library(here)
library(readr)
library(psych)
library(tidyverse)
library(apaTables)

US Longitudinal

This study examines whether people’s zero-sum mindsets measured 8 months before the onset of the COVID pandemic in the US would predict their strategic responses to dealing with the pandemic in March of 2020.

Load Data

cv <- read_csv(here("data", "s2.csv"))

Describing Demographics of Attrition between T1 and T2

library(psych)
t2 <- filter(cv, !is.na(cv$age_T2))
t1_only <- filter(cv, is.na(cv$age_T2))
nrow(t1_only) + nrow(t2) == nrow(cv)
## [1] TRUE
t.test(t1_only$income_T1, t2$income_T1) # no differences in income
## 
##  Welch Two Sample t-test
## 
## data:  t1_only$income_T1 and t2$income_T1
## t = 0.5901, df = 345.39, p-value = 0.5555
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.4508401  0.8373195
## sample estimates:
## mean of x mean of y 
##  6.252941  6.059701
t.test(t1_only$education_T1, t2$education_T1) # higher education
## 
##  Welch Two Sample t-test
## 
## data:  t1_only$education_T1 and t2$education_T1
## t = -2.9562, df = 372.22, p-value = 0.003313
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.6264440 -0.1259704
## sample estimates:
## mean of x mean of y 
##  3.805882  4.182090
t.test(t1_only$age, t2$age) # older people more likely to complete both timepoints
## 
##  Welch Two Sample t-test
## 
## data:  t1_only$age and t2$age
## t = -5.331, df = 406.27, p-value = 1.624e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.002336 -3.229372
## sample estimates:
## mean of x mean of y 
##  30.00000  35.11585
t.test(t1_only$`Socioeconomic Status`, t2$`Socioeconomic Status`)
## 
##  Welch Two Sample t-test
## 
## data:  t1_only$`Socioeconomic Status` and t2$`Socioeconomic Status`
## t = -1.2036, df = 373.5, p-value = 0.2295
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.5032683  0.1211025
## sample estimates:
## mean of x mean of y 
##  4.924419  5.115502

Cooperation Under Scarcity

# Descriptives
describe(cv$scarcity_means_cooperation_mean_T2)
##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 337 3.87 0.85      4    3.92 0.74   1   5     4 -0.4    -0.44 0.05
hist(cv$scarcity_means_cooperation_mean_T2)

# Plot Relationship

ggplot(cv, aes(zsm_mean_T1, scarcity_means_cooperation_mean_T2)) + geom_smooth()

Hypothesis 8a: Attitudes

Those with stronger zero-sum mindsets (measured the year prior) will be less likely to adopt cooperative attitudes towards dealing with the pandemic.

Cooperative Attitudes in General

# In General

# Unstandardized
test <- lm(scale(cv$scarcity_means_cooperation_mean_T2) ~ scale(cv$zsm_mean_T1))
summary(test)
## 
## Call:
## lm(formula = scale(cv$scarcity_means_cooperation_mean_T2) ~ scale(cv$zsm_mean_T1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0789 -0.7187  0.0834  0.7365  1.9567 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -0.02229    0.05238  -0.426    0.671    
## scale(cv$zsm_mean_T1) -0.29818    0.05391  -5.532 6.41e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9587 on 335 degrees of freedom
##   (172 observations deleted due to missingness)
## Multiple R-squared:  0.08369,    Adjusted R-squared:  0.08096 
## F-statistic:  30.6 on 1 and 335 DF,  p-value: 6.405e-08
test <- lm(cv$scarcity_means_cooperation_mean_T2 ~ cv$zsm_mean_T1 + cv$age_T2 + cv$education_T2 + cv$income_T2 +cv$sex_T2  + cv$race)
summary(test)
## 
## Call:
## lm(formula = cv$scarcity_means_cooperation_mean_T2 ~ cv$zsm_mean_T1 + 
##     cv$age_T2 + cv$education_T2 + cv$income_T2 + cv$sex_T2 + 
##     cv$race)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.60066 -0.52768  0.05305  0.59898  1.63265 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    3.9953207  0.3244390  12.315  < 2e-16 ***
## cv$zsm_mean_T1                -0.2184164  0.0535247  -4.081  5.7e-05 ***
## cv$age_T2                      0.0004958  0.0040536   0.122   0.9027    
## cv$education_T2                0.0343452  0.0348804   0.985   0.3256    
## cv$income_T2                   0.0020151  0.0138585   0.145   0.8845    
## cv$sex_T2                     -0.1282222  0.0936496  -1.369   0.1719    
## cv$raceBlack/African American  0.1121041  0.2528542   0.443   0.6578    
## cv$raceLatinx                  0.2634757  0.2476468   1.064   0.2882    
## cv$raceMixed                   0.3829346  0.2556259   1.498   0.1351    
## cv$raceOther                   0.6789743  0.3585575   1.894   0.0592 .  
## cv$raceWhite                   0.3535632  0.1970843   1.794   0.0738 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8148 on 313 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.1075, Adjusted R-squared:  0.07901 
## F-statistic: 3.771 on 10 and 313 DF,  p-value: 8.237e-05
# Standardized
test <- lm(scale(cv$scarcity_means_cooperation_mean_T2) ~ scale(cv$zsm_mean_T1) + scale(cv$age_T2) + cv$sex_T2  + scale(cv$education_T2) + scale(cv$income_T2) + cv$political_orientation)
summary(test)
## 
## Call:
## lm(formula = scale(cv$scarcity_means_cooperation_mean_T2) ~ scale(cv$zsm_mean_T1) + 
##     scale(cv$age_T2) + cv$sex_T2 + scale(cv$education_T2) + scale(cv$income_T2) + 
##     cv$political_orientation)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.71562 -0.66772  0.08862  0.68014  2.00063 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.29762    0.15664  -1.900   0.0583 .  
## scale(cv$zsm_mean_T1)            -0.28894    0.05684  -5.084 6.37e-07 ***
## scale(cv$age_T2)                  0.03251    0.05538   0.587   0.5575    
## cv$sex_T2                        -0.11702    0.11061  -1.058   0.2909    
## scale(cv$education_T2)            0.04266    0.05862   0.728   0.4673    
## scale(cv$income_T2)               0.02382    0.05813   0.410   0.6823    
## cv$political_orientationLiberal   0.42128    0.16683   2.525   0.0121 *  
## cv$political_orientationModerate  0.24331    0.17810   1.366   0.1729    
## cv$political_orientationN/A       0.81483    0.69835   1.167   0.2442    
## cv$political_orientationOther     0.38247    0.35684   1.072   0.2846    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9581 on 314 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.1113, Adjusted R-squared:  0.0858 
## F-statistic: 4.368 on 9 and 314 DF,  p-value: 2.189e-05
# Standardized
test <- lm(scale(cv$scarcity_means_cooperation_mean_T2) ~ scale(cv$zsm_mean_T1) + scale(cv$age_T2) + cv$sex_T2 + cv$race + scale(cv$education_T2) + scale(cv$income_T2) + cv$political_orientation + cv$unemployed)

summary(test)
## 
## Call:
## lm(formula = scale(cv$scarcity_means_cooperation_mean_T2) ~ scale(cv$zsm_mean_T1) + 
##     scale(cv$age_T2) + cv$sex_T2 + cv$race + scale(cv$education_T2) + 
##     scale(cv$income_T2) + cv$political_orientation + cv$unemployed)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.79164 -0.63645  0.08468  0.69760  1.95541 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.72002    0.26908  -2.676  0.00785 ** 
## scale(cv$zsm_mean_T1)            -0.25724    0.05992  -4.293 2.37e-05 ***
## scale(cv$age_T2)                  0.03327    0.05691   0.585  0.55930    
## cv$sex_T2                        -0.13275    0.11125  -1.193  0.23369    
## cv$raceBlack/African American     0.10070    0.29749   0.338  0.73522    
## cv$raceLatinx                     0.35497    0.29363   1.209  0.22763    
## cv$raceMixed                      0.48167    0.30549   1.577  0.11589    
## cv$raceOther                      0.82618    0.42367   1.950  0.05208 .  
## cv$raceWhite                      0.44418    0.23516   1.889  0.05985 .  
## scale(cv$education_T2)            0.04570    0.05882   0.777  0.43777    
## scale(cv$income_T2)               0.01534    0.05866   0.262  0.79383    
## cv$political_orientationLiberal   0.45995    0.16770   2.743  0.00645 ** 
## cv$political_orientationModerate  0.28701    0.17924   1.601  0.11035    
## cv$political_orientationN/A       0.78573    0.69866   1.125  0.26162    
## cv$political_orientationOther     0.32415    0.36794   0.881  0.37902    
## cv$unemployed                     0.04219    0.21352   0.198  0.84350    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9563 on 308 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.1315, Adjusted R-squared:  0.08918 
## F-statistic: 3.108 on 15 and 308 DF,  p-value: 9.808e-05

Attitudes about Cooperation as Best Response to COVID

e.g. The best way to deal with this pandemic is…

“Every man for himself” ——————-“We’re all in this together”

