.118
## pDemR -0.470 0.117
## pDemI -0.393 0.096 0.368
## indx_PE.:DR 0.071 -0.597 0.044 -0.055
## indx_PE.:DI 0.057 -0.480 -0.052 0.023 0.3102. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129484.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84285 -0.68133 0.02035 0.72368 2.23007
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001513 0.0389
## Residual 2.026295 1.4235
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.080e-02 1.698e-02 2.447e+01 2.403 0.0242 *
## index_PEexp.c 7.103e-02 4.125e-03 8.531e+03 17.216 < 2e-16 ***
## pRepD 9.509e-01 1.702e-02 3.618e+04 55.879 < 2e-16 ***
## pRepI 6.381e-01 2.117e-02 3.651e+04 30.136 < 2e-16 ***
## index_PEexp.c:pRepD -7.056e-02 5.135e-03 2.698e+04 -13.741 < 2e-16 ***
## index_PEexp.c:pRepI -4.316e-02 6.769e-03 3.591e+04 -6.376 1.84e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.143
## pRepD -0.562 -0.148
## pRepI -0.451 -0.112 0.450
## indx_PE.:RD -0.111 -0.774 0.044 0.089
## indx_PE.:RI -0.083 -0.581 0.082 0.127 0.4713. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129484.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84285 -0.68133 0.02035 0.72368 2.23007
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001513 0.0389
## Residual 2.026295 1.4235
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.789e-01 2.031e-02 5.019e+01 33.424 < 2e-16 ***
## index_PEexp.c 2.787e-02 5.515e-03 2.171e+04 5.053 4.38e-07 ***
## pIndD 3.128e-01 2.034e-02 3.649e+04 15.378 < 2e-16 ***
## pIndR -6.381e-01 2.117e-02 3.651e+04 -30.136 < 2e-16 ***
## index_PEexp.c:pIndD -2.740e-02 6.282e-03 3.645e+04 -4.362 1.29e-05 ***
## index_PEexp.c:pIndR 4.316e-02 6.769e-03 3.591e+04 6.376 1.84e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.079
## pIndD -0.694 -0.083
## pIndR -0.666 -0.072 0.665
## indx_PE.:ID -0.068 -0.855 0.023 0.064
## indx_PE.:IR -0.063 -0.793 0.063 0.127 0.693F. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 157153.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13927 -0.71634 0.05963 0.88731 1.55918
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01269 0.1126
## Residual 4.34000 2.0833
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.859e-01 3.455e-02 1.081e+01 14.06 2.73e-08 ***
## index_PEexp.c 8.606e-02 3.924e-03 1.530e+04 21.93 < 2e-16 ***
## pDem_Rep -8.698e-01 2.493e-02 3.646e+04 -34.89 < 2e-16 ***
## pInd_Not 5.783e-01 2.772e-02 3.647e+04 20.86 < 2e-16 ***
## index_PEexp.c:pDem_Rep 7.991e-02 7.544e-03 3.534e+04 10.59 < 2e-16 ***
## index_PEexp.c:pInd_Not -1.116e-02 8.789e-03 3.647e+04 -1.27 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.023
## pDem_Rep 0.030 0.137
## pInd_Not -0.103 -0.049 0.054
## ind_PE.:D_R 0.043 0.158 0.044 0.079
## ind_PE.:I_N -0.016 -0.340 0.073 0.084 0.104
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157153.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13927 -0.71634 0.05963 0.88731 1.55918
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01269 0.1126
## Residual 4.34000 2.0833
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.112e+00 3.646e-02 1.341e+01 30.486 8.82e-14 ***
## index_PEexp.c 4.242e-02 4.835e-03 2.249e+04 8.774 < 2e-16 ***
## pDemR -8.698e-01 2.493e-02 3.646e+04 -34.886 < 2e-16 ***
## pDemI -1.013e+00 2.978e-02 3.648e+04 -34.026 < 2e-16 ***
## index_PEexp.c:pDemR 7.991e-02 7.544e-03 3.534e+04 10.593 < 2e-16 ***
## index_PEexp.c:pDemI 5.112e-02 9.198e-03 3.648e+04 5.558 2.75e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.076
## pDemR -0.300 0.121
## pDemI -0.251 0.098 0.369
## indx_PE.:DR 0.045 -0.590 0.044 -0.055
## indx_PE.:DI 0.036 -0.475 -0.052 0.023 0.3112. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157153.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13927 -0.71634 0.05963 0.88731 1.55918
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01269 0.1126
## Residual 4.34000 2.0833
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.418e-01 3.749e-02 1.498e+01 6.450 1.10e-05 ***
## index_PEexp.c 1.223e-01 6.102e-03 2.683e+04 20.046 < 2e-16 ***
## pRepD 8.698e-01 2.493e-02 3.646e+04 34.886 < 2e-16 ***
## pRepI -1.434e-01 3.100e-02 3.647e+04 -4.626 3.75e-06 ***
## index_PEexp.c:pRepD -7.991e-02 7.544e-03 3.534e+04 -10.593 < 2e-16 ***
## index_PEexp.c:pRepI -2.879e-02 9.916e-03 3.645e+04 -2.903 0.00369 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.095
## pRepD -0.373 -0.150
## pRepI -0.299 -0.112 0.450
## indx_PE.:RD -0.073 -0.769 0.044 0.089
## indx_PE.:RI -0.055 -0.577 0.082 0.127 0.4723. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157153.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13927 -0.71634 0.05963 0.88731 1.55918
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01269 0.1126
## Residual 4.34000 2.0833
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.842e-02 4.088e-02 2.118e+01 2.408 0.02524 *
## index_PEexp.c 9.354e-02 8.110e-03 3.392e+04 11.534 < 2e-16 ***
## pIndD 1.013e+00 2.978e-02 3.648e+04 34.026 < 2e-16 ***
## pIndR 1.434e-01 3.100e-02 3.647e+04 4.626 3.75e-06 ***
## index_PEexp.c:pIndD -5.112e-02 9.198e-03 3.648e+04 -5.558 2.75e-08 ***
## index_PEexp.c:pIndR 2.879e-02 9.916e-03 3.645e+04 2.903 0.00369 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.058
## pIndD -0.504 -0.085
## pIndR -0.484 -0.071 0.664
## indx_PE.:ID -0.049 -0.851 0.023 0.064
## indx_PE.:IR -0.046 -0.789 0.063 0.127 0.691G. vulnerable worker
vulnerable worker ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ (index_PEexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149769.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3831 -0.7708 -0.2496 0.7603 2.5652
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003331 0.05771
## Residual 3.531389 1.87920
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.154e+00 1.971e-02 1.049e+01 -58.572 1.55e-14 ***
## index_PEexp.c 4.183e-02 3.485e-03 3.596e+03 12.003 < 2e-16 ***
## pDem_Rep 9.501e-01 2.247e-02 3.628e+04 42.289 < 2e-16 ***
## pInd_Not -6.400e-02 2.500e-02 3.651e+04 -2.560 0.0105 *
## index_PEexp.c:pDem_Rep -1.032e-02 6.784e-03 2.920e+04 -1.521 0.1284
## index_PEexp.c:pInd_Not 6.884e-03 7.927e-03 3.651e+04 0.868 0.3851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.037
## pDem_Rep 0.047 0.134
## pInd_Not -0.163 -0.049 0.054
## ind_PE.:D_R 0.067 0.159 0.045 0.079
## ind_PE.:I_N -0.025 -0.345 0.074 0.084 0.103
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ (index_PEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149769.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3831 -0.7708 -0.2496 0.7603 2.5652
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003331 0.05771
## Residual 3.531389 1.87920
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.651e+00 2.234e-02 1.728e+01 -73.894 <2e-16 ***
## index_PEexp.c 4.926e-02 4.314e-03 7.137e+03 11.417 <2e-16 ***
## pDemR 9.501e-01 2.247e-02 3.628e+04 42.289 <2e-16 ***
## pDemI 5.391e-01 2.685e-02 3.650e+04 20.075 <2e-16 ***
## index_PEexp.c:pDemR -1.032e-02 6.784e-03 2.920e+04 -1.521 0.128
## index_PEexp.c:pDemI -1.204e-02 8.294e-03 3.647e+04 -1.452 0.147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.112
## pDemR -0.442 0.118
## pDemI -0.369 0.096 0.368
## indx_PE.:DR 0.066 -0.595 0.045 -0.055
## indx_PE.:DI 0.053 -0.479 -0.052 0.023 0.3102. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ (index_PEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149769.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3831 -0.7708 -0.2496 0.7603 2.5652
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003331 0.05771
## Residual 3.531389 1.87920
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.004e-01 2.367e-02 2.178e+01 -29.591 < 2e-16 ***
## index_PEexp.c 3.894e-02 5.459e-03 1.075e+04 7.134 1.04e-12 ***
## pRepD -9.501e-01 2.247e-02 3.628e+04 -42.289 < 2e-16 ***
## pRepI -4.111e-01 2.795e-02 3.651e+04 -14.706 < 2e-16 ***
## index_PEexp.c:pRepD 1.032e-02 6.784e-03 2.920e+04 1.521 0.128
## index_PEexp.c:pRepI -1.726e-03 8.938e-03 3.610e+04 -0.193 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.136
## pRepD -0.532 -0.149
## pRepI -0.427 -0.112 0.450
## indx_PE.:RD -0.105 -0.772 0.045 0.089
## indx_PE.:RI -0.079 -0.580 0.082 0.127 0.4713. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157153.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13927 -0.71634 0.05963 0.88731 1.55918
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01269 0.1126
## Residual 4.34000 2.0833
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.842e-02 4.088e-02 2.118e+01 2.408 0.02524 *
## index_PEexp.c 9.354e-02 8.110e-03 3.392e+04 11.534 < 2e-16 ***
## pIndD 1.013e+00 2.978e-02 3.648e+04 34.026 < 2e-16 ***
## pIndR 1.434e-01 3.100e-02 3.647e+04 4.626 3.75e-06 ***
## index_PEexp.c:pIndD -5.112e-02 9.198e-03 3.648e+04 -5.558 2.75e-08 ***
## index_PEexp.c:pIndR 2.879e-02 9.916e-03 3.645e+04 2.903 0.00369 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.058
## pIndD -0.504 -0.085
## pIndR -0.484 -0.071 0.664
## indx_PE.:ID -0.049 -0.851 0.023 0.064
## indx_PE.:IR -0.046 -0.789 0.063 0.127 0.6915. NEGATIVE EMOTIONS
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134912.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12446 -0.78339 0.05861 0.73302 2.37875
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03548 0.1884
## Residual 2.35205 1.5336
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.488e+00 5.505e-02 1.072e+01 81.533 2.54e-16 ***
## index_NEexp.c 1.080e-01 3.793e-03 2.217e+04 28.466 < 2e-16 ***
## pDem_Rep -1.377e+00 1.833e-02 3.650e+04 -75.094 < 2e-16 ***
## pInd_Not 1.336e-01 2.037e-02 3.649e+04 6.558 5.52e-11 ***
## index_NEexp.c:pDem_Rep 9.323e-02 6.285e-03 3.650e+04 14.834 < 2e-16 ***
## index_NEexp.c:pInd_Not -2.441e-02 7.237e-03 3.649e+04 -3.373 0.000743 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.010
## pDem_Rep 0.014 0.148
## pInd_Not -0.048 -0.041 0.052
## ind_NE.:D_R 0.017 0.195 0.052 0.067
## ind_NE.:I_N -0.005 -0.284 0.062 0.072 0.141
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134912.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12446 -0.78339 0.05861 0.73302 2.37875
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03548 0.1884
## Residual 2.35205 1.5336
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.221e+00 5.571e-02 1.124e+01 93.715 <2e-16 ***
## index_NEexp.c 5.330e-02 4.251e-03 2.731e+04 12.538 <2e-16 ***
## pDemR -1.377e+00 1.833e-02 3.650e+04 -75.094 <2e-16 ***
## pDemI -8.219e-01 2.189e-02 3.650e+04 -37.543 <2e-16 ***
## index_NEexp.c:pDemR 9.323e-02 6.285e-03 3.650e+04 14.834 <2e-16 ***
## index_NEexp.c:pDemI 7.103e-02 7.473e-03 3.649e+04 9.504 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.033
## pDemR -0.144 0.128
## pDemI -0.121 0.095 0.370
## indx_NE.:DR 0.016 -0.487 0.052 -0.040
## indx_NE.:DI 0.013 -0.402 -0.038 0.023 0.2842. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134912.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12446 -0.78339 0.05861 0.73302 2.37875
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03548 0.1884
## Residual 2.35205 1.5336
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.844e+00 5.607e-02 1.154e+01 68.549 < 2e-16 ***
## index_NEexp.c 1.465e-01 5.619e-03 3.188e+04 26.079 < 2e-16 ***
## pRepD 1.377e+00 1.833e-02 3.650e+04 75.094 < 2e-16 ***
## pRepI 5.548e-01 2.277e-02 3.649e+04 24.370 < 2e-16 ***
## index_NEexp.c:pRepD -9.323e-02 6.285e-03 3.650e+04 -14.834 < 2e-16 ***
## index_NEexp.c:pRepI -2.220e-02 8.286e-03 3.650e+04 -2.679 0.00738 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.044
## pRepD -0.183 -0.155
## pRepI -0.147 -0.099 0.449
## indx_NE.:RD -0.033 -0.750 0.052 0.081
## indx_NE.:RI -0.024 -0.557 0.074 0.109 0.5023. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134912.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12446 -0.78339 0.05861 0.73302 2.37875
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03548 0.1884
## Residual 2.35205 1.5336
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.399e+00 5.734e-02 1.263e+01 76.705 < 2e-16 ***
## index_NEexp.c 1.243e-01 6.953e-03 3.448e+04 17.881 < 2e-16 ***
## pIndD 8.219e-01 2.189e-02 3.650e+04 37.543 < 2e-16 ***
## pIndR -5.548e-01 2.277e-02 3.649e+04 -24.370 < 2e-16 ***
## index_NEexp.c:pIndD -7.103e-02 7.473e-03 3.649e+04 -9.504 < 2e-16 ***
## index_NEexp.c:pIndR 2.220e-02 8.286e-03 3.650e+04 2.679 0.00738 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.026
## pIndD -0.265 -0.083
## pIndR -0.254 -0.050 0.664
## indx_NE.:ID -0.022 -0.829 0.023 0.053
## indx_NE.:IR -0.019 -0.741 0.051 0.109 0.686B. risk4
risk4 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2310 -0.6299 0.0882 0.7487 1.5294
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0004366 0.02089
## Residual 1.9380989 1.39216
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.113e+00 9.854e-03 5.096e+00 518.911 3.10e-13 ***
## index_NEexp.c 1.797e-02 2.955e-03 6.287e+01 6.081 7.77e-08 ***
## pDem_Rep -4.542e-01 1.658e-02 2.855e+04 -27.396 < 2e-16 ***
## pInd_Not 7.845e-02 1.854e-02 3.641e+04 4.232 2.32e-05 ***
## index_NEexp.c:pDem_Rep 9.151e-03 5.675e-03 2.052e+04 1.613 0.107
## index_NEexp.c:pInd_Not -7.487e-03 6.584e-03 3.640e+04 -1.137 0.255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.050
## pDem_Rep 0.069 0.117
## pInd_Not -0.246 -0.037 0.053
## ind_NE.:D_R 0.083 0.212 0.050 0.066
## ind_NE.:I_N -0.027 -0.327 0.063 0.072 0.139
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2310 -0.6299 0.0882 0.7487 1.5294
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0004365 0.02089
## Residual 1.9380989 1.39216
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.366e+00 1.253e-02 1.322e+01 428.299 < 2e-16 ***
## index_NEexp.c 1.093e-02 3.471e-03 1.405e+02 3.148 0.00201 **
## pDemR -4.542e-01 1.658e-02 2.855e+04 -27.396 < 2e-16 ***
## pDemI -3.056e-01 1.990e-02 3.521e+04 -15.357 < 2e-16 ***
## index_NEexp.c:pDemR 9.151e-03 5.675e-03 2.052e+04 1.613 0.10685
## index_NEexp.c:pDemI 1.206e-02 6.798e-03 3.622e+04 1.774 0.07602 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.118
## pDemR -0.582 0.098
## pDemI -0.484 0.078 0.367
## indx_NE.:DR 0.065 -0.550 0.050 -0.041
## indx_NE.:DI 0.054 -0.457 -0.040 0.023 0.2832. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2310 -0.6299 0.0882 0.7487 1.5294
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0004365 0.02089
## Residual 1.9380989 1.39216
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.912e+00 1.379e-02 1.938e+01 356.105 < 2e-16 ***
## index_NEexp.c 2.008e-02 4.751e-03 3.506e+02 4.226 3.03e-05 ***
## pRepD 4.542e-01 1.658e-02 2.855e+04 27.396 < 2e-16 ***
## pRepI 1.487e-01 2.071e-02 3.641e+04 7.181 7.08e-13 ***
## index_NEexp.c:pRepD -9.151e-03 5.675e-03 2.052e+04 -1.613 0.107
## index_NEexp.c:pRepI 2.911e-03 7.522e-03 3.485e+04 0.387 0.699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.150
## pRepD -0.674 -0.131
## pRepI -0.538 -0.097 0.448
## indx_NE.:RD -0.119 -0.792 0.050 0.079
## indx_NE.:RI -0.089 -0.594 0.074 0.109 0.4993. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127473.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2310 -0.6299 0.0882 0.7487 1.5294
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0004365 0.02089
## Residual 1.9380989 1.39216
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.061e+00 1.765e-02 5.235e+01 286.744 < 2e-16 ***
## index_NEexp.c 2.299e-02 6.059e-03 9.667e+02 3.794 0.000157 ***
## pIndD 3.056e-01 1.990e-02 3.521e+04 15.357 < 2e-16 ***
## pIndR -1.487e-01 2.071e-02 3.641e+04 -7.181 7.08e-13 ***
## index_NEexp.c:pIndD -1.206e-02 6.798e-03 3.622e+04 -1.774 0.076022 .
## index_NEexp.c:pIndR -2.911e-03 7.522e-03 3.485e+04 -0.387 0.698709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.075
## pIndD -0.784 -0.071
## pIndR -0.752 -0.059 0.667
## indx_NE.:ID -0.064 -0.860 0.023 0.054
## indx_NE.:IR -0.058 -0.776 0.051 0.109 0.690C. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148526.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9730 -0.8898 -0.1722 0.7792 2.5083
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.05092 0.2256
## Residual 3.47079 1.8630
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.297e+00 6.597e-02 1.071e+01 49.978 4.9e-14 ***
## index_NEexp.c 1.156e-01 4.611e-03 2.154e+04 25.062 < 2e-16 ***
## pDem_Rep -4.117e-01 2.231e-02 3.636e+04 -18.452 < 2e-16 ***
## pInd_Not -4.673e-01 2.476e-02 3.635e+04 -18.870 < 2e-16 ***
## index_NEexp.c:pDem_Rep 4.133e-02 7.638e-03 3.636e+04 5.411 6.3e-08 ***
## index_NEexp.c:pInd_Not 7.199e-03 8.795e-03 3.635e+04 0.819 0.413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.010
## pDem_Rep 0.014 0.146
## pInd_Not -0.049 -0.040 0.051
## ind_NE.:D_R 0.017 0.192 0.052 0.066
## ind_NE.:I_N -0.005 -0.284 0.062 0.071 0.139
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148526.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9730 -0.8898 -0.1722 0.7792 2.5083
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.05092 0.2256
## Residual 3.47079 1.8630
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.349e+00 6.679e-02 1.125e+01 50.136 1.38e-14 ***
## index_NEexp.c 9.726e-02 5.181e-03 2.681e+04 18.775 < 2e-16 ***
## pDemR -4.117e-01 2.231e-02 3.636e+04 -18.452 < 2e-16 ***
## pDemI 2.615e-01 2.663e-02 3.636e+04 9.817 < 2e-16 ***
## index_NEexp.c:pDemR 4.133e-02 7.638e-03 3.636e+04 5.411 6.30e-08 ***
## index_NEexp.c:pDemI 1.347e-02 9.089e-03 3.635e+04 1.482 0.138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.033
## pDemR -0.147 0.127
## pDemI -0.123 0.095 0.371
## indx_NE.:DR 0.016 -0.489 0.052 -0.040
## indx_NE.:DI 0.013 -0.404 -0.038 0.023 0.2862. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148526.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9730 -0.8898 -0.1722 0.7792 2.5083
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.05092 0.2256
## Residual 3.47079 1.8630
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.937e+00 6.723e-02 1.155e+01 43.683 3.42e-14 ***
## index_NEexp.c 1.386e-01 6.819e-03 3.148e+04 20.324 < 2e-16 ***
## pRepD 4.117e-01 2.231e-02 3.636e+04 18.452 < 2e-16 ***
## pRepI 6.731e-01 2.768e-02 3.635e+04 24.321 < 2e-16 ***
## index_NEexp.c:pRepD -4.133e-02 7.638e-03 3.636e+04 -5.411 6.30e-08 ***
## index_NEexp.c:pRepI -2.787e-02 1.006e-02 3.635e+04 -2.769 0.00563 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.044
## pRepD -0.186 -0.154
## pRepI -0.149 -0.098 0.449
## indx_NE.:RD -0.033 -0.749 0.052 0.080
## indx_NE.:RI -0.025 -0.556 0.074 0.108 0.5013. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148526.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9730 -0.8898 -0.1722 0.7792 2.5083
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.05092 0.2256
## Residual 3.47079 1.8630
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.610e+00 6.880e-02 1.267e+01 52.472 3.36e-16 ***
## index_NEexp.c 1.107e-01 8.450e-03 3.422e+04 13.104 < 2e-16 ***
## pIndD -2.615e-01 2.663e-02 3.636e+04 -9.817 < 2e-16 ***
## pIndR -6.731e-01 2.768e-02 3.635e+04 -24.321 < 2e-16 ***
## index_NEexp.c:pIndD -1.347e-02 9.089e-03 3.635e+04 -1.482 0.13844
## index_NEexp.c:pIndR 2.787e-02 1.006e-02 3.635e+04 2.769 0.00563 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.026
## pIndD -0.268 -0.082
## pIndR -0.257 -0.049 0.663
## indx_NE.:ID -0.022 -0.828 0.023 0.052
## indx_NE.:IR -0.019 -0.742 0.051 0.108 0.686D. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116794.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5397 -0.6468 -0.0222 0.6769 2.7560
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02355 0.1534
## Residual 1.46397 1.2099
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.296e+00 4.481e-02 1.073e+01 95.865 < 2e-16 ***
## index_NEexp.c 8.291e-02 3.002e-03 2.306e+04 27.620 < 2e-16 ***
## pDem_Rep -7.443e-01 1.450e-02 3.625e+04 -51.317 < 2e-16 ***
## pInd_Not -7.724e-02 1.613e-02 3.624e+04 -4.788 1.69e-06 ***
## index_NEexp.c:pDem_Rep 4.818e-02 4.969e-03 3.625e+04 9.697 < 2e-16 ***
## index_NEexp.c:pInd_Not -8.612e-03 5.728e-03 3.624e+04 -1.503 0.133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.010
## pDem_Rep 0.013 0.146
## pInd_Not -0.047 -0.041 0.051
## ind_NE.:D_R 0.016 0.192 0.052 0.066
## ind_NE.:I_N -0.005 -0.285 0.062 0.072 0.139
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_NEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116794.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5397 -0.6468 -0.0222 0.6769 2.7560
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02355 0.1534
## Residual 1.46397 1.2099
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.643e+00 4.532e-02 1.123e+01 102.434 < 2e-16 ***
## index_NEexp.c 5.598e-02 3.370e-03 2.796e+04 16.613 < 2e-16 ***
## pDemR -7.443e-01 1.450e-02 3.625e+04 -51.317 < 2e-16 ***
## pDemI -2.949e-01 1.735e-02 3.625e+04 -17.001 < 2e-16 ***
## index_NEexp.c:pDemR 4.818e-02 4.969e-03 3.625e+04 9.697 < 2e-16 ***
## index_NEexp.c:pDemI 3.270e-02 5.918e-03 3.624e+04 5.525 3.31e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.031
## pDemR -0.141 0.127
## pDemI -0.117 0.095 0.371
## indx_NE.:DR 0.015 -0.488 0.052 -0.040
## indx_NE.:DI 0.013 -0.403 -0.038 0.024 0.2852. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_NEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116794.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5397 -0.6468 -0.0222 0.6769 2.7560
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02355 0.1534
## Residual 1.46397 1.2099
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.898e+00 4.560e-02 1.151e+01 85.491 <2e-16 ***
## index_NEexp.c 1.042e-01 4.438e-03 3.211e+04 23.469 <2e-16 ***
## pRepD 7.443e-01 1.450e-02 3.625e+04 51.317 <2e-16 ***
## pRepI 4.494e-01 1.802e-02 3.624e+04 24.937 <2e-16 ***
## index_NEexp.c:pRepD -4.818e-02 4.969e-03 3.625e+04 -9.697 <2e-16 ***
## index_NEexp.c:pRepI -1.548e-02 6.553e-03 3.624e+04 -2.362 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.042
## pRepD -0.178 -0.154
## pRepI -0.142 -0.098 0.448
## indx_NE.:RD -0.032 -0.749 0.052 0.080
## indx_NE.:RI -0.024 -0.556 0.074 0.109 0.5013. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_NEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116794.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5397 -0.6468 -0.0222 0.6769 2.7560
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02355 0.1534
## Residual 1.46397 1.2099
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.348e+00 4.659e-02 1.254e+01 93.323 < 2e-16 ***
## index_NEexp.c 8.868e-02 5.504e-03 3.448e+04 16.111 < 2e-16 ***
## pIndD 2.949e-01 1.735e-02 3.625e+04 17.001 < 2e-16 ***
## pIndR -4.494e-01 1.802e-02 3.624e+04 -24.937 < 2e-16 ***
## index_NEexp.c:pIndD -3.270e-02 5.918e-03 3.624e+04 -5.525 3.31e-08 ***
## index_NEexp.c:pIndR 1.548e-02 6.553e-03 3.624e+04 2.362 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.026
## pIndD -0.258 -0.083
## pIndR -0.248 -0.050 0.664
## indx_NE.:ID -0.021 -0.828 0.024 0.053
## indx_NE.:IR -0.019 -0.742 0.051 0.109 0.687E. worst of Covid is behind/ahead
worstBorA ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129547.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.88594 -0.68842 0.01109 0.71401 2.21101
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004998 0.0707
## Residual 2.029245 1.4245
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.661e-01 2.191e-02 9.571e+00 25.842 3.55e-10 ***
## index_NEexp.c 4.295e-02 3.415e-03 1.726e+03 12.575 < 2e-16 ***
## pDem_Rep -9.526e-01 1.701e-02 3.605e+04 -56.011 < 2e-16 ***
## pInd_Not -1.678e-01 1.892e-02 3.651e+04 -8.871 < 2e-16 ***
## index_NEexp.c:pDem_Rep 6.956e-02 5.826e-03 3.609e+04 11.940 < 2e-16 ***
## index_NEexp.c:pInd_Not 1.020e-02 6.720e-03 3.651e+04 1.518 0.129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.023
## pDem_Rep 0.032 0.141
## pInd_Not -0.112 -0.040 0.052
## ind_NE.:D_R 0.039 0.197 0.051 0.066
## ind_NE.:I_N -0.012 -0.292 0.062 0.072 0.140## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129547.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.88594 -0.68842 0.01109 0.71401 2.21101
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004998 0.0707
## Residual 2.029245 1.4245
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.870e-01 2.330e-02 1.224e+01 42.361 1.18e-14 ***
## index_NEexp.c 1.154e-02 3.862e-03 3.234e+03 2.987 0.002841 **
## pDemR -9.526e-01 1.701e-02 3.605e+04 -56.011 < 2e-16 ***
## pDemI -3.085e-01 2.033e-02 3.648e+04 -15.176 < 2e-16 ***
## index_NEexp.c:pDemR 6.956e-02 5.826e-03 3.609e+04 11.940 < 2e-16 ***
## index_NEexp.c:pDemI 2.458e-02 6.941e-03 3.651e+04 3.541 0.000398 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.071
## pDemR -0.321 0.122
## pDemI -0.268 0.092 0.370
## indx_NE.:DR 0.035 -0.500 0.051 -0.040
## indx_NE.:DI 0.029 -0.413 -0.039 0.023 0.2842. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129547.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.88594 -0.68842 0.01109 0.71401 2.21101
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004998 0.0707
## Residual 2.029245 1.4245
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.441e-02 2.404e-02 1.388e+01 1.431 0.174
## index_NEexp.c 8.110e-02 5.135e-03 6.698e+03 15.793 < 2e-16 ***
## pRepD 9.526e-01 1.701e-02 3.605e+04 56.011 < 2e-16 ***
## pRepI 6.441e-01 2.114e-02 3.651e+04 30.468 < 2e-16 ***
## index_NEexp.c:pRepD -6.956e-02 5.826e-03 3.609e+04 -11.940 < 2e-16 ***
## index_NEexp.c:pRepI -4.498e-02 7.690e-03 3.651e+04 -5.850 4.96e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.093
## pRepD -0.397 -0.150
## pRepI -0.317 -0.098 0.449
## indx_NE.:RD -0.071 -0.759 0.051 0.080
## indx_NE.:RI -0.053 -0.564 0.074 0.108 0.5013. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129547.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.88594 -0.68842 0.01109 0.71401 2.21101
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004998 0.0707
## Residual 2.029245 1.4245
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.785e-01 2.651e-02 2.052e+01 25.596 < 2e-16 ***
## index_NEexp.c 3.612e-02 6.397e-03 1.297e+04 5.645 1.68e-08 ***
## pIndD 3.085e-01 2.033e-02 3.648e+04 15.176 < 2e-16 ***
## pIndR -6.441e-01 2.114e-02 3.651e+04 -30.468 < 2e-16 ***
## index_NEexp.c:pIndD -2.458e-02 6.941e-03 3.651e+04 -3.541 0.000398 ***
## index_NEexp.c:pIndR 4.498e-02 7.690e-03 3.651e+04 5.850 4.96e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.052
## pIndD -0.532 -0.080
## pIndR -0.510 -0.052 0.664
## indx_NE.:ID -0.043 -0.835 0.023 0.053
## indx_NE.:IR -0.039 -0.749 0.051 0.108 0.687F. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 157212.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39713 -0.69368 0.03946 0.89106 1.64871
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03947 0.1987
## Residual 4.34582 2.0847
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.817e-01 5.852e-02 1.054e+01 8.231 6.55e-06 ***
## index_NEexp.c 1.099e-01 5.132e-03 1.320e+04 21.424 < 2e-16 ***
## pDem_Rep -8.730e-01 2.492e-02 3.646e+04 -35.029 < 2e-16 ***
## pInd_Not 5.759e-01 2.768e-02 3.647e+04 20.802 < 2e-16 ***
## index_NEexp.c:pDem_Rep 8.443e-02 8.535e-03 3.647e+04 9.891 < 2e-16 ***
## index_NEexp.c:pInd_Not -7.714e-03 9.836e-03 3.647e+04 -0.784 0.433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.013
## pDem_Rep 0.018 0.146
## pInd_Not -0.061 -0.041 0.052
## ind_NE.:D_R 0.021 0.194 0.051 0.066
## ind_NE.:I_N -0.007 -0.286 0.062 0.072 0.140## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157212.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39713 -0.69368 0.03946 0.89106 1.64871
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03947 0.1987
## Residual 4.34582 2.0847
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.108e+00 5.967e-02 1.139e+01 18.574 7.12e-10 ***
## index_NEexp.c 6.518e-02 5.762e-03 1.902e+04 11.312 < 2e-16 ***
## pDemR -8.730e-01 2.492e-02 3.646e+04 -35.029 < 2e-16 ***
## pDemI -1.012e+00 2.976e-02 3.648e+04 -34.018 < 2e-16 ***
## index_NEexp.c:pDemR 8.443e-02 8.535e-03 3.647e+04 9.891 < 2e-16 ***
## index_NEexp.c:pDemI 4.993e-02 1.016e-02 3.647e+04 4.915 8.94e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.041
## pDemR -0.183 0.127
## pDemI -0.153 0.095 0.370
## indx_NE.:DR 0.020 -0.489 0.051 -0.040
## indx_NE.:DI 0.017 -0.404 -0.038 0.023 0.285
2. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157212.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39713 -0.69368 0.03946 0.89106 1.64871
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03947 0.1987
## Residual 4.34582 2.0847
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.352e-01 6.030e-02 1.188e+01 3.901 0.00214 **
## index_NEexp.c 1.496e-01 7.611e-03 2.605e+04 19.655 < 2e-16 ***
## pRepD 8.730e-01 2.492e-02 3.646e+04 35.029 < 2e-16 ***
## pRepI -1.394e-01 3.095e-02 3.647e+04 -4.504 6.68e-06 ***
## index_NEexp.c:pRepD -8.443e-02 8.535e-03 3.647e+04 -9.891 < 2e-16 ***
## index_NEexp.c:pRepI -3.450e-02 1.126e-02 3.648e+04 -3.065 0.00218 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.055
## pRepD -0.232 -0.153
## pRepI -0.185 -0.098 0.449
## indx_NE.:RD -0.041 -0.751 0.051 0.080
## indx_NE.:RI -0.031 -0.558 0.073 0.108 0.5013. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157212.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39713 -0.69368 0.03946 0.89106 1.64871
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03947 0.1987
## Residual 4.34582 2.0847
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.582e-02 6.246e-02 1.369e+01 1.534 0.14782
## index_NEexp.c 1.151e-01 9.438e-03 3.133e+04 12.196 < 2e-16 ***
## pIndD 1.012e+00 2.976e-02 3.648e+04 34.018 < 2e-16 ***
## pIndR 1.394e-01 3.095e-02 3.647e+04 4.504 6.68e-06 ***
## index_NEexp.c:pIndD -4.993e-02 1.016e-02 3.647e+04 -4.915 8.94e-07 ***
## index_NEexp.c:pIndR 3.450e-02 1.126e-02 3.648e+04 3.065 0.00218 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.032
## pIndD -0.330 -0.082
## pIndR -0.317 -0.050 0.664
## indx_NE.:ID -0.027 -0.830 0.023 0.053
## indx_NE.:IR -0.024 -0.743 0.051 0.108 0.687F. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ (index_NEexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149834.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4792 -0.7552 -0.2317 0.7959 2.5966
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008609 0.09278
## Residual 3.536965 1.88068
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.153e+00 2.877e-02 9.514e+00 -40.086 6.21e-12 ***
## index_NEexp.c 4.683e-02 4.507e-03 1.684e+03 10.389 < 2e-16 ***
## pDem_Rep 9.411e-01 2.245e-02 3.604e+04 41.913 < 2e-16 ***
## pInd_Not -5.988e-02 2.497e-02 3.651e+04 -2.398 0.0165 *
## index_NEexp.c:pDem_Rep -2.648e-03 7.692e-03 3.608e+04 -0.344 0.7307
## index_NEexp.c:pInd_Not 6.335e-03 8.873e-03 3.651e+04 0.714 0.4753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.023
## pDem_Rep 0.032 0.141
## pInd_Not -0.113 -0.040 0.052
## ind_NE.:D_R 0.039 0.197 0.051 0.066
## ind_NE.:I_N -0.012 -0.292 0.062 0.072 0.140## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ (index_NEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149834.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4792 -0.7552 -0.2317 0.7959 2.5966
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008609 0.09278
## Residual 3.536966 1.88068
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.644e+00 3.062e-02 1.220e+01 -53.676 7.27e-16 ***
## index_NEexp.c 5.024e-02 5.098e-03 3.160e+03 9.856 < 2e-16 ***
## pDemR 9.411e-01 2.245e-02 3.604e+04 41.913 < 2e-16 ***
## pDemI 5.304e-01 2.684e-02 3.648e+04 19.764 < 2e-16 ***
## index_NEexp.c:pDemR -2.648e-03 7.692e-03 3.608e+04 -0.344 0.731
## index_NEexp.c:pDemI -7.658e-03 9.163e-03 3.651e+04 -0.836 0.403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.071
## pDemR -0.322 0.122
## pDemI -0.269 0.092 0.370
## indx_NE.:DR 0.036 -0.500 0.051 -0.040
## indx_NE.:DI 0.029 -0.413 -0.039 0.023 0.284
2. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ (index_NEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149834.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4792 -0.7552 -0.2317 0.7959 2.5966
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008609 0.09278
## Residual 3.536965 1.88068
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.025e-01 3.161e-02 1.385e+01 -22.228 3.15e-12 ***
## index_NEexp.c 4.759e-02 6.778e-03 6.563e+03 7.021 2.42e-12 ***
## pRepD -9.411e-01 2.245e-02 3.604e+04 -41.913 < 2e-16 ***
## pRepI -4.107e-01 2.791e-02 3.651e+04 -14.714 < 2e-16 ***
## index_NEexp.c:pRepD 2.648e-03 7.692e-03 3.608e+04 0.344 0.731
## index_NEexp.c:pRepI -5.011e-03 1.015e-02 3.651e+04 -0.494 0.622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.094
## pRepD -0.398 -0.150
## pRepI -0.318 -0.098 0.449
## indx_NE.:RD -0.071 -0.759 0.051 0.080
## indx_NE.:RI -0.053 -0.564 0.074 0.108 0.5013. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vulnerableWorker ~ (index_NEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 149834.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4792 -0.7552 -0.2317 0.7959 2.5966
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008609 0.09278
## Residual 3.536965 1.88068
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.113e+00 3.487e-02 2.053e+01 -31.921 < 2e-16 ***
## index_NEexp.c 4.258e-02 8.445e-03 1.276e+04 5.042 4.66e-07 ***
## pIndD -5.304e-01 2.684e-02 3.648e+04 -19.764 < 2e-16 ***
## pIndR 4.107e-01 2.791e-02 3.651e+04 14.714 < 2e-16 ***
## index_NEexp.c:pIndD 7.658e-03 9.163e-03 3.651e+04 0.836 0.403
## index_NEexp.c:pIndR 5.011e-03 1.015e-02 3.651e+04 0.494 0.622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.052
## pIndD -0.534 -0.080
## pIndR -0.512 -0.052 0.664
## indx_NE.:ID -0.043 -0.835 0.023 0.053
## indx_NE.:IR -0.039 -0.749 0.051 0.108 0.6876. NEGATIVE VS. POSITIVE EMOTIONS
A. risk severity
risk severity ~ indexes * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c + index_NEexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116698.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2846 -0.6489 -0.0305 0.6692 2.8005
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008546 0.09244
## Residual 1.459658 1.20816
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.298e+00 2.754e-02 1.072e+01 156.089 < 2e-16 ***
## index_PEexp.c 6.413e-02 5.789e-03 7.261e+02 11.077 < 2e-16 ***
## index_NEexp.c 4.042e-03 7.546e-03 4.938e+02 0.536 0.5924
## pDem_Rep -7.401e-01 1.450e-02 3.624e+04 -51.030 < 2e-16 ***
## pInd_Not -7.834e-02 1.614e-02 3.624e+04 -4.854 1.21e-06 ***
## index_PEexp.c:pDem_Rep 3.630e-02 7.801e-03 3.445e+04 4.654 3.27e-06 ***
## index_PEexp.c:pInd_Not -1.724e-02 8.939e-03 3.624e+04 -1.928 0.0538 .
## index_NEexp.c:pDem_Rep 8.270e-03 8.876e-03 3.338e+04 0.932 0.3515
## index_NEexp.c:pInd_Not 6.476e-03 1.000e-02 3.624e+04 0.648 0.5173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. in_NE. pDm_Rp pInd_N i_PE.:D i_PE.:I i_NE.:D
## indx_PExp.c 0.005
## indx_NExp.c 0.002 -0.918
## pDem_Rep 0.021 0.033 0.024
## pInd_Not -0.076 -0.019 -0.001 0.052
## ind_PE.:D_R 0.017 0.224 -0.214 0.015 0.043
## ind_PE.:I_N -0.007 -0.242 0.171 0.040 0.047 0.123
## ind_NE.:D_R 0.001 -0.244 0.275 0.013 0.002 -0.826 -0.118
## ind_NE.:I_N 0.001 0.195 -0.202 0.002 0.002 -0.121 -0.820 0.158
## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_NEexp.c + index_PEexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116698.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2846 -0.6489 -0.0305 0.6692 2.8005
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008545 0.09244
## Residual 1.459658 1.20816
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.642e+00 2.836e-02 1.207e+01 163.681 < 2e-16 ***
## index_NEexp.c 2.044e-03 7.377e-03 7.587e+02 0.277 0.781805
## index_PEexp.c 4.029e-02 6.018e-03 1.418e+03 6.695 3.10e-11 ***
## pDemR -7.401e-01 1.450e-02 3.624e+04 -51.030 < 2e-16 ***
## pDemI -2.917e-01 1.734e-02 3.625e+04 -16.819 < 2e-16 ***
## index_NEexp.c:pDemR 8.270e-03 8.876e-03 3.338e+04 0.932 0.351483
## index_NEexp.c:pDemI -2.341e-03 1.028e-02 3.600e+04 -0.228 0.819874
## index_PEexp.c:pDemR 3.630e-02 7.801e-03 3.445e+04 4.654 3.27e-06 ***
## index_PEexp.c:pDemI 3.539e-02 9.302e-03 3.614e+04 3.804 0.000142 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDemR pDemI i_NE.:DR i_NE.:DI i_PE.:DR
## indx_NExp.c -0.003
## indx_PExp.c -0.024 -0.883
## pDemR -0.225 0.018 0.042
## pDemI -0.188 0.008 0.039 0.369
## indx_NE.:DR -0.002 -0.250 0.243 0.013 0.004
## indx_NE.:DI -0.001 -0.250 0.237 0.003 0.002 0.278
## indx_PE.:DR 0.020 0.224 -0.372 0.015 -0.034 -0.826 -0.239
## indx_PE.:DI 0.016 0.210 -0.327 -0.032 0.012 -0.233 -0.818 0.3012. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157169.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.70925 0.05774 0.88666 1.57607
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01542 0.1242
## Residual 4.33972 2.0832
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.427e-01 4.041e-02 1.329e+01 6.006 4.02e-05 ***
## index_NEexp.c 2.808e-02 1.677e-02 4.940e+02 1.674 0.0947 .
## index_PEexp.c 1.030e-01 1.327e-02 8.194e+02 7.762 2.48e-14 ***
## pRepD 8.684e-01 2.494e-02 3.645e+04 34.817 < 2e-16 ***
## pRepI -1.440e-01 3.100e-02 3.647e+04 -4.646 3.39e-06 ***
## index_NEexp.c:pRepD -9.490e-03 1.524e-02 3.116e+04 -0.623 0.5334
## index_NEexp.c:pRepI -7.009e-03 1.987e-02 3.647e+04 -0.353 0.7242
## index_PEexp.c:pRepD -7.347e-02 1.340e-02 3.273e+04 -5.484 4.18e-08 ***
## index_PEexp.c:pRepI -2.407e-02 1.750e-02 3.646e+04 -1.376 0.1689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pRepD pRepI i_NE.:RD i_NE.:RI i_PE.:RD
## indx_NExp.c 0.010
## indx_PExp.c 0.032 -0.888
## pRepD -0.346 -0.023 -0.049
## pRepI -0.278 -0.010 -0.042 0.450
## indx_NE.:RD -0.006 -0.710 0.636 0.013 0.007
## indx_NE.:RI -0.004 -0.494 0.443 0.007 0.005 0.522
## indx_PE.:RD -0.034 0.574 -0.714 0.015 0.044 -0.826 -0.423
## indx_PE.:RI -0.026 0.396 -0.506 0.041 0.068 -0.422 -0.824 0.4923. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_NEexp.c + index_PEexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116698.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2846 -0.6489 -0.0305 0.6692 2.8005
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008546 0.09244
## Residual 1.459658 1.20816
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.351e+00 3.034e-02 1.580e+01 143.417 < 2e-16 ***
## index_NEexp.c -2.967e-04 1.106e-02 1.990e+03 -0.027 0.978595
## index_PEexp.c 7.568e-02 9.282e-03 3.897e+03 8.153 4.73e-16 ***
## pIndD 2.917e-01 1.734e-02 3.625e+04 16.819 < 2e-16 ***
## pIndR -4.484e-01 1.804e-02 3.624e+04 -24.859 < 2e-16 ***
## index_NEexp.c:pIndD 2.341e-03 1.028e-02 3.600e+04 0.228 0.819875
## index_NEexp.c:pIndR 1.061e-02 1.156e-02 3.625e+04 0.918 0.358874
## index_PEexp.c:pIndD -3.539e-02 9.302e-03 3.614e+04 -3.804 0.000142 ***
## index_PEexp.c:pIndR 9.118e-04 1.018e-02 3.625e+04 0.090 0.928659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pIndD pIndR i_NE.:ID i_NE.:IR i_PE.:ID
## indx_NExp.c 0.001
## indx_PExp.c 0.022 -0.861
## pIndD -0.396 -0.008 -0.037
## pIndR -0.380 0.005 -0.040 0.665
## indx_NE.:ID 0.000 -0.763 0.666 0.002 -0.001
## indx_NE.:IR 0.000 -0.608 0.534 -0.001 0.005 0.676
## indx_PE.:ID -0.022 0.620 -0.790 0.012 0.038 -0.818 -0.548
## indx_PE.:IR -0.021 0.511 -0.676 0.037 0.068 -0.564 -0.824 0.683B. worst of Covid is behind/ahead
worstBorA ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129505.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8478 -0.6847 0.0067 0.7203 2.2333
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001961 0.04428
## Residual 2.026258 1.42347
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.701e-01 1.507e-02 9.247e+00 37.833 1.87e-11 ***
## index_NEexp.c 8.389e-03 7.163e-03 4.725e+01 1.171 0.247
## index_PEexp.c 2.754e-02 5.738e-03 7.949e+01 4.800 7.33e-06 ***
## pDem_Rep -9.501e-01 1.702e-02 3.615e+04 -55.817 < 2e-16 ***
## pInd_Not -1.627e-01 1.894e-02 3.651e+04 -8.590 < 2e-16 ***
## index_NEexp.c:pDem_Rep 2.633e-03 1.036e-02 2.531e+04 0.254 0.799
## index_NEexp.c:pInd_Not 3.554e-03 1.173e-02 3.651e+04 0.303 0.762
## index_PEexp.c:pDem_Rep 6.901e-02 9.110e-03 2.489e+04 7.575 3.73e-14 ***
## index_PEexp.c:pInd_Not 5.328e-03 1.048e-02 3.651e+04 0.508 0.611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDm_Rp pInd_N i_NE.:D i_NE.:I i_PE.:D
## indx_NExp.c 0.003
## indx_PExp.c 0.014 -0.888
## pDem_Rep 0.046 0.016 0.048
## pInd_Not -0.162 0.000 -0.023 0.054
## ind_NE.:D_R 0.002 0.262 -0.222 0.011 0.002
## ind_NE.:I_N 0.001 -0.238 0.220 0.003 0.002 0.162
## ind_PE.:D_R 0.036 -0.203 0.213 0.016 0.043 -0.825 -0.124
## ind_PE.:I_N -0.015 0.200 -0.275 0.040 0.047 -0.122 -0.820 0.126## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129505.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8478 -0.6847 0.0067 0.7203 2.2333
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001961 0.04428
## Residual 2.026258 1.42347
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.914e-01 1.704e-02 1.512e+01 58.174 < 2e-16 ***
## index_NEexp.c 8.245e-03 7.350e-03 8.863e+01 1.122 0.26501
## index_PEexp.c -5.204e-03 6.293e-03 1.925e+02 -0.827 0.40930
## pDemR -9.501e-01 1.702e-02 3.615e+04 -55.817 < 2e-16 ***
## pDemI -3.124e-01 2.034e-02 3.649e+04 -15.357 < 2e-16 ***
## index_NEexp.c:pDemR 2.633e-03 1.036e-02 2.531e+04 0.254 0.79930
## index_NEexp.c:pDemI -2.237e-03 1.203e-02 3.510e+04 -0.186 0.85251
## index_PEexp.c:pDemR 6.901e-02 9.110e-03 2.489e+04 7.575 3.73e-14 ***
## index_PEexp.c:pDemI 2.917e-02 1.089e-02 3.564e+04 2.679 0.00739 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDemR pDemI i_NE.:DR i_NE.:DI i_PE.:DR
## indx_NExp.c -0.003
## indx_PExp.c -0.055 -0.854
## pDemR -0.439 0.009 0.054
## pDemI -0.367 0.004 0.047 0.368
## indx_NE.:DR -0.003 -0.364 0.328 0.011 0.003
## indx_NE.:DI -0.002 -0.333 0.297 0.002 0.002 0.272
## indx_PE.:DR 0.039 0.319 -0.460 0.016 -0.033 -0.825 -0.234
## indx_PE.:DI 0.032 0.279 -0.392 -0.032 0.012 -0.228 -0.817 0.2972. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129505.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8478 -0.6847 0.0067 0.7203 2.2333
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001961 0.04429
## Residual 2.026258 1.42347
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.130e-02 1.805e-02 1.900e+01 2.289 0.033735 *
## index_NEexp.c 1.088e-02 1.029e-02 1.374e+02 1.057 0.292246
## index_PEexp.c 6.380e-02 8.355e-03 2.436e+02 7.636 5.08e-13 ***
## pRepD 9.501e-01 1.702e-02 3.615e+04 55.817 < 2e-16 ***
## pRepI 6.377e-01 2.117e-02 3.651e+04 30.118 < 2e-16 ***
## index_NEexp.c:pRepD -2.633e-03 1.036e-02 2.531e+04 -0.254 0.799298
## index_NEexp.c:pRepI -4.870e-03 1.357e-02 3.648e+04 -0.359 0.719665
## index_PEexp.c:pRepD -6.901e-02 9.110e-03 2.489e+04 -7.575 3.73e-14 ***
## index_PEexp.c:pRepI -3.983e-02 1.195e-02 3.632e+04 -3.334 0.000857 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pRepD pRepI i_NE.:RD i_NE.:RI i_PE.:RD
## indx_NExp.c 0.012
## indx_PExp.c 0.056 -0.869
## pRepD -0.529 -0.018 -0.058
## pRepI -0.424 -0.009 -0.048 0.450
## indx_NE.:RD -0.008 -0.747 0.653 0.011 0.006
## indx_NE.:RI -0.006 -0.538 0.471 0.006 0.005 0.522
## indx_PE.:RD -0.052 0.603 -0.744 0.016 0.045 -0.825 -0.422
## indx_PE.:RI -0.040 0.433 -0.541 0.041 0.068 -0.422 -0.824 0.4923. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129505.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8478 -0.6847 0.0067 0.7203 2.2333
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001961 0.04429
## Residual 2.026258 1.42347
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.790e-01 2.121e-02 3.630e+01 32.015 < 2e-16 ***
## index_NEexp.c 6.008e-03 1.183e-02 3.165e+02 0.508 0.611776
## index_PEexp.c 2.397e-02 1.022e-02 6.986e+02 2.345 0.019298 *
## pIndD 3.124e-01 2.034e-02 3.649e+04 15.357 < 2e-16 ***
## pIndR -6.377e-01 2.117e-02 3.651e+04 -30.118 < 2e-16 ***
## index_NEexp.c:pIndD 2.237e-03 1.203e-02 3.510e+04 0.186 0.852510
## index_NEexp.c:pIndR 4.870e-03 1.357e-02 3.648e+04 0.359 0.719665
## index_PEexp.c:pIndD -2.917e-02 1.089e-02 3.564e+04 -2.679 0.007391 **
## index_PEexp.c:pIndR 3.983e-02 1.195e-02 3.632e+04 3.334 0.000857 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pIndD pIndR i_NE.:ID i_NE.:IR i_PE.:ID
## indx_NExp.c 0.001
## indx_PExp.c 0.040 -0.841
## pIndD -0.664 -0.004 -0.042
## pIndR -0.637 0.002 -0.041 0.664
## indx_NE.:ID 0.000 -0.810 0.687 0.002 0.000
## indx_NE.:IR 0.000 -0.680 0.578 0.000 0.005 0.679
## indx_PE.:ID -0.037 0.658 -0.824 0.012 0.037 -0.817 -0.550
## indx_PE.:IR -0.034 0.569 -0.726 0.036 0.068 -0.566 -0.824 0.685C. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157169.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.70925 0.05774 0.88666 1.57607
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01542 0.1242
## Residual 4.33972 2.0832
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.861e-01 3.770e-02 1.006e+01 12.894 1.39e-07 ***
