Hierarchical multinomial modeling to explain individual differences in children's clustering in free recall

DOI10.1016/j.jmp.2020.102378OpenAlexW3029561997MaRDI QIDQ826862

Martha Michalkiewicz, Sebastian S. Horn, Ute J. Bayen

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.2020.102378

zbMATH Keywords

individual differencesBayesian hierarchical modelingcategory clusteringcognitive developmentfree recallmultinomial processing tree model

Mathematics Subject Classification ID

Cognitive psychology (91E10) Measurement and performance in psychology (91E45)

Related Items (1)

Cognitive psychometrics: the scientific legacy of William H. Batchelder (1940--2018)

Uses Software

• JAGS
• Stan
• TreeBUGS

Cites Work

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• How hierarchical models improve point estimates of model parameters at the individual level
• Hierarchical multinomial processing tree models: a latent-trait approach
• Beta-MPT: multinomial processing tree models for addressing individual differences
• Representing parametric order constraints in multi-trial applications of multinomial processing tree models
• Hierarchical multinomial processing tree models: a latent-class approach
• Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items
• The statistical analysis of a model for storage and retrieval processes in human memory

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