# During this Pandemic

test <- lm(scale(cv$strategy_statement_18_T2) ~ scale(cv$zsm_mean_T1)) # Simple
summary(test)
## 
## Call:
## lm(formula = scale(cv$strategy_statement_18_T2) ~ scale(cv$zsm_mean_T1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4559 -0.4299  0.2720  0.6881  1.5078 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -0.01976    0.05289  -0.374    0.709    
## scale(cv$zsm_mean_T1) -0.26440    0.05443  -4.858 1.83e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.968 on 335 degrees of freedom
##   (172 observations deleted due to missingness)
## Multiple R-squared:  0.0658, Adjusted R-squared:  0.06301 
## F-statistic:  23.6 on 1 and 335 DF,  p-value: 1.827e-06
# Standardized
test <- lm(scale(cv$strategy_statement_18_T2) ~ scale(cv$zsm_mean_T1) + scale(cv$age_T2) + cv$sex_T2  + scale(cv$education_T2) + scale(cv$income_T2) + cv$political_orientation)
summary(test)
## 
## Call:
## lm(formula = scale(cv$strategy_statement_18_T2) ~ scale(cv$zsm_mean_T1) + 
##     scale(cv$age_T2) + cv$sex_T2 + scale(cv$education_T2) + scale(cv$income_T2) + 
##     cv$political_orientation)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7008 -0.4170  0.2564  0.6336  1.6118 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.21708    0.15751  -1.378   0.1691    
## scale(cv$zsm_mean_T1)            -0.24786    0.05715  -4.337 1.95e-05 ***
## scale(cv$age_T2)                  0.08669    0.05568   1.557   0.1205    
## cv$sex_T2                         0.05670    0.11123   0.510   0.6105    
## scale(cv$education_T2)            0.01581    0.05894   0.268   0.7887    
## scale(cv$income_T2)              -0.00633    0.05845  -0.108   0.9138    
## cv$political_orientationLiberal   0.35198    0.16776   2.098   0.0367 *  
## cv$political_orientationModerate -0.02775    0.17908  -0.155   0.8770    
## cv$political_orientationN/A       0.15013    0.70222   0.214   0.8308    
## cv$political_orientationOther    -0.08726    0.35882  -0.243   0.8080    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9634 on 314 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.09996,    Adjusted R-squared:  0.07417 
## F-statistic: 3.875 on 9 and 314 DF,  p-value: 0.0001122
# With all controls

test <- lm(scale(cv$strategy_statement_18_T2) ~ scale(cv$zsm_mean_T1) + scale(cv$age_T2) + cv$sex_T2 + cv$race + scale(cv$education_T2) + scale(cv$income_T2) + cv$political_orientation + cv$unemployed)
summary(test)
## 
## Call:
## lm(formula = scale(cv$strategy_statement_18_T2) ~ scale(cv$zsm_mean_T1) + 
##     scale(cv$age_T2) + cv$sex_T2 + cv$race + scale(cv$education_T2) + 
##     scale(cv$income_T2) + cv$political_orientation + cv$unemployed)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7552 -0.3874  0.2519  0.6118  1.6722 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.507640   0.270670  -1.875 0.061671 .  
## scale(cv$zsm_mean_T1)            -0.211032   0.060276  -3.501 0.000532 ***
## scale(cv$age_T2)                  0.075516   0.057247   1.319 0.188102    
## cv$sex_T2                         0.034107   0.111905   0.305 0.760735    
## cv$raceBlack/African American     0.122719   0.299239   0.410 0.682014    
## cv$raceLatinx                    -0.015260   0.295365  -0.052 0.958830    
## cv$raceMixed                      0.187734   0.307292   0.611 0.541696    
## cv$raceOther                      0.713103   0.426169   1.673 0.095286 .  
## cv$raceWhite                      0.354812   0.236546   1.500 0.134647    
## scale(cv$education_T2)            0.015588   0.059170   0.263 0.792389    
## scale(cv$income_T2)              -0.006159   0.059010  -0.104 0.916941    
## cv$political_orientationLiberal   0.370005   0.168691   2.193 0.029027 *  
## cv$political_orientationModerate -0.006197   0.180298  -0.034 0.972605    
## cv$political_orientationN/A       0.073540   0.702781   0.105 0.916729    
## cv$political_orientationOther    -0.035823   0.370113  -0.097 0.922957    
## cv$unemployed                     0.002264   0.214776   0.011 0.991598    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9619 on 308 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.1198, Adjusted R-squared:  0.07695 
## F-statistic: 2.795 on 15 and 308 DF,  p-value: 0.0004342
# ZSM more imporant than age

Attitudes About Cooperation with Public Health Efforts

test <- lm(scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1)) # Simple
summary(test)
## 
## Call:
## lm(formula = scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7101 -0.1913  0.4070  0.5499  0.9786 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           -0.01264    0.05398  -0.234  0.81505   
## scale(cv$zsm_mean_T1) -0.16906    0.05555  -3.043  0.00253 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9879 on 335 degrees of freedom
##   (172 observations deleted due to missingness)
## Multiple R-squared:  0.0269, Adjusted R-squared:  0.024 
## F-statistic: 9.261 on 1 and 335 DF,  p-value: 0.002526
# Standardized
test <- lm(scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1) + scale(cv$age_T2) + cv$sex_T2  + scale(cv$education_T2) + scale(cv$income_T2) + cv$political_orientation)
summary(test)
## 
## Call:
## lm(formula = scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1) + 
##     scale(cv$age_T2) + cv$sex_T2 + scale(cv$education_T2) + scale(cv$income_T2) + 
##     cv$political_orientation)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4951 -0.1606  0.2869  0.5494  1.3467 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.48535    0.15959  -3.041  0.00255 ** 
## scale(cv$zsm_mean_T1)            -0.15640    0.05791  -2.701  0.00729 ** 
## scale(cv$age_T2)                  0.11036    0.05642   1.956  0.05136 .  
## cv$sex_T2                        -0.19571    0.11270  -1.737  0.08345 .  
## scale(cv$education_T2)           -0.08502    0.05972  -1.424  0.15554    
## scale(cv$income_T2)               0.09308    0.05923   1.572  0.11704    
## cv$political_orientationLiberal   0.70880    0.16998   4.170 3.95e-05 ***
## cv$political_orientationModerate  0.45186    0.18146   2.490  0.01328 *  
## cv$political_orientationN/A       1.02858    0.71152   1.446  0.14929    
## cv$political_orientationOther     0.79699    0.36357   2.192  0.02910 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9761 on 314 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.09942,    Adjusted R-squared:  0.07361 
## F-statistic: 3.852 on 9 and 314 DF,  p-value: 0.0001211
# With controls

