Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Paul De Boeck, Ivailo Partchev
Title: IRTrees: Tree-Based Item Response Models of the GLMM Family
Abstract: A category of item response models is presented with two defining 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 first 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.

Page views:: 6215. Submitted: 2011-09-16. Published: 2012-05-24.
Paper: IRTrees: Tree-Based Item Response Models of the GLMM Family     Download PDF (Downloads: 6789)
irtrees_0.1.0.tar.gz: R source package Download (Downloads: 1080; 13KB)
v48c01.R: R example code from the paper Download (Downloads: 1199; 4KB)
fsdatT.rda: Data in R binary format (fast vs. slow intelligence example) Download (Downloads: 1171; 43KB)
stressT.rda: Data in R binary format (fear and stress example) Download (Downloads: 1111; 25KB)

DOI: 10.18637/jss.v048.c01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.