Pooling data versus averaging model fits for some prototypical multinomial processing tree models

DOI10.1016/j.jmp.2009.06.005zbMath1182.91152OpenAlexW2008353407MaRDI QIDQ2654151

Richard A. Chechile

Publication date: 15 January 2010

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

Full work available at URL: https://doi.org/10.1016/j.jmp.2009.06.005

zbMATH Keywords

multinomial modelingaveraging maximum likelihood estimatesgroup data model fittingpopulation-parameter mapping

Mathematics Subject Classification ID

Measurement and performance in psychology (91E45) Applications of statistics to psychology (62P15)

Related Items (6)

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 ⋮ Sequential hypothesis tests for multinomial processing tree models ⋮ Obtaining separate measures for implicit and explicit memory ⋮ A novel method for assessing rival models of recognition memory ⋮ Measuring components of the memory of order

Uses Software

• HMMTree

Cites Work

• Unnamed Item
• Unnamed Item
• Modeling storage and retrieval processes with clinical populations with applications examining alcohol-induced amnesia and Korsakoff amnesia
• A new method for estimating model parameters for multinomial data
• New multinomial models for the Chechile-Meyer task
• Hierarchical multinomial processing tree models: a latent-class approach
• NOTES ON BIAS IN ESTIMATION
• Generating beta variates with nonintegral shape parameters
• BAYESIAN ANALYSIS OF GUMBEL DISTRIBUTED DATA
• Decision results for the parameters of the extreme value (Gumbel) distribution based on the mean and the standard deviation

This page was built for publication: Pooling data versus averaging model fits for some prototypical multinomial processing tree models