title: How to perform multiblock component analysis in practice.
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.
authors: Department of Educational Sciences, Katholieke Universiteit Leuven, Andreas Vesaliusstraat 2, B-3000 Leuven, Belgium. Kim.DeRoover@ped.kuleuven.be
authors: Kim
authors: K
authors: De Roover
authors: Eva
authors: E
authors: Ceulemans
authors: Marieke E
authors: ME
authors: Timmerman
mesh_terms: D008962:Models, Theoretical; D025341:Principal Component Analysis; D012107:Research Design