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This table shows two tests that indicate the suitability of your data
for structure detection.
The Kaiser-Meyer-Olkin Measure of Sampling Adequacy
is a statistic that indicates the proportion of variance in your
variables that might be caused by
underlying factors.
High values (close to 1.0) generally indicate that a
factor analysis may be useful with your data. If the value is less than
0.50, the results of the factor analysis probably won't be very useful.
Bartlett's test of sphericity tests the hypothesis that your correlation matrix
is an identity matrix, which would indicate that your variables are
unrelated and therefore unsuitable for structure detection.
Small
values (less than 0.05) of the significance level indicate that
a factor analysis may be useful with your data.
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KMO and Bartlett's Test |