difLord: Lord's chi-squared DIF method
In difR: Collection of Methods to Detect Dichotomous Differential Item Functioning (DIF)
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Performs DIF detection using Lord's chi-squared method.
Usage
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difLord(Data, group, focal.name, model, c = NULL, engine = "ltm", discr = 1,
irtParam = NULL, same.scale = TRUE, anchor = NULL, alpha = 0.05,
purify = FALSE, nrIter = 10, p.adjust.method = NULL, save.output = FALSE,
output = c("out", "default"))
## S3 method for class 'Lord'
print(x, ...)
## S3 method for class 'Lord'
plot(x, plot = "lordStat", item = 1, pch = 8, number = TRUE, col = "red",
colIC = rep("black", 2), ltyIC = c(1, 2), save.plot = FALSE,
save.options = c("plot", "default", "pdf"), group.names = NULL, ...)
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.
model
character: the IRT model to be fitted (either "1PL", "2PL" or "3PL").
c
optional numeric value or vector giving the values of the constrained pseudo-guessing parameters. See Details.
engine
character: the engine for estimating the 1PL model, either "ltm" (default) or "lme4".
discr
either NULL or a real positive value for the common discrimination parameter (default is 1). Used onlky if model is "1PL" and
engine is "ltm". 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.
anchor
either NULL (default) or a vector of item names (or identifiers) to specify the anchor items. See Details.
alpha
numeric: significance level (default is 0.05).
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).
p.adjust.method
either NULL (default) or the acronym of the method for p-value adjustment for multiple comparisons. See Details.
save.output
logical: should the output be saved into a text file? (Default is FALSE).
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 "default"
(default value). See Details.
x
the result from a Lord class object.
plot
character: the type of plot, either "lordStat" or "itemCurve". See Details.
item
numeric or character: either the number or the name of the item for which ICC curves are plotted. Used only when plot="itemCurve".
pch, col
type of usual pch and col graphical options.
number
logical: should the item number identification be printed (default is TRUE).
colIC, ltyIC
vectors of two elements of the usual col and lty arguments for ICC curves. Used only when plot="itemCurve".
save.plot
logical: should the plot be saved into a separate file? (default is FALSE).
save.options
character: a vector of three components. The first component is the name of the output file, the second component is either the file path or "default" (default value),
and the third component is the file extension, either "pdf" (default) or "jpeg". See Details.
group.names
either NULL (default) or a vector of two character strings giving the names of the reference group and the focal group (in this order)
for display in the legend. Ignored if plot is "lordStat".
...
other generic parameters for the plot or the print functions.
Details
Lord's chi-squared method (Lord, 1980) allows for detecting uniform or non-uniform differential item functioning
by setting an appropriate item response model. The input can be of two kinds: either by displaying the full data,
the group membership and the model, or by giving the item parameter estimates (through the option irtParam).
Both can be supplied, but in this case only the parameters in irtParam are used for computing Lord's
chi-squared statistic.
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 for item parameter estimation.
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.
If the model is not the 1PL model, or if engine is equal to "ltm", the selected IRT model is fitted using marginal maximum likelihood
by means of the functions from the ltm package (Rizopoulos, 2006). Otherwise, the 1PL model is fitted as a generalized
linear mixed model, by means of the glmer function of the lme4 package (Bates and Maechler, 2009).
With the "1PL" model and the "ltm" engine, the common discrimination parameter is set equal to 1 by default. It is possible to fix another value
through the argumentdiscr. Alternatively, this common discrimination parameter can be estimated (though not returned) by fixing discr to
NULL.
The 3PL model can be fitted either unconstrained (by setting c to NULL) or by fixing the pseudo-guessing values. In the latter
case, the argument c holds either a numeric vector of same length of the number of items, with one value per item pseudo-guessing parameter,
or a single value which is duplicated for all the items. If c is different from NULL then the 3PL model is always fitted (whatever the value of model).
