Editing Factor analysis (section)

==== Orthogonal methods ====
Two broad classes of orthogonal rotations exist: those that look for sparse rows (where each row is a case, i.e. subject), and those that look for sparse columns (where each column is a variable).

* Simple factors: these rotations try to explain all factors by using only a few important variables. This effect can be achieved by using ''Varimax'' (the most common rotation).
* Simple variables: these rotations try to explain all variables using only a few important factors. This effect can be achieved using either ''Quartimax'' or the unrotated components of PCA.
* Both: these rotations try to compromise between both of the above goals, but in the process, may achieve a fit that is poor at both tasks; as such, they are unpopular compared to the above methods. ''Equamax'' is one such rotation.