WJS International, Jan F. Hovden
1 Install required packages and set working directory
2 Import WJS consolidated data file from SPSS
2.1 Filter additional countries and make subsamples if needed
2.2 Functions
3 ROLE ORIENTATION (C12)
## Subsetting dataset and renaming variables
BAT<-dplyr::select(wjs,COUNTRY,C12A:C12U)
# C12C<-C12C %>% filter(complete.cases(.)) # note filter on only complete C12A-C12U cases
BAT<-plyr::rename(BAT, c(C12A="Detached.observer", C12B="Report.reality", C12C="Analyse.current.affairs", C12D="Monitor.political.leaders", C12E="Monitor.business", C12F="Set.political.agenda", C12G="Influence.public.opinion", C12H="Advocate.for.social.change", C12J="Adversary.government", C12K="Sup.national.development", C12L="Pos.image.politicians", C12M="Sup.gov.policy", C12O="Entertainment", C12P="Large.audience", C12R="Advice.for.daily.life", C12S="Info.people.pol.decisions",
C12T="Motivate.pol.particip.",C12U="Let.people.express.themselves"))
# C12W="Educate.audience", C12X="Tell.stories", C12Z="Promote.tolerance"
## Construct a matrix of averages and print means and sd
## make data numeric
BATm <- apply(dplyr::select(BAT, Detached.observer:Let.people.express.themselves), 2, function(x) {x <- recode(x,"'unimportant'=1; 'little important'=2; 'somewhat important'=3; 'very important'=4; 'extremely important'=5"); x})
### re-add country and add labels
BATm<-data.frame(BATm)
BATm$COUNTRY<-BAT$COUNTRY
##### BELOW MANY COUNTRIES DISAPPEARS -WHY?
# BATmFILT<-BATm[rowSums(is.na(BATm))<(length(BATm)-21),] #drops rows with all missings
BATm<-BATm %>% filter(complete.cases(.)) # note filter on only complete C12A-C12U cases
BATmORG<-BATm # make a copy of the dataset (orginal)
### make table of mean and sd by question and country
table.meansdC12<-tabular( (droplevels(BATmORG)$COUNTRY + 1) ~ (n=1) + Format(digits=2)*(Detached.observer+ Report.reality+ Analyse.current.affairs+Monitor.political.leaders+Monitor.business+ Set.political.agenda+ Influence.public.opinion+ Advocate.for.social.change+ Adversary.government+ Sup.national.development+ Pos.image.politicians+ Sup.gov.policy+ Entertainment+ Large.audience+ Advice.for.daily.life+ Info.people.pol.decisions+ Motivate.pol.particip.+ Let.people.express.themselves)*(Mean + sd), data=BATmORG)
#note function Mean=mean ignoring missing variables
### convert to matrix of averages by country and add rownames and labels
BATm<-BATm %>%
group_by(COUNTRY) %>%
summarise_each(funs(Mean), Detached.observer:Let.people.express.themselves)
BATm<-data.frame(BATm)
rownames(BATm)<-BATm[,1]
BATm<-BATm[,2:19]3.1 Various descriptive statistics and figures C12
# sjp.setTheme(geom.outline.size = 0, geom.label.size = 3, title.size = 1.5)
sjp.likert(BAT[,2:19], sort.frq = "pos.asc", show.prc.sign = TRUE, digits=0, show.n=FALSE,
title="How important are these things in your work? (C12, all countries)") # lickert plot## Warning: Detected uneven category count in items. Dropping last category.
# Average mean and spread of roles (based on country averages), boxplot and beanplot
sortedBATm<-BATm[ , order(colMeans(BATm))] # sort by column mean
par(mar = c(4,10,4,4)) # set margins
boxplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C12)")
par(mar = c(4,10,4,4)) # set margins
beanplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C12)")
##Bertin plot (centered)
cBATm<-t(BATm)
cBATm<-scale(cBATm, scale=FALSE) #centering of means
cBATm<-t(cBATm)
cBATmC12<-cBATm # make a copy
x <- scale_by_rank(as.matrix(scale(cBATm, scale=FALSE)))
order <- seriate(x, method="PCA")
bertinplot(x, order, options = list(panel=panel.squares, shading=TRUE, reverse=TRUE, mar = c(1,1,5,5)))
heatmap(cBATm, margins=c(5,5)) # heatmap (centered)
# network plot of correlations
c<-correlate(BATm)
#network_plot(c, min_cor=.2, colors=c("red", "green")) # not working for now
x<-rearrange(c, absolute = FALSE)
x<-shave(x)
rplot(x, print_cor=TRUE, legend=TRUE)
#plotluck(BAT, COUNTRY~Report.reality, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Monitor.political.leaders, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Advocate.for.social.change, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Sup.national.development, opts=plotluck.options(max.factor.levels=67))
# some nice HTML tables
sjt.frq(BATm)
sjt.corr(BATm, triangle="lower", digits=2)3.2 PCA and biplot, mean centered
x<-t(cBATm)
x<-scale(x, scale=FALSE)
x<-t(x)
paran(x)##
## Using eigendecomposition of correlation matrix.
## Computing: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
##
##
## Results of Horn's Parallel Analysis for component retention
## 540 iterations, using the mean estimate
##
## --------------------------------------------------
## Component Adjusted Unadjusted Estimated
## Eigenvalue Eigenvalue Bias
## --------------------------------------------------
## 1 5.066374 6.133136 1.066761
## 2 2.717756 3.550341 0.832585
## 3 1.431418 2.091970 0.660552
## --------------------------------------------------
##
## Adjusted eigenvalues > 1 indicate dimensions to retain.
## (3 components retained)res.pca<-PCA(x , scale.unit=FALSE, ncp=3, graph = TRUE)
fviz_pca_biplot(res.pca, geom = "text", title="Roles(C12)", axes =c(2,1), labelsize=2.5, repel=FALSE) + theme_minimal()
fviz_pca_biplot(res.pca, geom = "text", title="Roles(C12)", axes =c(3,1), labelsize=2.5, repel=FALSE) + theme_minimal()
fviz_screeplot(res.pca, ncp=10)
fviz_contrib(res.pca, choice = "var", axes = 1)
fviz_contrib(res.pca, choice = "var", axes = 2)
fviz_contrib(res.pca, choice = "var", axes = 3)
#fviz_contrib(res.pca, choice = "var", axes = 4)
indroles <- get_pca_ind(res.pca)
C12ind<-indroles$coord4 C13 ETHICS
## Subsetting dataset and renaming variables
BAT<-dplyr::select(wjs,COUNTRY,C13A:C14K)
BAT<-BAT %>% filter(complete.cases(.)) # note filter on only complete cases
BAT<-plyr::rename(BAT, c(C13A="Always.follow.codes", C13B="Depends.on.situation", C13C="Matter.of.personal.judgement", C13D="Can.be.set.aside",C14A="Paying.for.info", C14B="Using.conf.documents", C14C="False.identity", C14D="Pressure.informants",
C14E="Use.personal.doc", C14F="Undercover.empl", C14G="Hidden.miccam", C14H="Recreations", C14J="Publ.unverified", C14K="Accept.money"))#C13
BATm <- apply(dplyr::select(BAT, Always.follow.codes:Can.be.set.aside), 2, function(x) {x <- recode(x,"'strongly disagree'=1; 'somewhat disagree'=2; 'undecided'=3; 'somewhat agree'=4; 'strongly agree'=5"); x})
### re-add country and add labels
BATm<-data.frame(BATm)
BATm$COUNTRY<-BAT$COUNTRY
##### BELOW MANY COUNTRIES DISAPPEARS -WHY?
# BATmFILT<-BATm[rowSums(is.na(BATm))<(length(BATm)-21),] #drops rows with all missings
BATm<-BATm %>% filter(complete.cases(.)) # note filter on only complete C12A-C12U cases
BATmORG<-BATm # make a copy of the dataset (orginal)
### make table of mean and sd by question and country
table.meansdC13<-tabular( (droplevels(BATmORG)$COUNTRY + 1) ~ (n=1) + Format(digits=2)*(Always.follow.codes+Depends.on.situation+Matter.of.personal.judgement+Can.be.set.aside)*(Mean + sd), data=BATmORG)
#note function Mean=mean ignoring missing variables
options(max.print=5.5E5)
#print(table.meansd) # note: droplevels drop unused factor levels from COUNTRY
### convert to matrix of averages by country and add rownames and labels
BATm<-BATm %>%
group_by(COUNTRY) %>%
summarise_each(funs(Mean), Always.follow.codes:Can.be.set.aside)
BATm<-data.frame(BATm)
rownames(BATm)<-BATm[,1]
BATm<-BATm[,2:5]4.1 Various descriptive statistics and figures C13
# sjp.setTheme(geom.outline.size = 0, geom.label.size = 3, title.size = 1.5)
sjp.likert(BAT[,2:5], sort.frq = "pos.asc", show.prc.sign = TRUE, digits=0, show.n=FALSE,
title="Agreement? (C13, all countries)") # lickert plot## Warning: Detected uneven category count in items. Dropping last category.
# Average mean and spread of roles (based on country averages), boxplot and beanplot
sortedBATm<-BATm[ , order(colMeans(BATm))] # sort by column mean
par(mar = c(4,10,4,4)) # set margins
boxplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C13)")
par(mar = c(4,10,4,4)) # set margins
beanplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C13)")## log="y" selected
##Bertin plot (centered)
cBATm<-t(BATm)
cBATm<-scale(cBATm, scale=FALSE) #centering of means
cBATm<-t(cBATm)
cBATmC13<-cBATm
x <- scale_by_rank(as.matrix(scale(cBATm, scale=FALSE)))
order <- seriate(x, method="PCA")
bertinplot(x, order, options = list(panel=panel.squares, shading=TRUE, reverse=TRUE, mar = c(1,1,5,5)))
heatmap(cBATm, margins=c(5,5)) # heatmap (centered)
# network plot of correlations
c<-correlate(BATm)
#network_plot(c, min_cor=.2, colors=c("red", "green")) # not working for now
x<-rearrange(c, absolute = FALSE)
x<-shave(x)
rplot(x, print_cor=TRUE, legend=TRUE)
#plotluck(BAT, COUNTRY~Always.follow.codes, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Depends.on.situation, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Matter.of.personal.judgement, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Can.be.set.aside, opts=plotluck.options(max.factor.levels=67))
# some nice HTML tables
sjt.frq(BATm)
sjt.corr(BATm, triangle="lower", digits=2)4.2 PCA and biplot, mean centered
x<-t(cBATm)
x<-scale(x, scale=FALSE)
x<-t(x)
paran(x)##
## Using eigendecomposition of correlation matrix.
## Computing: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
##
##
## Results of Horn's Parallel Analysis for component retention
## 120 iterations, using the mean estimate
##
## --------------------------------------------------
## Component Adjusted Unadjusted Estimated
## Eigenvalue Eigenvalue Bias
## --------------------------------------------------
## 1 1.692088 1.983221 0.291132
## 2 1.121927 1.207337 0.085410
## --------------------------------------------------
##
## Adjusted eigenvalues > 1 indicate dimensions to retain.
## (2 components retained)res.pca<-PCA(x , scale.unit=FALSE, ncp=2, graph = TRUE)
fviz_pca_biplot(res.pca, geom = "text", title="Ethics(C13)", axes =c(2,1), labelsize=2.5, repel=FALSE) + theme_minimal()
fviz_screeplot(res.pca, ncp=10)
fviz_contrib(res.pca, choice = "var", axes = 1)
fviz_contrib(res.pca, choice = "var", axes = 2)
#fviz_contrib(res.pca, choice = "var", axes = 4)
indroles <- get_pca_ind(res.pca)
C13ind<-indroles$coord5 C14 ETHICS (PRACTICES)
#C14
BATm <- apply(dplyr::select(BAT, Paying.for.info:Accept.money), 2, function(x) {x <- recode(x,"'not approve under any circumstances'=1; 'justified on occasion'=2; 'always justified'=3"); x})
### re-add country and add labels
BATm<-data.frame(BATm)
BATm$COUNTRY<-BAT$COUNTRY
##### BELOW MANY COUNTRIES DISAPPEARS -WHY?
# BATmFILT<-BATm[rowSums(is.na(BATm))<(length(BATm)-21),] #drops rows with all missings
BATm<-BATm %>% filter(complete.cases(.)) # note filter on only complete C12A-C12U cases
BATmORG<-BATm # make a copy of the dataset (orginal)
### make table of mean and sd by question and country
table.meansdC14<-tabular( (droplevels(BATmORG)$COUNTRY + 1) ~ (n=1) + Format(digits=2)*(Paying.for.info+Using.conf.documents+False.identity+Pressure.informants+Use.personal.doc+Undercover.empl+Hidden.miccam+Recreations+Publ.unverified+Accept.money)*(Mean + sd), data=BATmORG)
#note function Mean=mean ignoring missing variables
options(max.print=5.5E5)
#print(table.meansd) # note: droplevels drop unused factor levels from COUNTRY
### convert to matrix of averages by country and add rownames and labels
BATm<-BATm %>%
group_by(COUNTRY) %>%
summarise_each(funs(Mean), Paying.for.info:Accept.money)
BATm<-data.frame(BATm)
rownames(BATm)<-BATm[,1]
BATm<-BATm[,2:5]5.1 Various descriptive statistics and figures C14
# sjp.setTheme(geom.outline.size = 0, geom.label.size = 3, title.size = 1.5)
sjp.likert(BAT[,6:15], sort.frq = "pos.asc", show.prc.sign = TRUE, digits=0, show.n=FALSE,
title="Agreement? (C14, all countries)") # lickert plot## Warning: Detected uneven category count in items. Dropping last category.
# Average mean and spread of roles (based on country averages), boxplot and beanplot
sortedBATm<-BATm[ , order(colMeans(BATm))] # sort by column mean
par(mar = c(4,10,4,4)) # set margins
boxplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C14)")
par(mar = c(4,10,4,4)) # set margins
beanplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C14)")## log="y" selected
##Bertin plot (centered)
cBATm<-t(BATm)
cBATm<-scale(cBATm, scale=FALSE) #centering of means
cBATm<-t(cBATm)
cBATmC14<-cBATm
x <- scale_by_rank(as.matrix(scale(cBATm, scale=FALSE)))
order <- seriate(x, method="PCA")
bertinplot(x, order, options = list(panel=panel.squares, shading=TRUE, reverse=TRUE, mar = c(1,1,5,5)))
heatmap(cBATm, margins=c(5,5)) # heatmap (centered)
# network plot of correlations
c<-correlate(BATm)
# network_plot(c, min_cor=.2, colors=c("red", "green")) # not working for now
x<-rearrange(c, absolute = FALSE)
x<-shave(x)
rplot(x, print_cor=TRUE, legend=TRUE)
#plotluck(BAT, COUNTRY~Always.follow.codes, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Depends.on.situation, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Matter.of.personal.judgement, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Can.be.set.aside, opts=plotluck.options(max.factor.levels=67))
# some nice HTML tables
sjt.frq(BATm)
sjt.corr(BATm, triangle="lower", digits=2)5.2 PCA and biplot, mean centered C14
x<-t(cBATm)
x<-scale(x, scale=FALSE)
x<-t(x)
paran(x)##
## Using eigendecomposition of correlation matrix.
## Computing: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
##
##
## Results of Horn's Parallel Analysis for component retention
## 120 iterations, using the mean estimate
##
## --------------------------------------------------
## Component Adjusted Unadjusted Estimated
## Eigenvalue Eigenvalue Bias
## --------------------------------------------------
## 1 1.646286 1.948396 0.302109
## 2 1.093789 1.165735 0.071946
## --------------------------------------------------
##
## Adjusted eigenvalues > 1 indicate dimensions to retain.
## (2 components retained)res.pca<-PCA(x , scale.unit=FALSE, ncp=2, graph = TRUE)
fviz_pca_biplot(res.pca, geom = "text", title="Ethics(C14)", axes =c(2,1), labelsize=2.5, repel=FALSE) + theme_minimal()
fviz_screeplot(res.pca, ncp=10)
fviz_contrib(res.pca, choice = "var", axes = 1)
fviz_contrib(res.pca, choice = "var", axes = 2)
#fviz_contrib(res.pca, choice = "var", axes = 4)
indroles <- get_pca_ind(res.pca)
C14ind<-indroles$coord6 C15 + C16
## Subsetting dataset and renaming variables C15 + C16
BAT<-dplyr::select(wjs,COUNTRY,C15A:C16N)
BAT<-plyr::rename(BAT, c(C15A="Personal.values", C15B="Peers.on.staff", C15C="Editorial.supervisors", C15D="Managers.of.news.org", C15E="Owners", C15F="Editorial.policy", C15G="Advertising.considerations", C15H="Profit.expectations", C15J="Audience.research", C15K="Availability.of.resources", C15L="Time.limits", C15M="Journalism.ethics", C16A="Friends.peers.family", C16B="Colleagues.other.media", C16C="Audience.feedback", C16D="Competing.newsorg", C16E="Media.laws.and.regul", C16F="Information.acceess", C16G="Censorship", C16H="Government.officials", C16J="Politicians", C16K="Pressure.groups", C16L="Business.people", C16M="Public.relations", C16N="Relationships.sources"))
BATm <- apply(dplyr::select(BAT, Personal.values:Relationships.sources), 2, function(x) {x <- recode(x,"'not influential'=1; 'little influential'=2; 'somewhat influential'=3; 'very influential'=4; 'extremely influential'=5"); x})
BATm<-subset(BATm, select = -c(C15N) ) # Drop optional variable
### re-add country and add labels
BATm<-data.frame(BATm)
BATm$COUNTRY<-BAT$COUNTRY
BATm<-BATm %>% filter(complete.cases(.)) # note filter on only complete cases
BATmORG<-BATm # make a copy of the dataset (orginal)
### make table of mean and sd by question and country
table.meansdC15C16<-tabular( (droplevels(BATmORG)$COUNTRY + 1) ~ (n=1) + Format(digits=2)*(Personal.values+ Peers.on.staff+ Editorial.supervisors+ Managers.of.news.org+ Owners+ Editorial.policy+ Advertising.considerations+ Profit.expectations+ Audience.research+ Availability.of.resources+ Time.limits+ Journalism.ethics+Friends.peers.family+ Colleagues.other.media+ Audience.feedback+ Competing.newsorg+ Media.laws.and.regul+ Information.acceess+ Censorship+ Government.officials+ Politicians+ Pressure.groups+ Business.people+ Public.relations+ Relationships.sources)*(Mean + sd), data=BATmORG)
### convert to matrix of averages by country and add rownames and labels
BATm<-BATm %>%
group_by(COUNTRY) %>%
summarise_each(funs(Mean), Personal.values:Relationships.sources)
BATm<-data.frame(BATm)
rownames(BATm)<-BATm[,1]
BATm<-BATm[,2:26]6.1 Various descriptive statistics and figures C15+C16
# sjp.setTheme(geom.outline.size = 0, geom.label.size = 3, title.size = 1.5)
###WRONG 1:11?
sjp.likert(BAT[,1:27], sort.frq = "pos.asc", show.prc.sign = TRUE, digits=0, show.n=FALSE,
title="Agreement? (C15, all countries)") # lickert plot## Warning: Length of labels for item categories `legend.labels` differs from
## detected amount of categories. Use `catcount` argument to define amount of
## item categories, if plotting does not work.## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette BrBG is 11
## Returning the palette you asked for with that many colors
# Average mean and spread of roles (based on country averages), boxplot and beanplot
sortedBATm<-BATm[ , order(colMeans(BATm))] # sort by column mean
par(mar = c(4,10,4,4)) # set margins
boxplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C15+C16)")
par(mar = c(4,10,4,4)) # set margins
beanplot(x = as.list(sortedBATm), horizontal = TRUE, las=1, main="Averages aggregated by country (C15+C16)")
##Bertin plot (centered)
cBATm<-t(BATm)
cBATm<-scale(cBATm, scale=FALSE) #centering of means
cBATm<-t(cBATm)
cBATmC14<-cBATm
x <- scale_by_rank(as.matrix(scale(cBATm, scale=FALSE)))
order <- seriate(x, method="PCA")
bertinplot(x, order, options = list(panel=panel.squares, shading=TRUE, reverse=TRUE, mar = c(1,1,5,5)))
heatmap(cBATm, margins=c(5,5)) # heatmap (centered)
# network plot of correlations
c<-correlate(BATm)
# network_plot(c, min_cor=.2, colors=c("red", "green")) # not working for now
x<-rearrange(c, absolute = FALSE)
x<-shave(x)
rplot(x, print_cor=TRUE, legend=TRUE)
#plotluck(BAT, COUNTRY~Always.follow.codes, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Depends.on.situation, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Matter.of.personal.judgement, opts=plotluck.options(max.factor.levels=67))
#plotluck(BAT, COUNTRY~Can.be.set.aside, opts=plotluck.options(max.factor.levels=67))
# some nice HTML tables
sjt.frq(BATm)
sjt.corr(BATm, triangle="lower", digits=2)6.2 PCA and biplot, mean centered C15+C16
x<-t(cBATm)
x<-scale(x, scale=FALSE)
x<-t(x)