test <- lm(scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1) + scale(cv$age_T2) + cv$sex_T2 + cv$race + scale(cv$education_T2) + scale(cv$income_T2) + cv$political_orientation + cv$unemployed)
summary(test)
## 
## Call:
## lm(formula = scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1) + 
##     scale(cv$age_T2) + cv$sex_T2 + cv$race + scale(cv$education_T2) + 
##     scale(cv$income_T2) + cv$political_orientation + cv$unemployed)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5067 -0.2384  0.2790  0.5523  1.3105 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.431881   0.275721  -1.566  0.11829    
## scale(cv$zsm_mean_T1)            -0.159297   0.061401  -2.594  0.00993 ** 
## scale(cv$age_T2)                  0.125341   0.058315   2.149  0.03238 *  
## cv$sex_T2                        -0.193726   0.113994  -1.699  0.09025 .  
## cv$raceBlack/African American    -0.116229   0.304823  -0.381  0.70324    
## cv$raceLatinx                    -0.257506   0.300877  -0.856  0.39274    
## cv$raceMixed                     -0.026744   0.313027  -0.085  0.93197    
## cv$raceOther                     -0.008301   0.434122  -0.019  0.98476    
## cv$raceWhite                     -0.061231   0.240961  -0.254  0.79958    
## scale(cv$education_T2)           -0.081369   0.060274  -1.350  0.17801    
## scale(cv$income_T2)               0.099794   0.060112   1.660  0.09790 .  
## cv$political_orientationLiberal   0.698957   0.171839   4.067 6.04e-05 ***
## cv$political_orientationModerate  0.448922   0.183663   2.444  0.01508 *  
## cv$political_orientationN/A       1.024579   0.715896   1.431  0.15339    
## cv$political_orientationOther     0.728548   0.377020   1.932  0.05423 .  
## cv$unemployed                     0.333539   0.218784   1.525  0.12841    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9799 on 308 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.1098, Adjusted R-squared:  0.06649 
## F-statistic: 2.534 on 15 and 308 DF,  p-value: 0.001455
test <- lm(scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1))
summary(test)
## 
## Call:
## lm(formula = scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7101 -0.1913  0.4070  0.5499  0.9786 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           -0.01264    0.05398  -0.234  0.81505   
## scale(cv$zsm_mean_T1) -0.16906    0.05555  -3.043  0.00253 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9879 on 335 degrees of freedom
##   (172 observations deleted due to missingness)
## Multiple R-squared:  0.0269, Adjusted R-squared:  0.024 
## F-statistic: 9.261 on 1 and 335 DF,  p-value: 0.002526
test <- lm(cv$compliance_importance_mean_T2 ~ cv$zsm_mean_T1 + cv$political_orientation)
summary(test)
## 
## Call:
## lm(formula = cv$compliance_importance_mean_T2 ~ cv$zsm_mean_T1 + 
##     cv$political_orientation)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8476 -0.1497  0.2115  0.3856  0.8149 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.64550    0.13734  33.824  < 2e-16 ***
## cv$zsm_mean_T1                   -0.13540    0.03963  -3.417 0.000715 ***
## cv$political_orientationLiberal   0.44084    0.11262   3.914 0.000111 ***
## cv$political_orientationModerate  0.26478    0.12124   2.184 0.029688 *  
## cv$political_orientationN/A       0.77424    0.47750   1.621 0.105896    
## cv$political_orientationOther     0.38091    0.23191   1.643 0.101454    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6589 on 324 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.07558,    Adjusted R-squared:  0.06131 
## F-statistic: 5.298 on 5 and 324 DF,  p-value: 0.0001077
test <- lm(scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1)  + scale(cv$age_T2) + cv$political_orientation + scale(cv$income_T2) + scale(cv$education_T2) + cv$sex_T2)
summary(test)
## 
## Call:
## lm(formula = scale(cv$compliance_importance_mean_T2) ~ scale(cv$zsm_mean_T1) + 
##     scale(cv$age_T2) + cv$political_orientation + scale(cv$income_T2) + 
##     scale(cv$education_T2) + cv$sex_T2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4951 -0.1606  0.2869  0.5494  1.3467 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.48535    0.15959  -3.041  0.00255 ** 
## scale(cv$zsm_mean_T1)            -0.15640    0.05791  -2.701  0.00729 ** 
## scale(cv$age_T2)                  0.11036    0.05642   1.956  0.05136 .  
## cv$political_orientationLiberal   0.70880    0.16998   4.170 3.95e-05 ***
## cv$political_orientationModerate  0.45186    0.18146   2.490  0.01328 *  
## cv$political_orientationN/A       1.02858    0.71152   1.446  0.14929    
## cv$political_orientationOther     0.79699    0.36357   2.192  0.02910 *  
## scale(cv$income_T2)               0.09308    0.05923   1.572  0.11704    
## scale(cv$education_T2)           -0.08502    0.05972  -1.424  0.15554    
## cv$sex_T2                        -0.19571    0.11270  -1.737  0.08345 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9761 on 314 degrees of freedom
##   (185 observations deleted due to missingness)
## Multiple R-squared:  0.09942,    Adjusted R-squared:  0.07361 
## F-statistic: 3.852 on 9 and 314 DF,  p-value: 0.0001211
# ZSM more important than age

Hypothesis 8b: Behavior

Those with stronger zero-sum mindsets (measured the year prior) will be less likely to behaviorally comply with public health recommendations to self-quarantine and socially distance.

Sheltering Behavior

# Describe
summary(cv$shelter_T2) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  1.0000  1.0000  0.8071  1.0000  1.0000     172
cv$shelter_T2
##   [1] NA NA  1 NA NA NA  1  1  1  0 NA  1  0  1  0  1  1  1  1 NA  0  1 NA  1 NA
##  [26]  1 NA NA NA NA  1 NA NA  1 NA NA  1  0  0 NA  0 NA  1 NA  1 NA NA  1  1 NA
##  [51] NA NA NA  1  0 NA  0 NA NA  0  1  1 NA  0  1  0  1  0  0  1 NA  1 NA  1 NA
##  [76]  1 NA NA  0  1  0  1 NA  1  1 NA NA  1 NA  1  1  1  1  1  1 NA  1 NA  1  1
## [101]  1  1  1  1  1 NA  1 NA NA  0  1  1 NA NA  1  1 NA  1 NA NA  1  1  1  1  1
## [126]  1  0  1  1  1  1  0 NA NA NA  1  0  1 NA NA  0 NA NA NA  1  0 NA  1 NA NA
## [151]  1  1  1  0 NA  1  1  1 NA  1 NA  1  1 NA NA  1 NA  1  1  1  1  1  1  1  1
## [176]  1  1 NA  1  1  1  0 NA  1  1  1  0  0  1 NA  1  0  1  1 NA  1  1  1  1  1
## [201]  1 NA  0  1  1 NA  1  1  1  1 NA  1 NA NA NA NA NA  0 NA  1  1  1  1 NA  1
## [226]  0  1 NA  1  1  1 NA NA  1  1  1  1  1  0  1 NA NA NA  1  1 NA NA  1  0  1
## [251]  1 NA  0  0 NA  1 NA NA  0  1 NA NA  1 NA  1 NA  1  1 NA  1  1  1  1 NA NA
## [276] NA  1 NA  0  1  1 NA  1 NA NA  1 NA  1 NA  1 NA NA  0  1  1  1  0  1  1  1
## [301]  1  1 NA  1 NA  1  1 NA NA NA  1  1  1 NA  1 NA  1  1  0 NA  1  0 NA  1  0
## [326] NA NA  1  0  1  0  1 NA  1  0 NA NA  0  1 NA  1 NA NA  0 NA  1  0  1  1 NA
## [351]  0  0  1  1  1 NA  1  1  1  1 NA NA NA NA  1  1  1  1 NA  1 NA  1  1 NA  1
## [376]  1  1  1  1 NA NA  1 NA  1  1 NA NA  1  1  1 NA NA NA  0  1  1 NA NA  0  1
## [401]  1  0 NA  1  1  1 NA  1  0  1 NA  1  1  1  1  1 NA  1 NA  1  1  1  1  0  1
## [426]  1  0  0  0  0  1  1  1  1  1  1  1 NA  1  1  1 NA  1  1  1  1  1 NA  1 NA
## [451]  1 NA NA NA  1  1  1  1  1 NA  1 NA NA  0  0  1  1  1  0 NA  1  1  1 NA  1
## [476]  1 NA NA  1 NA NA NA  1 NA NA  1 NA  0  0 NA  1  1  1  1  1  1  1  1  1  0
## [501] NA  1  1  1  1  1  1  0  1
#about 80% of people cooperating with sheltering recommendations

