Income and Job Satisfaction
Income and job satisfaction by gender.
jobsatisfaction
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"
.
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)
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