Repression Study 6 pilot analysis
Siwar Hasan-Aslih
2024-07-24
Summary: Consistent with our previous studies, police violence significantly decreases trust in the police. This effect persists even in the presence of protester violence, although the effect is smaller. This indicates that protester violence diminishes the negative impact of police violence on trust in the police. Similar to our previous findings, we do not observe any moderation by ideology. We find similar albeit weaker patterns for trust in the government and policy support.
Bar plot for the main outcomes
S5_long <- S5 %>%
pivot_longer(cols = c(HarmfulPolice_sc, MoralPolice_sc, LegitimacyPolice_sc, TrustPolice_sc, TrustGov_sc, PolicySupport),
names_to = "DV", values_to = "value")
S5_long$DV <- factor(S5_long$DV, levels = c("HarmfulPolice_sc", "MoralPolice_sc", "LegitimacyPolice_sc", "TrustPolice_sc", "TrustGov_sc", "PolicySupport"), labels = c(c("Harmful", "Moral", "Legitimacy", "TrustPolice", "TrustGov", "PolicySupport")))
# Plotting
p_trustpol <- ggplot(S5_long, aes(x = DV, y = value, fill = condition)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey",
position = position_dodge(width = 0.8), width = 0.2) +
scale_y_continuous(limits = c(0, 100)) +
theme_bw() +
scale_fill_manual(values = c("#ffffb3","#80b1d3", "#8dd3c7", "#fb8072")) +
labs(x = "Condition",
y = "Score",
title = "Effects on dependent variables") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
)
# Print the plot
print(p_trustpol)
Means
S5 %>%
group_by(condition) %>%
summarise_at(.vars = c("ViolentPolice_sc", "ViolentProtester_sc"), .funs = mean)## # A tibble: 4 × 3
## condition ViolentPolice_sc ViolentProtester_sc
## <fct> <dbl> <dbl>
## 1 control 8.01 8.44
## 2 police_violence 84.4 18.3
## 3 protester_violence 13.5 89.2
## 4 mutual_violence 64.8 73.9S5 %>%
group_by(condition) %>%
summarise_at(.vars = c("MoralPolice_sc", "MoralProtester_sc"), .funs = mean)## # A tibble: 4 × 3
## condition MoralPolice_sc MoralProtester_sc
## <fct> <dbl> <dbl>
## 1 control 88.8 83.0
## 2 police_violence 15.4 71.3
## 3 protester_violence 75.2 10.9
## 4 mutual_violence 34.7 24.4S5 %>%
group_by(condition) %>%
summarise_at(.vars = c("HarmfulPolice_sc", "HarmfulProtester_sc"), .funs = mean)## # A tibble: 4 × 3
## condition HarmfulPolice_sc HarmfulProtester_sc
## <fct> <dbl> <dbl>
## 1 control 8.06 9.97
## 2 police_violence 84.5 20.8
## 3 protester_violence 15.0 89.5
## 4 mutual_violence 67.5 75.9S5 %>%
group_by(condition) %>%
summarise_at(.vars = c("TrustPolice_sc", "TrustGov_sc"), .funs = mean)## # A tibble: 4 × 3
## condition TrustPolice_sc TrustGov_sc
## <fct> <dbl> <dbl>
## 1 control 71.4 58.3
## 2 police_violence 24.4 40.1
## 3 protester_violence 67.6 49.9
## 4 mutual_violence 43.4 45.8Effects on trust in the police
summary(model <- lm(TrustPolice_sc ~ condD1*condD2*condD3, S5, na.action=na.omit))#control as the reference group##
## Call:
## lm(formula = TrustPolice_sc ~ condD1 * condD2 * condD3, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.401 -21.401 1.629 22.642 65.635
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.401 3.079 23.191 < 2e-16 ***
## condD1 -47.036 4.367 -10.771 < 2e-16 ***
## condD2 -3.794 4.450 -0.853 0.395
## condD3 -28.030 4.317 -6.493 3.04e-10 ***
## condD1:condD2 NA NA NA NA
## condD1:condD3 NA NA NA NA
## condD2:condD3 NA NA NA NA
## condD1:condD2:condD3 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28.55 on 335 degrees of freedom
## Multiple R-squared: 0.3106, Adjusted R-squared: 0.3044
## F-statistic: 50.3 on 3 and 335 DF, p-value: < 2.2e-16summary(model <- lm(TrustPolice_sc ~ condD0*condD1*condD2, S5, na.action=na.omit))#mutual violence as the reference group. Mutual violence is significantly different from police violence and protester violence conditions##
## Call:
## lm(formula = TrustPolice_sc ~ condD0 * condD1 * condD2, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.401 -21.401 1.629 22.642 65.635
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.371 3.026 14.330 < 2e-16 ***
## condD0 28.030 4.317 6.493 3.04e-10 ***
## condD1 -19.006 4.330 -4.389 1.53e-05 ***
## condD2 24.237 4.413 5.492 7.89e-08 ***
## condD0:condD1 NA NA NA NA
## condD0:condD2 NA NA NA NA
## condD1:condD2 NA NA NA NA
## condD0:condD1:condD2 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28.55 on 335 degrees of freedom
## Multiple R-squared: 0.3106, Adjusted R-squared: 0.3044
## F-statistic: 50.3 on 3 and 335 DF, p-value: < 2.2e-16#anova
summary(model <- aov(TrustPolice_sc ~ condition, S5, na.action=na.omit))## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 123021 41007 50.3 <2e-16 ***
## Residuals 335 273096 815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(model, type="III")## Anova Table (Type III tests)
##
## Response: TrustPolice_sc
## Sum Sq Df F value Pr(>F)
## (Intercept) 438439 1 537.822 < 2.2e-16 ***
## condition 123021 3 50.302 < 2.2e-16 ***
## Residuals 273096 335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glht(model, linfct = mcp(condition= "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = TrustPolice_sc ~ condition, data = S5, na.action = na.omit)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## police_violence - control == 0 -47.036 4.367 -10.771 < 1e-05
## protester_violence - control == 0 -3.794 4.450 -0.853 0.829
## mutual_violence - control == 0 -28.030 4.317 -6.493 < 1e-05
## protester_violence - police_violence == 0 43.243 4.462 9.691 < 1e-05
## mutual_violence - police_violence == 0 19.006 4.330 4.389 7.25e-05
## mutual_violence - protester_violence == 0 -24.237 4.413 -5.492 < 1e-05
##
## police_violence - control == 0 ***
## protester_violence - control == 0
## mutual_violence - control == 0 ***
## protester_violence - police_violence == 0 ***
## mutual_violence - police_violence == 0 ***
## mutual_violence - protester_violence == 0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)#regression model with police violence and protester violence as predictors.
