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Inspection of a scree plot of unrotated components was used to determine the number of factors, followed by varimax rotation of the final solution to assist in the interpretation of components.
Principal component analysis (PCA) is a 100 year old mathematical technique credited to Karl Pearson [ 1] and its properties as well as the interpretation of components have been investigated extensively [ 2- 9].
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The interpretation of the components has been facilitated by the application of the varimax rotation to the component matrix.
To a large extent, the interpretation of principal components is generally guided by the degree to which each variable is associated with a particular component [ 47].
It should be mentioned, however, that the interpretation of mixture components as subpopulations is only one possible option.
Otherwise the biased influence of preceding trials makes the interpretation of ERP components disputable.
The interpretation of two components was consistent with previous research on the attitudes scale [ 11].
The interpretation of variance components is similar to that in Table 2.
The oblique rotation resulted in the same grouping of variables as the Varimax rotation and did not affect the interpretation of the components.
However, we have to be cautious with the interpretation of these component maps because of the way that STEM data is recorded.
The P-wave velocity structure of the crust and uppermost mantle was already obtained through the interpretation of vertical-component shot gathers.
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