LordChi2 {difR} | R Documentation |
Calculates the Lord's chi-square statistics for DIF detection.
LordChi2(mR, mF)
mR |
numeric: the matrix of item parameter estimates (one row per item) for the reference group. See Details. |
mF |
numeric: the matrix of item parameter estimates (one row per item) for the focal group. See Details. |
This command computes the Lord's chi-square statistic (Lord, 1980) in the specific framework of differential item functioning. It forms the basic command
of difLord
and is specifically designed for this call.
The matrices mR
and mF
must have the same format as the output of the command itemParEst
with one the possible models (1PL, 2PL,
3PL or constrained 3PL). The number of columns therefore equals two, five, nine or six, respectively. Moreover, item parameters of the focal must be on the
same scale of that of the reference group. If not, make use of e.g. equal means anchoring (Cook and Eignor, 1991) and itemRescale
to transform
them adequately.
A vector with the values of the Lord's chi-square DIF statistics.
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/
Cook, L. L. and Eignor, D. R. (1991). An NCME instructional module on IRT equating methods. Educational Measurement: Issues and Practice, 10, 37-45.
Lord, F. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Lawrence Erlbaum Associates.
itemParEst
, itemRescale
, difLord
, dichoDif
# Loading of the verbal data data(verbal) attach(verbal) # Splitting the data into reference and focal groups nF<-sum(Gender) nR<-nrow(verbal)-nF data.ref<-verbal[,1:24][order(Gender),][1:nR,] data.focal<-verbal[,1:24][order(Gender),][(nR+1):(nR+nF),] # Pre-estimation of the item parameters (1PL model) mR<-itemParEst(data.ref,model="1PL") mF<-itemParEst(data.focal,model="1PL") mF<-itemRescale(mR, mF) LordChi2(mR, mF) # Pre-estimation of the item parameters (2PL model) mR<-itemParEst(data.ref,model="2PL") mF<-itemParEst(data.focal,model="2PL") mF<-itemRescale(mR, mF) LordChi2(mR, mF) # Pre-estimation of the item parameters (constrained 3PL model) mR<-itemParEst(data.ref,model="3PL",c=0.05) mF<-itemParEst(data.focal,model="3PL",c=0.05) mF<-itemRescale(mR, mF) LordChi2(mR, mF)