## index_NEexp.c 2.259e-02 1.245e-02 2.203e+02 1.814 0.071 .
## index_PEexp.c 7.047e-02 9.623e-03 3.327e+02 7.323 1.84e-12 ***
## pDem_Rep -8.684e-01 2.494e-02 3.645e+04 -34.817 < 2e-16 ***
## pInd_Not 5.782e-01 2.772e-02 3.647e+04 20.860 < 2e-16 ***
## index_NEexp.c:pDem_Rep 9.490e-03 1.524e-02 3.116e+04 0.623 0.533
## index_NEexp.c:pInd_Not 2.264e-03 1.717e-02 3.647e+04 0.132 0.895
## index_PEexp.c:pDem_Rep 7.347e-02 1.340e-02 3.273e+04 5.484 4.18e-08 ***
## index_PEexp.c:pInd_Not -1.267e-02 1.535e-02 3.647e+04 -0.825 0.409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDm_Rp pInd_N i_NE.:D i_NE.:I i_PE.:D
## indx_NExp.c 0.002
## indx_PExp.c 0.007 -0.913
## pDem_Rep 0.027 0.022 0.036
## pInd_Not -0.095 -0.001 -0.019 0.054
## ind_NE.:D_R 0.001 0.272 -0.240 0.013 0.002
## ind_NE.:I_N 0.001 -0.208 0.199 0.002 0.002 0.160
## ind_PE.:D_R 0.021 -0.212 0.222 0.015 0.043 -0.826 -0.123
## ind_PE.:I_N -0.009 0.176 -0.247 0.040 0.047 -0.120 -0.820 0.125## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157169.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.70925 0.05774 0.88666 1.57607
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01542 0.1242
## Residual 4.33972 2.0832
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.111e+00 3.946e-02 1.208e+01 28.155 2.18e-12 ***
## index_NEexp.c 1.859e-02 1.227e-02 3.503e+02 1.515 0.13062
## index_PEexp.c 2.956e-02 1.009e-02 6.791e+02 2.928 0.00352 **
## pDemR -8.684e-01 2.494e-02 3.645e+04 -34.817 < 2e-16 ***
## pDemI -1.012e+00 2.978e-02 3.647e+04 -33.997 < 2e-16 ***
## index_NEexp.c:pDemR 9.490e-03 1.524e-02 3.116e+04 0.623 0.53339
## index_NEexp.c:pDemI 2.481e-03 1.764e-02 3.593e+04 0.141 0.88814
## index_PEexp.c:pDemR 7.347e-02 1.340e-02 3.273e+04 5.484 4.18e-08 ***
## index_PEexp.c:pDemI 4.940e-02 1.596e-02 3.621e+04 3.095 0.00197 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDemR pDemI i_NE.:DR i_NE.:DI i_PE.:DR
## indx_NExp.c -0.004
## indx_PExp.c -0.031 -0.877
## pDemR -0.277 0.016 0.045
## pDemI -0.232 0.008 0.041 0.369
## indx_NE.:DR -0.002 -0.271 0.260 0.013 0.004
## indx_NE.:DI -0.001 -0.265 0.249 0.003 0.002 0.276
## indx_PE.:DR 0.025 0.242 -0.389 0.015 -0.034 -0.826 -0.237
## indx_PE.:DI 0.020 0.222 -0.339 -0.032 0.011 -0.231 -0.817 0.3002. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157169.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.70925 0.05774 0.88666 1.57607
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01542 0.1242
## Residual 4.33972 2.0832
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.427e-01 4.041e-02 1.329e+01 6.006 4.02e-05 ***
## index_NEexp.c 2.808e-02 1.677e-02 4.940e+02 1.674 0.0947 .
## index_PEexp.c 1.030e-01 1.327e-02 8.194e+02 7.762 2.48e-14 ***
## pRepD 8.684e-01 2.494e-02 3.645e+04 34.817 < 2e-16 ***
## pRepI -1.440e-01 3.100e-02 3.647e+04 -4.646 3.39e-06 ***
## index_NEexp.c:pRepD -9.490e-03 1.524e-02 3.116e+04 -0.623 0.5334
## index_NEexp.c:pRepI -7.009e-03 1.987e-02 3.647e+04 -0.353 0.7242
## index_PEexp.c:pRepD -7.347e-02 1.340e-02 3.273e+04 -5.484 4.18e-08 ***
## index_PEexp.c:pRepI -2.407e-02 1.750e-02 3.646e+04 -1.376 0.1689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pRepD pRepI i_NE.:RD i_NE.:RI i_PE.:RD
## indx_NExp.c 0.010
## indx_PExp.c 0.032 -0.888
## pRepD -0.346 -0.023 -0.049
## pRepI -0.278 -0.010 -0.042 0.450
## indx_NE.:RD -0.006 -0.710 0.636 0.013 0.007
## indx_NE.:RI -0.004 -0.494 0.443 0.007 0.005 0.522
## indx_PE.:RD -0.034 0.574 -0.714 0.015 0.044 -0.826 -0.423
## indx_PE.:RI -0.026 0.396 -0.506 0.041 0.068 -0.422 -0.824 0.4923. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157169.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.70925 0.05774 0.88666 1.57607
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01542 0.1242
## Residual 4.33972 2.0832
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.868e-02 4.357e-02 1.796e+01 2.265 0.03613 *
## index_NEexp.c 2.107e-02 1.863e-02 9.817e+02 1.131 0.25826
## index_PEexp.c 7.896e-02 1.572e-02 2.010e+03 5.022 5.58e-07 ***
## pIndD 1.012e+00 2.978e-02 3.647e+04 33.997 < 2e-16 ***
## pIndR 1.440e-01 3.100e-02 3.647e+04 4.646 3.39e-06 ***
## index_NEexp.c:pIndD -2.481e-03 1.764e-02 3.593e+04 -0.141 0.88814
## index_NEexp.c:pIndR 7.009e-03 1.987e-02 3.647e+04 0.353 0.72425
## index_PEexp.c:pIndD -4.940e-02 1.596e-02 3.621e+04 -3.095 0.00197 **
## index_PEexp.c:pIndR 2.407e-02 1.750e-02 3.646e+04 1.376 0.16891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pIndD pIndR i_NE.:ID i_NE.:IR i_PE.:ID
## indx_NExp.c 0.001
## indx_PExp.c 0.027 -0.856
## pIndD -0.473 -0.007 -0.038
## pIndR -0.454 0.004 -0.040 0.664
## indx_NE.:ID 0.000 -0.772 0.670 0.002 0.000
## indx_NE.:IR 0.000 -0.622 0.542 -0.001 0.005 0.676
## indx_PE.:ID -0.026 0.627 -0.797 0.011 0.037 -0.817 -0.548
## indx_PE.:IR -0.024 0.522 -0.686 0.036 0.068 -0.564 -0.824 0.6837. AFFECT VS. ANALYTICAL THINKING
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 134721.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90529 -0.78375 -0.03965 0.65955 2.34437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004775 0.0691
## Residual 2.338271 1.5291
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.500e+00 2.172e-02 7.648e+00 207.179 1.28e-15 ***
## index_AFexp.c 1.886e-02 4.324e-03 1.036e+02 4.361 3.06e-05 ***
## index_ANexp.c 1.691e-03 2.266e-04 1.427e+02 7.466 7.51e-12 ***
## pDem_Rep -1.364e+00 1.837e-02 3.650e+04 -74.225 < 2e-16 ***
## pInd_Not 1.434e-01 2.038e-02 3.649e+04 7.038 1.98e-12 ***
## index_AFexp.c:pDem_Rep 2.772e-03 5.646e-03 3.485e+04 0.491 0.623
## index_AFexp.c:pInd_Not -6.260e-03 6.649e-03 3.645e+04 -0.941 0.347
## index_ANexp.c:pDem_Rep 3.146e-03 3.274e-04 3.583e+04 9.609 < 2e-16 ***
## index_ANexp.c:pInd_Not -5.862e-04 3.821e-04 3.647e+04 -1.534 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.001
## indx_ANxp.c 0.011 -0.926
## pDem_Rep 0.036 -0.001 0.057
## pInd_Not -0.119 -0.014 -0.003 0.057
## ind_AF.:D_R -0.001 0.169 -0.162 -0.007 -0.002
## ind_AF.:I_N -0.004 -0.265 0.243 -0.003 0.010 0.109
## ind_AN.:D_R 0.032 -0.148 0.180 0.040 0.052 -0.843 -0.093
## ind_AN.:I_N -0.005 0.226 -0.277 0.049 0.040 -0.094 -0.847 0.1161. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 134721.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90529 -0.78375 -0.03965 0.65955 2.34437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004775 0.0691
## Residual 2.338271 1.5291
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.229e+00 2.335e-02 1.022e+01 223.932 < 2e-16 ***
## index_AFexp.c 1.541e-02 4.580e-03 1.859e+02 3.364 0.000934 ***
## index_ANexp.c -7.497e-05 2.451e-04 2.892e+02 -0.306 0.759935
## pDemR -1.364e+00 1.837e-02 3.650e+04 -74.225 < 2e-16 ***
## pDemI -8.252e-01 2.187e-02 3.650e+04 -37.730 < 2e-16 ***
## index_AFexp.c:pDemR 2.772e-03 5.646e-03 3.485e+04 0.491 0.623410
## index_AFexp.c:pDemI 7.646e-03 6.934e-03 3.587e+04 1.103 0.270205
## index_ANexp.c:pDemR 3.146e-03 3.274e-04 3.583e+04 9.609 < 2e-16 ***
## index_ANexp.c:pDemI 2.159e-03 3.978e-04 3.602e+04 5.428 5.75e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.003
## indx_ANxp.c -0.039 -0.901
## pDemR -0.344 0.002 0.051
## pDemI -0.289 0.008 0.038 0.367
## indx_AF.:DR 0.001 -0.405 0.365 -0.007 -0.001
## indx_AF.:DI 0.000 -0.320 0.291 0.000 0.010 0.302
## indx_AN.:DR 0.030 0.335 -0.442 0.040 -0.032 -0.843 -0.254
## indx_AN.:DI 0.024 0.267 -0.356 -0.031 0.003 -0.256 -0.848 0.3002. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 134721.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90529 -0.78375 -0.03965 0.65955 2.34437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004775 0.0691
## Residual 2.338271 1.5291
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.866e+00 2.425e-02 1.188e+01 159.422 < 2e-16 ***
## index_AFexp.c 1.818e-02 5.650e-03 2.651e+02 3.217 0.00145 **
## index_ANexp.c 3.071e-03 3.104e-04 4.384e+02 9.893 < 2e-16 ***
## pRepD 1.364e+00 1.837e-02 3.650e+04 74.225 < 2e-16 ***
## pRepI 5.384e-01 2.282e-02 3.649e+04 23.591 < 2e-16 ***
## index_AFexp.c:pRepD -2.772e-03 5.646e-03 3.485e+04 -0.491 0.62341
## index_AFexp.c:pRepI 4.874e-03 7.502e-03 3.650e+04 0.650 0.51594
## index_ANexp.c:pRepD -3.146e-03 3.274e-04 3.583e+04 -9.609 < 2e-16 ***
## index_ANexp.c:pRepI -9.868e-04 4.328e-04 3.649e+04 -2.280 0.02262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c -0.006
## indx_ANxp.c 0.063 -0.890
## pRepD -0.427 0.006 -0.082
## pRepI -0.343 0.010 -0.070 0.453
## indx_AF.:RD 0.005 -0.671 0.601 -0.007 -0.005
## indx_AF.:RI 0.003 -0.465 0.417 -0.005 0.005 0.473
## indx_AN.:RD -0.059 0.570 -0.706 0.040 0.063 -0.843 -0.400
## indx_AN.:RI -0.044 0.395 -0.501 0.058 0.072 -0.402 -0.845 0.4813. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 134721.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90529 -0.78375 -0.03965 0.65955 2.34437
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004775 0.0691
## Residual 2.338271 1.5291
## Number of obs: 36508, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.404e+00 2.700e-02 1.825e+01 163.137 < 2e-16 ***
## index_AFexp.c 2.305e-02 6.982e-03 5.620e+02 3.302 0.00102 **
## index_ANexp.c 2.084e-03 3.859e-04 9.252e+02 5.400 8.46e-08 ***
## pIndD 8.252e-01 2.187e-02 3.650e+04 37.730 < 2e-16 ***
## pIndR -5.384e-01 2.282e-02 3.649e+04 -23.591 < 2e-16 ***
## index_AFexp.c:pIndD -7.646e-03 6.934e-03 3.587e+04 -1.103 0.27020
## index_AFexp.c:pIndR -4.874e-03 7.502e-03 3.650e+04 -0.650 0.51594
## index_ANexp.c:pIndD -2.159e-03 3.978e-04 3.602e+04 -5.428 5.75e-08 ***
## index_ANexp.c:pIndR 9.868e-04 4.328e-04 3.649e+04 2.280 0.02262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.011
## indx_ANxp.c 0.022 -0.879
## pIndD -0.560 -0.015 -0.027
## pIndR -0.537 -0.014 -0.025 0.663
## indx_AF.:ID -0.008 -0.783 0.689 0.010 0.010
## indx_AF.:IR -0.008 -0.698 0.613 0.010 0.005 0.697
## indx_AN.:ID -0.023 0.667 -0.805 0.003 0.027 -0.848 -0.591
## indx_AN.:IR -0.022 0.589 -0.718 0.027 0.072 -0.588 -0.845 0.692B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 127508.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.6243 0.0937 0.7322 1.5384
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 3.754e-15 6.127e-08
## Residual 1.938e+00 1.392e+00
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.115e+00 7.838e-03 3.640e+04 652.509 < 2e-16 ***
## index_AFexp.c 4.098e-03 2.496e-03 3.640e+04 1.642 0.1006
## index_ANexp.c 2.988e-04 1.441e-04 3.640e+04 2.073 0.0382 *
## pDem_Rep -4.499e-01 1.672e-02 3.640e+04 -26.908 < 2e-16 ***
## pInd_Not 7.789e-02 1.860e-02 3.640e+04 4.188 2.83e-05 ***
## index_AFexp.c:pDem_Rep -3.412e-04 5.110e-03 3.640e+04 -0.067 0.9468
## index_AFexp.c:pInd_Not -2.591e-03 6.069e-03 3.640e+04 -0.427 0.6694
## index_ANexp.c:pDem_Rep 3.130e-04 2.972e-04 3.640e+04 1.053 0.2923
## index_ANexp.c:pInd_Not -1.251e-04 3.489e-04 3.640e+04 -0.358 0.7200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.003
## indx_ANxp.c 0.041 -0.846
## pDem_Rep 0.090 -0.004 0.079
## pInd_Not -0.304 -0.011 -0.015 0.056
## ind_AF.:D_R -0.004 0.180 -0.156 -0.007 -0.003
## ind_AF.:I_N -0.010 -0.371 0.311 -0.003 0.009 0.110
## ind_AN.:D_R 0.082 -0.155 0.191 0.040 0.051 -0.845 -0.095
## ind_AN.:I_N -0.014 0.312 -0.360 0.049 0.041 -0.096 -0.848 0.118
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 127508.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.6243 0.0937 0.7322 1.5384
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 3.754e-15 6.127e-08
## Residual 1.938e+00 1.392e+00
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.365e+00 1.106e-02 3.640e+04 485.218 <2e-16 ***
## index_AFexp.c 3.414e-03 3.104e-03 3.640e+04 1.100 0.271
## index_ANexp.c 1.010e-04 1.788e-04 3.640e+04 0.565 0.572
## pDemR -4.499e-01 1.672e-02 3.640e+04 -26.908 <2e-16 ***
## pDemI -3.028e-01 1.996e-02 3.640e+04 -15.173 <2e-16 ***
## index_AFexp.c:pDemR -3.412e-04 5.110e-03 3.640e+04 -0.067 0.947
## index_AFexp.c:pDemI 2.420e-03 6.320e-03 3.640e+04 0.383 0.702
## index_ANexp.c:pDemR 3.130e-04 2.972e-04 3.640e+04 1.053 0.292
## index_ANexp.c:pDemI 2.816e-04 3.628e-04 3.640e+04 0.776 0.438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.001
## indx_ANxp.c -0.094 -0.847
## pDemR -0.661 0.001 0.062
## pDemI -0.554 0.000 0.052 0.366
## indx_AF.:DR 0.000 -0.607 0.514 -0.007 0.000
## indx_AF.:DI 0.000 -0.491 0.416 0.000 0.009 0.298
## indx_AN.:DR 0.056 0.509 -0.601 0.040 -0.031 -0.845 -0.250
## indx_AN.:DI 0.046 0.417 -0.493 -0.031 0.005 -0.253 -0.848 0.296
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 127508.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.6243 0.0937 0.7322 1.5384
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 3.754e-15 6.127e-08
## Residual 1.938e+00 1.392e+00
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.915e+00 1.254e-02 3.640e+04 391.926 < 2e-16 ***
## index_AFexp.c 3.073e-03 4.059e-03 3.640e+04 0.757 0.4491
## index_ANexp.c 4.140e-04 2.375e-04 3.640e+04 1.744 0.0812 .
## pRepD 4.499e-01 1.672e-02 3.640e+04 26.908 < 2e-16 ***
## pRepI 1.471e-01 2.082e-02 3.640e+04 7.064 1.65e-12 ***
## index_AFexp.c:pRepD 3.412e-04 5.110e-03 3.640e+04 0.067 0.9468
## index_AFexp.c:pRepI 2.762e-03 6.840e-03 3.640e+04 0.404 0.6864
## index_ANexp.c:pRepD -3.130e-04 2.972e-04 3.640e+04 -1.053 0.2923
## index_ANexp.c:pRepI -3.145e-05 3.950e-04 3.640e+04 -0.080 0.9365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c -0.011
## indx_ANxp.c 0.128 -0.843
## pRepD -0.750 0.008 -0.096
## pRepI -0.602 0.007 -0.077 0.452
## indx_AF.:RD 0.009 -0.794 0.670 -0.007 -0.005
## indx_AF.:RI 0.007 -0.593 0.500 -0.005 0.004 0.471
## indx_AN.:RD -0.102 0.674 -0.799 0.040 0.062 -0.845 -0.400
## indx_AN.:RI -0.077 0.507 -0.601 0.058 0.073 -0.403 -0.846 0.480
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 127508.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.6243 0.0937 0.7322 1.5384
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.938 1.392
## Number of obs: 36412, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.062e+00 1.662e-02 3.640e+04 304.669 < 2e-16 ***
## index_AFexp.c 5.834e-03 5.505e-03 3.640e+04 1.060 0.289
## index_ANexp.c 3.826e-04 3.157e-04 3.640e+04 1.212 0.226
## pIndD 3.028e-01 1.996e-02 3.640e+04 15.173 < 2e-16 ***
## pIndR -1.471e-01 2.082e-02 3.640e+04 -7.064 1.65e-12 ***
## index_AFexp.c:pIndD -2.420e-03 6.320e-03 3.640e+04 -0.383 0.702
## index_AFexp.c:pIndR -2.762e-03 6.840e-03 3.640e+04 -0.404 0.686
## index_ANexp.c:pIndD -2.816e-04 3.628e-04 3.640e+04 -0.776 0.438
## index_ANexp.c:pIndR 3.145e-05 3.950e-04 3.640e+04 0.080 0.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.013
## indx_ANxp.c 0.042 -0.848
## pIndD -0.833 -0.011 -0.035
## pIndR -0.798 -0.010 -0.033 0.664
## indx_AF.:ID -0.011 -0.871 0.739 0.009 0.009
## indx_AF.:IR -0.010 -0.805 0.683 0.009 0.004 0.701
## indx_AN.:ID -0.036 0.738 -0.870 0.005 0.029 -0.848 -0.594
## indx_AN.:IR -0.033 0.678 -0.799 0.028 0.073 -0.591 -0.846 0.695
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 148327.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8929 -0.8745 -0.1827 0.7574 2.3879
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00357 0.05975
## Residual 3.45018 1.85746
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.302e+00 2.017e-02 1.129e+01 163.717 < 2e-16 ***
## index_AFexp.c -4.287e-03 4.811e-03 7.314e+01 -0.891 0.376
## index_ANexp.c 3.123e-03 2.557e-04 1.062e+02 12.213 < 2e-16 ***
## pDem_Rep -3.850e-01 2.235e-02 3.635e+04 -17.223 < 2e-16 ***
## pInd_Not -4.635e-01 2.477e-02 3.635e+04 -18.710 < 2e-16 ***
## index_AFexp.c:pDem_Rep -6.334e-03 6.863e-03 3.414e+04 -0.923 0.356
## index_AFexp.c:pInd_Not -4.849e-04 8.081e-03 3.630e+04 -0.060 0.952
## index_ANexp.c:pDem_Rep 1.633e-03 3.983e-04 3.554e+04 4.100 4.15e-05 ***
## index_ANexp.c:pInd_Not 3.061e-04 4.643e-04 3.632e+04 0.659 0.510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.002
## indx_ANxp.c 0.015 -0.914
## pDem_Rep 0.046 -0.002 0.061
## pInd_Not -0.155 -0.014 -0.005 0.056
## ind_AF.:D_R -0.002 0.166 -0.158 -0.008 -0.002
## ind_AF.:I_N -0.006 -0.279 0.253 -0.003 0.010 0.109
## ind_AN.:D_R 0.042 -0.145 0.178 0.040 0.052 -0.843 -0.093
## ind_AN.:I_N -0.007 0.237 -0.288 0.049 0.039 -0.094 -0.847 0.1161. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 148327.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8929 -0.8745 -0.1827 0.7574 2.3879
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00357 0.05975
## Residual 3.45018 1.85746
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.341e+00 2.272e-02 1.818e+01 147.065 < 2e-16 ***
## index_AFexp.c -1.280e-03 5.234e-03 1.451e+02 -0.245 0.807
## index_ANexp.c 2.408e-03 2.840e-04 2.377e+02 8.477 2.46e-15 ***
## pDemR -3.850e-01 2.235e-02 3.635e+04 -17.223 < 2e-16 ***
## pDemI 2.711e-01 2.661e-02 3.635e+04 10.189 < 2e-16 ***
## index_AFexp.c:pDemR -6.334e-03 6.863e-03 3.414e+04 -0.923 0.356
## index_AFexp.c:pDemI -2.682e-03 8.429e-03 3.570e+04 -0.318 0.750
## index_ANexp.c:pDemR 1.633e-03 3.983e-04 3.554e+04 4.100 4.15e-05 ***
## index_ANexp.c:pDemI 5.103e-04 4.836e-04 3.585e+04 1.055 0.291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.003
## indx_ANxp.c -0.051 -0.890
## pDemR -0.431 0.002 0.053
## pDemI -0.362 0.006 0.041 0.368
## indx_AF.:DR 0.001 -0.447 0.398 -0.008 -0.001
## indx_AF.:DI 0.000 -0.356 0.320 0.000 0.010 0.303
## indx_AN.:DR 0.037 0.372 -0.478 0.040 -0.032 -0.843 -0.254
## indx_AN.:DI 0.030 0.300 -0.388 -0.031 0.002 -0.257 -0.848 0.3012. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 148327.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8929 -0.8745 -0.1827 0.7574 2.3879
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00357 0.05975
## Residual 3.45018 1.85746
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.956e+00 2.405e-02 2.282e+01 122.949 < 2e-16 ***
## index_AFexp.c -7.614e-03 6.508e-03 2.148e+02 -1.170 0.2433
## index_ANexp.c 4.040e-03 3.621e-04 3.729e+02 11.157 < 2e-16 ***
## pRepD 3.850e-01 2.235e-02 3.635e+04 17.223 < 2e-16 ***
## pRepI 6.560e-01 2.774e-02 3.635e+04 23.648 < 2e-16 ***
## index_AFexp.c:pRepD 6.334e-03 6.863e-03 3.414e+04 0.923 0.3561
## index_AFexp.c:pRepI 3.652e-03 9.116e-03 3.634e+04 0.401 0.6887
## index_ANexp.c:pRepD -1.633e-03 3.983e-04 3.554e+04 -4.100 4.15e-05 ***
## index_ANexp.c:pRepI -1.122e-03 5.260e-04 3.635e+04 -2.134 0.0329 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c -0.007
## indx_ANxp.c 0.080 -0.881
## pRepD -0.522 0.007 -0.085
## pRepI -0.421 0.009 -0.072 0.453
## indx_AF.:RD 0.006 -0.695 0.615 -0.008 -0.005
## indx_AF.:RI 0.004 -0.493 0.436 -0.005 0.005 0.473
## indx_AN.:RD -0.072 0.590 -0.725 0.040 0.062 -0.843 -0.400
## indx_AN.:RI -0.054 0.419 -0.524 0.058 0.072 -0.402 -0.846 0.4813. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) + (1 |
## media)
## Data: dl1
##
## REML criterion at convergence: 148327.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8929 -0.8745 -0.1827 0.7574 2.3879
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00357 0.05975
## Residual 3.45018 1.85746
## Number of obs: 36364, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.612e+00 2.804e-02 4.220e+01 128.821 < 2e-16 ***
## index_AFexp.c -3.962e-03 8.185e-03 4.900e+02 -0.484 0.6285
## index_ANexp.c 2.918e-03 4.562e-04 8.306e+02 6.397 2.64e-10 ***
## pIndD -2.711e-01 2.661e-02 3.635e+04 -10.189 < 2e-16 ***
## pIndR -6.560e-01 2.774e-02 3.635e+04 -23.648 < 2e-16 ***
## index_AFexp.c:pIndD 2.682e-03 8.429e-03 3.570e+04 0.318 0.7504
## index_AFexp.c:pIndR -3.652e-03 9.116e-03 3.634e+04 -0.401 0.6887
## index_ANexp.c:pIndD -5.103e-04 4.836e-04 3.585e+04 -1.055 0.2914
## index_ANexp.c:pIndR 1.122e-03 5.260e-04 3.635e+04 2.134 0.0329 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.012
## indx_ANxp.c 0.027 -0.872
## pIndD -0.655 -0.014 -0.028
## pIndR -0.628 -0.013 -0.026 0.662
## indx_AF.:ID -0.010 -0.802 0.700 0.010 0.010
## indx_AF.:IR -0.009 -0.722 0.629 0.010 0.005 0.697
## indx_AN.:ID -0.027 0.682 -0.819 0.002 0.027 -0.848 -0.591
## indx_AN.:IR -0.025 0.609 -0.737 0.026 0.072 -0.587 -0.846 0.692D. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116649.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3233 -0.6733 -0.0241 0.6714 2.7950
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001908 0.04368
## Residual 1.457282 1.20718
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.302e+00 1.433e-02 9.123e+00 300.219 < 2e-16 ***
## index_AFexp.c 7.581e-03 3.237e-03 7.413e+01 2.342 0.0219 *
## index_ANexp.c 1.644e-03 1.711e-04 1.055e+02 9.608 4.45e-16 ***
## pDem_Rep -7.314e-01 1.454e-02 3.625e+04 -50.297 < 2e-16 ***
## pInd_Not -7.270e-02 1.615e-02 3.624e+04 -4.502 6.77e-06 ***
## index_AFexp.c:pDem_Rep -1.232e-03 4.468e-03 3.406e+04 -0.276 0.7827
## index_AFexp.c:pInd_Not -3.533e-03 5.272e-03 3.619e+04 -0.670 0.5027
## index_ANexp.c:pDem_Rep 1.693e-03 2.592e-04 3.542e+04 6.531 6.61e-11 ***
## index_ANexp.c:pInd_Not -1.193e-04 3.030e-04 3.620e+04 -0.394 0.6938
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.001
## indx_ANxp.c 0.014 -0.918
## pDem_Rep 0.042 -0.002 0.059
## pInd_Not -0.144 -0.014 -0.005 0.056
## ind_AF.:D_R -0.002 0.166 -0.158 -0.008 -0.002
## ind_AF.:I_N -0.005 -0.275 0.251 -0.003 0.010 0.108
## ind_AN.:D_R 0.039 -0.145 0.178 0.040 0.052 -0.843 -0.092
## ind_AN.:I_N -0.007 0.234 -0.286 0.049 0.040 -0.093 -0.848 0.115## Warning: Removed 3 row(s) containing missing values (geom_path).
## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116649.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3233 -0.6733 -0.0241 0.6714 2.7950
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001908 0.04368
## Residual 1.457282 1.20718
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.644e+00 1.586e-02 1.369e+01 292.779 < 2e-16 ***
## index_AFexp.c 7.031e-03 3.486e-03 1.415e+02 2.017 0.04560 *
## index_ANexp.c 7.587e-04 1.882e-04 2.276e+02 4.031 7.59e-05 ***
## pDemR -7.314e-01 1.454e-02 3.625e+04 -50.297 < 2e-16 ***
## pDemI -2.930e-01 1.734e-02 3.625e+04 -16.897 < 2e-16 ***
## index_AFexp.c:pDemR -1.232e-03 4.468e-03 3.406e+04 -0.276 0.78268
## index_AFexp.c:pDemI 2.917e-03 5.499e-03 3.553e+04 0.530 0.59578
## index_ANexp.c:pDemR 1.693e-03 2.592e-04 3.542e+04 6.531 6.61e-11 ***
## index_ANexp.c:pDemI 9.656e-04 3.156e-04 3.570e+04 3.060 0.00222 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.003
## indx_ANxp.c -0.046 -0.894
## pDemR -0.402 0.002 0.052
## pDemI -0.337 0.007 0.039 0.368
## indx_AF.:DR 0.001 -0.433 0.387 -0.008 -0.001
## indx_AF.:DI 0.000 -0.343 0.309 0.000 0.010 0.303
## indx_AN.:DR 0.034 0.359 -0.466 0.040 -0.032 -0.843 -0.254
## indx_AN.:DI 0.028 0.287 -0.376 -0.030 0.004 -0.257 -0.848 0.3002. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116649.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3233 -0.6733 -0.0241 0.6714 2.7950
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001908 0.04368
## Residual 1.457282 1.20718
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.912e+00 1.667e-02 1.669e+01 234.738 < 2e-16 ***
## index_AFexp.c 5.799e-03 4.317e-03 2.061e+02 1.343 0.181
## index_ANexp.c 2.451e-03 2.391e-04 3.515e+02 10.253 < 2e-16 ***
## pRepD 7.314e-01 1.454e-02 3.625e+04 50.297 < 2e-16 ***
## pRepI 4.384e-01 1.808e-02 3.624e+04 24.252 < 2e-16 ***
## index_AFexp.c:pRepD 1.232e-03 4.468e-03 3.406e+04 0.276 0.783
## index_AFexp.c:pRepI 4.149e-03 5.943e-03 3.624e+04 0.698 0.485
## index_ANexp.c:pRepD -1.693e-03 2.592e-04 3.542e+04 -6.531 6.61e-11 ***
## index_ANexp.c:pRepI -7.270e-04 3.430e-04 3.624e+04 -2.120 0.034 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c -0.007
## indx_ANxp.c 0.074 -0.884
## pRepD -0.490 0.006 -0.084
## pRepI -0.394 0.009 -0.071 0.452
## indx_AF.:RD 0.005 -0.686 0.609 -0.008 -0.005
## indx_AF.:RI 0.004 -0.482 0.429 -0.005 0.005 0.472
## indx_AN.:RD -0.067 0.582 -0.717 0.040 0.062 -0.843 -0.399
## indx_AN.:RI -0.050 0.409 -0.515 0.058 0.073 -0.401 -0.846 0.4793. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 116649.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3233 -0.6733 -0.0241 0.6714 2.7950
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001908 0.04368
## Residual 1.457282 1.20718
## Number of obs: 36256, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.351e+00 1.916e-02 2.913e+01 227.116 < 2e-16 ***
## index_AFexp.c 9.948e-03 5.408e-03 4.617e+02 1.839 0.06649 .
## index_ANexp.c 1.724e-03 3.006e-04 7.759e+02 5.735 1.39e-08 ***
## pIndD 2.930e-01 1.734e-02 3.625e+04 16.897 < 2e-16 ***
## pIndR -4.384e-01 1.808e-02 3.624e+04 -24.252 < 2e-16 ***
## index_AFexp.c:pIndD -2.917e-03 5.499e-03 3.553e+04 -0.530 0.59578
## index_AFexp.c:pIndR -4.149e-03 5.943e-03 3.624e+04 -0.698 0.48509
## index_ANexp.c:pIndD -9.656e-04 3.156e-04 3.570e+04 -3.060 0.00222 **
## index_ANexp.c:pIndR 7.270e-04 3.430e-04 3.624e+04 2.120 0.03403 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.011
## indx_ANxp.c 0.026 -0.875
## pIndD -0.626 -0.014 -0.028
## pIndR -0.601 -0.013 -0.027 0.664
## indx_AF.:ID -0.009 -0.796 0.697 0.010 0.009
## indx_AF.:IR -0.008 -0.715 0.624 0.010 0.005 0.698
## indx_AN.:ID -0.027 0.677 -0.814 0.004 0.028 -0.848 -0.592
## indx_AN.:IR -0.025 0.603 -0.732 0.027 0.073 -0.589 -0.846 0.693E. worst of Covid is behind/ahead
worstBorA ~ indexes * party
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129445.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82407 -0.69239 0.01057 0.71418 2.26181
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.751e-01 7.991e-03 3.651e+04 71.974 < 2e-16 ***
## index_ANexp.c 9.218e-04 1.468e-04 3.651e+04 6.280 3.42e-10 ***
## index_AFexp.c 2.539e-03 2.543e-03 3.651e+04 0.998 0.318
## pDem_Rep -9.417e-01 1.707e-02 3.651e+04 -55.169 < 2e-16 ***
## pInd_Not -1.543e-01 1.895e-02 3.651e+04 -8.146 3.89e-16 ***
## index_ANexp.c:pDem_Rep 2.815e-03 3.033e-04 3.651e+04 9.281 < 2e-16 ***
## index_ANexp.c:pInd_Not 4.728e-04 3.550e-04 3.651e+04 1.332 0.183
## index_AFexp.c:pDem_Rep -3.509e-03 5.215e-03 3.651e+04 -0.673 0.501
## index_AFexp.c:pInd_Not -1.680e-03 6.177e-03 3.651e+04 -0.272 0.786
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDm_Rp pInd_N i_AN.:D i_AN.:I i_AF.:D
## indx_ANxp.c 0.040
## indx_AFxp.c 0.004 -0.846
## pDem_Rep 0.090 0.079 -0.004
## pInd_Not -0.301 -0.014 -0.011 0.057
## ind_AN.:D_R 0.082 0.193 -0.156 0.039 0.052
## ind_AN.:I_N -0.013 -0.357 0.309 0.049 0.040 0.119
## ind_AF.:D_R -0.004 -0.158 0.181 -0.007 -0.003 -0.845 -0.097
## ind_AF.:I_N -0.011 0.308 -0.369 -0.003 0.010 -0.096 -0.847 0.111
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular## Warning: Removed 3 row(s) containing missing values (geom_path).
## Warning: Removed 12 row(s) containing missing values (geom_path).
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129445.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82407 -0.69239 0.01057 0.71418 2.26181
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.950e-01 1.128e-02 3.651e+04 88.191 <2e-16 ***
## index_ANexp.c -3.297e-04 1.821e-04 3.651e+04 -1.810 0.0703 .
## index_AFexp.c 3.739e-03 3.163e-03 3.651e+04 1.182 0.2372
## pDemR -9.417e-01 1.707e-02 3.651e+04 -55.169 <2e-16 ***
## pDemI -3.165e-01 2.033e-02 3.651e+04 -15.567 <2e-16 ***
## index_ANexp.c:pDemR 2.815e-03 3.033e-04 3.651e+04 9.281 <2e-16 ***
## index_ANexp.c:pDemI 9.348e-04 3.691e-04 3.651e+04 2.533 0.0113 *
## index_AFexp.c:pDemR -3.509e-03 5.215e-03 3.651e+04 -0.673 0.5010
## index_AFexp.c:pDemI -7.438e-05 6.432e-03 3.651e+04 -0.012 0.9908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDemR pDemI i_AN.:DR i_AN.:DI i_AF.:DR
## indx_ANxp.c -0.095
## indx_AFxp.c -0.001 -0.847
## pDemR -0.661 0.063 0.001
## pDemI -0.555 0.053 0.000 0.367
## indx_AN.:DR 0.057 -0.601 0.509 0.039 -0.032
## indx_AN.:DI 0.047 -0.494 0.418 -0.031 0.003 0.296
## indx_AF.:DR 0.000 0.514 -0.607 -0.007 0.000 -0.845 -0.254
## indx_AF.:DI 0.000 0.417 -0.492 0.000 0.010 -0.250 -0.848 0.298
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129445.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82407 -0.69239 0.01057 0.71418 2.26181
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.337e-02 1.281e-02 3.651e+04 4.167 3.09e-05 ***
## index_ANexp.c 2.485e-03 2.425e-04 3.651e+04 10.248 < 2e-16 ***
## index_AFexp.c 2.302e-04 4.146e-03 3.651e+04 0.056 0.956
## pRepD 9.417e-01 1.707e-02 3.651e+04 55.169 < 2e-16 ***
## pRepI 6.251e-01 2.122e-02 3.651e+04 29.465 < 2e-16 ***
## index_ANexp.c:pRepD -2.815e-03 3.033e-04 3.651e+04 -9.281 < 2e-16 ***
## index_ANexp.c:pRepI -1.880e-03 4.023e-04 3.651e+04 -4.674 2.97e-06 ***
## index_AFexp.c:pRepD 3.509e-03 5.215e-03 3.651e+04 0.673 0.501
## index_AFexp.c:pRepI 3.435e-03 6.968e-03 3.651e+04 0.493 0.622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pRepD pRepI i_AN.:RD i_AN.:RI i_AF.:RD
## indx_ANxp.c 0.128
## indx_AFxp.c -0.011 -0.843
## pRepD -0.750 -0.096 0.008
## pRepI -0.604 -0.078 0.007 0.453
## indx_AN.:RD -0.103 -0.800 0.674 0.039 0.062
## indx_AN.:RI -0.077 -0.603 0.508 0.058 0.072 0.482
## indx_AF.:RD 0.009 0.670 -0.795 -0.007 -0.005 -0.845 -0.404
## indx_AF.:RI 0.006 0.502 -0.595 -0.005 0.005 -0.401 -0.846 0.473
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 129445.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82407 -0.69239 0.01057 0.71418 2.26181
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 2.022 1.422
## Number of obs: 36520, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.785e-01 1.691e-02 3.651e+04 40.115 < 2e-16 ***
## index_ANexp.c 6.051e-04 3.210e-04 3.651e+04 1.885 0.0595 .
## index_AFexp.c 3.665e-03 5.600e-03 3.651e+04 0.654 0.5128
## pIndD 3.165e-01 2.033e-02 3.651e+04 15.567 < 2e-16 ***
## pIndR -6.251e-01 2.122e-02 3.651e+04 -29.465 < 2e-16 ***
## index_ANexp.c:pIndD -9.348e-04 3.691e-04 3.651e+04 -2.533 0.0113 *
## index_ANexp.c:pIndR 1.880e-03 4.023e-04 3.651e+04 4.674 2.97e-06 ***
## index_AFexp.c:pIndD 7.438e-05 6.432e-03 3.651e+04 0.012 0.9908
## index_AFexp.c:pIndR -3.435e-03 6.968e-03 3.651e+04 -0.493 0.6221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pIndD pIndR i_AN.:ID i_AN.:IR i_AF.:ID
## indx_ANxp.c 0.040
## indx_AFxp.c 0.014 -0.848
## pIndD -0.832 -0.033 -0.011
## pIndR -0.797 -0.032 -0.011 0.663
## indx_AN.:ID -0.035 -0.870 0.737 0.003 0.028
## indx_AN.:IR -0.032 -0.798 0.676 0.027 0.072 0.694
## indx_AF.:ID -0.012 0.738 -0.871 0.010 0.009 -0.848 -0.589
## indx_AF.:IR -0.011 0.681 -0.804 0.009 0.005 -0.593 -0.846 0.700
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularF. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157117.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17580 -0.73454 0.06145 0.88944 1.53016
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001648 0.0406
## Residual 4.332684 2.0815
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.907e-01 1.656e-02 1.129e+01 29.630 4.59e-12 ***
## index_ANexp.c 2.512e-03 2.542e-04 5.958e+01 9.880 3.63e-14 ***
## index_AFexp.c 3.091e-03 4.652e-03 3.708e+01 0.664 0.511
## pDem_Rep -8.539e-01 2.501e-02 3.645e+04 -34.147 < 2e-16 ***
## pInd_Not 5.856e-01 2.774e-02 3.647e+04 21.115 < 2e-16 ***
## index_ANexp.c:pDem_Rep 2.382e-03 4.448e-04 3.496e+04 5.355 8.62e-08 ***
## index_ANexp.c:pInd_Not 1.758e-04 5.198e-04 3.642e+04 0.338 0.735
## index_AFexp.c:pDem_Rep 8.591e-03 7.660e-03 3.169e+04 1.122 0.262
## index_AFexp.c:pInd_Not -6.863e-03 9.046e-03 3.640e+04 -0.759 0.448
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDm_Rp pInd_N i_AN.:D i_AN.:I i_AF.:D
## indx_ANxp.c 0.023
## indx_AFxp.c 0.002 -0.890
## pDem_Rep 0.064 0.068 -0.003
## pInd_Not -0.212 -0.009 -0.012 0.057
## ind_AN.:D_R 0.058 0.184 -0.148 0.039 0.052
## ind_AN.:I_N -0.009 -0.314 0.262 0.049 0.040 0.118
## ind_AF.:D_R -0.003 -0.157 0.170 -0.007 -0.002 -0.843 -0.096
## ind_AF.:I_N -0.007 0.273 -0.310 -0.003 0.010 -0.095 -0.847 0.110## Warning: Removed 3 row(s) containing missing values (geom_path).
