Kaiser-Meyer-Olkin Test

The Kaiser-Meyer-Olkin (KMO) Test measures how suitable your data is for factor analysis, which is a statistical method used to identify underlying patterns. It evaluates whether variables are adequately related to each other to justify reducing them into fewer factors. A high KMO value (close to 1) indicates that the data has strong commonalities and is appropriate for factor analysis, while a low value suggests the data may not be well-suited. Essentially, it helps researchers decide if their data has enough structure to uncover meaningful insights through this technique.