paran(x)##
## Using eigendecomposition of correlation matrix.
## Computing: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
##
##
## Results of Horn's Parallel Analysis for component retention
## 750 iterations, using the mean estimate
##
## --------------------------------------------------
## Component Adjusted Unadjusted Estimated
## Eigenvalue Eigenvalue Bias
## --------------------------------------------------
## 1 7.705335 9.070829 1.365493
## 2 2.199633 3.316436 1.116803
## 3 1.414948 2.349109 0.934160
## 4 1.345005 2.130371 0.785365
## --------------------------------------------------
##
## Adjusted eigenvalues > 1 indicate dimensions to retain.
## (4 components retained)res.pca<-PCA(x , scale.unit=FALSE, ncp=4, graph = TRUE)
fviz_pca_biplot(res.pca, geom = "text", title="Ethics(C15)", axes =c(2,1), labelsize=2.5, repel=FALSE) + theme_minimal()
fviz_pca_biplot(res.pca, geom = "text", title="Ethics(C15)", axes =c(4,3), labelsize=2.5, repel=FALSE) + theme_minimal()
fviz_screeplot(res.pca, ncp=10)
fviz_contrib(res.pca, choice = "var", axes = 1)
fviz_contrib(res.pca, choice = "var", axes = 2)
fviz_contrib(res.pca, choice = "var", axes = 3)
fviz_contrib(res.pca, choice = "var", axes = 4)
indroles <- get_pca_ind(res.pca)
C15C16ind<-indroles$coord7 TABLES OF MEANS AND SD (RAW)
#note function Mean=mean ignoring missing variables
options(max.print=5.5E5)
