The KMO test (Kaiser-Meyer-Olkin measure of sampling adequacy) is a test that is used to decide whether our samples are suitable for conducting factor analysis. Factor analysis in statistics is about identifying underlying factors or causes that can be used to represent the relationship between two or more variables.

But before we apply factor analysis we must decide whether our sample is suitable for applying factor analysis or not. This can be done by calculating the KMO test statistic which is calculated in terms of the correlation and partial correlation between the variables.

**KMO test statistic formula and interpretation**:

The KMO (Kaiser Meyer Olkin) measure can be calculated using the formula,

The value of the KMO measure always lies between 0 and 1.

If the KMO value is closer to 1 then it means that our data is suited to factor analysis. Values of KMO above 0.5 are generally accepted as indicating the adequacy of the sample for factor analysis while values below 0.5 mean that the sample is inadequate.

KMO values between 0.8 and 1 mean that the sample is very well suited for factor analysis.

**Carrying out the KMO (Kaiser-Meyer-Olkin) test in statistical software**:

- This video explains how to carry out the KMO test on excel. We carry out the test in excel and other statistical software in order to avoid cumbersome calculations.
- Click here to understand how to carry out the KMO test in SPSS.
- This video explains how to carry out the KMO test on R software.