jobsatisfaction {coin} R Documentation

## Income and Job Satisfaction

### Description

Income and job satisfaction by gender.

### Usage

jobsatisfaction

### 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".

### Details

This data set was given in Agresti (2002, p. 288, Tab. 7.8). Winell and Lindbäck (2018) used the data to demonstrate a score-independent test for ordered categorical data.

### Source

Agresti, A. (2002). Categorical Data Analysis, Second Edition. Hoboken, New Jersey: John Wiley & Sons.

### References

Winell, H. and Lindbäck, J. (2018). A general score-independent test for order-restricted inference. Statistics in Medicine 37(21), 3078–3090. doi: 10.1002/sim.7690

### Examples

## 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]
## End(Not run)


[Package coin version 1.4-2 Index]