Description Details Author(s) References
The software Conquest (Wu, Adams, Wilson, & Haldane, 2007) is a computer program for fitting item response and latent regression models. It is based on the Muldi-dimensional mixed-Coefficients Multinomial Logit Model, which is a generalized form of the Rasch Model (Adams & Wu, 2007). For example, Conquest allows for the estimation of the Rasch model, the rating scale model, the partial credit model, the linear logistic test model, multifacet models, multidimensional and latent regression models.
Like Mplus, the interface of Conquest is command-line (cmd) based, where the syntax, the data and
fixed effects indicator names (i.e., names of items) have to be provided in separated ASCII files.
The package eatModel
was created to allow for more fail-save, less cumbersome
specification of IRT models in R, which subsequently can be estimated in Conquest. At the heart
of the package are several functions, which build on each other and should be called consecutively.
Not every function is mandatory to be called—it depends on the desired analyses.
splitModels
is only necessary if the user wants to estimate several IRT models in
a row, using only one function call. This might be useful, if, for example, one model
should be fitted separately in various person groups, for example the federal states in Germany,
or if one model should be fitted for several domains, say listening comprehension and reading
comprehension. Hence, the split may be defined according to person groups (deferal states,
for example), or item groups (domains) or both. If only one model should be defined and estimated,
calling splitModels
is not necessary may be skipped. Start directly with defineModel
instead. Hence, you may think of splitModels
as defining a loop, like lapply
or
by
.
defineModel
is used to specify the model and the analysis software (e.g., Conquest or
TAM) as well as the data. Several consistency checks are performed and all required input
for the estimation software is prepared. If several models shopuld be estimated in a row,
defineModel
needs the output from splitModels
.
runModel
needs the output generated by defineModel
and simply starts the
‘estimator’ (e.g. Conquest or TAM)
getResults
needs the output generated by runModel
and re-collects all created
model output which is represented then in a single R data frame. The aim of the function is
to provide the model output in a consistent framework which is independent from the software
used for analysis. Strictly speaking, getResults
is'nt necessary, i.e. the user is free
to work with the output provided by Conquest or TAM directly.
equat1pl
needs the output generated by getResults
. It provides equating
in a one-parameter (1pl) context, accounting for linking DIF and a clustered structure of
items (items nested in testlets). In the multiple model case (i.e., if splitModels
was called), the linking is executed for all defined models simultaneously.
transformToBista
needs the output generated by equat1pl
. It provides
transformation of item and person parameters to an arbitrary scale (for example the
“PISA”-metric or the metric of the german educational standards.
prepRep
needs the output generated by transformToBista
. The funtion
prepares the output for further (trend) analyses using the eatRep
package.
The multi-stage process of model estimation works for one single model as well as for a compilation
of several models in only one call. The estimation of these models may be accelerated using multicore
processing. Depending on the number of available logical CPUs, several models may be estimated
simultaneously. See the examples of splitModels
for further details. The help page of
defineModel
includes a variety of examples which are derived from the context of the
IQB “Laendervergleich”.
Basically, eatModel
is useful for Conquest analyses which are called from R. Alternatively, the R package
TAM
allows to estimate parameters of the mixed-Coefficients Multinomial Logit Model solely in an
R environment. Hence, eatModel
allows all models to be estimated in TAM
likewise.
Please note that some model specifications (for example, 2pl/3pl) lead to models only TAM is able to
estimate. Conversely, some other model specifications (for example, partial credit models with
many categories or differential item functioning) might be unstable in TAM.
Historically, eatModel
is a ‘reboot’ of the package eatRest
formerly known as
eat
. The development of the eat
package started in autumn 2010 at the Institute of
Educational Progress (IQB). In 2012, the functionality of the package was partitioned into several
small “sub packages”—by name eatPrep
for data preparation, eatTools
for several
auxiliary functions, eatRest
for Rasch modeling, eatRep
for replication methods,
eatDesign
for definition and modification of design properties. The further development
of eatRest
was expired because the package turned out to be enigmaticly written which
leads to undebugable problems. Thus, eatModel
is the second attempt. Its functionality
is closely related to eatRest
but adds some new features—for example, the support of
2pl models using the TAM
package. Important note: For consistency reasons, some functions
in eatModel
have identical names as the corresponding function in eatRest
, for example
‘get.shw’. It is strongly recommended to not have both packages attached simultaneously in one R session.
Package: | eatModel |
Type: | Package |
Version: | 0.6.22 |
Date: | 2018-10-11 |
License: | GPL(>=2) |
Author/maintainer: Sebastian Weirich <sebastian.weirich@iqb.hu-berlin.de>
Adams, R. J., Wilson, M., & Wang, W.-C. (1997). The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement, 21(1), 1-23.
Adams, R. J., & Wu, M. L. (2007). The Mixed-Coefficients Multinomial Logit Model: A Generalized Form of the Rasch Model. In M. Von Davier & C. H. Carstensen (Eds.), Multivariate and Mixture Distribution Rasch Models (pp. 57-75). New York: Springer.
Robitzsch, A., Kiefer, T., & Wu, M. (2018). TAM: Test analysis modules. R package version 2.13-15. https://CRAN.R-project.org/package=TAM
Wu, M.L., Adams, R.J., Wilson, M.R., & Haldane, S.A. (2007). ACER ConQuest Version 2.0. Generalised Item Response Modeling Software. Camberwell, Victoria: ACER Press.
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