The irtParam matrix has a number of rows equal to twice the number of items in the data set. The first J rows refer to
the item parameter estimates in the reference group, while the last J ones correspond to the same items in the focal group.
The number of columns depends on the selected IRT model: 2 for the 1PL model, 5 for the 2PL model, 6 for the constrained 3PL model
and 9 for the unconstrained 3PL model. The columns of irtParam have to follow the same structure as the output of
itemParEst command (the latter can actually be used to create the irtParam matrix).
In addition to the matrix of parameter estimates, one has to specify whether items in the focal group were rescaled to those of the
reference group. If not, rescaling is performed by equal means anchoring (Cook and Eignor, 1991). Argument same.scale is used for
this choice (default option is TRUE and assumes therefore that the parameters are already placed on the same scale).
The threshold (or cut-score) for classifying items as DIF is computed as the quantile of the chi-squared distribution with lower-tail
probability of one minus alpha and p degrees of freedom (p=1 for the 1PL model, p=2 for the 2PL model or the 3PL model
with constrained pseudo-guessing parameters, and p=3 for the unconstrained 3PL model).
Item purification can be performed by setting purify to TRUE. In this case, the purification occurs in the equal means anchoring process. Items
detected as DIF are iteratively removed from the set of items used for equal means anchoring, and the procedure is repeated until either the same items
are identified twice as functioning differently, or when nrIter iterations have been performed. In the latter case a warning message is printed.
See Candell and Drasgow (1988) for further details. Note that item purification is performed on original statistics and p-values; in case of adjustment for multiple comparisons this is performed after item purification.
Adjustment for multiple comparisons is possible with the argument p.adjust.method. The latter must be an acronym of one of the available adjustment methods of the p.adjust function. According to Kim and Oshima (2013), Holm and Benjamini-Hochberg adjustments (set respectively by "Holm" and "BH") perform best for DIF purposes. See p.adjust function for further details. Note that item purification is performed on original statistics and p-values; in case of adjustment for multiple comparisons this is performed after item purification.
A pre-specified set of anchor items can be provided through the anchor argument. It must be a vector of either item names (which must match exactly the column names of Data argument) or integer values (specifying the column numbers for item identification). In case anchor items are provided, they are used to rescale the item parameters on a common metric. None of the anchor items are tested for DIF: the output separates anchor items and tested items and DIF results are returned only for the latter. Note also that item purification is not activated when anchor items are provided (even if purify is set to TRUE). By default it is NULL so that no anchor item is specified. If item parameters are provided thorugh the irtParam argument and if they are on the same scale (i.e. if same.scale is TRUE), then anchor items are not used (even if they are specified).
Under the 1PL model, the displayed output also proposes an effect size measure, which is -2.35 times the difference between item difficulties of the reference group
and the focal group (Penfield and Camilli, 2007, p. 138). This effect size is similar Mantel-Haenszel's Δ_{MH} effect size, and the ETS delta scale is used
to classify the effect sizes (Holland and Thayer, 1985).
The output of the difLord, as displayed by the print.Lord function, can be stored in a text file provided that save.output is set to TRUE
(the default value FALSE does not execute the storage). In this case, the name of the text file must be given as a character string into the first component
of the output argument (default name is "out"), and the path for saving the text file can be given through the second component of output. The
default value is "default", meaning that the file will be saved in the current working directory. Any other path can be specified as a character string: see the
Examples section for an illustration.
Two types of plots are available. The first one is obtained by setting plot="lordStat" and it is the default option. The chi-squared statistics are displayed
on the Y axis, for each item. The detection threshold is displayed by a horizontal line, and items flagged as DIF are printed with the color defined by argument col.
By default, items are spotted with their number identification (number=TRUE); otherwise they are simply drawn as dots whose form is given by the option pch.
