\name{Abasement}
\alias{Abasement}
\alias{female}
\docType{data}
\title{
  Abasement data}
\description{
  Bounded continuous items responses of 244 subjects to the 10 items from the Abasement subscale of the Adjectives Check List (ACL; Gough & Heilbrun, 1980) including the covariate \code{female} which is code \code{0} and  \code{1}}

\format{
The item scores have originally been scored on a 60 milimeter line segment on which the subjects had to indicate to what degree each adjective applies to them. These scores have been recoded into a [0,1] interval. The Abasement scale does not contain 1 scores but it does contain 0 scores.
All subscales of the ACL including the Abasement scale have been analyzed using bounded continuous IRT models with zero-one inflation by Molenaar et al. (2022; see Table 10 for the model fit statistics of all ACL subscales and see Table 11 for the parameter estimates of the Abasement scale). The covariate \code{female} needs to be loaded seperately but subjects are in the same order so that data can be \code{cbind}-ed if needed.}
\references{
Gough, H. G., & Heilbrun, A. B. (1980). \emph{The adjective check list, manual 1980 Edition}.
Consulting Psychologists Press.

Molenaar, D., Cúri, M., & Bazán, J.L. (2022). Zero and One Inflated Item Response Theory Models for Bounded Continuous Data. \emph{Journal of Educational and Behavioral Statistics}, \bold{47}, 693-735. \doi{10.3102/10769986221108455}
}


\seealso{
\code{\link{fitBIRT}} to estimate various bounded continuous IRT models to data.

\code{\link{plot.BoundIRT}} to plot the item and test information curves and predicted densities for the different models.

\code{\link{coef.BoundIRT}} to extract item parameter estimates from a \code{BoundIRT} object.

\code{\link{bridgeBIRT}} to use bridge sampling to calculate the marginal loglikelihood for a model in a \code{BoundIRT} object.

\code{\link{latregBIRT}} to conduct a latent regression of the person parameters on some observed predictor variables.

\code{\link{rBIRT}} simulate data according to a (zero/one inflated) bounded IRT model.
}


\examples{

data(Abasement)
data(female)

}
\keyword{datasets}
