| difGenLogistic {difR} | R Documentation |
Generalized logistic regression DIF method
Description
Performs DIF detection among multiple groups using generalized logistic regression method.
Usage
difGenLogistic(Data, group, focal.names, anchor = NULL, match = "score",
type = "both", criterion = "LRT", alpha = 0.05, purify = FALSE, nrIter = 10,
p.adjust.method = NULL, save.output = FALSE, output = c("out", "default"))
## S3 method for class 'genLogistic'
print(x, ...)
## S3 method for class 'genLogistic'
plot(x, plot = "lrStat", item = 1, itemFit = "best",pch = 8, number = TRUE,
col = "red", colIC = rep("black", length(x$focal.names)+1),
ltyIC = 1:(length(x$focal.names)+1), title = NULL, save.plot = FALSE,
save.options = c("plot", "default", "pdf"), ref.name = 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.names |
numeric or character vector indicating the levels of |
anchor |
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 |
character: the type of test statistic used to detect DIF items. Possible values are |
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 logistic curves are plotted. Use only when |
itemFit |
character: the model to be selected for drawing the item curves. Possible values are |
pch, col |
type of usual |
number |
logical: should the item number identification be printed (default is |
colIC, ltyIC |
vectors of elements of the usual |
title |
either a character string with the title of the plot, or |
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
|
ref.name |
either |
... |
other generic parameters for the |
Details
The generalized logistic regression method (Magis, Raiche, Beland and Gerard, 2011) allows for detecting both uniform and non-uniform differential item
functioning among multiple groups without requiring an item response model approach. It consists in fitting a logistic model with the matching criterion,
the group membership and an interaction between both as covariates. The statistical significance of the parameters
related to group membership and the group-score interaction is then evaluated by means of the usual likelihood-ratio
test. 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 identification of DIF items can be performed with
either the Wald test or the likelihood ratio test, by setting the criterion argument to "Wald" or "LRT" respectively.
See genLogistik for further details.
The matching criterion can be either the test score or any other continuous or discrete variable to be passed in the genLogistik 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.
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 the fitting of the
logistic models (see glm for further details).
The vector of group membership must hold at least three values, either as numeric or character. The focal groups are defined by the values of the
argument focal.names. If there is a unique focal group, then difGenLogistic returns the output of difLogistic.
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 with J (if type="udif" or type="nudif") or 2J (if type="both") degrees of
freedom (J is the number of focal groups).
Item purification can be performed by setting purify to TRUE. Purification works as follows: if at least one item is detected as functioning
differently at the first step of the process, then the data set of the next step consists in all items that are currently anchor (DIF free) items, plus the
tested item (if necessary). The process stops when either two successive applications of the method yield the same classifications of the items
(Clauser and Mazor, 1998), or when nrIter iterations are run without obtaining two successive identical classifications. In the latter case a warning message is printed.
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 compute the test score (matching criterion), including also the tested item. 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. By default it is NULL so that no anchor item is specified. Note also that item purification is not activated when anchor items are provided (even if purify is set to TRUE). Moreover, if the match argument is not set to "score", anchor items will not be taken into account even if anchor is not NULL.
The measures of effect size are provided by the difference \Delta R^2 between the R^2 coefficients of the two nested models (Nagelkerke, 1991; Gomez-Benito, Dolores Hidalgo and Padilla, 2009). The effect sizes are classified as "negligible", "moderate" or "large". Two scales are available, one from Zumbo and Thomas (1997) and one from Jodoin and Gierl (2001). The output displays the \Delta R^2 measures, together with the two classifications.
The output of the difGenLogistic, as displayed by the print.genLogistic 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="lrStat" and it is the default option. The likelihood ratio 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 logistic 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. If the argument itemFit takes the value
"best", the curves are drawn according to the output of the best model among M_0 and M_1. That is, two curves are drawn if the item is flagged
as DIF, and only one if the item is flagged as non-DIF. If itemFit takes the value "null", then the two curves are drawn from the fitted parameters
of the null model M_0. See genLogistik for further details on the models. 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 J+1, the first element for the reference group and the others
for the focal groups. Finally, the ref.name argument permits to display the name if the reference group (instead of "Reference") 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 "genLogistic" with the following arguments:
genLogistik |
the values of the generalized logistic regression statistics. |
p.value |
the vector of p-values for the generalized logistic regression statistics. |
logitPar |
a matrix with one row per item and |
parM0 |
the matrix of fitted parameters of the null model |
covMat |
a 3-dimensional matrix of size p x p x K, where p is the number of estimated parameters and K is the number of items, holding the p x p covariance matrices of the estimated parameters (one matrix for each tested item). |
deltaR2 |
the differences in Nagelkerke's |
alpha |
the value of |
thr |
the threshold (cut-score) for DIF detection. |
DIFitems |
either the column indicators for the items which were detected as DIF items, or "No DIF item detected". |
type |
the value of |
p.adjust.method |
the value of the |
adjusted.p |
either |
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 of |
names |
the names of the items. |
anchor.names |
the value of the |
focal.names |
the value of |
criterion |
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
Clauser, B.E. and Mazor, K.M. (1998). Using statistical procedures to identify differential item functioning test items. Educational Measurement: Issues and Practice, 17, 31-44.
