ABSTRACT

This study examined the accuracy of commonly used model fit indexes in identifying number of factors in multilevel exploratory factor analysis using Monte Carlo simulations. Multilevel data were generated according to different scenarios of factor structures: cluster numbers, cluster sizes, and intraclass correlation coefficient (ICC) conditions. The results showed that when using the model-based approach, most of the commonly used fit indexes could identify the correct number of factors at the lower level, except for the within-level SRMR, AIC, CFI, and the within-level CFI. However, most of the fit indexes extracted fewer factors at the higher level when ICC and sample size were small. When using the design-based approach (assuming the same factor structure across levels), most of the fit indexes were able to identify the correct number of factors, except for SRMR, AIC, and ∆AIC, and CFI.

摘要

本研究使用蒙特卡罗模拟检验了多级探索性因子分析中常用模型拟合指数在识别因子数量方面的准确性。根据因子结构的不同场景生成多级数据：簇数、簇大小和类内相关系数（ICC）条件。结果表明，在使用基于模型的方法时，除层内SRMR、AIC、CFI和层内CFI外，大多数常用拟合指数都能识别出较低水平的正确因子数。然而，当ICC和样本量较小时，大多数拟合指数在较高水平上提取的因素较少。当使用基于设计的方法时（假设跨级别具有相同的因子结构），