selectDif {difR}  R Documentation 
This function performs DIF detection for one prespecified method.
selectDif(Data, group, focal.name, method, anchor = NULL, props = NULL, thrTID = 1.5, alpha = 0.05, MHstat = "MHChisq", correct = TRUE, exact = FALSE, stdWeight = "focal", thrSTD = 0.1, BDstat = "BD", member.type = "group", match = "score", type = "both", criterion = "LRT", model = "2PL", c = NULL, engine = "ltm", discr = 1, irtParam = NULL, same.scale = TRUE, signed = FALSE, purify = FALSE, purType = "IPP1", nrIter = 10, extreme = "constraint", const.range = c(0.001, 0.999), nrAdd = 1, p.adjust.method = NULL, save.output = FALSE, output = c("out", "default"))
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 
method 
character: the name of the selected method. Possible values are 
anchor 
either 
props 
either 
thrTID 
numeric: the threshold for detecting DIF items with TID method (default is 1.5). 
alpha 
numeric: significance level (default is 0.05). 
MHstat 
character: specifies the DIF statistic to be used for DIF identification. Possible values are 
correct 
logical: should the continuity correction be used? (default is TRUE). 
exact 
logical: should an exact test be computed? (default is 
stdWeight 
character: the type of weights used for the standardized PDIF statistic. Possible values are 
thrSTD 
numeric: the threshold (cutscore) for standardized PDIF statistic (default is 0.10). 
BDstat 
character specifying the DIF statistic to be used. Possible values are 
member.type 
character: either 
match 
specifies the type of matching criterion. Can be either 
type 
a character string specifying which DIF effects must be tested. Possible values are 
criterion 
a character string specifying which DIF statistic is computed. Possible values are 
model 
character: the IRT model to be fitted (either 
c 
optional numeric value or vector giving the values of the constrained pseudoguessing parameters. See Details. 
engine 
character: the engine for estimating the 1PL model, either 
discr 
either 
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 
signed 
logical: should the Raju's statistics be computed using the signed ( 
purify 
logical: should the method be used iteratively to purify the set of anchor items? (default is FALSE). 
purType 
character: the type of purification process to be run. Possible values are 
nrIter 
numeric: the maximal number of iterations in the item purification process (default is 10). 
extreme 
character: the method used to modify the extreme proportions. Possible values are 
const.range 
numeric: a vector of two constraining proportions. Default values are 0.001 and 0.999. Ignored if 
nrAdd 
integer: the number of successes and the number of failures to add to the data in order to adjust the proportions. Default value is 1. Ignored if 
p.adjust.method 
either 
save.output 
logical: should the output be saved into a text file? (Default is 
output 
character: a vector of two components. The first component is the name of the output file, the second component is either the file path or

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:
"TID"
for Transformed Item Difficulties (TID) method (Angoff and Ford, 1973),
"MH"
for mantelHaenszel (Holland and Thayer, 1988),
"Std"
for standardization (Dorans and Kulick, 1986),
"BD"
for BreslowDay method (Penfield, 2003),
"Logistic"
for logistic regression (Swaminathan and Rogers, 1990),
"SIBTEST"
for SIBTEST (Shealy and Stout) and CrossingSIBTEST (Chalmers, 2018; Li and Stout, 1996) methods,
"Lord"
for Lord's chisquare test (Lord, 1980),
"Raju"
for Raju's area method (Raju, 1990), and
"LRT"
for likelihoodratio test method (Thissen, Steinberg and Wainer, 1988).
The Data
is a matrix whose rows correspond to the subjects and columns to the items. 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)
.
Missing values are allowed for item responses (not for group membership) but must be coded as NA
values. They are discarded from either the computation of the sumscores, the fitting of the logistic models or the IRT models (according to the method).
The vector of group membership must hold only two different values, either as numeric or character. The focal group is defined by the argument focal.name
.
For "MH"
, "Std"
, "Logistic"
and "BD"
methods, the matching criterion can be either the test score or any other continuous or discrete variable to be passed in the selected DIF function. This is specified by the match
argument. By default, it takes the value "score"
and the test score (i.e. raw score) is computed. The second option is to assign to match
a vector of continuous or discrete numeric values, which acts as the matching criterion. Note that for consistency this vector should not belong to the Data
matrix.
For Lord and Raju methods, one can specify either the IRT model to be fitted (by means of model
, c
, engine
and discr
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), as well as for the TID method (through the thrTID
argument). For the other methods it is depending on the significance level set by alpha
.
For MantelHaenszel method, the DIF statistic can be either the MantelHaenszel chisquare statistic or the log oddsratio statistic. The method is specified by the argument MHstat
, and the default value is "MHChisq"
for the chisquare statistic. Moreover, the option correct
specifies whether the continuity correction has to be applied to MantelHaenszel statistic. See difMH
for further details.
By default, the asymptotic MantelHaenszel statistic is computed. However, the exact statistics and related Pvalues can be obtained by specifying the logical argument exact
to TRUE
. See Agresti (1990, 1992) for further details about exact inference.
The weights for computing the standardized PDIF statistics are defined through the argument stdWeight
, with possible values "focal"
(default value), "reference"
and "total"
. See stdPDIF
for further details.
For BreslowDay method, two test statistics are available: the usual BreslowDay statistic for testing homogeneous association (Aguerri, Galibert, Attorresi and Maranon, 2009) and the trend test statistic for assessing some monotonic trend in the odds ratios (Penfield, 2003). The DIF statistic is supplied by the BDstat
argument, with values "BD"
(default) for the usual statistic and "trend"
for the trend test statistic.
