Hierarchical Multinomial Processing Tree Models: A Latent-Class Approach
Psychometrika, 2006Multinomial processing tree models are widely used in many areas of psychology. Their application relies on the assumption of parameter homogeneity, that is, on the assumption that participants do not differ in their parameter values. Tests for parameter homogeneity are proposed that can be routinely used as part of multinomial model analyses to defend
Karl Christoph Klauer +1 more
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Beta-MPT: Multinomial processing tree models for addressing individual differences
Journal of Mathematical Psychology, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
William H Batchelder
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Multinomial Processing Tree Models of Factorial Categorization
Journal of Mathematical Psychology, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Batchelder, William H. +1 more
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Bounds on variances of estimators for multinomial processing tree models
Journal of Mathematical Psychology, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pierre Baldi, William H Batchelder
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HMMTree: A computer program for latent-class hierarchical multinomial processing tree models [PDF]
Behavior Research Methods, 2007 Latent-class hierarchical multinomial models are an important extension of the widely used family of multinomial processing tree models, in that they allow for testing the parameter homogeneity assumption and provide a framework for modeling parameter heterogeneity.
Christoph Stahl +2 more
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Using recursive partitioning to account for parameter heterogeneity in multinomial processing tree models [PDF]
Behavior Research Methods, 2017 In multinomial processing tree (MPT) models, individual differences between the participants in a study can lead to heterogeneity of the model parameters. While subject covariates may explain these differences, it is often unknown in advance how the parameters depend on the available covariates, that is, which variables play a role at all, interact, or
Achim Zeileis, Zeileis Achim
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Representing probabilistic models of knowledge space theory by multinomial processing tree models
Journal of Mathematical Psychology, 2020Supplementary material for the manuscript "Representing Probabilistic Models of Knowledge Space Theory by Multinomial Processing Tree Models"
Heck, Daniel W., Noventa, Stefano
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Julia Gross, Thorsten Pachur
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A context-free language for binary multinomial processing tree models
Journal of Mathematical Psychology, 2009Probabilistic binary multinomial processing tree models (BMPTs) are described for categorical data, in which the observed category is a result of occurrence or non-occurrence of some latent binary events. The correspondence between them is described by a binary tree in which the events are the internal nodes while the categories are leafs. An axiomatic
William H Batchelder
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Parametric order constraints in multinomial processing tree models: An extension of Knapp and Batchelder (2004) [PDF]
Journal of Mathematical Psychology, 2015 Multinomial processing tree (MPT) models are tools for disentangling the contributions of latent cognitive processes in a given experimental paradigm. The present note analyzes MPT models subject to order constraints on subsets of its parameters. The constraints that we consider frequently arise in cases where the response categories are ordered in ...
Karl Christoph Klauer +2 more
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