5.2. Factor analysis
The Kaiser criterion (Kaiser, 1960) was applied to determine the total number of factors for each dataset in this analysis. Under this criterion, only factors with eigenvalues greater than or equal to 1 will be accepted as possible sources of variance in the data, with the highest priority ascribed to the factor that has the highest eigenvector sum. The rationale for choosing 1 is that a factor must have a variance at least as large as that of a single standardized original variable to be acceptable. When the seemingly complete factor model was developed using this criterion, the first four factors that account for at least 80% of the variance were selected for the next analysis. This was to ward off factors which by virtue of their low loadings do not constitute unique sources of variance in the hydrochemistry and could therefore be dispensed with.