# Identifying potential confounds to add as covariates


test <- glm(cv$shelter_T2 ~ cv$age  + cv$sex_T2 + cv$income_T1 + cv$race + cv$education_T1 + cv$unemployed + cv$political_orientation + cv$household_number_T2, family = binomial(link = "logit"))  # Age and political orientation appear most meaningful as demographic predictors of sheltering
summary(test) # AIC: 320.84
## 
## Call:
## glm(formula = cv$shelter_T2 ~ cv$age + cv$sex_T2 + cv$income_T1 + 
##     cv$race + cv$education_T1 + cv$unemployed + cv$political_orientation + 
##     cv$household_number_T2, family = binomial(link = "logit"))
## 
## Coefficients:
##                                    Estimate Std. Error z value Pr(>|z|)   
## (Intercept)                        16.98741  852.30025   0.020  0.98410   
## cv$age                             -0.01999    0.01313  -1.523  0.12786   
## cv$sex_T2                          -0.12612    0.30327  -0.416  0.67751   
## cv$income_T1                        0.06151    0.04820   1.276  0.20195   
## cv$raceBlack/African American     -16.30463  852.30001  -0.019  0.98474   
## cv$raceLatinx                     -15.82163  852.30002  -0.019  0.98519   
## cv$raceMixed                      -15.69132  852.30008  -0.018  0.98531   
## cv$raceOther                      -17.06238  852.30027  -0.020  0.98403   
## cv$raceWhite                      -15.65991  852.29988  -0.018  0.98534   
## cv$education_T1                    -0.06544    0.11760  -0.556  0.57791   
## cv$unemployed                       1.16425    0.84331   1.381  0.16741   
## cv$political_orientationLiberal     1.20114    0.41716   2.879  0.00398 **
## cv$political_orientationModerate    0.63391    0.42324   1.498  0.13420   
## cv$political_orientationN/A        16.82701 2796.57272   0.006  0.99520   
## cv$political_orientationOther      -0.01921    0.87003  -0.022  0.98239   
## cv$household_number_T2             -0.01664    0.09985  -0.167  0.86762   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 321.15  on 321  degrees of freedom
## Residual deviance: 289.78  on 306  degrees of freedom
##   (187 observations deleted due to missingness)
## AIC: 321.78
## 
## Number of Fisher Scoring iterations: 16
exp(test$coefficients)
##                      (Intercept)                           cv$age 
##                     2.385274e+07                     9.802068e-01 
##                        cv$sex_T2                     cv$income_T1 
##                     8.815070e-01                     1.063440e+00 
##    cv$raceBlack/African American                    cv$raceLatinx 
##                     8.298277e-08                     1.345095e-07 
##                     cv$raceMixed                     cv$raceOther 
##                     1.532303e-07                     3.889589e-08 
##                     cv$raceWhite                  cv$education_T1 
##                     1.581208e-07                     9.366591e-01 
##                    cv$unemployed  cv$political_orientationLiberal 
##                     3.203534e+00                     3.323911e+00 
## cv$political_orientationModerate      cv$political_orientationN/A 
##                     1.884966e+00                     2.031777e+07 
##    cv$political_orientationOther           cv$household_number_T2 
##                     9.809766e-01                     9.834944e-01
test <- glm(cv$shelter_T2 ~ cv$zsm_mean_T1 + cv$age  + cv$sex + cv$income_T1 + cv$education_T1 + cv$unemployed + cv$political_orientation + cv$race, family = binomial(link = "logit"))
summary(test) # AIC: 314.32 -- Delta reduction = 6.52 by adding zero-sum mindset parameter 
## 
## Call:
## glm(formula = cv$shelter_T2 ~ cv$zsm_mean_T1 + cv$age + cv$sex + 
##     cv$income_T1 + cv$education_T1 + cv$unemployed + cv$political_orientation + 
##     cv$race, family = binomial(link = "logit"))
## 
## Coefficients:
##                                    Estimate Std. Error z value Pr(>|z|)   
## (Intercept)                       1.886e+01  8.255e+02   0.023  0.98177   
## cv$zsm_mean_T1                   -5.152e-01  1.853e-01  -2.780  0.00544 **
## cv$age                           -2.560e-02  1.351e-02  -1.894  0.05817 . 
## cv$sexMale                        5.522e-03  3.128e-01   0.018  0.98591   
## cv$income_T1                      5.201e-02  4.814e-02   1.080  0.27992   
## cv$education_T1                  -8.103e-02  1.178e-01  -0.688  0.49146   
## cv$unemployed                     1.344e+00  8.796e-01   1.528  0.12664   
## cv$political_orientationLiberal   1.352e+00  4.241e-01   3.188  0.00143 **
## cv$political_orientationModerate  7.099e-01  4.299e-01   1.651  0.09867 . 
## cv$political_orientationN/A       1.736e+01  2.752e+03   0.006  0.99497   
## cv$political_orientationOther    -1.924e-01  8.878e-01  -0.217  0.82841   
## cv$raceBlack/African American    -1.642e+01  8.255e+02  -0.020  0.98413   
## cv$raceLatinx                    -1.592e+01  8.255e+02  -0.019  0.98461   
## cv$raceMixed                     -1.592e+01  8.255e+02  -0.019  0.98461   
## cv$raceOther                     -1.732e+01  8.255e+02  -0.021  0.98326   
## cv$raceWhite                     -1.610e+01  8.255e+02  -0.020  0.98444   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 321.15  on 321  degrees of freedom
## Residual deviance: 281.85  on 306  degrees of freedom
##   (187 observations deleted due to missingness)
## AIC: 313.85
## 
## Number of Fisher Scoring iterations: 16
exp(test$coefficients)
##                      (Intercept)                   cv$zsm_mean_T1 
##                     1.552118e+08                     5.973966e-01 
##                           cv$age                       cv$sexMale 
##                     9.747253e-01                     1.005537e+00 
##                     cv$income_T1                  cv$education_T1 
##                     1.053390e+00                     9.221662e-01 
##                    cv$unemployed  cv$political_orientationLiberal 
##                     3.832971e+00                     3.865352e+00 
## cv$political_orientationModerate      cv$political_orientationN/A 
##                     2.033782e+00                     3.448458e+07 
##    cv$political_orientationOther    cv$raceBlack/African American 
##                     8.249531e-01                     7.400107e-08 
##                    cv$raceLatinx                     cv$raceMixed 
##                     1.215043e-07                     1.218173e-07 
##                     cv$raceOther                     cv$raceWhite 
##                     3.003147e-08                     1.016934e-07
test <- glm(cv$shelter_T2 ~ cv$zsm_mean_T1)
summary(test)
## 
## Call:
## glm(formula = cv$shelter_T2 ~ cv$zsm_mean_T1)
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.90734    0.06510  13.937   <2e-16 ***
## cv$zsm_mean_T1 -0.03819    0.02342  -1.631    0.104    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1553724)
## 
##     Null deviance: 52.463  on 336  degrees of freedom
## Residual deviance: 52.050  on 335  degrees of freedom
##   (172 observations deleted due to missingness)
## AIC: 332.89
## 
## Number of Fisher Scoring iterations: 2
#
test <- glm(cv$shelter_T2 ~ cv$zsm_mean_T1 + cv$political_orientation, family = binomial(link = "logit"))
summary(test)
## 
## Call:
## glm(formula = cv$shelter_T2 ~ cv$zsm_mean_T1 + cv$political_orientation, 
##     family = binomial(link = "logit"))
## 
## Coefficients:
##                                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                         1.2689     0.4869   2.606 0.009162 ** 
## cv$zsm_mean_T1                     -0.3133     0.1542  -2.032 0.042138 *  
## cv$political_orientationLiberal     1.4605     0.3937   3.709 0.000208 ***
## cv$political_orientationModerate    0.8693     0.4073   2.135 0.032795 *  
## cv$political_orientationN/A        15.2820  1023.8954   0.015 0.988092    
## cv$political_orientationOther       0.4728     0.7729   0.612 0.540669    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 324.64  on 329  degrees of freedom
## Residual deviance: 307.04  on 324  degrees of freedom
##   (179 observations deleted due to missingness)
## AIC: 319.04
## 
## Number of Fisher Scoring iterations: 14
test <- glm(cv$shelter_T2 ~ cv$zsm_mean_T1 + cv$age + cv$political_orientation, family = binomial(link = "logit"))
summary(test)
## 
## Call:
## glm(formula = cv$shelter_T2 ~ cv$zsm_mean_T1 + cv$age + cv$political_orientation, 
##     family = binomial(link = "logit"))
## 
## Coefficients:
##                                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                         2.79398    0.79317   3.523 0.000427 ***
## cv$zsm_mean_T1                     -0.39523    0.16208  -2.438 0.014750 *  
## cv$age                             -0.03226    0.01249  -2.583 0.009806 ** 
## cv$political_orientationLiberal     1.30420    0.40239   3.241 0.001190 ** 
## cv$political_orientationModerate    0.70342    0.41697   1.687 0.091607 .  
## cv$political_orientationN/A        15.25890 1022.03646   0.015 0.988088    
## cv$political_orientationOther       0.16068    0.79027   0.203 0.838885    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 323.78  on 327  degrees of freedom
## Residual deviance: 299.85  on 321  degrees of freedom
##   (181 observations deleted due to missingness)
## AIC: 313.85
## 
## Number of Fisher Scoring iterations: 14
exp(test$coefficients)
##                      (Intercept)                   cv$zsm_mean_T1 
##                     1.634593e+01                     6.735281e-01 
##                           cv$age  cv$political_orientationLiberal 
##                     9.682583e-01                     3.684753e+00 
## cv$political_orientationModerate      cv$political_orientationN/A 
##                     2.020656e+00                     4.235041e+06 
##    cv$political_orientationOther 
##                     1.174307e+00

Social Distancing

Participants reported the number of times within the past 48 hours that they had come within 6 feet of a non-household member for non-essential reasons (so not including for things like grocery shopping or as part of one’s job as an essential worker) – Higher numbers represent lower cooperation with public health guidance

describe(cv$compliance_social_d_T2)
##    vars   n mean    sd median trimmed mad min max range skew kurtosis   se
## X1    1 336 3.74 11.58      0    1.17   0   0 100   100  5.6    35.79 0.63
hist(cv$compliance_social_d_T2)

