Political knowledge in the US and Europe

Description

Data from McChesney and Nichols (2010) on domestic and international knowledge in Denmark, Finland, the UK and the US among college graduates, people with some college, and roughly 12th grade only.

Usage

1

Format

A data.frame containing 12 columns and 4 rows.

country

a character vector of Denmark, Finland, UK, and US, being the four countries comparied in this data set.

DomesticKnowledge.hs, DomesticKnowledge.sc, DomesticKnowledge.c

percent correct answers to calibrated questions regarding knowledge of prominent items in domestic news in a survey of residents of the four countries among college graduates (ending ".c"), some college (".sc") and high school ("hs"). Source: McChesney and Nichols (2010, chapter 1, chart 8).

InternationalKnowledge.hs, InternationalKnowledge.sc, InternationalKnowledge.c

percent correct answers to calibrated questions regarding knowledge of prominent items in international news in a survey of residents of the four countries by education level as for DomesticKnowledge. Source: McChesney and Nichols (2010, chapter 1, chart 7).

PoliticalKnowledge.hs, PoliticalKnowledge.sc, PoliticalKnowledge.c

average of domestic and international knowledge

PublicMediaPerCapita

Per capital spending on public media in 2007 in US dollars from McChesney and Nichols (2010, chapter 4, chart 1)

PublicMediaRel2US

Spending on public media relative to the US, being PublicMediaPerCapita / PublicMediaPerCapita[4].

Author(s)

Spencer Graves

Source

Robert W. McChesney and John Nichols (2010) The Death and Life of American Journalism (Nation Books)

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##
## 1. Combine first 2 rows 
##
data(politicalKnowledge)
pk <- politicalKnowledge[-1,]
pk[1, -1] <- ((politicalKnowledge[1, -1] + 
                 politicalKnowledge[2, -1])/2)
pk[1, 'country'] <- 'DK-FI'

##
## 2.  plot
##
xlim <- range(pk[, 'PublicMediaPerCapita'])
ylim <- 100*range(pk[2:7])
text.cex <- 2

# to label the lines 
(US.UK <- (pk[2, -1]+pk[3, -1])/2)

#png('Knowledge v. public media.png')
op <- par(mar=c(5, 7, 4, 2)+.1)
plot(c(0, 110), 100*ylim, type='n', axes=FALSE,
     xlab='public media $ per capita',
     ylab='Political Knowledge\n(% of standard questions)',
     cex.lab=2)
axis(1, cex.axis=2)
axis(2, las=2, cex.axis=2)
with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.hs,
              country, cex=text.cex, xpd=NA, 
              col=c('forestgreen', 'orange', 'red')))
with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.sc,
              country, cex=text.cex, xpd=NA, 
              col=c('forestgreen', 'orange', 'red')))
with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.c,
              country, cex=text.cex, xpd=NA, 
              col=c('forestgreen', 'orange', 'red')))
with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.hs,
               type='b', pch=' '))
with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.sc,
               type='b', pch=' '))
with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.c,
               type='b', pch=' '))
with(US.UK, text(PublicMediaPerCapita, 100*PoliticalKnowledge.hs,
                 'High School\nor less', srt=37, cex=1.5))
with(US.UK, text(PublicMediaPerCapita, 100*PoliticalKnowledge.sc,
                 'some\ncollege', srt=10.5, cex=1.5))
with(US.UK, text(PublicMediaPerCapita, 100*PoliticalKnowledge.c,
                 "Bachelor's\nor more", srt=-1, cex=1.5))

par(op)
#dev.off()

##
## redo for Wikimedia commons
## without English axis labels 
## to facilitate multilingual use 
##
#svg('Knowledge v. public media.svg')
op <- par(mar=c(3,3,2,2)+.1)
plot(c(0, 110), 100*ylim, type='n', axes=FALSE,
     xlab='', ylab='', cex.lab=2)
axis(1, cex.axis=2)
axis(2, las=2, cex.axis=2)
with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.hs,
              country, cex=text.cex, xpd=NA, 
              col=c('forestgreen', 'orange', 'red')))
with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.sc,
              country, cex=text.cex, xpd=NA, 
              col=c('forestgreen', 'orange', 'red')))
with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.c,
              country, cex=text.cex, xpd=NA, 
              col=c('forestgreen', 'orange', 'red')))
with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.hs,
               type='b', pch=' '))
with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.sc,
               type='b', pch=' '))
with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.c,
               type='b', pch=' '))
par(op)
#dev.off()

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