selectDif {difR} | R Documentation |
This function performs DIF detection for one pre-specified method.
selectDif(Data, group, focal.name, method, alpha=0.05, correct=TRUE,thr=0.1, model="2PL", c=NULL, irtParam=NULL, same.scale=TRUE, purify=FALSE, nrIter=10)
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. |
method |
character: the name of the selected method. See Details. |
alpha |
numeric: significance level (default is 0.05). |
correct |
logical: should the continuity correction be used? (default is TRUE). |
thr |
numeric: the threshold (cut-score) for standardized P-DIF statistic (default is 0.10). |
model |
character: the IRT model to be fitted (either "1PL" , "2PL" or "3PL" ). Default is "2PL" . |
c |
optional numeric value or vector giving the values of the constrained pseudo-guessing parameters. See Details. |
irtParam |
matrix with 2J rows (where J is the number of items) and at most 9 columns containing item parameters estimates. See Details. |
same.scale |
logical: are the item parameters of the irtParam matrix on the same scale? (default is "TRUE"). See Details. |
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. |
This is a generic function which calls one of the DIF detection methods and displays its output. It is mainly used as a routine for dichoDif
command.
The possible methods are: "MH"
for mantel-Haenszel (Holland and Thayer, 1988), "Std"
for standardization (Dorans and Kullick, 1986), "Logistic"
for logistic regression (Swaminathan and Rogers, 1990), "BD"
for Breslow-Day method (Penfield, 2003), "Lord"
for Lord's chi-square test (Lord, 1980),
"Raju"
for Raju's area method (Raju, 1990), and "LRT"
for likelihoo-ratio test method (Thissen, Steinberg and Wainer, 1988).
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
.
For Lord and Raju methods, one can specify either the IRT model to be fitted (by means of model
and c
arguments),
or the item parameter estimates with arguments irtParam
and same.scale
. See difLord
and difRaju
for further details.
The threshold for detecting DIF items depends on the method. For standardization it has to be fully specified (with the thr
argument),
while for the other methods it is depending on the significance level set by alpha
.
Option correct
specifies whether the continuity correction has to be applied to Mantel-Haenszel statistic (see difMH
).
Item purification can be requested by specifying purify
option to TRUE
. Recall that item purification is slightly different
for IRT and for non-IRT based methods. See the corresponding methods for further information.
The output of the selected DIF detection method.
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/
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.
Holland, P. W. and Thayer, D. T. (1988). Differential item performance and the Mantel-Haenszel procedure. In H. Wainer and H. I. Braun (Dirs.), Test validity. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
Lord, F. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Lawrence Erlbaum Associates.
Penfield, R.D. (2003). Application of the Breslow-Day test of trend in odds ratio heterogeneity to the detection of nonuniform DIF. Alberta Journal of Educational Research, 49, 231-243.
Raju, N. S. (1990). Determining the significance of estimated signed and unsigned areas between two item response functions. Applied Psychological Measurement, 14, 197-207.
Swaminathan, H. and Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27, 361-370.
Thissen, D., Steinberg, L. and Wainer, H. (1988). Use of item response theory in the study of group difference in trace lines. In H. Wainer and H. Braun (Eds.), Test validity. Hillsdale, NJ: Lawrence Erlbaum Associates.
difMH
, difStd
, difBD
, difLogistic
, difLord
, difRaju
, difLRT
, dichoDif
# Loading of the verbal data data(verbal) attach(verbal) # Excluding the "Anger" variable verbal<-verbal[colnames(verbal)!="Anger"] # Calling Mantel-Haenszel selectDif(verbal, group=25, focal.name=1, method="MH") # Calling Lord method # 2PL model, with item purification selectDif(verbal, group=25, focal.name=1, method="Lord",model="2PL", purify=TRUE)