jobsatisfaction | R Documentation |
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. \Sexpr[results=rd]{tools:::Rd_expr_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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