R/GMAT.R

In drabinova/difNLR: DIF and DDF Detection by Non-Linear Regression Models

#' Dichotomous dataset based on GMAT with the same total score distribution for groups.
#'
#' @description The \code{GMAT} is a generated dataset based on parameters from Graduate
#' Management Admission Test (GMAT, Kingston et al., 1985). First two items were
#' considered to function differently in uniform and non-uniform way respectively. The dataset
#' represents responses of 2,000 subjects to multiple-choice test of 20 items. A correct answer
#' is coded as 1 and incorrect answer as 0. The column \code{group} represents group membership,
#' where 0 indicates reference group and 1 indicates focal group. Groups are the same
#' size (i.e. 1,000 per group). The distributions of total scores (sum of correct answers) are the
#' same for both reference and focal group (Martinkova et al., 2017). The column \code{criterion}
#' represents generated continuous variable which is intended to be predicted by test.
#'
#' @usage data(GMAT)
#'
#' @author
#' Adela Hladka (nee Drabinova) \cr
#' Institute of Computer Science of the Czech Academy of Sciences \cr
#' Faculty of Mathematics and Physics, Charles University \cr
#' \email{hladka@@cs.cas.cz} \cr
#'
#' Patricia Martinkova \cr
#' Institute of Computer Science of the Czech Academy of Sciences \cr
#' \email{martinkova@@cs.cas.cz} \cr
#'
#' @references
#' Kingston, N., Leary, L., & Wightman, L. (1985). An exploratory study of the applicability of item response theory
#' methods to the Graduate Management Admission Test. ETS Research Report Series, 1985(2): 1--64.
#'
#' Martinkova, P., Drabinova, A., Liaw, Y. L., Sanders, E. A., McFarland, J. L., & Price, R. M. (2017).
#' Checking equity: Why differential item functioning analysis should be a routine part of developing conceptual
#' assessments. CBE--Life Sciences Education, 16(2), rm2, \doi{10.1187/cbe.16-10-0307}.
#'
#' @keywords datasets
#'
#' @seealso \code{\link{GMATtest}}, \code{\link{GMATkey}}
#'
#' @format A \code{GMAT} data frame consists of 2,000 observations on the following 22 variables:
#' \describe{
#' \item{Item1-Item20}{dichotomously scored items of the test}
#' \item{group}{group membership vector, \code{"0"} reference group, \code{"1"} focal group}
#' \item{criterion}{continuous critetion intended to be predicted by test}
#' }
"GMAT"