Journal of Data Science

Abstract: Principal components analysis (PCA) is a widely used technique in nutritional epidemiology, to extract dietary patterns. To improve the interpretation of the derived patterns, it has been suggested to rotate the axes defined by PCA. This study aimed to evaluate whether rotation influences the repeatability of these patterns. For this reason PCA was applied in nutrient data of 500 participants (37 ± 15 years, 38% male) who were voluntarily enrolled in the study and asked to complete a semi-quantitative food frequency questionnaire (FFQ), twice within 15 days. The varimax and the quartimax orthogonal rotation methods, as well as the non-orthogonal promax and the oblimin methods were applied. The degree of agreement between the similar extracted patterns by each rotation method was assessed using the Bland and Altman method and Kendall’s tau-b coefficient. Good agreement was observed between the two administrations of the FFQ for the un-rotated components, while low-to-moderate agreement was observed for all rotation types (the quartimax and the oblimin method lead to more repeatable results). To conclude, when rotation is needed to improve food patterns’ interpretation, the quartimax and the oblimin methods seems to produce more robust results.