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Hierarchical Multinomial Processing Tree Models: A Latent-Class Approach

Published online by Cambridge University Press:  01 January 2025

Karl Christoph Klauer
Karl Christoph Klauer*
Affiliation:
Albert-Ludwigs-Universität Freiburg, Germany
*
Requests for reprints should be sent to K. C. Klauer, Institut für Psychologie, Albert-Ludwigs-Universität Freiburg, D-79085 Freiburg, Germany. E-mail: christoph.klauer@psychologie.uni-freiburg.de
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Abstract

Multinomial 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 the assumption. If parameter homogeneity is found to be violated, a new family of models, termed latent-class multinomial processing tree models, can be applied that accommodates parameter heterogeneity and correlated parameters, yet preserves most of the advantages of the traditional multinomial method. Estimation, goodness-of-fit tests, and tests of other hypotheses of interest are considered for the new family of models.

Keywords

hierarchical modelsmultinomial processing-trxee models

Information

Type
Original Paper
Information
Psychometrika , Volume 71 , Issue 1 , March 2006 , pp. 7 - 31
Copyright
Copyright © 2006 The Psychometric Society

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Footnotes

The author thanks Bill Batchelder, Edgar Erdfelder, Thorsten Meiser, and Christoph Stahl for helpful comments on a previous version of this paper. The author is also grateful to Edgar Erdfelder for making available the data set analyzed in this paper.

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