summary(model <- lm(TrustPolice_sc ~ PoliceViolence.dum*ProtesterViolence.dum+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = TrustPolice_sc ~ PoliceViolence.dum * ProtesterViolence.dum +
## incomeN + Race4 + age + educationN + gender_dum, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73.220 -18.757 -0.933 19.719 60.952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.6780 9.2791 6.000 5.22e-09
## PoliceViolence.dum -46.4196 4.3619 -10.642 < 2e-16
## ProtesterViolence.dum -2.4882 4.4644 -0.557 0.577677
## incomeN 1.0708 0.4432 2.416 0.016234
## Race4Black 0.5662 5.0407 0.112 0.910637
## Race4Hispanic -2.4244 5.6774 -0.427 0.669636
## Race4Asian -3.6087 7.5723 -0.477 0.633984
## Race4Other 18.9388 14.2897 1.325 0.185979
## age 0.3630 0.1124 3.230 0.001361
## educationN -3.2862 1.7969 -1.829 0.068340
## gender_dum 3.9132 3.1084 1.259 0.208959
## PoliceViolence.dum:ProtesterViolence.dum 21.4903 6.1741 3.481 0.000568
##
## (Intercept) ***
## PoliceViolence.dum ***
## ProtesterViolence.dum
## incomeN *
## Race4Black
## Race4Hispanic
## Race4Asian
## Race4Other
## age **
## educationN .
## gender_dum
## PoliceViolence.dum:ProtesterViolence.dum ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.83 on 327 degrees of freedom
## Multiple R-squared: 0.3608, Adjusted R-squared: 0.3393
## F-statistic: 16.78 on 11 and 327 DF, p-value: < 2.2e-16sim_slopes(model, pred = PoliceViolence.dum, modx = ProtesterViolence.dum)## JOHNSON-NEYMAN INTERVAL
##
## When ProtesterViolence.dum is OUTSIDE the interval [1.52, 4.36], the slope
## of PoliceViolence.dum is p < .05.
##
## Note: The range of observed values of ProtesterViolence.dum is [0.00, 1.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 0.00 (0):
##
## Est. S.E. t val. p
## -------- ------ -------- ------
## -46.42 4.36 -10.64 0.00
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 1.00 (1):
##
## Est. S.E. t val. p
## -------- ------ -------- ------
## -24.93 4.34 -5.74 0.00summary(model <- lm(TrustPolice_sc ~ PoliceViolence.dum*ProtesterViolence.dum*ideology+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = TrustPolice_sc ~ PoliceViolence.dum * ProtesterViolence.dum *
## ideology + incomeN + Race4 + age + educationN + gender_dum,
## data = S5, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -82.72 -16.96 1.56 15.46 75.89
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 32.1905 10.2276 3.147
## PoliceViolence.dum -49.2927 8.8576 -5.565
## ProtesterViolence.dum -9.4385 9.6500 -0.978
## ideology 5.4482 1.4719 3.702
## incomeN 0.3718 0.4217 0.882
## Race4Black 2.5504 4.7221 0.540
## Race4Hispanic 1.9845 5.3041 0.374
## Race4Asian 1.4908 7.0717 0.211
## Race4Other 26.7328 13.3142 2.008
## age 0.3566 0.1044 3.417
## educationN -1.0787 1.6932 -0.637
## gender_dum 4.6284 2.8865 1.603
## PoliceViolence.dum:ProtesterViolence.dum 29.8546 12.9094 2.313
## PoliceViolence.dum:ideology 0.6683 2.1177 0.316
## ProtesterViolence.dum:ideology 1.1465 2.2289 0.514
## PoliceViolence.dum:ProtesterViolence.dum:ideology -1.6247 3.0301 -0.536
## Pr(>|t|)
## (Intercept) 0.001801 **
## PoliceViolence.dum 5.51e-08 ***
## ProtesterViolence.dum 0.328763
## ideology 0.000252 ***
## incomeN 0.378603
## Race4Black 0.589504
## Race4Hispanic 0.708545
## Race4Asian 0.833168
## Race4Other 0.045493 *
## age 0.000715 ***
## educationN 0.524534
## gender_dum 0.109812
## PoliceViolence.dum:ProtesterViolence.dum 0.021372 *
## PoliceViolence.dum:ideology 0.752530
## ProtesterViolence.dum:ideology 0.607347
## PoliceViolence.dum:ProtesterViolence.dum:ideology 0.592200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.79 on 323 degrees of freedom
## Multiple R-squared: 0.4575, Adjusted R-squared: 0.4323
## F-statistic: 18.16 on 15 and 323 DF, p-value: < 2.2e-16#plot
ggplot(S5, aes(x = ProtesterViolence, y = TrustPolice_sc, fill = PoliceViolence, group = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey",
position = position_dodge(width = 0.8), width = 0.2) +
scale_y_continuous(limits = c(0, 100)) +
theme_bw() +
scale_fill_manual(values = c("grey","#80b1d3")) +
labs(x = "",
y = "Score",
title = "Effects on trust in the police") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
) 
ggplot(subset(S5, ideology.cat %in% c('Liberal', 'Conservative')), aes(x = ProtesterViolence, y = TrustPolice_sc, fill = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey", position = position_dodge(width = 0.8), width = 0.2) +
facet_wrap(vars(ideology.cat)) +
theme_bw() +
scale_fill_manual(values = c("grey", "#80b1d3")) +
scale_y_continuous(limits = c(0, 100)) +
labs(x = "", y = "Score", title = "Effects on trust in the police by ideology") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
)
Effects on perceptions of harm
summary(model <- lm(HarmfulPolice_sc ~ condD1*condD2*condD3, S5, na.action=na.omit))#control as the reference group##
## Call:
## lm(formula = HarmfulPolice_sc ~ condD1 * condD2 * condD3, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -84.524 -12.029 -3.524 14.969 91.942
