The Kaiser–Meyer–Olkin (KMO) test is a statistical measure to determine how suited data is for factor analysis.
[1] Henry Kaiser introduced a Measure of Sampling Adequacy (MSA) of factor analytic data matrices in 1970.
[4] In flamboyant fashion, Kaiser proposed that a KMO > 0.9 was marvelous, in the 0.80s, meritorious, in the 0.70s, middling, in the 0.60s, mediocre, in the 0.50s, miserable, and less than 0.5 would be unacceptable.
In other words, there are widespread correlations which would be a large problem for factor analysis.
[1] If the following is run in R with the library(psych) The following is produced: This shows that the data is not that suited to Factor Analysis.