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Author: Lee,24
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English[en]
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.
Icelandic[is]
Eigingildi (Eigenvalue): determines the number of factors to retain in an analysis. An eigenvalue is a measure of how much of the common variance (the shared variance among variables) a factor explains. Imagine you perform a factor analysis and initially extract 16 factors. Each of these factors has a quality score called an eigenvalue. The factors with high eigenvalues are likely to represent real underlying factors.