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.058 2.618 3.078 0.00226 **
## condD1 76.465 3.714 20.590 < 2e-16 ***
## condD2 6.961 3.784 1.840 0.06672 .
## condD3 59.481 3.672 16.201 < 2e-16 ***
## condD1:condD2 NA NA NA NA
## condD1:condD3 NA NA NA NA
## condD2:condD3 NA NA NA NA
## condD1:condD2:condD3 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.28 on 335 degrees of freedom
## Multiple R-squared: 0.6497, Adjusted R-squared: 0.6465
## F-statistic: 207.1 on 3 and 335 DF, p-value: < 2.2e-16summary(model <- lm(HarmfulPolice_sc ~ condD0*condD1*condD2, S5, na.action=na.omit))#mutual violence as the reference group. ##
## Call:
## lm(formula = HarmfulPolice_sc ~ condD0 * condD1 * condD2, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -84.524 -12.029 -3.524 14.969 91.942
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 67.539 2.574 26.241 < 2e-16 ***
## condD0 -59.481 3.672 -16.201 < 2e-16 ***
## condD1 16.984 3.682 4.612 5.68e-06 ***
## condD2 -52.520 3.753 -13.993 < 2e-16 ***
## condD0:condD1 NA NA NA NA
## condD0:condD2 NA NA NA NA
## condD1:condD2 NA NA NA NA
## condD0:condD1:condD2 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.28 on 335 degrees of freedom
## Multiple R-squared: 0.6497, Adjusted R-squared: 0.6465
## F-statistic: 207.1 on 3 and 335 DF, p-value: < 2.2e-16#anova
summary(model <- aov(HarmfulPolice_sc ~ condition, S5, na.action=na.omit))## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 366272 122091 207.1 <2e-16 ***
## Residuals 335 197510 590
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(model, type="III")## Anova Table (Type III tests)
##
## Response: HarmfulPolice_sc
## Sum Sq Df F value Pr(>F)
## (Intercept) 5584 1 9.4716 0.002259 **
## condition 366272 3 207.0800 < 2.2e-16 ***
## Residuals 197510 335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glht(model, linfct = mcp(condition= "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = HarmfulPolice_sc ~ condition, data = S5, na.action = na.omit)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## police_violence - control == 0 76.465 3.714 20.590 <1e-04
## protester_violence - control == 0 6.961 3.784 1.840 0.257
## mutual_violence - control == 0 59.481 3.672 16.201 <1e-04
## protester_violence - police_violence == 0 -69.505 3.795 -18.316 <1e-04
## mutual_violence - police_violence == 0 -16.984 3.682 -4.612 <1e-04
## mutual_violence - protester_violence == 0 52.520 3.753 13.993 <1e-04
##
## police_violence - control == 0 ***
## protester_violence - control == 0
## mutual_violence - control == 0 ***
## protester_violence - police_violence == 0 ***
## mutual_violence - police_violence == 0 ***
## mutual_violence - protester_violence == 0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)#regression model with police violence and protester violence as predictors.
summary(model <- lm(HarmfulPolice_sc ~ PoliceViolence.dum*ProtesterViolence.dum+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = HarmfulPolice_sc ~ PoliceViolence.dum * ProtesterViolence.dum +
## incomeN + Race4 + age + educationN + gender_dum, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -92.82 -10.80 -2.24 14.25 91.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.377423 8.093345 1.406 0.1607
## PoliceViolence.dum 75.547568 3.804537 19.857 < 2e-16
## ProtesterViolence.dum 5.667009 3.893936 1.455 0.1465
## incomeN -0.593620 0.386554 -1.536 0.1256
## Race4Black 1.250238 4.396529 0.284 0.7763
## Race4Hispanic 6.114578 4.951895 1.235 0.2178
## Race4Asian 13.075819 6.604638 1.980 0.0486
## Race4Other -1.369047 12.463619 -0.110 0.9126
## age -0.007956 0.098015 -0.081 0.9354
## educationN 0.311420 1.567286 0.199 0.8426
## gender_dum -0.029173 2.711147 -0.011 0.9914
## PoliceViolence.dum:ProtesterViolence.dum -22.988731 5.385129 -4.269 2.58e-05
##
## (Intercept)
## PoliceViolence.dum ***
## ProtesterViolence.dum
## incomeN
## Race4Black
## Race4Hispanic
## Race4Asian *
## Race4Other
## age
## educationN
## gender_dum
## PoliceViolence.dum:ProtesterViolence.dum ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.27 on 327 degrees of freedom
## Multiple R-squared: 0.6583, Adjusted R-squared: 0.6469
## F-statistic: 57.28 on 11 and 327 DF, p-value: < 2.2e-16sim_slopes(model, pred = PoliceViolence.dum, modx = ProtesterViolence.dum)## JOHNSON-NEYMAN INTERVAL
##
## When ProtesterViolence.dum is OUTSIDE the interval [2.39, 5.69], the slope
## of PoliceViolence.dum is p < .05.
##
## Note: The range of observed values of ProtesterViolence.dum is [0.00, 1.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 0.00 (0):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## 75.55 3.80 19.86 0.00
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 1.00 (1):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## 52.56 3.79 13.89 0.00summary(model <- lm(HarmfulPolice_sc ~ PoliceViolence.dum*ProtesterViolence.dum*ideology+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = HarmfulPolice_sc ~ PoliceViolence.dum * ProtesterViolence.dum *
