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- W45861271 abstract "Factor analysis (FA) and principal-components analysis (PCA) are two important multivariate statistical analysis methods. The two methods are often used together for data reduction by structuring many variables into a much smaller number of components or factors. The techniques are particularly useful for eliminating variable collinearity and uncovering latent variables. However, FA and PCA are both conceptually and mathematically very different. PCA uses the same number of variables (components) to simply transform the original data, whereas FA uses fewer variables (factors) to reveal the interrelations between the original variables. This article discusses the mathematical foundations of the two methods with emphasis on their commonalities and differences. A case study is used to illustrate the implementations and interpretations of the methods. Finally, the application in social area analysis is discussed to demonstrate how PCA and FA methods are used in a geographical setting to advance the understanding of urban structure." @default.
- W45861271 created "2016-06-24" @default.
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- W45861271 date "2009-01-01" @default.
- W45861271 modified "2023-09-25" @default.
- W45861271 title "Factor Analysis and Principal-Components Analysis" @default.
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- W45861271 doi "https://doi.org/10.1016/b978-008044910-4.00434-x" @default.
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