The estimation of item response models with the lmer function from the lme4 package in R

By P. De Boeck, Marjan Bakker, Robert Zwitser, M. Nivard, Abe D. Hofman, Francis Tuerlinckx, I. Partchev. 2011

329 citations recorded by the source corpus.

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 effects the covariates have -- fixed vs. random, and if random, the mode across which the effects 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 specific 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, differential 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.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/955ae715eb005961bac449c67c7fa3ef5fe2987b

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Citations in the corpus

Bayesian Item Response Modeling in R with brms and Stan (2019, methodology): “De Boeck et al. (2011) provide a thoughtful and thorough discussion of the use of covariates in IRT models and I do not want to reiterate every detail, but simply note that all kinds of covariate models discussed in their paper may be specified in the here presented framework using the same formula ”
Working Memory and Dynamic Measures of Analogical Reasoning as Predictors of Children's Math and Reading Achievement (2014, methodology): “The multidimensional Rasch estimates were reliably computed using an aggregated dataset (N= 514) with the lme4 package (Bates, Maechler, & Bolker, 2010) for R (e.g. De Boeck et al., 2011).”
Developing an Instrument for Teacher Feedback : Using the Rasch Model to Explore Teachers' Development of Effective Teaching Strategies and Behaviors (2018, background): “Assumption tests to evaluate fit of Rasch models in nested data sets are still at a developmental phase (e.g., de Boeck et al., 2011) and have not yet been incorporated in standard software packages.”
IRTrees: Tree-Based Item Response Models of the GLMM Family (2012, methodology): “Another issue is that variance components cannot be easily tested on their signiﬁcance (De Boeck et al. 2011).”