An Overview of Models for Response Times and Processes in Cognitive Tests.

Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches

IRTrees: Tree-Based Item Response Models of the GLMM Family

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

Controlling speed in component skills of reading improves the explanation of reading comprehension

Efficiency in reading component skills is crucial for reading comprehension, as efficient subprocesses do not extensively consume limited cognitive resources, making them available for comprehension processes. Cognitive efficiency is typically measured with speeded tests of relatively easy items. Observed responses and response times indicate the latent variables of ability and speed. Interpreting only ability or speed as efficiency may be misleading because there is a within-person dependency between both variables (speed-ability tradeoff [SAT]). Therefore, the present study measures efficiency as ability conditional on speed by controlling speed experimentally with item-level time limits. The proposed timed ability measures of reading component skills are expected to have a clearer interpretation in terms of efficiency and to be better predictors for reading comprehension. To support this claim, this study investigates two component skills, visual word recognition and sentence-level semantic integration (sentence reading), to understand how differences in ability in a timed condition are related to differences in ability and speed in a traditional untimed condition. Moreover, untimed and timed reading component skill measures were used to explain reading comprehension. A German subsample from Programme for International Student Assessment (PISA) 2012 completed the reading component skills tasks with and without item-level time limits and PISA reading tasks. The results showed that timed ability is only moderately related to untimed ability. Furthermore, timed ability measures proved to be stronger predictors of sentence-level and text-level reading comprehension than the corresponding untimed ability and speed measures, although using untimed ability and speed jointly as predictors increased the amount of explained variance. (DIPF/Orig.

The Estimation of Item Response Models with the lmer Function from the lme4 Package in R

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.