Bayesian Estimation of Multinomial Processing Tree Models with Heterogeneity in Participants and Items

Dora Matzke (), Conor Dolan, William Batchelder and Eric-Jan Wagenmakers

Psychometrika, 2015, vol. 80, issue 1, 205-235

Abstract: Multinomial processing tree (MPT) models are theoretically motivated stochastic models for the analysis of categorical data. Here we focus on a crossed-random effects extension of the Bayesian latent-trait pair-clustering MPT model. Our approach assumes that participant and item effects combine additively on the probit scale and postulates (multivariate) normal distributions for the random effects. We provide a WinBUGS implementation of the crossed-random effects pair-clustering model and an application to novel experimental data. The present approach may be adapted to handle other MPT models. Copyright The Psychometric Society 2015

Keywords: multinomial processing tree model; parameter heterogeneity; crossed-random effects model; hierarchical Bayesian modeling (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11336-013-9374-9

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