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Exploratory Factor Analysis (EFA), How to interpret  KMO and Bartlett´s test 

The KMO test and Bartlett's test are used to assess the suitability of the data for factor analysis.

KMO test (Kaiser-Meyer-Olkin test)

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

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.



KMO > 0,5 variables are considered suitable for factor analysis

KMO > 0,6 acceptable factor analysis,

KMO > 0,7 good factor analysis,

KMO > 0,8 very good factor analysis,

KMO > 0,9 excellent factor analysis.

Kaiser and Meyer, 1974

Bartlett's test < 0,05 variables can be used in factor analysis

Bartlett, 1954



Cite this article in your research paper:

Statistische Beratung Leonardo Miljko (datum) Exploratory Factor Analysis (EFA) - How to interpret KMO and Bartlett´s test. Retrieved from https://www.StatistischeDatenAnalyse.de/images/Exploratory_Factor_Analysis-EFA-How_to_interpret_KMO_and_Bartletts_test.pdf.


Statistische Beratung Leonardo Miljko  January 10, 2020 Exploratory Factor Analysis (EFA) - How to interpret KMO and Bartlett´s test. viewed datum < https://www.StatistischeDatenAnalyse.de/images/Exploratory_Factor_Analysis-EFA-How_to_interpret_KMO_and_Bartletts_test.pdf >

Wichtiger Hinweis: Der Originalinhalt ist auf Kroatisch. Die Übersetzung ins Deutsche und Englische erfolgte über einen Web-Übersetzer. Wir entschuldigen uns für die Fehler.