difGMH: Generalized Mantel-Haenszel DIF method

In difR: Collection of Methods to Detect Dichotomous Differential Item Functioning (DIF)

Description

Performs DIF detection among multiple groups using the generalized Mantel-Haenszel method.

Usage

difGMH(Data, group, focal.names, anchor = NULL, match = "score", alpha = 0.05, 
  	purify = FALSE, nrIter = 10, p.adjust.method = NULL, save.output = FALSE, 
  	output = c("out", "default"))
## S3 method for class 'GMH'
print(x, ...)
## S3 method for class 'GMH'
plot(x, pch = 8, number = TRUE, col = "red", save.plot = FALSE, 
  	save.options = c("plot", "default", "pdf"), ...)

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.names	numeric or character vector indicating the levels of group which correspond to the focal groups.
anchor	either NULL (default) or a vector of item names (or identifiers) to specify the anchor items. See Details.
match	specifies the type of matching criterion. Can be either "score" (default) to compute the test score, or any continuous or discrete variable with the same length as the number of rows of Data. 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 GMH class object.
pch, col	type of usual pch and col graphical options.
number	logical: should the item number identification be printed (default is TRUE).
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.
...	other generic parameters for the plot or the print functions.

Details

The generalized Mantel-Haenszel statistic (Somes, 1986) can be used to detect uniform differential item functioning among multiple groups, without requiring an item response model approach (Penfield, 2001).

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 sum-score computation.

The vector of group membership must hold at least three value, 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 difGMH returns the output of difMH (without continuity correction).

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 as many degrees of freedom as the number of focal groups.

The matching criterion can be either the test score or any other continuous or discrete variable to be passed in the genMantelHaenszel 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.

Item purification can be performed by setting purify to TRUE. Purification works as follows: if at least one item 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. 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.

The output of the difGMH, as displayed by the print.GMH 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.

The plot.GMH function displays the DIF statistics in a plot, with each item on the X axis. The type of point and the colour are fixed by the usual pch and col arguments. Option number permits to display the item numbers instead. Also, the plot 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 "GMH" with the following arguments:

GMH	the values of the generalized Mantel-Haenszel statistics.
p.value	the vector of p-values for the generalized Mantel-Haenszel statistics.
alpha	the value of alpha argument.
thr	the threshold (cut-score) for DIF detection.
DIFitems	either the items which were detected as DIF items, or "No DIF item detected".
match	a character string, either "score" or "matching variable" depending on the match 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.
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 nrIter of allowed iterations. Returned only if purify is TRUE.
names	the names of the items.
anchor.names	the value of the anchor argument.
focal.names	the value of focal.names 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
[e-mail], http://www.cdame.uqam.ca/
David Magis
Department of Psychology, University of Liege
Research Group of Quantitative Psychology and Individual Differences, KU Leuven
[e-mail], 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
[e-mail], 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.

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

Penfield, R. D. (2001). Assessing differential item functioning among multiple groups: a comparison of three Mantel-Haenszel procedures. Applied Measurement in Education, 14, 235-259. doi: 10.1207/S15324818AME1403_3

Somes, G. W. (1986). The generalized Mantel-Haenszel statistic. The American Statistician, 40, 106-108. doi: 10.2307/2684866

See Also

difGMH, difMH

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")

 # Three equivalent settings of the data matrix and the group membership
 difGMH(Verbal, group = 25, focal.names = names)
 difGMH(Verbal, group = "group", focal.name = names)
 difGMH(Verbal[,1:24], group = Verbal[,25], focal.names = names)

 # Multiple comparisons adjustment using Benjamini-Hochberg method
 difGMH(Verbal, group = 25, focal.names = names, p.adjust.method = "BH")

 # With item purification 
 difGMH(Verbal, group = 25, focal.names = names, purify = TRUE)
 difGMH(Verbal, group = 25, focal.names = names, purify = TRUE, nrIter = 5)

 # With items 1 to 5 set as anchor items
 difMH(Verbal, group = 25, focal.name = names, anchor = 1:5)
 difMH(Verbal, group = 25, focal.name = names, anchor = 1:5, purify = TRUE)


 # Saving the output into the "GMHresults.txt" file (and default path)
 r <- difGMH(Verbal, group = 25, focal.name = names, save.output = TRUE, 
            output = c("GMHresults","default"))

 # Graphical devices
 plot(r)

 # 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.