##C12 ROLES
print(table.meansdC12) ##
## Detached.observer Report.reality
## droplevels(BATmORG)$COUNTRY n Mean sd Mean
## Albania 272 4.33 0.78 4.58
## Argentina 316 3.44 1.14 4.46
## Australia 565 4.07 0.99 4.62
## Austria 689 4.41 0.83 4.64
## Bangladesh 193 3.31 1.37 4.07
## Belgium 486 4.38 0.81 4.63
## Bhutan 83 3.90 1.16 4.10
## Botswana 142 3.81 1.33 4.81
## Brazil 375 4.12 1.02 4.57
## Canada 251 4.18 0.95 4.84
## Chile 382 3.45 1.34 4.54
## China 570 3.69 0.93 4.09
## Colombia 504 3.18 1.34 4.65
## Croatia 475 4.36 0.94 4.73
## Cyprus 167 4.26 0.96 4.84
## Czech Republic 248 4.52 0.79 4.92
## Denmark 1236 3.79 1.09 4.52
## Ecuador 299 3.16 1.40 4.44
## Egypt 396 4.65 0.67 4.36
## El Salvador 214 3.34 1.31 4.59
## Estonia 259 4.42 0.70 4.70
## Ethiopia 288 3.52 1.33 3.76
## Finland 325 4.51 0.74 4.53
## France\\_NON-REP 161 4.20 0.96 4.73
## Germany 695 4.25 0.97 4.59
## Greece 330 3.92 1.12 4.74
## Hong Kong 403 3.53 0.82 3.99
## Hungary 311 4.37 0.75 4.47
## Iceland 122 4.02 0.94 4.70
## India 360 4.08 1.18 4.46
## Indonesia 557 3.70 1.03 4.49
## Ireland 260 4.06 0.95 4.64
## Israel 288 3.34 1.39 4.67
## Italy 366 4.45 0.83 4.58
## Japan 711 3.36 0.93 3.80
## Kenya 267 3.57 1.19 4.48
## Kosovo 156 4.71 0.57 4.85
## Latvia 220 4.60 0.61 4.75
## Malawi 120 3.92 1.29 4.76
## Malaysia 314 3.90 0.87 4.11
## Mexico 354 3.47 1.30 4.70
## Moldova 194 4.31 0.84 4.79
## Netherlands 450 3.74 0.98 4.50
## New Zealand 421 3.93 0.99 4.59
## Norway 398 3.62 1.03 4.21
## Oman 257 4.23 0.83 3.75
## Philippines 333 3.88 0.86 4.49
## Qatar 138 4.08 1.07 3.99
## Romania 304 4.45 0.83 4.79
## Russia 389 3.80 0.94 4.22
## Serbia 347 3.76 1.27 4.78
## Sierra Leone 137 3.92 1.14 4.61
## Singapore\\_NON-REP 77 3.13 1.36 3.22
## South Africa 294 3.73 1.25 4.59
## South Korea 347 3.80 0.76 4.55
## Spain 364 3.91 1.01 4.79
## Sudan 235 4.75 0.57 4.58
## Sweden 465 4.39 0.77 4.61
## Switzerland 876 4.25 0.84 4.52
## Thailand 325 4.30 0.83 4.36
## Turkey\\_NON-REP 81 4.65 0.79 4.78
## UAE 113 3.42 1.33 3.92
## UK 595 4.11 0.89 4.58
## USA 355 4.07 0.92 4.82
## All 22225 3.97 1.07 4.50
##
## Analyse.current.affairs Monitor.political.leaders
## sd Mean sd Mean sd
## 0.56 3.93 0.72 3.01 1.17
## 0.77 4.09 0.86 3.96 0.93
## 0.65 3.48 1.26 3.57 1.38
## 0.61 4.43 0.81 3.16 1.38
## 1.09 4.10 0.88 3.81 1.01
## 0.59 3.83 0.95 3.30 1.28
## 1.20 4.02 1.12 3.89 0.95
## 0.48 4.56 0.74 4.15 1.06
## 0.75 3.85 0.99 3.56 1.11
## 0.43 4.09 0.91 3.82 1.32
## 0.86 4.46 0.91 3.89 1.24
## 0.75 3.84 0.85 3.13 1.11
## 0.75 4.54 0.78 4.20 1.05
## 0.58 4.47 0.72 4.50 0.81
## 0.44 4.68 0.55 3.71 1.32
## 0.36 3.85 0.95 3.49 1.22
## 0.69 3.98 0.89 4.19 0.88
## 0.93 4.31 0.84 3.96 1.08
## 0.80 4.17 0.86 4.05 1.00
## 0.74 3.78 1.23 4.28 0.94
## 0.59 4.04 0.91 3.38 1.24
## 1.30 4.21 0.90 3.27 1.33
## 0.74 4.25 0.81 3.66 1.33
## 0.54 4.07 0.87 3.63 0.95
## 0.74 4.30 0.96 2.74 1.46
## 0.54 4.17 0.95 3.82 1.23
## 0.75 3.82 0.87 4.08 0.89
## 0.74 4.07 0.99 3.07 1.45
## 0.66 4.47 0.84 3.10 1.24
## 0.77 4.31 0.77 3.79 1.12
## 0.70 3.93 0.88 3.60 0.91
## 0.65 3.70 1.11 3.64 1.25
## 0.70 4.14 1.13 3.67 1.43
## 0.73 4.13 0.90 3.24 1.23
## 0.94 4.23 0.73 4.48 0.68
## 0.75 4.11 1.02 3.58 1.23
## 0.39 4.58 0.60 3.73 1.16
## 0.50 4.11 0.89 3.37 1.34
## 0.65 4.53 0.69 4.19 0.98
## 0.82 3.91 0.82 3.54 0.89
## 0.63 4.37 0.88 4.34 0.90
## 0.45 4.20 0.91 3.54 1.33
## 0.70 3.80 1.05 2.71 1.30
## 0.62 3.79 1.04 3.69 1.30
## 0.92 3.30 1.16 2.95 1.30
## 1.22 3.90 1.04 3.30 0.88
## 0.60 4.14 0.85 4.18 0.88
## 1.04 3.85 1.06 3.41 1.34
## 0.54 4.23 1.00 3.44 1.34
## 0.82 3.82 1.01 2.97 1.09
## 0.56 4.51 0.80 3.93 1.20
## 0.68 4.45 0.72 4.22 1.03
## 1.69 3.06 1.35 3.30 1.31
## 0.76 3.93 1.23 3.61 1.39
## 0.67 4.32 0.69 4.35 0.77
## 0.55 4.38 0.77 4.15 1.05
## 0.77 4.58 0.77 4.46 0.91
## 0.56 4.03 0.94 4.34 0.93
## 0.64 4.20 0.81 3.22 1.20
## 0.68 4.23 0.68 4.05 0.83
## 0.59 4.27 0.87 4.10 0.94
## 1.16 3.96 1.06 3.42 1.36
## 0.64 3.76 1.16 3.18 1.48
## 0.49 3.78 1.00 4.29 0.98
## 0.78 4.09 0.96 3.68 1.25
##
## Monitor.business Set.political.agenda Influence.public.opinion
## Mean sd Mean sd Mean
## 2.73 1.12 2.26 1.18 4.00
## 3.69 1.09 3.38 1.10 3.45
## 3.42 1.22 2.31 1.20 2.61
## 3.02 1.36 2.20 1.00 2.58
## 3.51 1.21 3.05 1.25 3.65
## 3.18 1.26 2.67 1.15 2.48
## 3.83 0.92 3.41 1.15 3.31
## 4.18 1.04 3.04 1.36 3.35
## 3.10 1.18 2.65 1.10 3.22
## 3.73 1.31 2.53 1.22 2.78
## 3.77 1.26 3.36 1.42 3.93
## 3.18 1.01 2.77 1.12 3.57
## 3.86 1.23 3.82 1.21 4.09
## 4.48 0.79 3.47 1.19 3.91
## 3.44 1.27 2.77 1.30 3.32
## 3.16 1.18 2.92 1.11 2.60
## 4.05 0.87 2.95 1.07 2.64
## 3.50 1.15 3.53 1.27 3.62
## 3.56 1.14 3.60 1.10 4.20
## 3.76 1.10 3.96 1.03 4.09
## 3.32 1.21 3.11 1.24 3.59
## 3.47 1.25 3.16 1.31 3.19
## 3.50 1.26 2.33 1.06 3.13
## 3.39 1.05 2.45 1.05 2.21
## 2.75 1.39 2.05 1.03 2.67
## 3.66 1.23 2.40 1.16 2.83
## 4.03 0.89 2.99 0.99 3.23
## 3.01 1.35 2.63 1.31 3.39
## 3.20 1.35 1.41 0.74 1.77
## 3.41 1.17 2.91 1.33 3.65
## 3.39 0.89 2.87 1.11 3.34
## 3.44 1.25 2.55 1.16 2.81
## 3.50 1.44 3.34 1.49 4.24
## 3.26 1.19 2.52 1.19 2.51
## 3.83 0.85 3.74 0.87 3.33
## 3.50 1.24 3.39 1.33 3.81
## 3.36 1.19 2.97 1.35 3.99
## 2.87 1.25 2.69 1.26 3.75
## 4.15 0.95 3.86 1.18 4.03
## 3.46 0.86 3.02 1.01 3.58
## 3.74 1.09 3.91 1.05 4.21
## 3.05 1.26 2.87 1.27 3.44
## 2.84 1.23 2.41 1.07 2.70
## 3.61 1.24 2.40 1.16 2.74
## 2.85 1.28 2.98 1.16 2.79
## 3.43 1.17 3.19 1.08 3.75
## 3.98 0.94 3.29 1.10 3.79
## 3.49 1.27 3.54 1.29 4.13
## 3.12 1.30 2.78 1.33 3.33
## 2.84 1.11 2.44 1.14 3.43
## 3.88 1.18 3.20 1.40 4.08
## 3.47 1.13 3.46 1.22 3.81
## 3.04 1.20 3.08 1.46 3.04
## 3.64 1.27 2.24 1.24 3.25
## 4.37 0.74 3.76 0.88 3.02
## 4.12 1.09 3.16 1.13 3.49
## 4.43 0.95 3.62 1.43 4.65
## 4.19 0.94 2.51 1.15 2.64
## 3.04 1.17 2.56 1.06 2.53
## 3.89 0.80 3.77 0.73 3.72
## 3.59 1.08 3.64 1.18 3.96
## 3.15 1.39 2.81 1.49 3.89
## 3.57 1.33 2.23 1.15 2.70
## 3.87 0.98 2.10 1.12 2.39
## 3.51 1.22 2.89 1.27 3.23
##
## Advocate.for.social.change Adversary.government
## sd Mean sd Mean sd
## 1.01 3.88 1.02 3.65 1.18
## 1.11 3.79 1.20 1.97 1.16
## 1.14 3.03 1.22 1.96 1.11
## 1.06 2.92 1.17 2.43 1.24
## 1.23 4.10 1.10 2.35 1.42
## 1.05 2.55 1.13 1.45 0.72
## 1.06 3.35 1.28 3.00 1.34
## 1.37 4.11 1.10 2.57 1.41
## 1.17 3.83 1.19 1.76 0.98
## 1.19 2.92 1.32 2.04 1.21
## 1.18 3.82 1.24 2.04 1.17
## 0.92 3.31 1.03 2.41 1.31
## 1.06 4.40 0.94 2.49 1.29
## 0.98 4.07 1.04 2.39 1.32
## 1.25 4.08 0.93 2.65 1.36
## 1.19 2.69 1.26 1.55 0.83
## 1.15 2.69 1.17 3.25 1.22
## 1.22 4.01 1.10 2.25 1.41
## 0.95 4.18 0.96 3.58 1.14
## 1.03 4.24 0.97 2.24 1.29
## 0.99 3.53 1.05 2.47 1.16
## 1.40 4.09 1.00 2.25 1.35
## 0.98 2.95 1.06 2.07 1.09
## 1.13 2.39 1.19 1.35 0.62
## 1.13 2.74 1.21 2.23 1.29
## 1.26 3.90 1.14 2.24 1.29
## 0.93 3.17 1.01 2.35 1.06
## 1.25 3.47 1.13 2.43 1.33
## 0.93 2.18 1.16 1.85 1.04
## 1.25 4.12 1.02 2.99 1.31
## 1.10 4.05 0.75 2.69 0.93
## 1.16 3.04 1.23 2.34 1.22
## 1.02 4.30 1.09 3.22 1.39
## 1.22 3.23 1.31 1.52 0.88
## 1.07 3.06 1.01 2.24 0.97
## 1.14 4.05 1.14 2.77 1.43
## 1.17 4.08 1.23 4.04 1.07
## 0.98 3.78 0.99 1.78 0.96
## 1.09 4.37 0.81 2.98 1.57
## 0.97 3.68 0.94 3.26 1.13
## 1.04 4.51 0.83 2.39 1.25
## 1.24 4.06 1.10 2.72 1.39
## 1.08 2.66 1.09 3.26 1.23
## 1.21 3.01 1.26 2.13 1.17
## 1.11 2.94 1.14 2.59 1.17
## 1.09 4.03 1.01 2.54 1.23
## 0.95 4.31 0.84 2.80 1.12
## 1.08 4.01 1.01 2.62 1.53
## 1.27 4.09 1.11 2.24 1.25
## 1.11 3.87 1.05 2.34 1.19
## 1.03 4.12 1.09 2.86 1.35
## 1.23 4.52 0.79 2.56 1.32
## 1.11 3.10 1.24 2.97 1.55
## 1.20 3.53 1.19 2.00 1.14
## 0.92 3.71 0.90 2.72 0.94
## 1.08 3.90 1.04 2.48 1.20
## 0.74 4.73 0.67 3.23 1.62
## 1.20 2.94 1.25 2.75 1.38
## 1.06 2.60 1.15 2.54 1.20
## 0.78 4.02 0.76 3.70 1.00
## 1.16 4.12 0.97 2.85 1.46
## 1.26 3.24 1.46 1.88 1.35
## 1.14 2.71 1.28 1.89 1.05
## 1.24 2.77 1.32 2.32 1.27
## 1.25 3.45 1.27 2.49 1.31
##
## Sup.national.development Pos.image.politicians Sup.gov.policy
## Mean sd Mean sd Mean
## 3.84 1.14 2.19 1.24 2.07
## 3.31 1.29 1.93 1.03 2.52
## 2.63 1.28 1.58 0.86 1.56
## 2.31 1.07 1.28 0.58 1.34
## 4.13 1.10 2.81 1.34 2.67
## 2.40 1.12 1.52 0.76 1.51
## 3.14 1.46 2.92 1.42 2.95
## 4.56 0.83 2.35 1.38 3.18
## 3.56 1.20 1.62 0.96 2.03
## 2.37 1.21 1.29 0.57 1.25
## 3.71 1.26 2.01 1.13 2.51
## 3.90 0.94 3.40 1.16 3.52
## 4.13 1.11 2.41 1.33 3.01
## 4.02 1.11 1.90 1.27 1.94
## 4.21 1.09 2.26 1.48 1.99
## 2.63 1.20 1.52 0.90 1.36
## 2.75 1.14 1.23 0.50 1.11
## 4.13 1.03 2.89 1.37 3.20
## 4.04 1.10 2.60 1.27 2.61
## 4.23 0.99 2.49 1.31 3.00
## 3.39 1.26 1.53 0.76 1.72
## 4.37 0.96 3.26 1.36 3.49
## 2.94 1.03 1.26 0.56 1.21
## 2.37 1.22 1.23 0.52 1.29
## 2.11 1.13 1.26 0.58 1.28
## 3.54 1.26 1.66 1.00 1.42
## 2.30 1.04 1.73 0.97 1.90
## 3.50 1.23 2.07 1.25 2.19
## 2.42 1.32 1.13 0.38 1.07
## 4.26 1.02 2.87 1.37 3.04
## 3.96 0.82 3.12 1.05 3.26
## 2.73 1.18 1.48 0.77 1.38
## 3.55 1.45 1.75 1.08 1.65
## 1.33 0.68 1.37 0.76 1.33
## 3.42 1.04 1.60 0.69 1.68
## 3.78 1.25 2.71 1.38 3.23
## 3.99 1.09 2.65 1.28 2.14
## 3.66 1.20 1.36 0.76 1.72
## 4.44 0.85 2.99 1.47 3.61
## 3.81 0.98 3.26 1.15 3.47
## 4.31 0.94 2.30 1.16 3.08
## 3.90 1.29 1.68 0.95 2.05
## 2.15 1.01 1.54 0.64 1.62
## 2.62 1.22 1.47 0.79 1.38
## 2.59 1.14 1.59 0.79 1.35
## 4.04 1.14 3.35 0.96 3.32
## 4.01 0.97 2.39 1.11 2.85
## 4.14 0.97 3.61 1.26 3.69
## 4.03 1.16 1.65 0.94 1.97
## 3.59 1.21 2.01 1.05 2.04
## 4.14 1.16 2.04 1.24 1.94
## 4.62 0.70 2.80 1.38 3.16
## 3.18 1.16 2.99 1.40 3.05
## 3.41 1.23 1.72 1.08 1.88
## 3.39 0.99 2.00 0.85 2.15
## 3.56 1.18 1.87 0.92 2.15
## 4.80 0.58 2.91 1.57 3.00
## 2.11 1.14 1.37 0.70 1.04
## 2.26 1.04 1.30 0.55 1.50
## 4.09 0.76 3.66 0.97 3.65
## 3.41 1.44 2.09 1.24 1.90
## 4.05 1.10 4.03 1.08 4.17
## 2.42 1.19 1.44 0.74 1.38
## 2.35 1.19 1.52 0.92 1.51
## 3.25 1.37 1.96 1.20 2.07
##
## Entertainment Large.audience Advice.for.daily.life
## sd Mean sd Mean sd Mean sd
## 1.07 3.29 1.15 4.12 0.88 3.51 1.05
## 1.16 2.82 1.23 3.14 1.24 2.90 1.22
## 0.76 3.50 1.18 3.69 1.13 3.03 1.16
## 0.65 3.37 1.10 3.73 1.02 3.66 1.09
## 1.26 3.16 1.18 4.07 1.10 3.38 1.19
## 0.75 2.78 1.20 2.94 1.21 2.96 1.09
## 1.32 3.29 1.33 3.66 1.46 3.82 1.22
## 1.34 3.77 1.23 4.03 1.18 3.89 1.20
## 1.04 2.69 1.25 3.35 1.22 3.09 1.20
## 0.53 2.59 1.19 2.81 1.15 2.59 1.21
## 1.31 3.23 1.40 3.53 1.34 3.40 1.30
## 1.00 3.14 1.04 3.67 0.93 3.75 0.83
## 1.29 3.89 1.09 3.87 1.19 3.58 1.29
## 1.24 2.58 1.30 3.01 1.28 3.39 1.14
## 1.12 2.18 1.24 2.81 1.26 2.80 1.61
## 0.72 2.93 1.10 3.06 1.22 2.90 1.27
## 0.37 2.29 0.92 2.20 0.91 2.91 1.00
## 1.32 2.89 1.44 3.80 1.22 3.88 1.08
## 1.30 3.11 1.24 3.84 1.13 3.89 0.96
## 1.26 3.14 1.41 3.71 1.23 3.57 1.22
## 0.87 3.23 1.24 4.24 0.90 3.54 1.12
## 1.30 3.95 1.03 4.20 1.03 4.23 1.01
## 0.46 2.98 1.09 3.35 1.00 3.22 1.02
## 0.65 2.23 1.09 2.66 1.17 2.92 1.10
## 0.59 3.53 1.09 4.02 0.96 3.82 1.06
## 0.78 2.54 1.36 2.72 1.33 3.25 1.25
## 0.97 2.50 1.02 3.16 0.95 3.28 0.87
## 1.30 3.77 1.19 3.75 1.20 3.57 1.14
## 0.25 3.24 1.14 3.18 1.08 2.70 1.17
## 1.26 3.49 1.19 3.92 1.12 3.64 1.23
## 0.77 3.60 0.80 3.96 0.71 3.49 0.89
## 0.65 3.03 1.14 3.32 1.08 2.75 1.15
## 1.04 2.89 1.44 3.60 1.32 3.61 1.28
## 0.66 2.64 1.19 3.09 1.26 2.72 1.26
## 0.71 3.02 0.93 3.27 1.00 3.26 0.98
## 1.19 3.42 1.22 3.75 1.32 3.83 1.06
## 1.13 3.28 1.31 3.71 1.19 3.77 1.14
## 0.91 2.76 1.28 3.55 1.16 3.14 1.17
## 1.23 3.91 1.12 4.35 0.91 4.08 0.89
## 1.07 3.45 1.01 3.86 0.94 3.75 0.86
## 1.19 3.31 1.22 4.09 1.14 3.72 1.19
## 1.03 3.00 1.36 3.74 1.22 2.99 1.26
## 0.73 3.31 1.12 2.85 1.16 3.89 0.96
## 0.71 3.09 1.15 3.22 1.20 2.67 1.21
## 0.56 2.84 1.18 3.05 1.08 2.71 1.12
## 1.23 3.58 1.02 3.93 1.11 3.76 0.95
## 1.06 2.94 1.09 3.50 1.06 3.36 1.06
## 1.18 3.75 1.18 4.01 1.10 3.94 1.06
## 0.96 3.33 1.26 3.82 1.18 3.24 1.24
## 1.06 2.74 1.20 3.94 1.05 3.84 1.16
## 1.12 3.08 1.31 3.50 1.29 3.67 1.18
## 1.15 3.56 1.18 3.98 1.29 4.07 0.91
## 1.18 2.99 0.97 3.17 1.24 2.92 1.09
## 0.99 3.26 1.31 3.63 1.16 3.46 1.26
## 0.85 2.17 0.88 2.90 0.97 3.12 0.95
## 1.04 3.11 1.17 2.98 1.20 3.09 1.12
## 1.53 3.80 1.30 4.55 0.80 4.53 0.84
## 0.21 2.56 0.96 2.02 0.90 3.27 0.92
## 0.71 3.14 1.04 3.37 1.02 3.22 1.02
## 0.82 3.77 0.91 3.97 0.78 3.91 0.78
## 1.10 2.69 1.31 3.63 1.36 3.02 1.26
## 1.06 3.48 1.16 4.03 1.11 3.77 1.20
## 0.66 3.35 1.28 3.28 1.16 2.55 1.29
## 0.88 2.90 1.10 3.53 1.13 2.86 1.10
## 1.23 3.09 1.23 3.43 1.23 3.35 1.18
##
## Info.people.pol.decisions Motivate.pol.particip.
## Mean sd Mean sd
## 3.32 1.28 2.13 1.12
## 3.74 1.16 3.25 1.26
## 3.59 1.27 2.44 1.19
## 3.63 1.27 3.27 1.26
## 3.72 1.13 3.12 1.33
## 3.39 1.29 2.44 1.20
## 4.11 1.06 3.73 1.17
## 4.25 1.16 3.87 1.27
## 3.70 1.19 3.19 1.30
## 3.86 1.28 2.92 1.31
## 3.74 1.37 2.94 1.37
## 3.35 1.00 3.05 1.08
## 4.07 1.15 3.71 1.27
## 4.31 0.89 4.02 1.02
## 3.24 1.48 2.77 1.13
## 3.20 1.28 2.32 1.20
## 4.43 0.77 2.94 1.12
## 3.78 1.21 3.63 1.29
## 4.27 0.91 4.21 0.93
## 4.05 1.06 3.61 1.28
## 3.24 1.25 2.92 1.19
## 4.01 1.09 3.83 1.12
## 3.56 1.27 2.54 1.15
## 3.76 1.24 3.04 1.36
## 3.29 1.45 3.04 1.42
## 3.97 1.12 3.13 1.33
## 3.44 1.02 3.00 1.07
## 3.22 1.43 2.89 1.42
## 3.09 1.49 1.97 1.14
## 3.77 1.16 3.39 1.34
## 3.82 0.80 3.61 0.96
## 3.41 1.28 2.48 1.23
## 3.69 1.39 2.76 1.47
## 3.12 1.33 2.67 1.33
## 4.28 0.81 3.48 1.09
## 3.79 1.17 3.43 1.25
## 3.47 1.32 2.87 1.40
## 3.46 1.29 3.21 1.33
## 4.28 1.00 4.07 1.09
## 3.75 0.97 3.21 1.05
## 4.37 0.87 3.73 1.20
## 3.61 1.34 3.00 1.45
## 2.80 1.22 2.60 1.08
## 3.53 1.30 2.73 1.34
## 3.08 1.28 2.75 1.26
## 3.59 0.93 3.16 1.20
## 4.16 0.91 3.84 1.08
## 3.65 1.28 3.44 1.29
## 3.42 1.37 2.77 1.38
## 3.19 1.27 3.10 1.16
## 3.75 1.30 3.05 1.31
## 4.05 1.13 3.40 1.34
## 3.25 1.27 3.06 1.22
## 3.54 1.36 2.58 1.32
## 3.69 0.96 3.56 0.96
## 3.97 1.09 3.71 1.05
## 4.50 0.90 4.17 1.21
## 4.24 0.95 2.90 1.19
## 3.78 1.16 3.23 1.15
## 3.95 0.84 3.89 0.83
## 3.89 1.14 2.85 1.30
## 3.41 1.46 3.04 1.47
## 2.79 1.46 2.18 1.25
## 4.40 0.86 3.37 1.21
## 3.72 1.23 3.13 1.30
##
## Let.people.express.themselves
## Mean sd
## 4.04 0.84
## 3.96 1.06
## 3.94 1.02
## 3.37 1.12
## 4.13 0.95
## 3.47 1.11
## 4.22 0.90
## 4.53 0.86
## 4.08 1.08
## 3.86 1.20
## 3.97 1.22
## 3.66 1.00
## 4.26 1.05
## 4.43 0.78
## 3.95 0.95
## 3.76 1.18
## 3.48 1.01
## 4.23 0.99
## 4.29 0.89
## 4.27 0.84
## 4.04 0.96
## 4.10 1.09
## 3.79 1.01
## 4.05 0.96
## 3.24 1.24
## 4.39 0.96
## 3.81 0.99
## 3.65 1.10
## 3.77 1.12
## 4.33 0.89
## 4.12 0.64
## 3.69 1.10
## 3.90 1.25
## 3.80 1.16
## 2.82 1.05
## 4.24 0.89
## 4.08 1.15
## 4.04 1.04
## 4.53 0.69
## 3.95 0.87
## 4.51 0.80
## 4.04 1.09
## 3.59 1.03
## 3.93 0.97
## 3.63 1.16
## 3.67 1.11
## 4.21 0.81
## 3.80 1.38
## 4.07 1.09
## 3.73 1.13
## 4.22 1.02
## 4.64 0.59
## 3.35 1.27
## 4.25 0.91
## 3.67 0.94
## 4.45 0.77
## 4.64 0.78
## 4.40 0.77
## 3.52 1.06
## 4.23 0.83
## 4.21 1.09
## 3.69 1.30
## 3.41 1.24
## 4.23 1.04
## 3.88 1.09##C13 ETHICS
print(table.meansdC13) ##
## Always.follow.codes
## droplevels(BATmORG)$COUNTRY n Mean sd
## Albania 282 4.44 0.83
## Argentina 323 4.40 0.96
## Australia 564 4.67 0.58
## Austria 627 4.63 0.62
## Bangladesh 226 4.66 0.65
## Belgium 511 4.39 0.70
## Bhutan 82 4.71 0.53
## Botswana 177 4.80 0.49
## Brazil 374 4.41 0.80
## Canada 254 4.48 0.83
## Chile 427 4.50 0.93
## China 585 4.50 0.72
## Colombia 539 4.51 1.00
## Croatia 403 4.70 0.68
## Cyprus 196 4.55 0.70
## Czech Republic 240 4.30 0.94
## Denmark 1305 3.96 1.12
## Ecuador 333 4.78 0.59
## Egypt 392 4.55 1.02
## El Salvador 228 4.71 0.63
## Estonia 240 4.47 0.73
## Ethiopia 287 2.99 1.51
## Finland 304 4.63 0.62
## France\\_NON-REP 170 4.46 0.79
## Germany 647 4.60 0.64
## Greece 269 4.60 0.61
## Hong Kong 310 4.31 0.76
## Hungary 297 4.41 0.78
## Iceland 113 4.34 0.83
## India 442 4.10 1.24
## Indonesia 580 4.79 0.45
## Ireland 286 4.35 0.90
## Israel 279 4.26 1.24
## Italy 352 4.62 0.71
## Kenya 283 4.69 0.67
## Kosovo 169 4.73 0.61
## Latvia 241 4.60 0.84
## Malawi 133 4.65 0.70
## Malaysia 284 4.42 0.73
## Mexico 360 4.72 0.59
## Moldova 208 4.49 0.73
## Netherlands 421 4.33 0.89
## New Zealand 397 4.64 0.67
## Norway 383 4.72 0.61
## Oman 257 4.19 1.13
## Philippines 330 4.59 0.76
## Portugal 381 4.61 0.67
## Qatar 215 4.15 1.08
## Romania 324 4.50 0.75
## Russia 390 3.95 1.10
## Serbia 354 4.69 0.76
## Sierra Leone 136 4.82 0.49
## Singapore\\_NON-REP 83 3.17 1.72
## South Africa 293 4.55 0.82
## South Korea 347 4.37 0.74
## Spain 379 4.40 0.82
## Sudan 265 4.78 0.78
## Switzerland 874 4.50 0.72
## Thailand 331 4.75 0.50
## Turkey\\_NON-REP 88 4.40 0.75
## UAE 97 4.69 0.85
## UK 545 4.60 0.68
## USA 386 4.68 0.62
## All 21598 4.47 0.87
##
## Depends.on.situation Matter.of.personal.judgement
## Mean sd Mean sd
## 3.47 1.13 2.90 1.28
## 2.72 1.48 2.50 1.48
## 2.99 1.45 2.24 1.23
## 3.07 1.25 2.31 1.07
## 3.22 1.46 2.88 1.45
## 3.29 1.24 2.29 1.10
## 4.15 0.93 3.39 1.24
## 3.21 1.60 2.27 1.47
## 2.76 1.30 2.21 1.10
## 3.19 1.39 2.14 1.13
## 2.74 1.53 2.31 1.33
## 2.82 1.17 2.30 0.94
## 2.61 1.55 2.75 1.63
## 2.37 1.47 2.41 1.48
## 2.80 1.42 2.06 1.30
## 3.42 1.25 2.61 1.24
## 3.03 1.29 2.47 1.20
## 2.89 1.56 3.04 1.53
## 2.86 1.53 3.30 1.49
## 3.14 1.58 2.75 1.52
## 3.22 1.25 2.29 1.20
## 3.33 1.43 2.69 1.49
## 2.60 1.37 2.37 1.23
## 3.10 1.44 2.52 1.23
## 3.17 1.31 2.07 1.05
## 2.59 1.33 2.23 1.25
## 3.33 0.98 3.05 0.96
## 3.19 1.39 2.44 1.32
## 2.46 1.21 2.35 1.31
## 3.62 1.34 2.98 1.45
## 2.66 1.34 2.21 1.24
## 3.30 1.37 2.66 1.32
## 3.12 1.68 2.29 1.57
## 2.55 1.47 1.89 1.24
## 3.55 1.31 2.77 1.46
## 3.18 1.27 2.46 1.33
## 2.99 1.36 2.99 1.31
## 3.02 1.52 2.50 1.51
## 3.73 1.05 3.20 1.15
## 3.39 1.43 3.01 1.49
## 3.34 1.36 2.36 1.28
## 3.59 1.35 2.97 1.32
## 3.20 1.44 2.59 1.37
## 3.18 1.34 2.84 1.33
## 3.61 1.11 3.72 1.09
## 3.35 1.41 3.01 1.38
## 2.62 1.38 1.98 1.11
## 2.80 1.47 2.93 1.49
## 3.10 1.45 2.28 1.25
## 3.49 1.39 2.67 1.35
## 2.60 1.44 2.44 1.43
## 3.55 1.43 2.96 1.56
## 3.19 1.35 2.66 1.17
## 2.73 1.53 2.32 1.39
## 2.38 0.96 2.32 1.03
## 2.60 1.37 2.13 1.14
## 4.17 1.30 4.36 1.18
## 3.24 1.24 2.17 1.05
## 3.97 1.08 3.89 1.01
## 2.91 1.26 2.35 1.11
## 4.04 1.18 3.29 1.51
## 3.45 1.33 2.70 1.29
## 2.70 1.46 1.95 1.08
## 3.08 1.41 2.56 1.35
##
## Can.be.set.aside
## Mean sd
## 3.02 1.21
## 2.26 1.35
## 2.51 1.22
## 1.97 1.02
## 2.44 1.40
## 2.68 1.23
## 3.46 1.25
## 2.79 1.53
## 2.31 1.17
## 2.63 1.27
## 2.40 1.44
## 2.53 1.08
## 2.04 1.33
## 1.86 1.25
## 2.71 1.34
## 3.54 1.12
## 3.12 1.29
## 2.55 1.55
## 2.62 1.54
## 2.46 1.48
## 2.34 1.27
## 3.47 1.40
## 2.00 1.16
## 3.11 1.31
## 2.05 1.05
## 2.66 1.27
## 2.84 0.99
## 2.73 1.30
## 2.44 1.13
## 3.01 1.52
## 2.46 1.27
## 2.81 1.37
## 2.80 1.65
## 2.30 1.39
## 3.10 1.43
## 2.68 1.42
## 2.80 1.23
## 2.89 1.47
## 3.09 1.22
## 2.94 1.47
## 2.50 1.26
## 3.09 1.35
## 2.54 1.37
## 2.52 1.26
## 3.24 1.25
## 2.77 1.35
## 2.41 1.24
## 2.37 1.38
## 2.40 1.29
## 2.72 1.43
## 1.95 1.23
## 3.79 1.35
## 3.04 1.36
## 2.44 1.49
## 2.17 0.94
## 2.19 1.19
## 3.68 1.63
## 2.35 1.11
## 3.86 0.98
## 2.15 1.14
## 2.99 1.62
## 2.72 1.31
## 2.10 1.18
## 2.62 1.36##C14 ETHICS
print(table.meansdC14) ##
