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.

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.

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BackgroundIndividuals with social phobia are more likely to misinterpret ambiguous social situations as more threatening, i.e. they show an interpretive bias. This study investigated whether such a bias also exists in specific phobia.MethodsIndividuals with spider phobia or social phobia, spider aficionados and non-phobic controls saw morphed stimuli that gradually transformed from a schematic picture of a flower into a schematic picture of a spider by shifting the outlines of the petals until they turned into spider legs. Participants' task was to decide whether each stimulus was more similar to a spider, a flower or to neither object while EEG was recorded.ResultsAn interpretive bias was found in spider phobia on a behavioral level: with the first opening of the petals of the flower anchor, spider phobics rated the stimuli as more unpleasant and arousing than the control groups and showed an elevated latent trait to classify a stimulus as a spider and a response-time advantage for spider-like stimuli. No cortical correlates on the level of ERPs of this interpretive bias could be identified. However, consistent with previous studies, social and spider phobic persons exhibited generally enhanced visual P1 amplitudes indicative of hypervigilance in phobia.ConclusionResults suggest an interpretive bias and generalization of phobia-specific responses in specific phobia. Similar effects have been observed in other anxiety disorders, such as social phobia and posttraumatic stress disorder.