Experimental comparison of least-squares and maximum likelihood methods in factor analysis

Three popular methods to estimate the unknown parameters in the factor analysis model, simple (SLS) and weighted (WLS) least-squares methods and the maximum likelihood method (ML), are compared by a Monte Carlo study. The experiments were conducted with 200 replications for every combination of levels of the following three conditions: method (3 levels), sample size (3 levels) and uniquenesses (2 levels). It was found that SLS performed most favorably when the sample size is relatively small and unique variances are relatively large. WLS and ML proved to be rather alike.