1 Study 1

Create a dataset with data from Study 1

Data_Study1_Wide<-Data_Study1_Raw %>% filter(CCA==1 & Attention_check==1)

Transform the data in long format to run GEE

Data_Study1_Long<-Data_Study1_Wide %>% 
  pivot_longer(
    c(
       "Submarine_1", 
       "Trespassers_1", 
       "Hostages_1", 
       "Liferaft_1", 
       "PlaneCrash_1", 
       "PrisWar_1", 
       "Spelunkers_1", 
       "Soldier_1", 
       "Surgery_1", 
       "Footbridge_1", 
       "Cryingbaby_1"), 
    names_to = "Scenario", values_to = "Moral")

Data_Study1_Long$Trial<-rep(1:11, times=length(Data_Study1_Wide$Age))

1.1 Data analysis

Run Generalized Estimating Equations (GEE) for each sleep variable (IV) on moral judgments (DV)

GEE_SleepQualCroS1<-geeglm(Moral~SleepQualCro, family=gaussian, id=ResponseId, corstr="exchangeable", Data_Study1_Long)
GEE_SleepQualAcuS1<-geeglm(Moral~SleepQualAcu, family=gaussian, id=ResponseId, corstr="exchangeable", Data_Study1_Long)
GEE_SleepQuantCroS1<-geeglm(Moral~SleepQuantCro, family=gaussian, id=ResponseId, corstr="exchangeable", Data_Study1_Long)
GEE_SleepQuantAcuS1<-geeglm(Moral~SleepQuantAcu, family=gaussian, id=ResponseId, corstr="exchangeable", Data_Study1_Long)

Extract coefficients (unstandardized slopes, standard errors and p-values) from GEE models

#Chronic Sleep Quality
bGEEQualCroS1<-round(summary(GEE_SleepQualCroS1)$coefficients[2,1], digit=2)
SEGEEQualCroS1<- round(summary(GEE_SleepQualCroS1)$coefficients[2,2] , digit=2)
pGEEQualCroS1<- round(ifelse(summary(GEE_SleepQualCroS1)$coefficients[2,4]*4<.99, summary(GEE_SleepQualCroS1)$coefficients[2,4]*4, .99), digit=2)

#Acute Sleep Quality
bGEEQualAcuS1<- round(summary(GEE_SleepQualAcuS1)$coefficients[2,1] , digit=2)
SEGEEQualAcuS1 <- round(summary(GEE_SleepQualAcuS1)$coefficients[2,2] , digit=2)
pGEEQualAcuS1<- round(ifelse(summary(GEE_SleepQualAcuS1)$coefficients[2,4]*4<.99, summary(GEE_SleepQualAcuS1)$coefficients[2,4]*4, .99), digit=2)

#Chronic Sleep Quantity
bGEEQuantCroS1<- round(summary(GEE_SleepQuantCroS1)$coefficients[2,1] , digit=2)
SEGEEQuantCroS1 <- round(summary(GEE_SleepQuantCroS1)$coefficients[2,2] , digit=2)
pGEEQuantCroS1<- round(ifelse(summary(GEE_SleepQuantCroS1)$coefficients[2,4]*4<.99, summary(GEE_SleepQuantCroS1)$coefficients[2,4]*4, .99), digit=2)

#Acute Sleep Quantity
bGEEQuantAcuS1<- round(summary(GEE_SleepQuantAcuS1)$coefficients[2,1] , digit=2)
SEGEEQuantAcuS1 <- round(summary(GEE_SleepQuantAcuS1)$coefficients[2,2] , digit=2)
pGEEQuantAcuS1<- round(ifelse(summary(GEE_SleepQuantAcuS1)$coefficients[2,4]*4<.99, summary(GEE_SleepQuantAcuS1)$coefficients[2,4]*4, .99), digit=2)

Create datasets to display GEE summary in plots

ResumGEEQualCroS1<- paste0("b=",bGEEQualCroS1,", ", "SE=",SEGEEQualCroS1,", ","p=",pGEEQualCroS1)
ResumGEEQualAcuS1<- paste0("b=",bGEEQualAcuS1,", ", "SE=",SEGEEQualAcuS1,", ","p=",pGEEQualAcuS1)
ResumGEEQuantCroS1<- paste0("b=",bGEEQuantCroS1,", ", "SE=",SEGEEQuantCroS1,", ","p=",pGEEQuantCroS1)
ResumGEEQuantAcuS1<- paste0("b=",bGEEQuantAcuS1,", ", "SE=",SEGEEQuantAcuS1,", ","p=",pGEEQuantAcuS1)

annotationQualCroS1 <- data.frame(x = 5.5, y = 100,  label = ResumGEEQualCroS1)
annotationQualAcuS1 <- data.frame(x = 5.5, y = 100,  label = ResumGEEQualAcuS1)
annotationQuantCroS1 <- data.frame(x = 5.5, y = 100,  label = ResumGEEQuantCroS1)
annotationQuantAcuS1 <- data.frame(x = 5.5, y = 100,  label = ResumGEEQuantAcuS1)
annotationS1<-cbind(
  rbind(annotationQualCroS1, 
        annotationQualAcuS1, 
        annotationQuantCroS1, 
        annotationQuantAcuS1),
  SleepType=c(
        "Quality of Chronic Sleep", 
        "Quality of Acute Sleep", 
        "Quantity of Chronic Sleep", 
        "Quantity of Acute Sleep"))

