Liu Xinran, McNeish Daniel
Arizona State University, Tempe, USA.
Educ Psychol Meas. 2024 Nov 8:00131644241290172. doi: 10.1177/00131644241290172.
Factor analysis is commonly used in behavioral sciences to measure latent constructs, and researchers routinely consider approximate fit indices to ensure adequate model fit and to provide important validity evidence. Due to a lack of generalizable fit index cutoffs, methodologists suggest simulation-based methods to create customized cutoffs that allow researchers to assess model fit more accurately. However, simulation-based methods are computationally intensive. An open question is: How many simulation replications are needed for these custom cutoffs to stabilize? This Monte Carlo simulation study focuses on one such simulation-based method-dynamic fit index (DFI) cutoffs-to determine the optimal number of replications for obtaining stable cutoffs. Results indicated that the DFI approach generates stable cutoffs with 500 replications (the currently recommended number), but the process can be more efficient with fewer replications, especially in simulations with categorical data. Using fewer replications significantly reduces the computational time for determining cutoff values with minimal impact on the results. For one-factor or three-factor models, results suggested that in most conditions 200 DFI replications were optimal for balancing fit index cutoff stability and computational efficiency.
因子分析在行为科学中常用于测量潜在结构，研究人员通常会考虑近似拟合指数，以确保模型有足够的拟合度，并提供重要的效度证据。由于缺乏通用的拟合指数临界值，方法学家建议采用基于模拟的方法来创建定制的临界值，使研究人员能够更准确地评估模型拟合度。然而，基于模拟的方法计算量很大。一个悬而未决的问题是：这些定制的临界值需要多少次模拟重复才能稳定下来？这项蒙特卡洛模拟研究聚焦于一种基于模拟的方法——动态拟合指数（DFI）临界值，以确定获得稳定临界值所需的最佳重复次数。结果表明，DFI方法在500次重复（当前推荐的次数）时能生成稳定的临界值，但使用更少的重复次数该过程可能会更高效，尤其是在分类数据的模拟中。使用更少的重复次数能显著减少确定临界值的计算时间，且对结果的影响最小。对于单因素或三因素模型，结果表明在大多数情况下，200次DFI重复对于平衡拟合指数临界值的稳定性和计算效率是最优的。
