Kaiser Criterion

The Kaiser Criterion is a guideline used in data analysis, especially in principal component analysis (PCA), to decide how many factors or components to keep. It suggests retaining only those components whose associated eigenvalues are greater than 1. An eigenvalue reflects the amount of data variation a component explains. If it's less than 1, the component explains less variation than a single original variable, so excluding them simplifies the model without losing much information. This criterion helps analysts focus on the most meaningful patterns in complex data sets efficiently.