\name{JobSat}
\Rdversion{1.1}
\alias{JobSat}
\docType{data}
\title{Cross-classification of job satisfaction by income}
\description{
This data set is a contingency table of job satisfaction by income for a small sample
of black males from the 1996 General Social Survey, as used by Agresti (2002) for
an example.
}
\usage{data(JobSat)}
\format{
  A 4 x 4 contingency table of \code{income} by \code{satisfaction}, with the following structure:
\preformatted{
 table [1:4, 1:4] 1 2 1 0 3 3 6 1 10 10 ...
 - attr(*, "dimnames")=List of 2
  ..$ income      : chr [1:4] "< 15k" "15-25k" "25-40k" "> 40k"
  ..$ satisfaction: chr [1:4] "VeryD" "LittleD" "ModerateS" "VeryS"
}
}
\details{
Both \code{income} and \code{satisfaction} are ordinal variables, and are so ordered in the
table.  Measures of association, visualizations, and models should take ordinality into
account.
}
\source{
Agresti, A. Categorical Data Analysis John Wiley & Sons, 2002,
Table 2.8, p. 57.
}
%\references{
%  ~~ possibly secondary sources and usages ~~
%}
\examples{
data(JobSat)
assocstats(JobSat)
GKgamma(JobSat)
}
\keyword{datasets}
\concept{loglinear models}
\concept{ordinal variables}
