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#' IAT reliability
#'
#' Compute the practice -- test IAT reliability.
#'
#' @param data dataframe with class \code{"dscore"} (Gawronski et al., 2017).
#'
#' @return List of two objects:
#' \describe{
#' \item{\code{Test-practice reliability}}{contains the IAT reliability.}
#' \item{\code{Number of Participants}}{Contains the number of participants
#' on which the reliability was computed.}
#' }
#' @export
#'
#'
#' @examples
#' # compute D-score 2 for the IAT data ###
#' data("raw_data") # import data
#' iat_cleandata <- clean_iat(raw_data, sbj_id = "Participant",
#' block_id = "blockcode",
#' mapA_practice = "practice.iat.Milkbad",
#' mapA_test = "test.iat.Milkbad",
#' mapB_practice = "practice.iat.Milkgood",
#' mapB_test = "test.iat.Milkgood",
#' latency_id = "latency",
#' accuracy_id = "correct",
#' trial_id = "trialcode",
#' trial_eliminate = c("reminder", "reminder1"),
#' demo_id = "blockcode",
#' trial_demo = "demo")
#' iat_data <- iat_cleandata[[1]]
#' # calculate D-score
#' iat_dscore <- compute_iat(iat_data,
#' Dscore = "d2")
#' IAT_rel(iat_dscore)
IAT_rel <- function(data){
# check dataset class --------------------------
if(is.na(class(data)[2]) | class(data)[2] != "dscore"){
stop("data is not an object of class dscore")
}
# compute realiability --------------------------
IAT_reliability <- list(rel = round(
cor(data[ , c(grep("d_practice", colnames(data)),
grep("d_test",
colnames(data)))])[2, 1],
2),
number = round(nrow(data)))
names(IAT_reliability) <- c("Test-pratice Reliability",
"Number of participants")
class(IAT_reliability) <- "IAT_rel"
return(IAT_reliability)
}
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