## Warning: Removed 12 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157117.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17580 -0.73454 0.06145 0.88944 1.53016
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001648 0.0406
## Residual 4.332684 2.0815
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.111e+00 2.026e-02 2.526e+01 54.830 < 2e-16 ***
## index_ANexp.c 1.379e-03 2.938e-04 1.565e+02 4.692 5.85e-06 ***
## index_AFexp.c -3.470e-03 5.292e-03 8.800e+01 -0.656 0.5138
## pDemR -8.539e-01 2.501e-02 3.645e+04 -34.147 < 2e-16 ***
## pDemI -1.013e+00 2.977e-02 3.647e+04 -34.013 < 2e-16 ***
## index_ANexp.c:pDemR 2.382e-03 4.448e-04 3.496e+04 5.355 8.62e-08 ***
## index_ANexp.c:pDemI 1.015e-03 5.408e-04 3.579e+04 1.877 0.0605 .
## index_AFexp.c:pDemR 8.591e-03 7.660e-03 3.169e+04 1.122 0.2621
## index_AFexp.c:pDemI 1.116e-02 9.425e-03 3.559e+04 1.184 0.2365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDemR pDemI i_AN.:DR i_AN.:DI i_AF.:DR
## indx_ANxp.c -0.070
## indx_AFxp.c -0.002 -0.872
## pDemR -0.539 0.058 0.001
## pDemI -0.453 0.046 0.004 0.367
## indx_AN.:DR 0.046 -0.529 0.427 0.039 -0.032
## indx_AN.:DI 0.038 -0.432 0.347 -0.031 0.003 0.298
## indx_AF.:DR 0.001 0.446 -0.512 -0.007 -0.001 -0.843 -0.255
## indx_AF.:DI 0.000 0.360 -0.411 0.000 0.010 -0.252 -0.848 0.3002. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157117.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17580 -0.73454 0.06145 0.88944 1.53016
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001648 0.0406
## Residual 4.332684 2.0815
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.570e-01 2.212e-02 3.592e+01 11.618 1.01e-13 ***
## index_ANexp.c 3.760e-03 3.819e-04 2.645e+02 9.847 < 2e-16 ***
## index_AFexp.c 5.122e-03 6.724e-03 1.412e+02 0.762 0.4475
## pRepD 8.539e-01 2.501e-02 3.645e+04 34.147 < 2e-16 ***
## pRepI -1.587e-01 3.106e-02 3.647e+04 -5.109 3.25e-07 ***
## index_ANexp.c:pRepD -2.382e-03 4.448e-04 3.496e+04 -5.355 8.62e-08 ***
## index_ANexp.c:pRepI -1.367e-03 5.890e-04 3.647e+04 -2.320 0.0203 *
## index_AFexp.c:pRepD -8.591e-03 7.660e-03 3.169e+04 -1.122 0.2621
## index_AFexp.c:pRepI 2.567e-03 1.021e-02 3.640e+04 0.252 0.8014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pRepD pRepI i_AN.:RD i_AN.:RI i_AF.:RD
## indx_ANxp.c 0.102
## indx_AFxp.c -0.009 -0.865
## pRepD -0.637 -0.090 0.008
## pRepI -0.512 -0.074 0.008 0.453
## indx_AN.:RD -0.087 -0.758 0.625 0.039 0.062
## indx_AN.:RI -0.065 -0.558 0.456 0.058 0.072 0.482
## indx_AF.:RD 0.007 0.639 -0.737 -0.007 -0.005 -0.843 -0.403
## indx_AF.:RI 0.005 0.465 -0.536 -0.005 0.005 -0.401 -0.846 0.4733. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl1
##
## REML criterion at convergence: 157117.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17580 -0.73454 0.06145 0.88944 1.53016
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001648 0.0406
## Residual 4.332684 2.0815
## Number of obs: 36484, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.830e-02 2.739e-02 8.443e+01 3.588 0.000557 ***
## index_ANexp.c 2.394e-03 4.915e-04 6.443e+02 4.870 1.40e-06 ***
## index_AFexp.c 7.689e-03 8.710e-03 3.640e+02 0.883 0.377947
## pIndD 1.013e+00 2.977e-02 3.647e+04 34.013 < 2e-16 ***
## pIndR 1.587e-01 3.106e-02 3.647e+04 5.109 3.25e-07 ***
## index_ANexp.c:pIndD -1.015e-03 5.408e-04 3.579e+04 -1.877 0.060507 .
## index_ANexp.c:pIndR 1.367e-03 5.890e-04 3.647e+04 2.320 0.020338 *
## index_AFexp.c:pIndD -1.116e-02 9.425e-03 3.559e+04 -1.184 0.236461
## index_AFexp.c:pIndR -2.567e-03 1.021e-02 3.640e+04 -0.252 0.801386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pIndD pIndR i_AN.:ID i_AN.:IR i_AF.:ID
## indx_ANxp.c 0.034
## indx_AFxp.c 0.013 -0.861
## pIndD -0.752 -0.031 -0.013
## pIndR -0.720 -0.029 -0.012 0.663
## indx_AN.:ID -0.031 -0.842 0.707 0.003 0.028
## indx_AN.:IR -0.029 -0.765 0.639 0.027 0.072 0.693
## indx_AF.:ID -0.011 0.717 -0.833 0.010 0.010 -0.848 -0.588
## indx_AF.:IR -0.010 0.652 -0.758 0.010 0.005 -0.592 -0.846 0.698WAVE 2
1. AFFECT
A. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90310
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4218 -0.6716 -0.0450 0.6349 3.0699
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01111 0.1054
## Residual 1.33909 1.1572
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.161e+00 3.136e-02 1.094e+01 132.687 <2e-16 ***
## index_AFexp.c 4.086e-02 1.559e-03 2.175e+04 26.211 <2e-16 ***
## pDem_Rep -7.617e-01 1.533e-02 2.882e+04 -49.691 <2e-16 ***
## pInd_Not -5.442e-03 1.867e-02 2.882e+04 -0.291 0.771
## index_AFexp.c:pDem_Rep 2.448e-02 2.769e-03 2.871e+04 8.843 <2e-16 ***
## index_AFexp.c:pInd_Not 4.594e-04 3.548e-03 2.882e+04 0.129 0.897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDm_Rp pInd_N i_AF.:D
## indx_AFxp.c 0.026
## pDem_Rep 0.016 0.153
## pInd_Not -0.097 -0.096 0.037
## ind_AF.:D_R 0.032 0.170 0.050 0.081
## ind_AF.:I_N -0.022 -0.418 0.074 0.136 0.110## Warning: Removed 6 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90310
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4218 -0.6716 -0.0450 0.6349 3.0699
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01111 0.1054
## Residual 1.33909 1.1572
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.540e+00 3.212e-02 1.204e+01 141.362 <2e-16 ***
## index_AFexp.c 2.877e-02 1.761e-03 2.435e+04 16.342 <2e-16 ***
## pDemR -7.617e-01 1.533e-02 2.882e+04 -49.691 <2e-16 ***
## pDemI -3.754e-01 1.992e-02 2.882e+04 -18.847 <2e-16 ***
## index_AFexp.c:pDemR 2.448e-02 2.769e-03 2.871e+04 8.843 <2e-16 ***
## index_AFexp.c:pDemI 1.178e-02 3.663e-03 2.882e+04 3.216 0.0013 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDemR pDemI i_AF.:DR
## indx_AFxp.c -0.062
## pDemR -0.216 0.145
## pDemI -0.166 0.110 0.350
## indx_AF.:DR 0.035 -0.562 0.050 -0.056
## indx_AF.:DI 0.026 -0.414 -0.053 0.074 0.2712. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90310
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4218 -0.6716 -0.0450 0.6349 3.0699
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01111 0.1054
## Residual 1.33909 1.1572
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.778e+00 3.246e-02 1.257e+01 116.383 <2e-16 ***
## index_AFexp.c 5.325e-02 2.298e-03 2.640e+04 23.171 <2e-16 ***
## pRepD 7.617e-01 1.533e-02 2.882e+04 49.691 <2e-16 ***
## pRepI 3.863e-01 2.044e-02 2.881e+04 18.901 <2e-16 ***
## index_AFexp.c:pRepD -2.448e-02 2.769e-03 2.871e+04 -8.843 <2e-16 ***
## index_AFexp.c:pRepI -1.270e-02 3.948e-03 2.882e+04 -3.217 0.0013 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pRepD pRepI i_AF.:RD
## indx_AFxp.c 0.076
## pRepD -0.259 -0.172
## pRepI -0.193 -0.113 0.408
## indx_AF.:RD -0.058 -0.774 0.050 0.093
## indx_AF.:RI -0.040 -0.534 0.084 0.169 0.4503. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_AFexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90310
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4218 -0.6716 -0.0450 0.6349 3.0699
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01111 0.1054
## Residual 1.33909 1.1572
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.165e+00 3.487e-02 1.673e+01 119.436 <2e-16 ***
## index_AFexp.c 4.056e-02 3.343e-03 2.842e+04 12.130 <2e-16 ***
## pIndD 3.754e-01 1.992e-02 2.882e+04 18.847 <2e-16 ***
## pIndR -3.863e-01 2.044e-02 2.881e+04 -18.901 <2e-16 ***
## index_AFexp.c:pIndD -1.178e-02 3.663e-03 2.882e+04 -3.216 0.0013 **
## index_AFexp.c:pIndR 1.270e-02 3.948e-03 2.882e+04 3.217 0.0013 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pIndD pIndR i_AF.:ID
## indx_AFxp.c 0.075
## pIndD -0.418 -0.139
## pIndR -0.407 -0.122 0.712
## indx_AF.:ID -0.066 -0.878 0.074 0.112
## indx_AF.:IR -0.061 -0.814 0.108 0.169 0.738B. worst of Covid is behind/ahead
worstBorA ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105986.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0224 -0.6382 0.0446 0.7295 2.0855
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008032 0.08962
## Residual 2.297014 1.51559
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.641e-01 2.772e-02 1.089e+01 27.566 2e-11 ***
## index_AFexp.c 3.591e-02 2.028e-03 1.085e+04 17.710 < 2e-16 ***
## pDem_Rep -1.180e+00 2.006e-02 2.881e+04 -58.818 < 2e-16 ***
## pInd_Not -8.037e-02 2.445e-02 2.885e+04 -3.287 0.00101 **
## index_AFexp.c:pDem_Rep 4.320e-02 3.621e-03 2.809e+04 11.930 < 2e-16 ***
## index_AFexp.c:pInd_Not 9.650e-03 4.646e-03 2.885e+04 2.077 0.03780 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDm_Rp pInd_N i_AF.:D
## indx_AFxp.c 0.038
## pDem_Rep 0.024 0.151
## pInd_Not -0.144 -0.095 0.037
## ind_AF.:D_R 0.048 0.171 0.050 0.081
## ind_AF.:I_N -0.032 -0.421 0.074 0.136 0.110## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105986.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0224 -0.6382 0.0446 0.7295 2.0855
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008032 0.08962
## Residual 2.297014 1.51559
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.327e+00 2.916e-02 1.334e+01 45.518 4.98e-16 ***
## index_AFexp.c 1.749e-02 2.293e-03 1.484e+04 7.628 2.53e-14 ***
## pDemR -1.180e+00 2.006e-02 2.881e+04 -58.818 < 2e-16 ***
## pDemI -5.095e-01 2.608e-02 2.886e+04 -19.536 < 2e-16 ***
## index_AFexp.c:pDemR 4.320e-02 3.621e-03 2.809e+04 11.930 < 2e-16 ***
## index_AFexp.c:pDemI 1.195e-02 4.797e-03 2.886e+04 2.491 0.0127 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDemR pDemI i_AF.:DR
## indx_AFxp.c -0.089
## pDemR -0.311 0.143
## pDemI -0.239 0.109 0.350
## indx_AF.:DR 0.050 -0.565 0.050 -0.056
## indx_AF.:DI 0.037 -0.416 -0.053 0.074 0.2712. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105986.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0224 -0.6382 0.0446 0.7295 2.0855
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008032 0.08962
## Residual 2.297014 1.51559
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.478e-01 2.982e-02 1.458e+01 4.956 0.000188 ***
## index_AFexp.c 6.069e-02 2.998e-03 1.945e+04 20.244 < 2e-16 ***
## pRepD 1.180e+00 2.006e-02 2.881e+04 58.818 < 2e-16 ***
## pRepI 6.702e-01 2.676e-02 2.885e+04 25.041 < 2e-16 ***
## index_AFexp.c:pRepD -4.320e-02 3.621e-03 2.809e+04 -11.930 < 2e-16 ***
## index_AFexp.c:pRepI -3.125e-02 5.169e-03 2.884e+04 -6.046 1.5e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pRepD pRepI i_AF.:RD
## indx_AFxp.c 0.107
## pRepD -0.369 -0.170
## pRepI -0.275 -0.113 0.408
## indx_AF.:RD -0.083 -0.776 0.050 0.093
## indx_AF.:RI -0.057 -0.535 0.084 0.169 0.4493. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_AFexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105986.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0224 -0.6382 0.0446 0.7295 2.0855
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008032 0.08962
## Residual 2.297014 1.51559
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.180e-01 3.416e-02 2.513e+01 23.943 < 2e-16 ***
## index_AFexp.c 2.944e-02 4.372e-03 2.670e+04 6.735 1.68e-11 ***
## pIndD 5.095e-01 2.608e-02 2.886e+04 19.536 < 2e-16 ***
## pIndR -6.702e-01 2.676e-02 2.885e+04 -25.041 < 2e-16 ***
## index_AFexp.c:pIndD -1.195e-02 4.797e-03 2.886e+04 -2.491 0.0127 *
## index_AFexp.c:pIndR 3.125e-02 5.169e-03 2.884e+04 6.046 1.50e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pIndD pIndR i_AF.:ID
## indx_AFxp.c 0.101
## pIndD -0.559 -0.139
## pIndR -0.544 -0.122 0.712
## indx_AF.:ID -0.088 -0.879 0.074 0.112
## indx_AF.:IR -0.082 -0.815 0.108 0.169 0.738C. vaccine attitudes
vaxAttitudes ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 124038.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9980 -0.7798 0.0452 0.9551 1.6405
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01241 0.1114
## Residual 4.30153 2.0740
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.427e-01 3.492e-02 1.084e+01 6.949 2.62e-05 ***
## index_AFexp.c 4.720e-02 2.768e-03 8.604e+03 17.048 < 2e-16 ***
## pDem_Rep -4.937e-01 2.744e-02 2.876e+04 -17.989 < 2e-16 ***
## pInd_Not 4.507e-01 3.345e-02 2.884e+04 13.473 < 2e-16 ***
## index_AFexp.c:pDem_Rep 1.858e-02 4.953e-03 2.773e+04 3.752 0.000176 ***
## index_AFexp.c:pInd_Not -1.575e-02 6.357e-03 2.884e+04 -2.477 0.013247 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDm_Rp pInd_N i_AF.:D
## indx_AFxp.c 0.041
## pDem_Rep 0.026 0.150
## pInd_Not -0.156 -0.096 0.037
## ind_AF.:D_R 0.052 0.170 0.049 0.080
## ind_AF.:I_N -0.035 -0.422 0.074 0.135 0.109## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124038.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9980 -0.7798 0.0452 0.9551 1.6405
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01241 0.1114
## Residual 4.30153 2.0740
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.383e-01 3.706e-02 1.375e+01 17.221 1.07e-10 ***
## index_AFexp.c 3.271e-02 3.135e-03 1.231e+04 10.432 < 2e-16 ***
## pDemR -4.937e-01 2.744e-02 2.876e+04 -17.989 < 2e-16 ***
## pDemI -6.976e-01 3.569e-02 2.884e+04 -19.545 < 2e-16 ***
## index_AFexp.c:pDemR 1.858e-02 4.953e-03 2.773e+04 3.752 0.000176 ***
## index_AFexp.c:pDemI 2.504e-02 6.565e-03 2.885e+04 3.814 0.000137 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pDemR pDemI i_AF.:DR
## indx_AFxp.c -0.096
## pDemR -0.335 0.143
## pDemI -0.258 0.109 0.350
## indx_AF.:DR 0.055 -0.567 0.049 -0.057
## indx_AF.:DI 0.040 -0.417 -0.053 0.074 0.2712. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124038.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9980 -0.7798 0.0452 0.9551 1.6405
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01241 0.1114
## Residual 4.30153 2.0740
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.446e-01 3.802e-02 1.522e+01 3.803 0.001692 **
## index_AFexp.c 5.129e-02 4.094e-03 1.711e+04 12.527 < 2e-16 ***
## pRepD 4.937e-01 2.744e-02 2.876e+04 17.989 < 2e-16 ***
## pRepI -2.039e-01 3.662e-02 2.884e+04 -5.568 2.61e-08 ***
## index_AFexp.c:pRepD -1.858e-02 4.953e-03 2.773e+04 -3.752 0.000176 ***
## index_AFexp.c:pRepI 6.458e-03 7.071e-03 2.881e+04 0.913 0.361039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pRepD pRepI i_AF.:RD
## indx_AFxp.c 0.114
## pRepD -0.395 -0.168
## pRepI -0.295 -0.112 0.408
## indx_AF.:RD -0.088 -0.776 0.049 0.092
## indx_AF.:RI -0.061 -0.535 0.083 0.168 0.4493. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_AFexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124038.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9980 -0.7798 0.0452 0.9551 1.6405
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01241 0.1114
## Residual 4.30153 2.0740
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.930e-02 4.434e-02 2.816e+01 -1.337 0.191850
## index_AFexp.c 5.775e-02 5.980e-03 2.585e+04 9.657 < 2e-16 ***
## pIndD 6.976e-01 3.569e-02 2.884e+04 19.545 < 2e-16 ***
## pIndR 2.039e-01 3.662e-02 2.884e+04 5.568 2.61e-08 ***
## index_AFexp.c:pIndD -2.504e-02 6.565e-03 2.885e+04 -3.814 0.000137 ***
## index_AFexp.c:pIndR -6.458e-03 7.071e-03 2.881e+04 -0.913 0.361039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. pIndD pIndR i_AF.:ID
## indx_AFxp.c 0.106
## pIndD -0.590 -0.138
## pIndR -0.573 -0.122 0.712
## indx_AF.:ID -0.093 -0.879 0.074 0.112
## indx_AF.:IR -0.086 -0.816 0.108 0.168 0.7392. COGNITIVE PROCESSING
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103779.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1317 -0.8180 0.1077 0.6358 2.4343
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007782 0.08822
## Residual 2.130709 1.45969
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.510e+00 2.722e-02 1.118e+01 165.698 < 2e-16 ***
## index_CPexp.c 2.236e-02 9.199e-04 1.979e+04 24.309 < 2e-16 ***
## pDem_Rep -1.476e+00 1.931e-02 2.884e+04 -76.474 < 2e-16 ***
## pInd_Not 1.499e-01 2.360e-02 2.884e+04 6.350 2.19e-10 ***
## index_CPexp.c:pDem_Rep 2.344e-02 1.676e-03 2.726e+04 13.986 < 2e-16 ***
## index_CPexp.c:pInd_Not -5.970e-03 2.210e-03 2.884e+04 -2.701 0.00691 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.041
## pDem_Rep 0.023 0.141
## pInd_Not -0.142 -0.109 0.037
## ind_CP.:D_R 0.048 0.162 0.046 0.082
## ind_CP.:I_N -0.037 -0.460 0.074 0.150 0.098## Warning: Removed 12 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103779.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1317 -0.8180 0.1077 0.6358 2.4343
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007782 0.08822
## Residual 2.130709 1.45969
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.298e+00 2.858e-02 1.360e+01 185.344 < 2e-16 ***
## index_CPexp.c 8.673e-03 1.045e-03 2.242e+04 8.299 < 2e-16 ***
## pDemR -1.476e+00 1.931e-02 2.884e+04 -76.474 < 2e-16 ***
## pDemI -8.881e-01 2.516e-02 2.885e+04 -35.291 < 2e-16 ***
## index_CPexp.c:pDemR 2.344e-02 1.676e-03 2.726e+04 13.986 < 2e-16 ***
## index_CPexp.c:pDemI 1.769e-02 2.285e-03 2.885e+04 7.740 1.03e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.089
## pDemR -0.306 0.139
## pDemI -0.235 0.107 0.349
## indx_CP.:DR 0.053 -0.590 0.046 -0.060
## indx_CP.:DI 0.038 -0.423 -0.054 0.086 0.2722. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103779.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1317 -0.8180 0.1077 0.6358 2.4343
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007782 0.08822
## Residual 2.130709 1.45969
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.821e+00 2.919e-02 1.480e+01 130.889 <2e-16 ***
## index_CPexp.c 3.211e-02 1.354e-03 2.301e+04 23.720 <2e-16 ***
## pRepD 1.476e+00 1.931e-02 2.884e+04 76.474 <2e-16 ***
## pRepI 5.884e-01 2.583e-02 2.884e+04 22.779 <2e-16 ***
## index_CPexp.c:pRepD -2.344e-02 1.676e-03 2.726e+04 -13.986 <2e-16 ***
## index_CPexp.c:pRepI -5.748e-03 2.439e-03 2.881e+04 -2.356 0.0185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.105
## pRepD -0.362 -0.164
## pRepI -0.270 -0.114 0.408
## indx_CP.:RD -0.082 -0.782 0.046 0.092
## indx_CP.:RI -0.055 -0.528 0.083 0.181 0.4323. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103779.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1317 -0.8180 0.1077 0.6358 2.4343
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007782 0.08822
## Residual 2.130709 1.45969
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.409e+00 3.336e-02 2.523e+01 132.189 < 2e-16 ***
## index_CPexp.c 2.636e-02 2.072e-03 2.845e+04 12.723 < 2e-16 ***
## pIndD 8.881e-01 2.516e-02 2.885e+04 35.291 < 2e-16 ***
## pIndR -5.884e-01 2.583e-02 2.884e+04 -22.779 < 2e-16 ***
## index_CPexp.c:pIndD -1.769e-02 2.285e-03 2.885e+04 -7.740 1.03e-14 ***
## index_CPexp.c:pIndR 5.748e-03 2.439e-03 2.881e+04 2.356 0.0185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.110
## pIndD -0.553 -0.149
## pIndR -0.538 -0.138 0.714
## indx_CP.:ID -0.098 -0.889 0.086 0.125
## indx_CP.:IR -0.091 -0.832 0.122 0.181 0.750B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99409.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2095 -0.5808 0.1504 0.5862 1.7355
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 3.759e-14 1.939e-07
## Residual 1.833e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.931e+00 8.902e-03 2.885e+04 553.942 < 2e-16 ***
## index_CPexp.c 4.174e-03 8.086e-04 2.885e+04 5.162 2.46e-07 ***
## pDem_Rep -5.156e-01 1.786e-02 2.885e+04 -28.873 < 2e-16 ***
## pInd_Not 1.643e-01 2.188e-02 2.885e+04 7.509 6.13e-14 ***
## index_CPexp.c:pDem_Rep 3.157e-03 1.521e-03 2.885e+04 2.075 0.038 *
## index_CPexp.c:pInd_Not 8.433e-04 2.049e-03 2.885e+04 0.412 0.681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.115
## pDem_Rep 0.064 0.125
## pInd_Not -0.402 -0.108 0.039
## ind_CP.:D_R 0.133 0.161 0.043 0.081
## ind_CP.:I_N -0.105 -0.487 0.073 0.149 0.095
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular## Warning: Removed 12 row(s) containing missing values (geom_path).
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99409.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2095 -0.5808 0.1504 0.5862 1.7355
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 3.673e-14 1.917e-07
## Residual 1.833e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.243e+00 1.202e-02 2.885e+04 436.336 < 2e-16 ***
## index_CPexp.c 2.874e-03 9.282e-04 2.885e+04 3.096 0.00197 **
## pDemR -5.156e-01 1.786e-02 2.885e+04 -28.873 < 2e-16 ***
## pDemI -4.221e-01 2.331e-02 2.885e+04 -18.108 < 2e-16 ***
## index_CPexp.c:pDemR 3.157e-03 1.521e-03 2.885e+04 2.075 0.03797 *
## index_CPexp.c:pDemI 7.353e-04 2.117e-03 2.885e+04 0.347 0.72830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.189
## pDemR -0.673 0.127
## pDemI -0.515 0.097 0.347
## indx_CP.:DR 0.115 -0.610 0.043 -0.059
## indx_CP.:DI 0.083 -0.439 -0.056 0.087 0.268
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99409.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2095 -0.5808 0.1504 0.5862 1.7355
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 4.245e-14 2.060e-07
## Residual 1.833e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.728e+00 1.321e-02 2.885e+04 357.853 < 2e-16 ***
## index_CPexp.c 6.031e-03 1.205e-03 2.885e+04 5.003 5.67e-07 ***
## pRepD 5.156e-01 1.786e-02 2.885e+04 28.873 < 2e-16 ***
## pRepI 9.351e-02 2.395e-02 2.885e+04 3.904 9.47e-05 ***
## index_CPexp.c:pRepD -3.157e-03 1.521e-03 2.885e+04 -2.075 0.038 *
## index_CPexp.c:pRepI -2.422e-03 2.252e-03 2.885e+04 -1.075 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.205
## pRepD -0.740 -0.152
## pRepI -0.552 -0.113 0.408
## indx_CP.:RD -0.163 -0.792 0.043 0.090
## indx_CP.:RI -0.110 -0.535 0.081 0.179 0.424
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99409.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2095 -0.5808 0.1504 0.5862 1.7355
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 3.967e-14 1.992e-07
## Residual 1.833e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.821e+00 1.998e-02 2.885e+04 241.349 < 2e-16 ***
## index_CPexp.c 3.609e-03 1.902e-03 2.885e+04 1.897 0.0578 .
## pIndD 4.221e-01 2.331e-02 2.885e+04 18.108 < 2e-16 ***
## pIndR -9.351e-02 2.395e-02 2.885e+04 -3.904 9.47e-05 ***
## index_CPexp.c:pIndD -7.353e-04 2.117e-03 2.885e+04 -0.347 0.7283
## index_CPexp.c:pIndR 2.422e-03 2.252e-03 2.885e+04 1.075 0.2822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.168
## pIndD -0.857 -0.144
## pIndR -0.834 -0.140 0.715
## indx_CP.:ID -0.151 -0.899 0.087 0.126
## indx_CP.:IR -0.142 -0.845 0.122 0.179 0.759
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115159.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7673 -0.8627 -0.2452 0.7113 2.5915
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01587 0.126
## Residual 3.15559 1.776
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.043e+00 3.820e-02 1.112e+01 79.654 < 2e-16 ***
## index_CPexp.c 2.838e-02 1.121e-03 2.307e+04 25.311 < 2e-16 ***
## pDem_Rep -3.087e-01 2.349e-02 2.886e+04 -13.145 < 2e-16 ***
## pInd_Not -3.374e-01 2.872e-02 2.885e+04 -11.749 < 2e-16 ***
## index_CPexp.c:pDem_Rep 6.370e-03 2.040e-03 2.801e+04 3.123 0.00179 **
## index_CPexp.c:pInd_Not 1.366e-03 2.690e-03 2.885e+04 0.508 0.61142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.035
## pDem_Rep 0.020 0.141
## pInd_Not -0.123 -0.109 0.037
## ind_CP.:D_R 0.042 0.162 0.045 0.082
## ind_CP.:I_N -0.032 -0.460 0.073 0.150 0.098## Warning: Removed 12 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115159.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7673 -0.8627 -0.2452 0.7113 2.5915
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01587 0.126
## Residual 3.15559 1.776
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.086e+00 3.965e-02 1.291e+01 77.827 < 2e-16 ***
## index_CPexp.c 2.564e-02 1.274e-03 2.496e+04 20.136 < 2e-16 ***
## pDemR -3.087e-01 2.349e-02 2.886e+04 -13.145 < 2e-16 ***
## pDemI 1.831e-01 3.062e-02 2.886e+04 5.978 2.29e-09 ***
## index_CPexp.c:pDemR 6.370e-03 2.040e-03 2.801e+04 3.123 0.00179 **
## index_CPexp.c:pDemI 1.818e-03 2.781e-03 2.886e+04 0.654 0.51328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.078
## pDemR -0.268 0.139
## pDemI -0.206 0.107 0.349
## indx_CP.:DR 0.046 -0.590 0.045 -0.060
## indx_CP.:DI 0.033 -0.423 -0.054 0.087 0.2722. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115159.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7673 -0.8627 -0.2452 0.7113 2.5915
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01587 0.126
## Residual 3.15559 1.776
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.777e+00 4.030e-02 1.378e+01 68.913 < 2e-16 ***
## index_CPexp.c 3.201e-02 1.648e-03 2.536e+04 19.420 < 2e-16 ***
## pRepD 3.087e-01 2.349e-02 2.886e+04 13.145 < 2e-16 ***
## pRepI 4.918e-01 3.143e-02 2.885e+04 15.649 < 2e-16 ***
## index_CPexp.c:pRepD -6.370e-03 2.040e-03 2.801e+04 -3.123 0.00179 **
## index_CPexp.c:pRepI -4.551e-03 2.969e-03 2.884e+04 -1.533 0.12524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.092
## pRepD -0.319 -0.164
## pRepI -0.237 -0.113 0.407
## indx_CP.:RD -0.072 -0.782 0.045 0.092
## indx_CP.:RI -0.049 -0.528 0.082 0.180 0.4323. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115159.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7673 -0.8627 -0.2452 0.7113 2.5915
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01587 0.126
## Residual 3.15559 1.776
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.269e+00 4.484e-02 2.111e+01 72.910 < 2e-16 ***
## index_CPexp.c 2.746e-02 2.522e-03 2.866e+04 10.887 < 2e-16 ***
## pIndD -1.831e-01 3.062e-02 2.886e+04 -5.978 2.29e-09 ***
## pIndR -4.918e-01 3.143e-02 2.885e+04 -15.649 < 2e-16 ***
## index_CPexp.c:pIndD -1.818e-03 2.781e-03 2.886e+04 -0.654 0.513
## index_CPexp.c:pIndR 4.551e-03 2.969e-03 2.884e+04 1.533 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.099
## pIndD -0.501 -0.149
## pIndR -0.487 -0.138 0.714
## indx_CP.:ID -0.088 -0.889 0.087 0.125
## indx_CP.:IR -0.083 -0.832 0.122 0.180 0.750D. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90347.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4035 -0.6708 -0.0431 0.6376 3.0242
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006102 0.07811
## Residual 1.340933 1.15799
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.161e+00 2.380e-02 1.114e+01 174.824 <2e-16 ***
## index_CPexp.c 1.854e-02 7.308e-04 2.211e+04 25.372 <2e-16 ***
## pDem_Rep -7.669e-01 1.533e-02 2.882e+04 -50.042 <2e-16 ***
## pInd_Not -8.475e-03 1.872e-02 2.882e+04 -0.453 0.651
## index_CPexp.c:pDem_Rep 1.104e-02 1.331e-03 2.778e+04 8.298 <2e-16 ***
## index_CPexp.c:pInd_Not -1.225e-03 1.754e-03 2.882e+04 -0.699 0.485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.037
## pDem_Rep 0.021 0.142
## pInd_Not -0.128 -0.109 0.037
## ind_CP.:D_R 0.044 0.163 0.046 0.082
## ind_CP.:I_N -0.033 -0.459 0.074 0.150 0.099
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90347.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4035 -0.6708 -0.0431 0.6376 3.0242
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006102 0.07811
## Residual 1.340933 1.15799
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.542e+00 2.479e-02 1.311e+01 183.212 < 2e-16 ***
## index_CPexp.c 1.262e-02 8.300e-04 2.423e+04 15.199 < 2e-16 ***
## pDemR -7.669e-01 1.533e-02 2.882e+04 -50.042 < 2e-16 ***
## pDemI -3.750e-01 1.997e-02 2.882e+04 -18.781 < 2e-16 ***
## index_CPexp.c:pDemR 1.104e-02 1.331e-03 2.778e+04 8.298 < 2e-16 ***
## index_CPexp.c:pDemI 6.747e-03 1.813e-03 2.882e+04 3.721 0.000199 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.082
## pDemR -0.280 0.139
## pDemI -0.215 0.107 0.349
## indx_CP.:DR 0.048 -0.590 0.046 -0.060
## indx_CP.:DI 0.035 -0.423 -0.054 0.086 0.2722. simple effects for reps
| Â | riskSeverity | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.77 | 3.73 – 3.82 | <0.001 |
| index_CPexp.c | 0.02 | 0.02 – 0.03 | <0.001 |
| pRepD | 0.77 | 0.74 – 0.80 | <0.001 |
| pRepI | 0.39 | 0.35 – 0.43 | <0.001 |
| index_CPexp.c * pRepD | -0.01 | -0.01 – -0.01 | <0.001 |
| index_CPexp.c * pRepI | -0.00 | -0.01 – -0.00 | 0.026 |
| Random Effects | |||
| σ2 | 1.34 | ||
| Ï„00 media | 0.01 | ||
| ICC | 0.00 | ||
| N media | 12 | ||
| Observations | 28829 | ||
| Marginal R2 / Conditional R2 | 0.126 / 0.129 | ||
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90347.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4035 -0.6708 -0.0431 0.6376 3.0242
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006102 0.07811
## Residual 1.340933 1.15799
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.775e+00 2.524e-02 1.407e+01 149.588 <2e-16 ***
## index_CPexp.c 2.366e-02 1.076e-03 2.470e+04 21.994 <2e-16 ***
## pRepD 7.669e-01 1.533e-02 2.882e+04 50.042 <2e-16 ***
## pRepI 3.919e-01 2.049e-02 2.881e+04 19.124 <2e-16 ***
## index_CPexp.c:pRepD -1.104e-02 1.331e-03 2.778e+04 -8.298 <2e-16 ***
## index_CPexp.c:pRepI -4.296e-03 1.936e-03 2.880e+04 -2.219 0.0265 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.096
## pRepD -0.332 -0.164
## pRepI -0.248 -0.114 0.408
## indx_CP.:RD -0.075 -0.782 0.046 0.093
## indx_CP.:RI -0.051 -0.529 0.083 0.181 0.4333. simple effects for indep
| Â | riskSeverity | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.17 | 4.11 – 4.22 | <0.001 |
| index_CPexp.c | 0.02 | 0.02 – 0.02 | <0.001 |
| pIndD | 0.37 | 0.34 – 0.41 | <0.001 |
| pIndR | -0.39 | -0.43 – -0.35 | <0.001 |
| index_CPexp.c * pIndD | -0.01 | -0.01 – -0.00 | <0.001 |
| index_CPexp.c * pIndR | 0.00 | 0.00 – 0.01 | 0.026 |
| Random Effects | |||
| σ2 | 1.34 | ||
| Ï„00 media | 0.01 | ||
| ICC | 0.00 | ||
| N media | 12 | ||
| Observations | 28829 | ||
| Marginal R2 / Conditional R2 | 0.126 / 0.129 | ||
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90347.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4035 -0.6708 -0.0431 0.6376 3.0242
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006102 0.07811
## Residual 1.340933 1.15799
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.167e+00 2.830e-02 2.224e+01 147.268 < 2e-16 ***
## index_CPexp.c 1.936e-02 1.644e-03 2.858e+04 11.777 < 2e-16 ***
## pIndD 3.750e-01 1.997e-02 2.882e+04 18.781 < 2e-16 ***
## pIndR -3.919e-01 2.049e-02 2.881e+04 -19.124 < 2e-16 ***
## index_CPexp.c:pIndD -6.747e-03 1.813e-03 2.882e+04 -3.721 0.000199 ***
## index_CPexp.c:pIndR 4.296e-03 1.936e-03 2.880e+04 2.219 0.026476 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.103
## pIndD -0.517 -0.149
## pIndR -0.503 -0.138 0.713
## indx_CP.:ID -0.091 -0.889 0.086 0.125
## indx_CP.:IR -0.085 -0.832 0.122 0.181 0.750E. worst of Covid is behind/ahead
worstBorA ~ index * party + (1 |media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00114 -0.63500 0.03995 0.72965 2.07911
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004339 0.06587
## Residual 2.300142 1.51662
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.640e-01 2.147e-02 1.133e+01 35.580 5.57e-13 ***
## index_CPexp.c 1.581e-02 9.509e-04 1.176e+04 16.631 < 2e-16 ***
## pDem_Rep -1.186e+00 2.005e-02 2.881e+04 -59.166 < 2e-16 ***
## pInd_Not -8.148e-02 2.452e-02 2.886e+04 -3.323 0.000891 ***
## index_CPexp.c:pDem_Rep 2.002e-02 1.738e-03 2.410e+04 11.521 < 2e-16 ***
## index_CPexp.c:pInd_Not 3.473e-03 2.296e-03 2.886e+04 1.512 0.130433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.054
## pDem_Rep 0.030 0.140
## pInd_Not -0.187 -0.109 0.038
## ind_CP.:D_R 0.063 0.162 0.046 0.082
## ind_CP.:I_N -0.048 -0.463 0.074 0.150 0.098
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00114 -0.63500 0.03995 0.72965 2.07911
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004339 0.06587
## Residual 2.300142 1.51662
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.330e+00 2.331e-02 1.570e+01 57.081 < 2e-16 ***
## index_CPexp.c 6.950e-03 1.081e-03 1.504e+04 6.428 1.33e-10 ***
## pDemR -1.186e+00 2.005e-02 2.881e+04 -59.166 < 2e-16 ***
## pDemI -5.117e-01 2.614e-02 2.886e+04 -19.574 < 2e-16 ***
## index_CPexp.c:pDemR 2.002e-02 1.738e-03 2.410e+04 11.521 < 2e-16 ***
## index_CPexp.c:pDemI 6.538e-03 2.374e-03 2.886e+04 2.754 0.0059 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.113
## pDemR -0.389 0.137
## pDemI -0.299 0.106 0.348
## indx_CP.:DR 0.067 -0.592 0.046 -0.060
## indx_CP.:DI 0.048 -0.425 -0.054 0.087 0.2712. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00114 -0.63500 0.03995 0.72965 2.07911
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004339 0.06587
## Residual 2.300142 1.51662
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.440e-01 2.411e-02 1.799e+01 5.972 1.20e-05 ***
## index_CPexp.c 2.697e-02 1.401e-03 1.588e+04 19.246 < 2e-16 ***
## pRepD 1.186e+00 2.005e-02 2.881e+04 59.166 < 2e-16 ***
## pRepI 6.746e-01 2.684e-02 2.885e+04 25.139 < 2e-16 ***
## index_CPexp.c:pRepD -2.002e-02 1.738e-03 2.410e+04 -11.521 < 2e-16 ***
## index_CPexp.c:pRepI -1.348e-02 2.534e-03 2.866e+04 -5.322 1.03e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.131
## pRepD -0.455 -0.163
## pRepI -0.339 -0.114 0.408
## indx_CP.:RD -0.103 -0.783 0.046 0.092
## indx_CP.:RI -0.070 -0.529 0.083 0.180 0.4323. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00114 -0.63500 0.03995 0.72965 2.07911
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004339 0.06587
## Residual 2.300142 1.51662
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.186e-01 2.937e-02 3.961e+01 27.873 < 2e-16 ***
## index_CPexp.c 1.349e-02 2.151e-03 2.753e+04 6.271 3.63e-10 ***
## pIndD 5.117e-01 2.614e-02 2.886e+04 19.574 < 2e-16 ***
## pIndR -6.746e-01 2.684e-02 2.885e+04 -25.139 < 2e-16 ***
## index_CPexp.c:pIndD -6.538e-03 2.374e-03 2.886e+04 -2.754 0.0059 **
## index_CPexp.c:pIndR 1.348e-02 2.534e-03 2.866e+04 5.322 1.03e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.129
## pIndD -0.653 -0.149
## pIndR -0.635 -0.138 0.714
## indx_CP.:ID -0.115 -0.890 0.087 0.125
## indx_CP.:IR -0.108 -0.833 0.122 0.180 0.751F. vaccine attitudes
vaxAttitudes ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 124047.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9824 -0.7841 0.0384 0.9433 1.6073
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006181 0.07862
## Residual 4.303218 2.07442
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.425e-01 2.648e-02 1.135e+01 9.159 1.4e-06 ***
## index_CPexp.c 2.150e-02 1.297e-03 8.846e+03 16.579 < 2e-16 ***
## pDem_Rep -4.985e-01 2.742e-02 2.876e+04 -18.180 < 2e-16 ***
## pInd_Not 4.462e-01 3.354e-02 2.884e+04 13.305 < 2e-16 ***
## index_CPexp.c:pDem_Rep 7.468e-03 2.375e-03 2.195e+04 3.145 0.00166 **
## index_CPexp.c:pInd_Not -8.681e-03 3.141e-03 2.885e+04 -2.764 0.00571 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDm_Rp pInd_N i_CP.:D
## indx_CPxp.c 0.059
## pDem_Rep 0.033 0.138
## pInd_Not -0.207 -0.109 0.037
## ind_CP.:D_R 0.070 0.161 0.044 0.082
## ind_CP.:I_N -0.054 -0.464 0.073 0.149 0.097
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124047.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9824 -0.7841 0.0384 0.9433 1.6073
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006181 0.07862
## Residual 4.303218 2.07442
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.391e-01 2.924e-02 1.686e+01 21.855 8.35e-14 ***
## index_CPexp.c 1.491e-02 1.477e-03 1.182e+04 10.092 < 2e-16 ***
## pDemR -4.985e-01 2.742e-02 2.876e+04 -18.180 < 2e-16 ***
## pDemI -6.955e-01 3.576e-02 2.884e+04 -19.451 < 2e-16 ***
## index_CPexp.c:pDemR 7.468e-03 2.375e-03 2.195e+04 3.145 0.001664 **
## index_CPexp.c:pDemI 1.242e-02 3.248e-03 2.884e+04 3.823 0.000132 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pDemR pDemI i_CP.:DR
## indx_CPxp.c -0.123
## pDemR -0.425 0.137
## pDemI -0.326 0.106 0.349
## indx_CP.:DR 0.073 -0.594 0.044 -0.060
## indx_CP.:DI 0.053 -0.426 -0.055 0.086 0.2712. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124047.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9824 -0.7841 0.0384 0.9433 1.6073
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006181 0.07862
## Residual 4.303218 2.07442
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.405e-01 3.043e-02 1.976e+01 4.618 0.000171 ***
## index_CPexp.c 2.237e-02 1.912e-03 1.270e+04 11.702 < 2e-16 ***
## pRepD 4.985e-01 2.742e-02 2.876e+04 18.180 < 2e-16 ***
## pRepI -1.970e-01 3.670e-02 2.884e+04 -5.366 8.09e-08 ***
## index_CPexp.c:pRepD -7.468e-03 2.375e-03 2.195e+04 -3.145 0.001664 **
## index_CPexp.c:pRepI 4.947e-03 3.464e-03 2.852e+04 1.428 0.153287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pRepD pRepI i_CP.:RD
## indx_CPxp.c 0.141
## pRepD -0.493 -0.161
## pRepI -0.367 -0.113 0.408
## indx_CP.:RD -0.110 -0.783 0.044 0.091
## indx_CP.:RI -0.075 -0.529 0.082 0.180 0.4313. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_CPexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124047.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9824 -0.7841 0.0384 0.9433 1.6073
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.006181 0.07862
## Residual 4.303218 2.07442
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.643e-02 3.811e-02 4.864e+01 -1.481 0.145167
## index_CPexp.c 2.732e-02 2.940e-03 2.679e+04 9.293 < 2e-16 ***
## pIndD 6.955e-01 3.576e-02 2.884e+04 19.451 < 2e-16 ***
## pIndR 1.970e-01 3.670e-02 2.884e+04 5.366 8.09e-08 ***
## index_CPexp.c:pIndD -1.242e-02 3.248e-03 2.884e+04 -3.823 0.000132 ***
## index_CPexp.c:pIndR -4.947e-03 3.464e-03 2.852e+04 -1.428 0.153287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_CP. pIndD pIndR i_CP.:ID
## indx_CPxp.c 0.136
## pIndD -0.688 -0.149
## pIndR -0.670 -0.138 0.714
## indx_CP.:ID -0.121 -0.891 0.086 0.125
## indx_CP.:IR -0.114 -0.834 0.122 0.180 0.7513. ANALYTIC THINKING
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103678.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14567 -0.82500 0.04845 0.58625 2.44194
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001559 0.03948
## Residual 2.123328 1.45716
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.515e+00 1.491e-02 1.272e+01 302.888 < 2e-16 ***
## index_ANexp.c 2.564e-03 1.006e-04 1.931e+04 25.487 < 2e-16 ***
## pDem_Rep -1.462e+00 1.935e-02 2.884e+04 -75.557 < 2e-16 ***
## pInd_Not 1.612e-01 2.357e-02 2.884e+04 6.838 8.22e-12 ***
## index_ANexp.c:pDem_Rep 3.168e-03 1.943e-04 2.377e+04 16.301 < 2e-16 ***
## index_ANexp.c:pInd_Not -4.325e-04 2.485e-04 2.885e+04 -1.740 0.0818 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.077
## pDem_Rep 0.043 0.150
## pInd_Not -0.255 -0.099 0.040
## ind_AN.:D_R 0.098 0.186 0.061 0.093
## ind_AN.:I_N -0.062 -0.445 0.087 0.149 0.115## Warning: Removed 93 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103678.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14567 -0.82500 0.04845 0.58625 2.44194
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001559 0.03948
## Residual 2.123328 1.45716
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.299e+00 1.728e-02 2.294e+01 306.686 < 2e-16 ***
## index_ANexp.c 8.374e-04 1.161e-04 1.743e+04 7.215 5.63e-13 ***
## pDemR -1.462e+00 1.935e-02 2.884e+04 -75.557 < 2e-16 ***
## pDemI -8.921e-01 2.511e-02 2.885e+04 -35.522 < 2e-16 ***
## index_ANexp.c:pDemR 3.168e-03 1.943e-04 2.377e+04 16.301 < 2e-16 ***
## index_ANexp.c:pDemI 2.016e-03 2.562e-04 2.883e+04 7.870 3.66e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.157
## pDemR -0.505 0.140
## pDemI -0.389 0.109 0.347
## indx_AN.:DR 0.093 -0.595 0.061 -0.064
## indx_AN.:DI 0.069 -0.443 -0.061 0.079 0.2672. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103678.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14567 -0.82500 0.04845 0.58625 2.44194
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001559 0.03948
## Residual 2.123328 1.45716
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.837e+00 1.832e-02 2.898e+01 209.469 < 2e-16 ***
## index_ANexp.c 4.005e-03 1.562e-04 2.431e+04 25.633 < 2e-16 ***
## pRepD 1.462e+00 1.935e-02 2.884e+04 75.557 < 2e-16 ***
## pRepI 5.698e-01 2.583e-02 2.884e+04 22.056 < 2e-16 ***
## index_ANexp.c:pRepD -3.168e-03 1.943e-04 2.377e+04 -16.301 < 2e-16 ***
## index_ANexp.c:pRepI -1.151e-03 2.771e-04 2.857e+04 -4.155 3.26e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.189
## pRepD -0.580 -0.180
## pRepI -0.434 -0.133 0.411
## indx_AN.:RD -0.152 -0.802 0.061 0.108
## indx_AN.:RI -0.106 -0.559 0.099 0.189 0.4543. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103678.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14567 -0.82500 0.04845 0.58625 2.44194
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001559 0.03948
## Residual 2.123328 1.45716
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.407e+00 2.433e-02 9.008e+01 181.160 < 2e-16 ***
## index_ANexp.c 2.854e-03 2.297e-04 2.808e+04 12.424 < 2e-16 ***
## pIndD 8.921e-01 2.511e-02 2.885e+04 35.522 < 2e-16 ***
## pIndR -5.698e-01 2.583e-02 2.884e+04 -22.056 < 2e-16 ***
## index_ANexp.c:pIndD -2.016e-03 2.562e-04 2.883e+04 -7.870 3.66e-15 ***
## index_ANexp.c:pIndR 1.151e-03 2.771e-04 2.857e+04 4.155 3.26e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.146
## pIndD -0.756 -0.143
## pIndR -0.735 -0.137 0.712
## indx_AN.:ID -0.131 -0.891 0.079 0.123
## indx_AN.:IR -0.121 -0.826 0.118 0.189 0.737B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99411.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2114 -0.5814 0.1512 0.5828 1.7372
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.832 1.354
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.933e+00 8.921e-03 2.885e+04 552.918 < 2e-16 ***
## index_ANexp.c 5.323e-04 9.232e-05 2.885e+04 5.766 8.19e-09 ***
## pDem_Rep -5.097e-01 1.796e-02 2.885e+04 -28.378 < 2e-16 ***
## pInd_Not 1.668e-01 2.189e-02 2.885e+04 7.621 2.60e-14 ***
## index_ANexp.c:pDem_Rep 5.407e-04 1.790e-04 2.885e+04 3.021 0.00252 **
## index_ANexp.c:pInd_Not 1.822e-04 2.308e-04 2.885e+04 0.789 0.42991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.119
## pDem_Rep 0.067 0.146
## pInd_Not -0.397 -0.099 0.041
## ind_AN.:D_R 0.151 0.191 0.060 0.092
## ind_AN.:I_N -0.097 -0.449 0.087 0.149 0.114
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular## Warning: Removed 93 row(s) containing missing values (geom_path).
