The goal of PCA is to reduce the dimensionality of the data and to investigate which variables are most important in explaining the variability in the data. It reduces a set of correlated variables into a smaller set of uncorrelated components. In STATISTICA, there are a few methods for computing PCA. All methods should use all of the selected variables.

In response to your questions:

*What does the max number of factors entail? Are some of the original series excluded when building the factor components? (the number of Eigenvalues displayed seems to suggest that this is so)*

The max number of factors is an option in the factor analysis version of PCA that limits the number of components extracted from the full set of variables. The number of eigenvalues (eigenvalue in PCA is the variance of the component) displayed could only tell you how many factors were extracted out of the full set of variables. If the accuracy displayed is 100%, then you don’t need to extract anymore components since all of the variability has been explained.

*How does STATISTICA decide which series to include when building the factor components?*

All variables are used, so this is not an issue.

*What does this mean with respect to the interpretation of the factor loadings?*

Factor loading is the correlation between the variable and the component. The pattern of factor loadings can help give an interpretation to the component, however, sometimes interpretable components are simply not possible.