How to perform multiblock component analysis in practice

Kim De Roover*, Eva Ceulemans, Marieke E. Timmerman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

40 Citations (Scopus)
9 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)41-56
Number of pages16
JournalBehavior Research Methods
Volume44
Issue number1
DOIs
Publication statusPublished - Mar 2012
Externally publishedYes

Keywords

  • Multigroup data
  • Multilevel data
  • Simultaneous component analysis
  • Principal component analysis
  • Clusterwise simultaneous component analysis
  • COMMON FACTOR-ANALYSIS
  • PRINCIPAL-COMPONENTS
  • MISSING DATA
  • ROTATION
  • EMOTIONS
  • MODELS
  • NUMBER
  • HEALTH
  • FIT

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