The other type of plot is obtained by setting plot="itemCurve". In this case, the fitted ICC curves are displayed for one specific item set by the argument
item. The latter argument can hold either the name of the item or its number identification. The item parameters are extracted from the itemParFinal matrix
if the output argument purification is TRUE, otherwise from the itemParInit matrix and after a rescaling of the item parameters using the
itemRescale command. A legend is displayed in the upper left corner of the plot. The colors and types of traits for these curves are defined by means of
the arguments colIC and ltyIC respectively. These are set as vectors of length 2, the first element for the reference group and the second for the focal group.
Finally, the argument group.names permits to display the names of the reference and focal groups (instead of "Reference" and "Focal") in the legend.
Both types of plots can be stored in a figure file, either in PDF or JPEG format. Fixing save.plot to TRUE allows this process. The figure is defined through
the components of save.options. The first two components perform similarly as those of the output argument. The third component is the figure format, with allowed
values "pdf" (default) for PDF file and "jpeg" for JPEG file.
Value
A list of class "Lord" with the following arguments:
LordChi
the values of the Lord's chi-square statistics.
p.value
the vector of p-values for the Lord's chi-square 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 nrIterof allowed iterations.
Returned only if purify is TRUE.
model
the value of model argument.
c
The value of the c argument.
engine
The value of the engine argument.
discr
the value of the discr argument.
p.adjust.method
the value of the p.adjust.method argument.
adjusted.p
either NULL or the vector of adjusted p-values for multiple comparisons.
itemParInit
the matrix of initial parameter estimates,with the same format as irtParam either provided by the user (through irtParam) or estimated from the data
(and displayed without rescaling).
itemParFinal
the matrix of final parameter estimates, with the same format as irtParam, obtained after item purification. Returned
only if purify is TRUE.
estPar
a logical value indicating whether the item parameters were estimated (TRUE) or provided by the user (FALSE).
names
the names of the items.
anchor.names
the value of the anchor argument.
save.output
the value of the save.output argument.
output
the value of the output argument.
Author(s)
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/
References
Bates, D. and Maechler, M. (2009). lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-31. http://CRAN.R-project.org/package=lme4
Candell, G.L. and Drasgow, F. (1988). An iterative procedure for linking metrics and assessing item bias in item response theory. Applied Psychological Measurement, 12, 253–260. doi: 10.1177/014662168801200304
Cook, L. L. and Eignor, D. R. (1991). An NCME instructional module on IRT equating methods. Educational Measurement: Issues and Practice, 10, 37–45.
Holland, P. W. and Thayer, D. T. (1985). An alternative definition of the ETS delta scale of item difficulty. Research Report RR-85-43. Princeton, New-Jersey:
Educational Testing Service.
Kim, J., and Oshima, T. C. (2013). Effect of multiple testing adjustment in differential item functioning detection. Educational and Psychological Measurement, 73, 458–470. doi: 10.1177/0013164412467033
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, 847–862. doi: 10.3758/BRM.42.3.847
Penfield, R. D., and Camilli, G. (2007). Differential item functioning and item bias. In C. R. Rao and S. Sinharray (Eds.), Handbook of Statistics 26: Psychometrics
(pp. 125-167). Amsterdam, The Netherlands: Elsevier.
