Title: Common and cluster-specific simultaneous component analysis
Authors: De Roover, Kim ×
Timmerman, M. E.
Mesquita, Batja
Ceulemans, Eva #
Issue Date: 2013
Publisher: Public Library of Sciene
Series Title: PLoS One vol:8 issue:5 pages:1-14
Article number: e62280
Abstract: In many fields of research, so-called ‘multiblock’ data are collected, i.e., data containing multivariate observations that are
nested within higher-level research units (e.g., inhabitants of different countries). Each higher-level unit (e.g., country) then
corresponds to a ‘data block’. For such data, it may be interesting to investigate the extent to which the correlation
structure of the variables differs between the data blocks. More specifically, when capturing the correlation structure by
means of component analysis, one may want to explore which components are common across all data blocks and which
components differ across the data blocks. This paper presents a common and cluster-specific simultaneous component
method which clusters the data blocks according to their correlation structure and allows for common and cluster-specific
components. Model estimation and model selection procedures are described and simulation results validate their
performance. Also, the method is applied to data from cross-cultural values research to illustrate its empirical value.
ISSN: 1932-6203
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Methodology of Educational Sciences
Social and Cultural Psychology
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
De Roover et al. - 2013 - PLoS ONE.pdf Published 341KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science