difStd {difR}R Documentation

Standardization DIF method

Description

Performs DIF detection using standardization method.

Usage

 difStd(Data, group, focal.name,thr=0.1, purify=FALSE, nrIter=10)
 ## S3 method for class 'PDIF':
 print(x, ...)
 ## S3 method for class 'PDIF':
 plot(x, pch=8, number=TRUE, col="red", ...)
 

Arguments

Data numeric: either the data matrix only, or the data matrix plus the vector of group membership. See Details.
group numeric or character: either the vector of group membership or the column indicator (within data) of group membership. See Details.
focal.name numeric or character indicating the level of group which corresponds to the focal group.
thr numeric: the threshold (cut-score) for standardized P-DIF statistic (default is 0.10).
purify logical: should the method be used iteratively to purify the set of anchor items? (default is FALSE).
nrIter numeric: the maximal number of iterations in the item purification process. Default is 10.
x the result from a PDIF class object.
pch, col type of usual pch and col graphical options.
number logical: should the item number identification be printed (default is TRUE).
... other generic parameters for the plot or the print functions.

Details

The method of standardization (Dorans and Kullick, 1986) allows for detecting uniform differential item functioning without requiring an item response model approach.

The Data is a matrix whose rows correspond to the subjects and columns to the items. Missing values are not allowed. In addition, Data can hold the vector of group membership. If so, group indicates the column of Data which corresponds to the group membership, either by specifying its name or by giving the column number. Otherwise, group must be a vector of same length as nrow(Data).

The vector of group membership must hold only two different values, either as numeric or character. The focal group is defined by the value of the argument focal.name.

The threshold (or cut-score) for classifying items as DIF has to be set by the user by the argument thr. Default value is 0.10 but Dorans (1989) also recommends value 0.05. For this reason it is not possible to provide asymptotic p-values.

Item purification can be performed by setting purify to TRUE. Purification works as follows: if at least one item was detected as functioning differently at some step of the process, then the data set of the next step consists in all items that are currently anchor (DIF free) items, plus the tested item (if necessary). The process stops when either two successive applications of the method yield the same classifications of the items (Clauser and Mazor, 1998), or when nrIter iterations are run without obtaining two successive identical classifications. In the latter case a warning message is printed.

Value

A list of class "PDIF" with the following arguments:

PDIF the values of the Standardization statistics.
alpha the value of alpha argument.
thr the threshold (cut-score) for DIF detection.
DIFitems either the column indicators of the items which were detected as DIF items, or "No DIF item detected".
purification the value of purify option.
nrPur the number of iterations in the item purification process. Returned only if purify is TRUE.
difPur a binary matrix with one row per iteration in the item purification process and one column per item. Zeros and ones in the i-th row refer to items which were classified respectively as non-DIF and DIF items at the (i-1)-th step. The first row corresponds to the initial classification of the items. Returned only if purify is TRUE.
convergence logical indicating whether the iterative item purification process stopped before the maximal number nrIter of allowed iterations. Returned only if purify is TRUE.
names the names of the items.

Author(s)

Sebastien Beland
Centre sur les Applications des Modeles de Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
sebastien.beland.1@hotmail.com
David Magis
Research Group of Quantitative Psychology and Individual Differences
Katholieke Universiteit Leuven
David.Magis@psy.kuleuven.be, http://ppw.kuleuven.be/okp/home/
Gilles Raiche
Centre sur les Applications des Modeles de Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/

References

Clauser, B.E. and Mazor, K.M. (1998). Using statistical procedures to identify differential item functioning test items. Educational Measurement: Issues and Practice, 17, 31-44.

Dorans, N. J. (1989). Two new approaches to assessing differential item functioning. Standardization and the Mantel-Haenszel method. Applied Measurement in Education, 2, 217-233.

Dorans, N. J. and Kullick, E. (1986). Demonstrating the utility of the standardization approach to assessing unexpected differential item performance on the Scholastic Aptitude Test. Journal of Educational Measurement, 23, 355-368.

See Also

stdPDIF, dichoDif

Examples

# Loading of the verbal data
data(verbal)

# Excluding the "Anger" variable
verbal<-verbal[colnames(verbal)!="Anger"]

# Three equivalent settings of the data matrix and the group membership
difStd(verbal, group=25, focal.name=1)
difStd(verbal, group="Gender", focal.name=1)
difStd(verbal[,1:24], group=verbal[,25], focal.name=1)

# With item purification
difStd(verbal, group="Gender", focal.name=1, purify=TRUE)
difStd(verbal, group="Gender", focal.name=1, purify=TRUE, nrIter=5)

# With detection threshold of 0.05
difStd(verbal, group="Gender", focal.name=1, thr=0.05)

[Package difR version 1.1 Index]