What type of approach assumes there is no correlation between factors in factor analysis?

The orthogonal approach in factor analysis is characterized by the assumption that there is no correlation among the factors being analyzed. This means that the factors are treated as independent from one another, allowing for a clearer interpretation of how each factor contributes to the variance in the data without overlapping influences from other factors.

This approach is often associated with methods like Varimax rotation, which aims to simplify the factor structure by maximizing the variance of squared loadings within each factor while ensuring that the factors themselves do not correlate. The independence of factors is particularly useful in many applications where researchers seek distinct, non-overlapping constructs that can be interpreted separately.

In contrast, the oblique approach allows for correlated factors, thereby acknowledging potential relationships between them, which can be essential in certain contexts where factors are not truly independent. The maximal likelihood and Bayesian approaches refer more to the estimation methods used in factor analysis rather than the assumptions regarding factor independence.