## ideology + incomeN + Race4 + age + educationN + gender_dum,
## data = S5, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -102.661 -11.254 -1.751 12.118 85.001
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 22.557414 9.207903 2.450
## PoliceViolence.dum 83.363322 7.974502 10.454
## ProtesterViolence.dum 4.641886 8.687844 0.534
## ideology -2.433807 1.325111 -1.837
## incomeN -0.149099 0.379649 -0.393
## Race4Black 0.873966 4.251256 0.206
## Race4Hispanic 3.674261 4.775279 0.769
## Race4Asian 9.384492 6.366657 1.474
## Race4Other -5.885814 11.986742 -0.491
## age 0.004928 0.093964 0.052
## educationN -1.246012 1.524372 -0.817
## gender_dum -0.723822 2.598708 -0.279
## PoliceViolence.dum:ProtesterViolence.dum -17.066444 11.622274 -1.468
## PoliceViolence.dum:ideology -2.012203 1.906559 -1.055
## ProtesterViolence.dum:ideology 0.548762 2.006658 0.273
## PoliceViolence.dum:ProtesterViolence.dum:ideology -1.782334 2.727958 -0.653
## Pr(>|t|)
## (Intercept) 0.0148 *
## PoliceViolence.dum <2e-16 ***
## ProtesterViolence.dum 0.5935
## ideology 0.0672 .
## incomeN 0.6948
## Race4Black 0.8372
## Race4Hispanic 0.4422
## Race4Asian 0.1415
## Race4Other 0.6237
## age 0.9582
## educationN 0.4143
## gender_dum 0.7808
## PoliceViolence.dum:ProtesterViolence.dum 0.1430
## PoliceViolence.dum:ideology 0.2920
## ProtesterViolence.dum:ideology 0.7847
## PoliceViolence.dum:ProtesterViolence.dum:ideology 0.5140
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.22 on 323 degrees of freedom
## Multiple R-squared: 0.691, Adjusted R-squared: 0.6767
## F-statistic: 48.16 on 15 and 323 DF, p-value: < 2.2e-16#plot
ggplot(S5, aes(x = ProtesterViolence, y = HarmfulPolice_sc, fill = PoliceViolence, group = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey",
position = position_dodge(width = 0.8), width = 0.2) +
scale_y_continuous(limits = c(0, 100)) +
theme_bw() +
scale_fill_manual(values = c("grey","#80b1d3")) +
labs(x = "",
y = "Score",
title = "Effects on perceptions of harm") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
) 
ggplot(subset(S5, ideology.cat %in% c('Liberal', 'Conservative')), aes(x = ProtesterViolence, y = HarmfulPolice_sc, fill = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey", position = position_dodge(width = 0.8), width = 0.2) +
facet_wrap(vars(ideology.cat)) +
theme_bw() +
scale_fill_manual(values = c("grey", "#80b1d3")) +
scale_y_continuous(limits = c(0, 100)) +
labs(x = "", y = "Score", title = "Effects on perceptions of harm by ideology") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
)
Effects on morality
summary(model <- lm(MoralPolice_sc ~ condD1*condD2*condD3, S5, na.action=na.omit))#control as the reference group##
## Call:
## lm(formula = MoralPolice_sc ~ condD1 * condD2 * condD3, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.152 -15.353 1.151 14.598 83.647
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 88.849 2.810 31.621 < 2e-16 ***
## condD1 -73.496 3.985 -18.442 < 2e-16 ***
## condD2 -13.697 4.061 -3.373 0.000831 ***
## condD3 -54.107 3.940 -13.733 < 2e-16 ***
## condD1:condD2 NA NA NA NA
## condD1:condD3 NA NA NA NA
## condD2:condD3 NA NA NA NA
## condD1:condD2:condD3 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.06 on 335 degrees of freedom
## Multiple R-squared: 0.5683, Adjusted R-squared: 0.5644
## F-statistic: 147 on 3 and 335 DF, p-value: < 2.2e-16summary(model <- lm(MoralPolice_sc ~ condD0*condD1*condD2, S5, na.action=na.omit))#mutual violence as the reference group. ##
## Call:
## lm(formula = MoralPolice_sc ~ condD0 * condD1 * condD2, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.152 -15.353 1.151 14.598 83.647
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.742 2.762 12.578 < 2e-16 ***
## condD0 54.107 3.940 13.733 < 2e-16 ***
## condD1 -19.389 3.952 -4.906 1.45e-06 ***
## condD2 40.410 4.028 10.033 < 2e-16 ***
## condD0:condD1 NA NA NA NA
## condD0:condD2 NA NA NA NA
## condD1:condD2 NA NA NA NA
## condD0:condD1:condD2 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.06 on 335 degrees of freedom
## Multiple R-squared: 0.5683, Adjusted R-squared: 0.5644
## F-statistic: 147 on 3 and 335 DF, p-value: < 2.2e-16#anova
summary(model <- aov(MoralPolice_sc ~ condition, S5, na.action=na.omit))## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 299428 99809 147 <2e-16 ***
## Residuals 335 227455 679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(model, type="III")## Anova Table (Type III tests)
##
## Response: MoralPolice_sc
## Sum Sq Df F value Pr(>F)
## (Intercept) 678894 1 999.89 < 2.2e-16 ***
## condition 299428 3 147.00 < 2.2e-16 ***
## Residuals 227455 335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glht(model, linfct = mcp(condition= "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = MoralPolice_sc ~ condition, data = S5, na.action = na.omit)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## police_violence - control == 0 -73.496 3.985 -18.442 < 0.001
## protester_violence - control == 0 -13.697 4.061 -3.373 0.00463
## mutual_violence - control == 0 -54.107 3.940 -13.733 < 0.001
## protester_violence - police_violence == 0 59.799 4.072 14.685 < 0.001
## mutual_violence - police_violence == 0 19.389 3.952 4.906 < 0.001
## mutual_violence - protester_violence == 0 -40.410 4.028 -10.033 < 0.001
##
## police_violence - control == 0 ***
## protester_violence - control == 0 **
## mutual_violence - control == 0 ***
## protester_violence - police_violence == 0 ***
## mutual_violence - police_violence == 0 ***
## mutual_violence - protester_violence == 0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)#regression model with police violence and protester violence as predictors.
summary(model <- lm(MoralPolice_sc ~ PoliceViolence.dum*ProtesterViolence.dum+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = MoralPolice_sc ~ PoliceViolence.dum * ProtesterViolence.dum +
## incomeN + Race4 + age + educationN + gender_dum, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.285 -16.900 2.144 15.518 83.297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 87.7516 8.6497 10.145 < 2e-16
## PoliceViolence.dum -73.3667 4.0661 -18.044 < 2e-16
## ProtesterViolence.dum -13.4897 4.1616 -3.241 0.00131
## incomeN 0.7354 0.4131 1.780 0.07600
## Race4Black -3.3916 4.6988 -0.722 0.47093
## Race4Hispanic -8.2594 5.2923 -1.561 0.11958
## Race4Asian -6.8034 7.0587 -0.964 0.33585
## Race4Other 4.5585 13.3205 0.342 0.73241
## age 0.1108 0.1048 1.058 0.29099
## educationN -1.8596 1.6750 -1.110 0.26774
## gender_dum -3.4290 2.8975 -1.183 0.23751
## PoliceViolence.dum:ProtesterViolence.dum 33.3836 5.7553 5.800 1.56e-08
##
## (Intercept) ***
## PoliceViolence.dum ***
## ProtesterViolence.dum **
## incomeN .
## Race4Black
## Race4Hispanic
## Race4Asian
## Race4Other
## age
## educationN
## gender_dum
## PoliceViolence.dum:ProtesterViolence.dum ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.94 on 327 degrees of freedom
## Multiple R-squared: 0.5824, Adjusted R-squared: 0.5684
## F-statistic: 41.46 on 11 and 327 DF, p-value: < 2.2e-16sim_slopes(model, pred = PoliceViolence.dum, modx = ProtesterViolence.dum)## JOHNSON-NEYMAN INTERVAL
