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Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis

Published online by Cambridge University Press:  01 January 2025

Kim De Roover ,
Eva Ceulemans ,
Marieke E. Timmerman ,
John B. Nezlek  and
Patrick Onghena
Kim De Roover*
Affiliation:
KU Leuven
Eva Ceulemans
Affiliation:
KU Leuven
Marieke E. Timmerman
Affiliation:
University of Groningen
John B. Nezlek
Affiliation:
College of William & Mary University of Social Sciences and Humanities, Faculty in Poznań
Patrick Onghena
Affiliation:
KU Leuven
*
Requests for reprints should be sent to Kim De Roover, Methodology of Educational Sciences Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Andreas Vesaliusstraat 2, 3000 Leuven, Belgium. E-mail: [email protected]
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Abstract

Given multivariate multiblock data (e.g., subjects nested in groups are measured on multiple variables), one may be interested in the nature and number of dimensions that underlie the variables, and in differences in dimensional structure across data blocks. To this end, clusterwise simultaneous component analysis (SCA) was proposed which simultaneously clusters blocks with a similar structure and performs an SCA per cluster. However, the number of components was restricted to be the same across clusters, which is often unrealistic. In this paper, this restriction is removed. The resulting challenges with respect to model estimation and selection are resolved.

Keywords

multigroup datamultilevel dataprincipal component analysissimultaneous component analysisclusteringdimensionality
Type
Original Paper
Information
Psychometrika , Volume 78 , Issue 4 , October 2013 , pp. 648 - 668
Copyright
Copyright © 2013 The Psychometric Society

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