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 1.3-0 Index]