##
## When ProtesterViolence.dum is OUTSIDE the interval [1.74, 3.09], the slope
## of PoliceViolence.dum is p < .05.
##
## Note: The range of observed values of ProtesterViolence.dum is [0.00, 1.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 0.00 (0):
##
## Est. S.E. t val. p
## -------- ------ -------- ------
## -73.37 4.07 -18.04 0.00
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 1.00 (1):
##
## Est. S.E. t val. p
## -------- ------ -------- ------
## -39.98 4.05 -9.88 0.00summary(model <- lm(MoralPolice_sc ~ PoliceViolence.dum*ProtesterViolence.dum*ideology+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = MoralPolice_sc ~ PoliceViolence.dum * ProtesterViolence.dum *
## ideology + incomeN + Race4 + age + educationN + gender_dum,
## data = S5, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.294 -15.635 2.069 15.610 83.449
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 78.6650 9.9234 7.927
## PoliceViolence.dum -84.5899 8.5942 -9.843
## ProtesterViolence.dum -20.9358 9.3630 -2.236
## ideology 1.8326 1.4281 1.283
## incomeN 0.2853 0.4092 0.697
## Race4Black -2.7017 4.5816 -0.590
## Race4Hispanic -5.5552 5.1464 -1.079
## Race4Asian -3.1073 6.8614 -0.453
## Race4Other 9.1037 12.9182 0.705
## age 0.1046 0.1013 1.033
## educationN -0.3475 1.6428 -0.212
## gender_dum -2.7794 2.8006 -0.992
## PoliceViolence.dum:ProtesterViolence.dum 39.7709 12.5254 3.175
## PoliceViolence.dum:ideology 2.9347 2.0547 1.428
## ProtesterViolence.dum:ideology 1.5952 2.1626 0.738
## PoliceViolence.dum:ProtesterViolence.dum:ideology -1.3946 2.9399 -0.474
## Pr(>|t|)
## (Intercept) 3.68e-14 ***
## PoliceViolence.dum < 2e-16 ***
## ProtesterViolence.dum 0.02603 *
## ideology 0.20033
## incomeN 0.48618
## Race4Black 0.55582
## Race4Hispanic 0.28119
## Race4Asian 0.65094
## Race4Other 0.48149
## age 0.30244
## educationN 0.83261
## gender_dum 0.32175
## PoliceViolence.dum:ProtesterViolence.dum 0.00164 **
## PoliceViolence.dum:ideology 0.15417
## ProtesterViolence.dum:ideology 0.46129
## PoliceViolence.dum:ProtesterViolence.dum:ideology 0.63556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.03 on 323 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.5982
## F-statistic: 34.55 on 15 and 323 DF, p-value: < 2.2e-16#plot
ggplot(S5, aes(x = ProtesterViolence, y = MoralPolice_sc, fill = PoliceViolence, group = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey",
position = position_dodge(width = 0.8), width = 0.2) +
scale_y_continuous(limits = c(0, 100)) +
theme_bw() +
scale_fill_manual(values = c("grey","#80b1d3")) +
labs(x = "",
y = "Score",
title = "Effects on perceptions of morality") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
) 
ggplot(subset(S5, ideology.cat %in% c('Liberal', 'Conservative')), aes(x = ProtesterViolence, y = MoralPolice_sc, fill = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey", position = position_dodge(width = 0.8), width = 0.2) +
facet_wrap(vars(ideology.cat)) +
theme_bw() +
scale_fill_manual(values = c("grey", "#80b1d3")) +
scale_y_continuous(limits = c(0, 100)) +
labs(x = "", y = "Score", title = "Effects on perceptions of morality by ideology") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
)
Effects on trust in the government
summary(model <- lm(TrustGov_sc ~ condD1*condD2*condD3, S5, na.action=na.omit)) #control as the reference group##
## Call:
## lm(formula = TrustGov_sc ~ condD1 * condD2 * condD3, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56.785 -18.445 2.394 18.924 58.394
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58.285 2.771 21.031 < 2e-16 ***
## condD1 -18.179 3.931 -4.625 5.36e-06 ***
## condD2 -8.418 4.005 -2.102 0.03632 *
## condD3 -12.493 3.886 -3.215 0.00143 **
## condD1:condD2 NA NA NA NA
## condD1:condD3 NA NA NA NA
## condD2:condD3 NA NA NA NA
## condD1:condD2:condD3 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.7 on 335 degrees of freedom
## Multiple R-squared: 0.06358, Adjusted R-squared: 0.05519
## F-statistic: 7.582 on 3 and 335 DF, p-value: 6.404e-05summary(model <- lm(TrustGov_sc ~ condD0*condD1*condD2, S5, na.action=na.omit)) #mutual violence as the reference group. It is not significantly different from police violence and protester violence conditions##
## Call:
## lm(formula = TrustGov_sc ~ condD0 * condD1 * condD2, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56.785 -18.445 2.394 18.924 58.394
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.792 2.724 16.809 < 2e-16 ***
## condD0 12.493 3.886 3.215 0.00143 **
## condD1 -5.686 3.898 -1.459 0.14554
## condD2 4.075 3.973 1.026 0.30576
## condD0:condD1 NA NA NA NA
## condD0:condD2 NA NA NA NA
## condD1:condD2 NA NA NA NA
## condD0:condD1:condD2 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.7 on 335 degrees of freedom
## Multiple R-squared: 0.06358, Adjusted R-squared: 0.05519
## F-statistic: 7.582 on 3 and 335 DF, p-value: 6.404e-05summary(model <- aov(TrustGov_sc ~ condition, S5, na.action=na.omit))## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 15023 5008 7.582 6.4e-05 ***
## Residuals 335 221274 661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(model, type="III")## Anova Table (Type III tests)
##
## Response: TrustGov_sc
## Sum Sq Df F value Pr(>F)
## (Intercept) 292153 1 442.3078 < 2.2e-16 ***
## condition 15023 3 7.5816 6.404e-05 ***
## Residuals 221274 335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glht(model, linfct = mcp(condition= "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = TrustGov_sc ~ condition, data = S5, na.action = na.omit)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## police_violence - control == 0 -18.179 3.931 -4.625 < 0.001
## protester_violence - control == 0 -8.418 4.005 -2.102 0.15477
## mutual_violence - control == 0 -12.493 3.886 -3.215 0.00777
## protester_violence - police_violence == 0 9.761 4.016 2.430 0.07314
## mutual_violence - police_violence == 0 5.686 3.898 1.459 0.46361
## mutual_violence - protester_violence == 0 -4.075 3.973 -1.026 0.73444
##
## police_violence - control == 0 ***
## protester_violence - control == 0
## mutual_violence - control == 0 **
## protester_violence - police_violence == 0 .
## mutual_violence - police_violence == 0
## mutual_violence - protester_violence == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)summary(model <- lm(TrustGov_sc ~ PoliceViolence.dum*ProtesterViolence.dum+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = TrustGov_sc ~ PoliceViolence.dum * ProtesterViolence.dum +
## incomeN + Race4 + age + educationN + gender_dum, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60.365 -17.528 1.437 18.307 61.163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.7398 8.4915 4.562 7.17e-06
## PoliceViolence.dum -18.5136 3.9917 -4.638 5.09e-06
## ProtesterViolence.dum -8.6119 4.0855 -2.108 0.03580
## incomeN 0.7017 0.4056 1.730 0.08456
## Race4Black 3.7636 4.6128 0.816 0.41515
## Race4Hispanic 0.3456 5.1955 0.067 0.94700
## Race4Asian -1.2193 6.9296 -0.176 0.86043
## Race4Other 4.6494 13.0768 0.356 0.72241
## age 0.3166 0.1028 3.078 0.00226
## educationN -0.3031 1.6444 -0.184 0.85386
## gender_dum 0.2069 2.8445 0.073 0.94205
## PoliceViolence.dum:ProtesterViolence.dum 14.6345 5.6501 2.590 0.01002
##
## (Intercept) ***
## PoliceViolence.dum ***
## ProtesterViolence.dum *
## incomeN .
## Race4Black
## Race4Hispanic
## Race4Asian
## Race4Other
## age **
## educationN
## gender_dum
## PoliceViolence.dum:ProtesterViolence.dum *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.46 on 327 degrees of freedom
## Multiple R-squared: 0.1027, Adjusted R-squared: 0.07249
## F-statistic: 3.401 on 11 and 327 DF, p-value: 0.0001754sim_slopes(model, pred = PoliceViolence.dum, modx = ProtesterViolence.dum)## JOHNSON-NEYMAN INTERVAL
