The many-facet Rasch model in the analysis of the go/no-go association task

This article provides a many-facet Rasch measurement (MFRM) analysis of go/no-go association task (GNAT)-based measures of implicit attitudes toward sweet and salty food. We describe the statistical model and the strategy we adopted to score the GNAT, and we emphasize that, when analyzing implicit measures, MFRM indexes have to be interpreted in a peculiar way. In comparison with traditional scoring algorithms, an MFRM analysis of implicit measures provides some additional information and suffers from fewer limitations and assumptions. MFRM might help to overcome some limitations of current implicit measures, since it directly addresses some known issues and potential confounds, such as those related to a rational zero point, to the arbitrariness of the metric, and to participants’ task-set switching ability.     

Recovering a Probabilistic Knowledge Structure by Constraining its Parameter Space

In the Basic Local Independence Model (BLIM) of Doignon and Falmagne (Knowledge Spaces, Springer, Berlin, 1999), the probabilistic relationship between the latent knowledge states and the observable response patterns is established by the introduction of a pair of parameters for each of the problems: a lucky guess probability and a careless error probability. In estimating the parameters of the BLIM with an empirical data set, it is desirable that such probabilities remain reasonably small. A special case of the BLIM is proposed where the parameter space of such probabilities is constrained. A simulation study shows that the constrained BLIM is more effective than the unconstrained one, in recovering a probabilistic knowledge structure.     

An Upgrading Procedure for Adaptive Assessment of Knowledge

In knowledge space theory, existing adaptive assessment procedures can only be applied when suitable estimates of their parameters are available. In this paper, an iterative procedure is proposed, which upgrades its parameters with the increasing number of assessments. The first assessments are run using parameter values that favor accuracy over efficiency. Subsequent assessments are run using new parameter values estimated on the incomplete response patterns from previous assessments. Parameter estimation is carried out through a new probabilistic model for missing-at-random data. Two simulation studies show that, with the increasing number of assessments, the performance of the proposed procedure approaches that of gold standards.     

Assessing learning processes with the gain-loss model

Within the framework of knowledge space theory, a probabilistic skill multimap model for assessing learning processes is proposed. The learning process of a student is modeled as a function of the interaction between his competence state and the effect of a learning object on specific skills. Model parameters are initial probabilities of the skills, effects of learning objects on gaining and losing the skills, careless error, and lucky guess probabilities of the problems. A simulation study assessed model identifiability and goodness-of-recovery under several conditions. Practical implications of using the model are discussed, and the MATLAB code for simulating, estimating and testing it is available in the Psychonomic Society supplemental archive.