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首页> 外文期刊>Multivariate behavioral research >Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: AMonte Carlo Study
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Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: AMonte Carlo Study

机译：使用通用拟合指数检测错误指定的多层结构方程模型：AMonte Carlo研究

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摘要

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.
机译：这项研究调查了常见拟合指标（即RMSEA，CFI，TLI，SRMR-W和SRMR-B）对于检测错误指定的多级SEM的敏感性。蒙特卡洛研究的设计因素是组间模型（100、150和300）中的组数，组大小（10、20、30和60），组内相关性（低，中和高） ），以及模型错误指定的类型（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指定。此外，CFI，TLI和RMSEA对模式系数的错误指定更敏感，而SRMR-W对因子协方差的错误指定更敏感。此外，就检测因子协方差中的组内错误指定的命中率而言，TLI优于CFI和RMSEA。另一方面，SRMR-B是唯一的拟合指数，在组间模型中对错误指定敏感，并且比模式系数错误指定对因子协方差错误指定更敏感。最后，我们发现ICC对目标拟合指标的性能影响很小。

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