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99411.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2114 -0.5814 0.1512 0.5828 1.7372
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.832 1.354
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.243e+00 1.206e-02 2.885e+04 434.872 < 2e-16 ***
## index_ANexp.c 3.221e-04 1.063e-04 2.885e+04 3.029 0.00246 **
## pDemR -5.097e-01 1.796e-02 2.885e+04 -28.378 < 2e-16 ***
## pDemI -4.216e-01 2.332e-02 2.885e+04 -18.081 < 2e-16 ***
## index_ANexp.c:pDemR 5.407e-04 1.790e-04 2.885e+04 3.021 0.00252 **
## index_ANexp.c:pDemI 8.822e-05 2.378e-04 2.885e+04 0.371 0.71067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.206
## pDemR -0.671 0.138
## pDemI -0.517 0.106 0.347
## indx_AN.:DR 0.122 -0.594 0.060 -0.063
## indx_AN.:DI 0.092 -0.447 -0.062 0.079 0.266
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99411.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2114 -0.5814 0.1512 0.5828 1.7372
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.832 1.354
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.733e+00 1.331e-02 2.885e+04 355.544 < 2e-16 ***
## index_ANexp.c 8.628e-04 1.440e-04 2.885e+04 5.992 2.09e-09 ***
## pRepD 5.097e-01 1.796e-02 2.885e+04 28.378 < 2e-16 ***
## pRepI 8.802e-02 2.399e-02 2.885e+04 3.669 0.000244 ***
## index_ANexp.c:pRepD -5.407e-04 1.790e-04 2.885e+04 -3.021 0.002523 **
## index_ANexp.c:pRepI -4.525e-04 2.569e-04 2.885e+04 -1.762 0.078117 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.239
## pRepD -0.741 -0.177
## pRepI -0.555 -0.133 0.411
## indx_AN.:RD -0.192 -0.804 0.060 0.107
## indx_AN.:RI -0.134 -0.561 0.099 0.188 0.451
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99411.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2114 -0.5814 0.1512 0.5828 1.7372
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.832 1.354
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.821e+00 1.996e-02 2.885e+04 241.515 < 2e-16 ***
## index_ANexp.c 4.103e-04 2.127e-04 2.885e+04 1.929 0.053747 .
## pIndD 4.216e-01 2.332e-02 2.885e+04 18.081 < 2e-16 ***
## pIndR -8.802e-02 2.399e-02 2.885e+04 -3.669 0.000244 ***
## index_ANexp.c:pIndD -8.822e-05 2.378e-04 2.885e+04 -0.371 0.710670
## index_ANexp.c:pIndR 4.525e-04 2.569e-04 2.885e+04 1.762 0.078117 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.165
## pIndD -0.856 -0.142
## pIndR -0.832 -0.138 0.712
## indx_AN.:ID -0.148 -0.894 0.079 0.123
## indx_AN.:IR -0.137 -0.828 0.117 0.188 0.741
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115003
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8308 -0.8498 -0.2453 0.7092 2.6003
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004391 0.06627
## Residual 3.138419 1.77156
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.046e+00 2.241e-02 1.203e+01 135.899 < 2e-16 ***
## index_ANexp.c 3.337e-03 1.226e-04 2.417e+04 27.219 < 2e-16 ***
## pDem_Rep -2.837e-01 2.351e-02 2.886e+04 -12.065 < 2e-16 ***
## pInd_Not -3.286e-01 2.865e-02 2.885e+04 -11.468 < 2e-16 ***
## index_ANexp.c:pDem_Rep 1.112e-03 2.365e-04 2.668e+04 4.702 2.59e-06 ***
## index_ANexp.c:pInd_Not 6.058e-04 3.022e-04 2.886e+04 2.005 0.045 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.062
## pDem_Rep 0.035 0.150
## pInd_Not -0.207 -0.100 0.040
## ind_AN.:D_R 0.079 0.184 0.060 0.093
## ind_AN.:I_N -0.050 -0.445 0.086 0.149 0.115## Warning: Removed 93 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115003
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8308 -0.8498 -0.2453 0.7092 2.6003
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004391 0.06627
## Residual 3.138419 1.77156
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.079e+00 2.480e-02 1.803e+01 124.143 < 2e-16 ***
## index_ANexp.c 2.981e-03 1.415e-04 2.294e+04 21.062 < 2e-16 ***
## pDemR -2.837e-01 2.351e-02 2.886e+04 -12.065 < 2e-16 ***
## pDemI 1.867e-01 3.054e-02 2.886e+04 6.116 9.72e-10 ***
## index_ANexp.c:pDemR 1.112e-03 2.365e-04 2.668e+04 4.702 2.59e-06 ***
## index_ANexp.c:pDemI -4.975e-05 3.115e-04 2.886e+04 -0.160 0.873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.133
## pDemR -0.427 0.141
## pDemI -0.329 0.110 0.348
## indx_AN.:DR 0.079 -0.595 0.060 -0.065
## indx_AN.:DI 0.059 -0.442 -0.061 0.079 0.2682. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115003
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8308 -0.8498 -0.2453 0.7092 2.6003
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004391 0.06627
## Residual 3.138419 1.77156
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.795e+00 2.587e-02 2.136e+01 108.041 < 2e-16 ***
## index_ANexp.c 4.093e-03 1.901e-04 2.694e+04 21.528 < 2e-16 ***
## pRepD 2.837e-01 2.351e-02 2.886e+04 12.065 < 2e-16 ***
## pRepI 4.704e-01 3.140e-02 2.885e+04 14.982 < 2e-16 ***
## index_ANexp.c:pRepD -1.112e-03 2.365e-04 2.668e+04 -4.702 2.59e-06 ***
## index_ANexp.c:pRepI -1.162e-03 3.369e-04 2.877e+04 -3.448 0.000565 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.162
## pRepD -0.499 -0.179
## pRepI -0.373 -0.133 0.411
## indx_AN.:RD -0.130 -0.801 0.060 0.107
## indx_AN.:RI -0.091 -0.559 0.098 0.189 0.4543. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115003
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8308 -0.8498 -0.2453 0.7092 2.6003
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.004391 0.06627
## Residual 3.138419 1.77156
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.266e+00 3.238e-02 5.239e+01 100.852 < 2e-16 ***
## index_ANexp.c 2.931e-03 2.794e-04 2.858e+04 10.489 < 2e-16 ***
## pIndD -1.867e-01 3.054e-02 2.886e+04 -6.116 9.72e-10 ***
## pIndR -4.704e-01 3.140e-02 2.885e+04 -14.982 < 2e-16 ***
## index_ANexp.c:pIndD 4.975e-05 3.115e-04 2.886e+04 0.160 0.873098
## index_ANexp.c:pIndR 1.162e-03 3.369e-04 2.877e+04 3.448 0.000565 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.134
## pIndD -0.691 -0.143
## pIndR -0.671 -0.137 0.712
## indx_AN.:ID -0.119 -0.891 0.079 0.122
## indx_AN.:IR -0.111 -0.826 0.118 0.189 0.737D. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90222.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4662 -0.6615 -0.0519 0.6539 3.0212
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001465 0.03827
## Residual 1.335122 1.15547
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.164e+00 1.342e-02 1.226e+01 310.270 <2e-16 ***
## index_ANexp.c 2.152e-03 7.992e-05 2.246e+04 26.933 <2e-16 ***
## pDem_Rep -7.525e-01 1.535e-02 2.882e+04 -49.014 <2e-16 ***
## pInd_Not -6.940e-04 1.869e-02 2.882e+04 -0.037 0.970
## index_ANexp.c:pDem_Rep 1.607e-03 1.543e-04 2.573e+04 10.412 <2e-16 ***
## index_ANexp.c:pInd_Not 1.201e-04 1.971e-04 2.882e+04 0.609 0.542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.068
## pDem_Rep 0.038 0.150
## pInd_Not -0.225 -0.099 0.040
## ind_AN.:D_R 0.087 0.185 0.061 0.094
## ind_AN.:I_N -0.055 -0.444 0.087 0.149 0.116## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90222.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4662 -0.6615 -0.0519 0.6539 3.0212
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001465 0.03827
## Residual 1.335122 1.15547
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.540e+00 1.511e-02 1.966e+01 300.542 < 2e-16 ***
## index_ANexp.c 1.389e-03 9.223e-05 2.093e+04 15.057 < 2e-16 ***
## pDemR -7.525e-01 1.535e-02 2.882e+04 -49.014 < 2e-16 ***
## pDemI -3.755e-01 1.992e-02 2.882e+04 -18.854 < 2e-16 ***
## index_ANexp.c:pDemR 1.607e-03 1.543e-04 2.573e+04 10.412 < 2e-16 ***
## index_ANexp.c:pDemI 6.832e-04 2.032e-04 2.882e+04 3.363 0.000773 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.142
## pDemR -0.458 0.141
## pDemI -0.353 0.110 0.348
## indx_AN.:DR 0.085 -0.594 0.061 -0.065
## indx_AN.:DI 0.063 -0.442 -0.061 0.079 0.2672. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90222.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4662 -0.6615 -0.0519 0.6539 3.0212
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001465 0.03827
## Residual 1.335122 1.15547
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.788e+00 1.586e-02 2.389e+01 238.817 < 2e-16 ***
## index_ANexp.c 2.995e-03 1.241e-04 2.610e+04 24.139 < 2e-16 ***
## pRepD 7.525e-01 1.535e-02 2.882e+04 49.014 < 2e-16 ***
## pRepI 3.769e-01 2.049e-02 2.882e+04 18.396 < 2e-16 ***
## index_ANexp.c:pRepD -1.607e-03 1.543e-04 2.573e+04 -10.412 < 2e-16 ***
## index_ANexp.c:pRepI -9.234e-04 2.198e-04 2.868e+04 -4.201 2.67e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.174
## pRepD -0.532 -0.180
## pRepI -0.398 -0.134 0.411
## indx_AN.:RD -0.139 -0.802 0.061 0.108
## indx_AN.:RI -0.097 -0.559 0.099 0.189 0.4553. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90222.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4662 -0.6615 -0.0519 0.6539 3.0212
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.001465 0.03827
## Residual 1.335122 1.15547
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.165e+00 2.031e-02 6.421e+01 205.056 < 2e-16 ***
## index_ANexp.c 2.072e-03 1.822e-04 2.840e+04 11.371 < 2e-16 ***
## pIndD 3.755e-01 1.992e-02 2.882e+04 18.854 < 2e-16 ***
## pIndR -3.769e-01 2.049e-02 2.882e+04 -18.396 < 2e-16 ***
## index_ANexp.c:pIndD -6.832e-04 2.032e-04 2.882e+04 -3.363 0.000773 ***
## index_ANexp.c:pIndR 9.234e-04 2.198e-04 2.868e+04 4.201 2.67e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.139
## pIndD -0.718 -0.143
## pIndR -0.698 -0.137 0.712
## indx_AN.:ID -0.124 -0.891 0.079 0.122
## indx_AN.:IR -0.115 -0.825 0.118 0.189 0.737E. worst of Covid is behind/ahead
worstBorA ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105938.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00056 -0.62514 0.05042 0.74873 2.10351
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000523 0.02287
## Residual 2.293354 1.51438
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.691e-01 1.197e-02 1.416e+01 64.261 < 2e-16 ***
## index_ANexp.c 1.815e-03 1.040e-04 1.116e+04 17.452 < 2e-16 ***
## pDem_Rep -1.172e+00 2.010e-02 2.881e+04 -58.309 < 2e-16 ***
## pInd_Not -6.877e-02 2.449e-02 2.886e+04 -2.808 0.00499 **
## index_ANexp.c:pDem_Rep 2.844e-03 2.013e-04 1.688e+04 14.129 < 2e-16 ***
## index_ANexp.c:pInd_Not 7.515e-04 2.583e-04 2.885e+04 2.910 0.00362 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.100
## pDem_Rep 0.056 0.148
## pInd_Not -0.331 -0.099 0.041
## ind_AN.:D_R 0.127 0.189 0.061 0.093
## ind_AN.:I_N -0.080 -0.446 0.087 0.149 0.115## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105938.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00056 -0.62514 0.05042 0.74873 2.10351
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000523 0.02287
## Residual 2.293354 1.51438
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.332e+00 1.501e-02 3.497e+01 88.738 < 2e-16 ***
## index_ANexp.c 6.411e-04 1.199e-04 9.307e+03 5.348 9.09e-08 ***
## pDemR -1.172e+00 2.010e-02 2.881e+04 -58.309 < 2e-16 ***
## pDemI -5.172e-01 2.609e-02 2.885e+04 -19.823 < 2e-16 ***
## index_ANexp.c:pDemR 2.844e-03 2.013e-04 1.688e+04 14.129 < 2e-16 ***
## index_ANexp.c:pDemI 6.705e-04 2.662e-04 2.878e+04 2.519 0.0118 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.186
## pDemR -0.603 0.139
## pDemI -0.464 0.108 0.347
## indx_AN.:DR 0.110 -0.594 0.061 -0.064
## indx_AN.:DI 0.083 -0.445 -0.061 0.079 0.2662. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105938.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00056 -0.62514 0.05042 0.74873 2.10351
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000523 0.02287
## Residual 2.293354 1.51438
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.604e-01 1.630e-02 4.857e+01 9.839 3.78e-13 ***
## index_ANexp.c 3.485e-03 1.619e-04 1.783e+04 21.520 < 2e-16 ***
## pRepD 1.172e+00 2.010e-02 2.881e+04 58.309 < 2e-16 ***
## pRepI 6.548e-01 2.685e-02 2.886e+04 24.387 < 2e-16 ***
## index_ANexp.c:pRepD -2.844e-03 2.013e-04 1.688e+04 -14.129 < 2e-16 ***
## index_ANexp.c:pRepI -2.173e-03 2.878e-04 2.785e+04 -7.552 4.41e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.221
## pRepD -0.678 -0.179
## pRepI -0.507 -0.134 0.411
## indx_AN.:RD -0.177 -0.803 0.061 0.108
## indx_AN.:RI -0.124 -0.560 0.100 0.189 0.4533. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105938.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00056 -0.62514 0.05042 0.74873 2.10351
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000523 0.02287
## Residual 2.293354 1.51438
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.151e-01 2.329e-02 2.021e+02 35.000 < 2e-16 ***
## index_ANexp.c 1.312e-03 2.384e-04 2.640e+04 5.502 3.79e-08 ***
## pIndD 5.172e-01 2.609e-02 2.885e+04 19.823 < 2e-16 ***
## pIndR -6.548e-01 2.685e-02 2.886e+04 -24.387 < 2e-16 ***
## index_ANexp.c:pIndD -6.705e-04 2.662e-04 2.878e+04 -2.519 0.0118 *
## index_ANexp.c:pIndR 2.173e-03 2.878e-04 2.785e+04 7.552 4.41e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.159
## pIndD -0.821 -0.142
## pIndR -0.798 -0.137 0.712
## indx_AN.:ID -0.142 -0.893 0.079 0.123
## indx_AN.:IR -0.131 -0.827 0.118 0.189 0.739F. vaccine attitudes
vaxAttitudes ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 124002
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02397 -0.78335 0.04169 0.95003 1.61717
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0008659 0.02943
## Residual 4.2962466 2.07274
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.448e-01 1.609e-02 1.406e+01 15.218 3.95e-10 ***
## index_ANexp.c 2.535e-03 1.422e-04 1.044e+04 17.823 < 2e-16 ***
## pDem_Rep -4.852e-01 2.751e-02 2.879e+04 -17.637 < 2e-16 ***
## pInd_Not 4.482e-01 3.352e-02 2.885e+04 13.372 < 2e-16 ***
## index_ANexp.c:pDem_Rep 9.942e-04 2.753e-04 1.615e+04 3.611 0.000306 ***
## index_ANexp.c:pInd_Not -9.507e-04 3.535e-04 2.884e+04 -2.690 0.007157 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDm_Rp pInd_N i_AN.:D
## indx_ANxp.c 0.101
## pDem_Rep 0.057 0.147
## pInd_Not -0.337 -0.099 0.040
## ind_AN.:D_R 0.129 0.188 0.060 0.092
## ind_AN.:I_N -0.082 -0.447 0.087 0.149 0.114
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124002
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02397 -0.78335 0.04169 0.95003 1.61717
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000866 0.02943
## Residual 4.296247 2.07274
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.353e-01 2.033e-02 3.572e+01 31.254 < 2e-16 ***
## index_ANexp.c 1.724e-03 1.642e-04 8.648e+03 10.505 < 2e-16 ***
## pDemR -4.852e-01 2.751e-02 2.879e+04 -17.637 < 2e-16 ***
## pDemI -6.908e-01 3.572e-02 2.883e+04 -19.341 < 2e-16 ***
## index_ANexp.c:pDemR 9.942e-04 2.753e-04 1.615e+04 3.611 0.000306 ***
## index_ANexp.c:pDemI 1.448e-03 3.644e-04 2.875e+04 3.973 7.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pDemR pDemI i_AN.:DR
## indx_ANxp.c -0.188
## pDemR -0.610 0.139
## pDemI -0.470 0.108 0.347
## indx_AN.:DR 0.112 -0.595 0.060 -0.064
## indx_AN.:DI 0.084 -0.445 -0.062 0.079 0.2672. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124002
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02397 -0.78335 0.04169 0.95003 1.61717
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000866 0.02943
## Residual 4.296247 2.07274
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.502e-01 2.209e-02 4.978e+01 6.799 1.26e-08 ***
## index_ANexp.c 2.719e-03 2.213e-04 1.712e+04 12.285 < 2e-16 ***
## pRepD 4.852e-01 2.751e-02 2.879e+04 17.637 < 2e-16 ***
## pRepI -2.057e-01 3.674e-02 2.884e+04 -5.597 2.19e-08 ***
## index_ANexp.c:pRepD -9.942e-04 2.753e-04 1.615e+04 -3.611 0.000306 ***
## index_ANexp.c:pRepI 4.536e-04 3.937e-04 2.774e+04 1.152 0.249291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pRepD pRepI i_AN.:RD
## indx_ANxp.c 0.221
## pRepD -0.684 -0.177
## pRepI -0.512 -0.132 0.411
## indx_AN.:RD -0.177 -0.803 0.060 0.107
## indx_AN.:RI -0.124 -0.560 0.099 0.188 0.4523. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_ANexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124002
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02397 -0.78335 0.04169 0.95003 1.61717
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000866 0.02943
## Residual 4.296247 2.07274
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.550e-02 3.173e-02 2.116e+02 -1.749 0.0817 .
## index_ANexp.c 3.172e-03 3.262e-04 2.614e+04 9.724 < 2e-16 ***
## pIndD 6.908e-01 3.572e-02 2.883e+04 19.341 < 2e-16 ***
## pIndR 2.057e-01 3.674e-02 2.884e+04 5.597 2.19e-08 ***
## index_ANexp.c:pIndD -1.448e-03 3.644e-04 2.875e+04 -3.973 7.10e-05 ***
## index_ANexp.c:pIndR -4.536e-04 3.937e-04 2.774e+04 -1.152 0.2493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. pIndD pIndR i_AN.:ID
## indx_ANxp.c 0.160
## pIndD -0.825 -0.142
## pIndR -0.802 -0.137 0.712
## indx_AN.:ID -0.142 -0.893 0.079 0.123
## indx_AN.:IR -0.132 -0.827 0.118 0.188 0.7394. POSITIVE EMOTIONS
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103732.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14790 -0.80656 0.09893 0.63060 2.43987
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00938 0.09685
## Residual 2.12765 1.45865
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.510e+00 2.956e-02 1.111e+01 152.586 < 2e-16 ***
## index_PEexp.c 8.185e-02 3.275e-03 1.984e+04 24.988 < 2e-16 ***
## pDem_Rep -1.473e+00 1.930e-02 2.884e+04 -76.317 < 2e-16 ***
## pInd_Not 1.511e-01 2.356e-02 2.884e+04 6.411 1.46e-10 ***
## index_PEexp.c:pDem_Rep 8.528e-02 5.955e-03 2.802e+04 14.321 < 2e-16 ***
## index_PEexp.c:pInd_Not -1.945e-02 7.755e-03 2.884e+04 -2.509 0.0121 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.036
## pDem_Rep 0.021 0.145
## pInd_Not -0.130 -0.104 0.037
## ind_PE.:D_R 0.044 0.156 0.044 0.082
## ind_PE.:I_N -0.032 -0.446 0.074 0.144 0.097
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103732.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14790 -0.80656 0.09893 0.63060 2.43987
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00938 0.09685
## Residual 2.12765 1.45865
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.296e+00 3.082e-02 1.313e+01 171.845 < 2e-16 ***
## index_PEexp.c 3.278e-02 3.761e-03 2.302e+04 8.716 < 2e-16 ***
## pDemR -1.473e+00 1.930e-02 2.884e+04 -76.317 < 2e-16 ***
## pDemI -8.875e-01 2.513e-02 2.885e+04 -35.315 < 2e-16 ***
## index_PEexp.c:pDemR 8.528e-02 5.955e-03 2.802e+04 14.321 < 2e-16 ***
## index_PEexp.c:pDemI 6.209e-02 8.034e-03 2.885e+04 7.729 1.12e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.083
## pDemR -0.284 0.141
## pDemI -0.218 0.109 0.349
## indx_PE.:DR 0.049 -0.590 0.044 -0.060
## indx_PE.:DI 0.036 -0.427 -0.055 0.080 0.2772. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103732.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14790 -0.80656 0.09893 0.63060 2.43987
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00938 0.09685
## Residual 2.12765 1.45865
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.823e+00 3.139e-02 1.412e+01 121.821 < 2e-16 ***
## index_PEexp.c 1.181e-01 4.815e-03 2.416e+04 24.518 < 2e-16 ***
## pRepD 1.473e+00 1.930e-02 2.884e+04 76.317 < 2e-16 ***
## pRepI 5.854e-01 2.579e-02 2.884e+04 22.697 < 2e-16 ***
## index_PEexp.c:pRepD -8.528e-02 5.955e-03 2.802e+04 -14.321 < 2e-16 ***
## index_PEexp.c:pRepI -2.319e-02 8.571e-03 2.883e+04 -2.705 0.00683 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.097
## pRepD -0.336 -0.165
## pRepI -0.251 -0.113 0.408
## indx_PE.:RD -0.076 -0.776 0.044 0.092
## indx_PE.:RI -0.052 -0.530 0.083 0.176 0.4353. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_PEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103732.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14790 -0.80656 0.09893 0.63060 2.43987
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00938 0.09685
## Residual 2.12765 1.45865
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.409e+00 3.527e-02 2.252e+01 124.991 < 2e-16 ***
## index_PEexp.c 9.488e-02 7.273e-03 2.837e+04 13.045 < 2e-16 ***
## pIndD 8.875e-01 2.513e-02 2.885e+04 35.315 < 2e-16 ***
## pIndR -5.854e-01 2.579e-02 2.884e+04 -22.697 < 2e-16 ***
## index_PEexp.c:pIndD -6.209e-02 8.034e-03 2.885e+04 -7.729 1.12e-14 ***
## index_PEexp.c:pIndR 2.319e-02 8.571e-03 2.883e+04 2.705 0.00683 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.100
## pIndD -0.522 -0.145
## pIndR -0.508 -0.132 0.713
## indx_PE.:ID -0.088 -0.884 0.080 0.120
## indx_PE.:IR -0.083 -0.828 0.117 0.176 0.745B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99399.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2043 -0.5807 0.1514 0.5875 1.7392
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.833 1.354
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.931e+00 8.896e-03 2.885e+04 554.312 < 2e-16 ***
## index_PEexp.c 1.524e-02 2.852e-03 2.885e+04 5.342 9.26e-08 ***
## pDem_Rep -5.151e-01 1.786e-02 2.885e+04 -28.847 < 2e-16 ***
## pInd_Not 1.645e-01 2.186e-02 2.885e+04 7.525 5.43e-14 ***
## index_PEexp.c:pDem_Rep 1.181e-02 5.423e-03 2.885e+04 2.178 0.0294 *
## index_PEexp.c:pInd_Not 3.068e-03 7.192e-03 2.885e+04 0.427 0.6697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.110
## pDem_Rep 0.063 0.126
## pInd_Not -0.401 -0.103 0.039
## ind_PE.:D_R 0.133 0.158 0.042 0.081
## ind_PE.:I_N -0.100 -0.474 0.075 0.143 0.093
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## boundary (singular) fit: see ?isSingular
1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99399.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2043 -0.5807 0.1514 0.5875 1.7392
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.000 0.000
## Residual 1.833 1.354
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.243e+00 1.202e-02 2.885e+04 436.330 < 2e-16 ***
## index_PEexp.c 1.034e-02 3.326e-03 2.885e+04 3.109 0.00188 **
## pDemR -5.151e-01 1.786e-02 2.885e+04 -28.847 < 2e-16 ***
## pDemI -4.221e-01 2.329e-02 2.885e+04 -18.120 < 2e-16 ***
## index_PEexp.c:pDemR 1.181e-02 5.423e-03 2.885e+04 2.178 0.02944 *
## index_PEexp.c:pDemI 2.837e-03 7.446e-03 2.885e+04 0.381 0.70320
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.189
## pDemR -0.673 0.127
## pDemI -0.516 0.098 0.347
## indx_PE.:DR 0.116 -0.613 0.042 -0.060
## indx_PE.:DI 0.084 -0.447 -0.057 0.080 0.274
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99399.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2043 -0.5807 0.1514 0.5875 1.7392
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 2.646e-15 5.144e-08
## Residual 1.833e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.728e+00 1.321e-02 2.885e+04 357.917 < 2e-16 ***
## index_PEexp.c 2.215e-02 4.283e-03 2.885e+04 5.172 2.33e-07 ***
## pRepD 5.151e-01 1.786e-02 2.885e+04 28.847 < 2e-16 ***
## pRepI 9.308e-02 2.393e-02 2.885e+04 3.890 0.000101 ***
## index_PEexp.c:pRepD -1.181e-02 5.423e-03 2.885e+04 -2.178 0.029436 *
## index_PEexp.c:pRepI -8.973e-03 7.920e-03 2.885e+04 -1.133 0.257238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.205
## pRepD -0.740 -0.152
## pRepI -0.552 -0.113 0.408
## indx_PE.:RD -0.162 -0.790 0.042 0.089
## indx_PE.:RI -0.111 -0.541 0.082 0.175 0.427
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_PEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99399.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2043 -0.5807 0.1514 0.5875 1.7392
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 2.684e-15 5.180e-08
## Residual 1.833e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.821e+00 1.995e-02 2.885e+04 241.623 < 2e-16 ***
## index_PEexp.c 1.318e-02 6.662e-03 2.885e+04 1.978 0.047892 *
## pIndD 4.221e-01 2.329e-02 2.885e+04 18.120 < 2e-16 ***
## pIndR -9.308e-02 2.393e-02 2.885e+04 -3.890 0.000101 ***
## index_PEexp.c:pIndD -2.837e-03 7.446e-03 2.885e+04 -0.381 0.703200
## index_PEexp.c:pIndR 8.973e-03 7.920e-03 2.885e+04 1.133 0.257238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.162
## pIndD -0.857 -0.139
## pIndR -0.834 -0.135 0.714
## indx_PE.:ID -0.145 -0.895 0.080 0.121
## indx_PE.:IR -0.136 -0.841 0.117 0.175 0.753
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115130.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6987 -0.8666 -0.2413 0.7284 2.6053
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01886 0.1373
## Residual 3.15306 1.7757
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.042e+00 4.133e-02 1.107e+01 73.607 3.02e-16 ***
## index_PEexp.c 1.024e-01 3.993e-03 2.302e+04 25.655 < 2e-16 ***
## pDem_Rep -3.052e-01 2.348e-02 2.886e+04 -12.995 < 2e-16 ***
## pInd_Not -3.362e-01 2.868e-02 2.885e+04 -11.719 < 2e-16 ***
## index_PEexp.c:pDem_Rep 2.034e-02 7.250e-03 2.842e+04 2.806 0.00502 **
## index_PEexp.c:pInd_Not 9.424e-03 9.440e-03 2.885e+04 0.998 0.31811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.031
## pDem_Rep 0.018 0.145
## pInd_Not -0.113 -0.104 0.037
## ind_PE.:D_R 0.038 0.156 0.044 0.082
## ind_PE.:I_N -0.028 -0.445 0.074 0.144 0.096
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115130.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6987 -0.8666 -0.2413 0.7284 2.6053
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01886 0.1373
## Residual 3.15306 1.7757
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.083e+00 4.267e-02 1.258e+01 72.262 < 2e-16 ***
## index_PEexp.c 9.538e-02 4.585e-03 2.530e+04 20.805 < 2e-16 ***
## pDemR -3.052e-01 2.348e-02 2.886e+04 -12.995 < 2e-16 ***
## pDemI 1.836e-01 3.059e-02 2.886e+04 6.000 2e-09 ***
## index_PEexp.c:pDemR 2.035e-02 7.250e-03 2.842e+04 2.806 0.00502 **
## index_PEexp.c:pDemI 7.482e-04 9.780e-03 2.886e+04 0.076 0.93902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.073
## pDemR -0.249 0.141
## pDemI -0.191 0.109 0.350
## indx_PE.:DR 0.043 -0.589 0.044 -0.060
## indx_PE.:DI 0.031 -0.426 -0.055 0.080 0.2782. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115130.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6987 -0.8666 -0.2413 0.7284 2.6053
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01886 0.1373
## Residual 3.15306 1.7757
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.778e+00 4.327e-02 1.330e+01 64.205 < 2e-16 ***
## index_PEexp.c 1.157e-01 5.865e-03 2.606e+04 19.732 < 2e-16 ***
## pRepD 3.052e-01 2.348e-02 2.886e+04 12.995 < 2e-16 ***
## pRepI 4.887e-01 3.139e-02 2.885e+04 15.571 < 2e-16 ***
## index_PEexp.c:pRepD -2.034e-02 7.250e-03 2.842e+04 -2.806 0.00502 **
## index_PEexp.c:pRepI -1.960e-02 1.043e-02 2.885e+04 -1.878 0.06034 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.086
## pRepD -0.297 -0.165
## pRepI -0.221 -0.113 0.407
## indx_PE.:RD -0.066 -0.775 0.044 0.091
## indx_PE.:RI -0.045 -0.529 0.082 0.176 0.4353. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_PEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115130.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6987 -0.8666 -0.2413 0.7284 2.6053
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.01886 0.1373
## Residual 3.15306 1.7757
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.267e+00 4.751e-02 1.933e+01 68.769 <2e-16 ***
## index_PEexp.c 9.613e-02 8.857e-03 2.861e+04 10.854 <2e-16 ***
## pIndD -1.836e-01 3.059e-02 2.886e+04 -6.000 2e-09 ***
## pIndR -4.887e-01 3.139e-02 2.885e+04 -15.571 <2e-16 ***
## index_PEexp.c:pIndD -7.482e-04 9.780e-03 2.886e+04 -0.076 0.9390
## index_PEexp.c:pIndR 1.960e-02 1.043e-02 2.885e+04 1.878 0.0603 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.090
## pIndD -0.472 -0.145
## pIndR -0.459 -0.132 0.713
## indx_PE.:ID -0.080 -0.884 0.080 0.120
## indx_PE.:IR -0.075 -0.827 0.117 0.176 0.745D. risk severity
risk severity ~ indexes * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c) * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 90310.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3896 -0.6747 -0.0424 0.6382 3.0250
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007331 0.08562
## Residual 1.339442 1.15734
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.161e+00 2.586e-02 1.108e+01 160.888 <2e-16 ***
## index_PEexp.c 6.750e-02 2.602e-03 2.215e+04 25.936 <2e-16 ***
## pDem_Rep -7.642e-01 1.532e-02 2.882e+04 -49.875 <2e-16 ***
## pInd_Not -7.669e-03 1.870e-02 2.882e+04 -0.410 0.682
## index_PEexp.c:pDem_Rep 3.931e-02 4.729e-03 2.829e+04 8.312 <2e-16 ***
## index_PEexp.c:pInd_Not -2.270e-03 6.153e-03 2.882e+04 -0.369 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.033
## pDem_Rep 0.019 0.145
## pInd_Not -0.118 -0.104 0.037
## ind_PE.:D_R 0.040 0.157 0.045 0.082
## ind_PE.:I_N -0.029 -0.445 0.074 0.144 0.097## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90310.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3896 -0.6747 -0.0424 0.6382 3.0250
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007331 0.08562
## Residual 1.339442 1.15734
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.541e+00 2.678e-02 1.272e+01 169.576 < 2e-16 ***
## index_PEexp.c 4.709e-02 2.988e-03 2.469e+04 15.762 < 2e-16 ***
## pDemR -7.642e-01 1.532e-02 2.882e+04 -49.875 < 2e-16 ***
## pDemI -3.744e-01 1.994e-02 2.882e+04 -18.774 < 2e-16 ***
## index_PEexp.c:pDemR 3.931e-02 4.729e-03 2.829e+04 8.312 < 2e-16 ***
## index_PEexp.c:pDemI 2.193e-02 6.375e-03 2.882e+04 3.439 0.000584 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.076
## pDemR -0.259 0.141
## pDemI -0.199 0.109 0.350
## indx_PE.:DR 0.045 -0.589 0.045 -0.060
## indx_PE.:DI 0.032 -0.426 -0.055 0.080 0.2772. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124032.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95650 -0.79241 0.04414 0.95556 1.61786
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007639 0.0874
## Residual 4.301809 2.0741
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.425e-01 3.236e-02 1.812e+01 4.402 0.000339 ***
## index_PEexp.c 8.270e-02 6.810e-03 1.456e+04 12.145 < 2e-16 ***
## pRepD 4.965e-01 2.743e-02 2.877e+04 18.103 < 2e-16 ***
## pRepI -1.992e-01 3.667e-02 2.884e+04 -5.434 5.56e-08 ***
## index_PEexp.c:pRepD -2.922e-02 8.450e-03 2.473e+04 -3.458 0.000545 ***
## index_PEexp.c:pRepI 1.618e-02 1.218e-02 2.867e+04 1.328 0.184095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.133
## pRepD -0.464 -0.163
## pRepI -0.346 -0.113 0.408
## indx_PE.:RD -0.103 -0.777 0.043 0.091
## indx_PE.:RI -0.071 -0.531 0.082 0.175 0.4343. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90310.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3896 -0.6747 -0.0424 0.6382 3.0250
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007331 0.08562
## Residual 1.339442 1.15734
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.166e+00 3.003e-02 2.015e+01 138.707 < 2e-16 ***
## index_PEexp.c 6.902e-02 5.772e-03 2.852e+04 11.957 < 2e-16 ***
## pIndD 3.744e-01 1.994e-02 2.882e+04 18.774 < 2e-16 ***
## pIndR -3.898e-01 2.047e-02 2.881e+04 -19.044 < 2e-16 ***
## index_PEexp.c:pIndD -2.193e-02 6.375e-03 2.882e+04 -3.439 0.000584 ***
## index_PEexp.c:pIndR 1.739e-02 6.803e-03 2.881e+04 2.556 0.010604 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.093
## pIndD -0.487 -0.145
## pIndR -0.473 -0.132 0.713
## indx_PE.:ID -0.082 -0.884 0.080 0.120
## indx_PE.:IR -0.077 -0.827 0.117 0.176 0.744E. worst of Covid is ahead/behind
worstBorA ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105997.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00822 -0.63377 0.04405 0.72769 2.08455
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005204 0.07214
## Residual 2.298484 1.51608
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.640e-01 2.309e-02 1.119e+01 33.094 1.61e-12 ***
## index_PEexp.c 5.782e-02 3.387e-03 1.155e+04 17.074 < 2e-16 ***
## pDem_Rep -1.183e+00 2.005e-02 2.881e+04 -59.026 < 2e-16 ***
## pInd_Not -8.076e-02 2.449e-02 2.886e+04 -3.298 0.000976 ***
## index_PEexp.c:pDem_Rep 7.189e-02 6.182e-03 2.605e+04 11.630 < 2e-16 ***
## index_PEexp.c:pInd_Not 1.447e-02 8.060e-03 2.886e+04 1.795 0.072678 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.048
## pDem_Rep 0.028 0.143
## pInd_Not -0.173 -0.104 0.037
## ind_PE.:D_R 0.059 0.157 0.045 0.082
## ind_PE.:I_N -0.043 -0.448 0.074 0.144 0.096## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105997.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00822 -0.63377 0.04405 0.72769 2.08455
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005204 0.07214
## Residual 2.298484 1.51608
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.329e+00 2.480e-02 1.490e+01 53.588 < 2e-16 ***
## index_PEexp.c 2.665e-02 3.893e-03 1.566e+04 6.845 7.95e-12 ***
## pDemR -1.183e+00 2.005e-02 2.881e+04 -59.026 < 2e-16 ***
## pDemI -5.110e-01 2.611e-02 2.886e+04 -19.567 < 2e-16 ***
## index_PEexp.c:pDemR 7.189e-02 6.182e-03 2.605e+04 11.630 < 2e-16 ***
## index_PEexp.c:pDemI 2.148e-02 8.349e-03 2.886e+04 2.573 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.107
## pDemR -0.366 0.140
## pDemI -0.281 0.108 0.349
## indx_PE.:DR 0.063 -0.592 0.045 -0.060
## indx_PE.:DI 0.046 -0.428 -0.055 0.080 0.2772. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105997.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00822 -0.63377 0.04405 0.72769 2.08455
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005204 0.07214
## Residual 2.298484 1.51608
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.456e-01 2.556e-02 1.681e+01 5.698 2.73e-05 ***
## index_PEexp.c 9.854e-02 4.989e-03 1.748e+04 19.751 < 2e-16 ***
## pRepD 1.183e+00 2.005e-02 2.881e+04 59.026 < 2e-16 ***
## pRepI 6.725e-01 2.681e-02 2.885e+04 25.088 < 2e-16 ***
## index_PEexp.c:pRepD -7.189e-02 6.182e-03 2.605e+04 -11.630 < 2e-16 ***
## index_PEexp.c:pRepI -5.041e-02 8.906e-03 2.875e+04 -5.661 1.52e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.124
## pRepD -0.429 -0.164
## pRepI -0.320 -0.114 0.408
## indx_PE.:RD -0.096 -0.777 0.045 0.092
## indx_PE.:RI -0.066 -0.531 0.083 0.176 0.4343. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_PEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105997.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00822 -0.63377 0.04405 0.72769 2.08455
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005204 0.07214
## Residual 2.298484 1.51608
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.181e-01 3.055e-02 3.430e+01 26.780 < 2e-16 ***
## index_PEexp.c 4.813e-02 7.551e-03 2.721e+04 6.374 1.87e-10 ***
## pIndD 5.110e-01 2.611e-02 2.886e+04 19.567 < 2e-16 ***
## pIndR -6.725e-01 2.681e-02 2.885e+04 -25.088 < 2e-16 ***
## index_PEexp.c:pIndD -2.148e-02 8.349e-03 2.886e+04 -2.573 0.0101 *
## index_PEexp.c:pIndR 5.041e-02 8.906e-03 2.875e+04 5.661 1.52e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.120
## pIndD -0.627 -0.144
## pIndR -0.610 -0.132 0.713
## indx_PE.:ID -0.106 -0.885 0.080 0.120
## indx_PE.:IR -0.099 -0.829 0.117 0.176 0.745F. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 124032.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95650 -0.79241 0.04414 0.95556 1.61786
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007639 0.0874
## Residual 4.301809 2.0741
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.430e-01 2.868e-02 1.118e+01 8.474 3.36e-06 ***
## index_PEexp.c 7.825e-02 4.621e-03 8.851e+03 16.934 < 2e-16 ***
## pDem_Rep -4.965e-01 2.743e-02 2.877e+04 -18.103 < 2e-16 ***
## pInd_Not 4.475e-01 3.350e-02 2.884e+04 13.357 < 2e-16 ***
## index_PEexp.c:pDem_Rep 2.922e-02 8.450e-03 2.473e+04 3.458 0.000545 ***
## index_PEexp.c:pInd_Not -3.079e-02 1.103e-02 2.885e+04 -2.793 0.005233 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDm_Rp pInd_N i_PE.:D
## indx_PExp.c 0.053
## pDem_Rep 0.031 0.142
## pInd_Not -0.190 -0.104 0.037
## ind_PE.:D_R 0.064 0.156 0.043 0.082
## ind_PE.:I_N -0.048 -0.449 0.074 0.144 0.096
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124032.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95650 -0.79241 0.04414 0.95556 1.61786
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007639 0.0874
## Residual 4.301809 2.0741
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.390e-01 3.125e-02 1.576e+01 20.446 9.11e-13 ***
## index_PEexp.c 5.348e-02 5.321e-03 1.267e+04 10.051 < 2e-16 ***
## pDemR -4.965e-01 2.743e-02 2.877e+04 -18.103 < 2e-16 ***
## pDemI -6.958e-01 3.573e-02 2.884e+04 -19.473 < 2e-16 ***
## index_PEexp.c:pDemR 2.922e-02 8.450e-03 2.473e+04 3.458 0.000545 ***
## index_PEexp.c:pDemI 4.540e-02 1.142e-02 2.884e+04 3.974 7.07e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pDemR pDemI i_PE.:DR
## indx_PExp.c -0.116
## pDemR -0.398 0.140
## pDemI -0.305 0.107 0.349
## indx_PE.:DR 0.069 -0.593 0.043 -0.060
## indx_PE.:DI 0.050 -0.430 -0.056 0.080 0.2772. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124032.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95650 -0.79241 0.04414 0.95556 1.61786
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007639 0.0874
## Residual 4.301809 2.0741
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.425e-01 3.236e-02 1.812e+01 4.402 0.000339 ***
## index_PEexp.c 8.270e-02 6.810e-03 1.456e+04 12.145 < 2e-16 ***
## pRepD 4.965e-01 2.743e-02 2.877e+04 18.103 < 2e-16 ***
## pRepI -1.992e-01 3.667e-02 2.884e+04 -5.434 5.56e-08 ***
## index_PEexp.c:pRepD -2.922e-02 8.450e-03 2.473e+04 -3.458 0.000545 ***
## index_PEexp.c:pRepI 1.618e-02 1.218e-02 2.867e+04 1.328 0.184095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pRepD pRepI i_PE.:RD
## indx_PExp.c 0.133
## pRepD -0.464 -0.163
## pRepI -0.346 -0.113 0.408
## indx_PE.:RD -0.103 -0.777 0.043 0.091
## indx_PE.:RI -0.071 -0.531 0.082 0.175 0.4343. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_PEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124032.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95650 -0.79241 0.04414 0.95556 1.61786
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.007639 0.0874
## Residual 4.301809 2.0741
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.678e-02 3.964e-02 4.081e+01 -1.432 0.160
## index_PEexp.c 9.888e-02 1.032e-02 2.640e+04 9.578 < 2e-16 ***
## pIndD 6.957e-01 3.573e-02 2.884e+04 19.473 < 2e-16 ***
## pIndR 1.992e-01 3.667e-02 2.884e+04 5.434 5.56e-08 ***
## index_PEexp.c:pIndD -4.540e-02 1.142e-02 2.884e+04 -3.974 7.07e-05 ***
## index_PEexp.c:pIndR -1.618e-02 1.218e-02 2.867e+04 -1.328 0.184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. pIndD pIndR i_PE.:ID
## indx_PExp.c 0.126
## pIndD -0.661 -0.144
## pIndR -0.643 -0.133 0.713
## indx_PE.:ID -0.112 -0.885 0.080 0.120
## indx_PE.:IR -0.105 -0.829 0.117 0.175 0.7455. NEGATIVE EMOTIONS
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103804.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.8265 0.1394 0.6787 2.4842
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03341 0.1828
## Residual 2.13213 1.4602
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.503e+00 5.362e-02 1.072e+01 83.978 < 2e-16 ***
## index_NEexp.c 1.178e-01 4.761e-03 1.747e+04 24.749 < 2e-16 ***
## pDem_Rep -1.473e+00 1.929e-02 2.884e+04 -76.341 < 2e-16 ***
## pInd_Not 1.553e-01 2.345e-02 2.884e+04 6.623 3.58e-11 ***
## index_NEexp.c:pDem_Rep 1.025e-01 7.583e-03 2.885e+04 13.522 < 2e-16 ***
## index_NEexp.c:pInd_Not -8.927e-03 9.540e-03 2.884e+04 -0.936 0.349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.016
## pDem_Rep 0.012 0.161
## pInd_Not -0.071 -0.076 0.035
## ind_NE.:D_R 0.020 0.199 0.056 0.067
## ind_NE.:I_N -0.012 -0.357 0.064 0.109 0.130
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103804.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.8265 0.1394 0.6787 2.4842
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03341 0.1828
## Residual 2.13213 1.4602
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.291e+00 5.432e-02 1.129e+01 97.391 < 2e-16 ***
## index_NEexp.c 6.360e-02 5.094e-03 2.105e+04 12.485 < 2e-16 ***
## pDemR -1.473e+00 1.929e-02 2.884e+04 -76.341 < 2e-16 ***
## pDemI -8.918e-01 2.505e-02 2.885e+04 -35.609 < 2e-16 ***
## index_NEexp.c:pDemR 1.025e-01 7.583e-03 2.885e+04 13.522 < 2e-16 ***
## index_NEexp.c:pDemI 6.019e-02 9.797e-03 2.884e+04 6.144 8.13e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.041
## pDemR -0.161 0.148
## pDemI -0.123 0.107 0.353
## indx_NE.:DR 0.020 -0.478 0.056 -0.041
## indx_NE.:DI 0.015 -0.367 -0.040 0.059 0.2602. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103804.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.8265 0.1394 0.6787 2.4842
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03341 0.1828
## Residual 2.13213 1.4602
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.818e+00 5.465e-02 1.157e+01 69.857 < 2e-16 ***
## index_NEexp.c 1.661e-01 6.822e-03 2.436e+04 24.352 < 2e-16 ***
## pRepD 1.473e+00 1.929e-02 2.884e+04 76.341 < 2e-16 ***
## pRepI 5.812e-01 2.567e-02 2.884e+04 22.644 < 2e-16 ***
## index_NEexp.c:pRepD -1.025e-01 7.583e-03 2.885e+04 -13.522 < 2e-16 ***
## index_NEexp.c:pRepI -4.234e-02 1.072e-02 2.884e+04 -3.952 7.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.052
## pRepD -0.193 -0.173
## pRepI -0.144 -0.097 0.408
## indx_NE.:RD -0.039 -0.755 0.056 0.082
## indx_NE.:RI -0.027 -0.521 0.077 0.139 0.4703. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ index_NEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103804.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2133 -0.8265 0.1394 0.6787 2.4842
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.03341 0.1828
## Residual 2.13213 1.4602
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.399e+00 5.694e-02 1.363e+01 77.252 < 2e-16 ***