#Quasi-Poisson Models for count data and to address dispersion

#Examining possible confounders
test <- glm(cv$compliance_social_d_T2 ~ cv$age  + cv$race + cv$sex + cv$income_T2 + cv$education_T2 + cv$unemployed + cv$political_orientation, family = quasipoisson()) 
summary(test)
## 
## Call:
## glm(formula = cv$compliance_social_d_T2 ~ cv$age + cv$race + 
##     cv$sex + cv$income_T2 + cv$education_T2 + cv$unemployed + 
##     cv$political_orientation, family = quasipoisson())
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                        1.51515    0.91703   1.652  0.09951 . 
## cv$age                             0.01122    0.01297   0.865  0.38768   
## cv$raceBlack/African American     -0.76728    0.84780  -0.905  0.36616   
## cv$raceLatinx                     -0.77872    0.79162  -0.984  0.32604   
## cv$raceMixed                      -1.11035    0.93650  -1.186  0.23669   
## cv$raceOther                       1.42857    0.64620   2.211  0.02779 * 
## cv$raceWhite                      -0.60222    0.57016  -1.056  0.29170   
## cv$sexMale                         0.38244    0.28890   1.324  0.18656   
## cv$income_T2                      -0.05532    0.04812  -1.150  0.25120   
## cv$education_T2                    0.13976    0.11639   1.201  0.23076   
## cv$unemployed                      0.04047    0.56061   0.072  0.94250   
## cv$political_orientationLiberal   -1.18995    0.39511  -3.012  0.00281 **
## cv$political_orientationModerate  -0.25898    0.37614  -0.689  0.49165   
## cv$political_orientationN/A      -13.82050  981.44790  -0.014  0.98877   
## cv$political_orientationOther      0.36250    0.65596   0.553  0.58092   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 23.85134)
## 
##     Null deviance: 4223.8  on 320  degrees of freedom
## Residual deviance: 3173.6  on 306  degrees of freedom
##   (188 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 10
# Adding Zero-Sum Mindset
test <- glm(cv$compliance_social_d_T2 ~ cv$zsm_mean_T1 + cv$age + cv$sex + cv$race + cv$income_T2 + cv$education_T2  + cv$unemployed + cv$political_orientation, family = quasipoisson()) 
summary(test)
## 
## Call:
## glm(formula = cv$compliance_social_d_T2 ~ cv$zsm_mean_T1 + cv$age + 
##     cv$sex + cv$race + cv$income_T2 + cv$education_T2 + cv$unemployed + 
##     cv$political_orientation, family = quasipoisson())
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        -0.09285    1.12217  -0.083 0.934108    
## cv$zsm_mean_T1                      0.41752    0.16814   2.483 0.013560 *  
## cv$age                              0.01694    0.01275   1.329 0.184847    
## cv$sexMale                          0.25532    0.28589   0.893 0.372527    
## cv$raceBlack/African American      -0.68011    0.81550  -0.834 0.404941    
## cv$raceLatinx                      -0.70164    0.75663  -0.927 0.354490    
## cv$raceMixed                       -0.83109    0.90519  -0.918 0.359270    
## cv$raceOther                        1.61770    0.63047   2.566 0.010770 *  
## cv$raceWhite                       -0.28446    0.55436  -0.513 0.608228    
## cv$income_T2                       -0.06048    0.04695  -1.288 0.198623    
## cv$education_T2                     0.17616    0.11222   1.570 0.117517    
## cv$unemployed                      -0.12171    0.56456  -0.216 0.829462    
## cv$political_orientationLiberal    -1.27900    0.38310  -3.339 0.000947 ***
## cv$political_orientationModerate   -0.31823    0.36605  -0.869 0.385328    
## cv$political_orientationN/A       -15.19947 1517.20532  -0.010 0.992013    
## cv$political_orientationOther       0.35836    0.66500   0.539 0.590360    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 21.91186)
## 
##     Null deviance: 4223.8  on 320  degrees of freedom
## Residual deviance: 3038.8  on 305  degrees of freedom
##   (188 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 11
exp(test$coefficients)
##                      (Intercept)                   cv$zsm_mean_T1 
##                     9.113257e-01                     1.518192e+00 
##                           cv$age                       cv$sexMale 
##                     1.017089e+00                     1.290878e+00 
##    cv$raceBlack/African American                    cv$raceLatinx 
##                     5.065591e-01                     4.957716e-01 
##                     cv$raceMixed                     cv$raceOther 
##                     4.355745e-01                     5.041458e+00 
##                     cv$raceWhite                     cv$income_T2 
##                     7.524207e-01                     9.413079e-01 
##                  cv$education_T2                    cv$unemployed 
##                     1.192625e+00                     8.854090e-01 
##  cv$political_orientationLiberal cv$political_orientationModerate 
##                     2.783157e-01                     7.274340e-01 
##      cv$political_orientationN/A    cv$political_orientationOther 
##                     2.505842e-07                     1.430980e+00
test <- glm(cv$compliance_social_d_T2 ~ cv$zsm_mean_T1, family = quasipoisson()) # stronger with controls that without -- suggesting possible suppression effects
summary(test)
## 
## Call:
## glm(formula = cv$compliance_social_d_T2 ~ cv$zsm_mean_T1, family = quasipoisson())
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.4466     0.4975   0.898   0.3699  
## cv$zsm_mean_T1   0.3159     0.1614   1.957   0.0511 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 32.82856)
## 
##     Null deviance: 4300.9  on 335  degrees of freedom
## Residual deviance: 4181.6  on 334  degrees of freedom
##   (173 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 7
test <- glm(cv$compliance_social_d_T2 ~ cv$zsm_mean_T1 + cv$political_orientation, family = quasipoisson())
summary(test) #appears suppressed by political orientation, education, and age and race but not particularly by sex or income
## 
## Call:
## glm(formula = cv$compliance_social_d_T2 ~ cv$zsm_mean_T1 + cv$political_orientation, 
##     family = quasipoisson())
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        1.0817     0.4612   2.345 0.019614 *  
## cv$zsm_mean_T1                     0.3664     0.1445   2.536 0.011670 *  
## cv$political_orientationLiberal   -1.4172     0.3902  -3.632 0.000328 ***
## cv$political_orientationModerate  -0.5547     0.3687  -1.504 0.133445    
## cv$political_orientationN/A      -14.5270   983.2478  -0.015 0.988221    
## cv$political_orientationOther      0.3215     0.5377   0.598 0.550333    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 24.48599)
## 
##     Null deviance: 4266.6  on 328  degrees of freedom
## Residual deviance: 3678.4  on 323  degrees of freedom
##   (180 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 10

Graphing

ggplot(cv, aes(cv$zsm_mean_T1, cv$compliance_social_d_T2)) +geom_smooth(method ='lm' ) + ylab("Violations of Social Distancing") + xlab("Zero-Sum Mindset")

describe(cv$compliance_social_d_T2)
##    vars   n mean    sd median trimmed mad min max range skew kurtosis   se
## X1    1 336 3.74 11.58      0    1.17   0   0 100   100  5.6    35.79 0.63
hist(cv$compliance_social_d_T2)

Belgium Replication

This study replicates both directly (attitudes) and conceptually (behavior) findings in a new cultural context at a later stage in the pandemic: Belgium.

library(here)
library(readr)
library(tidyverse)
library(psych)

Hypothesis 8a: Cooperative Attitudes

Those with stronger zero-sum mindsets will be less likely to adopt cooperative attitudes towards dealing with the pandemic.

# Loading Data


bl1 <- read_csv(here("data", "s10.csv"))
bl2 <- read_csv(here("data", "s11.csv"))

Cooperative Attitudes in General

National Sample

test <- lm(scale(bl1$scarcity_cooperation_mean) ~ scale(bl1$zsm_mean))
summary(test)
## 
## Call:
## lm(formula = scale(bl1$scarcity_cooperation_mean) ~ scale(bl1$zsm_mean))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2858 -0.5112 -0.1165  0.6364  2.7371 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.001448   0.028765   0.050     0.96    
## scale(bl1$zsm_mean) -0.269483   0.029313  -9.193   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9648 on 1123 degrees of freedom
##   (398 observations deleted due to missingness)
## Multiple R-squared:  0.06999,    Adjusted R-squared:  0.06916 
## F-statistic: 84.52 on 1 and 1123 DF,  p-value: < 2.2e-16
test <- lm(scale(bl1$scarcity_cooperation_mean) ~ scale(bl1$zsm_mean) +  scale(bl1$age) + scale(bl1$income) + scale(bl1$education)  + bl1$sex )
summary(test)
## 
## Call:
## lm(formula = scale(bl1$scarcity_cooperation_mean) ~ scale(bl1$zsm_mean) + 
##     scale(bl1$age) + scale(bl1$income) + scale(bl1$education) + 
##     bl1$sex)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2944 -0.5330 -0.1010  0.6329  2.7283 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -0.008160   0.047584  -0.171    0.864    
## scale(bl1$zsm_mean)  -0.271734   0.030464  -8.920   <2e-16 ***
## scale(bl1$age)       -0.016866   0.030529  -0.552    0.581    
## scale(bl1$income)    -0.016290   0.031308  -0.520    0.603    
## scale(bl1$education)  0.011170   0.031032   0.360    0.719    
## bl1$sexMale           0.008586   0.061076   0.141    0.888    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9746 on 1085 degrees of freedom
##   (432 observations deleted due to missingness)
## Multiple R-squared:  0.06952,    Adjusted R-squared:  0.06524 
## F-statistic: 16.21 on 5 and 1085 DF,  p-value: 1.91e-15

