The time has come to stop rotations for the identification of structures in the Hamilton Depression Scale (HAM-D17)

Objective: To use principal component analysis (PCA) to test the hypothesis that the items of the Hamilton Depression Scale (HAM-D17) have been selected to reflect depression disability, whereas some of the items are specific for sub-typing depression into typical vs. atypical depression. Method: Our previous study using exploratory factor analysis on HAM-D17 has been re-analyzed with PCA and the results have been compared to a dataset from another randomized prospective study. Results: PCA showed that the first principal component was a general factor covering depression disability with factor loadings very similar to those obtained in the STAR*D study. The second principal component was a bi-directional factor contrasting typical vs. atypical depression symptoms. Varimax rotation gave no new insight into the factor structure of HAM-D17. Conclusion: With scales like the HAM-D17, it is very important to make a proper clinical interpretation of the PCA before attempting any form of exploratory factor analysis. For the HAM-D17, our results indicate that profile scores are needed because the total score of all 17 items in the HAM-D17 does not give sufficient information.