Beta-MPT: multinomial processing tree models for addressing individual differences
From MaRDI portal
Publication:972241
DOI10.1016/j.jmp.2009.06.007zbMath1203.91264OpenAlexW2013842439MaRDI QIDQ972241
William H. Batchelder, Jared B. Smith
Publication date: 25 May 2010
Published in: Journal of Mathematical Psychology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmp.2009.06.007
Bayesian inference (62F15) Medical applications (general) (92C50) Measurement and performance in psychology (91E45) Applications of statistics to psychology (62P15)
Related Items (16)
Hierarchical multinomial modeling to explain individual differences in children's clustering in free recall ⋮ A joint process model of consensus and longitudinal dynamics ⋮ Parameter validation in hierarchical MPT models by functional dissociation with continuous covariates: an application to contingency inference ⋮ A comparison of correlation and regression approaches for multinomial processing tree models ⋮ Parameter estimation approaches for multinomial processing tree models: a comparison for models of memory and judgment ⋮ Cognitive psychometrics: the scientific legacy of William H. Batchelder (1940--2018) ⋮ Adjusted priors for Bayes factors involving reparameterized order constraints ⋮ Sequential hypothesis tests for multinomial processing tree models ⋮ Random effects multinomial processing tree models: a maximum likelihood approach ⋮ Hierarchical Models for the Analysis of Likert Scales in Regression and Item Response Analysis ⋮ Selecting amongst multinomial models: an apologia for normalized maximum likelihood ⋮ Hierarchical multinomial processing tree models: a latent-trait approach ⋮ Hierarchical Bayesian modeling for test theory without an answer key ⋮ A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling ⋮ Parametric order constraints in multinomial processing tree models: an extension of Knapp and Batchelder (2004) ⋮ Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Marginal maximum a posteriori estimation using Markov chain Monte Carlo
- Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
- Tree inference with factors selectively influencing processes in a processing tree
- Individual differences and the all-or-none vs incremental learning controversy
- A new method for estimating model parameters for multinomial data
- A simulation-intensive approach for checking hierarchical models
- Inference from iterative simulation using multiple sequences
- Representing parametric order constraints in multi-trial applications of multinomial processing tree models
- The statistical analysis of general processing tree models with the EM algorithm
- Markov chain estimation for test theory without an answer key
- A hierarchical Bayesian statistical framework for response time distributions
- Hierarchical multinomial processing tree models: a latent-class approach
- Modeling individual differences using Dirichlet processes
- Signal detection models with random participant and item effects
- A context-free language for binary multinomial processing tree models
- Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
- The statistical analysis of a model for storage and retrieval processes in human memory
- Statistical analysis of finite mixture distributions
- A Bayesian approach to outlier detection and residual analysis
- The Selection of Prior Distributions by Formal Rules
- Bayesian Tests and Model Diagnostics in Conditionally Independent Hierarchical Models
- Bayesian Measures of Model Complexity and Fit
- Bayes Factors
- Bayesian Analysis of Binary and Polychotomous Response Data
This page was built for publication: Beta-MPT: multinomial processing tree models for addressing individual differences