Can fast and slow intelligence be differentiated?

Responses to items from an intelligence test may be fast or slow. The research issue dealt with in this paper is whether the intelligence involved in fast correct responses differs in nature from the intelligence involved in slow correct responses. There are two questions related to this issue: 1. Are the processes involved different? 2. Are the abilities involved different? An answer to these questions is provided making use of data from a Raven-like matrices test and a verbal analogies test, and the use of a psychometric branching model. The branching model is based on three latent traits: speed, fast accuracy and slow accuracy, and item parameters corresponding to each of these. The pattern of item difficulties is used to draw conclusions on the cognitive processes involved. The results are as follows: 1. The processes involved in fast and slow responses can be differentiated, as can be derived from qualitative differences in the patterns of item difficulty, and fast responses lead to a larger differentiation between items than slow responses do. 2. The abilities underlying fast and slow responses can also be differentiated, and fast responses allow for a better differentiation between the respondents

Parameter estimation of multiple item response profile model

Multiple item response profile (MIRP) models are models with crossed fixed and random effects. At least one between-person factor is crossed with at least one within-person factor, and the persons nested within the levels of the between-person factor are crossed with the items within levels of the within-person factor. Maximum likelihood estimation (MLE) of models for binary data with crossed random effects is challenging. This is because the marginal likelihood does not have a closed form, so that MLE requires numerical or Monte Carlo integration. In addition, the multidimensional structure of MIRPs makes the estimation complex. In this paper, three different estimation methods to meet these challenges are described: the Laplace approximation to the integrand; hierarchical Bayesian analysis, a simulation-based method; and an alternating imputation posterior with adaptive quadrature as the approximation to the integral. In addition, this paper discusses the advantages and disadvantages of these three estimation methods for MIRPs. The three algorithms are compared in a real data application and a simulation study was also done to compare their behaviour

Technical report for the first European Survey on Language Competences

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Distinguishing fast and slow processes in accuracy: Response time data

We investigate the relation between speed and accuracy within problem solving in its simplest non-trivial form. We consider tests with only two items and code the item responses in two binary variables: one indicating the response accuracy, and one indicating the response speed. Despite being a very basic setup, it enables us to study item pairs stemming from a broad range of domains such as basic arithmetic, first language learning, intelligence-related problems, and chess, with large numbers of observations for every pair of problems under consideration. We carry out a survey over a large number of such item pairs and compare three types of psychometric accuracy-response time models present in the literature: two 'one-process' models, the first of which models accuracy and response time as conditionally independent and the second of which models accuracy and response time as conditionally dependent, and a 'two-process' model which models accuracy contingent on response time. We find that the data clearly violates the restrictions imposed by both one-process models and requires additional complexity which is parsimoniously provided by the two-process model. We supplement our survey with an analysis of the erroneous responses for an example item pair and demonstrate that there are very significant differences between the types of errors in fast and slow responses