Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

Psychometrika volume 75, pages70–98(2010)Cite this article

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.

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  1. Institut für Psychologie, Universität Freiburg, 79085, Freiburg, Germany

    Karl Christoph Klauer

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  1. Karl Christoph Klauer
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Correspondence to Karl Christoph Klauer.

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

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Klauer, K.C. Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach. Psychometrika 75, 70–98 (2010). https://doi.org/10.1007/s11336-009-9141-0

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Keywords

  • multinomial processing tree models
  • hierarchical models
  • Gibbs sampler