jobsatisfaction

0th

Percentile

Income and Job Satisfaction

Income and job satisfaction by gender.

Keywords
datasets
Usage
jobsatisfaction
Details

This data set was given in Agresti (2002, p. 288, Tab. 7.8). Winell and Lindb<U+00E4>ck (2018) used the data to demonstrate a score-independent test for ordered categorical data.

Format

A contingency table with 104 observations on 3 variables.

Income

a factor with levels "<5000", "5000-15000", "15000-25000" and ">25000".

Job.Satisfaction

a factor with levels "Very Dissatisfied", "A Little Satisfied", "Moderately Satisfied" and "Very Satisfied".

Gender

a factor with levels "Female" and "Male".

References

Winell, H. and Lindb<U+00E4>ck, J. (2018). A general score-independent test for order-restricted inference. Statistics in Medicine 37(21), 3078--3090. 10.1002/sim.7690

Aliases
  • jobsatisfaction
Examples
# NOT RUN {
## Approximative (Monte Carlo) linear-by-linear association test
lbl_test(jobsatisfaction, distribution = approximate(nresample = 10000))

# }
# NOT RUN {
## Approximative (Monte Carlo) score-independent test
## Winell and Lindbaeck (2018)
(it <- independence_test(jobsatisfaction,
                         distribution = approximate(nresample = 10000),
                         xtrafo = function(data)
                             trafo(data, factor_trafo = function(x)
                                 zheng_trafo(as.ordered(x))),
                         ytrafo = function(data)
                             trafo(data, factor_trafo = function(y)
                                 zheng_trafo(as.ordered(y)))))

## Extract the "best" set of scores
ss <- statistic(it, type = "standardized")
idx <- which(abs(ss) == max(abs(ss)), arr.ind = TRUE)
ss[idx[1], idx[2], drop = FALSE]
# }
Documentation reproduced from package coin, version 1.3-1, License: GPL-2

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