Title: The estimation of item response models with the lmer function from the lme4 package in R
Authors: De Boeck, Paul ×
Bakker, Marjan
Zwitser, Robert
Nivard, Michel
Hofman, Abe
Tuerlinckx, Francis
Partchev, Ivailo #
Issue Date: 2011
Publisher: UCLA Statistics
Series Title: Journal of Statistical Software vol:39 issue:12 pages:1-28
Abstract: In this paper we elaborate on the potential of the lmer function from the lme4 package
in R for item response (IRT) modeling. In line with the package, an IRT framework is
described based on generalized linear mixed modeling. The aspects of the framework refer
to (a) the kind of covariates { their mode (person, item, person-by-item), and their being
external vs. internal to responses, and (b) the kind of e ects the covariates have { xed
vs. random, and if random, the mode across which the e ects are random (persons, items).
Based on this framework, three broad categories of models are described: Item covariate
models, person covariate models, and person-by-item covariate models, and within each
category three types of more speci c models are discussed. The models in question are
explained and the associated lmer code is given. Examples of models are the linear
logistic test model with an error term, di erential item functioning models, and local item
dependency models. Because the lme4 package is for univariate generalized linear mixed
models, neither the two-parameter, and three-parameter models, nor the item response
models for polytomous response data, can be estimated with the lmer function.
ISSN: 1548-7660
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Quantitative Psychology and Individual Differences
× corresponding author
# (joint) last author

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