The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
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.
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Cites
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Cited by
(2013) · Cited by 1,351
multiTree: A computer program for the analysis of multinomial processing tree models
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Model Comparison and the Principle of Parsimony
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Metadata
- Authors
- Klauer, K. C.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 2010-03
- Volume
- 75
- Issue
- 1
- Pages
- 70-98
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W2042024213
- Cited by
- 288
- References
- 53
- FWCI
- 12.63
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
