DIFSim | R Documentation |
Artificial data simulated from a Rasch model and a partial credit model, respectively, where the items exhibit differential item functioning (DIF).
data(DIFSim)
data(DIFSimPC)
Two data frames containing 200 and 500 observations, respectively, on 4 variables.
an itemresp
matrix with binary or polytomous
results for 20 or 8 items, respectively.
age in years.
factor indicating gender.
ordered factor indicating motivation level.
The data are employed for illustrations in Strobl et al. (2015)
and Komboz et al. (2018). See the manual pages for
raschtree
and pctree
for fitting the
tree models..
Komboz B, Zeileis A, Strobl C (2018). Tree-Based Global Model Tests for Polytomous Rasch Models. Educational and Psychological Measurement, 78(1), 128–166. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0013164416664394")}
Strobl C, Kopf J, Zeileis A (2015). Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model. Psychometrika, 80(2), 289–316. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-013-9388-3")}
raschtree
, pctree
## data
data("DIFSim", package = "psychotree")
data("DIFSimPC", package = "psychotree")
## summary of covariates
summary(DIFSim[, -1])
summary(DIFSimPC[, -1])
## empirical frequencies of responses
plot(DIFSim$resp)
plot(DIFSimPC$resp)
## histogram of raw scores
hist(rowSums(DIFSim$resp), breaks = 0:20 - 0.5)
hist(rowSums(DIFSimPC$resp), breaks = 0:17 - 0.5)
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