Logistic response models with item interactions

Revuelta, José Luis González de Rivera y

doi:10.1111/j.2044-8317.2010.02009.x

Cited by 2 publications

(1 citation statement)

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“…Therefore, if the vector 1 were in the column space of R, there would be a vector τ such that (I − 1 p )Rτ = 0, and J would be deficient in rank. Moreover, from the theory of multinomial maximum likelihood estimation, the information matrix for ξ can be computed from the Jacobian matrix by the equation (Revuelta, 2012):…”

Section: Model Identificationmentioning

confidence: 99%

A Bayesian Generalized Explanatory Item Response Model to Account for Learning During the Test

Lozano

Revuelta

2021

Psychometrika

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The present paper introduces a new explanatory item response model to account for the learning that takes place during a psychometric test due to the repeated use of the operations involved in the items. The proposed model is an extension of the operation-specific learning model (Fischer and Formann in Appl Psychol Meas 6:397–416, 1982; Scheiblechner in Z für Exp Angew Psychol 19:476–506, 1972; Spada in Spada and Kempf (eds.) Structural models of thinking and learning, Huber, Bern, Germany, pp 227–262, 1977). The paper discusses special cases of the model, which, together with the general formulation, differ in the type of response in which the model states that learning occurs: (1) correct and incorrect responses equally (non-contingent learning); (2) correct responses only (contingent learning); and (3) correct and incorrect responses to a different extent (differential contingent learning). A Bayesian framework is adopted for model estimation and evaluation. A simulation study is conducted to examine the performance of the estimation and evaluation methods in recovering the true parameters and selecting the true model. Finally, an empirical study is presented to illustrate the applicability of the model to detect learning effects using real data.

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Section: Model Identificationmentioning

confidence: 99%