IRTrees: Tree-Based Item Response Models of the GLMM Family
By P. De Boeck, I. Partchev. 2012
221 citations recorded by the source corpus.
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.
Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/07e2f911b19e6b85de09bdc5d61bb1d6a2a694e6
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In Experimental and Cognitive Psychology
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Citations in the corpus
- Use of Response Time for Measuring Cognitive Ability (2016, methodology): “They then fit a 1PL IRTree model [129], and found that three different latent traits were required to fit the data: speed, slow intelligence, and fast intelligence.”
- Ordinal regression: A review and a taxonomy of models (2021, unknown): “Symmetric binary split models have been considered extensively in item response theory under the name item response trees (Böckenholt, 2017; Böckenholt & Meiser, 2017; De Boeck & Partchev, 2012; Khorramdel & von Davier, 2014; Meiser et al., 2019; Plieninger & Meiser, 2014).”