stdPDIF {difR}R Documentation

Standardization DIF statistic

Description

Calculates standardized P-difference statistics for DIF detection.

Usage

 stdPDIF(data, member, anchor=1:ncol(data))
 

Arguments

data numeric: the data matrix (one row per subject, one column per item).
member numeric: the vector of group membership with zero and one entries only. See Details.
anchor a vector of integer values specifying which items (all by default) are currently considered as anchor (DIF free) items. See Details.

Details

This command computes the standardization statistic in the specific framework of differential item functioning (Dorans and Kullick, 1986). It forms the basic command of difStd and is specifically designed for this call.

The data are passed through the data argument, with one row per subject and one column per item. Missing values are not allowed.

The vector of group membership, specified with member argument, must hold only zeros and ones, a value of zero corresponding to the reference group and a value of one to the focal group.

Option anchor sets the items which are considered as anchor items for computing standardized P-DIF statistics. Items other than the anchor items and the tested item are discarded. anchor must hold integer values specifying the column numbers of the corresponding anchor items. It is mainly designed to perform item purification.

Value

A vector with the values of the standardization DIF statistics.

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

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

difStd, dichoDif

Examples

# Loading of the verbal data
data(verbal)

# All items as anchor items
stdPDIF(verbal[,1:24], verbal[,26])

# Removing item 6 from the set of anchor items
stdPDIF(verbal[,1:24], verbal[,26], anchor=c(1:5,7:24))

[Package difR version 1.1 Index]