If the tested KMO value is larger, it means that the simple sum of the squared correlation coefficients between the selected factor indicators is greater than the sum of the partial correlation coefficients, so this set of data is more suitable for principal component analysis.If the KMO value tested is larger, it means that the simple sum of the quadratic correlation coefficients between the selected factor indicators is greater than the sum of the partial correlation coefficients, making this data set more suitable for principal component analysis.If the kmo value is wide, it means that the sum of simple correlation coefficients between the selected factor indexes is greater than the sum of partial correlation coefficients. Therefore, this data set is more suitable for principal component analysis.