This study compared the effectiveness of 10 methods of determining the number of factors to retain in exploratory common factor analysis. The 10 methods included the Kaiser rule and a modified Kaiser criterion, 3 variations of parallel analysis, 4 regression-based variations of the scree procedure, and the minimum average partial procedure. The performance of these procedures was evaluated based on the average number of factors retained by each method, the proportion of samples retaining the same number of factors retained by each method, the proportion of samples retaining the same number of factors as the true number of factors in the population, and the proportion of samples retaining the same number of factors when a particular rule of thumb is applied to the population. The performance of the 10 procedures was investigated using Monte Carlo methods in which random samples were generated under known and controlled population conditions. Results clearly suggest that both the choice of method and the design of the factor analytic study play crucial roles in retaining the correct number of common factors. In terms of overall accuracy across the conditions examined in this study, one of the parallel analysis approaches (Montanelli and Humphreys, 1976) provided the largest proportion of samples retaining the correct value. Conditions under which other approaches may work well are discussed. (Contains 16 tables, 18 figures, 25 charts, and 44 references.) (SLD)