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 χ², 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.

尽管存在许多确定因素数量的方法，但没有一种方法在所有条件下都优于其他方法。本研究在现实场景下比较了传统并行分析 (TPA)、修订并行分析 (RPA)、凯泽规则、最小平均部分、序贯 χ ²和序贯均方根近似误差、比较拟合指数和 Tucker–Lewis 指数在行为研究中，研究人员采用具有K因子的闭拟合简约模型来近似总体模型，从而导致微不足道的模型数据失配。结果表明，虽然当存在零总体水平失配时，传统和 RPA 都表现出色，但当K因子模型可以非常接近总体时，RPA 的准确性会大幅下降。 TPA 对微小的失配最不敏感，并且在大多数模拟条件下都能获得最高的准确度。本研究建议在研究模型中使用 TPA。结果还表明，RPA 需要进一步修订，以适应一定程度的模型与数据不匹配的情况。