Transcription of 203-30: Principal Component Analysis versus …
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203-30: Principal Component Analysis versus …
1 Paper 203-30 Principal Component Analysis vs. Exploratory Factor Analysis Diana D. Suhr, University of Northern Colorado Abstract Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques and sometimes mistaken as the same statistical method. However, there are distinct differences between PCA and EFA. Similarities and differences between PCA and EFA will be examined. Examples of PCA and EFA with PRINCOMP and FACTOR will be illustrated and discussed. Introduction You want to run a regression Analysis with the data you ve collected. However, the measured (observed) variables are highly correlated.
3 In EFA, observed variables are a linear combination of the underlying factors (estimated factor and a unique factor). Communality is the variance of observed variables accounted for by a …
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