People are variables too: Multilevel structural equations modeling
People are variables too: Multilevel structural equations modeling
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DOI：
10.1037/1082-989x.10.3.259
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发表时间：
2005-09-01
影响因子：
7
通讯作者：
Neale, MC
中科院分区：
文献类型：
--
作者：
Mehta, PD;Neale, MC
本文使用验证性因子分析 (CFA) 作为模板来解释教学多层结构方程模型 (ML-SEM)，并证明一般混合效应模型和 ML-SEM 的等效性。引入了复杂 ML-SEM 的直观、有吸引力的图形表示，简洁地描述了基础模型及其假设。定义变量（即用于将模型参数固定为单个特定数据值的观察变量）的使用扩展到具有随机斜率的聚类数据的 ML-SEM 的情况。提供了具有随机斜率的多级 CFA 和 ML-SEM 的经验示例，以及用于在 SAS Proc Mixed、Mplus 和 Mx 中拟合此类模型的脚本。讨论了有关复杂 ML-SEM 估计和模型拟合评估的方法问题。探索了 ML-SEM 的进一步潜在应用。
The article uses confirmatory factor analysis (CFA) as a template to explain didactically multilevel structural equation models (ML-SEM) and to demonstrate the equivalence of general mixed-effects models and ML-SEM. An intuitively appealing graphical representation of complex ML-SEMs is introduced that succinctly describes the underlying model and its assumptions. The use of definition variables (i.e., observed variables used to fix model parameters to individual specific data values) is extended to the case of ML-SEMs for clustered data with random slopes. Empirical examples of multilevel CFA and ML-SEM with random slopes are provided along with scripts for fitting such models in SAS Proc Mixed, Mplus, and Mx. Methodological issues regarding estimation of complex ML-SEMs and the evaluation of model fit are discussed. Further potential applications of ML-SEMs are explored.