## index_NEexp.c 1.238e-01 9.233e-03 2.759e+04 13.409 < 2e-16 ***
## pIndD 8.918e-01 2.505e-02 2.885e+04 35.609 < 2e-16 ***
## pIndR -5.812e-01 2.567e-02 2.884e+04 -22.644 < 2e-16 ***
## index_NEexp.c:pIndD -6.019e-02 9.797e-03 2.884e+04 -6.144 8.13e-10 ***
## index_NEexp.c:pIndR 4.234e-02 1.072e-02 2.884e+04 3.952 7.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.047
## pIndD -0.322 -0.122
## pIndR -0.313 -0.090 0.711
## indx_NE.:ID -0.041 -0.858 0.059 0.088
## indx_NE.:IR -0.037 -0.776 0.084 0.139 0.730B. risk4
risk4 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99405.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2465 -0.5847 0.1500 0.5758 1.7235
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0003564 0.01888
## Residual 1.8328300 1.35382
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.931e+00 1.039e-02 5.130e+00 474.630 4.15e-13 ***
## index_NEexp.c 1.977e-02 3.763e-03 5.449e+01 5.252 2.56e-06 ***
## pDem_Rep -5.181e-01 1.778e-02 1.938e+04 -29.135 < 2e-16 ***
## pInd_Not 1.662e-01 2.173e-02 2.882e+04 7.647 2.13e-14 ***
## index_NEexp.c:pDem_Rep 1.781e-02 6.989e-03 1.464e+04 2.549 0.0108 *
## index_NEexp.c:pInd_Not 5.040e-03 8.843e-03 2.884e+04 0.570 0.5687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.076
## pDem_Rep 0.056 0.122
## pInd_Not -0.341 -0.070 0.038
## ind_NE.:D_R 0.096 0.210 0.052 0.068
## ind_NE.:I_N -0.058 -0.412 0.065 0.108 0.130
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99405.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2465 -0.5847 0.1500 0.5758 1.7235
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0003564 0.01888
## Residual 1.8328300 1.35382
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.245e+00 1.313e-02 1.292e+01 399.482 < 2e-16 ***
## index_NEexp.c 1.252e-02 4.203e-03 1.095e+02 2.980 0.00356 **
## pDemR -5.181e-01 1.778e-02 1.938e+04 -29.135 < 2e-16 ***
## pDemI -4.252e-01 2.316e-02 2.681e+04 -18.358 < 2e-16 ***
## index_NEexp.c:pDemR 1.781e-02 6.989e-03 1.464e+04 2.549 0.01082 *
## index_NEexp.c:pDemI 3.867e-03 9.077e-03 2.872e+04 0.426 0.67008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.141
## pDemR -0.612 0.112
## pDemI -0.470 0.084 0.348
## indx_NE.:DR 0.078 -0.553 0.052 -0.044
## indx_NE.:DI 0.060 -0.425 -0.044 0.058 0.2592. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99405.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2465 -0.5847 0.1500 0.5758 1.7235
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0003564 0.01888
## Residual 1.8328300 1.35382
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.727e+00 1.424e-02 1.784e+01 331.946 < 2e-16 ***
## index_NEexp.c 3.034e-02 5.833e-03 2.390e+02 5.201 4.26e-07 ***
## pRepD 5.181e-01 1.778e-02 1.938e+04 29.135 < 2e-16 ***
## pRepI 9.288e-02 2.379e-02 2.884e+04 3.903 9.51e-05 ***
## index_NEexp.c:pRepD -1.781e-02 6.989e-03 1.464e+04 -2.549 0.0108 *
## index_NEexp.c:pRepI -1.395e-02 9.921e-03 2.756e+04 -1.406 0.1598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.170
## pRepD -0.684 -0.142
## pRepI -0.510 -0.099 0.409
## indx_NE.:RD -0.136 -0.800 0.052 0.081
## indx_NE.:RI -0.095 -0.560 0.076 0.139 0.4683. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ index_NEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99405.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2465 -0.5847 0.1500 0.5758 1.7235
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0003564 0.01888
## Residual 1.8328300 1.35382
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.819e+00 2.057e-02 7.839e+01 234.278 < 2e-16 ***
## index_NEexp.c 1.639e-02 8.225e-03 1.076e+03 1.993 0.0465 *
## pIndD 4.252e-01 2.316e-02 2.681e+04 18.358 < 2e-16 ***
## pIndR -9.288e-02 2.379e-02 2.884e+04 -3.903 9.51e-05 ***
## index_NEexp.c:pIndD -3.867e-03 9.077e-03 2.872e+04 -0.426 0.6701
## index_NEexp.c:pIndR 1.395e-02 9.921e-03 2.756e+04 1.406 0.1598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.117
## pIndD -0.826 -0.107
## pIndR -0.803 -0.097 0.713
## indx_NE.:ID -0.103 -0.887 0.058 0.089
## indx_NE.:IR -0.094 -0.809 0.084 0.139 0.733C. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115274.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9679 -0.8687 -0.2426 0.7337 2.7314
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.06084 0.2467
## Residual 3.16764 1.7798
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.041e+00 7.215e-02 1.077e+01 42.149 2.65e-13 ***
## index_NEexp.c 1.421e-01 5.810e-03 2.013e+04 24.459 < 2e-16 ***
## pDem_Rep -3.172e-01 2.351e-02 2.886e+04 -13.490 < 2e-16 ***
## pInd_Not -3.242e-01 2.858e-02 2.885e+04 -11.342 < 2e-16 ***
## index_NEexp.c:pDem_Rep 4.197e-02 9.238e-03 2.886e+04 4.543 5.57e-06 ***
## index_NEexp.c:pInd_Not 1.890e-02 1.163e-02 2.885e+04 1.625 0.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.015
## pDem_Rep 0.011 0.160
## pInd_Not -0.065 -0.076 0.035
## ind_NE.:D_R 0.018 0.199 0.055 0.067
## ind_NE.:I_N -0.011 -0.357 0.063 0.109 0.130
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115274.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9679 -0.8687 -0.2426 0.7337 2.7314
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.06084 0.2467
## Residual 3.16764 1.7798
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.093e+00 7.293e-02 1.124e+01 42.408 9.23e-14 ***
## index_NEexp.c 1.274e-01 6.216e-03 2.316e+04 20.488 < 2e-16 ***
## pDemR -3.172e-01 2.351e-02 2.886e+04 -13.490 < 2e-16 ***
## pDemI 1.656e-01 3.053e-02 2.886e+04 5.425 5.84e-08 ***
## index_NEexp.c:pDemR 4.197e-02 9.238e-03 2.886e+04 4.543 5.57e-06 ***
## index_NEexp.c:pDemI 2.086e-03 1.194e-02 2.885e+04 0.175 0.861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.037
## pDemR -0.146 0.148
## pDemI -0.112 0.107 0.353
## indx_NE.:DR 0.018 -0.477 0.055 -0.041
## indx_NE.:DI 0.014 -0.367 -0.040 0.060 0.2602. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115274.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9679 -0.8687 -0.2426 0.7337 2.7314
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.06084 0.2467
## Residual 3.16764 1.7798
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.775e+00 7.328e-02 1.146e+01 37.872 2.11e-13 ***
## index_NEexp.c 1.693e-01 8.317e-03 2.572e+04 20.358 < 2e-16 ***
## pRepD 3.172e-01 2.351e-02 2.886e+04 13.490 < 2e-16 ***
## pRepI 4.828e-01 3.128e-02 2.885e+04 15.434 < 2e-16 ***
## index_NEexp.c:pRepD -4.197e-02 9.238e-03 2.886e+04 -4.543 5.57e-06 ***
## index_NEexp.c:pRepI -3.988e-02 1.306e-02 2.885e+04 -3.054 0.00226 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.047
## pRepD -0.176 -0.172
## pRepI -0.130 -0.096 0.407
## indx_NE.:RD -0.036 -0.754 0.055 0.082
## indx_NE.:RI -0.025 -0.520 0.076 0.139 0.4693. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ index_NEexp.c * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115274.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9679 -0.8687 -0.2426 0.7337 2.7314
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.06084 0.2467
## Residual 3.16764 1.7798
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.258e+00 7.583e-02 1.314e+01 42.964 1.59e-15 ***
## index_NEexp.c 1.294e-01 1.126e-02 2.803e+04 11.497 < 2e-16 ***
## pIndD -1.656e-01 3.053e-02 2.886e+04 -5.425 5.84e-08 ***
## pIndR -4.828e-01 3.128e-02 2.885e+04 -15.434 < 2e-16 ***
## index_NEexp.c:pIndD -2.086e-03 1.194e-02 2.885e+04 -0.175 0.86132
## index_NEexp.c:pIndR 3.988e-02 1.306e-02 2.885e+04 3.054 0.00226 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.043
## pIndD -0.295 -0.122
## pIndR -0.286 -0.090 0.711
## indx_NE.:ID -0.037 -0.858 0.060 0.088
## indx_NE.:IR -0.034 -0.776 0.084 0.139 0.730D. risk severity
risk severity ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90391.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5353 -0.6635 -0.0517 0.6319 3.1311
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02542 0.1594
## Residual 1.34270 1.1587
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.158e+00 4.664e-02 1.077e+01 89.153 <2e-16 ***
## index_NEexp.c 9.638e-02 3.784e-03 1.993e+04 25.472 <2e-16 ***
## pDem_Rep -7.679e-01 1.532e-02 2.882e+04 -50.119 <2e-16 ***
## pInd_Not -2.174e-03 1.861e-02 2.881e+04 -0.117 0.907
## index_NEexp.c:pDem_Rep 5.446e-02 6.021e-03 2.882e+04 9.045 <2e-16 ***
## index_NEexp.c:pInd_Not 4.926e-03 7.572e-03 2.881e+04 0.651 0.515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.015
## pDem_Rep 0.011 0.161
## pInd_Not -0.065 -0.076 0.035
## ind_NE.:D_R 0.019 0.199 0.056 0.067
## ind_NE.:I_N -0.011 -0.356 0.064 0.109 0.131## Warning: Removed 6 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_NEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90391.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5353 -0.6635 -0.0517 0.6319 3.1311
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02542 0.1594
## Residual 1.34270 1.1587
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.542e+00 4.715e-02 1.124e+01 96.316 < 2e-16 ***
## index_NEexp.c 7.077e-02 4.047e-03 2.299e+04 17.486 < 2e-16 ***
## pDemR -7.679e-01 1.532e-02 2.882e+04 -50.119 < 2e-16 ***
## pDemI -3.818e-01 1.988e-02 2.882e+04 -19.206 < 2e-16 ***
## index_NEexp.c:pDemR 5.446e-02 6.021e-03 2.882e+04 9.045 < 2e-16 ***
## index_NEexp.c:pDemI 2.231e-02 7.775e-03 2.881e+04 2.869 0.00412 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.038
## pDemR -0.147 0.148
## pDemI -0.113 0.107 0.353
## indx_NE.:DR 0.018 -0.477 0.056 -0.041
## indx_NE.:DI 0.014 -0.367 -0.040 0.059 0.2602. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124073
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13488 -0.78677 0.03072 0.94870 1.68627
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02982 0.1727
## Residual 4.30612 2.0751
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.414e-01 5.379e-02 1.221e+01 2.628 0.0218 *
## index_NEexp.c 1.233e-01 9.643e-03 1.507e+04 12.791 < 2e-16 ***
## pRepD 4.991e-01 2.741e-02 2.876e+04 18.211 < 2e-16 ***
## pRepI -2.092e-01 3.647e-02 2.884e+04 -5.736 9.79e-09 ***
## index_NEexp.c:pRepD -4.375e-02 1.077e-02 2.881e+04 -4.062 4.87e-05 ***
## index_NEexp.c:pRepI 3.086e-03 1.522e-02 2.885e+04 0.203 0.8394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.074
## pRepD -0.279 -0.170
## pRepI -0.207 -0.096 0.408
## indx_NE.:RD -0.056 -0.757 0.055 0.082
## indx_NE.:RI -0.039 -0.523 0.076 0.139 0.4693. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_NEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90391.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5353 -0.6635 -0.0517 0.6319 3.1311
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02542 0.1594
## Residual 1.34270 1.1587
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.160e+00 4.906e-02 1.318e+01 84.792 < 2e-16 ***
## index_NEexp.c 9.308e-02 7.330e-03 2.797e+04 12.699 < 2e-16 ***
## pIndD 3.818e-01 1.988e-02 2.882e+04 19.206 < 2e-16 ***
## pIndR -3.861e-01 2.037e-02 2.881e+04 -18.953 < 2e-16 ***
## index_NEexp.c:pIndD -2.231e-02 7.775e-03 2.881e+04 -2.869 0.004120 **
## index_NEexp.c:pIndR 3.216e-02 8.506e-03 2.882e+04 3.781 0.000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.044
## pIndD -0.297 -0.122
## pIndR -0.288 -0.090 0.710
## indx_NE.:ID -0.037 -0.858 0.059 0.088
## indx_NE.:IR -0.034 -0.775 0.084 0.139 0.730E. worst of Covid is ahead/behind
worstBorA ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pDem_Rep + pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.13328 -0.64698 0.02705 0.71578 2.10551
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02009 0.1417
## Residual 2.29965 1.5165
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.606e-01 4.210e-02 1.052e+01 18.066 2.95e-09 ***
## index_NEexp.c 8.808e-02 4.915e-03 9.474e+03 17.919 < 2e-16 ***
## pDem_Rep -1.184e+00 2.003e-02 2.881e+04 -59.103 < 2e-16 ***
## pInd_Not -8.331e-02 2.435e-02 2.885e+04 -3.421 0.000625 ***
## index_NEexp.c:pDem_Rep 8.853e-02 7.877e-03 2.884e+04 11.239 < 2e-16 ***
## index_NEexp.c:pInd_Not 2.102e-02 9.909e-03 2.885e+04 2.122 0.033862 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.021
## pDem_Rep 0.016 0.159
## pInd_Not -0.094 -0.075 0.035
## ind_NE.:D_R 0.027 0.200 0.056 0.067
## ind_NE.:I_N -0.016 -0.359 0.064 0.109 0.131## Warning: Removed 6 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.13328 -0.64698 0.02705 0.71578 2.10551
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02009 0.1417
## Residual 2.29965 1.5165
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.325e+00 4.306e-02 1.151e+01 30.772 2.07e-12 ***
## index_NEexp.c 5.076e-02 5.266e-03 1.332e+04 9.638 < 2e-16 ***
## pDemR -1.184e+00 2.003e-02 2.881e+04 -59.103 < 2e-16 ***
## pDemI -5.086e-01 2.600e-02 2.886e+04 -19.559 < 2e-16 ***
## index_NEexp.c:pDemR 8.853e-02 7.877e-03 2.884e+04 11.239 < 2e-16 ***
## index_NEexp.c:pDemI 2.324e-02 1.017e-02 2.885e+04 2.284 0.0224 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.054
## pDemR -0.210 0.146
## pDemI -0.162 0.106 0.352
## indx_NE.:DR 0.026 -0.480 0.056 -0.041
## indx_NE.:DI 0.020 -0.370 -0.041 0.059 0.2602. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.13328 -0.64698 0.02705 0.71578 2.10551
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02009 0.1417
## Residual 2.29965 1.5165
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.412e-01 4.351e-02 1.199e+01 3.245 0.00704 **
## index_NEexp.c 1.393e-01 7.067e-03 1.823e+04 19.708 < 2e-16 ***
## pRepD 1.184e+00 2.003e-02 2.881e+04 59.103 < 2e-16 ***
## pRepI 6.752e-01 2.666e-02 2.885e+04 25.331 < 2e-16 ***
## index_NEexp.c:pRepD -8.853e-02 7.877e-03 2.884e+04 -11.239 < 2e-16 ***
## index_NEexp.c:pRepI -6.529e-02 1.113e-02 2.886e+04 -5.866 4.52e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.068
## pRepD -0.252 -0.172
## pRepI -0.187 -0.097 0.408
## indx_NE.:RD -0.051 -0.757 0.056 0.083
## indx_NE.:RI -0.035 -0.522 0.077 0.139 0.4703. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106024.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.13328 -0.64698 0.02705 0.71578 2.10551
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02009 0.1417
## Residual 2.29965 1.5165
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.164e-01 4.657e-02 1.574e+01 17.532 9.52e-12 ***
## index_NEexp.c 7.400e-02 9.573e-03 2.514e+04 7.730 1.11e-14 ***
## pIndD 5.086e-01 2.600e-02 2.886e+04 19.559 < 2e-16 ***
## pIndR -6.752e-01 2.666e-02 2.885e+04 -25.331 < 2e-16 ***
## index_NEexp.c:pIndD -2.324e-02 1.017e-02 2.885e+04 -2.284 0.0224 *
## index_NEexp.c:pIndR 6.529e-02 1.113e-02 2.886e+04 5.866 4.52e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.060
## pIndD -0.409 -0.121
## pIndR -0.397 -0.090 0.711
## indx_NE.:ID -0.052 -0.859 0.059 0.088
## indx_NE.:IR -0.047 -0.777 0.084 0.139 0.730F. vaccine attitudes
vaxxAttitudes ~ negative emotion * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ index_NEexp.c * (pDem_Rep + pInd_Not) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 124073
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13488 -0.78677 0.03072 0.94870 1.68627
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02982 0.1727
## Residual 4.30612 2.0751
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.395e-01 5.166e-02 1.039e+01 4.637 0.000837 ***
## index_NEexp.c 1.097e-01 6.704e-03 6.853e+03 16.364 < 2e-16 ***
## pDem_Rep -4.991e-01 2.741e-02 2.876e+04 -18.211 < 2e-16 ***
## pInd_Not 4.588e-01 3.333e-02 2.884e+04 13.766 < 2e-16 ***
## index_NEexp.c:pDem_Rep 4.375e-02 1.077e-02 2.881e+04 4.062 4.87e-05 ***
## index_NEexp.c:pInd_Not -2.496e-02 1.356e-02 2.884e+04 -1.841 0.065607 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDm_Rp pInd_N i_NE.:D
## indx_NExp.c 0.024
## pDem_Rep 0.018 0.158
## pInd_Not -0.105 -0.076 0.035
## ind_NE.:D_R 0.030 0.199 0.055 0.067
## ind_NE.:I_N -0.018 -0.360 0.064 0.109 0.130## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pDemR + pDemI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124073
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13488 -0.78677 0.03072 0.94870 1.68627
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02982 0.1727
## Residual 4.30612 2.0751
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.405e-01 5.312e-02 1.162e+01 12.056 6.41e-08 ***
## index_NEexp.c 7.959e-02 7.193e-03 1.019e+04 11.065 < 2e-16 ***
## pDemR -4.991e-01 2.741e-02 2.876e+04 -18.211 < 2e-16 ***
## pDemI -7.083e-01 3.559e-02 2.884e+04 -19.904 < 2e-16 ***
## index_NEexp.c:pDemR 4.375e-02 1.077e-02 2.881e+04 4.062 4.87e-05 ***
## index_NEexp.c:pDemI 4.684e-02 1.392e-02 2.884e+04 3.364 0.000769 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pDemR pDemI i_NE.:DR
## indx_NExp.c -0.060
## pDemR -0.233 0.146
## pDemI -0.179 0.106 0.352
## indx_NE.:DR 0.029 -0.482 0.055 -0.042
## indx_NE.:DI 0.022 -0.371 -0.041 0.059 0.2602. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pRepD + pRepI) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124073
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13488 -0.78677 0.03072 0.94870 1.68627
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02982 0.1727
## Residual 4.30612 2.0751
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.414e-01 5.379e-02 1.221e+01 2.628 0.0218 *
## index_NEexp.c 1.233e-01 9.643e-03 1.507e+04 12.791 < 2e-16 ***
## pRepD 4.991e-01 2.741e-02 2.876e+04 18.211 < 2e-16 ***
## pRepI -2.092e-01 3.647e-02 2.884e+04 -5.736 9.79e-09 ***
## index_NEexp.c:pRepD -4.375e-02 1.077e-02 2.881e+04 -4.062 4.87e-05 ***
## index_NEexp.c:pRepI 3.086e-03 1.522e-02 2.885e+04 0.203 0.8394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pRepD pRepI i_NE.:RD
## indx_NExp.c 0.074
## pRepD -0.279 -0.170
## pRepI -0.207 -0.096 0.408
## indx_NE.:RD -0.056 -0.757 0.055 0.082
## indx_NE.:RI -0.039 -0.523 0.076 0.139 0.4693. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c) * (pIndD + pIndR) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124073
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13488 -0.78677 0.03072 0.94870 1.68627
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.02982 0.1727
## Residual 4.30612 2.0751
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.783e-02 5.840e-02 1.697e+01 -1.162 0.261516
## index_NEexp.c 1.264e-01 1.309e-02 2.339e+04 9.660 < 2e-16 ***
## pIndD 7.083e-01 3.559e-02 2.884e+04 19.904 < 2e-16 ***
## pIndR 2.092e-01 3.647e-02 2.884e+04 5.736 9.79e-09 ***
## index_NEexp.c:pIndD -4.684e-02 1.392e-02 2.884e+04 -3.364 0.000769 ***
## index_NEexp.c:pIndR -3.086e-03 1.522e-02 2.885e+04 -0.203 0.839379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. pIndD pIndR i_NE.:ID
## indx_NExp.c 0.065
## pIndD -0.446 -0.121
## pIndR -0.434 -0.091 0.711
## indx_NE.:ID -0.056 -0.860 0.059 0.088
## indx_NE.:IR -0.051 -0.778 0.084 0.139 0.7306. NEGATIVE VS. POSITIVE EMOTIONS
A. risk severity
risk severity ~ indexes * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c + index_NEexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90323.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3955 -0.6713 -0.0457 0.6338 3.0313
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008713 0.09334
## Residual 1.339087 1.15719
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.161e+00 2.800e-02 1.055e+01 148.616 < 2e-16 ***
## index_PEexp.c 5.680e-02 5.884e-03 7.443e+02 9.654 < 2e-16 ***
## index_NEexp.c 1.807e-02 8.573e-03 4.622e+02 2.107 0.035626 *
## pDem_Rep -7.625e-01 1.533e-02 2.881e+04 -49.738 < 2e-16 ***
## pInd_Not -7.400e-03 1.870e-02 2.881e+04 -0.396 0.692299
## index_PEexp.c:pDem_Rep 2.728e-02 7.773e-03 2.705e+04 3.510 0.000449 ***
## index_PEexp.c:pInd_Not -1.119e-02 1.012e-02 2.882e+04 -1.107 0.268454
## index_NEexp.c:pDem_Rep 2.000e-02 9.945e-03 2.633e+04 2.011 0.044340 *
## index_NEexp.c:pInd_Not 1.466e-02 1.243e-02 2.882e+04 1.179 0.238243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. in_NE. pDm_Rp pInd_N i_PE.:D i_PE.:I i_NE.:D
## indx_PExp.c 0.013
## indx_NExp.c 0.000 -0.896
## pDem_Rep 0.018 0.037 0.030
## pInd_Not -0.109 -0.058 0.013 0.037
## ind_PE.:D_R 0.021 0.212 -0.200 0.012 0.045
## ind_PE.:I_N -0.020 -0.345 0.233 0.041 0.097 0.104
## ind_NE.:D_R 0.002 -0.229 0.269 0.019 0.006 -0.793 -0.094
## ind_NE.:I_N 0.004 0.265 -0.272 0.006 -0.011 -0.100 -0.793 0.137## Warning: Removed 9 row(s) containing missing values (geom_path).
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_PEexp.c + index_NEexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90323.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3955 -0.6713 -0.0457 0.6338 3.0313
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008713 0.09334
## Residual 1.339087 1.15719
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.161e+00 2.800e-02 1.055e+01 148.616 < 2e-16 ***
## index_PEexp.c 5.680e-02 5.884e-03 7.443e+02 9.654 < 2e-16 ***
## index_NEexp.c 1.807e-02 8.573e-03 4.622e+02 2.107 0.035626 *
## pDem_Rep -7.625e-01 1.533e-02 2.881e+04 -49.738 < 2e-16 ***
## pInd_Not -7.400e-03 1.870e-02 2.881e+04 -0.396 0.692299
## index_PEexp.c:pDem_Rep 2.728e-02 7.773e-03 2.705e+04 3.510 0.000449 ***
## index_PEexp.c:pInd_Not -1.119e-02 1.012e-02 2.882e+04 -1.107 0.268454
## index_NEexp.c:pDem_Rep 2.000e-02 9.945e-03 2.633e+04 2.011 0.044340 *
## index_NEexp.c:pInd_Not 1.466e-02 1.243e-02 2.882e+04 1.179 0.238243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_PE. in_NE. pDm_Rp pInd_N i_PE.:D i_PE.:I i_NE.:D
## indx_PExp.c 0.013
## indx_NExp.c 0.000 -0.896
## pDem_Rep 0.018 0.037 0.030
## pInd_Not -0.109 -0.058 0.013 0.037
## ind_PE.:D_R 0.021 0.212 -0.200 0.012 0.045
## ind_PE.:I_N -0.020 -0.345 0.233 0.041 0.097 0.104
## ind_NE.:D_R 0.002 -0.229 0.269 0.019 0.006 -0.793 -0.094
## ind_NE.:I_N 0.004 0.265 -0.272 0.006 -0.011 -0.100 -0.793 0.137## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_NEexp.c + index_PEexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90323.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3955 -0.6713 -0.0457 0.6338 3.0313
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008713 0.09334
## Residual 1.339087 1.15719
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.540e+00 2.885e-02 1.188e+01 157.391 < 2e-16 ***
## index_NEexp.c 1.290e-02 8.210e-03 6.751e+02 1.572 0.116438
## index_PEexp.c 3.947e-02 5.909e-03 1.327e+03 6.680 3.51e-11 ***
## pDemR -7.625e-01 1.533e-02 2.881e+04 -49.738 < 2e-16 ***
## pDemI -3.738e-01 1.994e-02 2.882e+04 -18.746 < 2e-16 ***
## index_NEexp.c:pDemR 2.000e-02 9.945e-03 2.633e+04 2.011 0.044340 *
## index_NEexp.c:pDemI -4.661e-03 1.274e-02 2.868e+04 -0.366 0.714506
## index_PEexp.c:pDemR 2.728e-02 7.773e-03 2.705e+04 3.510 0.000449 ***
## index_PEexp.c:pDemI 2.483e-02 1.045e-02 2.874e+04 2.376 0.017507 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDemR pDemI i_NE.:DR i_NE.:DI i_PE.:DR
## indx_NExp.c -0.005
## indx_PExp.c -0.032 -0.862
## pDemR -0.240 0.023 0.052
## pDemI -0.185 0.005 0.051 0.349
## indx_NE.:DR -0.001 -0.257 0.240 0.019 0.002
## indx_NE.:DI -0.001 -0.229 0.209 0.002 -0.012 0.257
## indx_PE.:DR 0.026 0.222 -0.388 0.012 -0.038 -0.793 -0.212
## indx_PE.:DI 0.019 0.176 -0.292 -0.036 0.058 -0.204 -0.793 0.2712. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124049.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9604 -0.7763 0.0450 0.9542 1.6350
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.009048 0.09512
## Residual 4.301733 2.07406
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.436e-01 3.414e-02 1.576e+01 4.207 0.00069 ***
## index_NEexp.c 2.630e-02 1.846e-02 2.154e+02 1.425 0.15561
## index_PEexp.c 6.723e-02 1.305e-02 3.918e+02 5.154 4.06e-07 ***
## pRepD 4.949e-01 2.744e-02 2.872e+04 18.034 < 2e-16 ***
## pRepI -2.003e-01 3.668e-02 2.884e+04 -5.460 4.80e-08 ***
## index_NEexp.c:pRepD -1.573e-02 1.772e-02 2.189e+04 -0.888 0.37480
## index_NEexp.c:pRepI -1.724e-02 2.508e-02 2.884e+04 -0.687 0.49184
## index_PEexp.c:pRepD -1.985e-02 1.386e-02 2.191e+04 -1.432 0.15230
## index_PEexp.c:pRepI 2.646e-02 2.007e-02 2.878e+04 1.319 0.18731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pRepD pRepI i_NE.:RD i_NE.:RI i_PE.:RD
## indx_NExp.c 0.019
## indx_PExp.c 0.050 -0.853
## pRepD -0.440 -0.030 -0.060
## pRepI -0.328 -0.018 -0.043 0.408
## indx_NE.:RD -0.013 -0.725 0.621 0.018 0.012
## indx_NE.:RI -0.008 -0.471 0.403 0.011 -0.003 0.476
## indx_PE.:RD -0.050 0.561 -0.728 0.013 0.046 -0.792 -0.369
## indx_PE.:RI -0.034 0.351 -0.468 0.042 0.109 -0.360 -0.794 0.4403. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_NEexp.c + index_PEexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90323.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3955 -0.6713 -0.0457 0.6338 3.0313
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008713 0.09334
## Residual 1.339087 1.15719
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.166e+00 3.189e-02 1.776e+01 130.626 < 2e-16 ***
## index_NEexp.c 8.245e-03 1.348e-02 2.371e+03 0.612 0.5409
## index_PEexp.c 6.430e-02 1.039e-02 5.143e+03 6.186 6.65e-10 ***
## pIndD 3.738e-01 1.994e-02 2.882e+04 18.746 < 2e-16 ***
## pIndR -3.886e-01 2.047e-02 2.881e+04 -18.986 < 2e-16 ***
## index_NEexp.c:pIndD 4.661e-03 1.274e-02 2.868e+04 0.366 0.7145
## index_NEexp.c:pIndR 2.466e-02 1.401e-02 2.882e+04 1.761 0.0783 .
## index_PEexp.c:pIndD -2.483e-02 1.045e-02 2.874e+04 -2.376 0.0175 *
## index_PEexp.c:pIndR 2.448e-03 1.121e-02 2.882e+04 0.218 0.8271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pIndD pIndR i_NE.:ID i_NE.:IR i_PE.:ID
## indx_NExp.c -0.008
## indx_PExp.c 0.055 -0.831
## pIndD -0.458 0.008 -0.088
## pIndR -0.446 0.020 -0.090 0.713
## indx_NE.:ID 0.008 -0.805 0.678 -0.012 -0.013
## indx_NE.:IR 0.007 -0.671 0.568 -0.012 -0.003 0.727
## indx_PE.:ID -0.053 0.642 -0.839 0.058 0.083 -0.793 -0.576
## indx_PE.:IR -0.050 0.553 -0.746 0.081 0.110 -0.592 -0.794 0.745B. worst of Covid is ahead/behind
worstBorA ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106003.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.03215 -0.63674 0.04346 0.72727 2.09089
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008864 0.09415
## Residual 2.297143 1.51563
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.645e-01 2.895e-02 9.299e+00 26.410 4.61e-10 ***
## index_NEexp.c 3.689e-02 1.075e-02 1.886e+02 3.431 0.000738 ***
## index_PEexp.c 3.607e-02 7.449e-03 3.151e+02 4.842 2.01e-06 ***
## pDem_Rep -1.180e+00 2.006e-02 2.882e+04 -58.825 < 2e-16 ***
## pInd_Not -8.013e-02 2.449e-02 2.885e+04 -3.272 0.001068 **
## index_NEexp.c:pDem_Rep 2.852e-02 1.300e-02 2.389e+04 2.194 0.028220 *
## index_NEexp.c:pInd_Not 1.133e-02 1.628e-02 2.885e+04 0.696 0.486399
## index_PEexp.c:pDem_Rep 5.509e-02 1.016e-02 2.481e+04 5.423 5.92e-08 ***
## index_PEexp.c:pInd_Not 8.128e-03 1.325e-02 2.886e+04 0.614 0.539467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDm_Rp pInd_N i_NE.:D i_NE.:I i_PE.:D
## indx_NExp.c 0.000
## indx_PExp.c 0.018 -0.889
## pDem_Rep 0.023 0.029 0.041
## pInd_Not -0.138 0.013 -0.059 0.037
## ind_NE.:D_R 0.003 0.264 -0.223 0.019 0.006
## ind_NE.:I_N 0.005 -0.282 0.272 0.006 -0.011 0.137
## ind_PE.:D_R 0.026 -0.196 0.208 0.013 0.045 -0.793 -0.100
## ind_PE.:I_N -0.025 0.240 -0.354 0.041 0.097 -0.095 -0.793 0.104## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 9 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106003.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.03215 -0.63674 0.04346 0.72727 2.09089
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008864 0.09415
## Residual 2.297143 1.51563
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.328e+00 3.033e-02 1.121e+01 43.782 6.90e-14 ***
## index_NEexp.c 2.637e-02 1.038e-02 2.851e+02 2.541 0.0116 *
## index_PEexp.c 1.121e-02 7.546e-03 5.860e+02 1.485 0.1381
## pDemR -1.180e+00 2.006e-02 2.882e+04 -58.825 < 2e-16 ***
## pDemI -5.099e-01 2.611e-02 2.886e+04 -19.527 < 2e-16 ***
## index_NEexp.c:pDemR 2.852e-02 1.300e-02 2.389e+04 2.194 0.0282 *
## index_NEexp.c:pDemI 2.931e-03 1.668e-02 2.852e+04 0.176 0.8605
## index_PEexp.c:pDemR 5.509e-02 1.016e-02 2.481e+04 5.423 5.92e-08 ***
## index_PEexp.c:pDemI 1.942e-02 1.368e-02 2.865e+04 1.419 0.1559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDemR pDemI i_NE.:DR i_NE.:DI i_PE.:DR
## indx_NExp.c -0.005
## indx_PExp.c -0.041 -0.855
## pDemR -0.299 0.021 0.056
## pDemI -0.230 0.004 0.052 0.349
## indx_NE.:DR -0.002 -0.282 0.259 0.019 0.002
## indx_NE.:DI -0.001 -0.246 0.221 0.002 -0.012 0.256
## indx_PE.:DR 0.033 0.242 -0.407 0.013 -0.038 -0.793 -0.211
## indx_PE.:DI 0.023 0.190 -0.306 -0.035 0.058 -0.203 -0.792 0.2702. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106003.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.03215 -0.63674 0.04346 0.72727 2.09089
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008864 0.09415
## Residual 2.297143 1.51563
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.481e-01 3.096e-02 1.216e+01 4.782 0.000430 ***
## index_NEexp.c 5.489e-02 1.416e-02 3.818e+02 3.876 0.000125 ***
## index_PEexp.c 6.630e-02 9.885e-03 6.704e+02 6.707 4.22e-11 ***
## pRepD 1.180e+00 2.006e-02 2.882e+04 58.825 < 2e-16 ***
## pRepI 6.701e-01 2.681e-02 2.885e+04 25.000 < 2e-16 ***
## index_NEexp.c:pRepD -2.852e-02 1.300e-02 2.389e+04 -2.194 0.028220 *
## index_NEexp.c:pRepI -2.559e-02 1.834e-02 2.885e+04 -1.396 0.162874
## index_PEexp.c:pRepD -5.509e-02 1.016e-02 2.481e+04 -5.423 5.92e-08 ***
## index_PEexp.c:pRepI -3.567e-02 1.467e-02 2.884e+04 -2.431 0.015047 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pRepD pRepI i_NE.:RD i_NE.:RI i_PE.:RD
## indx_NExp.c 0.016
## indx_PExp.c 0.038 -0.862
## pRepD -0.355 -0.032 -0.056
## pRepI -0.264 -0.020 -0.040 0.408
## indx_NE.:RD -0.011 -0.711 0.617 0.019 0.013
## indx_NE.:RI -0.007 -0.455 0.394 0.012 -0.003 0.476
## indx_PE.:RD -0.040 0.550 -0.717 0.013 0.046 -0.793 -0.370
## indx_PE.:RI -0.028 0.337 -0.455 0.042 0.110 -0.360 -0.794 0.4403. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_NEexp.c + index_PEexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 106003.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.03215 -0.63674 0.04346 0.72727 2.09089
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.008864 0.09415
## Residual 2.297143 1.51563
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.182e-01 3.519e-02 2.030e+01 23.253 4.17e-16 ***
## index_NEexp.c 2.930e-02 1.734e-02 1.110e+03 1.690 0.0914 .
## index_PEexp.c 3.062e-02 1.346e-02 2.592e+03 2.276 0.0229 *
## pIndD 5.099e-01 2.611e-02 2.886e+04 19.527 < 2e-16 ***
## pIndR -6.701e-01 2.681e-02 2.885e+04 -25.000 < 2e-16 ***
## index_NEexp.c:pIndD -2.931e-03 1.668e-02 2.852e+04 -0.176 0.8605
## index_NEexp.c:pIndR 2.559e-02 1.834e-02 2.885e+04 1.396 0.1629
## index_PEexp.c:pIndD -1.942e-02 1.368e-02 2.865e+04 -1.419 0.1559
## index_PEexp.c:pIndR 3.567e-02 1.467e-02 2.884e+04 2.431 0.0150 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pIndD pIndR i_NE.:ID i_NE.:IR i_PE.:ID
## indx_NExp.c -0.010
## indx_PExp.c 0.066 -0.827
## pIndD -0.544 0.009 -0.089
## pIndR -0.529 0.019 -0.090 0.713
## indx_NE.:ID 0.009 -0.815 0.682 -0.012 -0.013
## indx_NE.:IR 0.008 -0.686 0.577 -0.012 -0.003 0.728
## indx_PE.:ID -0.063 0.649 -0.845 0.058 0.083 -0.792 -0.577
## indx_PE.:IR -0.059 0.565 -0.756 0.081 0.110 -0.593 -0.794 0.745C. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124049.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9604 -0.7763 0.0450 0.9542 1.6350
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.009048 0.09512
## Residual 4.301733 2.07406
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.433e-01 3.066e-02 1.026e+01 7.936 1.08e-05 ***
## index_NEexp.c 1.534e-02 1.372e-02 9.746e+01 1.118 0.2663
## index_PEexp.c 6.932e-02 9.668e-03 1.719e+02 7.170 2.13e-11 ***
## pDem_Rep -4.949e-01 2.744e-02 2.872e+04 -18.034 < 2e-16 ***
## pInd_Not 4.477e-01 3.351e-02 2.884e+04 13.361 < 2e-16 ***
## index_NEexp.c:pDem_Rep 1.573e-02 1.772e-02 2.189e+04 0.888 0.3748
## index_NEexp.c:pInd_Not 9.374e-03 2.227e-02 2.884e+04 0.421 0.6738
## index_PEexp.c:pDem_Rep 1.985e-02 1.387e-02 2.191e+04 1.432 0.1523
## index_PEexp.c:pInd_Not -3.639e-02 1.812e-02 2.884e+04 -2.008 0.0447 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDm_Rp pInd_N i_NE.:D i_NE.:I i_PE.:D
## indx_NExp.c 0.000
## indx_PExp.c 0.024 -0.878
## pDem_Rep 0.029 0.025 0.046
## pInd_Not -0.178 0.013 -0.062 0.037
## ind_NE.:D_R 0.004 0.255 -0.211 0.018 0.006
## ind_NE.:I_N 0.007 -0.299 0.284 0.006 -0.011 0.138
## ind_PE.:D_R 0.034 -0.189 0.201 0.013 0.045 -0.792 -0.100
## ind_PE.:I_N -0.032 0.253 -0.370 0.041 0.097 -0.095 -0.793 0.104## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124049.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9604 -0.7763 0.0450 0.9542 1.6350
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.009048 0.09512
## Residual 4.301733 2.07406
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.385e-01 3.308e-02 1.389e+01 19.303 1.98e-11 ***
## index_NEexp.c 1.057e-02 1.344e-02 1.558e+02 0.787 0.4326
## index_PEexp.c 4.739e-02 9.946e-03 3.387e+02 4.764 2.81e-06 ***
## pDemR -4.949e-01 2.744e-02 2.872e+04 -18.034 < 2e-16 ***
## pDemI -6.951e-01 3.573e-02 2.884e+04 -19.453 < 2e-16 ***
## index_NEexp.c:pDemR 1.573e-02 1.772e-02 2.189e+04 0.888 0.3748
## index_NEexp.c:pDemI -1.509e-03 2.281e-02 2.830e+04 -0.066 0.9473
## index_PEexp.c:pDemR 1.985e-02 1.387e-02 2.191e+04 1.432 0.1523
## index_PEexp.c:pDemI 4.631e-02 1.872e-02 2.848e+04 2.474 0.0134 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pDemR pDemI i_NE.:DR i_NE.:DI i_PE.:DR
## indx_NExp.c -0.005
## indx_PExp.c -0.054 -0.844
## pDemR -0.376 0.017 0.061
## pDemI -0.288 0.004 0.055 0.349
## indx_NE.:DR -0.002 -0.323 0.290 0.018 0.001
## indx_NE.:DI -0.001 -0.273 0.240 0.001 -0.012 0.254
## indx_PE.:DR 0.041 0.275 -0.439 0.013 -0.038 -0.792 -0.210
## indx_PE.:DI 0.029 0.211 -0.326 -0.035 0.058 -0.201 -0.792 0.2692. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124049.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9604 -0.7763 0.0450 0.9542 1.6350
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.009048 0.09512
## Residual 4.301733 2.07406
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.436e-01 3.414e-02 1.576e+01 4.207 0.00069 ***
## index_NEexp.c 2.630e-02 1.846e-02 2.154e+02 1.425 0.15561
## index_PEexp.c 6.723e-02 1.305e-02 3.918e+02 5.154 4.06e-07 ***
## pRepD 4.949e-01 2.744e-02 2.872e+04 18.034 < 2e-16 ***
## pRepI -2.003e-01 3.668e-02 2.884e+04 -5.460 4.80e-08 ***
## index_NEexp.c:pRepD -1.573e-02 1.772e-02 2.189e+04 -0.888 0.37480
## index_NEexp.c:pRepI -1.724e-02 2.508e-02 2.884e+04 -0.687 0.49184
## index_PEexp.c:pRepD -1.985e-02 1.386e-02 2.191e+04 -1.432 0.15230
## index_PEexp.c:pRepI 2.646e-02 2.007e-02 2.878e+04 1.319 0.18731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pRepD pRepI i_NE.:RD i_NE.:RI i_PE.:RD
## indx_NExp.c 0.019
## indx_PExp.c 0.050 -0.853
## pRepD -0.440 -0.030 -0.060
## pRepI -0.328 -0.018 -0.043 0.408
## indx_NE.:RD -0.013 -0.725 0.621 0.018 0.012
## indx_NE.:RI -0.008 -0.471 0.403 0.011 -0.003 0.476
## indx_PE.:RD -0.050 0.561 -0.728 0.013 0.046 -0.792 -0.369
## indx_PE.:RI -0.034 0.351 -0.468 0.042 0.109 -0.360 -0.794 0.4403. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_NEexp.c + index_PEexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124049.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9604 -0.7763 0.0450 0.9542 1.6350
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.009048 0.09512
## Residual 4.301733 2.07406
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.665e-02 4.110e-02 3.313e+01 -1.378 0.1774
## index_NEexp.c 9.063e-03 2.310e-02 6.863e+02 0.392 0.6949
## index_PEexp.c 9.370e-02 1.810e-02 1.673e+03 5.176 2.54e-07 ***
## pIndD 6.951e-01 3.573e-02 2.884e+04 19.453 < 2e-16 ***
## pIndR 2.003e-01 3.668e-02 2.884e+04 5.460 4.80e-08 ***
## index_NEexp.c:pIndD 1.509e-03 2.281e-02 2.830e+04 0.066 0.9473
## index_NEexp.c:pIndR 1.724e-02 2.508e-02 2.884e+04 0.687 0.4918
## index_PEexp.c:pIndD -4.631e-02 1.872e-02 2.848e+04 -2.474 0.0134 *
## index_PEexp.c:pIndR -2.646e-02 2.007e-02 2.878e+04 -1.319 0.1873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_NE. in_PE. pIndD pIndR i_NE.:ID i_NE.:IR i_PE.:ID
## indx_NExp.c -0.011
## indx_PExp.c 0.079 -0.821
## pIndD -0.637 0.010 -0.090
## pIndR -0.620 0.018 -0.090 0.713
## indx_NE.:ID 0.011 -0.829 0.687 -0.012 -0.013
## indx_NE.:IR 0.010 -0.709 0.590 -0.012 -0.003 0.730
## indx_PE.:ID -0.074 0.659 -0.855 0.058 0.083 -0.792 -0.578
## indx_PE.:IR -0.069 0.582 -0.771 0.080 0.109 -0.594 -0.794 0.7477. AFFECT VS. ANALYTICAL THINKING
A. risk3
risk3 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 103690.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12822 -0.82218 0.06169 0.58796 2.45196
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003239 0.05691
## Residual 2.122179 1.45677
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.515e+00 1.903e-02 8.635e+00 237.258 < 2e-16 ***
## index_AFexp.c 1.755e-02 4.864e-03 7.179e+01 3.608 0.000566 ***
## index_ANexp.c 1.739e-03 2.535e-04 9.807e+01 6.861 6.19e-10 ***
## pDem_Rep -1.461e+00 1.935e-02 2.884e+04 -75.507 < 2e-16 ***
## pInd_Not 1.600e-01 2.357e-02 2.884e+04 6.788 1.16e-11 ***
## index_AFexp.c:pDem_Rep 8.172e-03 6.494e-03 2.648e+04 1.258 0.208230
## index_AFexp.c:pInd_Not -3.881e-03 8.568e-03 2.878e+04 -0.453 0.650590
## index_ANexp.c:pDem_Rep 2.777e-03 3.639e-04 2.775e+04 7.632 2.38e-14 ***
## index_ANexp.c:pInd_Not -2.435e-04 4.768e-04 2.880e+04 -0.511 0.609570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.004
## indx_ANxp.c 0.021 -0.917
## pDem_Rep 0.034 0.006 0.055
## pInd_Not -0.200 -0.015 -0.026 0.040
## ind_AF.:D_R 0.003 0.165 -0.154 -0.003 0.003
## ind_AF.:I_N -0.007 -0.360 0.327 0.002 0.016 0.095
## ind_AN.:D_R 0.039 -0.146 0.176 0.035 0.048 -0.845 -0.080
## ind_AN.:I_N -0.020 0.310 -0.374 0.044 0.064 -0.081 -0.853 0.1011. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 103690.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12822 -0.82218 0.06169 0.58796 2.45196
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003239 0.05691
## Residual 2.122179 1.45677
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.299e+00 2.094e-02 1.266e+01 253.015 < 2e-16 ***
## index_AFexp.c 1.219e-02 5.035e-03 1.248e+02 2.420 0.01696 *
## index_ANexp.c 2.702e-04 2.650e-04 1.868e+02 1.020 0.30924
## pDemR -1.461e+00 1.935e-02 2.884e+04 -75.507 < 2e-16 ***
## pDemI -8.904e-01 2.511e-02 2.884e+04 -35.453 < 2e-16 ***
## index_AFexp.c:pDemR 8.172e-03 6.494e-03 2.648e+04 1.258 0.20823
## index_AFexp.c:pDemI 7.967e-03 8.871e-03 2.820e+04 0.898 0.36914
## index_ANexp.c:pDemR 2.777e-03 3.639e-04 2.775e+04 7.632 2.38e-14 ***
## index_ANexp.c:pDemI 1.632e-03 4.929e-04 2.827e+04 3.311 0.00093 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.008
## indx_ANxp.c -0.049 -0.898
## pDemR -0.416 0.008 0.054
## pDemI -0.321 0.010 0.039 0.348
## indx_AF.:DR 0.005 -0.432 0.384 -0.003 -0.004
## indx_AF.:DI 0.003 -0.306 0.275 -0.003 0.012 0.275
## indx_AN.:DR 0.037 0.358 -0.458 0.035 -0.031 -0.845 -0.232
## indx_AN.:DI 0.028 0.256 -0.330 -0.030 0.030 -0.234 -0.854 0.2722. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 103690.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12822 -0.82218 0.06169 0.58796 2.45196
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003239 0.05691
## Residual 2.122179 1.45677
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.838e+00 2.181e-02 1.488e+01 175.989 < 2e-16 ***
## index_AFexp.c 2.036e-02 6.266e-03 1.716e+02 3.249 0.00139 **
## index_ANexp.c 3.047e-03 3.380e-04 2.673e+02 9.015 < 2e-16 ***
## pRepD 1.461e+00 1.935e-02 2.884e+04 75.507 < 2e-16 ***
## pRepI 5.704e-01 2.583e-02 2.884e+04 22.083 < 2e-16 ***
## index_AFexp.c:pRepD -8.172e-03 6.494e-03 2.648e+04 -1.258 0.20823
## index_AFexp.c:pRepI -2.051e-04 9.446e-03 2.884e+04 -0.022 0.98268
## index_ANexp.c:pRepD -2.777e-03 3.639e-04 2.775e+04 -7.632 2.38e-14 ***
## index_ANexp.c:pRepI -1.145e-03 5.272e-04 2.884e+04 -2.172 0.02986 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c 0.002
## indx_ANxp.c 0.072 -0.886
## pRepD -0.487 -0.004 -0.080
## pRepI -0.365 0.001 -0.063 0.411
## indx_AF.:RD -0.002 -0.689 0.608 -0.003 0.002
## indx_AF.:RI -0.001 -0.437 0.386 0.001 0.014 0.430
## indx_AN.:RD -0.067 0.588 -0.717 0.035 0.056 -0.845 -0.363
## indx_AN.:RI -0.046 0.371 -0.464 0.052 0.087 -0.365 -0.851 0.4363. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk3 ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 103690.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12822 -0.82218 0.06169 0.58796 2.45196
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.003239 0.05691
## Residual 2.122179 1.45677
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.408e+00 2.705e-02 3.524e+01 162.958 < 2e-16 ***
## index_AFexp.c 2.015e-02 8.758e-03 5.736e+02 2.301 0.02174 *
## index_ANexp.c 1.902e-03 4.763e-04 8.919e+02 3.993 7.05e-05 ***
## pIndD 8.904e-01 2.511e-02 2.884e+04 35.453 < 2e-16 ***
## pIndR -5.704e-01 2.583e-02 2.884e+04 -22.083 < 2e-16 ***
## index_AFexp.c:pIndD -7.967e-03 8.871e-03 2.820e+04 -0.898 0.36914
## index_AFexp.c:pIndR 2.051e-04 9.446e-03 2.884e+04 0.022 0.98268
## index_ANexp.c:pIndD -1.632e-03 4.929e-04 2.827e+04 -3.311 0.00093 ***
## index_ANexp.c:pIndR 1.145e-03 5.272e-04 2.884e+04 2.172 0.02986 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.015
## indx_ANxp.c 0.050 -0.876
## pIndD -0.680 -0.018 -0.053
## pIndR -0.661 -0.016 -0.052 0.712
## indx_AF.:ID -0.013 -0.837 0.731 0.012 0.014
## indx_AF.:IR -0.013 -0.766 0.667 0.014 0.014 0.750
## indx_AN.:ID -0.050 0.718 -0.851 0.030 0.052 -0.854 -0.642
## indx_AN.:IR -0.047 0.652 -0.778 0.050 0.087 -0.639 -0.851 0.747B. risk4
risk4 ~ index * party + (1 | media)
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 99436.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2172 -0.5804 0.1492 0.5816 1.7353
