Abstract
In this chapter, we refer to exploratory factor analysis simply as factor analysis and consider the principal component analysis formulated as reduced rank approximation as in Chap. 5. Principal component analysis (PCA) and factor analysis (FA) can be performed for identical data sets, with the purpose of dimension reduction. This reduction means that p observed variables, i.e., the p-dimensional scores, are reduced to lower-dimensional scores. The lower dimensions correspond to the m principal components in PCA and the m common factors in FA, with m < p. A major purpose of this chapter is to introduce mathematical facts that contrast PCA and FA solutions for an identical data set. The facts elucidate crucial differences between PCA and FA, which can suggest whether PCA or FA should be used for a particular data set.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
