Factor Analysis attempts to identify underlying variables that explain the pattern of correlations within a set of observed variables. This procedure is often used to reduce the number of variables in a data set but can also be used to explore the latent structure of the variables in your data file.

- If you think the relationships between your variables are nonlinear, the Bivariate Correlations procedure offers correlation coefficients that are more appropriate for nonlinear associations.
- If your analysis variables are not scale variables, you can try Hierarchical Cluster Analysis on the variables as an alternative to Factor Analysis for structure detection.