The KMO test (Kaiser-Meyer-Olkin test) assesses the suitability of data for factor analysis by measuring the degree of coherence between variables. The test score varies between 0 and 1, and values greater than 0,5 are considered suitable for factor analysis.

It is basically considered that KMO test values should be greater than 0,6 for an acceptable analysis, greater than 0,7 for a good analysis, greater than 0,8 for a very good analysis and greater than 0,9 for an excellent analysis (Kaiser and Meyer, 1974).

It's with us KMO=0,749

Bartlett's test tests the hypothesis of homogeneity of the correlation matrix. In the example that the homogeneity hypothesis is rejected, this indicates that the variables in the correlation matrix are sufficiently interrelated that they could be used in factor analysis. Bartlett's test has a significant value when correlations between variables are large enough to be used in factor analysis.

So, Bartlett's test is appropriate when the significance value is less than 0,05 (Bartlett, 1954).

It's with us Bartlett´s test <0,001

Thus, for a suitable factor analysis, the value of the KMO test should be greater than 0,5, while the Bartlett's test should have a significance value less than 0,05.