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The variance explained by the initial solution,
extracted components, and rotated components is
displayed. This first section of the table shows the
Initial Eigenvalues.
The Total column gives the
eigenvalue, or amount of variance in the
original variables accounted for by each component.
The % of Variance column gives the
ratio of the variance accounted for by each component to the total
variance in all of the variables.
The
Cumulative % column gives the
percentage of variance
accounted for by the first n
components. For example, the cumulative percentage
for the second component is the sum of the percentage of variance
for the first and second components.
For the initial solution, there are as many components as variables, and in
a correlations analysis, the sum of the eigenvalues equals the number of components.
You have requested that eigenvalues greater than 1 be extracted, so the first
three principal components form the extracted solution.
The second section of the table shows the extracted components.
They explain nearly 88% of the
variability in the original ten variables, so you
can considerably reduce the complexity of the data
set by using these components, with only a 12% loss
of information.
The rotation maintains the cumulative percentage of variation
explained by the extracted components, but that variation
is now spread more evenly over the components. The large
changes in the individual totals suggest that the rotated
component matrix will be easier to interpret than the unrotated
matrix.
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Total Variance Explained |