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The CHIC Model: A Global Model for Coupled Binary Data

Published online by Cambridge University Press:  01 January 2025

Tom Wilderjans ,
Eva Ceulemans  and
Iven Van Mechelen
Tom Wilderjans*
Affiliation:
Katholieke Universiteit Leuven
Eva Ceulemans
Affiliation:
Katholieke Universiteit Leuven
Iven Van Mechelen
Affiliation:
Katholieke Universiteit Leuven
*
Requests for reprints should be sent to Tom Wilderjans, Department of Psychology, Katholieke Universiteit Leuven, Tiensestraat 102, 3000 Leuven, Belgium. E-mail: Tom.Wilderjans@psy.kuleuven.be
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Abstract

Often problems result in the collection of coupled data, which consist of different N-way N-mode data blocks that have one or more modes in common. To reveal the structure underlying such data, an integrated modeling strategy, with a single set of parameters for the common mode(s), that is estimated based on the information in all data blocks, may be most appropriate. Such a strategy implies a global model, consisting of different N-way N-mode submodels, and a global loss function that is a (weighted) sum of the partial loss functions associated with the different submodels. In this paper, such a global model for an integrated analysis of a three-way three-mode binary data array and a two-way two-mode binary data matrix that have one mode in common is presented. A simulated annealing algorithm to estimate the model parameters is described and evaluated in a simulation study. An application of the model to real psychological data is discussed.

Keywords

coupled datathree-way three-mode databinary datahierarchical classesmulti-way data analysisclusteringdata fusion
Type
Original Paper
Information
Psychometrika , Volume 73 , Issue 4 , December 2008 , pp. 729 - 751
Copyright
Copyright © 2008 The Psychometric Society

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Footnotes

T. Wilderjans is a Research Assistant of the Fund for Scientific Research—Flanders (Belgium). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/2005/04). We are grateful to Kristof Vansteelandt for providing us with an interesting data set. We also thank three anonymous reviewers for their useful comments.

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