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A random loading model is presented as a numerical model in a Monte Carlo experiment of factor analysis. Three methods for the number-of-factors problem, Guttman-Kaiser, likelihood ratio and AIC, and two least-squares estimators, one-step and final, were compared experimentally within each category. On the whole, AIC method using the final estimator fared best, whereas the one-step estimator behaved better than the final estimator in estimating the unique variances.