## Paying.for.info
## droplevels(BATmORG)$COUNTRY n Mean sd
## Albania 282 1.887 0.627
## Argentina 323 1.307 0.494
## Australia 564 1.293 0.482
## Austria 627 1.453 0.541
## Bangladesh 226 1.549 0.646
## Belgium 511 1.358 0.545
## Bhutan 82 2.049 0.768
## Botswana 177 1.384 0.583
## Brazil 374 1.214 0.454
## Canada 254 1.366 0.507
## Chile 427 1.405 0.587
## China 585 2.101 0.689
## Colombia 539 1.195 0.507
## Croatia 403 1.494 0.620
## Cyprus 196 1.367 0.514
## Czech Republic 240 1.654 0.565
## Denmark 1305 1.218 0.428
## Ecuador 333 1.369 0.590
## Egypt 392 1.577 0.643
## El Salvador 228 1.298 0.521
## Estonia 240 1.646 0.637
## Ethiopia 287 1.300 0.509
## Finland 304 1.447 0.524
## France\\_NON-REP 170 1.394 0.502
## Germany 647 1.635 0.607
## Greece 269 1.379 0.509
## Hong Kong 310 1.532 0.561
## Hungary 297 1.586 0.673
## Iceland 113 1.354 0.516
## India 442 1.627 0.652
## Indonesia 580 1.679 0.549
## Ireland 286 1.552 0.545
## Israel 279 1.778 0.754
## Italy 352 1.395 0.512
## Kenya 283 1.625 0.584
## Kosovo 169 1.509 0.656
## Latvia 241 1.444 0.553
## Malawi 133 1.489 0.623
## Malaysia 284 1.563 0.617
## Mexico 360 1.331 0.543
## Moldova 208 1.630 0.624
## Netherlands 421 1.437 0.547
## New Zealand 397 1.358 0.490
## Norway 383 1.285 0.474
## Oman 257 1.689 0.748
## Philippines 330 1.303 0.467
## Portugal 381 1.680 0.783
## Qatar 215 1.167 0.493
## Romania 324 1.682 0.640
## Russia 390 1.823 0.647
## Serbia 354 1.511 0.594
## Sierra Leone 136 1.654 0.703
## Singapore\\_NON-REP 83 1.205 0.406
## South Africa 293 1.222 0.432
## South Korea 347 1.631 0.507
## Spain 379 1.420 0.545
## Sudan 265 1.513 0.702
## Switzerland 874 1.292 0.470
## Thailand 331 1.556 0.682
## Turkey\\_NON-REP 88 1.511 0.587
## UAE 97 1.289 0.577
## UK 545 1.545 0.524
## USA 386 1.145 0.381
## All 21598 1.469 0.600
##
## Using.conf.documents False.identity Pressure.informants
## Mean sd Mean sd Mean sd
## 1.840 0.685 1.507 0.592 1.369 0.571
## 1.854 0.641 1.458 0.601 1.313 0.509
## 1.957 0.660 1.140 0.367 1.548 0.552
## 1.976 0.623 1.486 0.528 1.167 0.378
## 1.478 0.612 1.354 0.532 1.150 0.426
## 1.951 0.626 1.607 0.591 1.450 0.578
## 2.000 0.801 1.634 0.746 1.780 0.737
## 1.723 0.680 1.339 0.531 1.514 0.649
## 1.930 0.604 1.623 0.590 1.570 0.599
## 2.224 0.610 1.366 0.522 1.748 0.641
## 1.831 0.631 1.555 0.616 1.501 0.640
## 1.386 0.525 2.060 0.587 1.520 0.576
## 1.338 0.666 1.286 0.613 1.284 0.606
## 1.911 0.667 1.529 0.616 1.404 0.597
## 1.735 0.476 1.638 0.532 1.776 0.476
## 1.938 0.549 1.738 0.551 1.488 0.586
## 2.003 0.427 1.724 0.479 1.785 0.549
## 1.420 0.594 1.366 0.589 1.339 0.603
## 1.612 0.665 1.446 0.613 1.643 0.719
## 1.535 0.596 1.360 0.557 1.285 0.516
## 1.783 0.630 1.754 0.661 1.792 0.632
## 1.247 0.493 1.467 0.630 1.502 0.689
## 1.954 0.543 1.681 0.520 1.747 0.633
## 2.382 0.597 1.571 0.553 1.365 0.530
## 1.983 0.680 1.564 0.616 1.198 0.418
## 1.903 0.609 1.383 0.531 1.595 0.625
## 1.787 0.540 1.839 0.607 1.548 0.571
## 1.657 0.675 1.663 0.679 1.549 0.672
## 1.956 0.573 1.310 0.464 1.496 0.614
## 1.584 0.652 1.541 0.598 1.421 0.594
## 1.550 0.547 1.312 0.478 1.262 0.459
## 2.147 0.523 1.472 0.521 1.472 0.527
## 2.072 0.841 1.918 0.833 2.165 0.823
## 1.929 0.639 1.631 0.555 1.815 0.669
## 1.572 0.599 1.484 0.586 1.406 0.578
## 1.722 0.698 1.420 0.613 1.243 0.572
## 1.813 0.580 1.809 0.537 1.544 0.651
## 1.571 0.710 1.429 0.666 1.466 0.702
## 1.391 0.575 1.430 0.575 1.489 0.621
## 1.742 0.722 1.486 0.651 1.272 0.562
## 1.827 0.673 1.990 0.556 1.567 0.649
## 1.957 0.524 1.558 0.534 1.625 0.615
## 1.927 0.510 1.259 0.445 1.456 0.523
## 1.979 0.409 1.658 0.480 1.671 0.518
## 1.825 0.808 1.903 0.830 1.864 0.776
## 1.715 0.532 1.252 0.448 1.376 0.527
## 1.984 0.645 1.819 0.792 1.843 0.758
## 1.130 0.423 1.121 0.404 1.140 0.442
## 1.722 0.665 2.040 0.547 1.531 0.631
## 1.772 0.643 1.818 0.665 1.564 0.688
## 1.706 0.646 1.379 0.546 1.523 0.648
## 1.559 0.630 1.676 0.719 1.515 0.655
## 1.458 0.501 1.337 0.476 1.482 0.549
## 1.853 0.563 1.406 0.506 1.481 0.527
## 1.890 0.409 1.899 0.421 1.562 0.531
## 2.087 0.663 1.562 0.649 1.509 0.636
## 1.555 0.742 1.464 0.718 1.683 0.833
## 1.954 0.546 1.513 0.527 1.256 0.447
## 1.577 0.694 1.574 0.740 1.559 0.695
## 2.023 0.678 1.932 0.640 1.443 0.604
## 1.216 0.544 1.402 0.571 1.216 0.544
## 1.866 0.502 1.453 0.498 1.360 0.484
## 1.883 0.598 1.140 0.403 1.578 0.590
## 1.799 0.645 1.551 0.612 1.499 0.620
##
## Use.personal.doc Undercover.empl Hidden.miccam
## Mean sd Mean sd Mean sd
## 1.496 0.567 1.809 0.596 1.904 0.528
## 1.291 0.507 1.455 0.595 1.700 0.630
## 1.496 0.538 1.319 0.507 1.440 0.535
## 1.220 0.415 1.758 0.572 1.700 0.547
## 1.217 0.464 1.482 0.655 1.664 0.681
## 1.378 0.543 1.926 0.593 1.939 0.544
## 1.646 0.743 1.720 0.758 1.817 0.611
## 1.390 0.544 1.616 0.674 1.746 0.638
## 1.382 0.513 1.401 0.557 2.019 0.504
## 1.579 0.541 1.736 0.620 1.969 0.554
## 1.393 0.561 1.543 0.657 1.740 0.636
## 1.289 0.483 1.581 0.580 1.897 0.520
## 1.132 0.413 1.199 0.499 1.494 0.745
## 1.305 0.536 1.697 0.645 1.650 0.598
## 1.495 0.550 1.602 0.550 1.520 0.550
## 1.462 0.532 1.925 0.616 2.079 0.531
## 1.552 0.513 1.861 0.472 1.981 0.263
## 1.300 0.565 1.417 0.614 1.553 0.636
## 1.457 0.630 1.630 0.685 1.615 0.713
## 1.228 0.479 1.390 0.587 1.482 0.575
## 1.292 0.524 1.658 0.647 1.758 0.653
## 1.258 0.525 1.387 0.561 1.397 0.544
## 1.326 0.490 1.875 0.555 1.740 0.509
## 1.400 0.559 1.812 0.596 1.953 0.496
## 1.175 0.392 1.884 0.620 1.620 0.610
## 1.312 0.495 1.368 0.520 1.405 0.507
## 1.645 0.566 1.703 0.571 1.865 0.558
## 1.542 0.662 1.714 0.684 1.855 0.675
## 1.425 0.514 1.389 0.508 1.779 0.477
## 1.357 0.570 1.518 0.684 1.966 0.697
## 1.247 0.439 1.657 0.562 1.936 0.510
## 1.584 0.528 1.892 0.473 1.927 0.399
## 1.606 0.751 2.032 0.841 2.434 0.721
## 1.418 0.533 1.568 0.595 1.864 0.578
## 1.498 0.580 1.696 0.577 2.011 0.562
## 1.361 0.622 1.639 0.659 1.757 0.677
## 1.369 0.541 1.867 0.618 2.037 0.503
## 1.316 0.608 1.774 0.745 1.714 0.681
## 1.359 0.568 1.479 0.567 1.662 0.661
## 1.247 0.498 1.550 0.702 1.728 0.683
## 1.442 0.579 1.812 0.666 2.053 0.575
## 1.477 0.541 2.012 0.519 1.886 0.443
## 1.501 0.520 1.506 0.525 1.763 0.471
## 1.546 0.509 1.721 0.483 1.937 0.273
## 1.891 0.783 1.720 0.760 1.728 0.831
## 1.300 0.472 1.315 0.484 1.661 0.511
## 1.772 0.813 1.850 0.737 1.940 0.552
## 1.121 0.404 1.130 0.389 1.167 0.443
## 1.355 0.584 1.741 0.659 2.049 0.560
## 1.533 0.631 1.982 0.663 2.028 0.548
## 1.164 0.400 1.545 0.634 1.528 0.607
## 1.419 0.615 2.044 0.665 2.000 0.655
## 1.325 0.471 1.349 0.504 1.566 0.567
## 1.491 0.547 1.491 0.540 1.799 0.487
## 1.438 0.508 1.677 0.537 1.712 0.513
## 1.385 0.549 1.609 0.643 1.617 0.616
## 1.430 0.715 1.551 0.777 1.411 0.680
## 1.291 0.459 1.673 0.546 1.691 0.521
## 1.550 0.730 1.571 0.737 1.692 0.614
## 1.364 0.550 1.534 0.566 1.580 0.496
## 1.258 0.582 1.278 0.535 1.309 0.547
## 1.466 0.510 1.780 0.492 1.800 0.464
## 1.438 0.551 1.420 0.563 1.738 0.613
## 1.399 0.561 1.650 0.629 1.775 0.598
##
## Recreations Publ.unverified Accept.money
## Mean sd Mean sd Mean sd
## 1.089 0.320 1.103 0.327 1.085 0.304
## 1.523 0.632 1.053 0.250 1.053 0.274
## 1.837 0.576 1.372 0.512 1.018 0.157
## 1.604 0.627 1.466 0.521 1.019 0.148
## 1.031 0.198 1.044 0.263 1.040 0.237
## 1.601 0.639 1.127 0.393 1.047 0.283
## 1.659 0.671 1.500 0.671 1.622 0.811
## 1.446 0.602 1.130 0.399 1.068 0.312
## 1.281 0.484 1.035 0.197 1.016 0.146
## 1.630 0.594 1.173 0.389 1.012 0.140
## 1.546 0.657 1.180 0.538 1.162 0.535
## 2.041 0.618 1.200 0.453 1.210 0.448
## 1.542 0.776 1.085 0.356 1.058 0.309
## 1.804 0.606 1.159 0.457 1.104 0.422
## 1.117 0.431 1.128 0.463 1.112 0.461
## 1.483 0.634 1.225 0.428 1.021 0.170
## 1.827 0.514 1.424 0.514 1.034 0.214
## 1.880 0.747 1.198 0.546 1.171 0.495
## 1.079 0.330 1.112 0.354 1.084 0.344
## 1.557 0.678 1.118 0.397 1.066 0.325
## 2.038 0.705 1.154 0.426 1.204 0.553
## 1.369 0.576 1.160 0.483 1.195 0.484
## 1.980 0.624 1.457 0.543 1.013 0.162
## 1.318 0.538 1.029 0.169 1.006 0.077
## 1.844 0.707 1.397 0.563 1.045 0.222
## 1.197 0.426 1.074 0.263 1.019 0.135
## 1.719 0.609 1.374 0.565 1.132 0.409
## 1.525 0.698 1.556 0.656 1.421 0.698
## 1.283 0.472 1.416 0.495 1.009 0.094
## 1.690 0.694 1.226 0.529 1.152 0.428
## 1.109 0.322 1.071 0.270 1.159 0.370
## 1.927 0.508 1.199 0.450 1.056 0.320
## 2.272 0.834 1.366 0.691 1.251 0.630
## 1.756 0.587 1.080 0.291 1.031 0.204
## 1.654 0.653 1.163 0.397 1.212 0.435
## 1.189 0.567 1.142 0.467 1.136 0.462
## 1.568 0.616 1.162 0.369 1.025 0.156
## 1.459 0.646 1.158 0.505 1.331 0.612
## 1.563 0.606 1.278 0.535 1.292 0.541
## 1.525 0.684 1.117 0.419 1.086 0.359
## 1.293 0.507 1.135 0.382 1.072 0.294
## 1.435 0.529 1.171 0.383 1.050 0.229
## 1.700 0.521 1.232 0.422 1.023 0.149
## 1.530 0.564 1.253 0.441 1.005 0.072
## 2.027 0.854 2.093 0.874 1.615 0.890
## 1.658 0.574 1.115 0.329 1.048 0.215
## 1.869 0.749 1.743 0.866 1.680 0.944
## 1.047 0.251 1.098 0.380 1.056 0.285
## 1.349 0.566 1.164 0.467 1.154 0.480
## 1.431 0.668 1.377 0.660 1.369 0.663
## 1.458 0.620 1.130 0.369 1.073 0.311
## 1.838 0.712 1.140 0.407 1.184 0.459
## 1.602 0.583 1.169 0.408 1.012 0.110
## 1.522 0.527 1.089 0.285 1.014 0.165
## 1.331 0.513 1.084 0.287 1.040 0.211
## 1.493 0.652 1.024 0.169 1.024 0.169
## 1.053 0.321 1.060 0.295 1.049 0.278
## 1.747 0.627 1.347 0.497 1.011 0.106
## 1.689 0.672 1.559 0.734 1.498 0.740
## 1.068 0.254 1.386 0.556 1.034 0.183
## 1.113 0.405 1.113 0.405 1.134 0.492
## 1.793 0.524 1.261 0.460 1.042 0.210
## 1.407 0.575 1.122 0.392 1.057 0.317
## 1.589 0.654 1.242 0.503 1.113 0.403##C15+C16 INFLUENCES
print(table.meansdC15C16) ##
## Personal.values Peers.on.staff
## droplevels(BATmORG)$COUNTRY n Mean sd Mean
## Albania 216 3.39 1.21 2.53
## Argentina 227 3.98 0.98 3.07
## Australia 450 3.35 1.08 2.98
## Austria 640 3.59 1.00 2.66
## Bangladesh 157 3.50 1.40 2.69
## Belgium 428 3.23 1.03 2.91
## Bhutan 74 3.93 1.04 3.76
## Botswana 114 3.26 1.22 2.75
## Brazil 256 3.20 1.04 2.99
## Canada 188 3.11 1.11 2.78
## Chile 383 3.61 1.25 3.00
## China 320 3.52 0.94 2.89
## Colombia 484 3.44 1.39 2.93
## Croatia 478 3.90 1.33 2.68
## Cyprus 189 3.92 1.13 2.49
## Czech Republic 191 4.01 1.04 3.41
## Denmark 790 3.41 1.09 3.21
## Ecuador 289 3.92 1.17 3.02
## Egypt 268 4.02 1.05 3.01
## El Salvador 189 3.77 1.24 3.03
## Estonia 192 3.98 0.97 3.18
## Ethiopia 266 3.42 1.24 2.96
## Finland 259 3.36 0.92 2.90
## France\\_NON-REP 166 2.92 1.11 2.87
## Germany 545 3.69 0.98 2.99
## Greece 289 4.14 1.04 2.63
## Hong Kong 313 3.25 1.01 2.88
## Hungary 288 4.01 1.00 3.28
## Iceland 127 3.53 0.97 3.23
## India 314 3.22 1.42 2.87
## Indonesia 442 3.57 1.01 2.90
## Ireland 202 3.42 1.04 2.94
## Israel 127 4.17 0.97 2.91
## Italy 337 3.59 1.12 2.35
## Kenya 284 3.70 1.19 3.09
## Kosovo 108 3.11 1.47 2.73
## Latvia 233 4.12 0.93 3.09
## Malawi 114 3.89 1.11 3.41
## Malaysia 314 3.61 1.04 3.18
## Mexico 256 3.50 1.35 2.70
## Moldova 187 3.62 1.09 2.78
## Netherlands 292 3.34 0.93 3.03
## New Zealand 321 3.60 1.00 3.01
## Norway 313 3.39 0.78 3.15
## Oman 212 3.55 1.31 3.30
## Philippines 323 3.75 1.05 2.82
## Portugal 162 3.72 1.18 3.04
## Qatar 84 3.35 1.24 3.32
## Romania 284 3.94 0.99 2.98
## Russia 340 3.89 1.02 2.96
## Serbia 283 3.96 1.14 3.06
## Sierra Leone 98 3.57 1.31 3.11
## Singapore\\_NON-REP 70 3.04 1.24 2.94
## South Africa 282 3.52 1.25 3.03
## South Korea 347 3.97 0.77 3.37
## Spain 318 3.73 1.02 3.11
## Sudan 55 4.42 0.83 3.44
## Sweden 379 3.27 0.91 2.82
## Switzerland 571 3.50 0.96 2.84
## Thailand 304 3.74 0.84 3.67
## Turkey\\_NON-REP 83 2.49 1.14 2.41
## UAE 133 3.58 1.33 3.37
## UK 465 3.57 1.03 3.12
## USA 306 3.35 1.23 3.25
## All 17719 3.59 1.13 2.98
##
## Editorial.supervisors Managers.of.news.org Owners
## sd Mean sd Mean sd Mean sd
## 0.93 3.24 0.94 3.00 1.12 2.99 1.30
## 0.94 3.49 0.97 3.40 1.14 3.37 1.22
## 0.98 3.74 0.90 2.61 1.25 2.02 1.16
## 0.81 2.90 0.92 2.16 0.96 2.03 0.96
## 1.14 3.57 1.12 3.35 1.18 3.32 1.29
## 0.98 3.24 1.05 2.29 1.11 1.91 1.11
## 1.08 3.95 1.10 3.72 1.12 3.43 1.18
## 1.13 3.90 1.01 3.73 1.24 3.32 1.56
## 0.88 3.45 0.86 3.33 1.06 3.36 1.21
## 0.96 3.27 0.99 2.56 1.12 1.77 0.95
## 1.18 3.73 1.04 3.31 1.25 3.14 1.35
## 0.87 3.63 0.85 3.65 0.91 3.20 1.04
## 1.28 3.79 1.18 3.64 1.34 3.64 1.34
## 1.24 3.14 1.20 2.79 1.37 2.83 1.44
## 1.21 3.90 0.97 3.99 1.01 3.94 1.15
## 1.03 3.60 1.08 3.00 1.20 2.55 1.26
## 1.07 3.42 1.01 2.66 1.15 1.90 1.13
## 1.17 3.62 1.06 3.55 1.11 3.55 1.22
## 1.08 3.58 1.03 3.59 1.04 3.50 1.28
## 1.09 4.03 0.94 3.88 1.14 3.92 1.25
## 0.90 3.26 0.99 2.77 1.15 2.19 1.24
## 1.12 3.83 1.08 3.72 1.23 3.69 1.31
## 0.88 3.42 0.84 2.11 0.98 1.82 0.97
## 0.89 2.96 0.95 2.38 1.02 2.08 1.05
## 0.95 3.24 0.98 2.43 1.18 2.12 1.21
## 1.24 3.14 1.25 3.28 1.30 3.10 1.39
## 0.87 3.57 0.91 3.45 0.98 3.13 1.08
## 0.98 3.67 1.09 3.37 1.18 3.18 1.21
## 0.96 3.30 0.99 1.74 0.91 1.52 0.86
## 1.25 3.62 1.27 3.29 1.30 3.46 1.36
## 1.04 3.35 0.99 3.22 1.06 3.22 1.15
## 0.93 3.53 0.95 2.27 1.07 2.24 1.16
## 1.13 3.40 1.13 2.76 1.37 2.52 1.48
## 0.97 2.80 1.12 2.23 1.19 2.32 1.21
## 1.03 3.98 0.84 3.81 1.06 3.67 1.25
## 1.26 3.22 1.24 3.00 1.28 2.91 1.29
## 1.08 3.34 0.98 2.63 1.15 2.15 1.20
## 1.10 4.13 0.85 4.00 1.00 3.92 1.09
## 0.91 3.61 0.86 3.54 0.91 3.60 1.00
## 1.18 3.76 1.10 3.67 1.26 3.70 1.31
## 1.06 3.33 1.06 2.86 1.21 2.73 1.29
## 0.81 3.18 0.98 2.50 1.02 2.18 1.06
## 1.02 3.48 0.96 2.84 1.15 2.21 1.18
## 0.66 3.20 0.75 1.77 0.77 1.60 0.81
## 1.15 3.59 1.12 3.32 1.08 3.75 1.05
## 0.88 3.66 0.87 3.50 0.95 3.28 1.13
## 0.94 3.50 0.93 2.70 1.13 2.38 1.15
## 1.12 3.57 1.02 3.51 0.91 3.42 1.21
## 1.04 3.19 1.14 2.77 1.23 2.57 1.25
## 1.16 3.61 1.06 3.72 1.01 3.49 1.25
## 0.98 3.37 0.96 3.18 1.12 3.06 1.21
## 1.23 3.96 1.17 3.94 1.05 3.92 1.10
## 1.03 2.90 1.28 2.86 1.23 2.83 1.26
## 1.02 3.83 0.92 3.40 1.10 2.85 1.26
## 0.74 3.94 0.71 3.42 1.01 3.22 1.16
## 0.93 3.79 0.94 3.25 1.33 3.14 1.45
## 1.12 3.71 1.07 3.47 1.03 3.58 1.24
## 0.79 3.51 0.84 2.67 1.07 2.08 1.07
## 0.85 2.97 0.90 2.21 0.98 1.87 0.93
## 0.86 3.95 0.78 3.96 0.76 3.99 0.80
## 1.08 2.64 1.20 2.99 1.22 2.94 1.36
## 1.21 3.88 1.08 4.02 1.04 4.02 1.00
## 0.96 3.42 1.00 2.94 1.12 2.53 1.20
## 0.99 3.75 1.03 3.49 1.19 2.66 1.31
## 1.04 3.47 1.04 3.03 1.25 2.80 1.37
##
## Editorial.policy Advertising.considerations Profit.expectations
## Mean sd Mean sd Mean
## 3.37 1.05 2.56 1.30 2.40
## 3.57 1.00 2.46 1.11 2.51
## 3.71 1.12 2.09 1.12 2.10
## 3.07 1.02 1.97 1.01 2.10
## 3.87 1.18 2.99 1.15 2.39
## 3.42 1.02 1.78 1.03 1.79
## 3.54 1.15 3.24 1.18 3.23
## 4.45 0.82 2.50 1.33 2.61
## 3.68 0.98 2.62 1.21 2.70
## 3.03 1.42 1.52 0.77 1.58
## 3.68 1.12 2.57 1.35 2.64
## 3.62 0.90 2.86 0.95 2.83
## 3.81 1.24 3.11 1.44 2.97
## 3.43 1.23 2.65 1.45 2.71
## 3.71 1.00 2.78 1.45 2.81
## 3.41 1.10 2.38 1.25 2.21
## 3.57 1.07 1.58 0.96 2.14
## 3.67 1.12 2.81 1.32 3.05
## 4.03 0.99 3.18 1.21 3.10
## 3.90 1.06 3.07 1.29 3.13
## 3.28 1.13 2.11 1.21 2.19
## 3.92 1.22 2.70 1.31 2.83
## 3.84 0.90 1.57 0.76 2.06
## 3.20 1.07 1.87 0.81 1.96
## 3.26 1.13 2.36 1.25 2.39
## 3.07 1.24 2.68 1.24 2.32
## 3.44 0.93 2.37 1.07 2.30
## 3.44 1.14 2.96 1.12 3.03
## 3.35 1.01 1.47 0.75 1.77
## 4.08 1.00 3.23 1.21 3.06
## 3.57 0.98 2.72 1.10 2.72
## 3.58 1.04 2.08 1.11 1.95
## 3.24 1.29 2.49 1.41 2.02
## 3.16 1.08 2.07 1.11 2.19
## 4.19 1.03 3.48 1.04 3.38
## 3.37 1.22 2.44 1.28 1.94
## 3.38 1.14 2.35 1.23 2.19
## 4.27 0.83 3.29 1.04 3.04
## 3.83 0.90 3.32 0.94 3.33
## 3.97 1.00 2.72 1.30 2.91
## 3.59 1.16 2.55 1.28 2.82
## 3.33 0.93 2.34 