Principal Components and Factor Analysis
In this chapter, two methods of examining the relationships among a set of variables will be examined. The first, principal components analysis (PCA), is essentially a method of data reduction that aims to reduce the dimensionality of multivariate data and, thus, aid in its understanding. The second technique to be discussed is exploratory factor analysis, which has somewhat similar aims to principal components analysis, but in addition tries to uncover something more fundamental about the data.
KeywordsPrincipal Component Analysis Correlation Matrix Exploratory Factor Analysis Principal Component Score Cumulative Proportion
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