期刊
PSYCHOMETRIKA
卷 80, 期 1, 页码 205-235出版社
SPRINGER
DOI: 10.1007/s11336-013-9374-9
关键词
multinomial processing tree model; parameter heterogeneity; crossed-random effects model; hierarchical Bayesian modeling
Multinomial processing tree (MPT) models are theoretically motivated stochastic models for the analysis of categorical data. Here we focus on a crossed-random effects extension of the Bayesian latent-trait pair-clustering MPT model. Our approach assumes that participant and item effects combine additively on the probit scale and postulates (multivariate) normal distributions for the random effects. We provide a WinBUGS implementation of the crossed-random effects pair-clustering model and an application to novel experimental data. The present approach may be adapted to handle other MPT models.
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