The rule of thumb: any factor with an eigenvalue higher or equal to 1 explains more variance than a single observed variable. So, if a factor had an eigenvalue of 2.3, it would explain as much variance as 2.3 of the original variables. In other words, eigenvalues help us understand the importance of each factor in explaining the variability in our data. The higher the eigenvalue, the more important that factor is.
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