##
## When ProtesterViolence.dum is OUTSIDE the interval [0.82, 3.79], the slope
## of PoliceViolence.dum is p < .05.
##
## Note: The range of observed values of ProtesterViolence.dum is [0.00, 1.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 0.00 (0):
##
## Est. S.E. t val. p
## -------- ------ -------- ------
## -18.51 3.99 -4.64 0.00
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 1.00 (1):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -3.88 3.97 -0.98 0.33summary(model <- lm(TrustGov_sc ~ PoliceViolence.dum*ProtesterViolence.dum*ideology + incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = TrustGov_sc ~ PoliceViolence.dum * ProtesterViolence.dum *
## ideology + incomeN + Race4 + age + educationN + gender_dum,
## data = S5, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.508 -18.567 2.239 18.290 56.133
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 29.3946 10.0866 2.914
## PoliceViolence.dum -15.3293 8.7355 -1.755
## ProtesterViolence.dum 5.3301 9.5169 0.560
## ideology 2.5430 1.4516 1.752
## incomeN 0.5746 0.4159 1.382
## Race4Black 3.3916 4.6569 0.728
## Race4Hispanic 1.1056 5.2310 0.211
## Race4Asian -1.2980 6.9742 -0.186
## Race4Other 7.0558 13.1306 0.537
## age 0.3062 0.1029 2.975
## educationN 0.0769 1.6698 0.046
## gender_dum 0.1541 2.8467 0.054
## PoliceViolence.dum:ProtesterViolence.dum 6.6962 12.7313 0.526
## PoliceViolence.dum:ideology -0.8608 2.0885 -0.412
## ProtesterViolence.dum:ideology -3.6701 2.1982 -1.670
## PoliceViolence.dum:ProtesterViolence.dum:ideology 2.0471 2.9883 0.685
## Pr(>|t|)
## (Intercept) 0.00381 **
## PoliceViolence.dum 0.08024 .
## ProtesterViolence.dum 0.57582
## ideology 0.08074 .
## incomeN 0.16804
## Race4Black 0.46696
## Race4Hispanic 0.83274
## Race4Asian 0.85247
## Race4Other 0.59139
## age 0.00315 **
## educationN 0.96330
## gender_dum 0.95687
## PoliceViolence.dum:ProtesterViolence.dum 0.59928
## PoliceViolence.dum:ideology 0.68051
## ProtesterViolence.dum:ideology 0.09596 .
## PoliceViolence.dum:ProtesterViolence.dum:ideology 0.49380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.44 on 323 degrees of freedom
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.07439
## F-statistic: 2.811 on 15 and 323 DF, p-value: 0.0003924sim_slopes(model, pred = ProtesterViolence.dum, modx = ideology)## JOHNSON-NEYMAN INTERVAL
##
## The Johnson-Neyman interval could not be found. Is the p value for your
## interaction term below the specified alpha?
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of ProtesterViolence.dum when ideology = 1.906236 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 3.77 4.02 0.94 0.35
##
## Slope of ProtesterViolence.dum when ideology = 3.790560 (Mean):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -1.16 2.82 -0.41 0.68
##
## Slope of ProtesterViolence.dum when ideology = 5.674885 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -6.10 3.97 -1.53 0.13ggplot(S5, aes(x = ProtesterViolence, y = TrustGov_sc, fill = PoliceViolence, group = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey",
position = position_dodge(width = 0.8), width = 0.2) +
scale_y_continuous(limits = c(0, 100)) +
theme_bw() +
scale_fill_manual(values = c("grey","#80b1d3")) +
labs(x = "",
y = "Score",
title = "Effects on trust in the government") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
) 
ggplot(subset(S5, ideology.cat %in% c('Liberal', 'Conservative')), aes(x = ProtesterViolence, y = TrustGov_sc, fill = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey", position = position_dodge(width = 0.8), width = 0.2) +
facet_wrap(vars(ideology.cat)) +
theme_bw() +
scale_fill_manual(values = c("grey", "#80b1d3")) +
scale_y_continuous(limits = c(0, 100)) +
labs(x = "", y = "Score", title = "Effects on trust in the government by ideology") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
)
Effects on support for police reforms
summary(model <- lm(PolicySupport ~ condD1*condD2*condD3, S5, na.action=na.omit))#control as the reference group##
## Call:
## lm(formula = PolicySupport ~ condD1 * condD2 * condD3, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.471 -13.211 -0.747 14.029 47.657
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.093 2.298 20.055 < 2e-16 ***
## condD1 14.878 3.260 4.564 7.06e-06 ***
## condD2 2.581 3.322 0.777 0.4377
## condD3 7.404 3.223 2.297 0.0222 *
## condD1:condD2 NA NA NA NA
## condD1:condD3 NA NA NA NA
## condD2:condD3 NA NA NA NA
## condD1:condD2:condD3 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.31 on 335 degrees of freedom
## Multiple R-squared: 0.06665, Adjusted R-squared: 0.0583
## F-statistic: 7.975 on 3 and 335 DF, p-value: 3.772e-05summary(model <- lm(PolicySupport ~ condD0*condD1*condD2, S5, na.action=na.omit)) #mutual violence as the reference group. It is significantly different from the police violence condition##
## Call:
## lm(formula = PolicySupport ~ condD0 * condD1 * condD2, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.471 -13.211 -0.747 14.029 47.657
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.497 2.259 23.679 <2e-16 ***
## condD0 -7.404 3.223 -2.297 0.0222 *
## condD1 7.473 3.232 2.312 0.0214 *
## condD2 -4.823 3.295 -1.464 0.1442
## condD0:condD1 NA NA NA NA
## condD0:condD2 NA NA NA NA
## condD1:condD2 NA NA NA NA
## condD0:condD1:condD2 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.31 on 335 degrees of freedom
## Multiple R-squared: 0.06665, Adjusted R-squared: 0.0583
## F-statistic: 7.975 on 3 and 335 DF, p-value: 3.772e-05summary(model <- aov(PolicySupport ~ condition, S5, na.action=na.omit))## Df Sum Sq Mean Sq F value Pr(>F)
## condition 3 10868 3623 7.975 3.77e-05 ***
## Residuals 335 152185 454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(model, type="III")## Anova Table (Type III tests)
##
## Response: PolicySupport
## Sum Sq Df F value Pr(>F)
## (Intercept) 182713 1 402.2000 < 2.2e-16 ***
## condition 10868 3 7.9746 3.772e-05 ***
## Residuals 152185 335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glht(model, linfct = mcp(condition= "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = PolicySupport ~ condition, data = S5, na.action = na.omit)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## police_violence - control == 0 14.878 3.260 4.564 < 0.001