1.15 2.26
## 3.52 0.97 2.09 1.12 2.20
## 3.51 0.78 1.51 0.83 1.89
## 3.80 0.94 3.34 1.03 3.00
## 4.04 0.83 2.72 1.08 2.66
## 3.72 0.92 2.05 1.04 2.35
## 4.04 1.10 3.24 1.30 3.12
## 3.46 1.18 2.46 1.22 2.83
## 3.25 1.26 2.36 1.29 2.51
## 3.68 1.05 2.74 1.17 2.82
## 4.20 1.12 3.39 1.08 3.05
## 2.96 1.32 2.83 1.10 2.76
## 4.04 0.96 2.51 1.20 2.62
## 3.63 0.86 3.13 1.10 3.02
## 3.59 1.08 2.98 1.31 2.87
## 4.25 0.82 3.58 1.32 3.58
## 3.25 0.93 1.82 0.94 2.30
## 3.02 1.01 1.98 0.96 2.17
## 4.04 0.73 3.81 0.91 3.78
## 3.35 1.21 2.46 1.21 2.40
## 3.77 1.25 3.08 1.33 3.30
## 3.72 0.95 2.37 1.09 2.43
## 3.73 1.18 1.73 0.92 1.86
## 3.58 1.10 2.48 1.27 2.53
##
## Audience.research Availability.of.resources Time.limits
## sd Mean sd Mean sd Mean
## 1.35 3.31 1.14 3.58 1.10 3.82
## 1.23 2.71 1.19 3.28 1.13 3.52
## 1.21 3.22 1.15 3.95 0.85 4.03
## 1.06 2.58 1.05 3.53 0.99 3.69
## 1.25 3.51 1.03 3.36 1.08 3.28
## 1.09 2.62 1.09 3.31 1.00 3.54
## 1.21 3.23 1.33 3.30 1.31 3.31
## 1.40 3.72 1.18 4.25 0.95 4.21
## 1.25 3.25 1.19 3.57 1.06 3.92
## 0.94 2.65 1.12 3.65 0.89 3.73
## 1.38 2.89 1.32 3.36 1.29 3.78
## 0.98 3.12 0.96 3.47 0.91 3.33
## 1.43 3.27 1.35 2.65 1.45 3.62
## 1.46 2.92 1.21 3.72 1.06 3.85
## 1.55 2.23 1.17 3.54 1.21 4.07
## 1.19 3.09 1.14 3.95 0.95 3.80
## 1.23 2.63 1.05 3.59 1.05 3.84
## 1.27 3.40 1.18 3.36 1.27 3.57
## 1.24 3.67 1.02 4.20 0.89 4.06
## 1.31 3.24 1.27 3.59 1.19 3.66
## 1.31 3.18 1.12 3.85 1.00 3.77
## 1.30 3.23 1.19 3.65 1.02 3.77
## 1.08 3.03 0.90 3.90 0.91 3.87
## 0.98 2.67 1.02 3.31 1.00 3.81
## 1.24 2.85 1.12 3.56 0.99 3.73
## 1.21 2.85 1.24 3.53 1.10 3.71
## 1.12 2.51 1.01 3.16 1.04 3.45
## 1.21 3.45 1.08 3.55 0.97 3.84
## 1.00 2.11 1.06 3.84 0.99 3.91
## 1.25 3.86 1.01 3.94 0.99 4.18
## 1.15 3.63 0.88 3.72 0.88 3.47
## 1.01 2.88 1.11 3.88 0.89 4.04
## 1.31 2.93 1.27 3.43 1.08 3.61
## 1.14 2.73 1.19 3.15 1.23 3.63
## 1.17 3.84 1.03 4.06 0.89 3.90
## 1.13 3.31 1.34 2.81 1.36 3.66
## 1.20 2.99 1.20 3.64 0.98 3.63
## 1.25 3.92 0.98 4.16 0.84 3.97
## 0.99 3.60 0.91 3.78 0.87 3.79
## 1.33 3.16 1.18 3.20 1.22 3.55
## 1.29 3.30 1.21 3.64 1.04 3.66
## 1.18 2.70 1.03 3.39 0.94 3.63
## 1.21 3.17 1.16 3.77 0.91 3.91
## 1.03 2.34 1.04 2.86 0.96 3.54
## 1.09 3.42 1.01 3.98 0.96 2.92
## 1.10 3.58 0.99 4.07 0.79 3.91
## 1.13 2.68 1.16 3.71 0.89 3.98
## 1.33 3.81 1.02 4.14 0.98 3.71
## 1.27 3.21 1.17 3.82 1.05 3.86
## 1.32 3.08 1.18 2.96 1.31 3.02
## 1.25 3.20 1.20 3.48 1.13 3.55
## 1.16 3.73 1.07 4.08 1.02 4.11
## 1.17 3.07 0.97 2.90 1.16 3.01
## 1.25 3.54 1.06 4.08 0.88 4.11
## 1.13 3.60 0.84 3.97 0.76 3.78
## 1.22 2.88 1.15 3.56 1.15 3.94
## 1.29 3.78 1.15 4.44 0.79 4.13
## 1.27 2.50 0.96 3.55 0.93 3.45
## 1.01 2.55 0.97 3.51 0.95 3.59
## 0.81 3.70 0.79 3.81 0.77 3.76
## 1.24 2.59 1.16 2.83 1.12 3.28
## 1.30 3.62 1.15 3.75 1.06 3.89
## 1.12 3.29 1.09 3.66 0.97 3.84
## 0.98 3.08 1.10 3.84 0.91 3.82
## 1.28 3.08 1.17 3.59 1.08 3.72
##
## Journalism.ethics Friends.peers.family
## sd Mean sd Mean sd
## 1.00 4.25 0.70 2.37 1.15
## 1.00 4.19 0.85 2.63 1.08
## 0.81 4.31 0.70 2.05 0.96
## 0.99 3.85 0.95 2.11 0.85
## 1.00 4.14 1.17 2.22 1.38
## 1.01 4.00 0.98 2.17 0.98
## 1.54 3.38 1.54 3.72 1.44
## 0.89 4.63 0.60 2.15 1.22
## 1.02 4.08 1.09 2.50 1.03
## 0.87 4.53 0.71 1.96 0.93
## 1.13 4.33 0.91 2.20 1.18
## 0.89 3.83 0.91 2.44 0.95
## 1.23 4.20 1.11 2.29 1.26
## 1.07 4.27 0.97 2.85 1.30
## 0.89 4.08 1.09 1.38 0.84
## 0.96 4.38 0.87 2.96 1.36
## 1.05 3.93 0.96 1.88 0.87
## 1.09 4.43 0.85 2.80 1.27
## 1.02 4.41 0.98 2.61 1.31
## 1.11 4.32 0.90 2.50 1.24
## 1.05 4.24 0.95 2.47 1.04
## 1.08 3.59 1.30 2.37 1.19
## 0.81 4.33 0.73 2.44 0.95
## 1.05 4.18 0.90 2.34 0.92
## 0.97 4.04 0.90 2.01 0.90
## 1.07 4.17 0.97 2.07 1.13
## 0.93 3.55 0.97 2.41 0.94
## 0.97 3.99 0.96 2.85 1.18
## 0.97 3.71 1.11 2.65 1.07
## 0.96 4.20 0.96 2.38 1.30
## 0.83 4.08 0.87 2.79 1.01
## 0.87 4.13 0.86 2.17 0.91
## 1.13 4.39 0.86 2.19 1.12
## 1.08 4.20 0.92 2.01 0.99
## 1.03 4.13 1.00 2.77 1.24
## 1.16 4.40 1.01 2.30 1.31
## 1.00 3.79 1.13 2.23 1.06
## 0.94 4.35 0.86 2.82 1.26
## 0.89 3.98 0.92 3.18 1.15
## 1.11 4.56 0.75 2.26 1.19
## 1.07 4.19 0.93 2.32 1.16
## 0.90 3.34 1.07 2.29 0.92
## 0.87 4.14 0.85 2.54 1.01
## 0.81 3.90 0.76 2.21 0.76
## 1.41 3.60 1.22 2.67 1.21
## 0.95 4.48 0.65 2.28 0.95
## 0.91 4.51 0.77 2.42 1.03
## 1.02 4.05 0.92 2.52 1.17
## 1.03 4.26 0.92 2.43 1.27
## 1.25 3.14 1.26 2.56 1.20
## 1.11 4.37 0.90 2.47 1.20
## 0.96 4.45 0.95 2.40 1.38
## 1.32 3.29 1.42 2.86 1.12
## 0.92 4.54 0.69 2.24 1.09
## 0.80 3.82 0.79 2.90 0.94
## 1.02 4.41 0.78 2.13 1.10
## 1.11 4.76 0.47 2.62 1.50
## 0.99 3.72 0.82 2.17 0.75
## 0.99 3.63 0.94 2.37 0.89
## 0.70 4.10 0.80 3.87 1.01
## 1.25 3.48 1.25 2.47 0.98
## 1.05 4.15 1.08 3.06 1.30
## 0.91 4.05 0.91 2.33 0.98
## 0.96 4.58 0.79 2.22 1.05
## 1.03 4.08 0.99 2.40 1.13
##
## Colleagues.other.media Audience.feedback Competing.newsorg
## Mean sd Mean sd Mean sd
## 2.15 1.01 3.50 1.07 3.48 1.18
## 2.79 0.92 3.23 0.88 2.91 0.96
## 2.36 1.05 3.47 0.93 3.20 1.01
## 2.08 0.79 2.77 0.91 2.50 0.90
## 2.43 1.24 3.46 1.26 3.36 1.33
## 2.36 0.97 2.55 0.94 2.48 0.95
## 3.70 1.28 4.24 0.93 3.76 1.13
## 2.65 1.23 3.89 1.05 3.51 1.24
## 2.68 0.91 3.43 1.00 3.17 0.95
## 2.19 1.00 2.57 1.04 2.58 1.02
## 2.38 1.14 3.39 1.20 3.10 1.21
## 2.66 0.90 3.29 0.84 3.11 0.87
## 2.52 1.30 3.53 1.22 3.08 1.23
## 2.75 1.18 3.43 1.05 2.85 1.22
## 1.84 0.96 2.62 1.00 2.60 1.24
## 2.41 1.00 3.63 1.12 2.90 1.04
## 1.90 0.89 2.58 0.98 2.41 1.00
## 2.86 1.19 3.75 0.94 3.17 1.11
## 2.72 1.19 3.52 1.32 3.30 1.26
## 2.57 1.13 3.68 1.07 3.04 1.23
## 2.29 0.97 3.57 0.96 2.88 1.23
## 2.34 1.17 3.22 1.15 3.30 1.22
## 2.29 0.80 2.90 0.92 2.90 0.83
## 2.48 0.90 2.61 0.94 2.72 0.92
## 2.22 0.92 3.04 0.94 2.75 0.99
## 2.09 1.05 2.58 1.08 2.64 1.18
## 2.64 0.87 2.93 0.89 2.98 0.90
## 2.66 1.07 3.64 0.96 3.07 1.07
## 2.14 0.91 2.99 0.88 3.20 1.02
## 2.58 1.22 3.88 1.03 3.84 1.09
## 2.84 0.98 3.33 0.92 3.13 1.01
## 2.45 0.90 3.02 0.94 2.95 0.97
## 2.43 1.04 3.11 1.10 2.83 1.09
## 2.12 0.86 2.90 1.06 2.79 1.07
## 3.16 1.06 3.99 0.84 3.84 0.95
## 2.14 1.10 3.18 1.25 3.04 1.29
## 2.15 1.02 3.33 0.98 2.58 1.19
## 3.31 1.25 4.03 0.87 3.96 0.93
## 3.32 0.97 3.76 0.86 3.68 0.88
## 2.39 1.13 3.99 0.89 2.92 1.12
## 2.39 0.97 3.79 1.06 3.07 1.19
## 2.46 0.89 3.04 0.83 2.63 0.94
## 2.58 0.97 3.25 0.90 3.02 0.98
## 2.18 0.72 2.78 0.74 2.68 0.73
## 2.71 1.20 3.47 1.10 3.44 1.02
## 2.52 0.90 3.21 0.91 3.25 0.97
## 2.49 0.98 3.19 1.04 3.14 0.99
## 2.74 0.92 3.43 1.47 3.30 1.28
## 2.52 1.14 3.93 1.03 3.12 1.16
## 2.28 0.99 3.44 1.04 3.07 1.15
## 2.46 1.01 3.06 1.12 2.65 1.09
## 3.17 1.27 3.85 1.10 3.42 1.17
## 2.80 1.10 2.97 0.80 2.84 1.04
## 2.68 1.09 3.81 1.01 3.22 1.11
## 3.01 0.86 3.66 0.80 3.81 0.78
## 2.16 0.97 3.01 0.96 2.94 1.04
## 2.87 1.28 3.44 1.40 3.35 1.31
## 2.22 0.70 2.82 0.74 2.86 0.79
## 2.25 0.71 2.64 0.86 2.64 0.86
## 3.79 0.93 3.90 0.76 3.74 0.84
## 2.22 0.94 2.77 1.06 2.46 1.11
## 3.59 1.20 3.74 1.23 3.95 1.08
## 2.68 0.94 3.49 0.90 3.16 0.89
## 2.68 0.99 3.43 0.94 2.96 1.04
## 2.50 1.07 3.26 1.07 3.02 1.10
##
## Media.laws.and.regul Information.acceess Censorship
## Mean sd Mean sd Mean sd
## 3.85 0.98 3.96 0.81 2.63 1.24
## 2.97 1.06 3.85 0.86 2.71 1.37
## 4.06 0.94 3.96 0.79 2.53 1.28
## 2.75 1.08 3.44 1.07 1.28 0.63
## 3.96 1.13 3.62 1.13 3.34 1.35
## 3.13 1.18 3.68 0.91 1.67 0.99
## 3.59 1.33 4.28 0.73 3.88 1.05
## 4.37 0.93 4.51 0.84 3.61 1.37
## 3.14 1.05 3.87 0.91 2.19 1.14
## 3.76 1.19 3.69 1.00 1.75 1.03
## 2.88 1.24 3.70 1.26 2.56 1.36
## 3.58 0.88 3.57 0.80 3.74 0.89
## 3.65 1.18 3.81 1.25 2.75 1.38
## 3.59 1.09 3.99 0.91 2.61 1.37
## 3.47 1.25 4.01 0.92 3.14 1.33
## 3.92 1.12 4.38 0.82 1.90 1.27
## 2.70 1.15 2.93 1.13 1.27 0.59
## 3.77 0.98 4.05 0.96 3.44 1.15
## 3.41 1.37 3.63 1.46 3.22 1.24
## 3.50 1.12 4.10 0.92 3.17 1.28
## 2.90 1.18 4.11 0.96 1.56 0.98
## 3.82 1.16 3.92 1.09 3.41 1.44
## 4.00 1.00 3.12 1.22 1.38 0.76
## 3.14 1.04 3.56 0.96 1.77 0.90
## 2.98 1.17 3.76 0.96 1.43 0.80
## 3.52 1.09 3.97 1.00 2.54 1.42
## 3.06 0.87 3.20 0.87 2.83 0.97
## 3.55 1.09 3.84 0.98 2.74 1.21
## 3.00 1.18 4.22 0.77 1.63 0.85
## 4.02 0.97 4.09 0.92 3.49 1.17
## 3.82 0.81 3.75 0.72 3.35 1.00
## 3.60 0.97 3.87 0.79 2.32 1.10
## 3.10 1.32 3.53 1.16 3.35 1.47
## 3.04 1.07 3.60 0.96 2.10 1.12
## 4.20 0.86 4.20 0.85 3.69 1.00
## 3.99 1.04 3.80 1.09 2.92 1.33
## 3.07 1.33 3.94 0.92 1.45 0.75
## 4.27 0.76 4.13 0.80 4.01 0.86
## 3.94 0.80 3.98 0.78 3.79 0.85
## 3.46 1.16 3.97 1.00 3.17 1.38
## 3.73 1.18 4.27 0.91 2.53 1.37
## 2.53 0.94 2.85 0.93 1.64 0.90
## 3.79 1.00 3.83 0.90 2.58 1.28
## 3.57 0.87 3.63 0.74 1.54 0.72
## 3.87 0.92 3.77 0.97 3.42 1.12
## 3.84 0.90 4.13 0.77 3.01 1.18
## 3.06 1.06 4.06 0.86 1.70 1.02
## 3.31 1.34 3.45 1.40 2.83 1.25
## 3.93 1.06 4.35 0.85 2.45 1.38
## 3.39 1.07 3.68 0.94 2.47 1.19
## 3.69 1.05 3.75 1.08 2.67 1.25
## 4.30 0.86 4.21 0.90 3.49 1.23
## 3.16 1.33 2.99 1.21 3.16 1.09
## 4.03 0.98 4.10 0.98 2.40 1.27
## 3.35 0.85 3.97 0.69 2.76 1.07
## 2.94 1.16 4.03 0.89 2.16 1.21
## 3.55 1.45 3.78 1.56 3.64 1.46
## 3.02 0.93 3.61 0.85 1.49 0.71
## 2.77 0.99 3.53 0.95 1.57 0.74
## 3.95 0.75 3.98 0.71 3.81 0.77
## 3.84 1.02 3.53 1.04 3.48 1.27
## 4.24 0.96 4.10 1.09 4.10 1.11
## 3.73 1.04 3.76 0.95 2.34 1.16
## 4.01 1.10 4.02 0.85 2.24 1.30
## 3.45 1.15 3.78 1.02 2.47 1.36
##
## Government.officials Politicians Pressure.groups
## Mean sd Mean sd Mean sd
## 2.14 1.15 1.96 1.14 1.61 0.76
## 2.47 1.07 2.45 1.08 2.18 1.02
## 2.34 1.05 2.30 1.06 2.14 0.89
## 1.38 0.67 1.56 0.76 1.75 0.77
## 2.31 1.32 2.49 1.33 2.47 1.33
## 1.51 0.76 1.80 1.00 1.86 1.01
## 3.64 1.19 3.32 1.21 3.12 1.13
## 3.17 1.39 2.82 1.38 2.46 1.20
## 2.11 1.04 2.28 1.14 2.10 1.05
## 1.79 0.98 1.68 0.90 1.60 0.82
## 2.12 1.20 2.12 1.19 2.13 1.24
## 3.30 0.99 3.19 1.06 2.48 0.88
## 2.53 1.34 2.47 1.33 2.59 1.40
## 2.15 1.33 2.23 1.35 2.08 1.33
## 1.76 0.86 1.83 0.91 1.69 0.80
## 1.63 0.90 1.96 1.08 1.52 0.80
## 1.87 0.96 2.04 1.06 2.05 1.01
## 3.04 1.29 2.94 1.30 2.82 1.30
## 2.91 1.22 2.97 1.21 2.76 1.26
## 2.92 1.26 2.91 1.29 2.70 1.22
## 1.82 0.94 1.69 0.83 1.96 1.04
## 3.54 1.40 3.25 1.39 2.77 1.33
## 2.19 1.07 1.93 0.97 1.84 0.89
## 1.55 0.65 1.61 0.69 1.48 0.61
## 1.26 0.59 1.43 0.69 1.89 0.93
## 1.90 1.12 1.93 1.13 1.92 1.09
## 2.41 0.96 2.35 0.94 2.33 0.90
## 2.08 1.11 2.10 1.17 1.99 1.11
## 1.66 0.87 1.64 0.85 1.65 0.77
## 3.01 1.15 2.76 1.18 2.54 1.15
## 2.49 1.10 2.36 1.05 2.33 1.01
## 2.22 1.07 2.17 1.11 2.15 1.05
## 1.89 1.06 1.82 1.04 1.73 0.97
## 1.52 0.83 1.69 0.94 1.72 0.98
## 2.88 1.17 2.49 1.13 2.38 1.06
## 2.34 1.22 2.15 1.21 2.19 1.12
## 1.41 0.73 1.41 0.74 1.42 0.69
## 3.35 1.22 2.94 1.31 2.76 1.18
## 3.59 0.97 3.35 0.99 3.23 0.95
## 2.58 1.25 2.57 1.25 2.78 1.10
## 1.92 1.02 1.83 1.03 1.76 1.03
## 1.86 0.93 1.92 0.95 2.14 0.97
## 2.11 1.09 2.03 1.09 1.96 0.95
## 1.89 0.80 1.99 0.83 1.90 0.76
## 3.54 1.14 3.16 1.21 2.87 1.04
## 2.57 1.09 2.38 1.07 2.22 0.96
## 1.68 0.89 1.75 0.93 1.77 0.95
## 2.89 1.20 2.85 1.08 2.82 1.10
## 1.90 1.09 1.82 1.11 1.78 1.05
## 2.33 1.16 2.25 1.15 2.22 1.00
## 2.29 1.17 2.28 1.17 1.92 1.06
## 2.73 1.29 2.55 1.25 2.91 1.23
## 2.93 1.07 2.87 1.12 2.93 1.11
## 2.33 1.20 2.27 1.18 2.26 1.04
## 2.68 1.03 2.67 1.06 2.60 1.01
## 1.91 1.03 2.41 1.20 2.23 1.13
## 3.09 1.38 3.00 1.33 2.82 1.40
## 1.43 0.64 1.75 0.78 1.78 0.72
## 1.79 0.75 1.92 0.81 2.04 0.81
## 3.72 0.85 3.69 0.97 3.63 0.94
## 3.35 1.33 3.25 1.34 3.10 1.27
## 3.87 1.26 3.50 1.39 3.08 1.49
## 2.01 0.93 2.01 0.95 2.07 0.94
## 2.17 1.05 1.97 1.01 1.66 0.83
## 2.25 1.21 2.24 1.19 2.18 1.12
##
## Business.people Public.relations Relationships.sources
## Mean sd Mean sd Mean sd
## 1.89 1.02 2.31 1.06 2.68 1.12
## 2.17 1.01 2.45 1.04 3.33 1.02
## 2.33 0.96 2.66 1.08 3.59 0.98
## 1.67 0.80 2.07 0.85 2.83 1.12
## 2.36 1.32 2.36 1.22 2.81 1.28
## 1.75 1.07 2.16 1.07 2.84 1.12
## 2.81 1.13 3.11 1.31 3.38 1.27
## 2.75 1.31 2.97 1.27 3.60 1.29
## 2.30 1.09 2.19 0.94 3.27 1.09
## 1.68 0.81 2.01 0.96 3.18 1.07
## 2.22 1.24 2.57 1.31 3.19 1.34
## 2.45 0.90 2.80 0.86 3.18 0.93
## 2.65 1.33 3.07 1.35 3.57 1.28
## 2.29 1.35 2.46 1.28 3.20 1.25
## 1.63 0.86 2.16 0.99 3.34 1.09
## 1.68 0.94 2.06 0.94 3.52 1.12
## 1.81 0.96 2.19 0.93 3.22 1.10
## 2.72 1.31 3.12 1.24 3.26 1.27
## 2.63 1.40 2.98 1.39 3.47 1.43
## 2.84 1.25 3.14 1.22 3.53 1.12
## 1.72 0.89 2.39 1.08 3.70 1.04
## 2.70 1.32 3.01 1.14 3.22 1.19
## 1.79 0.81 2.07 0.82 3.09 1.02
## 1.45 0.73 1.81 0.81 2.72 1.07
## 1.80 0.93 2.29 1.03 3.05 1.07
## 1.95 1.17 2.30 1.22 3.09 1.33
## 2.32 0.93 2.47 0.94 2.90 0.93
## 2.01 1.06 2.95 1.12 3.39 1.15
## 1.63 0.81 1.85 0.85 3.45 1.11
## 2.53 1.19 3.38 1.26 3.81 1.08
## 2.27 1.01 2.33 1.07 2.56 1.08
## 2.15 0.98 2.34 0.91 3.37 1.02
## 1.65 0.98 2.34 1.06 3.31 1.12
## 1.63 0.88 2.59 1.21 3.52 1.07
## 2.68 1.16 2.96 1.19 3.60 1.07
## 2.02 1.10 2.47 1.19 2.70 1.31
## 1.58 0.80 2.09 1.01 3.11 1.20
## 3.04 1.15 3.17 1.11 3.54 1.10
## 3.16 1.00 3.36 0.96 3.67 0.87
## 2.42 1.13 2.95 1.18 3.52 1.21
## 1.75 0.96 2.32 1.13 3.36 1.17
## 2.11 1.04 2.47 0.99 2.88 1.04
## 2.14 1.04 2.31 1.06 3.43 1.05
## 1.73 0.75 1.94 0.77 2.78 0.93
## 2.69 1.17 3.05 1.20 3.59 1.01
## 2.38 1.02 2.35 0.99 3.10 1.02
## 1.78 0.92 2.02 0.93 3.48 1.08
## 2.80 1.00 2.79 1.04 3.50 1.28
## 1.73 0.98 2.43 1.22 3.39 1.27
## 2.24 1.03 2.58 1.08 3.25 1.10
## 2.11 1.06 2.51 1.13 2.90 1.18
## 2.67 1.22 3.22 1.25 3.29 1.32
## 2.80 1.03 2.86 1.05 2.96 0.91
## 2.61 1.16 2.83 1.22 3.83 1.09
## 2.74 1.01 2.71 0.90 3.56 0.83
## 2.44 1.18 2.32 1.09 3.62 0.99
## 2.85 1.46 2.69 1.33 3.51 1.44
## 1.80 0.73 1.85 0.76 2.86 1.02
## 1.98 0.80 2.31 0.90 2.90 1.00
## 3.67 0.95 3.67 0.91 3.76 0.85
## 2.64 1.27 2.47 1.00 2.86 0.93
## 2.95 1.39 3.65 1.19 3.71 1.31
## 2.42 1.12 2.69 1.01 3.22 1.07
## 2.02 0.93 2.04 0.91 3.29 1.00
## 2.20 1.14 2.52 1.15 3.24 1.158 TABLES OF MEANS (MEAN CENTERED)
#C12 ROLES
cBATmC12## Detached.observer Report.reality Analyse.current.affairs
## Albania 0.94424020 1.19424020 0.55085784
## Argentina 0.12095640 1.13361463 0.76969058
## Australia 1.01120944 1.56342183 0.42536873
## Austria 1.31059507 1.53991292 1.32801161
## Bangladesh -0.19717904 0.55929764 0.59038572
## Belgium 1.49474166 1.75194330 0.94330133
## Bhutan 0.31124498 0.50401606 0.43172691
## Botswana -0.02308294 0.97691706 0.72339593
## Brazil 0.90237037 1.34503704 0.62503704
## Canada 1.14984506 1.81119965 1.06617973
## Chile -0.01585224 1.07838860 0.99461896
## China 0.28031189 0.68031189 0.42241715
## Colombia -0.60482804 0.86541005 0.75628307
## Croatia 0.69614035 1.06035088 0.80561404
## Cyprus 0.93113772 1.50598802 1.35029940
## Czech Republic 1.55577957 1.95094086 0.88239247
## Denmark 0.71579468 1.44395002 0.90430601
## Ecuador -0.46451133 0.81642512 0.68933482
## Egypt 0.80695847 0.51655443 0.32716049
## El Salvador -0.34865005 0.90835929 0.09527518
## Estonia 1.06735307 1.34148434 0.68511369
## Ethiopia -0.16936728 0.07716049 0.52507716
## Finland 1.40888889 1.42735043 1.15350427
## France_NON-REP 1.31193927 1.83988958 1.18150449
## Germany 1.25427658 1.59096723 1.30895284
## Greece 0.68013468 1.50134680 0.92861953
## Hong Kong 0.40529363 0.86435070 0.69065343
## Hungary 1.06055734 1.16666667 0.76152197
## Iceland 1.27914390 1.96766849 1.72996357
## India 0.38348765 0.76682099 0.61404321
## Indonesia 0.08976661 0.88150808 0.32136445
## Ireland 1.03418803 1.61495726 0.67649573
## Israel -0.09027778 1.23611111 0.70486111
## Italy 1.58819065 1.72207043 1.27125076
## Japan 0.09063916 0.53227067 0.95561807
## Kenya -0.06554307 0.84082397 0.47752809
## Kosovo 1.02314815 1.17058405 0.90135328
## Latvia 1.34974747 1.49065657 0.85883838
## Malawi -0.13240741 0.70092593 0.46759259
## Malaysia 0.29564756 0.50265393 0.29883227
## Mexico -0.36189579 0.86409291 0.53358443
## Moldova 0.92554410 1.40492554 0.81214204
## Netherlands 0.79444444 1.55444444 0.85666667
## New Zealand 0.90366851 1.55925046 0.76115070
## Norway 0.74246231 1.33542714 0.41834171
## Oman 0.64872460 0.16623433 0.31798530
## Philippines 0.20303637 0.81564898 0.46730063
## Qatar 0.34943639 0.26247987 0.11755233
## Romania 1.07876462 1.41758041 0.85179094
## Russia 0.58811768 1.00457012 0.60611254
## Serbia 0.17178995 1.19772654 0.92683317
## Sierra Leone 0.06934307 0.76277372 0.60218978
## Singapore_NON-REP 0.02380952 0.11471861 -0.04112554
## South Africa 0.49886621 1.34920635 0.68934240
## South Korea 0.39833493 1.15049632 0.91706692
## Spain 0.39499389 1.27411477 0.86752137
## Sudan 0.53026005 0.36004728 0.36004728
## Sweden 1.26332139 1.47837515 0.89772999
## Switzerland 1.20941147 1.47539320 1.16032471
## Thailand 0.34290598 0.40752137 0.27829060
## Turkey_NON-REP 1.11728395 1.24074074 0.73456790
## UAE -0.10324484 0.40117994 0.43657817
## UK 1.24948646 1.71503268 0.89822596
## USA 0.90156495 1.65086072 0.61142410
## Monitor.political.leaders Monitor.business
## Albania -0.36825980 -0.6513480392
## Argentina 0.63994374 0.3646272855
## Australia 0.51032448 0.3581120944
## Austria 0.06386067 -0.0783744557
## Bangladesh 0.30023028 -0.0002878526
## Belgium 0.42272519 0.3013260174
## Bhutan 0.29919679 0.2389558233
## Botswana 0.31494523 0.3501564945
## Brazil 0.33970370 -0.1189629630
## Canada 0.79924745 0.6996458610
## Chile 0.43179174 0.3113728912
## China -0.28284600 -0.2372319688
## Colombia 0.41302910 0.0697751323
## Croatia 0.83298246 