Municipal Sample

test <- lm(scale(bl2$scarcity_cooperation_mean) ~ scale(bl2$zsm_mean))
summary(test)
## 
## Call:
## lm(formula = scale(bl2$scarcity_cooperation_mean) ~ scale(bl2$zsm_mean))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3459 -0.7428  0.0521  0.6509  2.3669 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.0001583  0.0272764  -0.006    0.995    
## scale(bl2$zsm_mean) -0.3109065  0.0273760 -11.357   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9512 on 1214 degrees of freedom
##   (150 observations deleted due to missingness)
## Multiple R-squared:  0.09604,    Adjusted R-squared:  0.09529 
## F-statistic:   129 on 1 and 1214 DF,  p-value: < 2.2e-16
test <- lm(scale(bl2$scarcity_cooperation_mean) ~ scale(bl2$zsm_mean) +  scale(bl2$age) + scale(bl2$income) + scale(bl2$education)  + bl2$sex)
summary(test)
## 
## Call:
## lm(formula = scale(bl2$scarcity_cooperation_mean) ~ scale(bl2$zsm_mean) + 
##     scale(bl2$age) + scale(bl2$income) + scale(bl2$education) + 
##     bl2$sex)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1690 -0.7023  0.0390  0.6570  2.3700 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.03605    0.03837   0.940  0.34758    
## scale(bl2$zsm_mean)  -0.27369    0.02978  -9.190  < 2e-16 ***
## scale(bl2$age)        0.02448    0.02886   0.848  0.39652    
## scale(bl2$income)     0.01851    0.03020   0.613  0.54011    
## scale(bl2$education)  0.08185    0.03024   2.707  0.00689 ** 
## bl2$sexMale          -0.06174    0.05689  -1.085  0.27804    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9467 on 1137 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.09072,    Adjusted R-squared:  0.08672 
## F-statistic: 22.69 on 5 and 1137 DF,  p-value: < 2.2e-16
cor.test(bl1$scarcity_coop, bl1$scarcity_coop_Recode)
## 
##  Pearson's product-moment correlation
## 
## data:  bl1$scarcity_coop and bl1$scarcity_coop_Recode
## t = 5.7613, df = 1123, p-value = 1.077e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1121003 0.2256480
## sample estimates:
##       cor 
## 0.1694364
(.17*2)/(1+.17)
## [1] 0.2905983
cor.test(bl2$scarcity_coop, bl2$scarcity_coop_Recode)
## 
##  Pearson's product-moment correlation
## 
## data:  bl2$scarcity_coop and bl2$scarcity_coop_Recode
## t = 17.592, df = 1214, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4047467 0.4943970
## sample estimates:
##       cor 
## 0.4507076
(.45*2)/(1+.45)
## [1] 0.6206897
cor.test(bl1$ph_compliance_2, bl1$ph_compli_1_Recoded)
## 
##  Pearson's product-moment correlation
## 
## data:  bl1$ph_compliance_2 and bl1$ph_compli_1_Recoded
## t = 22.367, df = 1122, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5135250 0.5944911
## sample estimates:
##       cor 
## 0.5553226
(.56*2)/1.56
## [1] 0.7179487
cor.test(bl2$ph_compliance_2, bl2$ph_compli_1_Recoded)
## 
##  Pearson's product-moment correlation
## 
## data:  bl2$ph_compliance_2 and bl2$ph_compli_1_Recoded
## t = 30.617, df = 1213, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6272966 0.6908307
## sample estimates:
##       cor 
## 0.6602432
.66*2/1.66
## [1] 0.7951807

Attitudes about Cooperation as Best Response to COVID

e.g. The best way to deal with this pandemic is…

“Every man for himself” ——————-“We’re all in this together”

National Sample

# Pilots
test <- lm(scale(bl1$strategy_slider_18) ~ scale(bl1$zsm_mean))
summary(test)
## 
## Call:
## lm(formula = scale(bl1$strategy_slider_18) ~ scale(bl1$zsm_mean))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2770 -0.3661  0.1614  0.7156  1.7257 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.001126   0.029189   0.039    0.969    
## scale(bl1$zsm_mean) -0.209652   0.029745  -7.048 3.16e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.979 on 1123 degrees of freedom
##   (398 observations deleted due to missingness)
## Multiple R-squared:  0.04236,    Adjusted R-squared:  0.04151 
## F-statistic: 49.68 on 1 and 1123 DF,  p-value: 3.157e-12
test <- lm(scale(bl1$strategy_slider_18) ~ scale(bl1$zsm_mean) + scale(bl1$income) + scale(bl1$education) + scale(bl1$age) + bl1$sex)
summary(test)
## 
## Call:
## lm(formula = scale(bl1$strategy_slider_18) ~ scale(bl1$zsm_mean) + 
##     scale(bl1$income) + scale(bl1$education) + scale(bl1$age) + 
##     bl1$sex)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3036 -0.3738  0.1528  0.7104  1.6662 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.039927   0.047644   0.838    0.402    
## scale(bl1$zsm_mean)  -0.201974   0.030502  -6.622 5.58e-11 ***
## scale(bl1$income)    -0.025173   0.031347  -0.803    0.422    
## scale(bl1$education)  0.005622   0.031071   0.181    0.856    
## scale(bl1$age)        0.026191   0.030567   0.857    0.392    
## bl1$sexMale          -0.064631   0.061153  -1.057    0.291    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9758 on 1085 degrees of freedom
##   (432 observations deleted due to missingness)
## Multiple R-squared:  0.04514,    Adjusted R-squared:  0.04074 
## F-statistic: 10.26 on 5 and 1085 DF,  p-value: 1.267e-09
#ZSM Stronger than Age

Municipal Sample

# City Survey
test <- lm(scale(bl2$strategy_slider_18) ~ scale(bl2$zsm_mean))
summary(test)
## 
## Call:
## lm(formula = scale(bl2$strategy_slider_18) ~ scale(bl2$zsm_mean))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9978 -0.4349  0.2110  0.7367  1.8327 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.0001108  0.0280059  -0.004    0.997    
## scale(bl2$zsm_mean) -0.2175925  0.0281082  -7.741 2.06e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9766 on 1214 degrees of freedom
##   (150 observations deleted due to missingness)
## Multiple R-squared:  0.04704,    Adjusted R-squared:  0.04626 
## F-statistic: 59.93 on 1 and 1214 DF,  p-value: 2.063e-14
test <- lm(scale(bl2$strategy_slider_18) ~ scale(bl2$zsm_mean) + scale(bl2$age) + bl2$income + bl2$education  + bl2$sex)
summary(test)
## 
## Call:
## lm(formula = scale(bl2$strategy_slider_18) ~ scale(bl2$zsm_mean) + 
##     scale(bl2$age) + bl2$income + bl2$education + bl2$sex)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8430 -0.4430  0.1976  0.7386  1.8298 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.250432   0.142896   1.753   0.0799 .  
## scale(bl2$zsm_mean) -0.221668   0.030403  -7.291 5.75e-13 ***
## scale(bl2$age)       0.024512   0.029459   0.832   0.4056    
## bl2$income           0.000373   0.007366   0.051   0.9596    
## bl2$education       -0.046866   0.025626  -1.829   0.0677 .  
## bl2$sexMale         -0.148496   0.058070  -2.557   0.0107 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9664 on 1137 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.05282,    Adjusted R-squared:  0.04866 
## F-statistic: 12.68 on 5 and 1137 DF,  p-value: 5.197e-12
# ZSM Stronger Than Age

Attitudes About Cooperation with Public Health Efforts

National Sample

test <- lm(scale(bl1$public_health_compliance_mean) ~ scale(bl1$zsm_mean))
summary(test)
## 
## Call:
## lm(formula = scale(bl1$public_health_compliance_mean) ~ scale(bl1$zsm_mean))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3176 -0.6720  0.0585  0.7939  2.0172 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.001693   0.028532   0.059    0.953    
## scale(bl1$zsm_mean) -0.298243   0.029065 -10.261   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9566 on 1122 degrees of freedom
##   (399 observations deleted due to missingness)
## Multiple R-squared:  0.08579,    Adjusted R-squared:  0.08498 
## F-statistic: 105.3 on 1 and 1122 DF,  p-value: < 2.2e-16
test <- lm(scale(bl1$public_health_compliance_mean) ~ scale(bl1$zsm_mean) +  scale(bl1$age) + scale(bl1$income) + scale(bl1$education)  + bl1$sex)
summary(test)
## 
## Call:
## lm(formula = scale(bl1$public_health_compliance_mean) ~ scale(bl1$zsm_mean) + 
##     scale(bl1$age) + scale(bl1$income) + scale(bl1$education) + 
##     bl1$sex)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3202 -0.6526  0.0341  0.7538  2.0871 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.0527099  0.0459637   1.147    0.252    
## scale(bl1$zsm_mean)  -0.2583460  0.0294261  -8.779  < 2e-16 ***
## scale(bl1$age)        0.1860879  0.0294891   6.310 4.05e-10 ***
## scale(bl1$income)    -0.0307108  0.0302417  -1.016    0.310    
## scale(bl1$education) -0.0007576  0.0299747  -0.025    0.980    
## bl1$sexMale          -0.0857575  0.0589957  -1.454    0.146    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9414 on 1085 degrees of freedom
##   (432 observations deleted due to missingness)
## Multiple R-squared:  0.1199, Adjusted R-squared:  0.1158 
## F-statistic: 29.56 on 5 and 1085 DF,  p-value: < 2.2e-16
#ZSM Stronger than Age

