Outline

Introspection_Condition: (1) Introspection Social Gain & (2) Introspection Social Control

Gain condition: introspection Q, then DV

Control condition: DV, then introspection Q

control for party manipulation and country (exclude Israel)

1. prepare codes and consolidated variables

sg$spread <- ifelse(is.na(sg$intSpread), sg$intSpreadCtrl, sg$intSpread)

sg$expSymptoms <- ifelse(is.na(sg$intExpSymp), sg$intExpSympCtrl, sg$intExpSymp)

sg$partyLikely <- ifelse(is.na(sg$partyLikelihood), sg$partyLikelihoodCtrl, sg$partyLikelihood)

sg$contLikely <- ifelse(is.na(sg$contLikelihood), sg$contLikelihoodCtrl, sg$contLikelihood)

#contrast code social gains: control = -.5; introspection = .5
sg$cond_cc <- ifelse(sg$Introspection_Condition == 1, .5, -.5)

sg$Introspection_Condition <- ifelse(sg$Introspection_Condition == 1, "introspect", "control")

sg$policy_condition_cc <-
  (sg$policy_condition == 1)*(-1/2) + 
  (sg$policy_condition == 2)*(-3/8) + 
  (sg$policy_condition == 3)*(-1/4) +
  (sg$policy_condition == 4)*(-1/8) +
  (sg$policy_condition == 5)*(1/8) + 
  (sg$policy_condition == 6)*(1/4) + 
  (sg$policy_condition == 7)*(3/8) +
  (sg$policy_condition == 8)*(1/2)

2. analyses

introspection screen questions

spread screen ~ condition + policy condition

summary(model <- lmer(spread ~ cond_cc + policy_condition_cc + (1|country_names), data = sg))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: spread ~ cond_cc + policy_condition_cc + (1 | country_names)
##    Data: sg
## 
## REML criterion at convergence: 21232.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8936 -0.5265  0.3277  0.8623  1.4316 
## 
## Random effects:
##  Groups        Name        Variance Std.Dev.
##  country_names (Intercept) 0.2336   0.4833  
##  Residual                  3.0825   1.7557  
## Number of obs: 5349, groups:  country_names, 6
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)         5.281e+00  1.989e-01 4.979e+00  26.554 1.48e-06 ***
## cond_cc             1.207e-01  4.803e-02 5.341e+03   2.514    0.012 *  
## policy_condition_cc 3.527e-02  6.960e-02 5.341e+03   0.507    0.612    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd_cc
## cond_cc      0.000       
## plcy_cndtn_  0.002 -0.009
mean(sg$spread[sg$Introspection_Condition == "introspect"], na.rm = T) #introspection = 5.37
## [1] 5.366667
mean(sg$spread[sg$Introspection_Condition == "control"], na.rm = T) #control = 5.24
## [1] 5.243748
sg %>% ggplot(aes(Introspection_Condition, spread, fill = Introspection_Condition)) +
  stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 6))
## Warning: Removed 49 rows containing non-finite values (stat_summary).

sg %>% ggplot(aes(country_names, spread, fill = Introspection_Condition)) +
  stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 6))
## Warning: Removed 49 rows containing non-finite values (stat_summary).

experience symptoms screen ~ condition + policy condition

summary(mExpSymp <- lmer(expSymptoms ~ cond_cc + policy_condition_cc + (1|country), data = sg))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: expSymptoms ~ cond_cc + policy_condition_cc + (1 | country)
##    Data: sg
## 
## REML criterion at convergence: 21464.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6825 -0.5190  0.2776  0.8410  1.3816 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept) 0.1513   0.389   
##  Residual             3.1917   1.787   
## Number of obs: 5361, groups:  country, 6
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)         5.184e+00  1.608e-01 4.977e+00  32.236 5.68e-07 ***
## cond_cc             1.632e-01  4.882e-02 5.353e+03   3.343 0.000835 ***
## policy_condition_cc 2.624e-02  7.078e-02 5.353e+03   0.371 0.710879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd_cc
## cond_cc      0.000       
## plcy_cndtn_  0.003 -0.008
mean(sg$expSymptoms[sg$Introspection_Condition == "introspect"], na.rm = T) #intro = 5.30
## [1] 5.297004
mean(sg$expSymptoms[sg$Introspection_Condition == "control"], na.rm = T) #control = 5.14
## [1] 5.137495
sg %>% ggplot(aes(Introspection_Condition, expSymptoms, fill = Introspection_Condition)) +
  stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 6))
## Warning: Removed 37 rows containing non-finite values (stat_summary).

sg %>% ggplot(aes(country_names, expSymptoms, fill = Introspection_Condition)) +
  stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 6))
## Warning: Removed 37 rows containing non-finite values (stat_summary).

judgment

going to party ~ condition + policy condition

summary(lmer(partyLikely ~ cond_cc + policy_condition_cc + (1|country), data = sg))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: partyLikely ~ cond_cc + policy_condition_cc + (1 | country)
##    Data: sg
## 
## REML criterion at convergence: 21758.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.1481 -0.7779 -0.4076  0.6427  2.7424 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept) 0.1021   0.3195  
##  Residual             3.3577   1.8324  
## Number of obs: 5367, groups:  country, 6
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            2.57095    0.13297    4.96336  19.335 7.28e-06 ***
## cond_cc               -0.23617    0.05004 5359.17342  -4.719 2.43e-06 ***
## policy_condition_cc    0.12071    0.07255 5359.59887   1.664   0.0962 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd_cc
## cond_cc      0.000       
## plcy_cndtn_  0.003 -0.009
mean(sg$partyLikely[sg$Introspection_Condition == "introspect"], na.rm = T) #intro = 2.44
## [1] 2.4394
mean(sg$partyLikely[sg$Introspection_Condition == "control"], na.rm = T) #control = 2.68
## [1] 2.678016
sg %>% ggplot(aes(Introspection_Condition, partyLikely, fill = Introspection_Condition)) +
  stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(1, 3))
## Warning: Removed 31 rows containing non-finite values (stat_summary).

