A factor analysis has the most interpretative value when:

• 1) Each factor loads strongly on only one factor
• 2) Each factor shows at least three strong loadings
• 3) Most loadings are either high or low
• 4) We get a “simple” factor structure

Rotation is a way of maximizing high loadings and minimizing low loadings so that we get the simplest factor structure possible. There are two main types of rotation:

Orthogonal	Assumes that the factors are uncorrelated Examples of sub types: varimax, quartimax, and equamax
Oblique	Assumes that the factors are correlated Examples of sub types: promax and oblimin

Thus, orthogonal rotation relies on the assumption that the factors are not correlated to each other, i.e. that the different factors represent different unrelated dimensions of what you are examining. This is not always the case.

For example, if you have several variables measuring health, and find one factor that reflects physical health and another one reflecting psychological health, it may not be reasonable to assume that physical and psychological health two unrelated dimension. In that case, you need to change the type of rotation to oblique.

In Stata, orthogonal rotation with the varimax option is default.