Select the type of matrix to use to calculate the principal components.
- Correlation: Use when your variables have different scales and you want to weight all the variables equally. For example, if some of the variables use a scale from 1-5 and others use a scale from 1-10, use the correlation matrix to standardize the scales.
- Covariance: Use when your variables use the same scale, or when your variables have different scales but you want to give more emphasis to variables with higher variances.
For example, suppose you count different species of organisms at several sample sites. If you select the covariance matrix, the more common species will show higher variances and be given more emphasis. Very rare species will not affect the analysis as much. If you select a correlation matrix, all species are weighted equally. Therefore, very rare species may contribute significantly to the analysis results. Therefore, the decision depends on the objective of your study.