Maydeu-Olivares Alberto
University of Barcelona, Barcelona, Spain.
Department of Psychology, University of South Carolina, Barnwell College, 1512 Pendleton St., Columbia, SC, 29208, USA.
Psychometrika. 2017 Feb 7. doi: 10.1007/s11336-016-9552-7.
When a statistically significant mean difference is found, the magnitude of the difference is judged qualitatively using an effect size such as Cohen's d. In contrast, in a structural equation model (SEM), the result of the statistical test of model fit is often disregarded if significant, and inferences are drawn using "close" models retained based on point estimates of sample statistics (goodness-of-fit indices). However, when a SEM cannot be retained using a test of exact fit, all substantive inferences drawn from it are suspect. It is therefore important to determine the size of the model misfit. Standardized residual covariances and residual correlations provide standardized effect sizes of the misfit of SEM models. They can be summarized using the Standardized Root Mean Squared Residual (SRMSR) and the Correlation Root Mean Squared Residual (CRMSR) which can be used as overall effect sizes of the misfit. Statistical theory is provided that allows the construction of confidence intervals and tests of close fit based on the SRMSR and CRMSR. It is hoped that the use of standardized effect sizes of misfit will help reconcile current practices in SEM and elsewhere in statistics.
当发现具有统计学显著意义的均值差异时,会使用诸如科恩d值之类的效应量对差异的大小进行定性判断。相比之下,在结构方程模型(SEM)中,模型拟合的统计检验结果即使具有显著性也常常被忽略,而是基于样本统计量的点估计(拟合优度指数)保留的“近似”模型进行推断。然而,当无法通过精确拟合检验保留SEM时,从中得出的所有实质性推断都值得怀疑。因此,确定模型失拟的大小很重要。标准化残差协方差和残差相关性提供了SEM模型失拟的标准化效应量。可以使用标准化均方根残差(SRMSR)和相关均方根残差(CRMSR)对它们进行汇总,这两者可作为失拟的总体效应量。文中提供了统计理论,允许基于SRMSR和CRMSR构建置信区间并进行近似拟合检验。希望使用失拟的标准化效应量将有助于协调SEM以及统计学其他领域中的当前做法。