Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/subtestLogistic.R
Performs the Wald test to identify DIF items among a subset of groups of examinees, using the results of generalized logistic regression for all groups.
1 2 3 4  subtestLogistic(x, items, groups, alpha = 0.05)
## S3 method for class 'subLogistic'
print(x, ...)

x 
an object of class "genLogistic", typically the output of the 
items 
numeric or character: a vector of items to be tested. See Details. 
groups 
numeric or character: a vector of groups of examinees to be compared. See Details. 
alpha 
numeric: the significance level (default is 0.05). 
... 
other generic parameters for the 
This command makes use of the results from the generalized logistic regression to perform subtests between two or more groups of examinees (Magis, Raiche, Beland and Gerard, 2010). The Wald test is used with an appropriate contrast matrix.
The subtestLogistic
command requires a preliminary output of the generalized logistic regression with all groups of examinees, preferable with the
difGenLogistic
command. The object x
is an object of class "genLogistic" from which subtests can be performed. The same DIF effect
(either uniform, nonuniform, or both types) is tested among the subset of groups of examinees as the one tested with all groups. It is provided b y the
argument type
argument of x
.
The argument items
is a vector of the names of the items to be tested, or their number in the data set. A single item can be specified.
The argument groups
specifies which groups of examinees are considered in this subtest routine. It is a vector of either group names or integer values.
In the latter case, the reference group is specified with the 0 (zero) value, while the focal groups are set up by their rank in the x$focal.names
argument.
At least two groups must be specified, and all groups can be included (which leads back to the generalized logistic regression with the Wald test).
The output provides, among others, the Wald statistics, the degrees of freedom and related asymptotic pvalues for each tested item, as well as the contrast matrix.
A list of class "subLogistic" with the following components:
stats 
a table with as many rows as tested items, and four columns: the item number, the Wald statistic, the degrees of freedom and the asymptotic pvalue. 
contrastMatrix 
the contrast matrix used for testing DIF among the groups set up by 
items 
the value of the 
groups 
the value of the 
type 
the value of the 
purification 
the value of the 
alpha 
the value of the 
Sebastien Beland
Collectif pour le Developpement et les Applications en Mesure et Evaluation (Cdame)
Universite du Quebec a Montreal
sebastien.beland.1@hotmail.com, http://www.cdame.uqam.ca/
David Magis
Department of Psychology, University of Liege
Research Group of Quantitative Psychology and Individual Differences, KU Leuven
David.Magis@uliege.be, http://ppw.kuleuven.be/okp/home/
Gilles Raiche
Collectif pour le Developpement et les Applications en Mesure et Evaluation (Cdame)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca, http://www.cdame.uqam.ca/
Magis, D., Beland, S., Tuerlinckx, F. and De Boeck, P. (2010). A general framework and an R package for the detection of dichotomous differential item functioning. Behavior Research Methods, 42, 847862. doi: 10.3758/BRM.42.3.847
Magis, D., Raiche, G., Beland, S. and Gerard, P. (2011). A logistic regression procedure to detect differential item functioning among multiple groups. International Journal of Testing, 11, 365–386. doi: 10.1080/15305058.2011.602810
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  ## Not run:
# Loading of the verbal data
data(verbal)
attach(verbal)
# Creating four groups according to gender (0 or 1) and trait anger score
# ("Low" or "High")
# Reference group: women with low trait anger score (<=20)
group < rep("WomanLow",nrow(verbal))
group[Anger>20 & Gender==0] < "WomanHigh"
group[Anger<=20 & Gender==1] < "ManLow"
group[Anger>20 & Gender==1] < "ManHigh"
# New data set
Verbal < cbind(verbal[,1:24], group)
# Reference group: "WomanLow"
names < c("WomanHigh", "ManLow", "ManHigh")
# Testing all types of DIF with all items
rDIF < difGenLogistic(Verbal, group = 25, focal.names = names)
rUDIF < difGenLogistic(Verbal, group = 25, focal.names = names, type = "udif")
rNUDIF < difGenLogistic(Verbal, group = 25, focal.names = names, type = "nudif")
# Subtests between the reference group and the first two focal groups
# for item "S2WantShout" (item 6) and the three types of DIF
subGroups < c("WomanLow", "WomanHigh", "ManLow")
subtestLogistic(rDIF, items = 6, groups = subGroups)
subtestLogistic(rUDIF, items = 6, groups = subGroups)
subtestLogistic(rNUDIF, items = 6, groups = subGroups)
# Subtests between the reference group and the first focal group
# for items "S2WantShout" (item 6) and "S3WantCurse" (item 7)
# (only both DIF effects)
subGroups < c("WomanLow", "WomanHigh")
items1 < c("S2WantShout", "S3WantCurse")
items2 < 6:7
subtestLogistic(rDIF, items = items1, groups = subGroups)
subtestLogistic(rDIF, items = items2, groups = subGroups)
## End(Not run)

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