Income and Job Satisfaction
Income and job satisfaction by gender.
jobsatisfaction
A contingency table with 104 observations on 3 variables.
Incomea factor with levels "<5000", "5000-15000",
"15000-25000" and ">25000".
Job.Satisfactiona factor with levels "Very Dissatisfied",
"A Little Satisfied", "Moderately Satisfied" and
"Very Satisfied".
Gendera factor with levels "Female" and "Male".
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.
Agresti, A. (2002). Categorical Data Analysis, Second Edition. Hoboken, New Jersey: John Wiley & Sons.
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
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.