Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: AMonte Carlo Study
Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: AMonte Carlo Study
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DOI：
10.1080/00273171.2014.977429
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发表时间：
2015-03-04
影响因子：
3.8
通讯作者：
Acosta, Sandra
中科院分区：
文献类型：
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作者：
Hsu, Hsien-Yuan;Kwok, Oi-man;Acosta, Sandra
本研究探讨了常用拟合指数（即RMSEA、CFI、TLI、SRMR-W和SRMR-B）检测错配多水平sem的敏感性。蒙特卡罗研究的设计因素是组间模型中的组数（100、150和300）、组大小（10、20、30和60）、类内相关性（低、中、高）以及模型错误说明的类型（简单和复杂）。仿真结果表明，CFI、TLI和RMSEA只能识别组内模型中的错误规范。此外，CFI、TLI和RMSEA对模式系数的错配更为敏感，而SRMR-W对因子协方差的错配更为敏感。此外，在因子协方差的组内错配检测命中率方面，TLI优于CFI和RMSEA。另一方面，SRMR-B是组间模型中唯一对错配敏感的拟合指标，对因子协方差的错配比对模式系数的错配更敏感。最后，我们发现ICC对目标拟合指标的影响是微不足道的。
This study investigated the sensitivity of common fit indices (i.e., RMSEA, CFI, TLI, SRMR-W, and SRMR-B) for detecting misspecified multilevel SEMs. The design factors for the Monte Carlo study were numbers of groups in between-group models (100, 150, and 300), group size (10, 20, 30, and 60), intra-class correlation (low, medium, and high), and the types of model misspecification (Simple and Complex). The simulation results showed that CFI, TLI, and RMSEA could only identify the misspecification in the within-group model. Additionally, CFI, TLI, and RMSEA were more sensitive to misspecification in pattern coefficients while SRMR-W was more sensitive to misspecification in factor covariance. Moreover, TLI outperformed both CFI and RMSEA in terms of the hit rates of detecting the within-group misspecification in factor covariance. On the other hand, SRMR-B was the only fit index sensitive to misspecification in the between-group model and more sensitive to misspecification in factor covariance than misspecification in pattern coefficients. Finally, we found that the influence of ICC on the performance of targeted fit indices was trivial.