102/replication_package/step2/Step2_Analyses

library(psych)

#Opening data

dat <- read.csv(""Step_Results_CLEAN_EnglishVersion.csv",header=TRUE)
attach(dat)

#Constructing utilitarian scores for each category

UD <- (U.D.01+U.D.02+U.D.03+U.D.04+U.D.05+U.D.06+U.D.07+U.D.08+U.D.09+U.D.10)/10
PD <- (P.D.01+P.D.02+P.D.03+P.D.04+P.D.05+P.D.06+P.D.07+P.D.08+P.D.09+P.D.10)/10
HC <- (H.C.01+H.C.02+H.C.03+H.C.04+H.C.05+H.C.06+H.C.07+H.C.08+H.C.09+H.C.10)/10
AO <- (A.O.01+A.O.02+A.O.03+A.O.04+A.O.05+A.O.06+A.O.07+A.O.08+A.O.09+A.O.10)/10
DE <- (D.E.01+D.E.02+D.E.03+D.E.04+D.E.05+D.E.06+D.E.07+D.E.08+D.E.09+D.E.10)/10
P <- (P.01+P.02+P.03+P.04+P.05+P.06+P.07+P.08+P.09+P.10)/10

#Reverse-coding utilitarian scores when necessary

UD_ut <- 8-UD
PD_ut <- 8-PD
HC_ut <- 8-HC
AO_ut <- 8-AO
DE_ut <- DE
P_ut <- 8-P

#Calculating Cronbach's Alpha for utilitarian scores in each category

UDframe <- data.frame(U.D.01,U.D.02,U.D.03,U.D.04,U.D.05,U.D.06,U.D.07,U.D.08,U.D.09,U.D.10)
alpha(UDframe)

PDframe <- data.frame(P.D.01,P.D.02,P.D.03,P.D.04,P.D.05,P.D.06,P.D.07,P.D.08,P.D.09,P.D.10)
alpha(PDframe)

HCframe <- data.frame(H.C.01,H.C.02,H.C.03,H.C.04,H.C.05,H.C.06,H.C.07,H.C.08,H.C.09,H.C.10)
alpha(HCframe)

AOframe <- data.frame(A.O.01,A.O.02,A.O.03,A.O.04,A.O.05,A.O.06,A.O.07,A.O.08,A.O.09,A.O.10)
alpha(AOframe)

DEframe <- data.frame(D.E.01,D.E.02,D.E.03,D.E.04,D.E.05,D.E.06,D.E.07,D.E.08,D.E.09,D.E.10)
alpha(DEframe)

Pframe <- data.frame(P.01,P.02,P.03,P.04,P.05,P.06,P.07,P.08,P.09,P.10)
alpha(Pframe)

#Constructing individual traits measures

CRT <- CRT1_RIGHT+CRT2_RIGHT+CRT3_RIGHT

NFCsum <- (6-NFC.01)+(6-NFC.02)+NFC.03+(6-NFC.04)+(6-NFC.05)+(6-NFC.06)+(6-NFC.07)+NFC.08+(6-NFC.09)+(6-NFC.10)+(6-NFC.11)+NFC.12+(6-NFC.13)+NFC.14+(6-NFC.15)+(6-NFC.16)+NFC.17+(6-NFC.18)+(6-NFC.19)
NFC <- NFCsum/19

mean(NFC)
sd(NFC)

FIsum <- FI.01+FI.02+FI.03+FI.04+FI.05+FI.06+FI.07+FI.08+FI.09+FI.10+FI.11+FI.12
FI <- FIsum/12

IRIsum <- IRI_01+(6-IRI_02)+IRI_03+(6-IRI_04)+(6-IRI_05)+IRI_06+IRI_07
IRI <- IRIsum/7

SRPsum <- SRP_01+SRP_02+SRP_03+SRP_04+SRP_05+SRP_06+SRP_07+SRP_08+SRP_09+SRP_10+SRP_11+SRP_12+SRP_13+SRP_14+SRP_15+SRP_16+SRP_17+SRP_18+SRP_19+SRP_20+SRP_21+SRP_22+(8-SRP_23)+(8-SRP_24)+(8-SRP_25)+(8-SRP_26)+SRP_27+(8-SRP_28)+SRP_29+SRP_30
SRP <- SRPsum/30

TASsum <- TAS_01+TAS_02+TAS_03+TAS_04+TAS_05+TAS_06+TAS_07+TAS_08+(6-TAS_09)+TAS_10+TAS_11+TAS_12+(6-TAS_13)+TAS_14+(6-TAS_15)+TAS_16+TAS_17+(6-TAS_18)+(6-TAS_19)+TAS_20
TAS <- TASsum/20

