How to perform multiblock component analysis in practice |
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Authors: | Kim De Roover Eva Ceulemans Marieke E Timmerman |
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Institution: | Department of Educational Sciences, Katholieke Universiteit Leuven, Andreas Vesaliusstraat 2, B-3000 Leuven, Belgium. Kim.DeRoover@ped.kuleuven.be |
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Abstract: | To explore structural differences and similarities in multivariate multiblock data (e.g., a number of variables have been
measured for different groups of subjects, where the data for each group constitute a different data block), researchers have
a variety of multiblock component analysis and factor analysis strategies at their disposal. In this article, we focus on
three types of multiblock component methods—namely, principal component analysis on each data block separately, simultaneous
component analysis, and the recently proposed clusterwise simultaneous component analysis, which is a generic and flexible
approach that has no counterpart in the factor analysis tradition. We describe the steps to take when applying those methods
in practice. Whereas plenty of software is available for fitting factor analysis solutions, up to now no easy-to-use software
has existed for fitting these multiblock component analysis methods. Therefore, this article presents the MultiBlock Component
Analysis program, which also includes procedures for missing data imputation and model selection. |
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