Abstract
Three methods commonly used to estimate unknown parameters in the factor analysis model, i.e., simple least-squares (SLS), weighted least-squares (WLS), and normal maximum likelihood (NML) methods, are compared by a Monte Carlo study with respect to their performances and robustness to the lack of normality. Our experiment was conducted with 200 replications for every combination of levels of the following four conditions : method (3 levels), sample size (2 levels), uniqueness (2 levels) and choice of non-normal variable (2 levels). It was found that SLS performed most favorably for all non-normal distribution, when sample size was relatively small and/or unique variances were relatively large, and that WLS was most robust and NML and SLS were equally sensitive when the distribution had non-zero kurtosis or skewness. Moreover, it was found that the effect of the kurtosis of the non-normal distribution on the estimation error was more serious than one of the skewess of the non-normal distribution.
