In factor analysis, oblique rotation allows

In factor analysis, oblique rotation allows

• A. Uncorrelated factors.
• B. Correlated factors.
• C. No rotation.
• D. Rotation to maximize eigenvalues.

Answer: (B)

Oblique rotation in factor analysis allows factors to be correlated. The idea behind rotation is to make the factor structure easier to interpret by shifting where the variance loads onto each factor. Orthogonal rotations keep factors completely independent, which is appropriate when the underlying constructs really are unrelated. Oblique rotations, on the other hand, let the factors relate to one another, which better fits many real-world data sets where underlying traits or abilities influence each other. This is why oblique rotation is used when you expect some overlap between factors, and why you might see a correlated factor solution with methods like Promax or Direct Oblimin. Rotation doesn’t change how much total variance is explained or the eigenvalues produced by extraction; it simply rearranges the loadings to achieve a simpler, more interpretable pattern. The option about maximizing eigenvalues isn’t what rotation does.

Oblique rotation in factor analysis allows factors to be correlated. The idea behind rotation is to make the factor structure easier to interpret by shifting where the variance loads onto each factor. Orthogonal rotations keep factors completely independent, which is appropriate when the underlying constructs really are unrelated. Oblique rotations, on the other hand, let the factors relate to one another, which better fits many real-world data sets where underlying traits or abilities influence each other. This is why oblique rotation is used when you expect some overlap between factors, and why you might see a correlated factor solution with methods like Promax or Direct Oblimin. Rotation doesn’t change how much total variance is explained or the eigenvalues produced by extraction; it simply rearranges the loadings to achieve a simpler, more interpretable pattern. The option about maximizing eigenvalues isn’t what rotation does.