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Four Simultaneous Component Models for the Analysis of Multivariate Time Series from More than One Subject to Model Intraindividual and Interindividual Differences

Published online by Cambridge University Press:  01 January 2025

Marieke E. Timmerman  and
Henk A. L. Kiers
Marieke E. Timmerman*
Affiliation:
University of Groningen
Henk A. L. Kiers
Affiliation:
University of Groningen
*
Requests for reprints should be sent to Marieke E. Timmerman, Heymans Institute of Psychology, DPMG, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, THE NETHERLANDS. E-Mail: m.e.timmerman@ppsw.rug.nl
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Abstract

A class of four simultaneous component models for the exploratory analysis of multivariate time series collected from more than one subject simultaneously is discussed. In each of the models, the multivariate time series of each subject is decomposed into a few series of component scores and a loading matrix. The component scores series reveal the latent data structure in the course of time. The interpretation of the components is based on the loading matrix. The simultaneous component models model not only intraindividual variability, but interindividual variability as well. The four models can be ordered hierarchically from weakly to severely constrained, thus allowing for big to small interindividual differences in the model. The use of the models is illustrated by an empirical example.

Keywords

three-way analysisexploratory longitudinal analysis
Type
Articles
Information
Psychometrika , Volume 68 , Issue 1 , March 2003 , pp. 105 - 121
Copyright
Copyright © 2003 The Psychometric Society

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Footnotes

This research has been made possible by funding from the Netherlands Organization of Scientific Research (NWO) to the first author. The authors are obliged to Tom A.B. Snijders, Jos M.F. ten Berge and three anonymous reviewers for comments on an earlier version of this paper, and to Kim Shifren for providing us with her data set, which was collected at Syracuse University.

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