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 2.683e-15 5.180e-08
## Residual 1.832e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.933e+00 8.923e-03 2.884e+04 552.849 < 2e-16 ***
## index_AFexp.c 3.258e-03 3.154e-03 2.884e+04 1.033 0.3015
## index_ANexp.c 3.776e-04 1.760e-04 2.884e+04 2.146 0.0319 *
## pDem_Rep -5.095e-01 1.796e-02 2.884e+04 -28.369 < 2e-16 ***
## pInd_Not 1.665e-01 2.189e-02 2.884e+04 7.608 2.88e-14 ***
## index_AFexp.c:pDem_Rep -7.187e-04 5.995e-03 2.884e+04 -0.120 0.9046
## index_AFexp.c:pInd_Not -1.661e-03 7.954e-03 2.884e+04 -0.209 0.8345
## index_ANexp.c:pDem_Rep 5.739e-04 3.367e-04 2.884e+04 1.705 0.0883 .
## index_ANexp.c:pInd_Not 2.610e-04 4.426e-04 2.884e+04 0.590 0.5554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.010
## indx_ANxp.c 0.054 -0.851
## pDem_Rep 0.067 0.004 0.073
## pInd_Not -0.397 -0.014 -0.040 0.041
## ind_AF.:D_R 0.004 0.164 -0.141 -0.002 0.003
## ind_AF.:I_N -0.013 -0.474 0.403 0.002 0.015 0.097
## ind_AN.:D_R 0.077 -0.140 0.175 0.034 0.047 -0.847 -0.083
## ind_AN.:I_N -0.039 0.404 -0.466 0.043 0.064 -0.084 -0.853 0.104
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular1. simple effects for dems
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 99436.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2172 -0.5804 0.1492 0.5816 1.7353
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 2.683e-15 5.180e-08
## Residual 1.832e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.243e+00 1.206e-02 2.884e+04 434.828 <2e-16 ***
## index_AFexp.c 3.069e-03 3.654e-03 2.884e+04 0.840 0.4010
## index_ANexp.c 1.768e-04 2.031e-04 2.884e+04 0.871 0.3840
## pDemR -5.095e-01 1.796e-02 2.884e+04 -28.369 <2e-16 ***
## pDemI -4.213e-01 2.332e-02 2.884e+04 -18.064 <2e-16 ***
## index_AFexp.c:pDemR -7.187e-04 5.995e-03 2.884e+04 -0.120 0.9046
## index_AFexp.c:pDemI 1.302e-03 8.224e-03 2.884e+04 0.158 0.8742
## index_ANexp.c:pDemR 5.739e-04 3.367e-04 2.884e+04 1.705 0.0883 .
## index_ANexp.c:pDemI 2.600e-05 4.569e-04 2.884e+04 0.057 0.9546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.010
## indx_ANxp.c -0.099 -0.852
## pDemR -0.671 0.007 0.066
## pDemI -0.517 0.005 0.051 0.347
## indx_AF.:DR 0.006 -0.610 0.519 -0.002 -0.003
## indx_AF.:DI 0.005 -0.444 0.379 -0.003 0.012 0.271
## indx_AN.:DR 0.060 0.514 -0.603 0.034 -0.031 -0.847 -0.228
## indx_AN.:DI 0.044 0.379 -0.444 -0.030 0.031 -0.231 -0.854 0.268
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular2. simple effects for reps
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 99436.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2172 -0.5804 0.1492 0.5816 1.7353
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 2.683e-15 5.180e-08
## Residual 1.832e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.733e+00 1.331e-02 2.884e+04 355.533 < 2e-16 ***
## index_AFexp.c 2.350e-03 4.753e-03 2.884e+04 0.495 0.620927
## index_ANexp.c 7.507e-04 2.685e-04 2.884e+04 2.796 0.005184 **
## pRepD 5.095e-01 1.796e-02 2.884e+04 28.369 < 2e-16 ***
## pRepI 8.822e-02 2.400e-02 2.884e+04 3.676 0.000237 ***
## index_AFexp.c:pRepD 7.187e-04 5.995e-03 2.884e+04 0.120 0.904574
## index_AFexp.c:pRepI 2.021e-03 8.767e-03 2.884e+04 0.230 0.817707
## index_ANexp.c:pRepD -5.739e-04 3.367e-04 2.884e+04 -1.705 0.088256 .
## index_ANexp.c:pRepI -5.479e-04 4.896e-04 2.884e+04 -1.119 0.263047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c 0.003
## indx_ANxp.c 0.125 -0.844
## pRepD -0.741 -0.002 -0.093
## pRepI -0.555 -0.002 -0.069 0.411
## indx_AF.:RD -0.003 -0.793 0.669 -0.002 0.001
## indx_AF.:RI -0.002 -0.542 0.458 0.001 0.014 0.430
## indx_AN.:RD -0.100 0.673 -0.798 0.034 0.055 -0.847 -0.365
## indx_AN.:RI -0.069 0.463 -0.549 0.051 0.087 -0.367 -0.851 0.438
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular3. simple effects for indep
## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk4 ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 99436.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2172 -0.5804 0.1492 0.5816 1.7353
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 2.683e-15 5.180e-08
## Residual 1.832e+00 1.354e+00
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.821e+00 1.997e-02 2.884e+04 241.478 < 2e-16 ***
## index_AFexp.c 4.371e-03 7.367e-03 2.884e+04 0.593 0.552964
## index_ANexp.c 2.028e-04 4.093e-04 2.884e+04 0.495 0.620318
## pIndD 4.213e-01 2.332e-02 2.884e+04 18.064 < 2e-16 ***
## pIndR -8.822e-02 2.400e-02 2.884e+04 -3.676 0.000237 ***
## index_AFexp.c:pIndD -1.302e-03 8.224e-03 2.884e+04 -0.158 0.874197
## index_AFexp.c:pIndR -2.021e-03 8.767e-03 2.884e+04 -0.230 0.817707
## index_ANexp.c:pIndD -2.600e-05 4.569e-04 2.884e+04 -0.057 0.954630
## index_ANexp.c:pIndR 5.479e-04 4.896e-04 2.884e+04 1.119 0.263047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.018
## indx_ANxp.c 0.070 -0.854
## pIndD -0.856 -0.016 -0.060
## pIndR -0.832 -0.015 -0.058 0.712
## indx_AF.:ID -0.016 -0.896 0.765 0.012 0.014
## indx_AF.:IR -0.015 -0.840 0.718 0.013 0.014 0.753
## indx_AN.:ID -0.063 0.765 -0.896 0.031 0.052 -0.854 -0.643
## indx_AN.:IR -0.059 0.714 -0.836 0.050 0.087 -0.640 -0.851 0.749
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularC. risk5
risk5 ~ index * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 115021.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8669 -0.8501 -0.2479 0.7058 2.6044
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005883 0.0767
## Residual 3.137903 1.7714
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.046e+00 2.503e-02 1.038e+01 121.668 < 2e-16 ***
## index_AFexp.c -1.239e-02 6.069e-03 1.074e+02 -2.041 0.043694 *
## index_ANexp.c 3.938e-03 3.150e-04 1.446e+02 12.501 < 2e-16 ***
## pDem_Rep -2.837e-01 2.351e-02 2.886e+04 -12.063 < 2e-16 ***
## pInd_Not -3.282e-01 2.866e-02 2.885e+04 -11.454 < 2e-16 ***
## index_AFexp.c:pDem_Rep -9.514e-03 7.895e-03 2.724e+04 -1.205 0.228172
## index_AFexp.c:pInd_Not -8.805e-04 1.042e-02 2.881e+04 -0.085 0.932651
## index_ANexp.c:pDem_Rep 1.568e-03 4.422e-04 2.809e+04 3.545 0.000394 ***
## index_ANexp.c:pInd_Not 6.415e-04 5.797e-04 2.882e+04 1.106 0.268530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.003
## indx_ANxp.c 0.019 -0.921
## pDem_Rep 0.031 0.006 0.053
## pInd_Not -0.185 -0.015 -0.025 0.040
## ind_AF.:D_R 0.002 0.165 -0.155 -0.003 0.003
## ind_AF.:I_N -0.006 -0.354 0.323 0.002 0.016 0.094
## ind_AN.:D_R 0.036 -0.147 0.176 0.034 0.047 -0.845 -0.080
## ind_AN.:I_N -0.018 0.305 -0.369 0.043 0.064 -0.080 -0.853 0.1001. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 115021.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8669 -0.8501 -0.2479 0.7058 2.6044
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005883 0.0767
## Residual 3.137903 1.7714
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.079e+00 2.720e-02 1.446e+01 113.226 < 2e-16 ***
## index_AFexp.c -7.920e-03 6.245e-03 1.822e+02 -1.268 0.206365
## index_ANexp.c 3.366e-03 3.275e-04 2.688e+02 10.278 < 2e-16 ***
## pDemR -2.837e-01 2.351e-02 2.886e+04 -12.063 < 2e-16 ***
## pDemI 1.864e-01 3.054e-02 2.886e+04 6.104 1.05e-09 ***
## index_AFexp.c:pDemR -9.514e-03 7.895e-03 2.724e+04 -1.205 0.228172
## index_AFexp.c:pDemI -3.877e-03 1.079e-02 2.839e+04 -0.359 0.719395
## index_ANexp.c:pDemR 1.568e-03 4.422e-04 2.809e+04 3.545 0.000394 ***
## index_ANexp.c:pDemI 1.423e-04 5.995e-04 2.844e+04 0.237 0.812349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.008
## indx_ANxp.c -0.045 -0.902
## pDemR -0.390 0.009 0.053
## pDemI -0.300 0.011 0.038 0.348
## indx_AF.:DR 0.004 -0.420 0.374 -0.003 -0.004
## indx_AF.:DI 0.003 -0.296 0.267 -0.003 0.012 0.275
## indx_AN.:DR 0.035 0.347 -0.448 0.034 -0.031 -0.845 -0.232
## indx_AN.:DI 0.026 0.247 -0.321 -0.029 0.030 -0.234 -0.854 0.2722. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 115021.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8669 -0.8501 -0.2479 0.7058 2.6044
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005883 0.0767
## Residual 3.137903 1.7714
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.796e+00 2.817e-02 1.666e+01 99.224 < 2e-16 ***
## index_AFexp.c -1.743e-02 7.743e-03 2.470e+02 -2.252 0.025221 *
## index_ANexp.c 4.934e-03 4.162e-04 3.793e+02 11.855 < 2e-16 ***
## pRepD 2.837e-01 2.351e-02 2.886e+04 12.063 < 2e-16 ***
## pRepI 4.701e-01 3.140e-02 2.885e+04 14.969 < 2e-16 ***
## index_AFexp.c:pRepD 9.514e-03 7.895e-03 2.724e+04 1.205 0.228172
## index_AFexp.c:pRepI 5.638e-03 1.148e-02 2.885e+04 0.491 0.623459
## index_ANexp.c:pRepD -1.568e-03 4.422e-04 2.809e+04 -3.545 0.000394 ***
## index_ANexp.c:pRepI -1.425e-03 6.408e-04 2.885e+04 -2.224 0.026148 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c 0.002
## indx_ANxp.c 0.067 -0.889
## pRepD -0.458 -0.004 -0.078
## pRepI -0.343 0.001 -0.062 0.411
## indx_AF.:RD -0.002 -0.681 0.603 -0.003 0.002
## indx_AF.:RI -0.001 -0.429 0.380 0.001 0.014 0.429
## indx_AN.:RD -0.062 0.581 -0.710 0.034 0.056 -0.845 -0.363
## indx_AN.:RI -0.043 0.364 -0.456 0.051 0.087 -0.364 -0.851 0.4363. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: risk5 ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) + (1 |
## media)
## Data: dl2
##
## REML criterion at convergence: 115021.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8669 -0.8501 -0.2479 0.7058 2.6044
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.005883 0.0767
## Residual 3.137903 1.7714
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.266e+00 3.425e-02 3.638e+01 95.342 < 2e-16 ***
## index_AFexp.c -1.180e-02 1.075e-02 7.978e+02 -1.098 0.2727
## index_ANexp.c 3.508e-03 5.835e-04 1.228e+03 6.013 2.40e-09 ***
## pIndD -1.864e-01 3.054e-02 2.886e+04 -6.104 1.05e-09 ***
## pIndR -4.701e-01 3.140e-02 2.885e+04 -14.969 < 2e-16 ***
## index_AFexp.c:pIndD 3.877e-03 1.079e-02 2.839e+04 0.359 0.7194
## index_AFexp.c:pIndR -5.638e-03 1.148e-02 2.885e+04 -0.491 0.6235
## index_ANexp.c:pIndD -1.423e-04 5.995e-04 2.844e+04 -0.237 0.8123
## index_ANexp.c:pIndR 1.425e-03 6.408e-04 2.885e+04 2.224 0.0261 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.015
## indx_ANxp.c 0.048 -0.878
## pIndD -0.653 -0.018 -0.053
## pIndR -0.635 -0.016 -0.051 0.712
## indx_AF.:ID -0.013 -0.832 0.728 0.012 0.014
## indx_AF.:IR -0.012 -0.759 0.663 0.014 0.014 0.750
## indx_AN.:ID -0.048 0.714 -0.847 0.030 0.052 -0.854 -0.642
## indx_AN.:IR -0.045 0.647 -0.773 0.050 0.087 -0.639 -0.851 0.748D. risk severity
risk severity ~ indexes * party + (1 | media)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_ANexp.c + index_AFexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90247.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4572 -0.6635 -0.0553 0.6498 3.0275
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00159 0.03988
## Residual 1.33517 1.15550
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.164e+00 1.381e-02 1.179e+01 301.628 < 2e-16 ***
## index_ANexp.c 1.961e-03 1.955e-04 1.076e+02 10.032 < 2e-16 ***
## index_AFexp.c 4.032e-03 3.734e-03 7.735e+01 1.080 0.284
## pDem_Rep -7.523e-01 1.535e-02 2.882e+04 -48.998 < 2e-16 ***
## pInd_Not -1.026e-03 1.870e-02 2.882e+04 -0.055 0.956
## index_ANexp.c:pDem_Rep 1.631e-03 2.886e-04 2.785e+04 5.650 1.62e-08 ***
## index_ANexp.c:pInd_Not 2.299e-04 3.782e-04 2.878e+04 0.608 0.543
## index_AFexp.c:pDem_Rep -5.509e-04 5.151e-03 2.661e+04 -0.107 0.915
## index_AFexp.c:pInd_Not -2.288e-03 6.796e-03 2.877e+04 -0.337 0.736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDm_Rp pInd_N i_AN.:D i_AN.:I i_AF.:D
## indx_ANxp.c 0.023
## indx_AFxp.c 0.004 -0.913
## pDem_Rep 0.037 0.056 0.006
## pInd_Not -0.219 -0.027 -0.015 0.040
## ind_AN.:D_R 0.043 0.175 -0.145 0.035 0.047
## ind_AN.:I_N -0.022 -0.381 0.317 0.044 0.064 0.101
## ind_AF.:D_R 0.003 -0.153 0.164 -0.003 0.003 -0.845 -0.081
## ind_AF.:I_N -0.007 0.333 -0.368 0.002 0.016 -0.081 -0.853 0.095## Warning: Removed 9 row(s) containing missing values (geom_path).
## Warning: Removed 3 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_ANexp.c + index_AFexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90247.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4572 -0.6635 -0.0553 0.6498 3.0275
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00159 0.03988
## Residual 1.33517 1.15550
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.540e+00 1.545e-02 1.847e+01 293.856 < 2e-16 ***
## index_ANexp.c 1.222e-03 2.061e-04 2.115e+02 5.930 1.22e-08 ***
## index_AFexp.c 3.553e-03 3.897e-03 1.389e+02 0.912 0.364
## pDemR -7.523e-01 1.535e-02 2.882e+04 -48.998 < 2e-16 ***
## pDemI -3.751e-01 1.992e-02 2.882e+04 -18.828 < 2e-16 ***
## index_ANexp.c:pDemR 1.631e-03 2.886e-04 2.785e+04 5.650 1.62e-08 ***
## index_ANexp.c:pDemI 5.854e-04 3.909e-04 2.834e+04 1.498 0.134
## index_AFexp.c:pDemR -5.509e-04 5.151e-03 2.661e+04 -0.107 0.915
## index_AFexp.c:pDemI 2.012e-03 7.035e-03 2.828e+04 0.286 0.775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDemR pDemI i_AN.:DR i_AN.:DI i_AF.:DR
## indx_ANxp.c -0.054
## indx_AFxp.c -0.009 -0.894
## pDemR -0.448 0.056 0.008
## pDemI -0.345 0.040 0.010 0.348
## indx_AN.:DR 0.040 -0.473 0.373 0.035 -0.031
## indx_AN.:DI 0.030 -0.342 0.267 -0.029 0.031 0.271
## indx_AF.:DR 0.005 0.397 -0.449 -0.003 -0.004 -0.845 -0.233
## indx_AF.:DI 0.003 0.285 -0.319 -0.003 0.012 -0.231 -0.854 0.2742. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124023.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05264 -0.78366 0.04051 0.94417 1.61639
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0009917 0.03149
## Residual 4.2963644 2.07277
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.503e-01 2.232e-02 4.571e+01 6.733 2.36e-08 ***
## index_ANexp.c 2.382e-03 4.306e-04 2.167e+02 5.532 9.09e-08 ***
## index_AFexp.c 7.132e-03 7.738e-03 1.241e+02 0.922 0.359
## pRepD 4.851e-01 2.751e-02 2.878e+04 17.635 < 2e-16 ***
## pRepI -2.056e-01 3.675e-02 2.884e+04 -5.596 2.21e-08 ***
## index_ANexp.c:pRepD -4.293e-04 5.164e-04 2.715e+04 -0.831 0.406
## index_ANexp.c:pRepI 6.908e-04 7.497e-04 2.884e+04 0.921 0.357
## index_AFexp.c:pRepD -1.189e-02 9.202e-03 2.361e+04 -1.292 0.196
## index_AFexp.c:pRepI -4.966e-03 1.343e-02 2.873e+04 -0.370 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pRepD pRepI i_AN.:RD i_AN.:RI i_AF.:RD
## indx_ANxp.c 0.110
## indx_AFxp.c 0.003 -0.858
## pRepD -0.677 -0.089 -0.003
## pRepI -0.507 -0.067 -0.001 0.411
## indx_AN.:RD -0.091 -0.771 0.644 0.034 0.056
## indx_AN.:RI -0.063 -0.522 0.433 0.051 0.087 0.437
## indx_AF.:RD -0.003 0.649 -0.758 -0.003 0.002 -0.846 -0.366
## indx_AF.:RI -0.002 0.435 -0.508 0.001 0.014 -0.364 -0.851 0.4303. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskSeverity ~ (index_ANexp.c + index_AFexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 90247.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4572 -0.6635 -0.0553 0.6498 3.0275
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00159 0.03988
## Residual 1.33517 1.15550
## Number of obs: 28829, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.165e+00 2.057e-02 5.802e+01 202.490 < 2e-16 ***
## index_ANexp.c 1.807e-03 3.744e-04 1.049e+03 4.827 1.59e-06 ***
## index_AFexp.c 5.565e-03 6.869e-03 6.709e+02 0.810 0.4181
## pIndD 3.751e-01 1.992e-02 2.882e+04 18.828 < 2e-16 ***
## pIndR -3.772e-01 2.049e-02 2.881e+04 -18.404 < 2e-16 ***
## index_ANexp.c:pIndD -5.854e-04 3.909e-04 2.834e+04 -1.498 0.1342
## index_ANexp.c:pIndR 1.045e-03 4.182e-04 2.882e+04 2.499 0.0125 *
## index_AFexp.c:pIndD -2.012e-03 7.035e-03 2.828e+04 -0.286 0.7749
## index_AFexp.c:pIndR -2.563e-03 7.493e-03 2.881e+04 -0.342 0.7323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pIndD pIndR i_AN.:ID i_AN.:IR i_AF.:ID
## indx_ANxp.c 0.053
## indx_AFxp.c 0.016 -0.874
## pIndD -0.709 -0.054 -0.018
## pIndR -0.689 -0.053 -0.016 0.712
## indx_AN.:ID -0.052 -0.856 0.723 0.031 0.052
## indx_AN.:IR -0.049 -0.784 0.659 0.050 0.087 0.748
## indx_AF.:ID -0.014 0.735 -0.843 0.012 0.014 -0.854 -0.639
## indx_AF.:IR -0.013 0.673 -0.773 0.014 0.014 -0.642 -0.851 0.750E. worst of Covid is behind/ahead
worstBorA ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pDem_Rep + pInd_Not) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105957
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00697 -0.62755 0.05285 0.74877 2.10977
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00129 0.03592
## Residual 2.29272 1.51417
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.693e-01 1.439e-02 8.753e+00 53.453 2.58e-12 ***
## index_ANexp.c 1.296e-03 2.352e-04 4.965e+01 5.509 1.28e-06 ***
## index_AFexp.c 1.124e-02 4.416e-03 3.354e+01 2.546 0.01568 *
## pDem_Rep -1.171e+00 2.010e-02 2.883e+04 -58.248 < 2e-16 ***
## pInd_Not -6.975e-02 2.449e-02 2.886e+04 -2.848 0.00441 **
## index_ANexp.c:pDem_Rep 2.887e-03 3.778e-04 2.693e+04 7.641 2.22e-14 ***
## index_ANexp.c:pInd_Not 9.591e-04 4.954e-04 2.879e+04 1.936 0.05287 .
## index_AFexp.c:pDem_Rep -8.449e-04 6.738e-03 2.396e+04 -0.125 0.90021
## index_AFexp.c:pInd_Not -4.335e-03 8.903e-03 2.876e+04 -0.487 0.62632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDm_Rp pInd_N i_AN.:D i_AN.:I i_AF.:D
## indx_ANxp.c 0.032
## indx_AFxp.c 0.005 -0.896
## pDem_Rep 0.047 0.062 0.005
## pInd_Not -0.275 -0.031 -0.014 0.040
## ind_AN.:D_R 0.054 0.175 -0.143 0.035 0.047
## ind_AN.:I_N -0.027 -0.405 0.340 0.044 0.064 0.103
## ind_AF.:D_R 0.003 -0.149 0.163 -0.003 0.003 -0.845 -0.083
## ind_AF.:I_N -0.009 0.352 -0.396 0.002 0.015 -0.082 -0.853 0.096## Warning: Removed 9 row(s) containing missing values (geom_path).
## Warning: Removed 15 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105957
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00697 -0.62755 0.05285 0.74877 2.10977
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00129 0.03591
## Residual 2.29272 1.51417
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.332e+00 1.701e-02 1.706e+01 78.284 < 2e-16 ***
## index_ANexp.c 1.685e-04 2.543e-04 1.082e+02 0.662 0.5091
## index_AFexp.c 1.023e-02 4.738e-03 6.721e+01 2.160 0.0344 *
## pDemR -1.171e+00 2.010e-02 2.883e+04 -58.248 < 2e-16 ***
## pDemI -5.157e-01 2.610e-02 2.885e+04 -19.761 < 2e-16 ***
## index_ANexp.c:pDemR 2.887e-03 3.778e-04 2.693e+04 7.641 2.22e-14 ***
## index_ANexp.c:pDemI 4.845e-04 5.118e-04 2.808e+04 0.947 0.3438
## index_AFexp.c:pDemR -8.449e-04 6.738e-03 2.396e+04 -0.125 0.9002
## index_AFexp.c:pDemI 3.913e-03 9.211e-03 2.799e+04 0.425 0.6710
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDemR pDemI i_AN.:DR i_AN.:DI i_AF.:DR
## indx_ANxp.c -0.069
## indx_AFxp.c -0.010 -0.881
## pDemR -0.532 0.059 0.008
## pDemI -0.410 0.044 0.008 0.347
## indx_AN.:DR 0.047 -0.515 0.417 0.035 -0.031
## indx_AN.:DI 0.035 -0.376 0.303 -0.029 0.031 0.270
## indx_AF.:DR 0.006 0.437 -0.500 -0.003 -0.004 -0.845 -0.232
## indx_AF.:DI 0.004 0.316 -0.359 -0.003 0.012 -0.230 -0.854 0.2732. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105957
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00697 -0.62755 0.05285 0.74877 2.10977
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00129 0.03591
## Residual 2.29272 1.51417
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.609e-01 1.815e-02 2.214e+01 8.861 9.86e-09 ***
## index_ANexp.c 3.056e-03 3.293e-04 1.638e+02 9.281 < 2e-16 ***
## index_AFexp.c 9.388e-03 5.996e-03 9.827e+01 1.566 0.121
## pRepD 1.171e+00 2.010e-02 2.883e+04 58.248 < 2e-16 ***
## pRepI 6.552e-01 2.685e-02 2.885e+04 24.402 < 2e-16 ***
## index_ANexp.c:pRepD -2.887e-03 3.778e-04 2.693e+04 -7.641 2.22e-14 ***
## index_ANexp.c:pRepI -2.403e-03 5.480e-04 2.886e+04 -4.384 1.17e-05 ***
## index_AFexp.c:pRepD 8.449e-04 6.738e-03 2.396e+04 0.125 0.900
## index_AFexp.c:pRepI 4.757e-03 9.817e-03 2.880e+04 0.485 0.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pRepD pRepI i_AN.:RD i_AN.:RI i_AF.:RD
## indx_ANxp.c 0.096
## indx_AFxp.c 0.002 -0.870
## pRepD -0.609 -0.086 -0.003
## pRepI -0.456 -0.066 0.000 0.411
## indx_AN.:RD -0.083 -0.750 0.620 0.035 0.056
## indx_AN.:RI -0.057 -0.499 0.408 0.052 0.087 0.438
## indx_AF.:RD -0.002 0.632 -0.729 -0.003 0.002 -0.845 -0.366
## indx_AF.:RI -0.001 0.416 -0.479 0.001 0.014 -0.365 -0.851 0.4303. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: worstBorA ~ (index_ANexp.c + index_AFexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 105957
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00697 -0.62755 0.05285 0.74877 2.10977
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.00129 0.03592
## Residual 2.29272 1.51417
## Number of obs: 28865, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.161e-01 2.463e-02 7.492e+01 33.139 < 2e-16 ***
## index_ANexp.c 6.530e-04 4.783e-04 6.254e+02 1.365 0.173
## index_AFexp.c 1.415e-02 8.715e-03 3.893e+02 1.623 0.105
## pIndD 5.157e-01 2.610e-02 2.885e+04 19.761 < 2e-16 ***
## pIndR -6.552e-01 2.685e-02 2.885e+04 -24.402 < 2e-16 ***
## index_ANexp.c:pIndD -4.845e-04 5.118e-04 2.808e+04 -0.947 0.344
## index_ANexp.c:pIndR 2.403e-03 5.480e-04 2.886e+04 4.384 1.17e-05 ***
## index_AFexp.c:pIndD -3.913e-03 9.211e-03 2.799e+04 -0.425 0.671
## index_AFexp.c:pIndR -4.757e-03 9.817e-03 2.880e+04 -0.485 0.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pIndD pIndR i_AN.:ID i_AN.:IR i_AF.:ID
## indx_ANxp.c 0.060
## indx_AFxp.c 0.017 -0.867
## pIndD -0.777 -0.056 -0.017
## pIndR -0.754 -0.055 -0.016 0.712
## indx_AN.:ID -0.057 -0.870 0.738 0.031 0.052
## indx_AN.:IR -0.053 -0.802 0.678 0.050 0.087 0.748
## indx_AF.:ID -0.015 0.746 -0.862 0.012 0.014 -0.854 -0.639
## indx_AF.:IR -0.014 0.689 -0.797 0.014 0.014 -0.642 -0.851 0.751F. vaccine attitudes
vaxAttitudes ~ indexes * party
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pDem_Rep +
## pInd_Not) + (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124023.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05264 -0.78366 0.04051 0.94417 1.61639
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0009917 0.03149
## Residual 4.2963644 2.07277
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.450e-01 1.641e-02 1.340e+01 14.925 9.85e-10 ***
## index_ANexp.c 2.466e-03 2.962e-04 5.659e+01 8.324 2.10e-11 ***
## index_AFexp.c 1.509e-03 5.454e-03 3.568e+01 0.277 0.784
## pDem_Rep -4.851e-01 2.751e-02 2.878e+04 -17.635 < 2e-16 ***
## pInd_Not 4.482e-01 3.353e-02 2.884e+04 13.368 < 2e-16 ***
## index_ANexp.c:pDem_Rep 4.293e-04 5.164e-04 2.715e+04 0.831 0.406
## index_ANexp.c:pInd_Not -9.054e-04 6.779e-04 2.880e+04 -1.336 0.182
## index_AFexp.c:pDem_Rep 1.189e-02 9.202e-03 2.361e+04 1.292 0.196
## index_AFexp.c:pInd_Not -9.800e-04 1.218e-02 2.878e+04 -0.080 0.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDm_Rp pInd_N i_AN.:D i_AN.:I i_AF.:D
## indx_ANxp.c 0.041
## indx_AFxp.c 0.007 -0.877
## pDem_Rep 0.055 0.067 0.005
## pInd_Not -0.330 -0.036 -0.014 0.040
## ind_AN.:D_R 0.064 0.172 -0.140 0.034 0.047
## ind_AN.:I_N -0.033 -0.432 0.367 0.043 0.064 0.102
## ind_AF.:D_R 0.004 -0.144 0.161 -0.003 0.003 -0.846 -0.083
## ind_AF.:I_N -0.011 0.375 -0.429 0.002 0.015 -0.082 -0.853 0.096
## Warning: Removed 12 row(s) containing missing values (geom_path).
1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pDemR + pDemI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124023.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05264 -0.78366 0.04051 0.94417 1.61639
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0009917 0.03149
## Residual 4.2963644 2.07277
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.354e-01 2.059e-02 3.305e+01 30.868 < 2e-16 ***
## index_ANexp.c 1.952e-03 3.301e-04 1.378e+02 5.915 2.49e-08 ***
## index_AFexp.c -4.759e-03 6.052e-03 8.097e+01 -0.786 0.434
## pDemR -4.851e-01 2.751e-02 2.878e+04 -17.635 < 2e-16 ***
## pDemI -6.908e-01 3.572e-02 2.882e+04 -19.336 < 2e-16 ***
## index_ANexp.c:pDemR 4.293e-04 5.164e-04 2.715e+04 0.831 0.406
## index_ANexp.c:pDemI 1.120e-03 7.003e-04 2.833e+04 1.599 0.110
## index_AFexp.c:pDemR 1.189e-02 9.202e-03 2.361e+04 1.292 0.196
## index_AFexp.c:pDemI 6.926e-03 1.260e-02 2.827e+04 0.550 0.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pDemR pDemI i_AN.:DR i_AN.:DI i_AF.:DR
## indx_ANxp.c -0.084
## indx_AFxp.c -0.010 -0.867
## pDemR -0.602 0.063 0.007
## pDemI -0.464 0.048 0.007 0.347
## indx_AN.:DR 0.054 -0.558 0.463 0.034 -0.031
## indx_AN.:DI 0.040 -0.409 0.338 -0.030 0.031 0.270
## indx_AF.:DR 0.006 0.477 -0.551 -0.003 -0.004 -0.846 -0.232
## indx_AF.:DI 0.004 0.346 -0.399 -0.003 0.012 -0.230 -0.854 0.2722. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pRepD + pRepI) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124023.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05264 -0.78366 0.04051 0.94417 1.61639
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0009917 0.03149
## Residual 4.2963644 2.07277
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.503e-01 2.232e-02 4.571e+01 6.733 2.36e-08 ***
## index_ANexp.c 2.382e-03 4.306e-04 2.167e+02 5.532 9.09e-08 ***
## index_AFexp.c 7.132e-03 7.738e-03 1.241e+02 0.922 0.359
## pRepD 4.851e-01 2.751e-02 2.878e+04 17.635 < 2e-16 ***
## pRepI -2.056e-01 3.675e-02 2.884e+04 -5.596 2.21e-08 ***
## index_ANexp.c:pRepD -4.293e-04 5.164e-04 2.715e+04 -0.831 0.406
## index_ANexp.c:pRepI 6.908e-04 7.497e-04 2.884e+04 0.921 0.357
## index_AFexp.c:pRepD -1.189e-02 9.202e-03 2.361e+04 -1.292 0.196
## index_AFexp.c:pRepI -4.966e-03 1.343e-02 2.873e+04 -0.370 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pRepD pRepI i_AN.:RD i_AN.:RI i_AF.:RD
## indx_ANxp.c 0.110
## indx_AFxp.c 0.003 -0.858
## pRepD -0.677 -0.089 -0.003
## pRepI -0.507 -0.067 -0.001 0.411
## indx_AN.:RD -0.091 -0.771 0.644 0.034 0.056
## indx_AN.:RI -0.063 -0.522 0.433 0.051 0.087 0.437
## indx_AF.:RD -0.003 0.649 -0.758 -0.003 0.002 -0.846 -0.366
## indx_AF.:RI -0.002 0.435 -0.508 0.001 0.014 -0.364 -0.851 0.4303. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ (index_ANexp.c + index_AFexp.c) * (pIndD + pIndR) +
## (1 | media)
## Data: dl2
##
## REML criterion at convergence: 124023.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05264 -0.78366 0.04051 0.94417 1.61639
##
## Random effects:
## Groups Name Variance Std.Dev.
## media (Intercept) 0.0009917 0.03149
## Residual 4.2963644 2.07277
## Number of obs: 28853, groups: media, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.535e-02 3.190e-02 1.903e+02 -1.735 0.0843 .
## index_ANexp.c 3.073e-03 6.406e-04 9.020e+02 4.796 1.89e-06 ***
## index_AFexp.c 2.166e-03 1.160e-02 5.550e+02 0.187 0.8520
## pIndD 6.908e-01 3.572e-02 2.882e+04 19.336 < 2e-16 ***
## pIndR 2.056e-01 3.675e-02 2.884e+04 5.596 2.21e-08 ***
## index_ANexp.c:pIndD -1.120e-03 7.003e-04 2.833e+04 -1.599 0.1097
## index_ANexp.c:pIndR -6.908e-04 7.497e-04 2.884e+04 -0.921 0.3569
## index_AFexp.c:pIndD -6.926e-03 1.260e-02 2.827e+04 -0.550 0.5827
## index_AFexp.c:pIndR 4.966e-03 1.343e-02 2.873e+04 0.370 0.7116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AN. in_AF. pIndD pIndR i_AN.:ID i_AN.:IR i_AF.:ID
## indx_ANxp.c 0.065
## indx_AFxp.c 0.018 -0.861
## pIndD -0.820 -0.058 -0.016
## pIndR -0.797 -0.057 -0.016 0.712
## indx_AN.:ID -0.060 -0.883 0.751 0.031 0.052
## indx_AN.:IR -0.056 -0.819 0.696 0.050 0.087 0.748
## indx_AF.:ID -0.016 0.755 -0.878 0.012 0.014 -0.854 -0.640
## indx_AF.:IR -0.015 0.703 -0.819 0.014 0.014 -0.643 -0.851 0.752G. vaccine policy support + (1 | policy condition)
1. policySupport ~ index * party + (1 | policy condition) singular with (1|media), so excluded
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep +
## pInd_Not) + (1 | Policy_Condition)
## Data: dl2
##
## REML criterion at convergence: 111126.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.76870 -0.62300 -0.00133 0.83758 2.11489
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Condition (Intercept) 0.2046 0.4523
## Residual 2.7353 1.6539
## Number of obs: 28877, groups: Policy_Condition, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.716e-01 1.603e-01 7.013e+00 4.190 0.00407 **
## index_AFexp.c 4.651e-03 3.852e-03 2.886e+04 1.207 0.22735
## index_ANexp.c 1.004e-03 2.150e-04 2.886e+04 4.667 3.07e-06 ***
## pDem_Rep 6.967e-02 2.197e-02 2.886e+04 3.171 0.00152 **
## pInd_Not 4.192e-01 2.676e-02 2.886e+04 15.663 < 2e-16 ***
## index_AFexp.c:pDem_Rep 3.723e-03 7.322e-03 2.886e+04 0.509 0.61110
## index_AFexp.c:pInd_Not 1.418e-03 9.718e-03 2.886e+04 0.146 0.88402
## index_ANexp.c:pDem_Rep 3.706e-04 4.114e-04 2.886e+04 0.901 0.36761
## index_ANexp.c:pInd_Not -1.012e-03 5.409e-04 2.886e+04 -1.871 0.06138 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDm_Rp pInd_N i_AF.:D i_AF.:I i_AN.:D
## indx_AFxp.c 0.001
## indx_ANxp.c 0.004 -0.851
## pDem_Rep 0.004 0.004 0.073
## pInd_Not -0.027 -0.013 -0.040 0.041
## ind_AF.:D_R 0.000 0.163 -0.141 -0.002 0.003
## ind_AF.:I_N -0.001 -0.474 0.403 0.002 0.015 0.097
## ind_AN.:D_R 0.005 -0.140 0.175 0.034 0.047 -0.847 -0.083
## ind_AN.:I_N -0.003 0.404 -0.466 0.043 0.065 -0.084 -0.853 0.1041. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) +
## (1 | Policy_Condition)
## Data: dl2
##
## REML criterion at convergence: 111126.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.76870 -0.62300 -0.00133 0.83758 2.11489
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Condition (Intercept) 0.2046 0.4523
## Residual 2.7353 1.6539
## Number of obs: 28877, groups: Policy_Condition, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.751e-01 1.606e-01 7.067e+00 4.826 0.00186 **
## index_AFexp.c 3.257e-03 4.464e-03 2.886e+04 0.730 0.46569
## index_ANexp.c 4.843e-04 2.481e-04 2.886e+04 1.952 0.05092 .
## pDemR 6.967e-02 2.197e-02 2.886e+04 3.171 0.00152 **
## pDemI -3.844e-01 2.852e-02 2.886e+04 -13.479 < 2e-16 ***
## index_AFexp.c:pDemR 3.723e-03 7.322e-03 2.886e+04 0.509 0.61110
## index_AFexp.c:pDemI 4.441e-04 1.005e-02 2.886e+04 0.044 0.96475
## index_ANexp.c:pDemR 3.706e-04 4.114e-04 2.886e+04 0.901 0.36761
## index_ANexp.c:pDemI 1.197e-03 5.584e-04 2.886e+04 2.144 0.03203 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pDemR pDemI i_AF.:DR i_AF.:DI i_AN.:DR
## indx_AFxp.c -0.001
## indx_ANxp.c -0.009 -0.852
## pDemR -0.062 0.007 0.066
## pDemI -0.047 0.005 0.051 0.347
## indx_AF.:DR 0.001 -0.610 0.519 -0.002 -0.003
## indx_AF.:DI 0.000 -0.444 0.378 -0.003 0.012 0.271
## indx_AN.:DR 0.005 0.514 -0.603 0.034 -0.031 -0.847 -0.228
## indx_AN.:DI 0.004 0.379 -0.444 -0.029 0.032 -0.231 -0.854 0.2682. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) +
## (1 | Policy_Condition)
## Data: dl2
##
## REML criterion at convergence: 111126.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.76870 -0.62300 -0.00133 0.83758 2.11489
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Condition (Intercept) 0.2046 0.4523
## Residual 2.7353 1.6539
## Number of obs: 28877, groups: Policy_Condition, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.448e-01 1.607e-01 7.093e+00 5.255 0.00113 **
## index_AFexp.c 6.980e-03 5.803e-03 2.886e+04 1.203 0.22908
## index_ANexp.c 8.550e-04 3.281e-04 2.886e+04 2.605 0.00918 **
## pRepD -6.967e-02 2.197e-02 2.886e+04 -3.171 0.00152 **
## pRepI -4.540e-01 2.934e-02 2.886e+04 -15.474 < 2e-16 ***
## index_AFexp.c:pRepD -3.723e-03 7.322e-03 2.886e+04 -0.509 0.61110
## index_AFexp.c:pRepI -3.279e-03 1.071e-02 2.886e+04 -0.306 0.75948
## index_ANexp.c:pRepD -3.706e-04 4.114e-04 2.886e+04 -0.901 0.36761
## index_ANexp.c:pRepI 8.267e-04 5.983e-04 2.886e+04 1.382 0.16711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pRepD pRepI i_AF.:RD i_AF.:RI i_AN.:RD
## indx_AFxp.c 0.000
## indx_ANxp.c 0.013 -0.844
## pRepD -0.075 -0.002 -0.093
## pRepI -0.056 -0.002 -0.070 0.411
## indx_AF.:RD 0.000 -0.793 0.669 -0.002 0.001
## indx_AF.:RI 0.000 -0.542 0.457 0.001 0.014 0.429
## indx_AN.:RD -0.010 0.673 -0.798 0.034 0.055 -0.847 -0.365
## indx_AN.:RI -0.007 0.463 -0.549 0.051 0.087 -0.367 -0.851 0.4383. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) +
## (1 | Policy_Condition)
## Data: dl2
##
## REML criterion at convergence: 111126.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.76870 -0.62300 -0.00133 0.83758 2.11489
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Condition (Intercept) 0.2046 0.4523
## Residual 2.7353 1.6539
## Number of obs: 28877, groups: Policy_Condition, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.908e-01 1.618e-01 7.276e+00 2.415 0.045102 *
## index_AFexp.c 3.701e-03 9.002e-03 2.886e+04 0.411 0.680978
## index_ANexp.c 1.682e-03 5.003e-04 2.886e+04 3.362 0.000776 ***
## pIndD 3.844e-01 2.852e-02 2.886e+04 13.479 < 2e-16 ***
## pIndR 4.540e-01 2.934e-02 2.886e+04 15.474 < 2e-16 ***
## index_AFexp.c:pIndD -4.441e-04 1.005e-02 2.886e+04 -0.044 0.964750
## index_AFexp.c:pIndR 3.279e-03 1.071e-02 2.886e+04 0.306 0.759477
## index_ANexp.c:pIndD -1.197e-03 5.584e-04 2.886e+04 -2.144 0.032027 *
## index_ANexp.c:pIndR -8.267e-04 5.983e-04 2.886e+04 -1.382 0.167108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) in_AF. in_AN. pIndD pIndR i_AF.:ID i_AF.:IR i_AN.:ID
## indx_AFxp.c 0.003
## indx_ANxp.c 0.011 -0.854
## pIndD -0.129 -0.016 -0.061
## pIndR -0.126 -0.015 -0.059 0.712
## indx_AF.:ID -0.002 -0.896 0.765 0.012 0.014
## indx_AF.:IR -0.002 -0.840 0.718 0.013 0.014 0.753
## indx_AN.:ID -0.010 0.765 -0.896 0.032 0.053 -0.854 -0.643
## indx_AN.:IR -0.009 0.714 -0.836 0.051 0.087 -0.640 -0.851 0.749H. vaccine policy support + prop v. US 1st + (1 | policy group)
policySupport ~ index * party * propVUS1st + (1 | policy_group) singular with (1|media), so excluded
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pDem_Rep +
## pInd_Not) * PropvUS1st + (1 | Policy_Group)
## Data: dl2
##
## REML criterion at convergence: 110948.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90565 -0.60778 0.06409 0.80157 1.98909
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Group (Intercept) 0.08946 0.2991
## Residual 2.71416 1.6475
## Number of obs: 28877, groups: Policy_Group, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.700e-01 1.499e-01 3.007e+00 4.468
