Statistics: Orthogonal and Oblique Factor Rotation
Factor rotation enhances the interpretability of factor analysis by adjusting factor loadings. Orthogonal rotation preserves factor independence, simplifying results and improving replicability but may oversimplify real-world relationships. Oblique rotation allows factor correlation, providing a better model fit but making interpretation more complex. Researchers choose oblique rotation when orthogonal methods fail to maximize factor loadings or oversimplify data. Selecting the appropriate rotation method depends on the nature of the data and research objectives.
References
Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Thousand Oaks, CA: SAGE Publications.
Kieffer, K. M. (1998). Orthogonal versus oblique factor rotation: A review of the literature regarding the pros and cons. Web.
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.
Reference
StudyCorgi. (2025, February 13). Statistics: Orthogonal and Oblique Factor Rotation. https://studycorgi.video/statistics-orthogonal-and-oblique-factor-rotation/