0.8140350877
## Cyprus 0.37425150 0.1047904192
## Czech Republic 0.52352151 0.1969086022
## Denmark 1.11547105 0.9666037397
## Ecuador 0.34150873 -0.1233742103
## Egypt 0.20089787 -0.2890011223
## El Salvador 0.59060228 0.0719106957
## Estonia 0.02488202 -0.0368940369
## Ethiopia -0.41589506 -0.2214506173
## Finland 0.56273504 0.3965811966
## France_NON-REP 0.74672188 0.5044858523
## Germany -0.25651479 -0.2406874500
## Greece 0.57710438 0.4195286195
## Hong Kong 0.95616212 0.9015715467
## Hungary -0.23204716 -0.2995712755
## Iceland 0.36111111 0.4676684882
## India 0.10015432 -0.2776234568
## Indonesia -0.01077199 -0.2208258528
## Ireland 0.61111111 0.4111111111
## Israel 0.23611111 0.0694444444
## Italy 0.38054038 0.3969338191
## Japan 1.20456321 0.5589935927
## Kenya -0.05805243 -0.1367041199
## Kosovo 0.04878917 -0.3230056980
## Latvia 0.11338384 -0.3866161616
## Malawi 0.13425926 0.0925925926
## Malaysia -0.06740977 -0.1533970276
## Mexico 0.49968613 -0.0991839297
## Moldova 0.15234822 -0.3424971363
## Netherlands -0.23222222 -0.1011111111
## New Zealand 0.65663764 0.5806281341
## Norway 0.06909548 -0.0288944724
## Oman -0.28512754 -0.1528318201
## Philippines 0.50633967 0.3051384718
## Qatar -0.31723027 -0.2375201288
## Romania 0.06560673 -0.2567616959
## Russia -0.24992859 -0.3810339903
## Serbia 0.34181876 0.2928274095
## Sierra Leone 0.36861314 -0.3832116788
## Singapore_NON-REP 0.19264069 -0.0670995671
## South Africa 0.37641723 0.4036281179
## South Korea 0.94300352 0.9689401217
## Spain 0.63125763 0.6092796093
## Sudan 0.24515366 0.2153664303
## Sweden 1.21385902 1.0590203106
## Switzerland 0.17402334 0.0005073567
## Thailand 0.09367521 -0.0601709402
## Turkey_NON-REP 0.56172840 0.0555555556
## UAE -0.09439528 -0.3687315634
## UK 0.32007470 0.7032679739
## USA 1.12128326 0.6959311424
## Set.political.agenda Influence.public.opinion
## Albania -1.12561275 0.617034314
## Argentina 0.06082982 0.124120956
## Australia -0.74985251 -0.445427729
## Austria -0.89695210 -0.519593614
## Bangladesh -0.45624640 0.144789868
## Belgium -0.21307727 -0.402377686
## Bhutan -0.18273092 -0.279116466
## Botswana -0.79068858 -0.487871674
## Brazil -0.56962963 -0.004296296
## Canada -0.49955733 -0.240593183
## Chile -0.10223967 0.468440954
## China -0.64424951 0.161013645
## Colombia 0.03406085 0.301917989
## Croatia -0.19438596 0.247719298
## Cyprus -0.55988024 -0.008982036
## Czech Republic -0.04502688 -0.363575269
## Denmark -0.13048364 -0.433881697
## Ecuador -0.09661836 -0.006317354
## Egypt -0.24607183 0.354938272
## El Salvador 0.27284528 0.403686397
## Estonia -0.24924925 0.237237237
## Ethiopia -0.53047840 -0.495756173
## Finland -0.76649573 0.027350427
## France_NON-REP -0.43961353 -0.675638371
## Germany -0.94284572 -0.327018385
## Greece -0.84107744 -0.407744108
## Hong Kong -0.13813069 0.097601323
## Hungary -0.67899250 0.079849946
## Iceland -1.32741348 -0.966757741
## India -0.77762346 -0.038734568
## Indonesia -0.74506284 -0.271095153
## Ireland -0.47735043 -0.219658120
## Israel -0.09027778 0.805555556
## Italy -0.33803886 -0.348967820
## Japan 0.47038600 0.061103297
## Kenya -0.24157303 0.174157303
## Kosovo -0.70762108 0.311609687
## Latvia -0.56843434 0.490656566
## Malawi -0.19907407 -0.032407407
## Malaysia -0.59288747 -0.032377919
## Mexico 0.07313245 0.378217200
## Moldova -0.52290951 0.049255441
## Netherlands -0.53666667 -0.241111111
## New Zealand -0.62602270 -0.291105833
## Norway 0.10175879 -0.084170854
## Oman -0.39407696 0.162343277
## Philippines -0.38254922 0.118952286
## Qatar -0.19404187 0.400161031
## Romania -0.59886696 -0.046235380
## Russia -0.77949157 0.210225650
## Serbia -0.38728786 0.500320205
## Sierra Leone -0.39051095 -0.040145985
## Singapore_NON-REP -0.02813853 -0.067099567
## South Africa -0.99773243 0.015873016
## South Korea 0.35222542 -0.379763048
## Spain -0.35775336 -0.025335775
## Sudan -0.59739953 0.432387707
## Sweden -0.62270012 -0.489366786
## Switzerland -0.48579401 -0.515474378
## Thailand -0.18324786 -0.229401709
## Turkey_NON-REP 0.10493827 0.425925926
## UAE -0.71386431 0.374631268
## UK -0.63622782 -0.165639589
## USA -1.07308294 -0.777308294
## Advocate.for.social.change Adversary.government
## Albania 0.499387255 0.2640931
## Argentina 0.469057665 -1.3473980
## Australia -0.025958702 -1.0949853
## Austria -0.181422351 -0.6632801
## Bangladesh 0.590385723 -1.1557283
## Belgium -0.332418839 -1.4332419
## Bhutan -0.242971888 -0.5923695
## Botswana 0.272691706 -1.2625196
## Brazil 0.614370370 -1.4602963
## Canada -0.105135015 -0.9895972
## Chile 0.363728912 -1.4242292
## China -0.107407407 -1.0038986
## Colombia 0.615410053 -1.2992725
## Croatia 0.403508772 -1.2722807
## Cyprus 0.751497006 -0.6796407
## Czech Republic -0.274865591 -1.4159946
## Denmark -0.393428623 0.1761507
## Ecuador 0.384986994 -1.3708658
## Egypt 0.332210999 -0.2662738
## El Salvador 0.557892004 -1.4421080
## Estonia 0.171600172 -0.8863149
## Ethiopia 0.400077160 -1.4332562
## Finland -0.151111111 -1.0249573
## France_NON-REP -0.501725328 -1.5327812
## Germany -0.252198241 -0.7687450
## Greece 0.664983165 -1.0016835
## Hong Kong 0.040529363 -0.7808106
## Hungary 0.163451233 -0.8719185
## Iceland -0.556921676 -0.8847905
## India 0.425154321 -0.6970679
## Indonesia 0.439856373 -0.9210054
## Ireland 0.014957265 -0.6888889
## Israel 0.864583333 -0.2118056
## Italy 0.369611415 -1.3462356
## Japan -0.211751836 -1.0331302
## Kenya 0.417602996 -0.8632959
## Kosovo 0.394943020 0.3628917
## Latvia 0.522474747 -1.4775253
## Malawi 0.309259259 -1.0824074
## Malaysia 0.069532909 -0.3476645
## Mexico 0.669177652 -1.4494664
## Moldova 0.672966781 -0.6723940
## Netherlands -0.278888889 0.3211111
## New Zealand -0.017946688 -0.8968065
## Norway 0.059045226 -0.2927136
## Oman 0.446389970 -1.0438824
## Philippines 0.638471805 -0.8750417
## Qatar 0.284219002 -1.1143317
## Romania 0.710343567 -1.1383406
## Russia 0.652385033 -0.8771779
## Serbia 0.534902337 -0.7244637
## Sierra Leone 0.667883212 -1.2883212
## Singapore_NON-REP -0.002164502 -0.1320346
## South Africa 0.298185941 -1.2358277
## South Korea 0.308997759 -0.6823567
## Spain 0.386752137 -1.0390720
## Sudan 0.513238771 -0.9931442
## Sweden -0.183990442 -0.3775388
## Switzerland -0.446981228 -0.5017757
## Thailand 0.062905983 -0.2478632
## Turkey_NON-REP 0.586419753 -0.6851852
## UAE -0.280235988 -1.6342183
## UK -0.150513539 -0.9740430
## USA -0.397026604 -0.8561815
## Sup.national.development Pos.image.politicians
## Albania 0.458946078 -1.18811275
## Argentina -0.008790436 -1.39486639
## Australia -0.431268437 -1.47551622
## Austria -0.782293179 -1.82002903
## Bangladesh 0.621473805 -0.69458837
## Belgium -0.480566987 -1.35916781
## Bhutan -0.447791165 -0.67670683
## Botswana 0.723395931 -1.48082942
## Brazil 0.339703704 -1.60162963
## Canada -0.658919876 -1.73860115
## Chile 0.251163467 -1.45564282
## China 0.487329435 -0.01267057
## Colombia 0.343584656 -1.38062169
## Croatia 0.352982456 -1.76491228
## Cyprus 0.877245509 -1.07485030
## Czech Republic -0.335349462 -1.44825269
## Denmark -0.333558073 -1.84488493
## Ecuador 0.508732813 -0.73206986
## Egypt 0.195847363 -1.24607183
## El Salvador 0.548546210 -1.19911734
## Estonia 0.036465036 -1.83226083
## Ethiopia 0.681327160 -0.42631173
## Finland -0.157264957 -1.84034188
## France_NON-REP -0.520358868 -1.65700483
## Germany -0.889608313 -1.73709033
## Greece 0.301346801 -1.57744108
## Hong Kong -0.830438379 -1.39867659
## Hungary 0.195605573 -1.23847803
## Iceland -0.319216758 -1.60610200
## India 0.572376543 -0.81929012
## Indonesia 0.348294434 -0.49192101
## Ireland -0.296581197 -1.54273504
## Israel 0.111111111 -1.68750000
## Italy -1.534760170 -1.49377656
## Japan 0.144085013 -1.67448039
## Kenya 0.144194757 -0.92696629
## Kosovo 0.311609687 -1.03454416
## Latvia 0.408838384 -1.89116162
## Malawi 0.384259259 -1.06574074
## Malaysia 0.196921444 -0.35084926
## Mexico 0.474262398 -1.53703704
## Moldova 0.513172967 -1.70847652
## Netherlands -0.794444444 -1.40777778
## New Zealand -0.409870678 -1.55713909
## Norway -0.287688442 -1.29020101
## Oman 0.450281020 -0.23454388
## Philippines 0.335168502 -1.28645312
## Qatar 0.407407407 -0.12157810
## Romania 0.654422515 -1.72386696
## Russia 0.377320765 -1.20879749
## Serbia 0.560838937 -1.54002562
## Sierra Leone 0.770072993 -1.05474453
## Singapore_NON-REP 0.075757576 -0.11904762
## South Africa 0.175736961 -1.51133787
## South Korea -0.013768812 -1.40569965
## Spain 0.046092796 -1.64896215
## Sudan 0.581323877 -1.31229314
## Sweden -1.014097969 -1.75818399
## Switzerland -0.783739219 -1.74835109
## Thailand 0.136752137 -0.29709402
## Turkey_NON-REP -0.129629630 -1.45061728
## UAE 0.533923304 0.50737463
## UK -0.442950514 -1.41942110
## USA -0.825195618 -1.65054773
## Sup.gov.policy Entertainment Large.audience
## Albania -1.31311275 -0.0925245098 0.7383578431
## Argentina -0.80309423 -0.5056258790 -0.1828410689
## Australia -1.49498525 0.4466076696 0.6359882006
## Austria -1.75326560 0.2757619739 0.6328011611
## Bangladesh -0.83448474 -0.3474381117 0.5592976396
## Belgium -1.37562872 -0.0999085505 0.0564700503
## Bhutan -0.64056225 -0.3032128514 0.0702811245
## Botswana -0.65688576 -0.0582942097 0.1952269171
## Brazil -1.18829630 -0.5269629630 0.1343703704
## Canada -1.77844179 -0.4358123063 -0.2127047366
## Chile -0.95564282 -0.2278941245 0.0731529959
## China 0.11189084 -0.2723196881 0.2557504873
## Colombia -0.77546296 0.1054894180 0.0777116402
## Croatia -1.72491228 -1.0849122807 -0.6512280702
## Cyprus -1.33832335 -1.1526946108 -0.5179640719
## Czech Republic -1.60551075 -0.0369623656 0.0920698925
## Denmark -1.96705322 -0.7866325063 -0.8772473930
## Ecuador -0.42437755 -0.7287253809 0.1742846525
## Egypt -1.23849607 -0.7309203143 -0.0011223345
## El Salvador -0.68509865 -0.5449117342 0.0205088266
## Estonia -1.63534964 -0.1295581296 0.8820248820
## Ethiopia -0.20061728 0.2646604938 0.5181327160
## Finland -1.88649573 -0.1203418803 0.2519658120
## France_NON-REP -1.59489303 -0.6570048309 -0.2222222222
## Germany -1.71694644 0.5362909672 1.0254996003
## Greece -1.81683502 -0.7016835017 -0.5138047138
## Hong Kong -1.22497932 -0.6319272126 0.0330851944
## Hungary -1.11307610 0.4657020364 0.4431939979
## Iceland -1.67167577 0.5004553734 0.4430783242
## India -0.64706790 -0.1998456790 0.2279320988
## Indonesia -0.34829443 -0.0143626571 0.3536804309
## Ireland -1.65042735 -0.0004273504 0.2918803419
## Israel -1.78472222 -0.5416666667 0.1666666667
## Italy -1.53202793 -0.2205525197 0.2275349120
## Japan -1.59009220 -0.2525394593 0.0006250977
## Kenya -0.40262172 -0.2153558052 0.1104868914
## Kosovo -1.54095442 -0.4063390313 0.0295584046
## Latvia -1.53207071 -0.4957070707 0.2906565657
## Malawi -0.44907407 -0.1490740741 0.2925925926
## Malaysia -0.13428875 -0.1597664544 0.2510615711
## Mexico -0.75455116 -0.5257376020 0.2567482737
## Moldova -1.33734250 -0.3888888889 0.3533791523
## Netherlands -1.32111111 0.3677777778 -0.0966666667
## New Zealand -1.64977567 0.0580628134 0.1863288467
## Norway -1.52889447 -0.0339195980 0.1670854271
## Oman -0.26567229 -0.0088629486 0.3452226546
## Philippines -0.82699366 -0.7308975642 -0.1753420087
## Qatar -0.04186795 0.0161030596 0.2769726248
## Romania -1.40478801 -0.0495248538 0.4438961988
## Russia -1.18051985 -0.4761496715 0.7217937732
## Serbia -1.64377201 -0.4996797951 -0.0846942043
## Sierra Leone -0.68978102 -0.2883211679 0.1277372263
## Singapore_NON-REP -0.05411255 -0.1190476190 0.0627705628
## South Africa -1.35827664 0.0226757370 0.3934240363
## South Korea -1.25584374 -1.2327889850 -0.4979186679
## Spain -1.36050061 -0.4044566545 -0.5390720391
## Sudan -1.21442080 -0.4144208038 0.3302600473
## Sweden -2.08936679 -0.5646356033 -1.1130227001
## Switzerland -1.53944698 0.0986808727 0.3258498224
## Thailand -0.30632479 -0.1832478632 0.0198290598
## Turkey_NON-REP -1.63580247 -0.8456790123 0.0925925926
## UAE 0.64896755 -0.0412979351 0.5073746313
## UK -1.48328665 0.4898225957 0.4175536881
## USA -1.66463224 -0.2730829421 0.3607198748
## Advice.for.daily.life Info.people.pol.decisions
## Albania 0.12438725 -0.059436275
## Argentina -0.42651195 0.421589311
## Australia -0.02949853 0.529793510
## Austria 0.56458636 0.532656023
## Bangladesh -0.12464018 0.206966033
## Belgium 0.07910380 0.505029721
## Bhutan 0.22690763 0.516064257
## Botswana 0.05438185 0.413536776
## Brazil -0.13496296 0.478370370
## Canada -0.43581231 0.831119965
## Chile -0.06559046 0.282577080
## China 0.33996101 -0.058284600
## Colombia -0.20403439 0.286044974
## Croatia -0.27438596 0.639298246
## Cyprus -0.52994012 -0.092814371
## Czech Republic -0.06518817 0.237231183
## Denmark -0.17174577 1.352526070
## Ecuador 0.25455221 0.157562245
## Egypt 0.04938272 0.428170595
## El Salvador -0.11967809 0.366303219
## Estonia 0.18704419 -0.114114114
## Ethiopia 0.53896605 0.327160494
## Finland 0.12581197 0.464273504
## France_NON-REP 0.03243616 0.870945480
## Germany 0.82693845 0.294564349
## Greece 0.01043771 0.728619529
## Hong Kong 0.14722911 0.315963606
## Hungary 0.26312969 -0.090568060
## Iceland -0.03233151 0.352914390
## India -0.05540123 0.080709877
## Indonesia -0.12387792 0.211849192
## Ireland -0.27735043 0.384188034
## Israel 0.17361111 0.253472222
## Italy -0.14131755 0.254857316
## Japan -0.01625254 1.003438037
## Kenya 0.19662921 0.151685393
## Kosovo 0.08725071 -0.214031339
## Latvia -0.11388889 0.204292929
## Malawi 0.01759259 0.225925926
## Malaysia 0.14278132 0.136411890
## Mexico -0.11895794 0.536409291
## Moldova -0.39404353 0.219358534
## Netherlands 0.94555556 -0.138888889
## New Zealand -0.36474004 0.502243336
## Norway -0.17211055 0.199748744
## Oman 0.17790748 0.002810203
## Philippines -0.31047714 0.488321655
## Qatar 0.21175523 -0.078099839
## Romania -0.13505117 0.042580409
## Russia 0.62410740 -0.028848900
## Serbia 0.08245277 0.168908101
## Sierra Leone 0.22262774 0.200729927
## Singapore_NON-REP -0.18398268 0.140692641
## South Africa 0.21995465 0.304988662
## South Korea -0.28178034 0.288824848
## Spain -0.42094017 0.458180708
## Sudan 0.31323877 0.279196217
## Sweden 0.14074074 1.114934289
## Switzerland 0.17630644 0.737950279
## Thailand -0.04478632 0.001367521
## Turkey_NON-REP -0.51234568 0.351851852
## UAE 0.25073746 -0.112094395
## UK -0.31353875 -0.068160598
## USA -0.30688576 1.231142410
## Motivate.pol.particip. Let.people.express.themselves
## Albania -1.254289216 0.66115196
## Argentina -0.072081575 0.63677918
## Australia -0.615339233 0.88200590
## Austria 0.177068215 0.26995646
## Bangladesh -0.388888889 0.62665515
## Belgium -0.443529950 0.58527663
## Bhutan 0.142570281 0.62449799
## Botswana 0.040297340 0.69522692
## Brazil -0.030962963 0.85703704
## Canada -0.101150952 0.83908809
## Chile -0.521087842 0.51294357
## China -0.367056530 0.24697856
## Colombia -0.079034392 0.47453704
## Croatia 0.355087719 0.75929825
## Cyprus -0.559880240 0.61976048
## Czech Republic -0.641801075 0.79368280
## Denmark -0.136147069 0.40026070
## Ecuador 0.010405054 0.60906726
## Egypt 0.362514029 0.44332211
## El Salvador -0.077622015 0.58125649
## Estonia -0.434577435 0.68511369
## Ethiopia 0.143132716 0.41743827
## Finland -0.560341880 0.68888889
## France_NON-REP 0.150448585 1.16287095
## Germany 0.045643485 0.24852118
## Greece -0.104713805 1.15286195
## Hong Kong -0.128205128 0.68072787
## Hungary -0.418542337 0.34351554
## Iceland -0.770036430 1.03324226
## India -0.299845679 0.64182099
## Indonesia -0.005385996 0.50628366
## Ireland -0.546581197 0.66111111
## Israel -0.677083333 0.46180556
## Italy -0.187765634 0.93245294
## Japan 0.205969683 -0.44944523
## Kenya -0.204119850 0.60112360
## Kosovo -0.816595442 0.40135328
## Latvia -0.045707071 0.78156566
## Malawi 0.009259259 0.47592593
## Malaysia -0.395435244 0.34023355
## Mexico -0.107658506 0.66917765
## Moldova -0.388888889 0.65234822
## Netherlands -0.341111111 0.65000000
## New Zealand -0.298231723 0.90366851
## Norway -0.129396985 0.75502513
## Oman -0.421314310 0.08841332
## Philippines 0.170003337 0.53937271
## Qatar -0.288244767 0.06682770
## Romania -0.602156433 0.69060673
## Russia -0.113681805 0.51099686
## Serbia -0.537143772 0.63864874
## Sierra Leone -0.448905109 0.79197080
## Singapore_NON-REP -0.041125541 0.24458874
## South Africa -0.657596372 1.01247166
## South Korea 0.156260006 0.26577009
## Spain 0.194444444 0.93345543
## Sudan -0.052718676 0.42387707
## Sweden -0.224850657 1.26977300
## Switzerland 0.187721969 0.47539320
## Thailand -0.066324786 0.27521368
## Turkey_NON-REP -0.685185185 0.67283951
## UAE -0.483775811 0.17109145
## UK -0.681605976 0.54192344
## USA 0.197339593 1.05367762
## attr(,"scaled:center")
## Albania Argentina Australia Austria
## 3.382966 3.322082 3.057817 3.097242
## Bangladesh Belgium Bhutan Botswana
## 3.508060 2.881802 3.592369 3.832942
## Brazil Canada Chile China
## 3.220296 3.025454 3.460878 3.412671
## Colombia Croatia Cyprus Czech Republic
## 3.787368 3.665965 3.332335 2.964382
## Denmark Ecuador Egypt El Salvador
## 3.078704 3.621702 3.844557 3.685099
## Estonia Ethiopia Finland France_NON-REP
## 3.357357 3.686728 3.098803 2.886818
## Germany Greece Hong Kong Hungary
## 2.994644 3.238047 3.128205 3.306002
## Iceland India Indonesia Ireland
## 2.737250 3.691512 3.610413 3.027350
## Israel Italy Japan Kenya
## 3.434028 2.862629 3.272230 3.634831
## Kosovo Latvia Malawi Malaysia
## 3.681980 3.254798 4.057407 3.608811
## Mexico Moldova Netherlands New Zealand
## 3.836472 3.388889 2.943333 3.029823
## Norway Oman Philippines Qatar
## 2.878141 3.584738 3.673841 3.730274
## Romania Russia Serbia Sierra Leone
## 3.375183 3.216510 3.583253 3.850365
## Singapore_NON-REP South Africa South Korea Spain
## 3.106061 3.235828 3.402818 3.514347
## Sudan Sweden Switzerland Thailand
## 4.218676 3.128076 3.044013 3.952479
## Turkey_NON-REP UAE UK USA
## 3.537037 3.519174 2.863119 3.171674##C13 ETHICS
cBATmC13## Always.follow.codes Depends.on.situation
