eatModel-package: Specify and run IRT models from R (using Conquest or TAM).
In eatModel: Educational Assessment Tools for IRT Modeling using Conquest or TAM
Description
Details
Author(s)
References
Description
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.
Details
Package: eatModel
Type: Package
Version: 0.6.22
Date: 2018-10-11
License: GPL(>=2)
Author(s)
Author/maintainer: Sebastian Weirich <sebastian.weirich@iqb.hu-berlin.de>
References
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.
eatModel documentation built on May 2, 2019, 6:49 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References
Description
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.
-
splitModelsis 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, callingsplitModelsis not necessary may be skipped. Start directly withdefineModelinstead. Hence, you may think ofsplitModelsas defining a loop, likelapplyorby. -
defineModelis 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,defineModelneeds the output fromsplitModels. -
runModelneeds the output generated bydefineModeland simply starts the ‘estimator’ (e.g. Conquest or TAM) -
getResultsneeds the output generated byrunModeland 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,getResultsis'nt necessary, i.e. the user is free to work with the output provided by Conquest or TAM directly. -
equat1plneeds the output generated bygetResults. 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., ifsplitModelswas called), the linking is executed for all defined models simultaneously. -
transformToBistaneeds the output generated byequat1pl. 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. -
prepRepneeds the output generated bytransformToBista. The funtion prepares the output for further (trend) analyses using theeatReppackage.
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.
Details
| Package: | eatModel |
| Type: | Package |
| Version: | 0.6.22 |
| Date: | 2018-10-11 |
| License: | GPL(>=2) |
Author(s)
Author/maintainer: Sebastian Weirich <sebastian.weirich@iqb.hu-berlin.de>
References
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.