Functional Principal Component Analysis (FPCA) is an important dimension reduction technique to interpret the main modes of functional data variation in terms of a small set of uncorrelated variables. The principal components can not always be simply interpreted and rotation is one of the main solutions to improve the interpretation. In this paper, two new functional Varimax rotation approaches are introduced. They are based on the equivalence between FPCA of basis expansion of the sample curves and Principal Component Analysis (PCA) of a transformation of the matrix of basis coefficients. The first approach consists of a rotation of the eigenvectors that preserves the orthogonality between the eigenfunctions but the rotated principal component scores are not uncorrelated. The second approach is based on rotation of the loadings of the standardized principal component scores that provides uncorrelated rotated scores but non-orthogonal eigenfunctions. A simulation study and an application with data from the curves of infections by COVID-19 pandemic in Spain are developed to study the performance of these methods by comparing the results with other existing approaches.

This work applies a factor analysis with VARIMAX rotation to develop a bibliometric indicator, named the Weighted Factor Index, in order to derive a new classification for journals belonging to a certain category, alternative to the one provided by the Journal Impact Factor. For this, 16 metrics from three different databases (Web of Science, Scopus and SCImago Journal Rank) are considered. The Weighed Factor Index entails the advantage of incorporating and summarizing information from all the indicators; so as to test its performance, it was applied to rank journals belonging to the category Information Science & Library Science.

Aims: The aims of this study were: a) to analyse the association between the mode of commuting to go and come back from school and b) to analyse the association between the usual mode of commuting and weekly counts of school trips by mode of commuting to and from school. Methods: A total of 5960 students (7–20 year old) participated in the study fulfilling the Mode and Frequency of Commuting to and from School Questionnaire. This is a self-reported questionnaire that included questions about personal data and the usual and weekly mode of commuting to go and come back from school. Results: There were differences between the modes of commuting to and from school in commuting by car and on foot in children and adolescents and by bus only in adolescents (all p ≤ 0.001). Reporting a usual mode of commuting indicated 7.7 travels/week (the maximum number of journeys is 10) using that same mode. Conclusion: Our results show the importance of assessing the mode of commuting in both directions and only one recall period (i.e., usual or the weekly mode of commuting).