Multiple factor analysis: Difference between revisions

Revision as of 14:56, 23 March 2021

Revision as of 11:12, 2 January 2023

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'''Multiple factor analysis (MFA)''' is a [[Factorial experiment|factorial]] method<ref name="GreenacreBlasius2006">{{cite book|last1=Greenacre|first1=Michael|last2=Blasius|first2=Jorg|author-link2=Jörg Blasius|title=Multiple Correspondence Analysis and Related Methods|url=https://books.google.com/books?id=ZvYV1lfU5zIC&pg=PA352|accessdate=11 June 2014|date=2006-06-23|publisher=CRC Press|isbn=9781420011319|pages=352–}}</ref> devoted to the study of tables in which a group of individuals is described by a set of variables (quantitative and / or qualitative) structured in groups. It is a [[Multivariate statistics|multivariate method]] from the field of [[Ordination (statistics)|ordination]] used to simplify [[Dimensionality reduction|multidimensional data]] structures. MFA treats all involved tables in the same way (symmetrical analysis). It may be seen as an extension of:

* [[Principal component analysis]] (PCA) when variables are quantitative,

* [[Multiple correspondence analysis]] (MCA) when variables are qualitative,

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The core of MFA is a weighted factorial analysis: MFA firstly provides the classical results of the factorial analyses.

1. ''Representations of individuals'' in which two individuals are muchclose closerto thaneach other if they haveexhibit similar values for allmany variables in allthe different variable groups; in practice the user particularly studies the first factorial plane.

2.''Representations of quantitative variables'' as in PCA (correlation circle).