Principal component analysis (PCA) and factor analysis (FA) are two time-honored dimension reduction methods. In this paper, some inequalities are presented to contrast the parameters’ estimates in PCA and FA. For this reason, we take advantage of the recently established matrix decomposition (MD) formulation of FA. In summary, the resulting inequalities show that (1) FA gives a better fit to a data set than PCA, (2) PCA extracts a larger amount of common “information” than FA, and (3) for each variable, its unique variance in FA is larger than its residual variance in PCA minus the one in FA. The resulting inequalities can be useful to suggest whether PCA or FA should be used for a particular data set. The answers can also be valid for the classic FA formulation not relying on the MD-FA definition, as both “types” FA provide almost equal solutions. Additionally, the inequalities give theoretical explanation of some empirically observed tendencies in PCA and FA solutions, e.g., that the absolute values of PCA loadings tend to be larger than those for FA loadings and that the unique variances in FA tend to be larger than the residual variances of PCA.
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Funding was provided by the Japan Society of the Promotion of Sciences [Grant (C)-18K11191]. The authors thank the anonymous reviewers for their useful comments.
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Adachi, K., Trendafilov, N.T. Some inequalities contrasting principal component and factor analyses solutions. Jpn J Stat Data Sci 2, 31–47 (2019). https://doi.org/10.1007/s42081-018-0024-4
- Matrix decomposition
- Dimension reduction
- Common parts
- Unique parts