Municipal Sample

test <- lm(scale(bl2$public_health_compliance_mean) ~ scale(bl2$zsm_mean))
summary(test)
## 
## Call:
## lm(formula = scale(bl2$public_health_compliance_mean) ~ scale(bl2$zsm_mean))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9413 -0.4013  0.1936  0.7885  1.6458 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.000135   0.028128   0.005    0.996    
## scale(bl2$zsm_mean) -0.199556   0.028243  -7.066 2.69e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9804 on 1213 degrees of freedom
##   (151 observations deleted due to missingness)
## Multiple R-squared:  0.03953,    Adjusted R-squared:  0.03874 
## F-statistic: 49.92 on 1 and 1213 DF,  p-value: 2.689e-12
test <- lm(scale(bl2$public_health_compliance_mean) ~ scale(bl2$zsm_mean) + scale(bl2$income) + scale(bl2$education) + scale(bl2$age) + bl2$sex)
summary(test)
## 
## Call:
## lm(formula = scale(bl2$public_health_compliance_mean) ~ scale(bl2$zsm_mean) + 
##     scale(bl2$income) + scale(bl2$education) + scale(bl2$age) + 
##     bl2$sex)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9739 -0.4276  0.2027  0.7432  1.8015 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.010984   0.038124   0.288 0.773318    
## scale(bl2$zsm_mean)  -0.174937   0.029593  -5.911 4.48e-09 ***
## scale(bl2$income)     0.099671   0.030012   3.321 0.000925 ***
## scale(bl2$education)  0.075488   0.030046   2.512 0.012130 *  
## scale(bl2$age)        0.100676   0.028675   3.511 0.000464 ***
## bl2$sexMale          -0.005996   0.056524  -0.106 0.915534    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9407 on 1137 degrees of freedom
##   (223 observations deleted due to missingness)
## Multiple R-squared:  0.07572,    Adjusted R-squared:  0.07165 
## F-statistic: 18.63 on 5 and 1137 DF,  p-value: < 2.2e-16
# ZSM stronger than age

Hypothesis 8b: Behavior

Those with stronger zero-sum mindsets will be less likely to behaviorally cooperate with public vaccination efforts.

Vaccination Behavior

National Sample

# Simple Test
test <- glm(bl1$vaccine_binary ~ bl1$zsm_mean, family = binomial(link = "logit"))
summary(test)
## 
## Call:
## glm(formula = bl1$vaccine_binary ~ bl1$zsm_mean, family = binomial(link = "logit"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   1.75135    0.26985   6.490 8.58e-11 ***
## bl1$zsm_mean -0.30083    0.07615  -3.951 7.80e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1129.0  on 896  degrees of freedom
## Residual deviance: 1112.9  on 895  degrees of freedom
##   (626 observations deleted due to missingness)
## AIC: 1116.9
## 
## Number of Fisher Scoring iterations: 4
exp(test$coefficients)
##  (Intercept) bl1$zsm_mean 
##    5.7623760    0.7402007
exp(confint(test))
##                  2.5 %    97.5 %
## (Intercept)  3.4212144 9.8636991
## bl1$zsm_mean 0.6365913 0.8582772
# Examining potential demographic confounds/covariates
test <- glm(bl1$vaccine_binary ~ bl1$age + bl1$sex  + bl1$education + bl1$income , family = binomial(link = "logit")) 
summary(test) 
## 
## Call:
## glm(formula = bl1$vaccine_binary ~ bl1$age + bl1$sex + bl1$education + 
##     bl1$income, family = binomial(link = "logit"))
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.80021    0.35740  -2.239   0.0252 *  
## bl1$age        0.32640    0.05510   5.924 3.15e-09 ***
## bl1$sexMale    0.12712    0.15387   0.826   0.4087    
## bl1$education  0.07844    0.05378   1.459   0.1447    
## bl1$income     0.02078    0.01755   1.184   0.2364    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1087.1  on 867  degrees of freedom
## Residual deviance: 1045.6  on 863  degrees of freedom
##   (655 observations deleted due to missingness)
## AIC: 1055.6
## 
## Number of Fisher Scoring iterations: 4
exp(test$coefficients)
##   (Intercept)       bl1$age   bl1$sexMale bl1$education    bl1$income 
##     0.4492349     1.3859646     1.1355511     1.0816011     1.0209933
exp(confint(test))
##                   2.5 %    97.5 %
## (Intercept)   0.2216893 0.9011743
## bl1$age       1.2458992 1.5465724
## bl1$sexMale   0.8389240 1.5341371
## bl1$education 0.9738426 1.2026581
## bl1$income    0.9867324 1.0570801
# Robust Test
test <- glm(bl1$vaccine_binary ~ bl1$zsm_mean + bl1$age + bl1$sex + bl1$education + bl1$income , family = binomial(link = "logit")) 
summary(test) 
## 
## Call:
## glm(formula = bl1$vaccine_binary ~ bl1$zsm_mean + bl1$age + bl1$sex + 
##     bl1$education + bl1$income, family = binomial(link = "logit"))
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    0.007327   0.449943   0.016  0.98701    
## bl1$zsm_mean  -0.236029   0.079874  -2.955  0.00313 ** 
## bl1$age        0.303603   0.055716   5.449 5.06e-08 ***
## bl1$sexMale    0.160730   0.155085   1.036  0.30002    
## bl1$education  0.085367   0.054032   1.580  0.11412    
## bl1$income     0.020923   0.017595   1.189  0.23440    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1087.1  on 867  degrees of freedom
## Residual deviance: 1036.7  on 862  degrees of freedom
##   (655 observations deleted due to missingness)
## AIC: 1048.7
## 
## Number of Fisher Scoring iterations: 4
exp(test$coefficients)
##   (Intercept)  bl1$zsm_mean       bl1$age   bl1$sexMale bl1$education 
##     1.0073537     0.7897578     1.3547305     1.1743677     1.0891168 
##    bl1$income 
##     1.0211432
exp(confint(test))
##                   2.5 %    97.5 %
## (Intercept)   0.4164558 2.4344164
## bl1$zsm_mean  0.6743301 0.9225714
## bl1$age       1.2162305 1.5134120
## bl1$sexMale   0.8656927 1.5906601
## bl1$education 0.9801270 1.2116029
## bl1$income    0.9867762 1.0573271

Municipal Sample

summary(as.factor(bl2$vaccine_binary)) # no = 171, yes = 1041, NA = 154
##   no  yes NA's 
##  171 1041  154
bl2$vaccine_binary <- if_else(bl2$vaccine_binary == "yes", 1,0)
summary(as.factor(bl2$vaccine_binary))
##    0    1 NA's 
##  171 1041  154
# Simple Test

