Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach
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.
Keywordsmultinomial processing tree models-hierarchical models-Gibbs sampler