Principal Component Analysis Versus Factor Analysis

Kohei Adachi ()
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Kohei Adachi: Osaka University, Graduate School of Human Sciences

Chapter Chapter 19 in Matrix-Based Introduction to Multivariate Data Analysis, 2020, pp 297-310 from Springer

Abstract: Abstract In this chapter, we refer to exploratoryPrincipal Component Analysis (PCA) factor analysisExploratory Factor Analysis (EFA) simply as factor analysisFactor Analysis (FA) and consider the principal component analysisPrincipal Component Analysis (PCA) formulated as reduced rank approximationReduced rank approximation as in Chap. 5 . Principal component analysisPrincipal Component Analysis (PCA) (PCA) and factor analysis (FA) can be performed for identical data sets, with the purpose of dimension reduction. This reduction means that p observed variablesVariables, i.e., the p-dimensional scores, are reduced to lower-dimensional scores. The lower dimensions correspond to the m principal components in PCAPrincipal Component Analysis (PCA) and the m common factorsCommon factor in FA, with m

Date: 2020
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DOI: 10.1007/978-981-15-4103-2_19

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