## Albania 0.9804965 0.0159574468
## Argentina 1.4295666 -0.2484520124
## Australia 1.5695922 -0.1165780142
## Austria 1.6399522 0.0705741627
## Bangladesh 1.3628319 -0.0840707965
## Belgium 1.2284736 0.1227984344
## Bhutan 0.7804878 0.2195121951
## Botswana 1.5310734 -0.0508474576
## Brazil 1.4859626 -0.1584224599
## Canada 1.3740157 0.0748031496
## Chile 1.5111241 -0.2453161593
## China 1.4653846 -0.2200854701
## Colombia 1.5352505 -0.3645640074
## Croatia 1.8660050 -0.4689826303
## Cyprus 1.5178571 -0.2321428571
## Czech Republic 0.8354167 -0.0520833333
## Denmark 0.8118774 -0.1191570881
## Ecuador 1.4654655 -0.4204204204
## Egypt 1.2187500 -0.4751275510
## El Salvador 1.4473684 -0.1271929825
## Estonia 1.3937500 0.1395833333
## Ethiopia -0.1332753 0.2081881533
## Finland 1.7310855 -0.3018092105
## France_NON-REP 1.1647059 -0.2000000000
## Germany 1.6263524 0.1982225657
## Greece 1.5780669 -0.4293680297
## Hong Kong 0.9290323 -0.0548387097
## Hungary 1.2163300 -0.0058922559
## Iceland 1.4402655 -0.4358407080
## India 0.6730769 0.1934389140
## Indonesia 1.7590517 -0.3702586207
## Ireland 1.0690559 0.0201048951
## Israel 1.1442652 0.0008960573
## Italy 1.7812500 -0.2869318182
## Kenya 1.1598940 0.0220848057
## Kosovo 1.4718935 -0.0843195266
## Latvia 1.2541494 -0.3558091286
## Malawi 1.3815789 -0.2500000000
## Malaysia 0.8133803 0.1161971831
## Mexico 1.2076389 -0.1229166667
## Moldova 1.3161058 0.1670673077
## Netherlands 0.8349169 0.0938242280
## New Zealand 1.4005038 -0.0428211587
## Norway 1.4040470 -0.1312010444
## Oman 0.4980545 -0.0778210117
## Philippines 1.1643939 -0.0780303030
## Portugal 1.7040682 -0.2801837270
## Qatar 1.0872093 -0.2662790698
## Romania 1.4282407 0.0331790123
## Russia 0.7435897 0.2820512821
## Serbia 1.7662429 -0.3213276836
## Sierra Leone 1.0367647 -0.2279411765
## Singapore_NON-REP 0.1536145 0.1777108434
## South Africa 1.5383959 -0.2807167235
## South Korea 1.5576369 -0.4308357349
## Spain 1.5639842 -0.2275725594
## Sudan 0.5367925 -0.0783018868
## Switzerland 1.4370709 0.1796338673
## Thailand 0.6306647 -0.1518126888
## Turkey_NON-REP 1.4460227 -0.0426136364
## UAE 0.9381443 0.2886597938
## UK 1.2288991 0.0802752294
## USA 1.8218912 -0.1522020725
## Matter.of.personal.judgement Can.be.set.aside
## Albania -0.55851064 -0.437943262
## Argentina -0.47136223 -0.709752322
## Australia -0.86125887 -0.591755319
## Austria -0.68700159 -1.023524721
## Bangladesh -0.42035398 -0.858407080
## Belgium -0.87133072 -0.479941292
## Bhutan -0.53658537 -0.463414634
## Botswana -1.00000000 -0.480225989
## Brazil -0.71457219 -0.612967914
## Canada -0.97244094 -0.476377953
## Chile -0.67857143 -0.587236534
## China -0.73803419 -0.507264957
## Colombia -0.23098330 -0.939703154
## Croatia -0.42183623 -0.975186104
## Cyprus -0.96683673 -0.318877551
## Czech Republic -0.85625000 0.072916667
## Denmark -0.67088123 -0.021839080
## Ecuador -0.27627628 -0.768768769
## Egypt -0.02869898 -0.714923469
## El Salvador -0.51754386 -0.802631579
## Estonia -0.79375000 -0.739583333
## Ethiopia -0.42595819 0.351045296
## Finland -0.52878289 -0.900493421
## France_NON-REP -0.77647059 -0.188235294
## Germany -0.90533230 -0.919242658
## Greece -0.78624535 -0.362453532
## Hong Kong -0.33225806 -0.541935484
## Hungary -0.75000000 -0.460437710
## Iceland -0.55088496 -0.453539823
## India -0.44683258 -0.419683258
## Indonesia -0.81681034 -0.571982759
## Ireland -0.61975524 -0.469405594
## Israel -0.83064516 -0.314516129
## Italy -0.95454545 -0.539772727
## Kenya -0.75530035 -0.426678445
## Kosovo -0.80621302 -0.581360947
## Latvia -0.35580913 -0.542531120
## Malawi -0.76127820 -0.370300752
## Malaysia -0.41197183 -0.517605634
## Mexico -0.50625000 -0.578472222
## Moldova -0.81370192 -0.669471154
## Netherlands -0.52137767 -0.407363420
## New Zealand -0.65239295 -0.705289673
## Norway -0.47584856 -0.796997389
## Oman 0.03112840 -0.451361868
## Philippines -0.42348485 -0.662878788
## Portugal -0.92585302 -0.498031496
## Qatar -0.13139535 -0.689534884
## Romania -0.78780864 -0.673611111
## Russia -0.53846154 -0.487179487
## Serbia -0.47669492 -0.968220339
## Sierra Leone -0.81617647 0.007352941
## Singapore_NON-REP -0.35240964 0.021084337
## South Africa -0.68686007 -0.570819113
## South Korea -0.48559078 -0.641210375
## Spain -0.69722955 -0.639182058
## Sudan 0.11415094 -0.572641509
## Switzerland -0.89702517 -0.719679634
## Thailand -0.22432024 -0.254531722
## Turkey_NON-REP -0.59943182 -0.803977273
## UAE -0.46391753 -0.762886598
## UK -0.66467890 -0.644495413
## USA -0.90867876 -0.761010363
## attr(,"scaled:center")
## Albania Argentina Australia Austria
## 3.455674 2.969814 3.102394 2.994817
## Bangladesh Belgium Bhutan Botswana
## 3.300885 3.164873 3.926829 3.265537
## Brazil Canada Chile China
## 2.923128 3.110236 2.985363 3.038889
## Colombia Croatia Cyprus Czech Republic
## 2.976809 2.836228 3.028061 3.468750
## Denmark Ecuador Egypt El Salvador
## 3.144444 3.315315 3.332270 3.263158
## Estonia Ethiopia Finland France_NON-REP
## 3.081250 3.119338 2.900493 3.300000
## Germany Greece Hong Kong Hungary
## 2.971793 3.020446 3.383871 3.194444
## Iceland India Indonesia Ireland
## 2.896018 3.426471 3.027155 3.284091
## Israel Italy Kenya Kosovo
## 3.117384 2.840909 3.529152 3.261834
## Latvia Malawi Malaysia Mexico
## 3.343361 3.265038 3.609155 3.514583
## Moldova Netherlands New Zealand Norway
## 3.174279 3.495249 3.244332 3.313969
## Oman Philippines Portugal Qatar
## 3.692607 3.429545 2.904856 3.061628
## Romania Russia Serbia Sierra Leone
## 3.068673 3.205128 2.920198 3.779412
## Singapore_NON-REP South Africa South Korea Spain
## 3.015060 3.011092 2.808357 2.831794
## Sudan Switzerland Thailand Turkey_NON-REP
## 4.248113 3.065217 4.118580 2.951705
## UAE UK USA
## 3.752577 3.369266 2.856865##C14 ETHICS
cBATmC14## Personal.values Peers.on.staff Editorial.supervisors
## Albania 0.47000000 -0.391111111 0.32185185
## Argentina 0.95118943 0.043700441 0.46220264
## Australia 0.34426667 -0.029066667 0.73315556
## Austria 1.09725000 0.164437500 0.41287500
## Bangladesh 0.40942675 -0.393121019 0.47949045
## Belgium 0.67214953 0.349719626 0.68149533
## Bhutan 0.38972973 0.214054054 0.40324324
## Botswana -0.16877193 -0.686315789 0.47157895
## Brazil 0.18093750 -0.026093750 0.43484375
## Canada 0.52234043 0.197872340 0.68723404
## Chile 0.62704961 0.013472585 0.74976501
## China 0.33487500 -0.299500000 0.44425000
## Colombia 0.25264463 -0.249421488 0.60801653
## Croatia 0.88669456 -0.335062762 0.12100418
## Cyprus 0.99851852 -0.424761905 0.98264550
## Czech Republic 1.03979058 0.437696335 0.62617801
## Denmark 0.82197468 0.618177215 0.83083544
## Ecuador 0.56885813 -0.330795848 0.27128028
## Egypt 0.64328358 -0.364179104 0.19925373
## El Salvador 0.40613757 -0.334603175 0.66539683
## Estonia 1.13729167 0.340416667 0.41854167
## Ethiopia 0.13353383 -0.325112782 0.54706767
## Finland 0.63691120 0.173590734 0.69868726
## France_NON-REP 0.38240964 0.328192771 0.42457831
## Germany 1.02311927 0.329541284 0.57908257
## Greece 1.29743945 -0.214671280 0.29397924
## Hong Kong 0.37086262 0.006645367 0.69035144
## Hungary 0.86388889 0.138194444 0.52013889
## Iceland 0.92000000 0.620787402 0.69165354
## India -0.18853503 -0.538853503 0.20636943
## Indonesia 0.47375566 -0.204977376 0.25203620
## Ireland 0.55108911 0.070891089 0.66000000
## Israel 1.31968504 0.067716535 0.55590551
## Italy 0.96201780 -0.272403561 0.16973294
## Kenya 0.17788732 -0.431267606 0.45253521
## Kosovo 0.22555556 -0.154074074 0.33666667
## Latvia 1.44223176 0.407896996 0.65682403
## Malawi 0.21824561 -0.255438596 0.46385965
## Malaysia 0.04394904 -0.382802548 0.04076433
## Mexico 0.28109375 -0.511875000 0.54281250
## Moldova 0.65604278 -0.188877005 0.36192513
## Netherlands 0.70054795 0.388904110 0.53616438
## New Zealand 0.66105919 0.066043614 0.53956386
## Norway 0.85290735 0.613290735 0.65801917
## Oman 0.19811321 -0.056603774 0.24056604
## Philippines 0.56123839 -0.364458204 0.46835913
## Portugal 0.88740741 0.202222222 0.66518519
## Qatar 0.04523810 0.021428571 0.27142857
## Romania 0.93732394 -0.030985915 0.18028169
## Russia 0.94470588 0.009411765 0.65647059
## Serbia 0.95279152 0.048197880 0.36268551
## Sierra Leone 0.06204082 -0.397142857 0.44979592
## Singapore_NON-REP 0.10285714 0.002857143 -0.04000000
## South Africa 0.29035461 -0.195460993 0.60241135
## South Korea 0.64000000 0.037694524 0.60541787
## Spain 0.71056604 0.087924528 0.77031447
## Sudan 0.88436364 -0.097454545 0.17527273
## Sweden 0.68992084 0.238733509 0.92738786
## Switzerland 0.91614711 0.254150613 0.38900175
## Thailand -0.08000000 -0.145789474 0.12723684
## Turkey_NON-REP -0.39759036 -0.481927711 -0.25301205
## UAE -0.09984962 -0.310375940 0.20090226
## UK 0.57926882 0.129806452 0.42873118
## USA 0.39947712 0.301437908 0.79490196
## Managers.of.news.org Owners Editorial.policy
## Albania 0.07648148 0.06722222 0.451481481
## Argentina 0.36969163 0.34325991 0.541497797
## Australia -0.39795556 -0.98462222 0.704266667
## Austria -0.33400000 -0.46525000 0.581625000
## Bangladesh 0.26292994 0.23745223 0.785222930
## Belgium -0.26943925 -0.65028037 0.863738318
## Bhutan 0.17351351 -0.11027027 -0.002162162
## Botswana 0.29614035 -0.10736842 1.015438596
## Brazil 0.31765625 0.34109375 0.665312500
## Canada -0.02021277 -0.81808511 0.442553191
## Chile 0.32678851 0.15185379 0.694934726
## China 0.45675000 0.00675000 0.425500000
## Colombia 0.45925620 0.45925620 0.624545455
## Croatia -0.22209205 -0.18025105 0.413891213
## Cyprus 1.07259259 1.01968254 0.792169312
## Czech Republic 0.02931937 -0.41570681 0.437696335
## Denmark 0.07513924 -0.68562025 0.976405063
## Ecuador 0.20553633 0.20207612 0.323183391
## Egypt 0.21044776 0.11716418 0.650746269
## El Salvador 0.51195767 0.54899471 0.538412698
## Estonia -0.07625000 -0.64916667 0.434166667
## Ethiopia 0.43052632 0.40045113 0.633533835
## Finland -0.61791506 -0.90362934 1.111814672
## France_NON-REP -0.15975904 -0.45493976 0.665542169
## Germany -0.23009174 -0.53834862 0.595596330
## Greece 0.43238754 0.25245675 0.228235294
## Hong Kong 0.57853035 0.25584665 0.562555911
## Hungary 0.22152778 0.03402778 0.294444444
## Iceland -0.86740157 -1.08787402 0.746771654
## India -0.12165605 0.04394904 0.664968153
## Indonesia 0.11628959 0.12081448 0.471493213
## Ireland -0.59742574 -0.63207921 0.709504950
## Israel -0.08976378 -0.32598425 0.398425197
## Italy -0.39406528 -0.30801187 0.534718101
## Kenya 0.29056338 0.14971831 0.667323944
## Kosovo 0.11444444 0.02185185 0.484814815
## Latvia -0.05133047 -0.52772532 0.699742489
## Malawi 0.33228070 0.25333333 0.604210526
## Malaysia -0.02611465 0.02802548 0.263694268
## Mexico 0.45296875 0.48421875 0.753750000
## Moldova -0.10866310 -0.23165775 0.623957219
## Netherlands -0.14191781 -0.46383562 0.683424658
## New Zealand -0.10218069 -0.73146417 0.576947040
## Norway -0.76690096 -0.93942492 0.974313099
## Oman -0.03301887 0.40094340 0.443396226
## Philippines 0.31356037 0.08755418 0.855356037
## Portugal -0.13728395 -0.45827160 0.881234568
## Qatar 0.21190476 0.11666667 0.735714286
## Romania -0.23169014 -0.43239437 0.454929577
## Russia 0.76823529 0.54176471 0.297647059
## Serbia 0.17187279 0.05526502 0.673639576
## Sierra Leone 0.42938776 0.40897959 0.694693878
## Singapore_NON-REP -0.08285714 -0.11142857 0.017142857
## South Africa 0.16978723 -0.37276596 0.811631206
## South Korea 0.09244957 -0.11504323 0.297060519
## Spain 0.22314465 0.11622642 0.565911950
## Sudan -0.06109091 0.04800000 0.720727273
## Sweden 0.08833773 -0.50269129 0.668812665
## Switzerland -0.37106830 -0.71257443 0.438038529
## Thailand 0.14039474 0.17328947 0.216052632
## Turkey_NON-REP 0.09638554 0.04819277 0.457831325
## UAE 0.33624060 0.34375940 0.095639098
## UK -0.05298925 -0.46159140 0.721204301
## USA 0.53673203 -0.29660131 0.778562092
## Advertising.considerations Profit.expectations
## Albania -0.354074074 -0.51611111
## Argentina -0.564229075 -0.51577093
## Australia -0.913511111 -0.90017778
## Austria -0.526187500 -0.39493750
## Bangladesh -0.093757962 -0.69248408
## Belgium -0.781121495 -0.76476636
## Bhutan -0.299459459 -0.31297297
## Botswana -0.931929825 -0.82666667
## Brazil -0.397187500 -0.31125000
## Canada -1.062765957 -1.00425532
## Chile -0.417336815 -0.34684073
## China -0.327625000 -0.35575000
## Colombia -0.075867769 -0.21016529
## Croatia -0.366443515 -0.30786611
## Cyprus -0.139047619 -0.10201058
## Czech Republic -0.593717277 -0.76125654
## Denmark -1.004607595 -0.45017722
## Ecuador -0.534948097 -0.29619377
## Egypt -0.203731343 -0.27835821
## El Salvador -0.292275132 -0.23407407
## Estonia -0.732500000 -0.64916667
## Ethiopia -0.588270677 -0.45669173
## Finland -1.158455598 -0.66424710
## France_NON-REP -0.671807229 -0.57542169
## Germany -0.301651376 -0.27412844
## Greece -0.166228374 -0.52262976
## Hong Kong -0.501341853 -0.57801917
## Hungary -0.184722222 -0.11875000
## Iceland -1.135118110 -0.83590551
## India -0.182165605 -0.35414013
## Indonesia -0.376923077 -0.37918552
## Ireland -0.785544554 -0.91920792
## Israel -0.357480315 -0.82204724
## Italy -0.557270030 -0.43857567
## Kenya -0.043943662 -0.14605634
## Kosovo -0.441111111 -0.94111111
## Latvia -0.330300429 -0.49339056
## Malawi -0.378245614 -0.62385965
## Malaysia -0.249044586 -0.23312102
## Mexico -0.496250000 -0.30093750
## Moldova -0.413475936 -0.14074866
## Netherlands -0.299452055 -0.38506849
## New Zealand -0.849844237 -0.74392523
## Norway -1.025686901 -0.64549521
## Oman -0.009433962 -0.35849057
## Philippines -0.463529412 -0.53164087
## Portugal -0.785432099 -0.48296296
## Qatar -0.061904762 -0.18095238
## Romania -0.541549296 -0.17535211
## Russia -0.590588235 -0.44352941
## Serbia -0.269823322 -0.18855124
## Sierra Leone -0.121632653 -0.45836735
## Singapore_NON-REP -0.111428571 -0.18285714
## South Africa -0.716737589 -0.61035461
## South Korea -0.198616715 -0.30812680
## Spain -0.041006289 -0.14792453
## Sudan 0.048000000 0.04800000
## Sweden -0.763905013 -0.28633245
## Switzerland -0.605744308 -0.40784588
## Thailand -0.014210526 -0.03723684
## Turkey_NON-REP -0.433734940 -0.49397590
## UAE -0.603609023 -0.37804511
## UK -0.620731183 -0.56051613
## USA -1.221437908 -1.09398693
## Audience.research Availability.of.resources Time.limits
## Albania 0.39592593 0.664444444 0.90055556
## Argentina -0.31312775 0.255154185 0.48863436
## Australia 0.21760000 0.948711111 1.02426667
## Austria 0.08787500 1.034750000 1.20193750
## Bangladesh 0.42216561 0.269299363 0.19286624
## Belgium 0.05766355 0.746915888 0.98056075
## Bhutan -0.31297297 -0.245405405 -0.23189189
## Botswana 0.28736842 0.822456140 0.77859649
## Brazil 0.23171875 0.552031250 0.90750000
## Canada 0.07021277 1.070212766 1.14468085
## Chile -0.09879896 0.373785901 0.79154047
## China -0.06825000 0.281750000 0.13487500
## Colombia 0.08322314 -0.532479339 0.43652893
## Croatia -0.09029289 0.704686192 0.83020921
## Cyprus -0.68931217 0.622857143 1.15195767
## Czech Republic 0.11832461 0.982198953 0.83036649
## Denmark 0.03843038 0.999189873 1.25108861
## Ecuador 0.05674740 0.008304498 0.22283737
## Egypt 0.28880597 0.822388060 0.68059701
## El Salvador -0.12825397 0.220952381 0.28973545
## Estonia 0.34041667 1.007083333 0.92375000
## Ethiopia -0.05819549 0.362857143 0.48691729
## Finland 0.30872587 1.169729730 1.14656371
## France_NON-REP 0.12939759 0.773975904 1.26795181
## Germany 0.18275229 0.896513761 1.06532110
## Greece 0.01024221 0.684982699 0.86491349
## Hong Kong -0.36396166 0.281405751 0.57533546
## Hungary 0.30833333 0.402083333 0.69722222
## Iceland -0.49732283 1.234960630 1.29795276
## India 0.44522293 0.528025478 0.77324841
## Indonesia 0.53031674 0.616289593 0.37194570
## Ireland 0.01148515 1.006534653 1.16990099
## Israel 0.08346457 0.579527559 0.76850394
## Italy 0.10741840 0.519881306 1.00059347
## Kenya 0.31521127 0.533521127 0.38211268
## Kosovo 0.42925926 -0.080000000 0.77185185
## Latvia 0.30918455 0.952961373 0.94866953
## Malawi 0.25333333 0.490175439 0.30596491
## Malaysia 0.03439490 0.209554140 0.21910828
## Mexico -0.05093750 -0.019687500 0.33578125
## Moldova 0.34053476 0.677433155 0.69882353
## Netherlands 0.06013699 0.748493151 0.98821918
## New Zealand 0.22803738 0.832398754 0.97258567
## Norway -0.20460064 0.319361022 1.00306709
## Oman 0.07075472 0.627358491 -0.43396226
## Philippines 0.39405573 0.883219814 0.71913313
## Portugal -0.15580247 