Evaluating SEM Model Fit with Small Degrees of Freedom
Evaluating SEM Model Fit with Small Degrees of Freedom
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DOI：
10.1080/00273171.2020.1868965
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发表时间：
2020-12-30
影响因子：
3.8
通讯作者：
Lee, Taehun
中科院分区：
文献类型：
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作者：
Shi, Dexin;DiStefano, Christine;Lee, Taehun
研究表明，均方根误差近似（RMSEA）在评估小自由度（DF）的结构方程模型的性能是次优的，往往会导致拒绝正确指定或密切拟合的模型。本研究探讨了标准化均方根残差（SRMR）和比较拟合指数（CFI）的性能在小DF模型与各种水平的因素负荷，样本量，模型误设。我们发现，与RMSEA相比，群体SRMR和CFI对DF的影响较小。在小的模型中，样本SRMR和CFI可以提供更多有用的信息，以区分模型与不同水平的失配。对于所有三个拟合指数，紧密拟合的置信区间和p值通常准确。我们建议研究人员在解释具有小df的模型的RMSEA时要谨慎，并更多地依赖SRMR和CFI。
Research has revealed that the performance of root mean square error of approximation (RMSEA) in assessing structural equation models with small degrees of freedom (df) is suboptimal, often resulting in the rejection of correctly specified or closely fitted models. This study investigates the performance of standardized root mean square residual (SRMR) and comparative fit index (CFI) in small df models with various levels of factor loadings, sample sizes, and model misspecifications. We find that, in comparison with RMSEA, population SRMR and CFI are less susceptible to the effects of df. In small df models, the sample SRMR and CFI could provide more useful information to differentiate models with various levels of misfit. The confidence intervals and p-values of a close fit were generally accurate for all three fit indices. We recommend researchers use caution when interpreting RMSEA for models with small df and to rely more on SRMR and CFI.