Prepare data for plots

Data_PlotS1<- Data_Study1_Wide %>%
  pivot_longer(
    cols=c(SleepQualCro, SleepQualAcu, SleepQuantCro, SleepQuantAcu),
    names_to="SleepType") %>%
  rename("SleepValue"=value) %>%
  mutate(
    SleepLength=case_when(
      SleepType=="SleepQualCro" | SleepType=="SleepQuantCro"~"Chronic",
      SleepType=="SleepQualAcu" | SleepType=="SleepQuantAcu"~"Acute"),
    SleepQuanthist=case_when(
      SleepType=="SleepQualCro" | SleepType=="SleepQualAcu"~"Sleep Quality",
      SleepType=="SleepQuantCro" | SleepType=="SleepQuantAcu"~"Sleep Quantity"))

Data_PlotS1$SleepType<-dplyr::recode(Data_PlotS1$SleepType, 
        "SleepQualCro" = "Quality of Chronic Sleep",
        "SleepQualAcu" = "Quality of Acute Sleep",
        "SleepQuantCro" = "Quantity of Chronic Sleep",
        "SleepQuantAcu" = "Quantity of Acute Sleep")

1.2 Summary of Study 1 results

Put results in a table

SleepMarker<-c("Quality Chronic","Quality Acute","Quantity Chronic","Quantity Acute")
bGEES1<-c(bGEEQualCroS1,bGEEQualAcuS1,bGEEQuantCroS1,bGEEQuantAcuS1)
SEGEES1<-c(SEGEEQualCroS1,SEGEEQualAcuS1,SEGEEQuantCroS1,SEGEEQuantAcuS1)
pvalGEES1<-c(pGEEQualCroS1,pGEEQualAcuS1,pGEEQuantCroS1,pGEEQuantAcuS1)
NS1<-c(length(GEE_SleepQualCroS1$fitted.values)/11,
     length(GEE_SleepQualAcuS1$fitted.values)/11,
     length(GEE_SleepQuantCroS1$fitted.values)/11,
     length(GEE_SleepQuantAcuS1$fitted.values)/11)
ResultsStudy1<-data.frame("Sleep Indicator"=SleepMarker, "Unstandardized Slope"=bGEES1, "Standard Errors"=SEGEES1, "p values"=pvalGEES1, "N"=NS1)
gt(ResultsStudy1)

Sleep.Indicator	Unstandardized.Slope	Standard.Errors	p.values	N
Quality Chronic	0.03	0.11	0.99	122
Quality Acute	0.11	0.10	0.99	122
Quantity Chronic	0.00	0.19	0.99	122
Quantity Acute	0.14	0.17	0.99	122

1.3 Plot Study 1 results

Plot distribution for each sleep indicator

DistQualityS1<-ggplot(Data_PlotS1, aes(x=SleepValue, fill=SleepLength)) + 
  geom_density(alpha=0.5, size=0.5,adjust = 2) + 
  scale_fill_manual(values=c("#193B94", "#BDCAEE", "#193B94", "#BDCAEE")) +
  theme_bw() + 
  ylab("Density") + xlab("") +
  facet_wrap(~SleepQuanthist, scale="free_x") + 
  guides(fill=guide_legend("Sleep Length")) +
  theme(
    axis.title.y = element_text(size = 11, hjust = 0.5, face="bold"),
    axis.title.x = element_text(face="bold", size = 11, hjust = 0.5),
    legend.position="top",
    legend.title = element_text(colour="black", size=10, face="bold")) 

DistQualityS1

Plot distribution of the scores to the sacrificial dilemmas

DistMoralS1<-ggplot(Data_Study1_Raw, aes(x=Moral_DIL, fill="#193B94")) + 
  geom_density(alpha=0.5, size=0.5,adjust = 2) + 
  theme_bw() + 
  ylab("Density") + xlab("Sacrificial Dilemmas") +
  guides(fill="none") +
  theme(
    axis.title.y = element_text(size = 11, hjust = 0.5, face="bold"),
    axis.title.x = element_text(face="bold", size = 11, hjust = 0.5))
  
DistMoralS1

Scatterplots

ggplot(Data_PlotS1, aes(x=SleepValue, y=Moral_DIL)) + 
       geom_jitter(alpha=0.6, color="#545454", size=1.2) + 
       geom_smooth(method="lm") +
       facet_wrap(~SleepType, nrow = 2, ncol = 2, scales="free_x") +
       theme_bw() + ylab("Utilitirianism") + xlab("") + 
       theme(axis.title.y = element_text(size = 11, hjust = 0.5, face="bold"), 
             axis.title.x = element_text(face="bold", size = 11, hjust = 0.5)) +
       guides(size=FALSE, colour=FALSE, fill=FALSE) + 
       geom_label(data=annotationS1, aes( x=x, y=y, label=label), 
                  color="black",  size=3.5 , angle=45, alpha=7/10, inherit.aes = FALSE)

## `geom_smooth()` using formula 'y ~ x'