IprMG: Item parameter replication

In herulor/DFIT: Differential Functioning of Items and Tests

Item parameter replication

Description

Generates a sample of item parameters assuming multivariate normality of estimates

Usage

IprMG(itemParameters, itemCovariances, nReplicates = 5000)

Arguments

itemParameters	A list of three or more sets of item parameters. Item parameters are assumed to be on the same scale. Item parameters for each group should be a matrix with nrow equal to the number of items. One set of item parameters must be called "base" and contain the item parameters to which all other sets will be compared to; the other group item parameters must have some name but it can be arbitrarily given.
itemCovariances	A list containing matrices of covariance for item estimates. See 'itemParameters' for list structure. Each list element may be either a list of covariance matrices for each item or a single matrix of covariance of all parameters.
nReplicates	A numeric value indicating the number of replications to perform

Value

itemParameters A list with item parameters for focal and reference groups

Author(s)

Victor H. Cervantes <vhcervantesb at unal.edu.co>

References

Oshima, T. C., Wright, K., & White, N. (2014). Multiple-Group Noncompensatory Differential Item Functioning in Raju’s Differential Functioning of Items and Tests. International Journal of Testing, 15, 254–273.

Oshima, T., Raju, N. & Nanda, A. (2006). A new method for assessing the statistical significance in the Differential Functioning of Items and Tests (DFIT) framework. Journal of educational measurement, 43(1), 1–17. doi:10.1111/j.1745-3984.2006.00001.x

Examples

#' # Data fom Oshima, Wright and White
baseParameters <- matrix(c(0.49, -0.07, 0.19,
                           0.92,  0.21, 0.15,
                           1.26,  0.54, 0.05,
                           0.61, -0.03, 0.18,
                           1.74,  0.01, 0.12,
                           0.50,  1.96, 0.12,
                           0.96,  0.04, 0.13,
                           0.59, -0.09, 0.18,
                           0.82, -1.16, 0.17,
                           1.26,  0.02, 0.11,
                           0.82,  0.20, 0.07,
                           0.75, -0.43, 0.15,
                           1.49, -0.06, 0.09,
                           0.97, -0.34, 0.12,
                           1.49,  0.05, 0.12,
                           0.89, -0.25, 0.15,
                           1.45,  0.06, 0.07,
                           0.75,  0.31, 0.18,
                           1.43,  0.04, 0.08,
                           0.60,  0.13, 0.22,
                           0.83,  0.52, 0.09,
                           0.56, -0.96, 0.19,
                           0.67, -0.79, 0.20,
                           0.70,  0.37, 0.18,
                           1.03, -0.71, 0.14,
                           0.89, -0.19, 0.21,
                           1.23,  0.74, 0.06,
                           0.90, -0.44, 0.18,
                           1.23, -0.17, 0.12,
                           0.69, 0.53, 0.17),
                           byrow = TRUE, ncol = 3)


group1Pars <- group2Pars <- group3Pars <- group4Pars <- group5Pars <- baseParameters

group1Pars[c(4,  22), 1] <- group1Pars[c(4,  22), 1] + .4
group1Pars[c(7,  22), 2] <- group1Pars[c(7,  22), 2] + .7

group2Pars[c(13, 28), 1] <- group2Pars[c(13, 28), 1] - .4
group2Pars[c(19, 28), 2] <- group2Pars[c(19, 28), 2] - .7

group3Pars[c(4,  13), 1] <- group3Pars[c(4,  13), 1] + .4
group3Pars[c(4,  22), 2] <- group3Pars[c(4,  22), 2] - .7

group4Pars[c(7,  28), 1] <- group4Pars[c(7,  28), 1] - .4
group4Pars[c(7,  19), 2] <- group4Pars[c(7,  19), 2] + .7

itemParameters <- list(base = baseParameters,
                       group1 = group1Pars,
                       group2 = group2Pars,
                       group3 = group3Pars,
                       group4 = group4Pars,
                       group5 = group5Pars
                       )

itemCovariances <- lapply(itemParameters, AseIrt, irtModel = "3pl", sampleSize = 5000)

mgIpr <- IprMG(itemParameters = itemParameters,
               itemCovariances = itemCovariances,
               nReplicates = 100)

herulor/DFIT documentation built on June 25, 2024, 8:23 a.m.