Gomez-Benito, J., Dolores Hidalgo, M. and Padilla, J.-L. (2009). Efficacy of effect size measures in logistic regression: an application for detecting DIF. Methodology, 5, 18-25. doi: 10.1027/1614-2241.5.1.18
Hidalgo, M. D. and Lopez-Pina, J.A. (2004). Differential item functioning detection and effect size: a comparison between logistic regression and Mantel-Haenszel procedures. Educational and Psychological Measurement, 64, 903-915. doi: 10.1177/0013164403261769
Jodoin, M. G. and Gierl, M. J. (2001). Evaluating Type I error and power rates using an effect size measure with logistic regression procedure for DIF detection. Applied Measurement in Education, 14, 329-349. doi: 10.1207/S15324818AME1404_2
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
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
Magis, D., Raiche, G., Beland, S. and Gerard, P. (2011). A logistic regression procedure to detect differential item functioning among multiple groups. International Journal of Testing, 11, 365–386. doi: 10.1080/15305058.2011.602810
Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78, 691-692. doi: 10.1093/biomet/78.3.691
Zumbo, B. D. and Thomas, D. R. (1997). A measure of effect size for a model-based approach for studying DIF. Prince George, Canada: University of Northern British Columbia, Edgeworth Laboratory for Quantitative Behavioral Science.
See Also
genLogistik, genDichoDif, subtestLogistic
Examples
## Not run:
# Loading of the verbal data
data(verbal)
attach(verbal)
# Creating four groups according to gender ("Man" or "Woman") and
# trait anger score ("Low" or "High")
group <- rep("WomanLow", nrow(verbal))
group[Anger>20 & Gender==0] <- "WomanHigh"
group[Anger<=20 & Gender==1] <- "ManLow"
group[Anger>20 & Gender==1] <- "ManHigh"
# New data set
Verbal <- cbind(verbal[,1:24], group)
# Reference group: "WomanLow"
names <- c("WomanHigh", "ManLow", "ManHigh")
# Testing both types of DIF effects
# Three equivalent settings of the data matrix and the group membership
r <- difGenLogistic(Verbal, group = 25, focal.names = names)
difGenLogistic(Verbal, group = "group", focal.name = names)
difGenLogistic(Verbal[,1:24], group = Verbal[,25], focal.names = names)
# Using the Wald test
difGenLogistic(Verbal, group = 25, focal.names = names, criterion = "Wald")
# Multiple comparisons adjustment using Benjamini-Hochberg method
difGenLogistic(Verbal, group = 25, focal.names = names, p.adjust.method = "BH")
# With item purification
difGenLogistic(Verbal, group = 25, focal.names = names, purify = TRUE)
difGenLogistic(Verbal, group = 25, focal.names = names, purify = TRUE,
nrIter = 5)
# With items 1 to 5 set as anchor items
difGenLogistic(Verbal, group = 25, focal.name = names, anchor = 1:5)
# Testing for nonuniform DIF effect
difGenLogistic(Verbal, group = 25, focal.names = names, type = "nudif")
# Testing for uniform DIF effect
difGenLogistic(Verbal, group = 25, focal.names = names, type = "udif")
# User anger trait score as matching criterion
anger <- verbal[,25]
difGenLogistic(Verbal, group = 25, focal.names = names, match = anger)
# Saving the output into the "GLresults.txt" file (and default path)
r <- difGenLogistic(Verbal, group = 25, focal.name = names,
save.output = TRUE, output = c("GLresults","default"))
# Graphical devices
plot(r)
plot(r, plot = "itemCurve", item = 1)
plot(r, plot = "itemCurve", item = 1, itemFit = "best")
plot(r, plot = "itemCurve", item = 6)
plot(r, plot = "itemCurve", item = 6, itemFit = "best")
# 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)