preplink: Data Preparation for IRT Linking

In cswells1/MeasInv: Collection of Methods to Detect Dichotomous and Polytomous Differential Item Functioning (DIF)

Description

This function creates an object of class prelink that is used with the irtlink, irteq, scatter, and plot.preplink functions.

Usage

preplink(dat.base, dat.new, common.b, common.n)

Arguments

dat.base	A data.frame containing the item meta data in the base test form (e.g., item parameter, categories, models ...). This data.frame can be easily obtained using the function shape_df. See below for details.
dat.new	A data.frame containing meta data for items in the new test form (e.g., item parameter, categories, models ...). This data.frame can be easily obtained using the function shape_df. See below for details.
common.b	A numeric vector specifying the position (or location) of common items on the base test form.
common.n	A numeric vector specifying the position (or location) of common items on the new test form.

Details

A specific form of the data.frame should be specified in dat.base and dat.new. In each argument, the first column should have item IDs, the second column should contain the number of score categories for the items, and the third column should include IRT models. The available IRT models are "1PLM", "2PLM", "3PLM", and "DRM" for dichotomous items, and "GRM" and "GPCM" for polytomous items. Note that "DRM" covers all dichotomous IRT models (i.e, "1PLM", "2PLM", and "3PLM") and "GRM" and "GPCM" represent the graded response model and (generalized) partial credit model, respectively. From the fourth column, item parameters should be included. For dichotomous items, the fourth, fifth, and sixth columns represent the item discrimination (or slope), item difficulty, and item guessing parameters, respectively. When "1PLM" or "2PLM" is specified for any items in the third column, NAs should be inserted for the item guessing parameters. For polytomous items, the item discrimination (or slope) parameters should be contained in the fourth column and the item threshold (or step) parameters should be included from the fifth to the last column. When the number of categories differs between items, the empty cells of item parameters should be filled with NAs. In this package, item step parameters should be used for the (generalized) partial credit model. Item step parameters are the overall item difficulty (or location) parameter subtracted by the difficulty (or threshold) parameter for each category. Thus, the number of step parameters for an item with m categories is m-1 because a step parameter for the first category does not affect the category probabilities. For example, if an item has five categories under the (generalized) partial credit model, four step parameters should be specified. An example of a data.frame with a single-format test is as follows:

ITEM1	2	1PLM	1.000	1.461	NA
ITEM2	2	2PLM	1.921	-1.049	NA
ITEM3	2	3PLM	1.736	1.501	0.203
ITEM4	2	3PLM	0.835	-1.049	0.182
ITEM5	2	DRM	0.926	0.394	0.099

And an example of a data.frame for a mixed-format test is as follows:

ITEM1	2	1PLM	1.000	1.461	NA	NA	NA
ITEM2	2	2PLM	1.921	-1.049	NA	NA	NA
ITEM3	2	3PLM	0.926	0.394	0.099	NA	NA
ITEM4	2	DRM	1.052	-0.407	0.201	NA	NA
ITEM5	4	GRM	1.913	-1.869	-1.238	-0.714	NA
ITEM6	5	GRM	1.278	-0.724	-0.068	0.568	1.072
ITEM7	4	GPCM	1.137	-0.374	0.215	0.848	NA
ITEM8	5	GPCM	1.233	-2.078	-1.347	-0.705	-0.116

Although the base and new test forms contain the same common items, the position (or location) of common items could differ by test form. Therefore, it is important to accurately match the position of the same common item for both test forms in the arguments common.b and common.n. For example, suppose that three common items on the base test form are located in the 7th, 9th, and 11th positions and the same common items on the new test form are located in the 2nd, 3rd, and 10th positions. Specifically, the 7th item in the base form is matched with the 2nd item in the new form, the 9th item in the base form is matched with the the 3rd item in the new form, and the 11th item in the base form is matched with the the 10th item in the new form. The arguments to indicate that those common items are the same across the forms (in spite of having different positions) would be common.b = c(7, 9, 11) and common.n = c(2, 3, 10).

Note that when using the function irtlink in this package, the scale of item parameters from the new test form are converted onto the the scale of the item parameters of the base test form.

Value

This function returns an object of class preplink.

Author(s)

Hwanggyu Lim [e-mail]

See Also

shape_df, irtlink, irteq, scatter, plot.preplink

Examples

# read 'prm' files of flexMIRT
base <- system.file("extdata", "flexmirt_base-prm.txt", package = "irteQ")
new <- system.file("extdata", "flexmirt_new-prm.txt", package = "irteQ")
dat_base <- bring.flexmirt(file=base, "par")$Group1$full_df
dat_new <- bring.flexmirt(file=new, "par")$Group1$full_df

# specify common items
common.b <- c(41:55)
common.n <- c(41:55)

# prepare a set of data for the IRT linking
preplink(dat_base, dat_new, common.b, common.n)

cswells1/MeasInv documentation built on Dec. 19, 2021, 7 p.m.