Orthogonal Rotation Method

The orthogonal rotation method is a statistical technique used in factor analysis, a multivariate data analysis method. It involves rotating the axes or factors in order to simplify and improve the interpretability of the factor structure obtained from the initial extraction method.

Orthogonal rotation ensures that the rotated factors are uncorrelated or orthogonal to each other. This means that the factors do not share any common variance, making their interpretation easier. The most commonly used orthogonal rotation method is the Varimax method, which maximizes the variance of the squared loadings in each factor.

Orthogonal rotation provides distinct and independent factors, but it may not accurately represent the underlying structure of the data. It assumes that the factors are independent and unrelated to each other, which may not always be the case in real-world datasets.

Oblique Rotation Method

The oblique rotation method, also known as non-orthogonal rotation, is an alternative to orthogonal rotation in factor analysis. Unlike orthogonal rotation, oblique rotation allows for correlation between the rotated factors. It aims to uncover the underlying structure of the data by allowing the factors to be correlated, reflecting the potential interdependence that exists between variables.

Oblique rotation methods, such as Promax and Direct Oblimin, provide a more flexible approach in factor analysis as they allow for the identification of complex relationships between factors. This can be particularly useful when the factors are expected to be related or when the assumption of orthogonality between factors is violated.

However, oblique rotation can make factor interpretation more challenging compared to orthogonal rotation. The interpretation of correlated factors requires careful consideration of the relationship and overlap between factors, and it may result in a less straightforward and clear representation of the factor structure compared to orthogonal rotation methods.