0.875061728 1.14049383
## Qatar 0.50952381 0.842857143 0.41428571
## Romania 0.20845070 0.817605634 0.85633803
## Russia 0.13294118 0.015294118 0.07411765
## Serbia 0.18954064 0.468692580 0.53936396
## Sierra Leone 0.22530612 0.572244898 0.60285714
## Singapore_NON-REP 0.13142857 -0.040000000 0.07428571
## South Africa 0.31517730 0.854184397 0.88255319
## South Korea 0.26824207 0.637118156 0.45268012
## Spain -0.14163522 0.540754717 0.92125786
## Sudan 0.24800000 0.902545455 0.59345455
## Sweden -0.08316623 0.961688654 0.86934037
## Switzerland -0.03306480 0.926654991 1.00896673
## Thailand -0.11947368 -0.007631579 -0.06355263
## Turkey_NON-REP -0.30120482 -0.060240964 0.38554217
## UAE -0.06225564 0.073082707 0.21593985
## UK 0.29324731 0.667440860 0.84808602
## USA 0.13150327 0.883137255 0.86352941
## Journalism.ethics Friends.peers.family
## Albania 1.3311111 -0.548518519
## Argentina 1.1626432 -0.401233480
## Australia 1.3064889 -0.951288889
## Austria 1.3566250 -0.382437500
## Bangladesh 1.0527389 -0.864458599
## Belgium 1.4361682 -0.393271028
## Bhutan -0.1643243 0.173513514
## Botswana 1.1996491 -1.282807018
## Brazil 1.0637500 -0.514375000
## Canada 1.9425532 -0.626595745
## Chile 1.3450653 -0.788093995
## China 0.6442500 -0.749500000
## Colombia 1.0191736 -0.896115702
## Croatia 1.2590795 -0.165606695
## Cyprus 1.1678307 -1.541164021
## Czech Republic 1.4062827 -0.007329843
## Denmark 1.3434937 -0.708405063
## Ecuador 1.0775087 -0.545328720
## Egypt 1.0276119 -0.767164179
## El Salvador 0.9564021 -0.863703704
## Estonia 1.3977083 -0.373125000
## Ethiopia 0.2989474 -0.915338346
## Finland 1.6021622 -0.289729730
## France_NON-REP 1.6414458 -0.195903614
## Germany 1.3735780 -0.652110092
## Greece 1.3285813 -0.778685121
## Hong Kong 0.6711821 -0.469392971
## Hungary 0.8430556 -0.295833333
## Iceland 1.1011024 0.038110236
## India 0.7859873 -1.035668790
## Indonesia 0.9782805 -0.311312217
## Ireland 1.2639604 -0.701386139
## Israel 1.5401575 -0.656692913
## Italy 1.5762611 -0.610682493
## Kenya 0.6109859 -0.751690141
## Kosovo 1.5125926 -0.589259259
## Latvia 1.1117597 -0.454763948
## Malawi 0.6831579 -0.843157895
## Malaysia 0.4165605 -0.382802548
## Mexico 1.3435938 -0.957187500
## Moldova 1.2228877 -0.643422460
## Netherlands 0.6971233 -0.347397260
## New Zealand 1.2031153 -0.401246106
## Norway 1.3608946 -0.329201278
## Oman 0.2452830 -0.679245283
## Philippines 1.2887926 -0.912445820
## Portugal 1.6775309 -0.415061728
## Qatar 0.7476190 -0.776190476
## Romania 1.2542254 -0.580281690
## Russia 0.1888235 -0.390588235
## Serbia 1.3662191 -0.538374558
## Sierra Leone 0.9395918 -1.111428571
## Singapore_NON-REP 0.3457143 -0.082857143
## South Africa 1.3151773 -0.986241135
## South Korea 0.4843804 -0.429164265
## Spain 1.3835220 -0.890062893
## Sudan 1.2298182 -0.915636364
## Sweden 1.1305541 -0.410343008
## Switzerland 1.0439930 -0.208196147
## Thailand 0.2818421 0.051578947
## Turkey_NON-REP 0.5903614 -0.421686747
## UAE 0.4715789 -0.618646617
## UK 1.0502366 -0.663741935
## USA 1.6249673 -0.737777778
## Colleagues.other.media Audience.feedback
## Albania -0.77074074 0.585740741
## Argentina -0.23823789 0.202290749
## Australia -0.64240000 0.468711111
## Austria -0.41212500 0.273812500
## Bangladesh -0.65426752 0.371210191
## Belgium -0.20401869 -0.012429907
## Bhutan 0.16000000 0.700540541
## Botswana -0.78280702 0.462807018
## Brazil -0.33468750 0.411406250
## Canada -0.39787234 -0.009574468
## Chile -0.60271540 0.402506527
## China -0.53075000 0.097375000
## Colombia -0.66471074 0.349752066
## Croatia -0.26602510 0.415983264
## Cyprus -1.07555556 -0.297777778
## Czech Republic -0.55706806 0.662827225
## Denmark -0.68815190 -0.013468354
## Ecuador -0.48650519 0.406228374
## Egypt -0.65895522 0.143283582
## El Salvador -0.79492063 0.316190476
## Estonia -0.55020833 0.725833333
## Ethiopia -0.94917293 -0.069473684
## Finland -0.44030888 0.173590734
## France_NON-REP -0.05734940 0.075180723
## Germany -0.44293578 0.373577982
## Greece -0.75446367 -0.266574394
## Hong Kong -0.23297125 0.057763578
## Hungary -0.48333333 0.495833333
## Iceland -0.46582677 0.384566929
## India -0.83503185 0.470700637
## Indonesia -0.26380090 0.229411765
## Ireland -0.41920792 0.150099010
## Israel -0.41259843 0.264566929
## Italy -0.50089021 0.273590504
## Kenya -0.36436620 0.470140845
## Kosovo -0.74666667 0.290370370
## Latvia -0.53630901 0.643948498
## Malawi -0.36070175 0.358596491
## Malaysia -0.24267516 0.190445860
## Mexico -0.82046875 0.777187500
## Moldova -0.57390374 0.821818182
## Netherlands -0.18301370 0.399178082
## New Zealand -0.36386293 0.312149533
## Norway -0.36115016 0.239488818
## Oman -0.64622642 0.113207547
## Philippines -0.66476780 0.019442724
## Portugal -0.34716049 0.356543210
## Qatar -0.56190476 0.128571429
## Romania -0.48521127 0.923239437
## Russia -0.66705882 0.485882353
## Serbia -0.55250883 0.048197880
## Sierra Leone -0.33591837 0.337551020
## Singapore_NON-REP -0.14000000 0.031428571
## South Africa -0.55007092 0.581134752
## South Korea -0.32541787 0.328760807
## Spain -0.85861635 -0.012704403
## Sudan -0.66109091 -0.097454545
## Sweden -0.36548813 0.236094987
## Switzerland -0.33078809 0.061506130
## Thailand -0.02736842 0.084473684
## Turkey_NON-REP -0.67469880 -0.120481928
## UAE -0.08481203 0.058045113
## UK -0.31320430 0.491096774
## USA -0.27372549 0.477908497
## Competing.newsorg Media.laws.and.regul
## Albania 0.557962963 0.93296296
## Argentina -0.114889868 -0.05762115
## Australia 0.190933333 1.05760000
## Austria 0.003500000 0.25975000
## Bangladesh 0.269299363 0.87439490
## Belgium -0.080186916 0.57168224
## Bhutan 0.214054054 0.05189189
## Botswana 0.076842105 0.93649123
## Brazil 0.157500000 0.12625000
## Canada -0.004255319 1.17127660
## Chile 0.120522193 -0.10140992
## China -0.083875000 0.39112500
## Colombia -0.104793388 0.46545455
## Croatia -0.165606695 0.57916318
## Cyprus -0.313650794 0.55407407
## Czech Republic -0.075392670 0.95078534
## Denmark -0.183088608 0.11058228
## Ecuador -0.182006920 0.42006920
## Egypt -0.076865672 0.02761194
## El Salvador -0.329312169 0.13629630
## Estonia 0.033125000 0.05916667
## Ethiopia 0.013233083 0.52827068
## Finland 0.173590734 1.27397683
## France_NON-REP 0.183614458 0.59927711
## Germany 0.083669725 0.31853211
## Greece -0.200830450 0.67114187
## Hong Kong 0.102492013 0.18236422
## Hungary -0.073611111 0.39861111
## Iceland 0.589291339 0.39244094
## India 0.426114650 0.60445860
## Indonesia 0.030316742 0.72036199
## Ireland 0.080792079 0.73425743
## Israel -0.018897638 0.25669291
## Italy 0.166765579 0.41008902
## Kenya 0.318732394 0.67436620
## Kosovo 0.151481481 1.10518519
## Latvia -0.107124464 0.38643777
## Malawi 0.288421053 0.60421053
## Malaysia 0.107643312 0.37197452
## Mexico -0.293125000 0.24203125
## Moldova 0.105240642 0.76299465
## Netherlands -0.008356164 -0.10767123
## New Zealand 0.084735202 0.84797508
## Norway 0.137252396 1.03182109
## Oman 0.089622642 0.51415094
## Philippines 0.065882353 0.65102167
## Portugal 0.307160494 0.22074074
## Qatar -0.002380952 0.00952381
## Romania 0.109859155 0.92676056
## Russia 0.118235294 0.43882353
## Serbia -0.361696113 0.67717314
## Sierra Leone -0.091020408 0.78653061
## Singapore_NON-REP -0.097142857 0.21714286
## South Africa -0.003971631 0.80099291
## South Korea 0.478616715 0.02040346
## Spain -0.085031447 -0.07874214
## Sudan -0.188363636 0.01163636
## Sweden 0.275672823 0.43926121
## Switzerland 0.056252189 0.18760070
## Thailand -0.083289474 0.12723684
## Turkey_NON-REP -0.433734940 0.95180723
## UAE 0.268571429 0.56180451
## UK 0.166365591 0.73840860
## USA 0.004052288 1.05307190
## Information.acceess Censorship Government.officials
## Albania 1.03944444 -0.28925926 -0.77537037
## Argentina 0.81903084 -0.31753304 -0.55541850
## Australia 0.95982222 -0.47128889 -0.66017778
## Austria 0.95037500 -1.21368750 -1.10743750
## Bangladesh 0.53681529 0.25656051 -0.77528662
## Belgium 1.11607477 -0.89093458 -1.05214953
## Bhutan 0.74108108 0.33567568 0.09243243
## Botswana 1.07684211 0.18210526 -0.26526316
## Brazil 0.85281250 -0.82687500 -0.90890625
## Canada 1.10744681 -0.83404255 -0.79148936
## Chile 0.71843342 -0.41994778 -0.86120104
## China 0.38175000 0.54737500 0.11300000
## Colombia 0.63074380 -0.43123967 -0.65644628
## Croatia 0.97246862 -0.40410042 -0.86435146
## Cyprus 1.09375661 0.22603175 -1.16021164
## Czech Republic 1.40628272 -1.07539267 -1.33717277
## Denmark 0.34475949 -1.32232911 -0.71726582
## Ecuador 0.70034602 0.09134948 -0.30311419
## Egypt 0.24776119 -0.16268657 -0.46492537
## El Salvador 0.73417989 -0.19703704 -0.44571429
## Estonia 1.27270833 -1.28458333 -1.01895833
## Ethiopia 0.63729323 0.12225564 0.25383459
## Finland 0.39366795 -1.34764479 -0.53683398
## France_NON-REP 1.02096386 -0.76819277 -0.98506024
## Germany 1.09651376 -1.22825688 -1.40256881
## Greece 1.12442907 -0.30117647 -0.94477509
## Hong Kong 0.32613419 -0.04127796 -0.46619808
## Hungary 0.69375000 -0.40694444 -1.06319444
## Iceland 1.61291339 -0.97763780 -0.94614173
## India 0.67452229 0.07898089 -0.40191083
## Indonesia 0.64570136 0.24977376 -0.60995475
## Ireland 1.00158416 -0.55287129 -0.64693069
## Israel 0.68188976 0.50078740 -0.95590551
## Italy 0.97388724 -0.52462908 -1.10623145
## Kenya 0.67788732 0.17084507 -0.64605634
## Kosovo 0.91074074 0.03111111 -0.54296296
## Latvia 1.26197425 -1.23587983 -1.27021459
## Malawi 0.46385965 0.34105263 -0.31684211
## Malaysia 0.41337580 0.22547771 0.02165605
## Mexico 0.75375000 -0.04312500 -0.63296875
## Moldova 1.30310160 -0.42951872 -1.04449198
## Netherlands 0.20739726 -1.00493151 -0.78232877
## New Zealand 0.88535826 -0.35763240 -0.83115265
## Norway 1.09252396 -1.00012780 -0.65188498
## Oman 0.41981132 0.06132075 0.18867925
## Philippines 0.94204334 -0.17560372 -0.61832817
## Portugal 1.22074074 -1.13111111 -1.15580247
## Qatar 0.15238095 -0.46666667 -0.40714286
## Romania 1.34577465 -0.55211268 -1.10845070
## Russia 0.73294118 -0.47588235 -0.61705882
## Serbia 0.73724382 -0.34049470 -0.71505300
## Sierra Leone 0.70489796 -0.01959184 -0.77469388
## Singapore_NON-REP 0.04571429 0.21714286 -0.01142857
## South Africa 0.87546099 -0.82312057 -0.89404255
## South Korea 0.63423631 -0.57325648 -0.65394813
## Spain 1.00616352 -0.86490566 -1.11018868
## Sudan 0.24800000 0.10254545 -0.44290909
## Sweden 1.02765172 -1.09635884 -1.15704485
## Switzerland 0.94416813 -1.01204904 -0.79313485
## Thailand 0.15684211 -0.01092105 -0.09973684
## Turkey_NON-REP 0.63855422 0.59036145 0.45783133
## UAE 0.41894737 0.41894737 0.19338346
## UK 0.76636559 -0.65729032 -0.98847312
## USA 1.06614379 -0.71816993 -0.78679739
## Politicians Pressure.groups Business.people
## Albania -0.96055556 -1.31240741 -1.0253704
## Argentina -0.57744493 -0.85057269 -0.8593833
## Australia -0.70017778 -0.86906667 -0.6735111
## Austria -0.93400000 -0.74025000 -0.8215000
## Bangladesh -0.59694268 -0.61605096 -0.7307006
## Belgium -0.75542056 -0.69934579 -0.8068224
## Bhutan -0.21837838 -0.42108108 -0.7318919
## Botswana -0.60736842 -0.96701754 -0.6863158
## Brazil -0.73703125 -0.91281250 -0.7096875
## Canada -0.90319149 -0.98829787 -0.9085106
## Chile -0.86120104 -0.85075718 -0.7672063
## China 0.00050000 -0.70887500 -0.7370000
## Colombia -0.71429752 -0.59239669 -0.5345455
## Croatia -0.78276151 -0.93757322 -0.7241841
## Cyprus -1.09142857 -1.22899471 -1.2871958
## Czech Republic -1.00732984 -1.44712042 -1.2900524
## Denmark -0.54637975 -0.53878481 -0.7818228
## Ecuador -0.40692042 -0.53148789 -0.6249135
## Egypt -0.41268657 -0.61417910 -0.7447761
## El Salvador -0.45629630 -0.66264550 -0.5303704
## Estonia -1.15437500 -0.88354167 -1.1231250
## Ethiopia -0.03939850 -0.51684211 -0.5845113
## Finland -0.79166023 -0.88432432 -0.9345174
## France_NON-REP -0.92481928 -1.06337349 -1.0874699
## Germany -1.23009174 -0.77321101 -0.8631193
## Greece -0.91363322 -0.92747405 -0.8963322
## Hong Kong -0.52051118 -0.54607029 -0.5588498
## Hungary -1.04583333 -1.15694444 -1.1361111
## Iceland -0.96976378 -0.95401575 -0.9776378
## India -0.64713376 -0.87324841 -0.8859873
## Indonesia -0.74343891 -0.77511312 -0.8316742
## Ireland -0.69643564 -0.71623762 -0.7162376
## Israel -1.02677165 -1.11338583 -1.1921260
## Italy -0.93412463 -0.91038576 -0.9994065
## Kenya -1.03690141 -1.14253521 -0.8467606
## Kosovo -0.73740741 -0.69111111 -0.8670370
## Latvia -1.27021459 -1.26592275 -1.1071245
## Malawi -0.72912281 -0.90456140 -0.6238596
## Malaysia -0.22038217 -0.33503185 -0.4050955
## Mexico -0.64468750 -0.43765625 -0.7970312
## Moldova -1.13540107 -1.20491979 -1.2102674
## Netherlands -0.71726027 -0.50150685 -0.5357534
## New Zealand -0.90591900 -0.98068536 -0.7968847
## Norway -0.54964856 -0.63591054 -0.8116294
## Oman -0.19339623 -0.48113208 -0.6603774
## Philippines -0.81027864 -0.96507740 -0.8102786
## Portugal -1.08172840 -1.06320988 -1.0508642
## Qatar -0.45476190 -0.47857143 -0.5023810
## Romania -1.18591549 -1.22464789 -1.2774648
## Russia -0.69941176 -0.73176471 -0.7141176
## Serbia -0.72918728 -1.08961131 -0.8952650
## Sierra Leone -0.95836735 -0.60122449 -0.8359184
## Singapore_NON-REP -0.06857143 -0.01142857 -0.1400000
## South Africa -0.96141844 -0.96851064 -0.6139007
## South Korea -0.66547550 -0.73175793 -0.5876657
## Spain -0.61333333 -0.79257862 -0.5818868
## Sudan -0.53381818 -0.71563636 -0.6792727
## Sweden -0.83778364 -0.80612137 -0.7876517
## Switzerland -0.65828371 -0.54269702 -0.6022417
## Thailand -0.12605263 -0.18855263 -0.1490789
## Turkey_NON-REP 0.36144578 0.20481928 -0.2530120
## UAE -0.17503759 -0.59609023 -0.7314286
## UK -0.98202151 -0.92610753 -0.5734194
## USA -0.98287582 -1.29006536 -0.9338562
## Public.relations Relationships.sources
## Albania -0.61333333 -0.23833333
## Argentina -0.57303965 0.29920705
## Australia -0.34462222 0.58204444
## Austria -0.42618750 0.33318750
## Bangladesh -0.72433121 -0.27847134
## Belgium -0.39794393 0.28196262
## Bhutan -0.43459459 -0.16432432
## Botswana -0.45824561 0.16456140
## Brazil -0.82296875 0.25906250
## Canada -0.57872340 0.59148936
## Chile -0.40950392 0.20929504
## China -0.38700000 -0.01200000
## Colombia -0.11305785 0.38694215
## Croatia -0.55263598 0.18167364
## Cyprus -0.75809524 0.42708995
## Czech Republic -0.90785340 0.54764398
## Denmark -0.40207595 0.63210127
## Ecuador -0.22352941 -0.08858131
## Egypt -0.39776119 0.08731343
## El Salvador -0.22349206 0.16804233
## Estonia -0.45645833 0.86125000
## Ethiopia -0.27624060 -0.06947368
## Finland -0.65652510 0.36277992
## France_NON-REP -0.73204819 0.18361446
## Germany -0.36954128 0.38825688
## Greece -0.54685121 0.24553633
## Hong Kong -0.40869010 0.02581470
## Hungary -0.19166667 0.24583333
## Iceland -0.75716535 0.84125984
## India -0.03566879 0.39745223
## Indonesia -0.77058824 -0.53981900
## Ireland -0.52811881 0.50158416
## Israel -0.50708661 0.46141732
## Italy -0.03501484 0.89673591
## Kenya -0.55802817 0.07577465
## Kosovo -0.41333333 -0.18185185
## Latvia -0.59639485 0.42506438
## Malawi -0.50105263 -0.12385965
## Malaysia -0.20764331 0.09808917
## Mexico -0.26578125 0.30453125
## Moldova -0.64342246 0.39401070
## Netherlands -0.17273973 0.24164384
## New Zealand -0.63489097 0.48971963
## Norway -0.60396166 0.24268371
## Oman -0.30188679 0.24056604
## Philippines -0.84123839 -0.09201238
## Portugal -0.81629630 0.64666667
## Qatar -0.51428571 0.20000000
## Romania -0.57676056 0.38802817
## Russia -0.37294118 0.29764706
## Serbia -0.49950530 -0.11081272
## Sierra Leone -0.28489796 -0.22367347
## Singapore_NON-REP -0.08285714 0.01714286
## South Africa -0.40113475 0.59886525
## South Korea -0.61648415 0.22789625
## Spain -0.70138365 0.59421384
## Sudan -0.84290909 -0.02472727
## Sweden -0.73224274 0.27567282
## Switzerland -0.26949212 0.32070053
## Thailand -0.14907895 -0.05697368
## Turkey_NON-REP -0.42168675 -0.03614458
## UAE -0.03218045 0.03548872
## UK -0.30030108 0.22012903
## USA -0.91424837 0.33411765
## attr(,"scaled:center")
## Albania Argentina Australia Austria
## 2.918889 3.026784 3.004622 2.491812
## Bangladesh Belgium Bhutan Botswana
## 3.087389 2.559159 3.542703 3.431930
## Brazil Canada Chile China
## 3.014375 2.584043 2.983916 3.190125
## Colombia Croatia Cyprus Czech Republic
## 3.183306 3.014979 2.916825 2.970681
## Denmark Ecuador Egypt El Salvador
## 2.589418 3.348097 3.379104 3.366349
## Estonia Ethiopia Finland France_NON-REP
## 2.841875 3.287519 2.726023 2.539277
## Germany Greece Hong Kong Hungary
## 2.663119 2.844429 2.875144 3.146528
## Iceland India Indonesia Ireland
## 2.607559 3.411465 3.100905 2.869703
## Israel Italy Kenya Kosovo
## 2.845669 2.625519 3.522817 2.885556
## Latvia Malawi Malaysia Mexico
## 2.682232 3.667719 3.567516 3.215000
## Moldova Netherlands New Zealand Norway
## 2.964278 2.641918 2.940187 2.540064
## Oman Philippines Portugal Qatar
## 3.353774 3.187988 2.834815 3.300000
## Romania Russia Serbia Sierra Leone
## 3.006338 2.949412 3.008339 3.509388
## Singapore_NON-REP South Africa South Korea Spain
## 2.940000 3.227376 3.331182 3.022138
## Sudan Sweden Switzerland Thailand
## 3.533818 2.584485 2.581226 3.820132
## Turkey_NON-REP UAE UK USA
## 2.891566 3.678797 2.994925 2.953464