difTID {difR} | R Documentation |

Performs DIF detection using Transformed Item Difficulties (TID) method.

difTID(Data, group, focal.name, thrTID = 1.5, purify = FALSE, purType = "IPP1", nrIter = 10, alpha = 0.05, extreme = "constraint", const.range = c(0.001, 0.999), nrAdd = 1, save.output = FALSE, output = c("out", "default")) ## S3 method for class 'TID' print(x, only.final = TRUE, ...) ## S3 method for class 'TID' plot(x, plot = "dist",pch = 2, pch.mult = 17, axis.draw = TRUE, thr.draw = FALSE, dif.draw = c(1, 3), print.corr = FALSE, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL, main = NULL, col = "red", number = TRUE, save.plot = FALSE, save.options = c("plot", "default", "pdf"), ...)

`Data` |
numeric: either the data matrix only, or the data matrix plus the vector of group membership. See |

`group` |
numeric or character: either the vector of group membership or the column indicator (within |

`focal.name` |
numeric or character indicating the level of |

`thrTID` |
either the threshold for detecting DIF items (default is 1.5) or |

`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). |

`alpha` |
numeric: the significance level for calculating the detection threshold (default is 0.05). Ignored if |

`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 |

`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 |

`only.final` |
logical: should only the first and last steps of the purification process be printed? (default is |

`plot` |
character: either |

`pch` |
integer: the usual point character type for point display. Default value is 2, that is, Delta points are drawn as empty triangles. |

`pch.mult` |
integer: the type of point to be used for superposing onto Delta points that correspond to several items. Default value is 17, that is, full black traingles are drawn onto existing Delta plots wherein multiple items are located. |

`axis.draw` |
logical: should the major axis be drawn? (default is |

`thr.draw` |
logical: should the upper and lower bounds for DIF detection be drawn? (default is |

`dif.draw` |
numeric: a vector of two integer values to specify how the DIF items should be displayed. The first component of |

`print.corr` |
logical: should the sample correlation of Delta scores be printed? (default is |

`xlim, ylim, xlab, ylab, main` |
either the usual plot arguments |

`col` |
character: the color type for the items. Used only when |

`number` |
logical: should the item number identification be printed (default is |

`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 |

`...` |
other generic parameters for the |

The Transformed Item Difficulties (TID) method, also known as Angoff's Delta method (Angoff, 1982; Angoff and Ford, 1973) allows for detecting uniform differential item functioning without requiring an item response model approach. The presnt implementation relies on the `deltaPlot`

and `diagPlot`

functions from package**deltaPlotR** (Magis and Facon, 2014).

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 computation of
proportions of success.

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`

.

The threshold for flaging items as DIF can be of two types and is specified by the `thr`

argument.

It can be fixed to some arbitrary positive value by the user, for instance 1.5 (Angoff and Ford, 1973). In this case,

`thr`

takes the required numeric threshold value.Alternatively, it can be derived from the bivariate normal approximation of the Delta points (Magis and Facon, 2012). In this case,

`thr`

must be given the character value`"norm"`

(which is the default value). This threshold equals*Φ^{-1}(1-α/2) \; √{\frac{b^2\,{s_0}^2-2\,b\,s_{01}+{s_1}^2}{b^2+1}}*where

*Φ*is the density of the standard normal distribution,*α*is the significance level (set by the argument`alpha`

with default value 0.05),*b*is the slope parameter of the major axis,*s_0*and*s_1*are the sample standard deviations of the Delta scores in the reference group and the focal group, respecively, and*s_{01}*is the sample covariance of the Delta scores (see Magis and Facon, 2012, for further details).

Item purification can be performed by setting the argument `purify`

to `TRUE`

(by default it is `FALSE`

so
no purification is performed). The item purification process (IPP) starts when at least one item was flagged as DIF after
the first run of the Delta plot, and proceeds as follows.

The intercept and slope parameters of the major axis are re-calculated by removing all DIF that are currently flagged as DIF. This yields updated values

*a^**,*b^**,*s_0^**,*s_1^**and*s_{01}^**of the intercept and slope parameters, sample stanbdard deviations and sample covariance of the Delta scores.Perpendicular distances (for all items) are updated with respect to the updated major axis.

Detection threshold is also updated. Three possible updates are possible: see below.

All items are now tested for the presence of DIF, given the updated perpendicular distances and major axis.

If the set of items flagged as DIF is the same as the one from the previous loop, stop the process. Otherwise go back to step 1.

Unlike traditional DIF methods, the detection threshold may also be updated since it depends on the sample estimates (when
the normal approximation is considered). Three approaches are currently implemented and are specified by the `purType`

argument.

Method 1 (

`purType=="IPP1"`

): the same threshold is used throughout the purification process, it is not iteratively updated. The threshold is the one obtained after the first run of the Delta plot.Method 2 (

`purType=="IPP2"`

): only the slope parameter is updated in the threshold formula. By this way, one keeps the full data structure (i.e. neither the sample variances nor the sample covariance of the Delta scores are modified) but only the slope parameter is adjusted to lessen the impact of DIF items.Method 3 (

`purType=="IPP3"`

): all adjusted parameters are plugged in the threshold formula. This approach completely discards the effect of items flagged as DIF from the computation of the threshold.

