Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser's rule, minimum average partial, sequential X[superscript 2], and sequential root mean square error of approximation, comparative fit index, and Tucker-Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing-fitting parsimonious model with "K" factors to approximate a population model, leading to a trivial model-data misfit. Results show that while traditional and RPA both stand out when zero population-level misfits exist, the accuracy of RPA substantially deteriorates when a "K"-factor model can closely approximate the population. TPA is the least sensitive to trivial misfits and results in the highest accuracy across most simulation conditions. This study suggests the use of TPA for the investigated models. Results also imply that RPA requires further revision to accommodate a degree of model-data misfit that can be tolerated.