Rizopoulos, D. (2006). ltm: An R package for latent variable modelling and item response theory analyses. Journal of Statistical Software, 17, 1-25. doi: 10.18637/jss.v017.i05
See Also
itemParEst, dichoDif, p.adjust
Examples
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## Not run:
# Loading of the verbal data
data(verbal)
attach(verbal)
# Excluding the "Anger" variable
verbal <- verbal[colnames(verbal)!="Anger"]
# Three equivalent settings of the data matrix and the group membership
# (1PL model, "ltm" engine)
r <- difLord(verbal, group = 25, focal.name = 1, model = "1PL")
difLord(verbal, group = "Gender", focal.name = 1, model = "1PL")
difLord(verbal[,1:24], group = verbal[,25], focal.name = 1, model = "1PL")
# With items 1 to 5 set as anchor items
difLord(verbal, group = 25, focal.name = 1, model = "1PL", anchor = 1:5)
# Multiple comparisons adjustment of p-values with Benjamini-Hochberg method
difLord(verbal, group = 25, focal.name = 1, model = "1PL", anchor = 1:5, p.adjust.method = "BH")
# 1PL model, "lme4" engine
difLord(verbal, group = 25, focal.name = 1, model = "1PL", engine = "lme4")
# 2PL model
difLord(verbal, group = "Gender", focal.name = 1, model = "2PL")
# 3PL model with all pseudo-guessing parameters constrained to 0.05
difLord(verbal, group = "Gender", focal.name = 1, model = "3PL", c = 0.05)
# Same models, with item purification
difLord(verbal, group = 25, focal.name = 1, model = "1PL", purify = TRUE)
difLord(verbal, group = "Gender", focal.name = 1, model = "2PL", purify = TRUE)
difLord(verbal, group = "Gender", focal.name = 1, model = "3PL", c = 0.05,
purify = TRUE)
# Saving the output into the "LordResults.txt" file (and default path)
r <- difLord(verbal, group = 25, focal.name = 1, model = "1PL",
save.output = TRUE, output = c("LordResults","default"))
# 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, "ltm" engine)
item.1PL<-rbind(itemParEst(data.ref, model = "1PL"),
itemParEst(data.focal, model = "1PL"))
difLord(irtParam = item.1PL, same.scale = FALSE)
## Pre-estimation of the item parameters (1PL model, "lme4" engine)
item.1PL<-rbind(itemParEst(data.ref, model = "1PL", engine = "lme4"),
itemParEst(data.focal, model = "1PL", engine = "lme4"))
difLord(irtParam = item.1PL, same.scale = FALSE)
## Pre-estimation of the item parameters (2PL model)
item.2PL<-rbind(itemParEst(data.ref, model = "2PL"),
itemParEst(data.focal, model = "2PL"))
difLord(irtParam = item.2PL, same.scale = FALSE)
## Pre-estimation of the item parameters (constrained 3PL model)
item.3PL<-rbind(itemParEst(data.ref, model = "3PL", c = 0.05),
itemParEst(data.focal, model = "3PL", c = 0.05))
difLord(irtParam = item.3PL, same.scale = FALSE)
# Graphical devices
plot(r)
plot(r, plot = "itemCurve", item = 1)
plot(r, plot = "itemCurve", item = 6)
# Plotting results and saving it in a PDF figure
plot(r, save.plot = TRUE, save.options = c("plot", "default", "pdf"))
# Changing the path, JPEG figure
path <- "c:/Program Files/"
plot(r, save.plot = TRUE, save.options = c("plot", path, "jpeg"))
## End(Not run)
difR documentation built on July 2, 2020, 3:34 a.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Performs DIF detection using Lord's chi-squared method.
Usage
1 2 3 4 5 6 7 8 9 10 11 | difLord(Data, group, focal.name, model, c = NULL, engine = "ltm", discr = 1,
irtParam = NULL, same.scale = TRUE, anchor = NULL, alpha = 0.05,
purify = FALSE, nrIter = 10, p.adjust.method = NULL, save.output = FALSE,
output = c("out", "default"))
## S3 method for class 'Lord'
print(x, ...)
## S3 method for class 'Lord'
plot(x, plot = "lordStat", item = 1, pch = 8, number = TRUE, col = "red",
colIC = rep("black", 2), ltyIC = c(1, 2), save.plot = FALSE,
save.options = c("plot", "default", "pdf"), group.names = NULL, ...)
|
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 |
focal.name |
numeric or character indicating the level of |
model |
character: the IRT model to be fitted (either |
c |
optional numeric value or vector giving the values of the constrained pseudo-guessing 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 |
anchor |
either |
alpha |
numeric: significance level (default is 0.05). |
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). |
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 |
x |
the result from a |
plot |
character: the type of plot, either |
item |
numeric or character: either the number or the name of the item for which ICC curves are plotted. Used only when |
pch, col |
type of usual |
number |
logical: should the item number identification be printed (default is |
colIC, ltyIC |
vectors of two elements of the usual |
save.plot |
logical: should the plot be saved into a separate file? (default is |
save.options |
character: a vector of three components. The first component is the name of the output file, the second component is either the file path or |
group.names |
either |
... |
other generic parameters for the |
Details
Lord's chi-squared method (Lord, 1980) allows for detecting uniform or non-uniform differential item functioning
by setting an appropriate item response model. The input can be of two kinds: either by displaying the full data,
the group membership and the model, or by giving the item parameter estimates (through the option irtParam).
