Title: IRTrees: Tree-based item response models of the GLMM family
Authors: De Boeck, Paul ×
Partchev, Ivailo #
Issue Date: 2012
Publisher: UCLA Statistics
Series Title: Journal of Statistical Software vol:48 issue:1 pages:1-28
Abstract: A category of item response models is presented with two de ning features: they all
(i) have a tree representation, and (ii) are members of the family of generalized linear
mixed models (GLMM). Because the models are based on trees, they are denoted as
IRTree models. The GLMM nature of the models implies that they can all be estimated
with the glmer function of the lme4 package in R. The aim of the article is to present four
subcategories of models, the rst two of which are based on a tree representation for response
categories: 1. linear response tree models (e.g., missing response models), 2. nested
response tree models (e.g., models for parallel observations regarding item responses such
as agreement and certainty), while the last two are based on a tree representation for
latent variables: 3. linear latent-variable tree models (e.g., models for change processes),
and 4. nested latent-variable tree models (e.g., bi-factor models). The use of the glmer
function is illustrated for all four subcategories. Simulated example data sets and two service
functions useful in preparing the data for IRTree modeling with glmer are provided
in the form of an R package, irtrees. For all four subcategories also a real data application
is discussed.
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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