Pornprasertmanit Sunthud, Lee Jaehoon, Preacher Kristopher J
a Texas Tech University.
b Vanderbilt University.
Multivariate Behav Res. 2014 Nov-Dec;49(6):518-43. doi: 10.1080/00273171.2014.933762.
In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro level. Standardized parameter estimates from the disaggregation and aggregation approaches are deviated toward the values of the macro- and micro-level standardized parameter estimates, respectively. The degree of deviation depends on ICC and cluster size, particularly for the aggregation method. The standard errors of standardized parameter estimates from the disaggregation approach depend on the macro-level item communalities. Those from the aggregation approach underestimate the standard errors in multilevel CFA (MCFA), especially when ICC is low. Thus, we conclude that MCFA or an alternative approach should be used if possible.
在许多情况下，研究人员收集了多层次（聚类或嵌套）数据，但在分析数据时要么忽略聚类（分解），要么对每个聚类中的微观层面单位进行平均，并在宏观层面分析汇总后的数据（聚合）。在本研究中，我们调查了在验证性因子分析（CFA）中忽略数据嵌套性质的影响。从模型拟合和标准化参数估计的角度检查了忽略聚类所产生的偏差，而模型拟合和标准化参数估计通常是使用CFA的研究人员所感兴趣的。我们发现，分解方法会增加模型失拟，尤其是当组内相关系数（ICC）较高时，而聚合方法在宏观层面能准确检测到模型失拟。分解和聚合方法的标准化参数估计分别向宏观和微观层面标准化参数估计值偏移。偏移程度取决于ICC和聚类大小，聚合方法尤其如此。分解方法的标准化参数估计的标准误差取决于宏观层面的项目共同度。聚合方法的标准误差在多层次CFA（MCFA）中会低估标准误差，尤其是当ICC较低时。因此，我们得出结论，如有可能应使用MCFA或替代方法。