Both can be supplied, but in this case only the parameters in irtParam are used for computing Lord's
chi-squared statistic.
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 for item parameter estimation.
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.
If the model is not the 1PL model, or if engine is equal to "ltm", the selected IRT model is fitted using marginal maximum likelihood
by means of the functions from the ltm package (Rizopoulos, 2006). Otherwise, the 1PL model is fitted as a generalized
linear mixed model, by means of the glmer function of the lme4 package (Bates and Maechler, 2009).
With the "1PL" model and the "ltm" engine, the common discrimination parameter is set equal to 1 by default. It is possible to fix another value
through the argumentdiscr. Alternatively, this common discrimination parameter can be estimated (though not returned) by fixing discr to
NULL.
The 3PL model can be fitted either unconstrained (by setting c to NULL) or by fixing the pseudo-guessing values. In the latter
case, the argument c holds either a numeric vector of same length of the number of items, with one value per item pseudo-guessing parameter,
or a single value which is duplicated for all the items. If c is different from NULL then the 3PL model is always fitted (whatever the value of model).
The irtParam matrix has a number of rows equal to twice the number of items in the data set. The first J rows refer to
the item parameter estimates in the reference group, while the last J ones correspond to the same items in the focal group.
The number of columns depends on the selected IRT model: 2 for the 1PL model, 5 for the 2PL model, 6 for the constrained 3PL model
and 9 for the unconstrained 3PL model. The columns of irtParam have to follow the same structure as the output of
itemParEst command (the latter can actually be used to create the irtParam matrix).
In addition to the matrix of parameter estimates, one has to specify whether items in the focal group were rescaled to those of the
reference group. If not, rescaling is performed by equal means anchoring (Cook and Eignor, 1991). Argument same.scale is used for
this choice (default option is TRUE and assumes therefore that the parameters are already placed on the same scale).
The threshold (or cut-score) for classifying items as DIF is computed as the quantile of the chi-squared distribution with lower-tail
probability of one minus alpha and p degrees of freedom (p=1 for the 1PL model, p=2 for the 2PL model or the 3PL model
with constrained pseudo-guessing parameters, and p=3 for the unconstrained 3PL model).
Item purification can be performed by setting purify to TRUE. In this case, the purification occurs in the equal means anchoring process. Items
detected as DIF are iteratively removed from the set of items used for equal means anchoring, and the procedure is repeated until either the same items
are identified twice as functioning differently, or when nrIter iterations have been performed. In the latter case a warning message is printed.
See Candell and Drasgow (1988) for further details. Note that item purification is performed on original statistics and p-values; in case of adjustment for multiple comparisons this is performed after item purification.
Adjustment for multiple comparisons is possible with the argument p.adjust.method. The latter must be an acronym of one of the available adjustment methods of the p.adjust function. According to Kim and Oshima (2013), Holm and Benjamini-Hochberg adjustments (set respectively by "Holm" and "BH") perform best for DIF purposes. See p.adjust function for further details. Note that item purification is performed on original statistics and p-values; in case of adjustment for multiple comparisons this is performed after item purification.
A pre-specified set of anchor items can be provided through the anchor argument. It must be a vector of either item names (which must match exactly the column names of Data argument) or integer values (specifying the column numbers for item identification). In case anchor items are provided, they are used to rescale the item parameters on a common metric. None of the anchor items are tested for DIF: the output separates anchor items and tested items and DIF results are returned only for the latter. Note also that item purification is not activated when anchor items are provided (even if purify is set to TRUE). By default it is NULL so that no anchor item is specified. If item parameters are provided thorugh the irtParam argument and if they are on the same scale (i.e. if same.scale is TRUE), then anchor items are not used (even if they are specified).