## index_AFexp.c 5.050e-03 3.858e-03 2.886e+04 1.309
## index_ANexp.c 9.936e-04 2.159e-04 2.886e+04 4.602
## pDem_Rep 8.214e-02 2.189e-02 2.886e+04 3.753
## pInd_Not 4.200e-01 2.678e-02 2.886e+04 15.683
## PropvUS1st 6.040e-01 2.179e-02 2.886e+04 27.717
## index_AFexp.c:pDem_Rep 3.546e-03 7.303e-03 2.886e+04 0.486
## index_AFexp.c:pInd_Not 1.257e-03 9.750e-03 2.886e+04 0.129
## index_ANexp.c:pDem_Rep 3.927e-04 4.102e-04 2.886e+04 0.957
## index_ANexp.c:pInd_Not -1.044e-03 5.450e-04 2.886e+04 -1.915
## index_AFexp.c:PropvUS1st 1.768e-03 7.716e-03 2.886e+04 0.229
## index_ANexp.c:PropvUS1st -8.922e-04 4.319e-04 2.886e+04 -2.065
## pDem_Rep:PropvUS1st 7.284e-01 4.378e-02 2.886e+04 16.637
## pInd_Not:PropvUS1st 1.871e-01 5.354e-02 2.886e+04 3.494
## index_AFexp.c:pDem_Rep:PropvUS1st -9.523e-03 1.461e-02 2.886e+04 -0.652
## index_AFexp.c:pInd_Not:PropvUS1st -4.085e-03 1.950e-02 2.886e+04 -0.210
## index_ANexp.c:pDem_Rep:PropvUS1st -1.336e-03 8.204e-04 2.886e+04 -1.628
## index_ANexp.c:pInd_Not:PropvUS1st -1.578e-03 1.090e-03 2.886e+04 -1.448
## Pr(>|t|)
## (Intercept) 0.020786 *
## index_AFexp.c 0.190616
## index_ANexp.c 4.21e-06 ***
## pDem_Rep 0.000175 ***
## pInd_Not < 2e-16 ***
## PropvUS1st < 2e-16 ***
## index_AFexp.c:pDem_Rep 0.627260
## index_AFexp.c:pInd_Not 0.897442
## index_ANexp.c:pDem_Rep 0.338345
## index_ANexp.c:pInd_Not 0.055488 .
## index_AFexp.c:PropvUS1st 0.818768
## index_ANexp.c:PropvUS1st 0.038885 *
## pDem_Rep:PropvUS1st < 2e-16 ***
## pInd_Not:PropvUS1st 0.000476 ***
## index_AFexp.c:pDem_Rep:PropvUS1st 0.514426
## index_AFexp.c:pInd_Not:PropvUS1st 0.834042
## index_ANexp.c:pDem_Rep:PropvUS1st 0.103532
## index_ANexp.c:pInd_Not:PropvUS1st 0.147527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it1. simple effects for dems
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pDemR + pDemI) *
## PropvUS1st + (1 | Policy_Group)
## Data: dl2
##
## REML criterion at convergence: 110948.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90565 -0.60778 0.06409 0.80157 1.98909
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Group (Intercept) 0.08946 0.2991
## Residual 2.71416 1.6475
## Number of obs: 28877, groups: Policy_Group, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 7.675e-01 1.503e-01 3.033e+00 5.107
## index_AFexp.c 3.691e-03 4.456e-03 2.886e+04 0.828
## index_ANexp.c 4.529e-04 2.477e-04 2.886e+04 1.828
## pDemR 8.214e-02 2.189e-02 2.886e+04 3.753
## pDemI -3.789e-01 2.853e-02 2.886e+04 -13.284
## PropvUS1st 3.015e-01 2.938e-02 2.886e+04 10.262
## index_AFexp.c:pDemR 3.546e-03 7.303e-03 2.886e+04 0.486
## index_AFexp.c:pDemI 5.166e-04 1.008e-02 2.886e+04 0.051
## index_ANexp.c:pDemR 3.927e-04 4.102e-04 2.886e+04 0.957
## index_ANexp.c:pDemI 1.240e-03 5.624e-04 2.886e+04 2.205
## index_AFexp.c:PropvUS1st 5.181e-03 8.911e-03 2.886e+04 0.581
## index_ANexp.c:PropvUS1st -7.452e-04 4.954e-04 2.886e+04 -1.504
## pDemR:PropvUS1st 7.284e-01 4.378e-02 2.886e+04 16.637
## pDemI:PropvUS1st 1.771e-01 5.702e-02 2.886e+04 3.106
## index_AFexp.c:pDemR:PropvUS1st -9.523e-03 1.461e-02 2.886e+04 -0.652
## index_AFexp.c:pDemI:PropvUS1st -6.760e-04 2.016e-02 2.886e+04 -0.034
## index_ANexp.c:pDemR:PropvUS1st -1.336e-03 8.204e-04 2.886e+04 -1.628
## index_ANexp.c:pDemI:PropvUS1st 9.105e-04 1.125e-03 2.886e+04 0.810
## Pr(>|t|)
## (Intercept) 0.014137 *
## index_AFexp.c 0.407448
## index_ANexp.c 0.067520 .
## pDemR 0.000175 ***
## pDemI < 2e-16 ***
## PropvUS1st < 2e-16 ***
## index_AFexp.c:pDemR 0.627260
## index_AFexp.c:pDemI 0.959123
## index_ANexp.c:pDemR 0.338345
## index_ANexp.c:pDemI 0.027463 *
## index_AFexp.c:PropvUS1st 0.560933
## index_ANexp.c:PropvUS1st 0.132509
## pDemR:PropvUS1st < 2e-16 ***
## pDemI:PropvUS1st 0.001895 **
## index_AFexp.c:pDemR:PropvUS1st 0.514426
## index_AFexp.c:pDemI:PropvUS1st 0.973245
## index_ANexp.c:pDemR:PropvUS1st 0.103532
## index_ANexp.c:pDemI:PropvUS1st 0.418148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it2. simple effects for reps
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pRepD + pRepI) *
## PropvUS1st + (1 | Policy_Group)
## Data: dl2
##
## REML criterion at convergence: 110948.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90565 -0.60778 0.06409 0.80157 1.98909
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Group (Intercept) 0.08946 0.2991
## Residual 2.71416 1.6475
## Number of obs: 28877, groups: Policy_Group, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.496e-01 1.504e-01 3.046e+00 5.648
## index_AFexp.c 7.237e-03 5.787e-03 2.886e+04 1.251
## index_ANexp.c 8.456e-04 3.269e-04 2.886e+04 2.587
## pRepD -8.214e-02 2.189e-02 2.886e+04 -3.753
## pRepI -4.611e-01 2.933e-02 2.886e+04 -15.721
## PropvUS1st 1.030e+00 3.244e-02 2.886e+04 31.750
## index_AFexp.c:pRepD -3.546e-03 7.303e-03 2.886e+04 -0.486
## index_AFexp.c:pRepI -3.030e-03 1.073e-02 2.886e+04 -0.282
## index_ANexp.c:pRepD -3.927e-04 4.102e-04 2.886e+04 -0.957
## index_ANexp.c:pRepI 8.473e-04 6.015e-04 2.886e+04 1.409
## index_AFexp.c:PropvUS1st -4.342e-03 1.157e-02 2.886e+04 -0.375
## index_ANexp.c:PropvUS1st -2.081e-03 6.539e-04 2.886e+04 -3.182
## pRepD:PropvUS1st -7.284e-01 4.378e-02 2.886e+04 -16.637
## pRepI:PropvUS1st -5.513e-01 5.866e-02 2.886e+04 -9.398
## index_AFexp.c:pRepD:PropvUS1st 9.523e-03 1.461e-02 2.886e+04 0.652
## index_AFexp.c:pRepI:PropvUS1st 8.847e-03 2.147e-02 2.886e+04 0.412
## index_ANexp.c:pRepD:PropvUS1st 1.336e-03 8.204e-04 2.886e+04 1.628
## index_ANexp.c:pRepI:PropvUS1st 2.246e-03 1.203e-03 2.886e+04 1.867
## Pr(>|t|)
## (Intercept) 0.010546 *
## index_AFexp.c 0.211064
## index_ANexp.c 0.009700 **
## pRepD 0.000175 ***
## pRepI < 2e-16 ***
## PropvUS1st < 2e-16 ***
## index_AFexp.c:pRepD 0.627260
## index_AFexp.c:pRepI 0.777727
## index_ANexp.c:pRepD 0.338345
## index_ANexp.c:pRepI 0.158970
## index_AFexp.c:PropvUS1st 0.707560
## index_ANexp.c:PropvUS1st 0.001464 **
## pRepD:PropvUS1st < 2e-16 ***
## pRepI:PropvUS1st < 2e-16 ***
## index_AFexp.c:pRepD:PropvUS1st 0.514426
## index_AFexp.c:pRepI:PropvUS1st 0.680252
## index_ANexp.c:pRepD:PropvUS1st 0.103532
## index_ANexp.c:pRepI:PropvUS1st 0.061861 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it3. simple effects for indep
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: policySupport ~ (index_AFexp.c + index_ANexp.c) * (pIndD + pIndR) *
## PropvUS1st + (1 | Policy_Group)
## Data: dl2
##
## REML criterion at convergence: 110948.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90565 -0.60778 0.06409 0.80157 1.98909
##
## Random effects:
## Groups Name Variance Std.Dev.
## Policy_Group (Intercept) 0.08946 0.2991
## Residual 2.71416 1.6475
## Number of obs: 28877, groups: Policy_Group, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.886e-01 1.515e-01 3.136e+00 2.564
## index_AFexp.c 4.208e-03 9.040e-03 2.886e+04 0.465
## index_ANexp.c 1.693e-03 5.049e-04 2.886e+04 3.353
## pIndD 3.789e-01 2.853e-02 2.886e+04 13.284
## pIndR 4.611e-01 2.933e-02 2.886e+04 15.721
## PropvUS1st 4.786e-01 4.887e-02 2.886e+04 9.794
## index_AFexp.c:pIndD -5.166e-04 1.008e-02 2.886e+04 -0.051
## index_AFexp.c:pIndR 3.030e-03 1.073e-02 2.886e+04 0.282
## index_ANexp.c:pIndD -1.240e-03 5.624e-04 2.886e+04 -2.205
## index_ANexp.c:pIndR -8.473e-04 6.015e-04 2.886e+04 -1.409
## index_AFexp.c:PropvUS1st 4.505e-03 1.808e-02 2.886e+04 0.249
## index_ANexp.c:PropvUS1st 1.653e-04 1.010e-03 2.886e+04 0.164
## pIndD:PropvUS1st -1.771e-01 5.702e-02 2.886e+04 -3.106
## pIndR:PropvUS1st 5.513e-01 5.866e-02 2.886e+04 9.398
## index_AFexp.c:pIndD:PropvUS1st 6.760e-04 2.016e-02 2.886e+04 0.034
## index_AFexp.c:pIndR:PropvUS1st -8.847e-03 2.147e-02 2.886e+04 -0.412
## index_ANexp.c:pIndD:PropvUS1st -9.105e-04 1.125e-03 2.886e+04 -0.810
## index_ANexp.c:pIndR:PropvUS1st -2.246e-03 1.203e-03 2.886e+04 -1.867
## Pr(>|t|)
## (Intercept) 0.0793 .
## index_AFexp.c 0.6416
## index_ANexp.c 0.0008 ***
## pIndD <2e-16 ***
## pIndR <2e-16 ***
## PropvUS1st <2e-16 ***
## index_AFexp.c:pIndD 0.9591
## index_AFexp.c:pIndR 0.7777
## index_ANexp.c:pIndD 0.0275 *
## index_ANexp.c:pIndR 0.1590
## index_AFexp.c:PropvUS1st 0.8032
## index_ANexp.c:PropvUS1st 0.8699
## pIndD:PropvUS1st 0.0019 **
## pIndR:PropvUS1st <2e-16 ***
## index_AFexp.c:pIndD:PropvUS1st 0.9732
## index_AFexp.c:pIndR:PropvUS1st 0.6803
## index_ANexp.c:pIndD:PropvUS1st 0.4181
## index_ANexp.c:pIndR:PropvUS1st 0.0619 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itAVERAGE OF W1 & W2
1. Analytic + Affect
A. avgRisk3
avgRisk3 ~ index * party
##
## Call:
## lm(formula = avgRisk3 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9249 -0.9514 0.0719 0.9678 3.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5602977 0.0317816 143.489 < 2e-16 ***
## avgIndex_AFexp.c 0.0502206 0.0076366 6.576 6.01e-11 ***
## avgIndex_ANexp.c -0.0018273 0.0003665 -4.985 6.68e-07 ***
## pDem_Rep.y -1.2253223 0.0662268 -18.502 < 2e-16 ***
## pInd_Not.y 0.1352679 0.0764971 1.768 0.07715 .
## avgIndex_AFexp.c:pDem_Rep.y 0.0407835 0.0144180 2.829 0.00472 **
## avgIndex_AFexp.c:pInd_Not.y -0.0062478 0.0193199 -0.323 0.74643
## avgIndex_ANexp.c:pDem_Rep.y -0.0013857 0.0006889 -2.012 0.04439 *
## avgIndex_ANexp.c:pInd_Not.y 0.0001166 0.0009292 0.125 0.90018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.31 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.2884, Adjusted R-squared: 0.2858
## F-statistic: 111.1 on 8 and 2194 DF, p-value: < 2.2e-161. simple effects for dems
##
## Call:
## lm(formula = avgRisk3 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDemR.y + pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9249 -0.9514 0.0719 0.9678 3.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2175972 0.0444252 117.447 < 2e-16 ***
## avgIndex_AFexp.c 0.0277671 0.0090181 3.079 0.00210 **
## avgIndex_ANexp.c -0.0010959 0.0004304 -2.546 0.01096 *
## pDemR.y -1.2253223 0.0662268 -18.502 < 2e-16 ***
## pDemI.y -0.7479291 0.0820299 -9.118 < 2e-16 ***
## avgIndex_AFexp.c:pDemR.y 0.0407835 0.0144180 2.829 0.00472 **
## avgIndex_AFexp.c:pDemI.y 0.0266395 0.0200652 1.328 0.18443
## avgIndex_ANexp.c:pDemR.y -0.0013857 0.0006889 -2.012 0.04439 *
## avgIndex_ANexp.c:pDemI.y -0.0008094 0.0009643 -0.839 0.40137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.31 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.2884, Adjusted R-squared: 0.2858
## F-statistic: 111.1 on 8 and 2194 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk3 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pRepD.y + pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9249 -0.9514 0.0719 0.9678 3.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9922749 0.0491161 81.282 < 2e-16 ***
## avgIndex_AFexp.c 0.0685506 0.0112496 6.094 1.30e-09 ***
## avgIndex_ANexp.c -0.0024817 0.0005379 -4.614 4.18e-06 ***
## pRepD.y 1.2253223 0.0662268 18.502 < 2e-16 ***
## pRepI.y 0.4773932 0.0846623 5.639 1.93e-08 ***
## avgIndex_AFexp.c:pRepD.y -0.0407835 0.0144180 -2.829 0.00472 **
## avgIndex_AFexp.c:pRepI.y -0.0141439 0.0211622 -0.668 0.50398
## avgIndex_ANexp.c:pRepD.y 0.0013857 0.0006889 2.012 0.04439 *
## avgIndex_ANexp.c:pRepI.y 0.0005763 0.0010169 0.567 0.57094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.31 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.2884, Adjusted R-squared: 0.2858
## F-statistic: 111.1 on 8 and 2194 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk3 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pIndD.y + pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9249 -0.9514 0.0719 0.9678 3.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4696681 0.0689587 64.817 < 2e-16 ***
## avgIndex_AFexp.c 0.0544067 0.0179245 3.035 0.00243 **
## avgIndex_ANexp.c -0.0019054 0.0008630 -2.208 0.02735 *
## pIndD.y 0.7479291 0.0820299 9.118 < 2e-16 ***
## pIndR.y -0.4773932 0.0846623 -5.639 1.93e-08 ***
## avgIndex_AFexp.c:pIndD.y -0.0266395 0.0200652 -1.328 0.18443
## avgIndex_AFexp.c:pIndR.y 0.0141439 0.0211622 0.668 0.50398
## avgIndex_ANexp.c:pIndD.y 0.0008094 0.0009643 0.839 0.40137
## avgIndex_ANexp.c:pIndR.y -0.0005763 0.0010169 -0.567 0.57094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.31 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.2884, Adjusted R-squared: 0.2858
## F-statistic: 111.1 on 8 and 2194 DF, p-value: < 2.2e-16B. avgRisk4
avgRisk4 ~ index * party
##
## Call:
## lm(formula = avgRisk4 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1245 -0.7756 0.1737 0.8318 2.3640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0314690 0.0291896 172.372 < 2e-16 ***
## avgIndex_AFexp.c 0.0230368 0.0070262 3.279 0.00106 **
## avgIndex_ANexp.c -0.0009771 0.0003373 -2.897 0.00381 **
## pDem_Rep.y -0.4652521 0.0607278 -7.661 2.75e-14 ***
## pInd_Not.y 0.0546541 0.0703241 0.777 0.43714
## avgIndex_AFexp.c:pDem_Rep.y -0.0082462 0.0132208 -0.624 0.53287
## avgIndex_AFexp.c:pInd_Not.y -0.0116860 0.0178017 -0.656 0.51160
## avgIndex_ANexp.c:pDem_Rep.y 0.0004252 0.0006318 0.673 0.50099
## avgIndex_ANexp.c:pInd_Not.y 0.0005260 0.0008564 0.614 0.53910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.201 on 2188 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.05069, Adjusted R-squared: 0.04722
## F-statistic: 14.6 on 8 and 2188 DF, p-value: < 2.2e-161. simple effects for dems
##
## Call:
## lm(formula = avgRisk4 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDemR.y + pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1245 -0.7756 0.1737 0.8318 2.3640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2821309 0.0407637 129.579 < 2e-16 ***
## avgIndex_AFexp.c 0.0233036 0.0082703 2.818 0.004880 **
## avgIndex_ANexp.c -0.0010161 0.0003947 -2.574 0.010112 *
## pDemR.y -0.4652521 0.0607278 -7.661 2.75e-14 ***
## pDemI.y -0.2872802 0.0754002 -3.810 0.000143 ***
## avgIndex_AFexp.c:pDemR.y -0.0082462 0.0132208 -0.624 0.532868
## avgIndex_AFexp.c:pDemI.y 0.0075629 0.0184825 0.409 0.682438
## avgIndex_ANexp.c:pDemR.y 0.0004252 0.0006318 0.673 0.500995
## avgIndex_ANexp.c:pDemI.y -0.0003134 0.0008885 -0.353 0.724283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.201 on 2188 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.05069, Adjusted R-squared: 0.04722
## F-statistic: 14.6 on 8 and 2188 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk4 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pRepD.y + pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1245 -0.7756 0.1737 0.8318 2.3640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8168789 0.0450131 107.011 < 2e-16 ***
## avgIndex_AFexp.c 0.0150573 0.0103146 1.460 0.1445
## avgIndex_ANexp.c -0.0005909 0.0004933 -1.198 0.2311
## pRepD.y 0.4652521 0.0607278 7.661 2.75e-14 ***
## pRepI.y 0.1779719 0.0777798 2.288 0.0222 *
## avgIndex_AFexp.c:pRepD.y 0.0082462 0.0132208 0.624 0.5329
## avgIndex_AFexp.c:pRepI.y 0.0158091 0.0194832 0.811 0.4172
## avgIndex_ANexp.c:pRepD.y -0.0004252 0.0006318 -0.673 0.5010
## avgIndex_ANexp.c:pRepI.y -0.0007386 0.0009364 -0.789 0.4303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.201 on 2188 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.05069, Adjusted R-squared: 0.04722
## F-statistic: 14.6 on 8 and 2188 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk4 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pIndD.y + pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1245 -0.7756 0.1737 0.8318 2.3640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9948508 0.0634312 78.744 < 2e-16 ***
## avgIndex_AFexp.c 0.0308665 0.0165289 1.867 0.061975 .
## avgIndex_ANexp.c -0.0013295 0.0007960 -1.670 0.094996 .
## pIndD.y 0.2872802 0.0754002 3.810 0.000143 ***
## pIndR.y -0.1779719 0.0777798 -2.288 0.022224 *
## avgIndex_AFexp.c:pIndD.y -0.0075629 0.0184825 -0.409 0.682438
## avgIndex_AFexp.c:pIndR.y -0.0158091 0.0194832 -0.811 0.417210
## avgIndex_ANexp.c:pIndD.y 0.0003134 0.0008885 0.353 0.724283
## avgIndex_ANexp.c:pIndR.y 0.0007386 0.0009364 0.789 0.430322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.201 on 2188 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.05069, Adjusted R-squared: 0.04722
## F-statistic: 14.6 on 8 and 2188 DF, p-value: < 2.2e-16C. avgRisk5
avgRisk5 ~ index * party
##
## Call:
## lm(formula = avgRisk5 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3843 -1.3260 -0.1639 1.0864 4.5813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1259939 0.0385306 81.130 < 2e-16 ***
## avgIndex_AFexp.c -0.0125264 0.0092641 -1.352 0.17647
## avgIndex_ANexp.c 0.0012571 0.0004447 2.827 0.00474 **
## pDem_Rep.y -0.1230758 0.0802708 -1.533 0.12536
## pInd_Not.y -0.4234660 0.0927548 -4.565 5.26e-06 ***
## avgIndex_AFexp.c:pDem_Rep.y -0.0152028 0.0174979 -0.869 0.38504
## avgIndex_AFexp.c:pInd_Not.y -0.0007205 0.0234332 -0.031 0.97547
## avgIndex_ANexp.c:pDem_Rep.y 0.0007492 0.0008362 0.896 0.37035
## avgIndex_ANexp.c:pInd_Not.y 0.0001607 0.0011271 0.143 0.88660
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.586 on 2184 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1146, Adjusted R-squared: 0.1114
## F-statistic: 35.34 on 8 and 2184 DF, p-value: < 2.2e-161. simple effects for dems
##
## Call:
## lm(formula = avgRisk5 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDemR.y + pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3843 -1.3260 -0.1639 1.0864 4.5813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0477880 0.0539027 56.542 < 2e-16 ***
## avgIndex_AFexp.c -0.0051628 0.0109613 -0.471 0.637685
## avgIndex_ANexp.c 0.0009356 0.0005233 1.788 0.073926 .
## pDemR.y -0.1230758 0.0802708 -1.533 0.125357
## pDemI.y 0.3619282 0.0994892 3.638 0.000281 ***
## avgIndex_AFexp.c:pDemR.y -0.0152028 0.0174979 -0.869 0.385035
## avgIndex_AFexp.c:pDemI.y -0.0068808 0.0243459 -0.283 0.777489
## avgIndex_ANexp.c:pDemR.y 0.0007492 0.0008362 0.896 0.370353
## avgIndex_ANexp.c:pDemI.y 0.0002138 0.0011702 0.183 0.855017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.586 on 2184 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1146, Adjusted R-squared: 0.1114
## F-statistic: 35.34 on 8 and 2184 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk5 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pRepD.y + pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3843 -1.3260 -0.1639 1.0864 4.5813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9247122 0.0594803 49.171 < 2e-16 ***
## avgIndex_AFexp.c -0.0203656 0.0136392 -1.493 0.13554
## avgIndex_ANexp.c 0.0016848 0.0006522 2.583 0.00985 **
## pRepD.y 0.1230758 0.0802708 1.533 0.12536
## pRepI.y 0.4850039 0.1026183 4.726 2.43e-06 ***
## avgIndex_AFexp.c:pRepD.y 0.0152028 0.0174979 0.869 0.38504
## avgIndex_AFexp.c:pRepI.y 0.0083219 0.0256632 0.324 0.74576
## avgIndex_ANexp.c:pRepD.y -0.0007492 0.0008362 -0.896 0.37035
## avgIndex_ANexp.c:pRepI.y -0.0005353 0.0012332 -0.434 0.66426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.586 on 2184 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1146, Adjusted R-squared: 0.1114
## F-statistic: 35.34 on 8 and 2184 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk5 ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pIndD.y + pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3843 -1.3260 -0.1639 1.0864 4.5813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4097161 0.0836218 40.775 < 2e-16 ***
## avgIndex_AFexp.c -0.0120436 0.0217387 -0.554 0.579623
## avgIndex_ANexp.c 0.0011494 0.0010467 1.098 0.272266
## pIndD.y -0.3619282 0.0994892 -3.638 0.000281 ***
## pIndR.y -0.4850039 0.1026183 -4.726 2.43e-06 ***
## avgIndex_AFexp.c:pIndD.y 0.0068808 0.0243459 0.283 0.777489
## avgIndex_AFexp.c:pIndR.y -0.0083219 0.0256632 -0.324 0.745761
## avgIndex_ANexp.c:pIndD.y -0.0002138 0.0011702 -0.183 0.855017
## avgIndex_ANexp.c:pIndR.y 0.0005353 0.0012332 0.434 0.664265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.586 on 2184 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1146, Adjusted R-squared: 0.1114
## F-statistic: 35.34 on 8 and 2184 DF, p-value: < 2.2e-16D. avgRiskSeverity
risk severity ~ index * party
##
## Call:
## lm(formula = avgRiskSeverity ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6253 -0.7025 -0.0510 0.6864 3.3539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.235e+00 2.523e-02 167.835 < 2e-16 ***
## avgIndex_AFexp.c 2.021e-02 6.080e-03 3.324 0.000902 ***
## avgIndex_ANexp.c -5.166e-04 2.919e-04 -1.770 0.076889 .
## pDem_Rep.y -6.083e-01 5.253e-02 -11.580 < 2e-16 ***
## pInd_Not.y -6.909e-02 6.077e-02 -1.137 0.255756
## avgIndex_AFexp.c:pDem_Rep.y 6.073e-03 1.146e-02 0.530 0.596349
## avgIndex_AFexp.c:pInd_Not.y -7.427e-03 1.539e-02 -0.483 0.629447
## avgIndex_ANexp.c:pDem_Rep.y -8.438e-05 5.477e-04 -0.154 0.877564
## avgIndex_ANexp.c:pInd_Not.y 3.324e-04 7.404e-04 0.449 0.653509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2176 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1952, Adjusted R-squared: 0.1923
## F-statistic: 65.98 on 8 and 2176 DF, p-value: < 2.2e-161. simple effects for dems
##
## Call:
## lm(formula = avgRiskSeverity ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDemR.y + pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6253 -0.7025 -0.0510 0.6864 3.3539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.516e+00 3.527e-02 128.041 < 2e-16 ***
## avgIndex_AFexp.c 1.472e-02 7.163e-03 2.055 0.039955 *
## avgIndex_ANexp.c -3.647e-04 3.419e-04 -1.067 0.286248
## pDemR.y -6.083e-01 5.253e-02 -11.580 < 2e-16 ***
## pDemI.y -2.351e-01 6.518e-02 -3.606 0.000317 ***
## avgIndex_AFexp.c:pDemR.y 6.073e-03 1.146e-02 0.530 0.596349
## avgIndex_AFexp.c:pDemI.y 1.046e-02 1.598e-02 0.655 0.512653
## avgIndex_ANexp.c:pDemR.y -8.438e-05 5.477e-04 -0.154 0.877564
## avgIndex_ANexp.c:pDemI.y -3.746e-04 7.682e-04 -0.488 0.625858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2176 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1952, Adjusted R-squared: 0.1923
## F-statistic: 65.98 on 8 and 2176 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRiskSeverity ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pRepD.y + pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6253 -0.7025 -0.0510 0.6864 3.3539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.908e+00 3.892e-02 100.414 < 2e-16 ***
## avgIndex_AFexp.c 2.080e-02 8.950e-03 2.324 0.0202 *
## avgIndex_ANexp.c -4.491e-04 4.278e-04 -1.050 0.2940
## pRepD.y 6.083e-01 5.253e-02 11.580 < 2e-16 ***
## pRepI.y 3.732e-01 6.722e-02 5.552 3.16e-08 ***
## avgIndex_AFexp.c:pRepD.y -6.073e-03 1.146e-02 -0.530 0.5963
## avgIndex_AFexp.c:pRepI.y 4.391e-03 1.686e-02 0.260 0.7945
## avgIndex_ANexp.c:pRepD.y 8.438e-05 5.477e-04 0.154 0.8776
## avgIndex_ANexp.c:pRepI.y -2.902e-04 8.101e-04 -0.358 0.7202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2176 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1952, Adjusted R-squared: 0.1923
## F-statistic: 65.98 on 8 and 2176 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRiskSeverity ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pIndD.y + pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6253 -0.7025 -0.0510 0.6864 3.3539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2814288 0.0548070 78.118 < 2e-16 ***
## avgIndex_AFexp.c 0.0251866 0.0142836 1.763 0.077987 .
## avgIndex_ANexp.c -0.0007393 0.0006879 -1.075 0.282629
## pIndD.y 0.2350525 0.0651770 3.606 0.000317 ***
## pIndR.y -0.3732276 0.0672209 -5.552 3.16e-08 ***
## avgIndex_AFexp.c:pIndD.y -0.0104634 0.0159790 -0.655 0.512653
## avgIndex_AFexp.c:pIndR.y -0.0043909 0.0168559 -0.260 0.794507
## avgIndex_ANexp.c:pIndD.y 0.0003746 0.0007682 0.488 0.625858
## avgIndex_ANexp.c:pIndR.y 0.0002902 0.0008101 0.358 0.720185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2176 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1952, Adjusted R-squared: 0.1923
## F-statistic: 65.98 on 8 and 2176 DF, p-value: < 2.2e-16E. avgVaxxAttitudes
avgVaxxAttitudes ~ (affect + analytical) * party
##
## Call:
## lm(formula = avgVaxxAttitudes ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4579 -1.3158 0.1858 1.5952 3.7400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4532464 0.0459732 9.859 < 2e-16 ***
## avgIndex_AFexp.c 0.0255370 0.0111018 2.300 0.021527 *
## avgIndex_ANexp.c -0.0007068 0.0005331 -1.326 0.184982
## pDem_Rep.y -0.5472590 0.0957382 -5.716 1.24e-08 ***
## pInd_Not.y 0.4856998 0.1106970 4.388 1.20e-05 ***
## avgIndex_AFexp.c:pDem_Rep.y 0.0811162 0.0208339 3.893 0.000102 ***
## avgIndex_AFexp.c:pInd_Not.y -0.0073240 0.0281599 -0.260 0.794822
## avgIndex_ANexp.c:pDem_Rep.y -0.0035821 0.0009956 -3.598 0.000328 ***
## avgIndex_ANexp.c:pInd_Not.y 0.0002600 0.0013549 0.192 0.847828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.896 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.08729, Adjusted R-squared: 0.08396
## F-statistic: 26.23 on 8 and 2194 DF, p-value: < 2.2e-16## Warning: Removed 60 row(s) containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## Warning: Removed 30 row(s) containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
1. simple effects for dems
##
## Call:
## lm(formula = avgVaxxAttitudes ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pDemR.y + pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4579 -1.3158 0.1858 1.5952 3.7400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8871568 0.0642167 13.815 < 2e-16 ***
## avgIndex_AFexp.c -0.0174380 0.0130467 -1.337 0.181497
## avgIndex_ANexp.c 0.0011700 0.0006227 1.879 0.060389 .
## pDemR.y -0.5472590 0.0957382 -5.716 1.24e-08 ***
## pDemI.y -0.7593293 0.1186851 -6.398 1.92e-10 ***
## avgIndex_AFexp.c:pDemR.y 0.0811162 0.0208339 3.893 0.000102 ***
## avgIndex_AFexp.c:pDemI.y 0.0478821 0.0292350 1.638 0.101599
## avgIndex_ANexp.c:pDemR.y -0.0035821 0.0009956 -3.598 0.000328 ***
## avgIndex_ANexp.c:pDemI.y -0.0020511 0.0014056 -1.459 0.144634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.896 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.08729, Adjusted R-squared: 0.08396
## F-statistic: 26.23 on 8 and 2194 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgVaxxAttitudes ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pRepD.y + pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4579 -1.3158 0.1858 1.5952 3.7400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3398978 0.0710072 4.787 1.81e-06 ***
## avgIndex_AFexp.c 0.0636782 0.0162430 3.920 9.11e-05 ***
## avgIndex_ANexp.c -0.0024121 0.0007769 -3.105 0.001928 **
## pRepD.y 0.5472590 0.0957382 5.716 1.24e-08 ***
## pRepI.y -0.2120703 0.1224924 -1.731 0.083540 .
## avgIndex_AFexp.c:pRepD.y -0.0811162 0.0208339 -3.893 0.000102 ***
## avgIndex_AFexp.c:pRepI.y -0.0332341 0.0307945 -1.079 0.280607
## avgIndex_ANexp.c:pRepD.y 0.0035821 0.0009956 3.598 0.000328 ***
## avgIndex_ANexp.c:pRepI.y 0.0015311 0.0014803 1.034 0.301121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.896 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.08729, Adjusted R-squared: 0.08396
## F-statistic: 26.23 on 8 and 2194 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgVaxxAttitudes ~ (avgIndex_AFexp.c + avgIndex_ANexp.c) *
## (pIndD.y + pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4579 -1.3158 0.1858 1.5952 3.7400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1278276 0.0998117 1.281 0.2004
## avgIndex_AFexp.c 0.0304441 0.0261623 1.164 0.2447
## avgIndex_ANexp.c -0.0008811 0.0012601 -0.699 0.4845
## pIndD.y 0.7593293 0.1186851 6.398 1.92e-10 ***
## pIndR.y 0.2120703 0.1224924 1.731 0.0835 .
## avgIndex_AFexp.c:pIndD.y -0.0478821 0.0292350 -1.638 0.1016
## avgIndex_AFexp.c:pIndR.y 0.0332341 0.0307945 1.079 0.2806
## avgIndex_ANexp.c:pIndD.y 0.0020511 0.0014056 1.459 0.1446
## avgIndex_ANexp.c:pIndR.y -0.0015311 0.0014803 -1.034 0.3011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.896 on 2194 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.08729, Adjusted R-squared: 0.08396
## F-statistic: 26.23 on 8 and 2194 DF, p-value: < 2.2e-162. Positive emotion
A. avgRisk3
avgRisk ~ positive emotion
avgRisk3 ~ index * party
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_PEexp.c * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0975 -0.9797 0.0933 0.9514 3.6896
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.552355 0.031722 143.508 < 2e-16 ***
## avgIndex_PEexp.c 0.020584 0.001444 14.258 < 2e-16 ***
## pDem_Rep.y -1.235172 0.066595 -18.548 < 2e-16 ***
## pInd_Not.y 0.166658 0.076019 2.192 0.0285 *
## avgIndex_PEexp.c:pDem_Rep.y 0.020073 0.003052 6.577 5.98e-11 ***
## avgIndex_PEexp.c:pInd_Not.y -0.006761 0.003445 -1.963 0.0498 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.321 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.276, Adjusted R-squared: 0.2744
## F-statistic: 167.5 on 5 and 2197 DF, p-value: < 2.2e-16## Warning: Removed 42 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_PEexp.c * (pDemR.y + pDemI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0975 -0.9797 0.0933 0.9514 3.6896
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.224938 0.044659 116.997 < 2e-16 ***
## avgIndex_PEexp.c 0.008316 0.001918 4.335 1.52e-05 ***
## pDemR.y -1.235172 0.066595 -18.548 < 2e-16 ***
## pDemI.y -0.784244 0.081637 -9.607 < 2e-16 ***
## avgIndex_PEexp.c:pDemR.y 0.020073 0.003052 6.577 5.98e-11 ***
## avgIndex_PEexp.c:pDemI.y 0.016798 0.003636 4.620 4.06e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.321 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.276, Adjusted R-squared: 0.2744
## F-statistic: 167.5 on 5 and 2197 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_PEexp.c * (pRepD.y + pRepI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0975 -0.9797 0.0933 0.9514 3.6896
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.989766 0.049401 80.763 < 2e-16 ***
## avgIndex_PEexp.c 0.028389 0.002374 11.961 < 2e-16 ***
## pRepD.y 1.235172 0.066595 18.548 < 2e-16 ***
## pRepI.y 0.450928 0.084324 5.348 9.84e-08 ***
## avgIndex_PEexp.c:pRepD.y -0.020073 0.003052 -6.577 5.98e-11 ***
## avgIndex_PEexp.c:pRepI.y -0.003275 0.003895 -0.841 0.401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.321 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.276, Adjusted R-squared: 0.2744
## F-statistic: 167.5 on 5 and 2197 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_PEexp.c * (pIndD.y + pIndR.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0975 -0.9797 0.0933 0.9514 3.6896
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.440694 0.068338 64.981 < 2e-16 ***
## avgIndex_PEexp.c 0.025114 0.003089 8.131 7.02e-16 ***
## pIndD.y 0.784244 0.081637 9.607 < 2e-16 ***
## pIndR.y -0.450928 0.084324 -5.348 9.84e-08 ***
## avgIndex_PEexp.c:pIndD.y -0.016798 0.003636 -4.620 4.06e-06 ***
## avgIndex_PEexp.c:pIndR.y 0.003275 0.003895 0.841 0.401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.321 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.276, Adjusted R-squared: 0.2744
## F-statistic: 167.5 on 5 and 2197 DF, p-value: < 2.2e-16B. avgRisk4
avgRisk4 ~ index * party
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_PEexp.c * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1842 -0.7866 0.1786 0.7852 2.3154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0266002 0.0289761 173.474 < 2e-16 ***
## avgIndex_PEexp.c 0.0044960 0.0013192 3.408 0.000666 ***
## pDem_Rep.y -0.4737525 0.0607087 -7.804 9.23e-15 ***
## pInd_Not.y 0.0768968 0.0695218 1.106 0.268813
## avgIndex_PEexp.c:pDem_Rep.y 0.0008796 0.0027858 0.316 0.752214
## avgIndex_PEexp.c:pInd_Not.y -0.0015014 0.0031501 -0.477 0.633676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.204 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04548, Adjusted R-squared: 0.0433
## F-statistic: 20.88 on 5 and 2191 DF, p-value: < 2.2e-16## Warning: Removed 42 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_PEexp.c * (pDemR.y + pDemI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1842 -0.7866 0.1786 0.7852 2.3154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2888524 0.0407366 129.831 < 2e-16 ***
## avgIndex_PEexp.c 0.0035607 0.0017564 2.027 0.0428 *
## pDemR.y -0.4737525 0.0607087 -7.804 9.23e-15 ***
## pDemI.y -0.3137730 0.0746416 -4.204 2.73e-05 ***
## avgIndex_PEexp.c:pDemR.y 0.0008796 0.0027858 0.316 0.7522
## avgIndex_PEexp.c:pDemI.y 0.0019412 0.0033269 0.584 0.5596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.204 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04548, Adjusted R-squared: 0.0433
## F-statistic: 20.88 on 5 and 2191 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_PEexp.c * (pRepD.y + pRepI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1842 -0.7866 0.1786 0.7852 2.3154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8151000 0.0450119 106.974 < 2e-16 ***
## avgIndex_PEexp.c 0.0044403 0.0021623 2.053 0.0401 *
## pRepD.y 0.4737525 0.0607087 7.804 9.23e-15 ***
## pRepI.y 0.1599794 0.0770583 2.076 0.0380 *
## avgIndex_PEexp.c:pRepD.y -0.0008796 0.0027858 -0.316 0.7522
## avgIndex_PEexp.c:pRepI.y 0.0010616 0.0035579 0.298 0.7654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.204 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04548, Adjusted R-squared: 0.0433
## F-statistic: 20.88 on 5 and 2191 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_PEexp.c * (pIndD.y + pIndR.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1842 -0.7866 0.1786 0.7852 2.3154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.975079 0.062545 79.544 < 2e-16 ***
