% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\name{ej2021_data}
\alias{ej2021_data}
\alias{ej2021_study1}
\alias{ej2021_study2}
\alias{ej2021_combined}
\alias{ej2021_study1_itercal}
\alias{ej2021_study2_itercal}
\alias{ej2021_combined_itercal}
\title{Simulated MFRM datasets based on Eckes and Jin (2021)}
\format{
A data.frame with 5 columns:
\describe{
\item{Study}{Study label (\code{"Study1"} or \code{"Study2"}).}
\item{Person}{Person/respondent identifier.}
\item{Rater}{Rater identifier.}
\item{Criterion}{Criterion facet label.}
\item{Score}{Observed category score.}
}
}
\source{
Simulated for this package with design settings informed by Eckes
and Jin (2021). The Eckes & Jin (2021) Method section reports the
following design parameters that motivated the synthetic versions
shipped here: Study 1 had 307 examinees (149 males, 158 females), 18
raters (4 males, 14 females), and 3 criteria (global impression, task
fulfillment, linguistic realization) on a 4-category rating scale (TDN
levels rescored 1-4); Study 2 had 206 examinees (66 males, 140 females),
12 raters (1 male, 11 females), and 9 criteria on the same 4-category
scale. The packaged datasets reproduce these
(examinees, raters, criteria, categories) shapes but use simulated
responses, so they are not the real TestDaF data.
}
\description{
Synthetic many-facet rating datasets in long format.
All datasets include one row per observed rating.
}
\details{
Available data objects:
\itemize{
\item \code{mfrmr_example_core}
\item \code{mfrmr_example_bias}
\item \code{ej2021_study1}
\item \code{ej2021_study2}
\item \code{ej2021_combined}
\item \code{ej2021_study1_itercal}
\item \code{ej2021_study2_itercal}
\item \code{ej2021_combined_itercal}
}

Naming convention:
\itemize{
\item \code{study1} / \code{study2}: separate simulation studies
\item \code{combined}: row-bind of study1 and study2
\item \verb{_itercal}: iterative-calibration variant
}

Use \code{\link[=load_mfrmr_data]{load_mfrmr_data()}} for programmatic selection by key.
}
\section{Data dimensions}{

\tabular{lrrrr}{
\strong{Dataset} \tab \strong{Rows} \tab \strong{Persons} \tab \strong{Raters} \tab \strong{Criteria} \cr
study1 \tab 1842 \tab 307 \tab 18 \tab 3 \cr
study2 \tab 3287 \tab 206 \tab 12 \tab 9 \cr
combined \tab 5129 \tab 307 \tab 18 \tab 12 \cr
study1_itercal \tab 1842 \tab 307 \tab 18 \tab 3 \cr
study2_itercal \tab 3341 \tab 206 \tab 12 \tab 9 \cr
combined_itercal \tab 5183 \tab 307 \tab 18 \tab 12
}
Score range: 1--4 (four-category rating scale).
}

\section{Simulation design}{

Person ability is drawn from N(0, 1).  Rater severity effects span
approximately -0.5 to +0.5 logits.  Criterion difficulty effects span
approximately -0.3 to +0.3 logits.  Scores are generated from the
resulting linear predictor plus Gaussian noise, then discretized into
four categories.  The \verb{_itercal} variants use a second iteration of
calibrated rater severity parameters.
}

\section{Interpreting output}{

Each dataset is already in long format and can be passed directly to
\code{\link[=fit_mfrm]{fit_mfrm()}} after confirming column-role mapping.
}

\section{Typical workflow}{

\enumerate{
\item Inspect available datasets with \code{\link[=list_mfrmr_data]{list_mfrmr_data()}}.
\item Load one dataset using \code{\link[=load_mfrmr_data]{load_mfrmr_data()}}.
\item Fit and diagnose with \code{\link[=fit_mfrm]{fit_mfrm()}} and \code{\link[=diagnose_mfrm]{diagnose_mfrm()}}.
}
}

\examples{
data("ej2021_study1", package = "mfrmr")
head(ej2021_study1)
table(ej2021_study1$Study)
}
\references{
Eckes, T., & Jin, K.-Y. (2021). Measuring rater centrality effects in
writing assessment: A Bayesian facets modeling approach.
\emph{Psychological Test and Assessment Modeling, 63}(1), 65--94.
}