Under the 1PL model, the displayed output also proposes an effect size measure, which is -2.35 times the difference between item difficulties of the reference group and the focal group (Penfield and Camilli, 2007, p. 138). This effect size is similar Mantel-Haenszel's Δ_{MH} effect size, and the ETS delta scale is used to classify the effect sizes (Holland and Thayer, 1985).
The output of the difLord, as displayed by the print.Lord function, can be stored in a text file provided that save.output is set to TRUE
(the default value FALSE does not execute the storage). In this case, the name of the text file must be given as a character string into the first component
of the output argument (default name is "out"), and the path for saving the text file can be given through the second component of output. The
default value is "default", meaning that the file will be saved in the current working directory. Any other path can be specified as a character string: see the
Examples section for an illustration.
Two types of plots are available. The first one is obtained by setting plot="lordStat" and it is the default option. The chi-squared statistics are displayed
on the Y axis, for each item. The detection threshold is displayed by a horizontal line, and items flagged as DIF are printed with the color defined by argument col.
By default, items are spotted with their number identification (number=TRUE); otherwise they are simply drawn as dots whose form is given by the option pch.
The other type of plot is obtained by setting plot="itemCurve". In this case, the fitted ICC curves are displayed for one specific item set by the argument
item. The latter argument can hold either the name of the item or its number identification. The item parameters are extracted from the itemParFinal matrix
if the output argument purification is TRUE, otherwise from the itemParInit matrix and after a rescaling of the item parameters using the
itemRescale command. A legend is displayed in the upper left corner of the plot. The colors and types of traits for these curves are defined by means of
the arguments colIC and ltyIC respectively. These are set as vectors of length 2, the first element for the reference group and the second for the focal group.
Finally, the argument group.names permits to display the names of the reference and focal groups (instead of "Reference" and "Focal") in the legend.
Both types of plots can be stored in a figure file, either in PDF or JPEG format. Fixing save.plot to TRUE allows this process. The figure is defined through
the components of save.options. The first two components perform similarly as those of the output argument. The third component is the figure format, with allowed
values "pdf" (default) for PDF file and "jpeg" for JPEG file.
Value
A list of class "Lord" with the following arguments:
LordChi |
the values of the Lord's chi-square statistics. |
p.value |
the vector of p-values for the Lord's chi-square statistics. |
alpha |
the value of |
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 |
nrPur |
the number of iterations in the item purification process. Returned only if |
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 |
convergence |
logical indicating whether the iterative item purification process stopped before the maximal number |
model |
the value of |
c |
The value of the |
engine |
The value of the |
discr |
the value of the |
p.adjust.method |
the value of the |
adjusted.p |
either |
itemParInit |
the matrix of initial parameter estimates,with the same format as |
itemParFinal |
the matrix of final parameter estimates, with the same format as |
estPar |
a logical value indicating whether the item parameters were estimated ( |
names |
the names of the items. |
anchor.names |
the value of the |
save.output |
the value of the |
output |
the value of the |
Author(s)
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/
References
Bates, D. and Maechler, M. (2009). lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-31. http://CRAN.R-project.org/package=lme4
Candell, G.L. and Drasgow, F. (1988). An iterative procedure for linking metrics and assessing item bias in item response theory. Applied Psychological Measurement, 12, 253–260. doi: 10.1177/014662168801200304
Cook, L. L. and Eignor, D. R. (1991). An NCME instructional module on IRT equating methods. Educational Measurement: Issues and Practice, 10, 37–45.
Holland, P. W. and Thayer, D. T. (1985). An alternative definition of the ETS delta scale of item difficulty. Research Report RR-85-43. Princeton, New-Jersey: Educational Testing Service.