#Calculating average utilitarian scores for each category

mean(UD_ut)
sd(UD_ut)

mean(PD_ut)
sd(PD_ut)

mean(HC_ut)
sd(HC_ut)

mean(AO_ut)
sd(AO_ut)

mean(DE_ut)
sd(DE_ut)

mean(P_ut)
sd(P_ut)

#Calculating correlations utilitarian scores between each category

cor.test(UD_ut,PD_ut)
cor.test(UD_ut,HC_ut)
cor.test(UD_ut,AO_ut)
cor.test(UD_ut,DE_ut)
cor.test(UD_ut,P_ut)
cor.test(PD_ut,HC_ut)
cor.test(PD_ut,AO_ut)
cor.test(PD_ut,DE_ut)
cor.test(PD_ut,P_ut)
cor.test(HC_ut,AO_ut)
cor.test(HC_ut,DE_ut)
cor.test(HC_ut,P_ut)
cor.test(AO_ut,DE_ut)
cor.test(AO_ut,P_ut)
cor.test(DE_ut,P_ut)

#Calculating coherence between utilitarian scores using Cronbach's Alpha

alpha(data.frame(UD_ut,PD_ut,HC_ut,AO_ut,DE_ut,P_ut))

#Calculating Cronbach's Alpha for individual traits measures

NFCframe <- data.frame((6-NFC.01),(6-NFC.02),NFC.03,(6-NFC.04),(6-NFC.05),(6-NFC.06),(6-NFC.07),NFC.08,(6-NFC.09),(6-NFC.10),(6-NFC.11),NFC.12,(6-NFC.13),NFC.14,(6-NFC.15),(6-NFC.16),NFC.17,(6-NFC.18),(6-NFC.19))
alpha(NFCframe)

FIframe <- data.frame(FI.01,FI.02,FI.03,FI.04,FI.05,FI.06,FI.07,FI.08,FI.09,FI.10,FI.11,FI.12)
alpha(FIframe)

IRIframe <- data.frame(IRI_01,(6-IRI_02),IRI_03,(6-IRI_04),(6-IRI_05),IRI_06,IRI_07)
alpha(IRIframe)

SRPframe <- data.frame(SRP_01,SRP_02,SRP_03,SRP_04,SRP_05,SRP_06,SRP_07,SRP_08,SRP_09,SRP_10,SRP_11,SRP_12,SRP_13,SRP_14,SRP_15,SRP_16,SRP_17,SRP_18,SRP_19,SRP_20,SRP_21,SRP_22,(8-SRP_23),(8-SRP_24),(8-SRP_25),(8-SRP_26),SRP_27,(8-SRP_28),SRP_29,SRP_30)
alpha(SRPframe)

TASframe <- data.frame(TAS_01,TAS_02,TAS_03,TAS_04,TAS_05,TAS_06,TAS_07,TAS_08,(6-TAS_09),TAS_10,TAS_11,TAS_12,(6-TAS_13),TAS_14,(6-TAS_15),TAS_16,TAS_17,(6-TAS_18),(6-TAS_19),TAS_20)
alpha(TASframe)

#Correlation between different individual traits measures

cor.test(CRT,NFC)
cor.test(CRT,FI)
cor.test(CRT,IRI)
cor.test(CRT,SRP)
cor.test(CRT,TAS)
cor.test(NFC,FI)
cor.test(NFC,IRI)
cor.test(NFC,SRP)
cor.test(NFC,TAS)
cor.test(FI,IRI)
cor.test(FI,SRP)
cor.test(FI,TAS)
cor.test(IRI,SRP)
cor.test(IRI,TAS)
cor.test(SRP,TAS)

#Correlations between individual traits and utilitarian scores

cor.test(CRT,UD_ut)
cor.test(CRT,PD_ut)
cor.test(CRT,HC_ut)
cor.test(CRT,AO_ut)
cor.test(CRT,DE_ut)
cor.test(CRT,P_ut)
cor.test(NFC,UD_ut)
cor.test(NFC,PD_ut)
cor.test(NFC,HC_ut)
cor.test(NFC,AO_ut)
cor.test(NFC,DE_ut)
cor.test(NFC,P_ut)
cor.test(FI,UD_ut)
cor.test(FI,PD_ut)
cor.test(FI,HC_ut)
cor.test(FI,AO_ut)
cor.test(FI,DE_ut)
cor.test(FI,P_ut)
cor.test(IRI,UD_ut)
cor.test(IRI,PD_ut)
cor.test(IRI,HC_ut)
cor.test(IRI,AO_ut)
cor.test(IRI,DE_ut)
cor.test(IRI,P_ut)
cor.test(SRP,PD_ut)
cor.test(SRP,HC_ut)
cor.test(SRP,AO_ut)
cor.test(SRP,DE_ut)
cor.test(SRP,P_ut)
cor.test(TAS,UD_ut)
cor.test(TAS,PD_ut)
cor.test(TAS,HC_ut)
cor.test(TAS,AO_ut)
cor.test(TAS,DE_ut)
cor.test(TAS,P_ut)

