Previous: Results Up: Ext. Abst.

Conclusions

One must be careful when analyzing data filtered by a PCA or EOF technique. Due to the post-hoc analysis applied to the interpretation of the PC spatial and temporal patterns, one is tempted to over-analyze the results. Moreover, following a standard approach based on inspection of eigenvalue magnitudes or spacing will likely lead to underestimating the information embedded in the data (or similarity matrix used). Reconciling these two points is not an easy task. This research suggests that careful analysis of a range of solutions (unrotated, simple structure rotation) may provide insight into the variability. That said, the use of Procrustes should be encouraged where prior theory exists. Such theory can be incorporated directly into the elements of the target and fit to the data. The degree of fit can then be assessed to determine how well the theory fits the data. In the examples presented herein, the data do not fit a random theory or that of a linear or sigmoidal trend very well. The capability to reject such hypotheses, provided by Procrustes, underscores the advantage of this tool.