The SIBTEST method (Shealy and Stout, 1993) and its modified version, the CrossingSIBTEST (Chalmers, 2018; Li and Stout, 1996) are returned by the difSIBTEST
function. SIBTEST method is returned when type
argument is set to "udif"
, while CrossingSIBTEST is set with "nudif"
value for the type
argument. Note that type
takes the bydefault value "both"
which is not allowed within the difSIBTEST
function; however, within this fucntion, keeping the bydefault value yields selection of CrossingSIBTEST.
The difSIBTEST
function is a wrapper to the SIBTEST
function from the mirt package (Chalmers, 2012) to fit within the difR
framework (Magis et al., 2010). Therefore, if you are using this function for publication purposes please cite Chalmers (2018; 2012) and Magis et al. (2010).
For logistic regression, the argument type
permits to test either both uniform and nonuniform effects simultaneously (type="both"
), only uniform DIF effect (type="udif"
) or only nonuniform DIF effect (type="nudif"
). The criterion
argument specifies the DIF statistic to be computed, either the likelihood ratio test statistic (with criterion="LRT"
) or the Wald test (with criterion="Wald"
). Moreover, the group membership can be either a vector of two distinct values, one for the reference group and one for the focal group, or a continuous or discrete variable that acts as the "group" membership variable. In the former case, the member.type
argument is set to "group"
and the focal.name
defines which value in the group
variable stands for the focal group. In the latter case, member.type
is set to "cont"
, focal.name
is ignored and each value of the group
represents one "group" of data (that is, the DIF effects are investigated among participants relying on different values of some discrete or continuous trait). See Logistik
for further details.
For Raju's method, the type of area (signed or unsigned) is fixed by the logical signed
argument, with default value FALSE
(i.e. unsigned areas). See RajuZ
for further details.
Item purification can be requested by specifying purify
option to TRUE
. Recall that item purification is slightly different for IRT and for nonIRT based methods. See the corresponding methods for further information.
Adjustment for multiple comparisons is possible with the argument p.adjust.method
. See the corresponding methods for further information.
A prespecified set of anchor items can be provided through the anchor
argument. For nonIRT methods, anchor items are used to compute the test score (as matching criterion). For IRT methods, anchor items are used to rescale the item parameters on a common metric. See the corresponding methods for further information. Note that anchor
argument is not working with "LRT"
method.
The output of the selected method can be stored in a text file by fixing save.output
and output
appropriately. See the help file of the corresponding method for further information.
The output of the selected DIF detection method.
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/
Agresti, A. (1990). Categorical data analysis. New York: Wiley.
Agresti, A. (1992). A survey of exact inference for contingency tables. Statistical Science, 7, 131177. doi: 10.1214/ss/1177011454
Aguerri, M.E., Galibert, M.S., Attorresi, H.F. and Maranon, P.P. (2009). Erroneous detection of nonuniform DIF using the BreslowDay test in a short test. Quality and Quantity, 43, 3544. doi: 10.1007/s1113500791302
Angoff, W. H., and Ford, S. F. (1973). Itemrace interaction on a test of scholastic aptitude. Journal of Educational Measurement, 2, 95106. doi: 10.1111/j.17453984.1973.tb00787.x
Chalmers, R. P. (2012). mirt: A Multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 129. doi: 10.18637/jss.v048.i06
Chalmers, R. P. (2018). Improving the CrossingSIBTEST statistic for detecting nonuniform DIF. Psychometrika, 83(2), 376–386. doi: 10.1007/s1133601795838
Dorans, N. J. and Kulick, 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, 355368. doi: 10.1111/j.17453984.1986.tb00255.x
Holland, P. W. and Thayer, D. T. (1988). Differential item performance and the MantelHaenszel procedure. In H. Wainer and H. I. Braun (Dirs.), Test validity. Hillsdale, NJ: Lawrence Erlbaum Associates.
Li, H.H., and Stout, W. (1996). A new procedure for detection of crossing DIF. Psychometrika, 61, 647–677. doi: 10.1007/BF02294041
Lord, F. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Lawrence Erlbaum Associates.
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
Penfield, R.D. (2003). Application of the BreslowDay test of trend in odds ratio heterogeneity to the detection of nonuniform DIF. Alberta Journal of Educational Research, 49, 231243.
Raju, N. S. (1990). Determining the significance of estimated signed and unsigned areas between two item response functions. Applied Psychological Measurement, 14, 197207. doi: 10.1177/014662169001400208
Shealy, R. and Stout, W. (1993). A modelbased standardization approach that separates true bias/DIF from group ability differences and detect test bias/DTF as well as item bias/DIF. Psychometrika, 58, 159194. doi: 10.1007/BF02294572
Swaminathan, H. and Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27, 361370. doi: 10.1111/j.17453984.1990.tb00754.x
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.
difTID
, difMH
, difStd
, difBD
, difLogistic
, difSIBTEST
, difLord
,
difRaju
, difLRT
, dichoDif
## Not run: # Loading of the verbal data data(verbal) attach(verbal) # Excluding the "Anger" variable verbal < verbal[colnames(verbal)!="Anger"] # Calling MantelHaenszel selectDif(verbal, group = 25, focal.name = 1, method = "MH") # Calling MantelHaenszel and saving output in 'MH.txt' file selectDif(verbal, group = 25, focal.name = 1, method = "MH", save.output = TRUE, output = c("MH", "default")) # Calling Lord method # 2PL model, with item purification selectDif(verbal, group = 25, focal.name = 1, method = "Lord", model = "2PL", purify = TRUE) ## End(Not run)