#Calculating a global utilitarian score

util <- (UD_ut+PD_ut+HC_ut+DE_ut+AO_ut+P_ut)/6
mean(util)
sd(util)

#Correlations between global utilitarian score and individual traits measures

cor.test(util,CRT)
cor.test(util,NFC)
cor.test(util,FI)
cor.test(util,IRI)
cor.test(util,SRP)
cor.test(util,TAS)
cor.test(util,TAS)
cor.test(util,CRT)
cor.test(util,NFC)

#Calculating average utilitarian score (and standard deviation) for each individual scenario

8-mean(U.D.01)
sd(U.D.01)
8-mean(U.D.02)
sd(U.D.02)
8-mean(U.D.03)
sd(U.D.03)
8-mean(U.D.04)
sd(U.D.04)
8-mean(U.D.05)
sd(U.D.05)
8-mean(U.D.06)
sd(U.D.06)
8-mean(U.D.07)
sd(U.D.07)
8-mean(U.D.08)
sd(U.D.08)
8-mean(U.D.09)
sd(U.D.09)
8-mean(U.D.10)
sd(U.D.10)
8-mean(P.D.01)
sd(P.D.01)
8-mean(P.D.02)
sd(P.D.02)
8-mean(P.D.03)
sd(P.D.03)
8-mean(P.D.04)
sd(P.D.04)
8-mean(P.D.05)
sd(P.D.05)
8-mean(P.D.06)
sd(P.D.06)
8-mean(P.D.07)
sd(P.D.07)
8-mean(P.D.08)
sd(P.D.08)
8-mean(P.D.09)
sd(P.D.09)
8-mean(P.D.10)
sd(P.D.10)
8-mean(H.C.01)
sd(H.C.01)
8-mean(H.C.02)
sd(H.C.02)
8-mean(H.C.03)
sd(H.C.03)
8-mean(H.C.04)
sd(H.C.04)
8-mean(H.C.05)
sd(H.C.05)
8-mean(H.C.06)
sd(H.C.06)
8-mean(H.C.07)
sd(H.C.07)
8-mean(H.C.08)
sd(H.C.08)
8-mean(H.C.09)
sd(H.C.09)
8-mean(H.C.10)
sd(H.C.10)
8-mean(A.O.01)
sd(A.O.01)
8-mean(A.O.02)
sd(A.O.02)
8-mean(A.O.03)
sd(A.O.03)
8-mean(A.O.04)
sd(A.O.04)
8-mean(A.O.05)
sd(A.O.05)
8-mean(A.O.06)
sd(A.O.06)
8-mean(A.O.07)
sd(A.O.07)
8-mean(A.O.08)
sd(A.O.08)
8-mean(A.O.09)
sd(A.O.09)
8-mean(A.O.10)
sd(A.O.10)
8-mean(P.01)
sd(P.01)
8-mean(P.02)
sd(P.02)
8-mean(P.03)
sd(P.03)
8-mean(P.04)
sd(P.04)
8-mean(P.05)
sd(P.05)
8-mean(P.06)
sd(P.06)
8-mean(P.07)
sd(P.07)
8-mean(P.08)
sd(P.08)
8-mean(P.09)
sd(P.09)
8-mean(P.10)
sd(P.10)
mean(D.E.01)
sd(D.E.01)
mean(D.E.02)
sd(D.E.02)
mean(D.E.03)
sd(D.E.03)
mean(D.E.04)
sd(D.E.04)
mean(D.E.05)
sd(D.E.05)
mean(D.E.06)
sd(D.E.06)
mean(D.E.07)
sd(D.E.07)
mean(D.E.08)
sd(D.E.08)
mean(D.E.09)
sd(D.E.09)
mean(D.E.10)
sd(D.E.10)

#Cluster analysis

library(pvclust)

mydata <- data.frame(UD_ut,PD_ut,HC_ut,AO_ut,DE_ut,P_ut)
fit <- pvclust(mydata, method.hclust="ward", method.dist="euclidean")
plot(fit)

pvrect(fit, alpha=.95)

#Correlation plot

> pairs.panels(mydata, 
+              method = "pearson", # correlation method
+              hist.col = "#00AFBB",
+              density = FALSE,  # show density plots
+              ellipses = FALSE, # show correlation ellipses
+              lm = TRUE,
+              stars = TRUE)