## protester_violence - control == 0 2.581 3.322 0.777 0.86479
## mutual_violence - control == 0 7.404 3.223 2.297 0.10071
## protester_violence - police_violence == 0 -12.297 3.331 -3.692 0.00137
## mutual_violence - police_violence == 0 -7.473 3.232 -2.312 0.09725
## mutual_violence - protester_violence == 0 4.823 3.295 1.464 0.46051
##
## police_violence - control == 0 ***
## protester_violence - control == 0
## mutual_violence - control == 0
## protester_violence - police_violence == 0 **
## mutual_violence - police_violence == 0 .
## mutual_violence - protester_violence == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)summary(model <- lm(PolicySupport ~ PoliceViolence.dum*ProtesterViolence.dum+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = PolicySupport ~ PoliceViolence.dum * ProtesterViolence.dum +
## incomeN + Race4 + age + educationN + gender_dum, data = S5,
## na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60.871 -12.033 -1.008 13.777 52.619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.73144 6.95385 7.583 3.53e-13
## PoliceViolence.dum 14.75555 3.26888 4.514 8.89e-06
## ProtesterViolence.dum 2.31621 3.34569 0.692 0.48924
## incomeN -1.01775 0.33213 -3.064 0.00236
## Race4Black 1.33943 3.77753 0.355 0.72313
## Race4Hispanic 5.48397 4.25470 1.289 0.19834
## Race4Asian 1.05003 5.67475 0.185 0.85332
## Race4Other -0.60256 10.70882 -0.056 0.95516
## age -0.22014 0.08421 -2.614 0.00936
## educationN 2.92898 1.34662 2.175 0.03034
## gender_dum 0.48754 2.32943 0.209 0.83435
## PoliceViolence.dum:ProtesterViolence.dum -9.87734 4.62694 -2.135 0.03352
##
## (Intercept) ***
## PoliceViolence.dum ***
## ProtesterViolence.dum
## incomeN **
## Race4Black
## Race4Hispanic
## Race4Asian
## Race4Other
## age **
## educationN *
## gender_dum
## PoliceViolence.dum:ProtesterViolence.dum *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.85 on 327 degrees of freedom
## Multiple R-squared: 0.1279, Adjusted R-squared: 0.09858
## F-statistic: 4.36 on 11 and 327 DF, p-value: 4.191e-06sim_slopes(model, pred = PoliceViolence.dum, modx = ProtesterViolence.dum)## JOHNSON-NEYMAN INTERVAL
##
## When ProtesterViolence.dum is OUTSIDE the interval [0.90, 13.25], the slope
## of PoliceViolence.dum is p < .05.
##
## Note: The range of observed values of ProtesterViolence.dum is [0.00, 1.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 0.00 (0):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## 14.76 3.27 4.51 0.00
##
## Slope of PoliceViolence.dum when ProtesterViolence.dum = 1.00 (1):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 4.88 3.25 1.50 0.13summary(model <- lm(PolicySupport ~ PoliceViolence.dum*ProtesterViolence.dum*ideology+ incomeN + Race4 + age + educationN + gender_dum, S5, na.action=na.omit))##
## Call:
## lm(formula = PolicySupport ~ PoliceViolence.dum * ProtesterViolence.dum *
## ideology + incomeN + Race4 + age + educationN + gender_dum,
## data = S5, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.721 -11.416 0.356 11.012 51.928
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 73.7234 7.4179 9.939
## PoliceViolence.dum 12.0253 6.4243 1.872
## ProtesterViolence.dum 6.7507 6.9990 0.965
## ideology -4.8483 1.0675 -4.542
## incomeN -0.4372 0.3059 -1.429
## Race4Black -0.6423 3.4248 -0.188
## Race4Hispanic 2.0530 3.8470 0.534
## Race4Asian -3.8201 5.1290 -0.745
## Race4Other -6.5683 9.6566 -0.680
## age -0.2131 0.0757 -2.815
## educationN 1.0266 1.2280 0.836
## gender_dum -0.2418 2.0935 -0.116
## PoliceViolence.dum:ProtesterViolence.dum -6.8517 9.3630 -0.732
## PoliceViolence.dum:ideology 0.8353 1.5359 0.544
## ProtesterViolence.dum:ideology -0.5925 1.6166 -0.367
## PoliceViolence.dum:ProtesterViolence.dum:ideology -1.3329 2.1977 -0.607
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## PoliceViolence.dum 0.06213 .
## ProtesterViolence.dum 0.33550
## ideology 7.89e-06 ***
## incomeN 0.15384
## Race4Black 0.85136
## Race4Hispanic 0.59393
## Race4Asian 0.45693
## Race4Other 0.49687
## age 0.00517 **
## educationN 0.40379
## gender_dum 0.90811
## PoliceViolence.dum:ProtesterViolence.dum 0.46483
## PoliceViolence.dum:ideology 0.58695
## ProtesterViolence.dum:ideology 0.71421
## PoliceViolence.dum:ProtesterViolence.dum:ideology 0.54460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.71 on 323 degrees of freedom
## Multiple R-squared: 0.3067, Adjusted R-squared: 0.2745
## F-statistic: 9.526 on 15 and 323 DF, p-value: < 2.2e-16ggplot(S5, aes(x = ProtesterViolence, y = PolicySupport, fill = PoliceViolence, group = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey",
position = position_dodge(width = 0.8), width = 0.2) +
scale_y_continuous(limits = c(0, 100)) +
theme_bw() +
scale_fill_manual(values = c("grey","#80b1d3")) +
labs(x = "",
y = "Score",
title = "Effects on support for police reforms") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
) 
ggplot(subset(S5, ideology.cat %in% c('Liberal', 'Conservative')), aes(x = ProtesterViolence, y = PolicySupport, fill = PoliceViolence)) +
stat_summary(fun = mean, geom = "bar", position = position_dodge(width = 0.8), width = 0.75) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", colour = "grey", position = position_dodge(width = 0.8), width = 0.2) +
facet_wrap(vars(ideology.cat)) +
theme_bw() +
scale_fill_manual(values = c("grey", "#80b1d3")) +
scale_y_continuous(limits = c(0, 100)) +
labs(x = "", y = "Score", title = "Effects on support for police reforms by ideology") +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.5, face = "italic")
)
#violin plot with ideology
Mediation analysis
library(lavaan)
S5$MoralPolice_std <- scale(S5$MoralPolice_sc)
S5$HarmfulPolice_std <- scale(S5$HarmfulPolice_sc)
