## 3.1 Kaiser-Meyer-Olkin (KMO)

Kaiser-Meyer-Olkin (Kaiser 1974) is a statistical test used in factor analysis to determine if the data is suitable for factor analysis. KMO measures the sampling adequacy of each observed variables in the model as well as the complete model. KMO is calculated based on the correlation between the variables. It ranges from 0 to 1 with values closer to 1 suggesting the variables are correlated and the data is well-suited for factor analysis, otherwise the variables are uncorrelated and there may not be a common factor influencing them.

The following criteria are used for evaluating KMO:

- Above 0.90 - Marvelous
- 0.80 to 0.90 - Meritorious
- 0.7 to 0.80 - Average
- 0.60 to 0.70 - Mediocre
- 0.50 to 0.60 - Terrible
- Below 0.50 - Unacceptable

### References

Kaiser, Henry F. 1974. “An Index of Factorial Simplicity.”

*Psychometrika*39 (1): 31–36.