colombia {basicspace}

R Documentation

2004 PELA Liberal-Conservative Scales.

Description

Liberal-Conservative 10-point scales from the University of Salamanca's Parliamentary Elites of Latin America (PELA) survey. Stored as a matrix of integers. The number 99 is a missing value. These data come from Sebastian Saiegh and are used in the paper and book cited below.

Usage

data(colombia)

Value

The data is formatted as an integer matrix with the following elements.

colombia

matrix, containing reported placements of various stimuli on a 10 point Liberal-Conservative scale: id Respondent ID. party Respondent party. departam Respondent district. entrey Interviewer ID. pl_uribista Placement of “Partido Liberal Uribista” on 10 point scale. pl_oficial Placement of “Partido Liberal Oficial” on 10 point scale. conservator Placement of “Partido Conservador” on 10 point scale. polo Placement of “Polo” on 10 point scale. union_cristiana Placement of “Union Cristiana” on 10 point scale. salvation Placement of “Salvacion” on 10 point scale. urine Placement of Mr. Uribe on 10 point scale. antanas Placement of Mr. Antanas on 10 point scale. gomez Placement of Mr. Gomez on 10 point scale. garzon Placement of Garzon on 10 point scale. holgin Placement of Holguin on 10 point scale. rivera Placement of Rivera on 10 point scale. self Respondent self placement on 10 point scale.

Author(s)

Keith Poole ktpoole@uga.edu

Howard Rosenthal hr31@nyu.edu

Jeffrey Lewis jblewis@ucla.edu

James Lo lojames@usc.edu

Royce Carroll rcarroll@rice.edu

Source

Sebastian Saiegh. 2009. ‘Recovering a Basic Space from Elite Surveys: Evidence from Latin America.’ Legislative Studies Quarterly. 34(1): 117-145.

Sebastian Saiegh. 2011. Ruling By Statute: How Uncertainty and Vote-Buying Shape Lawmaking. New York: Cambridge University Press.

See Also

'aldmck', 'summary.aldmck', 'plot.aldmck', 'plot.cdf'.

Examples

### Loads and scales the Liberal-Conservative scales from the 2004 PELA survey
data(colombia)
tmp <- colombia[,c(5:8,12:17)]
result <- aldmck(data=tmp, polarity=7, respondent=10, missing=c(99),verbose=TRUE)
summary(result)
plot.cdf(result)

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