Kim, J., and Oshima, T. C. (2013). Effect of multiple testing adjustment in differential item functioning detection. Educational and Psychological Measurement, 73, 458–470. doi: 10.1177/0013164412467033
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, 847–862. doi: 10.3758/BRM.42.3.847
Penfield, R. D., and Camilli, G. (2007). Differential item functioning and item bias. In C. R. Rao and S. Sinharray (Eds.), Handbook of Statistics 26: Psychometrics (pp. 125-167). Amsterdam, The Netherlands: Elsevier.
Rizopoulos, D. (2006). ltm: An R package for latent variable modelling and item response theory analyses. Journal of Statistical Software, 17, 1-25. doi: 10.18637/jss.v017.i05
See Also
itemParEst, dichoDif, p.adjust
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ## Not run:
# Loading of the verbal data
data(verbal)
attach(verbal)
# Excluding the "Anger" variable
verbal <- verbal[colnames(verbal)!="Anger"]
# Three equivalent settings of the data matrix and the group membership
# (1PL model, "ltm" engine)
r <- difLord(verbal, group = 25, focal.name = 1, model = "1PL")
difLord(verbal, group = "Gender", focal.name = 1, model = "1PL")
difLord(verbal[,1:24], group = verbal[,25], focal.name = 1, model = "1PL")
# With items 1 to 5 set as anchor items
difLord(verbal, group = 25, focal.name = 1, model = "1PL", anchor = 1:5)
# Multiple comparisons adjustment of p-values with Benjamini-Hochberg method
difLord(verbal, group = 25, focal.name = 1, model = "1PL", anchor = 1:5, p.adjust.method = "BH")
# 1PL model, "lme4" engine
difLord(verbal, group = 25, focal.name = 1, model = "1PL", engine = "lme4")
# 2PL model
difLord(verbal, group = "Gender", focal.name = 1, model = "2PL")
# 3PL model with all pseudo-guessing parameters constrained to 0.05
difLord(verbal, group = "Gender", focal.name = 1, model = "3PL", c = 0.05)
# Same models, with item purification
difLord(verbal, group = 25, focal.name = 1, model = "1PL", purify = TRUE)
difLord(verbal, group = "Gender", focal.name = 1, model = "2PL", purify = TRUE)
difLord(verbal, group = "Gender", focal.name = 1, model = "3PL", c = 0.05,
purify = TRUE)
# Saving the output into the "LordResults.txt" file (and default path)
r <- difLord(verbal, group = 25, focal.name = 1, model = "1PL",
save.output = TRUE, output = c("LordResults","default"))
# 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, "ltm" engine)
item.1PL<-rbind(itemParEst(data.ref, model = "1PL"),
itemParEst(data.focal, model = "1PL"))
difLord(irtParam = item.1PL, same.scale = FALSE)
## Pre-estimation of the item parameters (1PL model, "lme4" engine)
item.1PL<-rbind(itemParEst(data.ref, model = "1PL", engine = "lme4"),
itemParEst(data.focal, model = "1PL", engine = "lme4"))
difLord(irtParam = item.1PL, same.scale = FALSE)
## Pre-estimation of the item parameters (2PL model)
item.2PL<-rbind(itemParEst(data.ref, model = "2PL"),
itemParEst(data.focal, model = "2PL"))
difLord(irtParam = item.2PL, same.scale = FALSE)
## Pre-estimation of the item parameters (constrained 3PL model)
item.3PL<-rbind(itemParEst(data.ref, model = "3PL", c = 0.05),
itemParEst(data.focal, model = "3PL", c = 0.05))
difLord(irtParam = item.3PL, same.scale = FALSE)
# Graphical devices
plot(r)
plot(r, plot = "itemCurve", item = 1)
plot(r, plot = "itemCurve", item = 6)
# Plotting results and saving it in a PDF figure
plot(r, save.plot = TRUE, save.options = c("plot", "default", "pdf"))
# Changing the path, JPEG figure
path <- "c:/Program Files/"
plot(r, save.plot = TRUE, save.options = c("plot", path, "jpeg"))
## End(Not run)
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