bl2$vaccine_binary
##    [1]  0  1  1  0  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  0  1  1
##   [25]  1  1  1  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##   [49]  1  1  0  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1
##   [73]  0  1  1  0  1  1  0  0  0  0  1  1  1  1  1  1  1  1 NA  1  1  1  1  1
##   [97]  1 NA  1 NA  1  1  0 NA  1  0  0  0  1  1  0  0  0 NA  1  1  1  1  1  1
##  [121]  1  1  1 NA  1  0  1  1  1  1 NA NA  1  1  1  1  1  1  1  0  1  1  1  1
##  [145]  1  1  0  1  1  0  1  0 NA  1  1  1  1  1  1  1 NA NA  0  1  1  1  0  1
##  [169]  1  1  0  1  1  1  0  1  1 NA  1  1  1  1  1 NA  1  0  1  1  1  1  1  1
##  [193]  1  1 NA  1  1  0  1  0  0  1 NA NA  0  0  1  1  1  0  1  1  1  1  1  1
##  [217]  1  1  1  0  1  1  1  1  1  1  1  1 NA  1  1  1  1  1  1  1  1  1  1  1
##  [241]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
##  [265]  1  1  1  1  1  1  1  0  0  1  0  0  0  0  1  1  0  1  1  1  0  1 NA  1
##  [289]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1  0  1  1  1
##  [313]  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  0  1 NA  0  1  1  1  1  1
##  [337]  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [361]  1  1  1  1  1  1  1  0  1  1  0  1  1 NA  1 NA NA  1  1  1 NA  0  1 NA
##  [385]  1  1  1  1  1  1  1  1  1  0  1 NA  1  1  1  1  1 NA  1 NA  1 NA  1  1
##  [409] NA NA  1  1  0  1  1  1  1  1  1  1 NA NA  1  1  0  1  1  1  1  1  1  1
##  [433]  0  1  1  1  1  1 NA  0 NA  1  1 NA  0  1  1  1  1  1  1 NA  1  0  1  1
##  [457]  1  1  1  1  1  1  0  1  1  1  0 NA  1  1  1  1  1 NA  1 NA  1  0  1  1
##  [481]  0  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  0 NA  1  1  1  1  1
##  [505]  1  0  1  0  1  0  1  1  1  1  1  1  1 NA  1  1 NA  1  1  1  1  1  1 NA
##  [529] NA  1  1  1  1  1  1 NA NA  1 NA  1  1 NA NA  0  1  0  1  0  1  1  1 NA
##  [553]  1  1  0  1  1  1  1  1  1  1  1 NA  1  1  1  1 NA  0  1  1 NA  0  1  1
##  [577]  1  1 NA  0  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  0  1  1
##  [601]  0  1  1  1  0  1  1  1  1  1  1 NA  1  1  1  1  1  0  1  1  1  1  1 NA
##  [625] NA  1 NA  1  1 NA NA  0 NA  1  0  1  0  1  1  1  0  1  1  1 NA  1  1  1
##  [649]  0  0  0  0 NA  1 NA  0  1  1  1 NA  1  1  1  1  1  1  1  1  0  1  0  1
##  [673]  0  1  1  1  1  1  1  1  1  1 NA  1  0  1  1  1  1  1  1  1  1  1  1 NA
##  [697]  1  1  1  1  1  1  0  1  1  1  0  1  0  1  1 NA  1  1  0  1  1  0  1  0
##  [721]  1  0  1  1  1  0  1  1 NA NA  1  1  1 NA  1  1  1 NA  1  1  1  1  1  1
##  [745]  1  1  1  1  1 NA  1  1  1 NA  0 NA  1 NA  0  0  0  1  1  1  0  1  1  1
##  [769]  1  1  1  1  1  0  1  0  1 NA  1  1  1  1  1  1  1 NA  1  1  1  1  1  1
##  [793] NA  1  1  1  1 NA  0  1  1  1  1  1  1  0  1  1  1  1  0  1  1  1  1 NA
##  [817]  1  1  1  1 NA  0  1  1  1  1  1  1  1  1  1  1  0  1  1  0  1  1  1  1
##  [841]  1  1  1  1  1  1  1  1  1  0  0  1 NA  1  1  1  1  0  1  1  1  1  1  1
##  [865] NA  1  1  0  1 NA  1  0  1  1  1  1  0  1  1  1  1  1  1  1  0 NA  1  1
##  [889]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  0  0  1 NA
##  [913]  1 NA  0  1  1 NA NA NA  0  1  1 NA  1 NA  1  0  1 NA NA  1  1  1  1  1
##  [937] NA NA NA  1 NA  1 NA NA  1  1 NA  0 NA NA  1  1  1  1  1  1  1  0  0 NA
##  [961] NA  1 NA  1 NA NA NA  1 NA NA  0 NA  1 NA  1  1  1 NA  1  1 NA NA  1 NA
##  [985]  1  1  0  1  1  0  1  1  1 NA  0 NA NA  1  1  1  1 NA  1  1  1  1  1  1
## [1009]  1  1  1  1  1  1  1  1  1  1  1  0  1 NA  0  1  1  1  1  1  1  1  0  1
## [1033]  1  1  1  1  1  1  1  1  1  1 NA  1  1  1  1  1  1  1 NA  1  1 NA  1  0
## [1057]  1  0  1  1 NA  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  0  1  1
## [1081]  1  1  1  1  1 NA  1 NA  1  1  0  1  1  1  1  0  1  1  1  1 NA  1  1  1
## [1105]  1  1  1  1  1  1  1  1  0  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1
## [1129]  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  0  1  1  1  1
## [1153]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## [1177]  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1 NA  1  0 NA  1  1  0  1  1
## [1201]  1  1  1  1  1  1  1  1  1 NA  1  1 NA NA  1  1  1  1  0  0  1  1  0 NA
## [1225] NA  0  1  1 NA  1  1  1  1  1  1  1  1  0  1  1  1 NA  1  1 NA  0  0  0
## [1249]  1 NA NA  1  1  1  1  1  1  1  1  1  1  1  1  1 NA  1  1  1  1  1  0  1
## [1273]  1  1 NA  0  1  1  1  1  1 NA  1  1  1 NA  1  1  1  1  1  1  1  1  0  1
## [1297] NA  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  0  1  1  1  1
## [1321]  1  1  1  1  1  1  1 NA NA NA  1  1  1  0  1  0 NA  1  1 NA  1  1  1  1
## [1345]  1  1 NA  1  1  1  1  1  1  1  1  1  1  1  1  1  1 NA  1  1  1  1
test <- glm(bl2$vaccine_binary ~ bl2$zsm_mean, family = binomial(link = "logit"))
summary(test)
## 
## Call:
## glm(formula = bl2$vaccine_binary ~ bl2$zsm_mean, family = binomial(link = "logit"))
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.60139    0.22611  11.505  < 2e-16 ***
## bl2$zsm_mean -0.31816    0.08068  -3.943 8.03e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 986.41  on 1211  degrees of freedom
## Residual deviance: 971.34  on 1210  degrees of freedom
##   (154 observations deleted due to missingness)
## AIC: 975.34
## 
## Number of Fisher Scoring iterations: 4
exp(test$coefficients)
##  (Intercept) bl2$zsm_mean 
##   13.4824957    0.7274881
exp(confint(test))
##                 2.5 %     97.5 %
## (Intercept)  8.706170 21.1416397
## bl2$zsm_mean 0.621421  0.8529968
# Examining potential demographic confounds/covariates
test <- glm(bl2$vaccine_binary ~ bl2$age + bl2$sex + bl2$education + bl2$income , family = binomial(link = "logit")) 
summary(test) 
## 
## Call:
## glm(formula = bl2$vaccine_binary ~ bl2$age + bl2$sex + bl2$education + 
##     bl2$income, family = binomial(link = "logit"))
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -2.668304   0.525670  -5.076 3.85e-07 ***
## bl2$age        0.027029   0.006691   4.040 5.35e-05 ***
## bl2$sexMale    0.304971   0.184870   1.650    0.099 .  
## bl2$education  0.314093   0.069664   4.509 6.52e-06 ***
## bl2$income     0.123723   0.028460   4.347 1.38e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 918.38  on 1142  degrees of freedom
## Residual deviance: 834.45  on 1138  degrees of freedom
##   (223 observations deleted due to missingness)
## AIC: 844.45
## 
## Number of Fisher Scoring iterations: 5
exp(test$coefficients)
##   (Intercept)       bl2$age   bl2$sexMale bl2$education    bl2$income 
##    0.06936975    1.02739716    1.35658521    1.36901720    1.13170240
exp(confint(test))
##                    2.5 %    97.5 %
## (Intercept)   0.02433497 0.1915928
## bl2$age       1.01427931 1.0412749
## bl2$sexMale   0.94702100 1.9571051
## bl2$education 1.19346390 1.5688210
## bl2$income    1.07210645 1.1988026
# Robust Test
test <- glm(bl2$vaccine_binary ~ bl2$zsm_mean + bl2$age + bl2$sex + bl2$education + bl2$income , family = binomial(link = "logit")) 
summary(test) 
## 
## Call:
## glm(formula = bl2$vaccine_binary ~ bl2$zsm_mean + bl2$age + bl2$sex + 
##     bl2$education + bl2$income, family = binomial(link = "logit"))
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.888070   0.624553  -3.023  0.00250 ** 
## bl2$zsm_mean  -0.210505   0.090910  -2.316  0.02058 *  
## bl2$age        0.025902   0.006697   3.868  0.00011 ***
## bl2$sexMale    0.318705   0.185731   1.716  0.08617 .  
## bl2$education  0.291896   0.070694   4.129 3.64e-05 ***
## bl2$income     0.115716   0.028560   4.052 5.08e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 918.38  on 1142  degrees of freedom
## Residual deviance: 829.16  on 1137  degrees of freedom
##   (223 observations deleted due to missingness)
## AIC: 841.16
## 
## Number of Fisher Scoring iterations: 5
exp(test$coefficients)
##   (Intercept)  bl2$zsm_mean       bl2$age   bl2$sexMale bl2$education 
##     0.1513637     0.8101752     1.0262400     1.3753457     1.3389644 
##    bl2$income 
##     1.1226772
exp(confint(test))
##                    2.5 %    97.5 %
## (Intercept)   0.04395423 0.5100098
## bl2$zsm_mean  0.67828482 0.9692159
## bl2$age       1.01312017 1.0401094
## bl2$sexMale   0.95857360 1.9876891
## bl2$education 1.16479927 1.5373350
## bl2$income    1.06332888 1.1894581
test <- glm(bl2$vaccine_binary ~ bl2$zsm_mean + bl2$age + bl2$sex + bl2$education + bl2$income, family = binomial(link = "logit")) 
summary(test) 
## 
## Call:
## glm(formula = bl2$vaccine_binary ~ bl2$zsm_mean + bl2$age + bl2$sex + 
##     bl2$education + bl2$income, family = binomial(link = "logit"))
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.888070   0.624553  -3.023  0.00250 ** 
## bl2$zsm_mean  -0.210505   0.090910  -2.316  0.02058 *  
## bl2$age        0.025902   0.006697   3.868  0.00011 ***
## bl2$sexMale    0.318705   0.185731   1.716  0.08617 .  
## bl2$education  0.291896   0.070694   4.129 3.64e-05 ***
## bl2$income     0.115716   0.028560   4.052 5.08e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 918.38  on 1142  degrees of freedom
## Residual deviance: 829.16  on 1137  degrees of freedom
##   (223 observations deleted due to missingness)
## AIC: 841.16
## 
## Number of Fisher Scoring iterations: 5