R/data_LearningToLearn.R

In netique/ShinyItemAnalysis: Test and Item Analysis via Shiny

#' Dichotomous dataset of learning to learn test
#'
#' @md # allow RMarkdown formatting usage
#'
#' @description `LearningToLearn` is a real longitudinal dataset used in
#'   Martinkova et al (2020) study, demonstrating differential item functioning
#'   in change (DIF-C) on Learning to Learn (LtL) test. Among other variables,
#'   it primarily contains binary-coded responses of 782 subjects to (mostly)
#'   multiple-choice test consisting of 41 items within 7 subscales (see
#'   **Format** for details). Each respondent was tested twice in total -- the
#'   first time in Grade 6 and the second time in Grade 9. Most importantly,
#'   school track (variable `track_01` or `track`) is available, with 391
#'   students attending basic school (BS) and 391 pursuing selective academic
#'   school (AS). This dataset was created using propensity score matching
#'   algorithm to achieve similar characteristics in both tracks (see
#'   **References** for details). To further simplify the work with `LtL`
#'   dataset, we provide computed total scores as well as 7 subscores, both for
#'   Grade 6 and Grade 9. The dataset also includes *change* variables for each
#'   item (see **Format** for details) for more detailed DIF-C analysis using
#'   multinomial regression model.
#'
#' @usage data(LearningToLearn)
#'
#' @author
#' Patricia Martinkova \cr
#' Faculty of Education, Charles University \cr
#' Institute of Computer Science of the Czech Academy of Sciences \cr
#' \email{martinkova@@cs.cas.cz} \cr
#'
#' Adela Hladka \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
#'
#' Eva Potuznikova \cr
#' Faculty of Education, Charles University \cr
#'
#' @references Martinkova, P., Hladka, A., & Potuznikova, E. (2020). Is academic
#' tracking related to gains in learning competence? Using propensity score
#' matching and differential item change functioning analysis for better
#' understanding of tracking implications. \emph{Learning and Instruction},
#' \emph{66}, 101286. \doi{10.1016/j.learninstruc.2019.101286}
#'
#' @keywords datasets
#'
#' @format A \code{LearningToLearn} data frame consists of 782 observations on the following 141 variables:
#' \describe{
#' \item{track_01}{Dichotomously scored school track, where \code{"1"} denotes the selective academic school one. }
#' \item{track}{School track, where \code{"AS"} represents the selective academic school track, and \code{"BS"} stands
#' for basic school track. }
#' \item{score_6 & score_9}{Total test score value obtained by summing all 41 items of `LtL`, the number denotes
#' the Grade which the respondent was taking at the time of testing. }
#' \item{score_6_subtest1--score_6_subtest7}{Scores of respective cognitive subtest (1--7) of `LtL` in Grade 6. }
#' \item{score_9_subtest1--score_9_subtest7}{Scores of respective cognitive subtest (1--7) of `LtL` in Grade 9. }
#' \item{Item1A_6--Item7F_6}{Dichotomously coded 41 individual items obtained at Grade 6, \code{"1"} represents
#' the correct answer to the particular item. }
#' \item{Item1A_9--Item7F_9}{Dichotomously coded 41 individual items obtained at Grade 9, \code{"1"} represents
#' the correct answer to the particular item. }
#' \item{Item1A_changes--Item7F_changes}{Change patterns with those possible values:
#' * a student responded correctly in neither Grade 6 nor in Grade 9 (did not improve, `"00"`)
#' * a student responded correctly in Grade 6 but not in Grade 9 (deteriorated, `"10"`)
#' * a student did not respond correctly in Grade 6 but responded correctly in Grade 9 (improved, `"01"`), and
#' * a student responded correctly in both grades (did not deteriorate, `"11"`)}
#' }
"LearningToLearn"