New Insights into PCA + Varimax for Psychological Researchers: A short commentary on Rohe & Zeng (2023)
Online repository of a commentary published in the journal Royal Statistical Society, Series B:
Pargent, F., Goretzko, D. & von Oertzen, T. (2023). Florian Pargent, David Goretzko and Timo von Oertzen’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng. Journal of the Royal Statistical Society Series B: Statistical Methodology, qkad054, https://doi.org/10.1093/jrsssb/qkad054
The electronic supplemental materials (the content of the electronic_supplemental_materials.html file) are hosted online at:
https://florianpargent.github.io/pca_varimax_commentary/
In their paper “Vintage factor analysis with varimax performs statistical inference”, Rohe and Zeng (R&Z; Rohe & Zeng, 2023) demonstrate the usefulness of principal component analysis with varimax rotation (PCA+VR), a combination they call vintage factor analysis. The authors show that PCA+VR can be used to estimate factor scores and factor loadings, if a certain leptokurtic condition is fulfilled that can be assessed by simple visual diagnostics. In a side result, they also imply that PCA+VR is able to estimate factor scores even if the latent factors are correlated. In our commentary "New Insights into PCA + Varimax for Psychological Researchers", we briefly discuss some implications of these results for psychological research and note that the suggested diagnostics of “radial streaks” might give less clear results in typical psychological applications. The commentary includes extensive electronic supplemental materials, including a data example and a small simulation on estimating correlated factors, that can be found at https://osf.io/5symf/.
Rohe, K. & Zeng, M. (2023). Vintage Factor Analysis with Varimax Performs Statistical Inference. Journal of the Royal Statistical Society Series B: Statistical Methodology, qkad029, https://doi.org/10.1093/jrsssb/qkad029