We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
HIERARCHICAL MULTINOMIAL PROCESSING TREE MODELS: A LATENT-TRAIT APPROACH. .
- Authors
KLAUER, KARL CHRISTOPH
- 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.
- Subjects
STOCHASTIC models; MULTILEVEL models; STATISTICAL correlation; BAYESIAN analysis; SAMPLING (Process)
- Publication
Psychometrika, 2010, Vol 75, Issue 1, p70
- ISSN
0033-3123
- Publication type
Article
- DOI
10.1007/S11336-009-9141-0