sg %>% ggplot(aes(country_names, partyLikely, fill = Introspection_Condition)) +
  stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(1, 3))
## Warning: Removed 31 rows containing non-finite values (stat_summary).

risk estimates

if attend party, likelihood catching ~ condition + policy condition

summary(lmer(contLikely ~ cond_cc + policy_condition_cc + (1|country), data = sg))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: contLikely ~ cond_cc + policy_condition_cc + (1 | country)
##    Data: sg
## 
## REML criterion at convergence: 21171.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2398 -0.5099  0.1235  0.7393  1.7064 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept) 0.1235   0.3514  
##  Residual             3.0088   1.7346  
## Number of obs: 5367, groups:  country, 6
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)          4.485e+00  1.455e-01  4.993e+00  30.816 6.86e-07 ***
## cond_cc             -3.057e-02  4.737e-02  5.359e+03  -0.645    0.519    
## policy_condition_cc  1.499e-03  6.871e-02  5.359e+03   0.022    0.983    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd_cc
## cond_cc      0.000       
## plcy_cndtn_  0.003 -0.010
mean(sg$contLikely[sg$Introspection_Condition == "introspect"], na.rm = T) #intro = 4.48
## [1] 4.484234
mean(sg$contLikely[sg$Introspection_Condition == "control"], na.rm = T) #control = 4.52
## [1] 4.523862
sg %>% ggplot(aes(Introspection_Condition, contLikely, fill = Introspection_Condition)) + stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 5))
## Warning: Removed 31 rows containing non-finite values (stat_summary).

sg %>% ggplot(aes(country_names, contLikely, fill = Introspection_Condition)) + stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 5))
## Warning: Removed 31 rows containing non-finite values (stat_summary).

if attend party, likelihood of spreading ~ condition + policy condition

summary(lmer(spreadLikelihood ~ cond_cc + policy_condition_cc + (1|country), data = sg))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: spreadLikelihood ~ cond_cc + policy_condition_cc + (1 | country)
##    Data: sg
## 
## REML criterion at convergence: 21950.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3366 -0.5581  0.2503  0.8889  1.3322 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept) 0.06551  0.256   
##  Residual             3.48990  1.868   
## Number of obs: 5364, groups:  country, 6
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            4.98245    0.10775    4.96966  46.242 9.67e-08 ***
## cond_cc                0.02127    0.05103 5356.25058   0.417    0.677    
## policy_condition_cc   -0.06833    0.07402 5356.94606  -0.923    0.356    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd_cc
## cond_cc      0.000       
## plcy_cndtn_  0.004 -0.009
mean(sg$spreadLikelihood[sg$Introspection_Condition == "introspect"], na.rm = T) #intro = 4.99
## [1] 4.993251
mean(sg$spreadLikelihood[sg$Introspection_Condition == "control"], na.rm = T) #control = 4.98
## [1] 4.977753
sg %>% ggplot(aes(Introspection_Condition, spreadLikelihood, fill = Introspection_Condition)) + stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 6))
## Warning: Removed 34 rows containing non-finite values (stat_summary).

sg %>% ggplot(aes(country_names, spreadLikelihood, fill = Introspection_Condition)) + stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(4, 6))
## Warning: Removed 34 rows containing non-finite values (stat_summary).

social norms

e. groupLikelihood ~ condition + policy condition

summary(lmer(groupLikelihood ~ cond_cc + policy_condition_cc + (1|country), data = sg))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: groupLikelihood ~ cond_cc + policy_condition_cc + (1 | country)
##    Data: sg
## 
## REML criterion at convergence: 17790.2
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.92853 -0.83385 -0.06196  0.82617  2.30728 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  country  (Intercept) 0.2518   0.5018  
##  Residual             3.0012   1.7324  
## Number of obs: 4512, groups:  country, 5
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            3.75005    0.22600    3.99477  16.593  7.8e-05 ***
## cond_cc               -0.05786    0.05160 4505.05620  -1.121   0.2622    
## policy_condition_cc    0.12421    0.07508 4505.18357   1.654   0.0981 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd_cc
## cond_cc      0.000       
## plcy_cndtn_  0.001 -0.012
mean(sg$groupLikelihood[sg$Introspection_Condition == "introspect"], na.rm = T) #intro = 3.65
## [1] 3.658929
mean(sg$groupLikelihood[sg$Introspection_Condition == "control"], na.rm = T) #control = 3.72
## [1] 3.720511
sg %>% ggplot(aes(Introspection_Condition, groupLikelihood, fill = Introspection_Condition)) + stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(2, 5))
## Warning: Removed 886 rows containing non-finite values (stat_summary).

mean(sg$groupLikelihood[sg$country_names == "Italy"], na.rm = T)
## [1] NaN
View(sg)


sg %>% ggplot(aes(country_names, groupLikelihood, fill = Introspection_Condition)) + stat_summary(fun.data = mean_sdl, geom = "bar", position = "dodge") + theme_minimal() + coord_cartesian(ylim = c(0, 5))
## Warning: Removed 886 rows containing non-finite values (stat_summary).