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

Abstract

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.

DOI: 10.1590/1516-4446-2013-1116

Cite this paper

@article{Bech2013TheTH, title={The time has come to stop rotations for the identification of structures in the Hamilton Depression Scale (HAM-D₁₇).}, author={Per Bech and Cl{\'a}udio Csillag and Lone Christina Hellstr{\"{o}m and Marcelo Pio de Almeida Fleck}, journal={Revista brasileira de psiquiatria}, year={2013}, volume={35 4}, pages={360-3} }