## avgIndex_PEexp.c 0.005502 0.002825 1.947 0.0516 .
## pIndD.y 0.313773 0.074642 4.204 2.73e-05 ***
## pIndR.y -0.159979 0.077058 -2.076 0.0380 *
## avgIndex_PEexp.c:pIndD.y -0.001941 0.003327 -0.584 0.5596
## avgIndex_PEexp.c:pIndR.y -0.001062 0.003558 -0.298 0.7654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.204 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04548, Adjusted R-squared: 0.0433
## F-statistic: 20.88 on 5 and 2191 DF, p-value: < 2.2e-16C. avgRisk5
avgRisk5 ~ index * party
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_PEexp.c * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3855 -1.3219 -0.1533 1.1043 4.6175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1285113 0.0381794 81.942 < 2e-16 ***
## avgIndex_PEexp.c 0.0230601 0.0017374 13.273 < 2e-16 ***
## pDem_Rep.y -0.1231845 0.0801547 -1.537 0.124
## pInd_Not.y -0.4461117 0.0914910 -4.876 1.16e-06 ***
## avgIndex_PEexp.c:pDem_Rep.y 0.0003082 0.0036766 0.084 0.933
## avgIndex_PEexp.c:pInd_Not.y 0.0045672 0.0041434 1.102 0.270
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.588 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1118, Adjusted R-squared: 0.1097
## F-statistic: 55.03 on 5 and 2187 DF, p-value: < 2.2e-16## Warning: Removed 42 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_PEexp.c * (pDemR.y + pDemI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3855 -1.3219 -0.1533 1.1043 4.6175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0428866 0.0538068 56.552 < 2e-16 ***
## avgIndex_PEexp.c 0.0244132 0.0023183 10.530 < 2e-16 ***
## pDemR.y -0.1231845 0.0801547 -1.537 0.124
## pDemI.y 0.3845195 0.0982832 3.912 9.42e-05 ***
## avgIndex_PEexp.c:pDemR.y 0.0003082 0.0036766 0.084 0.933
## avgIndex_PEexp.c:pDemI.y -0.0044131 0.0043776 -1.008 0.314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.588 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1118, Adjusted R-squared: 0.1097
## F-statistic: 55.03 on 5 and 2187 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_PEexp.c * (pRepD.y + pRepI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3855 -1.3219 -0.1533 1.1043 4.6175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9197022 0.0594104 49.145 < 2e-16 ***
## avgIndex_PEexp.c 0.0247213 0.0028535 8.663 < 2e-16 ***
## pRepD.y 0.1231845 0.0801547 1.537 0.124
## pRepI.y 0.5077040 0.1014594 5.004 6.06e-07 ***
## avgIndex_PEexp.c:pRepD.y -0.0003082 0.0036766 -0.084 0.933
## avgIndex_PEexp.c:pRepI.y -0.0047213 0.0046831 -1.008 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.588 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1118, Adjusted R-squared: 0.1097
## F-statistic: 55.03 on 5 and 2187 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_PEexp.c * (pIndD.y + pIndR.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3855 -1.3219 -0.1533 1.1043 4.6175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.427406 0.082246 41.673 < 2e-16 ***
## avgIndex_PEexp.c 0.020000 0.003713 5.386 7.97e-08 ***
## pIndD.y -0.384520 0.098283 -3.912 9.42e-05 ***
## pIndR.y -0.507704 0.101459 -5.004 6.06e-07 ***
## avgIndex_PEexp.c:pIndD.y 0.004413 0.004378 1.008 0.314
## avgIndex_PEexp.c:pIndR.y 0.004721 0.004683 1.008 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.588 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.1118, Adjusted R-squared: 0.1097
## F-statistic: 55.03 on 5 and 2187 DF, p-value: < 2.2e-16D. avgRiskSeverity
risk severity ~ index * party
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_PEexp.c * (pDem_Rep.y +
## pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7058 -0.7009 -0.0549 0.6830 3.3447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.231401 0.025005 169.222 <2e-16 ***
## avgIndex_PEexp.c 0.015967 0.001137 14.042 <2e-16 ***
## pDem_Rep.y -0.614255 0.052431 -11.716 <2e-16 ***
## pInd_Not.y -0.058091 0.059965 -0.969 0.3328
## avgIndex_PEexp.c:pDem_Rep.y 0.007069 0.002403 2.942 0.0033 **
## avgIndex_PEexp.c:pInd_Not.y -0.001023 0.002714 -0.377 0.7064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1914
## F-statistic: 104.4 on 5 and 2179 DF, p-value: < 2.2e-16## Warning: Removed 42 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_PEexp.c * (pDemR.y +
## pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7058 -0.7009 -0.0549 0.6830 3.3447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.519359 0.035196 128.406 < 2e-16 ***
## avgIndex_PEexp.c 0.012095 0.001516 7.980 2.35e-15 ***
## pDemR.y -0.614255 0.052431 -11.716 < 2e-16 ***
## pDemI.y -0.249037 0.064400 -3.867 0.000113 ***
## avgIndex_PEexp.c:pDemR.y 0.007069 0.002403 2.942 0.003299 **
## avgIndex_PEexp.c:pDemI.y 0.004557 0.002867 1.589 0.112120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1914
## F-statistic: 104.4 on 5 and 2179 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_PEexp.c * (pRepD.y +
## pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7058 -0.7009 -0.0549 0.6830 3.3447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.905104 0.038862 100.487 < 2e-16 ***
## avgIndex_PEexp.c 0.019165 0.001865 10.278 < 2e-16 ***
## pRepD.y 0.614255 0.052431 11.716 < 2e-16 ***
## pRepI.y 0.365218 0.066474 5.494 4.38e-08 ***
## avgIndex_PEexp.c:pRepD.y -0.007069 0.002403 -2.942 0.0033 **
## avgIndex_PEexp.c:pRepI.y -0.002512 0.003066 -0.819 0.4127
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1914
## F-statistic: 104.4 on 5 and 2179 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_PEexp.c * (pIndD.y +
## pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7058 -0.7009 -0.0549 0.6830 3.3447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.270322 0.053931 79.181 < 2e-16 ***
## avgIndex_PEexp.c 0.016653 0.002434 6.842 1.01e-11 ***
## pIndD.y 0.249037 0.064400 3.867 0.000113 ***
## pIndR.y -0.365218 0.066474 -5.494 4.38e-08 ***
## avgIndex_PEexp.c:pIndD.y -0.004557 0.002867 -1.589 0.112120
## avgIndex_PEexp.c:pIndR.y 0.002512 0.003066 0.819 0.412685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1914
## F-statistic: 104.4 on 5 and 2179 DF, p-value: < 2.2e-16E. avgVaxxAttitudes
avgVaxxAttitudes ~ positive emotion * party
##
## Call:
## lm(formula = avgVaxxAttitudes ~ avgIndex_PEexp.c * (pDem_Rep.y +
## pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4872 -1.3324 0.1792 1.5757 3.4882
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.447258 0.045679 9.791 < 2e-16 ***
## avgIndex_PEexp.c 0.018230 0.002078 8.773 < 2e-16 ***
## pDem_Rep.y -0.543974 0.095784 -5.679 1.53e-08 ***
## pInd_Not.y 0.496169 0.109543 4.529 6.23e-06 ***
## avgIndex_PEexp.c:pDem_Rep.y 0.011175 0.004391 2.545 0.011 *
## avgIndex_PEexp.c:pInd_Not.y -0.003412 0.004960 -0.688 0.492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.901 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.08086, Adjusted R-squared: 0.07876
## F-statistic: 38.65 on 5 and 2197 DF, p-value: < 2.2e-16## Warning: Removed 60 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_PEexp.c * (pDemR.y +
## pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7058 -0.7009 -0.0549 0.6830 3.3447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.519359 0.035196 128.406 < 2e-16 ***
## avgIndex_PEexp.c 0.012095 0.001516 7.980 2.35e-15 ***
## pDemR.y -0.614255 0.052431 -11.716 < 2e-16 ***
## pDemI.y -0.249037 0.064400 -3.867 0.000113 ***
## avgIndex_PEexp.c:pDemR.y 0.007069 0.002403 2.942 0.003299 **
## avgIndex_PEexp.c:pDemI.y 0.004557 0.002867 1.589 0.112120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1914
## F-statistic: 104.4 on 5 and 2179 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_PEexp.c * (pRepD.y +
## pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7058 -0.7009 -0.0549 0.6830 3.3447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.905104 0.038862 100.487 < 2e-16 ***
## avgIndex_PEexp.c 0.019165 0.001865 10.278 < 2e-16 ***
## pRepD.y 0.614255 0.052431 11.716 < 2e-16 ***
## pRepI.y 0.365218 0.066474 5.494 4.38e-08 ***
## avgIndex_PEexp.c:pRepD.y -0.007069 0.002403 -2.942 0.0033 **
## avgIndex_PEexp.c:pRepI.y -0.002512 0.003066 -0.819 0.4127
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1914
## F-statistic: 104.4 on 5 and 2179 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_PEexp.c * (pIndD.y +
## pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7058 -0.7009 -0.0549 0.6830 3.3447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.270322 0.053931 79.181 < 2e-16 ***
## avgIndex_PEexp.c 0.016653 0.002434 6.842 1.01e-11 ***
## pIndD.y 0.249037 0.064400 3.867 0.000113 ***
## pIndR.y -0.365218 0.066474 -5.494 4.38e-08 ***
## avgIndex_PEexp.c:pIndD.y -0.004557 0.002867 -1.589 0.112120
## avgIndex_PEexp.c:pIndR.y 0.002512 0.003066 0.819 0.412685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1914
## F-statistic: 104.4 on 5 and 2179 DF, p-value: < 2.2e-163. Negative emotion
A. avgRisk3
avgRisk3 ~ index * party
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_NEexp.c * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9961 -0.9679 0.0931 0.9436 3.7259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.556645 0.031698 143.750 < 2e-16 ***
## avgIndex_NEexp.c 0.031647 0.002163 14.628 < 2e-16 ***
## pDem_Rep.y -1.218168 0.066653 -18.276 < 2e-16 ***
## pInd_Not.y 0.168852 0.075888 2.225 0.0262 *
## avgIndex_NEexp.c:pDem_Rep.y 0.030989 0.004552 6.807 1.28e-11 ***
## avgIndex_NEexp.c:pInd_Not.y -0.010230 0.005177 -1.976 0.0483 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.317 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.28, Adjusted R-squared: 0.2783
## F-statistic: 170.8 on 5 and 2197 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_NEexp.c * (pDemR.y + pDemI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9961 -0.9679 0.0931 0.9436 3.7259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.221451 0.044667 116.898 < 2e-16 ***
## avgIndex_NEexp.c 0.012777 0.002861 4.466 8.39e-06 ***
## pDemR.y -1.218168 0.066653 -18.276 < 2e-16 ***
## pDemI.y -0.777936 0.081508 -9.544 < 2e-16 ***
## avgIndex_NEexp.c:pDemR.y 0.030989 0.004552 6.807 1.28e-11 ***
## avgIndex_NEexp.c:pDemI.y 0.025724 0.005460 4.711 2.61e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.317 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.28, Adjusted R-squared: 0.2783
## F-statistic: 170.8 on 5 and 2197 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_NEexp.c * (pRepD.y + pRepI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9961 -0.9679 0.0931 0.9436 3.7259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.003282 0.049472 80.920 < 2e-16 ***
## avgIndex_NEexp.c 0.043766 0.003541 12.361 < 2e-16 ***
## pRepD.y 1.218168 0.066653 18.276 < 2e-16 ***
## pRepI.y 0.440232 0.084237 5.226 1.89e-07 ***
## avgIndex_NEexp.c:pRepD.y -0.030989 0.004552 -6.807 1.28e-11 ***
## avgIndex_NEexp.c:pRepI.y -0.005264 0.005845 -0.901 0.368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.317 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.28, Adjusted R-squared: 0.2783
## F-statistic: 170.8 on 5 and 2197 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk3 ~ avgIndex_NEexp.c * (pIndD.y + pIndR.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9961 -0.9679 0.0931 0.9436 3.7259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.443515 0.068179 65.174 < 2e-16 ***
## avgIndex_NEexp.c 0.038501 0.004650 8.280 < 2e-16 ***
## pIndD.y 0.777936 0.081508 9.544 < 2e-16 ***
## pIndR.y -0.440232 0.084237 -5.226 1.89e-07 ***
## avgIndex_NEexp.c:pIndD.y -0.025724 0.005460 -4.711 2.61e-06 ***
## avgIndex_NEexp.c:pIndR.y 0.005264 0.005845 0.901 0.368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.317 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.28, Adjusted R-squared: 0.2783
## F-statistic: 170.8 on 5 and 2197 DF, p-value: < 2.2e-16B. avgRisk4
avgRisk4 ~ index * party
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_NEexp.c * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1723 -0.7849 0.1785 0.7951 2.3290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.028527 0.029013 173.320 < 2e-16 ***
## avgIndex_NEexp.c 0.007516 0.001981 3.794 0.000152 ***
## pDem_Rep.y -0.464094 0.060883 -7.623 3.67e-14 ***
## pInd_Not.y 0.076128 0.069544 1.095 0.273782
## avgIndex_NEexp.c:pDem_Rep.y 0.001895 0.004162 0.455 0.648950
## avgIndex_NEexp.c:pInd_Not.y -0.002388 0.004744 -0.503 0.614697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.203 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04675, Adjusted R-squared: 0.04458
## F-statistic: 21.49 on 5 and 2191 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_NEexp.c * (pDemR.y + pDemI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1723 -0.7849 0.1785 0.7951 2.3290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.285697 0.040829 129.458 < 2e-16 ***
## avgIndex_NEexp.c 0.005780 0.002624 2.203 0.0277 *
## pDemR.y -0.464094 0.060883 -7.623 3.67e-14 ***
## pDemI.y -0.308175 0.074678 -4.127 3.82e-05 ***
## avgIndex_NEexp.c:pDemR.y 0.001895 0.004162 0.455 0.6490
## avgIndex_NEexp.c:pDemI.y 0.003336 0.005006 0.666 0.5052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.203 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04675, Adjusted R-squared: 0.04458
## F-statistic: 21.49 on 5 and 2191 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_NEexp.c * (pRepD.y + pRepI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1723 -0.7849 0.1785 0.7951 2.3290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.821602 0.045162 106.762 < 2e-16 ***
## avgIndex_NEexp.c 0.007675 0.003231 2.375 0.0176 *
## pRepD.y 0.464094 0.060883 7.623 3.67e-14 ***
## pRepI.y 0.155919 0.077132 2.021 0.0434 *
## avgIndex_NEexp.c:pRepD.y -0.001895 0.004162 -0.455 0.6490
## avgIndex_NEexp.c:pRepI.y 0.001441 0.005349 0.269 0.7877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.203 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04675, Adjusted R-squared: 0.04458
## F-statistic: 21.49 on 5 and 2191 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk4 ~ avgIndex_NEexp.c * (pIndD.y + pIndR.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1723 -0.7849 0.1785 0.7951 2.3290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.977521 0.062528 79.605 < 2e-16 ***
## avgIndex_NEexp.c 0.009116 0.004263 2.139 0.0326 *
## pIndD.y 0.308175 0.074678 4.127 3.82e-05 ***
## pIndR.y -0.155919 0.077132 -2.021 0.0434 *
## avgIndex_NEexp.c:pIndD.y -0.003336 0.005006 -0.666 0.5052
## avgIndex_NEexp.c:pIndR.y -0.001441 0.005349 -0.269 0.7877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.203 on 2191 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.04675, Adjusted R-squared: 0.04458
## F-statistic: 21.49 on 5 and 2191 DF, p-value: < 2.2e-16C. avgRisk5
avgRisk5 ~ index * party
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_NEexp.c * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3242 -1.3234 -0.1411 1.1004 4.6048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.129167 0.038356 81.582 < 2e-16 ***
## avgIndex_NEexp.c 0.033573 0.002617 12.827 < 2e-16 ***
## pDem_Rep.y -0.122029 0.080651 -1.513 0.130
## pInd_Not.y -0.441556 0.091829 -4.808 1.62e-06 ***
## avgIndex_NEexp.c:pDem_Rep.y 0.001192 0.005512 0.216 0.829
## avgIndex_NEexp.c:pInd_Not.y 0.006950 0.006260 1.110 0.267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.592 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.107, Adjusted R-squared: 0.1049
## F-statistic: 52.4 on 5 and 2187 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_NEexp.c * (pDemR.y + pDemI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3242 -1.3234 -0.1411 1.1004 4.6048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.044468 0.054111 56.263 < 2e-16 ***
## avgIndex_NEexp.c 0.035270 0.003475 10.148 < 2e-16 ***
## pDemR.y -0.122029 0.080651 -1.513 0.130412
## pDemI.y 0.380541 0.098663 3.857 0.000118 ***
## avgIndex_NEexp.c:pDemR.y 0.001192 0.005512 0.216 0.828811
## avgIndex_NEexp.c:pDemI.y -0.006354 0.006609 -0.961 0.336433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.592 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.107, Adjusted R-squared: 0.1049
## F-statistic: 52.4 on 5 and 2187 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_NEexp.c * (pRepD.y + pRepI.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3242 -1.3234 -0.1411 1.1004 4.6048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.922440 0.059804 48.867 < 2e-16 ***
## avgIndex_NEexp.c 0.036462 0.004278 8.523 < 2e-16 ***
## pRepD.y 0.122029 0.080651 1.513 0.130
## pRepI.y 0.502570 0.101897 4.932 8.75e-07 ***
## avgIndex_NEexp.c:pRepD.y -0.001192 0.005512 -0.216 0.829
## avgIndex_NEexp.c:pRepI.y -0.007546 0.007064 -1.068 0.286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.592 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.107, Adjusted R-squared: 0.1049
## F-statistic: 52.4 on 5 and 2187 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRisk5 ~ avgIndex_NEexp.c * (pIndD.y + pIndR.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3242 -1.3234 -0.1411 1.1004 4.6048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.425010 0.082501 41.515 < 2e-16 ***
## avgIndex_NEexp.c 0.028916 0.005621 5.144 2.93e-07 ***
## pIndD.y -0.380541 0.098663 -3.857 0.000118 ***
## pIndR.y -0.502570 0.101897 -4.932 8.75e-07 ***
## avgIndex_NEexp.c:pIndD.y 0.006354 0.006609 0.961 0.336433
## avgIndex_NEexp.c:pIndR.y 0.007546 0.007064 1.068 0.285529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.592 on 2187 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.107, Adjusted R-squared: 0.1049
## F-statistic: 52.4 on 5 and 2187 DF, p-value: < 2.2e-16D. avgRiskSeverity
risk severity ~ index * party +
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_NEexp.c * (pDem_Rep.y +
## pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6324 -0.6944 -0.0528 0.6862 3.3441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.233772 0.025034 169.118 < 2e-16 ***
## avgIndex_NEexp.c 0.024118 0.001707 14.127 < 2e-16 ***
## pDem_Rep.y -0.604991 0.052578 -11.507 < 2e-16 ***
## pInd_Not.y -0.055875 0.059977 -0.932 0.35165
## avgIndex_NEexp.c:pDem_Rep.y 0.011394 0.003591 3.173 0.00153 **
## avgIndex_NEexp.c:pInd_Not.y -0.001571 0.004087 -0.384 0.70078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1945, Adjusted R-squared: 0.1927
## F-statistic: 105.2 on 5 and 2179 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
1. simple effects for dems
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_NEexp.c * (pDemR.y +
## pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6324 -0.6944 -0.0528 0.6862 3.3441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.517829 0.035273 128.081 < 2e-16 ***
## avgIndex_NEexp.c 0.017902 0.002265 7.905 4.21e-15 ***
## pDemR.y -0.604991 0.052578 -11.507 < 2e-16 ***
## pDemI.y -0.246621 0.064423 -3.828 0.000133 ***
## avgIndex_NEexp.c:pDemR.y 0.011394 0.003591 3.173 0.001530 **
## avgIndex_NEexp.c:pDemI.y 0.007268 0.004313 1.685 0.092157 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1945, Adjusted R-squared: 0.1927
## F-statistic: 105.2 on 5 and 2179 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_NEexp.c * (pRepD.y +
## pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6324 -0.6944 -0.0528 0.6862 3.3441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.912838 0.038990 100.354 < 2e-16 ***
## avgIndex_NEexp.c 0.029296 0.002787 10.513 < 2e-16 ***
## pRepD.y 0.604991 0.052578 11.507 < 2e-16 ***
## pRepI.y 0.358370 0.066531 5.386 7.96e-08 ***
## avgIndex_NEexp.c:pRepD.y -0.011394 0.003591 -3.173 0.00153 **
## avgIndex_NEexp.c:pRepI.y -0.004126 0.004609 -0.895 0.37074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1945, Adjusted R-squared: 0.1927
## F-statistic: 105.2 on 5 and 2179 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_NEexp.c * (pIndD.y +
## pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6324 -0.6944 -0.0528 0.6862 3.3441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.271208 0.053909 79.230 < 2e-16 ***
## avgIndex_NEexp.c 0.025170 0.003671 6.856 9.18e-12 ***
## pIndD.y 0.246621 0.064423 3.828 0.000133 ***
## pIndR.y -0.358370 0.066531 -5.386 7.96e-08 ***
## avgIndex_NEexp.c:pIndD.y -0.007268 0.004313 -1.685 0.092157 .
## avgIndex_NEexp.c:pIndR.y 0.004126 0.004609 0.895 0.370735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1945, Adjusted R-squared: 0.1927
## F-statistic: 105.2 on 5 and 2179 DF, p-value: < 2.2e-16E. avgVaxxAttitudes
avgVaxxAttitudes ~ negative emotion * party
##
## Call:
## lm(formula = avgVaxxAttitudes ~ avgIndex_NEexp.c * (pDem_Rep.y +
## pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.513 -1.327 0.164 1.586 3.497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.450183 0.045724 9.846 < 2e-16 ***
## avgIndex_NEexp.c 0.027922 0.003119 8.952 < 2e-16 ***
## pDem_Rep.y -0.529106 0.096032 -5.510 4.02e-08 ***
## pInd_Not.y 0.498342 0.109544 4.549 5.68e-06 ***
## avgIndex_NEexp.c:pDem_Rep.y 0.017570 0.006560 2.678 0.00746 **
## avgIndex_NEexp.c:pInd_Not.y -0.004947 0.007466 -0.663 0.50766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.899 on 2197 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.08252, Adjusted R-squared: 0.08043
## F-statistic: 39.52 on 5 and 2197 DF, p-value: < 2.2e-16## Warning: Removed 12 row(s) containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
1. simple effects for dems
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_NEexp.c * (pDemR.y +
## pDemI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6324 -0.6944 -0.0528 0.6862 3.3441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.517829 0.035273 128.081 < 2e-16 ***
## avgIndex_NEexp.c 0.017902 0.002265 7.905 4.21e-15 ***
## pDemR.y -0.604991 0.052578 -11.507 < 2e-16 ***
## pDemI.y -0.246621 0.064423 -3.828 0.000133 ***
## avgIndex_NEexp.c:pDemR.y 0.011394 0.003591 3.173 0.001530 **
## avgIndex_NEexp.c:pDemI.y 0.007268 0.004313 1.685 0.092157 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1945, Adjusted R-squared: 0.1927
## F-statistic: 105.2 on 5 and 2179 DF, p-value: < 2.2e-162. simple effects for reps
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_NEexp.c * (pRepD.y +
## pRepI.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6324 -0.6944 -0.0528 0.6862 3.3441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.912838 0.038990 100.354 < 2e-16 ***
## avgIndex_NEexp.c 0.029296 0.002787 10.513 < 2e-16 ***
## pRepD.y 0.604991 0.052578 11.507 < 2e-16 ***
## pRepI.y 0.358370 0.066531 5.386 7.96e-08 ***
## avgIndex_NEexp.c:pRepD.y -0.011394 0.003591 -3.173 0.00153 **
## avgIndex_NEexp.c:pRepI.y -0.004126 0.004609 -0.895 0.37074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1945, Adjusted R-squared: 0.1927
## F-statistic: 105.2 on 5 and 2179 DF, p-value: < 2.2e-163. simple effects for indep
##
## Call:
## lm(formula = avgRiskSeverity ~ avgIndex_NEexp.c * (pIndD.y +
## pIndR.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6324 -0.6944 -0.0528 0.6862 3.3441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.271208 0.053909 79.230 < 2e-16 ***
## avgIndex_NEexp.c 0.025170 0.003671 6.856 9.18e-12 ***
## pIndD.y 0.246621 0.064423 3.828 0.000133 ***
## pIndR.y -0.358370 0.066531 -5.386 7.96e-08 ***
## avgIndex_NEexp.c:pIndD.y -0.007268 0.004313 -1.685 0.092157 .
## avgIndex_NEexp.c:pIndR.y 0.004126 0.004609 0.895 0.370735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 2179 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.1945, Adjusted R-squared: 0.1927
## F-statistic: 105.2 on 5 and 2179 DF, p-value: < 2.2e-16F. Negative emotion + Risk Severity
1. riskSeverity + negemo + party
##
## Call:
## lm(formula = avgVaxxAttitudes ~ avgRiskSeverity.c + avgIndex_NEexp.c +
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9693 -1.3331 0.1742 1.5488 3.4640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.413634 0.044094 9.381 < 2e-16 ***
## avgRiskSeverity.c 0.144834 0.039264 3.689 0.000231 ***
## avgIndex_NEexp.c 0.022600 0.003044 7.425 1.61e-13 ***
## pDem_Rep.y -0.446476 0.097998 -4.556 5.50e-06 ***
## pInd_Not.y 0.482646 0.107541 4.488 7.56e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.9 on 2178 degrees of freedom
## (1286 observations deleted due to missingness)
## Multiple R-squared: 0.08538, Adjusted R-squared: 0.0837
## F-statistic: 50.83 on 4 and 2178 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
2. riskSeverity x negemo + party
##
## Call:
## lm(formula = avgVaxxAttitudes ~ avgRiskSeverity.c * avgIndex_NEexp.c +
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.432 -1.345 0.164 1.570 3.745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.463484 0.046316 10.007 < 2e-16 ***
## avgRiskSeverity.c 0.140721 0.039185 3.591 0.000336 ***
## avgIndex_NEexp.c 0.025486 0.003150 8.090 9.81e-16 ***
## pDem_Rep.y -0.408391 0.098383 -4.151 3.44e-05 ***
## pInd_Not.y 0.484824 0.107277 4.519 6.53e-06 ***
## avgRiskSeverity.c:avgIndex_NEexp.c -0.007647 0.002226 -3.436 0.000602 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.895 on 2177 degrees of freedom
## (1286 observations deleted due to missingness)
## Multiple R-squared: 0.09031, Adjusted R-squared: 0.08822
## F-statistic: 43.23 on 5 and 2177 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
3. riskSeverity + negemo x party
##
## Call:
## lm(formula = avgVaxxAttitudes ~ avgRiskSeverity.c + avgIndex_NEexp.c *
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7394 -1.3282 0.1539 1.5679 3.5249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.445742 0.045911 9.709 < 2e-16 ***
## avgRiskSeverity.c 0.138169 0.039320 3.514 0.00045 ***
## avgIndex_NEexp.c 0.024765 0.003269 7.575 5.27e-14 ***
## pDem_Rep.y -0.447207 0.099230 -4.507 6.93e-06 ***
## pInd_Not.y 0.508176 0.110053 4.618 4.11e-06 ***
## avgIndex_NEexp.c:pDem_Rep.y 0.015306 0.006596 2.321 0.02040 *
## avgIndex_NEexp.c:pInd_Not.y -0.005132 0.007489 -0.685 0.49326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.898 on 2176 degrees of freedom
## (1286 observations deleted due to missingness)
## Multiple R-squared: 0.08797, Adjusted R-squared: 0.08546
## F-statistic: 34.98 on 6 and 2176 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
4. riskSeverity x negemo x party
##
## Call:
## lm(formula = avgVaxxAttitudes ~ avgRiskSeverity.c * avgIndex_NEexp.c *
## (pDem_Rep.y + pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4085 -1.3503 0.1594 1.5730 3.8930
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.4947760 0.0483010 10.244
## avgRiskSeverity.c 0.1525497 0.0417285 3.656
## avgIndex_NEexp.c 0.0247223 0.0034300 7.208
## pDem_Rep.y -0.4366174 0.1020694 -4.278
## pInd_Not.y 0.5551539 0.1152886 4.815
## avgRiskSeverity.c:avgIndex_NEexp.c -0.0045969 0.0024916 -1.845
## avgRiskSeverity.c:pDem_Rep.y 0.2643292 0.0950976 2.780
## avgRiskSeverity.c:pInd_Not.y -0.1796278 0.0945028 -1.901
## avgIndex_NEexp.c:pDem_Rep.y 0.0062102 0.0072653 0.855
## avgIndex_NEexp.c:pInd_Not.y -0.0023807 0.0081751 -0.291
## avgRiskSeverity.c:avgIndex_NEexp.c:pDem_Rep.y -0.0035282 0.0057823 -0.610
## avgRiskSeverity.c:avgIndex_NEexp.c:pInd_Not.y -0.0009905 0.0055597 -0.178
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## avgRiskSeverity.c 0.000263 ***
## avgIndex_NEexp.c 7.82e-13 ***
## pDem_Rep.y 1.97e-05 ***
## pInd_Not.y 1.57e-06 ***
## avgRiskSeverity.c:avgIndex_NEexp.c 0.065178 .
## avgRiskSeverity.c:pDem_Rep.y 0.005490 **
## avgRiskSeverity.c:pInd_Not.y 0.057465 .
## avgIndex_NEexp.c:pDem_Rep.y 0.392766
## avgIndex_NEexp.c:pInd_Not.y 0.770917
## avgRiskSeverity.c:avgIndex_NEexp.c:pDem_Rep.y 0.541811
## avgRiskSeverity.c:avgIndex_NEexp.c:pInd_Not.y 0.858619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.892 on 2171 degrees of freedom
## (1286 observations deleted due to missingness)
## Multiple R-squared: 0.09645, Adjusted R-squared: 0.09187
## F-statistic: 21.07 on 11 and 2171 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
LONGITUDINAL WITH AVG(LIWC)
risk measures
analytic + affect
1. W2Risk3 ~ W1Risk3 * party + (avgAnalytic + avgAffect)
##
## Call:
## lm(formula = risk3.x ~ risk3.y * (pDem_Rep.y + pInd_Not.y) +
## (avgIndex_ANexp.c + avgIndex_AFexp.c), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4861 -0.6956 0.0600 0.7343 4.0059
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8490696 0.0787606 23.477 < 2e-16 ***
## risk3.y 0.6024059 0.0164483 36.624 < 2e-16 ***
## pDem_Rep.y -1.1963501 0.1696665 -7.051 2.37e-12 ***
## pInd_Not.y 0.6333128 0.1760492 3.597 0.000329 ***
## avgIndex_ANexp.c -0.0003036 0.0002677 -1.134 0.256841
## avgIndex_AFexp.c 0.0097938 0.0056181 1.743 0.081425 .
## risk3.y:pDem_Rep.y 0.1297214 0.0343447 3.777 0.000163 ***
## risk3.y:pInd_Not.y -0.1169409 0.0373164 -3.134 0.001749 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.112 on 2195 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5428, Adjusted R-squared: 0.5413
## F-statistic: 372.3 on 7 and 2195 DF, p-value: < 2.2e-162. W2Risk4 ~ W1Risk4 * party + (avgAnalytic + avgAffect)
##
## Call:
## lm(formula = risk4.x ~ risk4.y * (pDem_Rep.y + pInd_Not.y) +
## (avgIndex_ANexp.c + avgIndex_AFexp.c), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0995 -0.6020 -0.0214 0.8053 4.3327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.178e+00 9.690e-02 22.477 < 2e-16 ***
## risk4.y 5.407e-01 1.826e-02 29.618 < 2e-16 ***
## pDem_Rep.y -4.317e-01 2.090e-01 -2.066 0.03894 *
## pInd_Not.y 6.466e-01 2.264e-01 2.856 0.00433 **
## avgIndex_ANexp.c -6.857e-05 2.726e-04 -0.252 0.80140
## avgIndex_AFexp.c 2.093e-03 5.702e-03 0.367 0.71360
## risk4.y:pDem_Rep.y 2.360e-02 3.927e-02 0.601 0.54781
## risk4.y:pInd_Not.y -1.068e-01 4.274e-02 -2.498 0.01256 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.136 on 2189 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.325, Adjusted R-squared: 0.3229
## F-statistic: 150.6 on 7 and 2189 DF, p-value: < 2.2e-163. W2Risk5 ~ W1Risk5 * party + (avgAnalytic + avgAffect)
##
## Call:
## lm(formula = risk5.x ~ risk5.y * (pDem_Rep.y + pInd_Not.y) +
## (avgIndex_ANexp.c + avgIndex_AFexp.c), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9877 -0.7739 -0.2404 0.8379 5.3938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1910128 0.0645361 18.455 <2e-16 ***
## risk5.y 0.5866334 0.0173800 33.753 <2e-16 ***
## pDem_Rep.y 0.0771920 0.1280792 0.603 0.5468
## pInd_Not.y -0.1395218 0.1539318 -0.906 0.3648
## avgIndex_ANexp.c 0.0008506 0.0003314 2.566 0.0103 *
## avgIndex_AFexp.c -0.0119498 0.0069201 -1.727 0.0843 .
## risk5.y:pDem_Rep.y -0.0520430 0.0360628 -1.443 0.1491
## risk5.y:pInd_Not.y -0.0112945 0.0398655 -0.283 0.7770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 2185 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.4325, Adjusted R-squared: 0.4307
## F-statistic: 237.9 on 7 and 2185 DF, p-value: < 2.2e-164. W2RiskSeverity ~ W1RiskSeverity * party + (avgAanalytic + avgAffect)
##
## Call:
## lm(formula = dm$riskSeverity.x ~ riskSeverity.y * (pDem_Rep.y +
## pInd_Not.y) + (avgIndex_ANexp.c + avgIndex_AFexp.c), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7356 -0.5399 -0.0120 0.5341 4.1345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5946472 0.0739744 21.557 < 2e-16 ***
## riskSeverity.y 0.6068065 0.0166988 36.338 < 2e-16 ***
## pDem_Rep.y -0.5280199 0.1562070 -3.380 0.000737 ***
## pInd_Not.y 0.2901475 0.1662869 1.745 0.081150 .
## avgIndex_ANexp.c 0.0002097 0.0002138 0.981 0.326852
## avgIndex_AFexp.c -0.0011882 0.0044801 -0.265 0.790862
## riskSeverity.y:pDem_Rep.y 0.0500541 0.0355152 1.409 0.158868
## riskSeverity.y:pInd_Not.y -0.0662540 0.0373326 -1.775 0.076088 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8886 on 2177 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.4883, Adjusted R-squared: 0.4867
## F-statistic: 296.8 on 7 and 2177 DF, p-value: < 2.2e-16positive emotion
1. W2Risk3 ~ W1Risk3 * party + positive emotion
##
## Call:
## lm(formula = risk3.x ~ risk3.y * (pDem_Rep.y + pInd_Not.y) +
## dm$avgIndex_PEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4724 -0.6840 0.0594 0.7371 4.0266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.837243 0.078257 23.477 < 2e-16 ***
## risk3.y 0.604798 0.016351 36.988 < 2e-16 ***
## pDem_Rep.y -1.195010 0.169619 -7.045 2.47e-12 ***
## pInd_Not.y 0.633839 0.176094 3.599 0.000326 ***
## dm$avgIndex_PEexp.c 0.005782 0.001171 4.938 8.48e-07 ***
## risk3.y:pDem_Rep.y 0.129455 0.034347 3.769 0.000168 ***
## risk3.y:pInd_Not.y -0.115807 0.037315 -3.104 0.001937 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.113 on 2196 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5424, Adjusted R-squared: 0.5412
## F-statistic: 433.9 on 6 and 2196 DF, p-value: < 2.2e-162. W2Risk4 ~ W1Risk4 * party + positive emotion
##
## Call:
## lm(formula = risk4.x ~ risk4.y * (pDem_Rep.y + pInd_Not.y) +
## avgIndex_PEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0978 -0.6007 -0.0196 0.8085 4.3290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.175662 0.096587 22.525 < 2e-16 ***
## risk4.y 0.541170 0.018207 29.724 < 2e-16 ***
## pDem_Rep.y -0.431443 0.208845 -2.066 0.03896 *
## pInd_Not.y 0.647288 0.226340 2.860 0.00428 **
## avgIndex_PEexp.c 0.001083 0.001162 0.933 0.35106
## risk4.y:pDem_Rep.y 0.023357 0.039246 0.595 0.55181
## risk4.y:pInd_Not.y -0.106644 0.042731 -2.496 0.01264 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.136 on 2190 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.325, Adjusted R-squared: 0.3231
## F-statistic: 175.7 on 6 and 2190 DF, p-value: < 2.2e-163. W2Risk5 ~ W1Risk5 * party + positive emotion
##
## Call:
## lm(formula = risk5.x ~ risk5.y * (pDem_Rep.y + pInd_Not.y) +
## avgIndex_PEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8880 -0.7934 -0.2432 0.8414 5.3973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.189227 0.064600 18.409 < 2e-16 ***
## risk5.y 0.588049 0.017384 33.826 < 2e-16 ***
## pDem_Rep.y 0.075779 0.128135 0.591 0.554
## pInd_Not.y -0.156502 0.153961 -1.017 0.310
## avgIndex_PEexp.c 0.009754 0.001462 6.669 3.25e-11 ***
## risk5.y:pDem_Rep.y -0.050462 0.036091 -1.398 0.162
## risk5.y:pInd_Not.y -0.010878 0.039900 -0.273 0.785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 2186 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.4313, Adjusted R-squared: 0.4297
## F-statistic: 276.3 on 6 and 2186 DF, p-value: < 2.2e-164. W2RiskSeverity ~ W1RiskSeverity * party + positive emotion
##
## Call:
## lm(formula = dm$riskSeverity.x ~ riskSeverity.y * (pDem_Rep.y +
## pInd_Not.y) + avgIndex_PEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7232 -0.5354 -0.0139 0.5366 4.1481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5986915 0.0738348 21.652 < 2e-16 ***
## riskSeverity.y 0.6060130 0.0166730 36.347 < 2e-16 ***
## pDem_Rep.y -0.5310260 0.1561545 -3.401 0.000684 ***
## pInd_Not.y 0.2871290 0.1662678 1.727 0.084326 .
## avgIndex_PEexp.c 0.0053750 0.0009427 5.702 1.35e-08 ***
## riskSeverity.y:pDem_Rep.y 0.0506375 0.0355097 1.426 0.154006
## riskSeverity.y:pInd_Not.y -0.0664440 0.0373260 -1.780 0.075199 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8885 on 2178 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.4882, Adjusted R-squared: 0.4868
## F-statistic: 346.2 on 6 and 2178 DF, p-value: < 2.2e-16negative emotion
1. W2Risk3 ~ W1Risk3 * party + positive emotion
##
## Call:
## lm(formula = risk3.x ~ risk3.y * (pDem_Rep.y + pInd_Not.y) +
## dm$avgIndex_NEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4637 -0.6818 0.0553 0.7352 4.0352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.841720 0.078331 23.512 < 2e-16 ***
## risk3.y 0.603831 0.016368 36.891 < 2e-16 ***
## pDem_Rep.y -1.188691 0.169658 -7.006 3.24e-12 ***
## pInd_Not.y 0.634808 0.176011 3.607 0.000317 ***
## dm$avgIndex_NEexp.c 0.008907 0.001757 5.069 4.32e-07 ***
## risk3.y:pDem_Rep.y 0.128856 0.034341 3.752 0.000180 ***
## risk3.y:pInd_Not.y -0.116195 0.037299 -3.115 0.001862 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.112 on 2196 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5427, Adjusted R-squared: 0.5414
## F-statistic: 434.3 on 6 and 2196 DF, p-value: < 2.2e-162. W2Risk4 ~ W1Risk4 * party + negative emotion
##
## Call:
## lm(formula = risk4.x ~ risk4.y * (pDem_Rep.y + pInd_Not.y) +
## avgIndex_NEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1045 -0.6033 -0.0187 0.8076 4.3303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.177188 0.096646 22.527 <2e-16 ***
## risk4.y 0.540903 0.018216 29.694 <2e-16 ***
## pDem_Rep.y -0.429255 0.208887 -2.055 0.0400 *
## pInd_Not.y 0.646943 0.226318 2.859 0.0043 **
## avgIndex_NEexp.c 0.001792 0.001741 1.029 0.3035
## risk4.y:pDem_Rep.y 0.023350 0.039244 0.595 0.5519
## risk4.y:pInd_Not.y -0.106665 0.042728 -2.496 0.0126 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.136 on 2190 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.325, Adjusted R-squared: 0.3232
## F-statistic: 175.8 on 6 and 2190 DF, p-value: < 2.2e-163. W2Risk5 ~ W1Risk5 * party + negative emotion
##
## Call:
## lm(formula = risk5.x ~ risk5.y * (pDem_Rep.y + pInd_Not.y) +
## avgIndex_PEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8880 -0.7934 -0.2432 0.8414 5.3973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.189227 0.064600 18.409 < 2e-16 ***
## risk5.y 0.588049 0.017384 33.826 < 2e-16 ***
## pDem_Rep.y 0.075779 0.128135 0.591 0.554
## pInd_Not.y -0.156502 0.153961 -1.017 0.310
## avgIndex_PEexp.c 0.009754 0.001462 6.669 3.25e-11 ***
## risk5.y:pDem_Rep.y -0.050462 0.036091 -1.398 0.162
## risk5.y:pInd_Not.y -0.010878 0.039900 -0.273 0.785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 2186 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.4313, Adjusted R-squared: 0.4297
## F-statistic: 276.3 on 6 and 2186 DF, p-value: < 2.2e-164. W2RiskSeverity ~ W1RiskSeverity * party + negative emotion
##
## Call:
## lm(formula = dm$riskSeverity.x ~ riskSeverity.y * (pDem_Rep.y +
## pInd_Not.y) + avgIndex_NEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7257 -0.5381 -0.0175 0.5428 4.1455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.597593 0.073890 21.621 < 2e-16 ***
## riskSeverity.y 0.606240 0.016686 36.332 < 2e-16 ***
## pDem_Rep.y -0.527435 0.156273 -3.375 0.000751 ***
## pInd_Not.y 0.290542 0.166298 1.747 0.080757 .
## avgIndex_NEexp.c 0.007925 0.001414 5.605 2.34e-08 ***
## riskSeverity.y:pDem_Rep.y 0.050049 0.035521 1.409 0.158974
## riskSeverity.y:pInd_Not.y -0.067205 0.037336 -1.800 0.071996 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8888 on 2178 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.4879, Adjusted R-squared: 0.4865
## F-statistic: 345.9 on 6 and 2178 DF, p-value: < 2.2e-16vaccine attitudes
1. W2VaxxAttitude ~ W1VaxxAttitudes * party + (avgAnalytic + avgAffect)
##
## Call:
## lm(formula = vaxxAttitudes.x ~ vaxxAttitudes.y * (pDem_Rep.y +
## pInd_Not.y) + (avgIndex_ANexp.c + avgIndex_AFexp.c), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3097 -0.7195 0.0749 0.9873 5.4249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1311435 0.0346598 -3.784 0.000159 ***
## vaxxAttitudes.y 0.7018399 0.0157699 44.505 < 2e-16 ***
## pDem_Rep.y 0.1073852 0.0765619 1.403 0.160879
## pInd_Not.y 0.2302997 0.0822960 2.798 0.005180 **
## avgIndex_ANexp.c 0.0006602 0.0003424 1.928 0.053964 .
## avgIndex_AFexp.c -0.0119867 0.0071645 -1.673 0.094456 .
## vaxxAttitudes.y:pDem_Rep.y 0.1199654 0.0324565 3.696 0.000224 ***
## vaxxAttitudes.y:pInd_Not.y 0.0401789 0.0373425 1.076 0.282065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2195 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5402, Adjusted R-squared: 0.5388
## F-statistic: 368.5 on 7 and 2195 DF, p-value: < 2.2e-162. W2RiskSeverity ~ W1RiskSeverity * party + positive emotion
##
## Call:
## lm(formula = vaxxAttitudes.x ~ vaxxAttitudes.y * (pDem_Rep.y +
## pInd_Not.y) + avgIndex_PEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3932 -0.7179 0.0766 1.0059 5.3800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.129489 0.034668 -3.735 0.000192 ***
## vaxxAttitudes.y 0.700963 0.015768 44.454 < 2e-16 ***
## pDem_Rep.y 0.111954 0.076496 1.464 0.143465
## pInd_Not.y 0.217592 0.082091 2.651 0.008092 **
## avgIndex_PEexp.c 0.002973 0.001478 2.011 0.044411 *
## vaxxAttitudes.y:pDem_Rep.y 0.116295 0.032419 3.587 0.000342 ***
## vaxxAttitudes.y:pInd_Not.y 0.040581 0.037364 1.086 0.277565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.43 on 2196 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5395, Adjusted R-squared: 0.5382
## F-statistic: 428.8 on 6 and 2196 DF, p-value: < 2.2e-163. W2VaxxAttitude ~ W1VaxxAttitudes * party + negative emotion
##
## Call:
## lm(formula = vaxxAttitudes.x ~ vaxxAttitudes.y * (pDem_Rep.y +
## pInd_Not.y) + avgIndex_NEexp.c, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3869 -0.7139 0.0753 1.0135 5.3776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.129783 0.034677 -3.743 0.000187 ***
## vaxxAttitudes.y 0.701176 0.015781 44.431 < 2e-16 ***
## pDem_Rep.y 0.110538 0.076674 1.442 0.149542
## pInd_Not.y 0.218329 0.082097 2.659 0.007884 **
## avgIndex_NEexp.c 0.004184 0.002217 1.887 0.059231 .
## vaxxAttitudes.y:pDem_Rep.y 0.116286 0.032424 3.586 0.000343 ***
## vaxxAttitudes.y:pInd_Not.y 0.040322 0.037367 1.079 0.280675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.43 on 2196 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5394, Adjusted R-squared: 0.5381
## F-statistic: 428.6 on 6 and 2196 DF, p-value: < 2.2e-16LONGITUDINAL W1-W2
W2VaxxAttitude ~ party * (W1VaxxAttitudes + W1Analytic + W1Affect + W2Analytic + W2Affect)
##
## Call:
## lm(formula = vaxxAttitudes.x ~ (vaxxAttitudes.y.c + index_ANexp.y.c +
## index_AFexp.y.c + index_ANexp.x.c + index_AFexp.x.c) * (pDem_Rep.y +
## pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3991 -0.7300 0.0603 0.9274 5.5702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2749155 0.0351416 7.823 7.96e-15 ***
## vaxxAttitudes.y.c 0.7009677 0.0158469 44.234 < 2e-16 ***
## index_ANexp.y.c -0.0007565 0.0004264 -1.774 0.07613 .
## index_AFexp.y.c 0.0144656 0.0088514 1.634 0.10235
## index_ANexp.x.c 0.0012235 0.0004738 2.582 0.00988 **
## index_AFexp.x.c -0.0223656 0.0099529 -2.247 0.02473 *
## pDem_Rep.y 0.1519954 0.0736273 2.064 0.03910 *
## pInd_Not.y 0.2106495 0.0843138 2.498 0.01255 *
## vaxxAttitudes.y.c:pDem_Rep.y 0.1334135 0.0329956 4.043 5.45e-05 ***
## vaxxAttitudes.y.c:pInd_Not.y 0.0541473 0.0381608 1.419 0.15606
## index_ANexp.y.c:pDem_Rep.y -0.0007395 0.0008406 -0.880 0.37909
## index_ANexp.y.c:pInd_Not.y 0.0003306 0.0010572 0.313 0.75450
## index_AFexp.y.c:pDem_Rep.y 0.0112548 0.0175428 0.642 0.52123
## index_AFexp.y.c:pInd_Not.y -0.0091663 0.0218913 -0.419 0.67546
## index_ANexp.x.c:pDem_Rep.y -0.0005687 0.0009635 -0.590 0.55505
## index_ANexp.x.c:pInd_Not.y 0.0007093 0.0011564 0.613 0.53970
## index_AFexp.x.c:pDem_Rep.y 0.0125234 0.0202830 0.617 0.53701
## index_AFexp.x.c:pInd_Not.y -0.0153017 0.0242595 -0.631 0.52827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.425 on 2185 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5448, Adjusted R-squared: 0.5413
## F-statistic: 153.9 on 17 and 2185 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
W2risk3 ~ party * (W1risk3 + W1Analytic + W1Affect + W2Analytic + W2Affect)
##
## Call:
## lm(formula = risk3.x ~ (risk3.y.c + index_ANexp.y.c + index_AFexp.y.c +
## index_ANexp.x.c + index_AFexp.x.c) * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3742 -0.6844 0.0593 0.7604 3.9444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6099438 0.0278953 165.259 < 2e-16 ***
## risk3.y.c 0.6000054 0.0166513 36.034 < 2e-16 ***
## index_ANexp.y.c -0.0002308 0.0003315 -0.696 0.48636
## index_AFexp.y.c 0.0046021 0.0068915 0.668 0.50434
## index_ANexp.x.c -0.0004951 0.0003672 -1.348 0.17769
## index_AFexp.x.c 0.0144104 0.0077117 1.869 0.06181 .
## pDem_Rep.y -0.5974422 0.0600522 -9.949 < 2e-16 ***
## pInd_Not.y 0.1165935 0.0658083 1.772 0.07658 .
## risk3.y.c:pDem_Rep.y 0.0983269 0.0355553 2.765 0.00573 **
## risk3.y.c:pInd_Not.y -0.1252325 0.0394884 -3.171 0.00154 **
## index_ANexp.y.c:pDem_Rep.y -0.0004173 0.0006544 -0.638 0.52372
## index_ANexp.y.c:pInd_Not.y 0.0008258 0.0008217 1.005 0.31498
## index_AFexp.y.c:pDem_Rep.y 0.0054973 0.0136689 0.402 0.68760
## index_AFexp.y.c:pInd_Not.y -0.0145451 0.0170377 -0.854 0.39337
## index_ANexp.x.c:pDem_Rep.y -0.0009091 0.0007489 -1.214 0.22489
## index_ANexp.x.c:pInd_Not.y 0.0002986 0.0008945 0.334 0.73858
## index_AFexp.x.c:pDem_Rep.y 0.0266447 0.0157758 1.689 0.09137 .
## index_AFexp.x.c:pInd_Not.y -0.0077729 0.0187576 -0.414 0.67863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 2185 degrees of freedom
## (1266 observations deleted due to missingness)
## Multiple R-squared: 0.5486, Adjusted R-squared: 0.5451
## F-statistic: 156.2 on 17 and 2185 DF, p-value: < 2.2e-16## Warning: Removed 3 row(s) containing missing values (geom_path).
W2risk4 ~ party * (W1risk4 + W1Analytic + W1Affect + W2Analytic + W2Affect)
##
## Call:
## lm(formula = risk4.x ~ (risk4.y.c + index_ANexp.y.c + index_AFexp.y.c +
## index_ANexp.x.c + index_AFexp.x.c) * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0659 -0.6014 -0.0158 0.8320 4.3620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.960e+00 2.788e-02 177.944 < 2e-16 ***
## risk4.y.c 5.411e-01 1.832e-02 29.542 < 2e-16 ***
## index_ANexp.y.c -3.080e-04 3.418e-04 -0.901 0.3675
## index_AFexp.y.c 5.522e-03 7.091e-03 0.779 0.4363
## index_ANexp.x.c 1.287e-04 3.790e-04 0.340 0.7342
## index_AFexp.x.c -1.239e-03 7.953e-03 -0.156 0.8762
## pDem_Rep.y -2.955e-01 5.832e-02 -5.067 4.37e-07 ***
## pInd_Not.y 1.061e-01 6.694e-02 1.584 0.1133
## risk4.y.c:pDem_Rep.y 2.540e-02 3.952e-02 0.643 0.5206
## risk4.y.c:pInd_Not.y -1.058e-01 4.315e-02 -2.453 0.0142 *
## index_ANexp.y.c:pDem_Rep.y 1.346e-04 6.713e-04 0.201 0.8411
## index_ANexp.y.c:pInd_Not.y 6.593e-04 8.490e-04 0.777 0.4375
## index_AFexp.y.c:pDem_Rep.y -4.699e-03 1.401e-02 -0.335 0.7373
## index_AFexp.y.c:pInd_Not.y -1.367e-02 1.757e-02 -0.778 0.4364
## index_ANexp.x.c:pDem_Rep.y 2.586e-04 7.686e-04 0.336 0.7366
## index_ANexp.x.c:pInd_Not.y 7.798e-05 9.262e-04 0.084 0.9329
## index_AFexp.x.c:pDem_Rep.y -2.546e-03 1.618e-02 -0.157 0.8750
## index_AFexp.x.c:pInd_Not.y 6.545e-05 1.940e-02 0.003 0.9973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.137 on 2179 degrees of freedom
## (1272 observations deleted due to missingness)
## Multiple R-squared: 0.3268, Adjusted R-squared: 0.3215
## F-statistic: 62.22 on 17 and 2179 DF, p-value: < 2.2e-16## Warning: Removed 3 row(s) containing missing values (geom_path).
W2risk5 ~ party * (W1risk5 + W1Analytic + W1Affect + W2Analytic + W2Affect)
##
## Call:
## lm(formula = risk5.x ~ (risk5.y.c + index_ANexp.y.c + index_AFexp.y.c +
## index_ANexp.x.c + index_AFexp.x.c) * (pDem_Rep.y + pInd_Not.y),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8416 -0.7913 -0.2289 0.8183 5.4268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.108e+00 3.387e-02 91.763 <2e-16 ***
## risk5.y.c 5.883e-01 1.745e-02 33.724 <2e-16 ***
## index_ANexp.y.c -9.879e-05 4.128e-04 -0.239 0.8109
## index_AFexp.y.c 3.204e-03 8.565e-03 0.374 0.7084
## index_ANexp.x.c 6.880e-04 4.578e-04 1.503 0.1330
## index_AFexp.x.c -1.003e-02 9.611e-03 -1.043 0.2968
## pDem_Rep.y -7.737e-02 7.064e-02 -1.095 0.2735
## pInd_Not.y -1.744e-01 8.150e-02 -2.140 0.0325 *
## risk5.y.c:pDem_Rep.y -5.466e-02 3.759e-02 -1.454 0.1460
## risk5.y.c:pInd_Not.y -2.474e-02 4.114e-02 -0.601 0.5476
## index_ANexp.y.c:pDem_Rep.y -1.804e-05 8.156e-04 -0.022 0.9824
## index_ANexp.y.c:pInd_Not.y -2.687e-04 1.023e-03 -0.263 0.7928
## index_AFexp.y.c:pDem_Rep.y 5.228e-04 1.701e-02 0.031 0.9755
## index_AFexp.y.c:pInd_Not.y 6.698e-03 2.116e-02 0.317 0.7516
## index_ANexp.x.c:pDem_Rep.y 2.726e-04 9.332e-04 0.292 0.7702
## index_ANexp.x.c:pInd_Not.y 2.096e-03 1.116e-03 1.879 0.0604 .
## index_AFexp.x.c:pDem_Rep.y -5.098e-03 1.965e-02 -0.260 0.7953
## index_AFexp.x.c:pInd_Not.y -4.222e-02 2.339e-02 -1.805 0.0712 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 2175 degrees of freedom
## (1276 observations deleted due to missingness)
## Multiple R-squared: 0.4355, Adjusted R-squared: 0.4311
## F-statistic: 98.7 on 17 and 2175 DF, p-value: < 2.2e-16## Warning: Removed 6 row(s) containing missing values (geom_path).
W2riskSeverity ~ party * (W1riskSeverity + W1Analytic + W1Affect + W2Analytic + W2Affect)
##
## Call:
## lm(formula = riskSeverity.x ~ (riskSeverity.y.c + index_ANexp.y.c +
## index_AFexp.y.c + index_ANexp.x.c + index_AFexp.x.c) * (pDem_Rep.y +
## pInd_Not.y), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7059 -0.5419 -0.0097 0.5291 4.1280
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.220e+00 2.201e-02 191.764 < 2e-16 ***
## riskSeverity.y.c 6.087e-01 1.679e-02 36.249 < 2e-16 ***
## index_ANexp.y.c -2.208e-04 2.666e-04 -0.828 0.4076
## index_AFexp.y.c 4.483e-03 5.535e-03 0.810 0.4180
## index_ANexp.x.c 1.506e-04 2.962e-04 0.508 0.6112
## index_AFexp.x.c 2.934e-05 6.217e-03 0.005 0.9962
## pDem_Rep.y -2.989e-01 4.649e-02 -6.429 1.58e-10 ***
## pInd_Not.y 1.360e-02 5.253e-02 0.259 0.7957
## riskSeverity.y.c:pDem_Rep.y 3.844e-02 3.702e-02 1.038 0.2992
## riskSeverity.y.c:pInd_Not.y -7.876e-02 3.898e-02 -2.020 0.0435 *
## index_ANexp.y.c:pDem_Rep.y -1.522e-04 5.245e-04 -0.290 0.7717
## index_ANexp.y.c:pInd_Not.y 4.651e-04 6.618e-04 0.703 0.4823
## index_AFexp.y.c:pDem_Rep.y 1.274e-03 1.095e-02 0.116 0.9074
## index_AFexp.y.c:pInd_Not.y -8.265e-03 1.370e-02 -0.603 0.5464
## index_ANexp.x.c:pDem_Rep.y -1.445e-04 6.008e-04 -0.240 0.8100
## index_ANexp.x.c:pInd_Not.y 8.144e-04 7.238e-04 1.125 0.2606
## index_AFexp.x.c:pDem_Rep.y 6.607e-03 1.265e-02 0.522 0.6017
## index_AFexp.x.c:pInd_Not.y -1.617e-02 1.516e-02 -1.066 0.2863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8875 on 2167 degrees of freedom
## (1284 observations deleted due to missingness)
## Multiple R-squared: 0.492, Adjusted R-squared: 0.488
## F-statistic: 123.5 on 17 and 2167 DF, p-value: < 2.2e-16## Warning: Removed 3 row(s) containing missing values (geom_path).