S5$TrustPolice_std <- scale(S5$TrustPolice_sc)
#Morality as a mediator
model <- '
# Direct effects
MoralPolice_std ~ a1*condD1 + a2*condD2 + a3*condD3
TrustPolice_std ~ b1*MoralPolice_std + c1*condD1 + c2*condD2 +c3*condD3
# Indirect effects
Ind_Eff_D1 := b1*a1
Ind_Eff_D2 := b1*a2
Ind_Eff_D3 := b1*a3
'
fit <- sem(model, data=S5)
summary(fit, standardized=TRUE, fit.measures = TRUE, rsq = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 339
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 565.824
## Degrees of freedom 7
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -678.127
## Loglikelihood unrestricted model (H1) -678.127
##
## Akaike (AIC) 1374.254
## Bayesian (BIC) 1408.688
## Sample-size adjusted Bayesian (SABIC) 1380.138
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MoralPolice_std ~
## condD1 (a1) -1.862 0.100 -18.551 0.000 -1.862 -0.808
## condD2 (a2) -0.347 0.102 -3.393 0.001 -0.347 -0.147
## condD3 (a3) -1.370 0.099 -13.814 0.000 -1.370 -0.604
## TrustPolice_std ~
## MrlPlc_st (b1) 0.766 0.055 14.018 0.000 0.766 0.766
## condD1 (c1) 0.051 0.143 0.356 0.722 0.051 0.022
## condD2 (c2) 0.155 0.105 1.480 0.139 0.155 0.066
## condD3 (c3) 0.230 0.125 1.847 0.065 0.230 0.101
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MoralPolic_std 0.430 0.033 13.019 0.000 0.430 0.432
## .TrustPolic_std 0.435 0.033 13.019 0.000 0.435 0.436
##
## R-Square:
## Estimate
## MoralPolic_std 0.568
## TrustPolic_std 0.564
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Ind_Eff_D1 -1.425 0.127 -11.184 0.000 -1.425 -0.619
## Ind_Eff_D2 -0.266 0.081 -3.298 0.001 -0.266 -0.112
## Ind_Eff_D3 -1.049 0.107 -9.839 0.000 -1.049 -0.462#Harm is a mediator
model <- '
# Direct effects
HarmfulPolice_std ~ a1*condD1 + a2*condD2 + a3*condD3
TrustPolice_std ~ b1*HarmfulPolice_std + c1*condD1 + c2*condD2 +c3*condD3
# Indirect effects
Ind_Eff_D1 := b1*a1
Ind_Eff_D2 := b1*a2
Ind_Eff_D3 := b1*a3
'
fit <- sem(model, data=S5)
summary(fit, standardized=TRUE, fit.measures = TRUE, rsq = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 339
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 577.928
## Degrees of freedom 7
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -672.075
## Loglikelihood unrestricted model (H1) -672.075
##
## Akaike (AIC) 1362.150
## Bayesian (BIC) 1396.584
## Sample-size adjusted Bayesian (SABIC) 1368.034
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## HarmfulPolice_std ~
## condD1 (a1) 1.872 0.090 20.712 0.000 1.872 0.813
## condD2 (a2) 0.170 0.092 1.850 0.064 0.170 0.072
## condD3 (a3) 1.456 0.089 16.297 0.000 1.456 0.642
## TrustPolice_std ~
## HrmflPlc_ (b1) -0.698 0.066 -10.553 0.000 -0.698 -0.698
## condD1 (c1) -0.068 0.166 -0.410 0.682 -0.068 -0.029
## condD2 (c2) 0.008 0.113 0.072 0.943 0.008 0.003
## condD3 (c3) 0.197 0.145 1.357 0.175 0.197 0.087
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HarmfulPlc_std 0.349 0.027 13.019 0.000 0.349 0.350
## .TrustPolic_std 0.517 0.040 13.019 0.000 0.517 0.519
##
## R-Square:
## Estimate
## HarmfulPlc_std 0.650
## TrustPolic_std 0.481
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Ind_Eff_D1 -1.306 0.139 -9.403 0.000 -1.306 -0.567
## Ind_Eff_D2 -0.119 0.065 -1.823 0.068 -0.119 -0.050
## Ind_Eff_D3 -1.016 0.115 -8.858 0.000 -1.016 -0.448#Serial mediation
model <- '
# Direct effects
HarmfulPolice_std ~ a1*condD1 + a2*condD2 + a3*condD3
MoralPolice_std ~ b1*HarmfulPolice_std + d1*condD1 + d2*condD2 + d3*condD3
TrustPolice_std ~ c1*MoralPolice_std + e1*HarmfulPolice_std + f1*condD1 + f2*condD2 + f3*condD3
# Indirect effects
Ind_Eff_D1 := b1*a1
Ind_Eff_D2 := b1*a2
Ind_Eff_D3 := b1*a3
Ind_Eff_D1_Serial := c1*b1*a1
Ind_Eff_D2_Serial := c1*b1*a2
Ind_Eff_D3_Serial := c1*b1*a3
'
fit <- sem(model, data=S5)
summary(fit, standardized=TRUE, fit.measures=TRUE, rsq=TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 15
##
## Number of observations 339
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 1098.572
## Degrees of freedom 12
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -892.272
## Loglikelihood unrestricted model (H1) -892.272
##
## Akaike (AIC) 1814.545
## Bayesian (BIC) 1871.935
## Sample-size adjusted Bayesian (SABIC) 1824.352
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## HarmfulPolice_std ~
## condD1 (a1) 1.872 0.090 20.712 0.000 1.872 0.813
## condD2 (a2) 0.170 0.092 1.850 0.064 0.170 0.072
## condD3 (a3) 1.456 0.089 16.297 0.000 1.456 0.642
## MoralPolice_std ~
## HrmflPlc_ (b1) -0.687 0.047 -14.521 0.000 -0.687 -0.687
## condD1 (d1) -0.574 0.119 -4.844 0.000 -0.574 -0.249
## condD2 (d2) -0.230 0.081 -2.848 0.004 -0.230 -0.097
## condD3 (d3) -0.369 0.104 -3.550 0.000 -0.369 -0.163
## TrustPolice_std ~
## MrlPlc_st (c1) 0.610 0.068 8.950 0.000 0.610 0.610
## HrmflPlc_ (e1) -0.278 0.076 -3.670 0.000 -0.278 -0.278
## condD1 (f1) 0.283 0.154 1.836 0.066 0.283 0.123
## condD2 (f2) 0.148 0.103 1.447 0.148 0.148 0.063
## condD3 (f3) 0.423 0.133 3.176 0.001 0.423 0.186
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HarmfulPlc_std 0.349 0.027 13.019 0.000 0.349 0.350
## .MoralPolic_std 0.265 0.020 13.019 0.000 0.265 0.266
## .TrustPolic_std 0.419 0.032 13.019 0.000 0.419 0.420
##
## R-Square:
## Estimate
## HarmfulPlc_std 0.650
## MoralPolic_std 0.734
## TrustPolic_std 0.580
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Ind_Eff_D1 -1.287 0.108 -11.890 0.000 -1.287 -0.559
## Ind_Eff_D2 -0.117 0.064 -1.836 0.066 -0.117 -0.050
## Ind_Eff_D3 -1.001 0.092 -10.842 0.000 -1.001 -0.441
## Ind_Eff_D1_Srl -0.786 0.110 -7.151 0.000 -0.786 -0.341
## Ind_Eff_D2_Srl -0.072 0.040 -1.798 0.072 -0.072 -0.030
## Ind_Eff_D3_Srl -0.611 0.089 -6.902 0.000 -0.611 -0.269