Orthogonal And Oblique Rotation Methods

Orthogonal and oblique are two different types of rotation methods used to analyze information from a factor analysis. Factor analysis is a type of statistical procedure that is conducted to identify clusters or groups of related items (called factors) on a test.

For example, when you take a multiple choice Introductory Psychology test, a factor analysis can be done to see what types of questions you did best on and worst on (maybe they did best on factual types of questions but really poorly on conceptual types of questions). Factors are taken from a data set and then rotation of the different factors typically occurs.

Rotation methods simplify factors and make results more reliable and easier to interpret. Rotation methods in which correlations between the factors are allowed are oblique. Rotations where factors are not correlated are orthogonal. So rotation methods that are correlated are oblique while rotation of uncorrelated factors is orthogonal.