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Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

Published online by Cambridge University Press:  01 January 2025

Karl Christoph Klauer
Karl Christoph Klauer*
Affiliation:
Albert-Ludwigs-Universität Freiburg
*
Requests for reprints should be sent to Karl Christoph Klauer, Institut für Psychologie, Universität Freiburg, 79085 Freiburg, Germany. E-mail: christoph.klauer@psychologie.uni-freiburg.de
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Abstract

Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into account and to assess parameter correlations. The model is estimated using Bayesian methods with weakly informative hyperprior distribution and a Gibbs sampler based on two steps of data augmentation. Estimation, model checks, and hypotheses tests are discussed. The new method is illustrated using a real data set, and its performance is evaluated in a simulation study.

Keywords

multinomial processing tree modelshierarchical modelsGibbs sampler

Information

Type
Theory and Methods
Information
Psychometrika , Volume 75 , Issue 1 , March 2010 , pp. 70 - 98
Copyright
Copyright © 2010 The Psychometric Society

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Footnotes

The research reported in this paper was supported by grant Kl 614/31-1 from the Deutsche Forschungsgemeinschaft.

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