- Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items
- Volume | Issue number
- 80 | 1
- Pages (from-to)
- Document type
- Faculty of Social and Behavioural Sciences (FMG)
- Psychology Research Institute (PsyRes)
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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