Article

Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

Psychometrika (impact factor: 1.77). 04/2012; 75(1):70-98. DOI:10.1007/s11336-009-9141-0 pp.70-98

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

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Keywords

covariance matrix
 
Gibbs sampler
 
hierarchical extension
 
Keywordsmultinomial processing tree models-hierarchical models-Gibbs sampler
 
model checks
 
Multinomial processing tree models
 
multivariate normal distribution
 
new method
 
person-level parameters
 
persons
 
simulation study
 
steps
 
weakly informative hyperprior distribution
 

Karl Christoph Klauer