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

Published online by Cambridge University Press:  01 January 2025

Dora Matzke ,
Conor V. Dolan ,
William H. Batchelder  and
Eric-Jan Wagenmakers
Dora Matzke*
Affiliation:
University of Amsterdam
Conor V. Dolan
Affiliation:
University of Amsterdam
William H. Batchelder
Affiliation:
University of California, Irvine
Eric-Jan Wagenmakers
Affiliation:
University of Amsterdam
*
Requests for reprints should be sent to Dora Matzke, Department of Psychology, University of Amsterdam, Weesperplein 4, 1018 XA, Amsterdam, The Netherlands. E-mail: d.matzke@uva.nl
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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.

Keywords

multinomial processing tree modelparameter heterogeneitycrossed-random effects modelhierarchical Bayesian modeling
Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 1 , March 2015 , pp. 205 - 235
Copyright
Copyright © 2013 The Psychometric Society

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