See Magis and Facon (2013) for further details. Note that purification can also be performed with fixed threshold (i.e. specified by the user), but then only IPP1 process is performed.

In order to avoid possible infinite loops in the purification process, a maximal number of iterations must be specified
through the argument `maxIter`

. The default maximal number of iterations is 10.

The output contains all input information, the Delta scores and perpendicular distances, the parameter of the major axis and the items flagged as DIF (if none, a character sentence is returned). In addition, the detection threshold and the type of threshold (fixed or normal approximation) is provided.

If item purification was run, several additional elements are returned: the number of iterations, a logical indicator whether the convergence was reached (or not, meaning that the process stopped because of reaching the maximal number of allowed iterations), a matrix with indicators of which items were flagged as DIF at each iteration, and the type of item purification process. Moreover, perpendicular distances are returned in a matrix format (one column per iteration), as well as successive major axis parameters (one row per iteration) and successive thresholds (as a vector).

The output is managed and printed in a more user-friendly way. When item purification is performed, only the first and
last steps are displayed. Specifying the argument `only.final`

to `FALSE`

prints in addition all intermediate steps of the process (successive perpendicular distances, parameters of the major axis, and detection thresholds).

The output of the `difTID`

, as displayed by the `print.TID`

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 through the `plot.TID`

function. If the argument `plot`

is set to `"dist"`

(the default value), then the perpendicular distances are represented on the Y axis of a scatter plot, with each item on the X axis. If `plot`

is set to `"delta"`

, the Delta plot is returned. In the latter, all particular options can be found from the `diagPlot`

function.
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.

A list of class "TID" with the following arguments:

`Props` |
the matrix of proportions of correct responses, or |

`adjProps` |
the restricted proportions, in the same format as the output |

`Deltas` |
the matrix of Delta scores. |

`Dist` |
a matrix with perpendicular distances, one row per item and one column per run of the Delta plot. If |

`axis.par` |
a matrix with two columns, holding respectively the intercepts and the slope parameters of the major axis. Each row refers to one step of the purification process. If |

`nrIter` |
the number of iterations invloved in the purification process. Returned only if |

`maxIter` |
the value of the |

`convergence` |
a logical value indicating whether convergence was reached in the purification process. Returned only if |

`difPur` |
a matrix with one column per item and one row per iteration in the purification process, holding zeros and ones to indicate which items were flagged as DIF or not at each step of the process. Returned only if |

`thr` |
a vector of successive threshold values used during the purification process. If |

`rule` |
a character value indicating whether the threshold was |

`purType` |
the value of the |

`DIFitems` |
either |

`adjust.extreme` |
the value of the |

`const.range` |
the value of the |

`nrAdd` |
the value of the |

`purify` |
the value of the |

`alpha` |
the value of the |

`save.output` |
the value of the |

`output` |
the value of the |

`names` |
either the names of the items (defined by the column names of the |

`number` |
a boolean value, being |

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/

Angoff, W. H. (1982). Use of difficulty and discrimination indices for detecting item bias. In R. A. Berck (Ed.), *Handbook of methods for detecting item bias* (pp. 96-116). Baltimore, MD: Johns Hopkins University Press.

Angoff, W. H., and Ford, S. F. (1973). Item-race interaction on a test of scholastic aptitude. *Journal of Educational Measurement, 2*, 95-106. doi: 10.1111/j.1745-3984.1973.tb00787.x

Magis, D., and Facon, B. (2012). Angoff's Delta method revisited: improving the DIF detection under small samples.
*British Journal of Mathematical and Statistical Psychology, 65*, 302-321. doi: 10.1111/j.2044-8317.2011.02025.x

Magis, D., and Facon, B. (2013). Item purification does not always improve DIF detection: a counter-example with Angoff's Delta plot. *Educational and Psychological Measurement, 73*, 293-311. doi: 10.1177/0013164412451903

Magis, D. and Facon, B. (2014). *deltaPlotR*: An R Package for Differential Item Functioning Analysis with Angoff's Delta Plot. *Journal of Statistical Software, Code Snippets, 59(1)*, 1-19. doi: 10.18637/jss.v059.c01

`deltaPlot`

, codediagPlot, `dichoDif`

## Not run: # Loading of the verbal data data(verbal) # Excluding the "Anger" variable verbal <- verbal[colnames(verbal) != "Anger"] # Three equivalent settings of the data matrix and the group membership r <- difTID(verbal, group = 25, focal.name = 1) difTID(verbal, group = "Gender", focal.name = 1) difTID(verbal[,1:24], group = verbal[,25], focal.name = 1) # With item purification and threshold 1 r2 <- difTID(verbal, group = "Gender", focal.name = 1, purify = TRUE, thrTID = 1) # Saving the output into the "TIDresults.txt" file (and default path) difTID(verbal, group = 25, focal.name = 1, save.output = TRUE, output = c("TIDresults", "default")) # Graphical devices plot(r2) plot(r2, plot = "delta") # Plotting results and saving it in a PDF figure plot(r2, save.plot = TRUE, save.options = c("plot", "default", "pdf")) # Changing the path, JPEG figure path <- "c:/Program Files/" plot(r2, save.plot = TRUE, save.options = c("plot", path, "jpeg")) ## End(Not run)

[Package *difR* version 5.1 Index]