Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items
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| Publication date | 2015 |
| Journal | Psychometrika |
| Volume | Issue number | 80 | 1 |
| Pages (from-to) | 205-235 |
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| Abstract | 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. |
| Document type | Article |
| Language | English |
| Published at | https://doi.org/10.1007/s11336-013-9374-9 |
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