Parameter validation in hierarchical MPT models by functional dissociation with continuous covariates: an application to contingency inference

DOI10.1016/j.jmp.2020.102388zbMath1455.91185OpenAlexW3036900253MaRDI QIDQ826874

Daniel W. Heck, Franziska M. Bott, Thorsten Meiser

Publication date: 6 January 2021

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2020.102388

zbMATH Keywords

continuous covariateshierarchical MPTparameter validationpseudocontingency

Mathematics Subject Classification ID

Cognitive psychology (91E10) Applications of statistics to psychology (62P15)

Uses Software

• R
• BayesFactor
• TreeBUGS

Cites Work

• Default Bayes factors for ANOVA designs
• Hierarchical multinomial processing tree models: a latent-trait approach
• Beta-MPT: multinomial processing tree models for addressing individual differences
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Hierarchical multinomial processing tree models: a latent-class approach
• A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling
• Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items
• Signal detection models with random participant and item effects
• A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

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