Structural equation modeling of multitrait-multimethod data: Different models for different types of methods
Structural equation modeling of multitrait-multimethod data: Different models for different types of methods
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DOI：
10.1037/a0013219
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发表时间：
2008-09-01
影响因子：
7
通讯作者：
Lischetzke, Tanja
中科院分区：
文献类型：
--
作者：
Eid, Michael;Nussbeck, Fridtjof W.;Lischetzke, Tanja
在多特征多方法（MTMM）数据分析中，选择何种结构方程模型是许多研究者感兴趣的问题。在过去，寻找良好拟合模型的尝试通常是数据驱动的并且是高度任意的。在本文中，作者认为，测量设计（使用的方法类型）应指导选择的统计模型来分析数据。在这方面，作者区分了（a）可互换的方法，（B）结构不同的方法，以及（c）两种方法的组合。作者提出了一个适当的模型，每种类型的方法。所有的模型允许分离测量误差的性状影响和性状特异性方法的影响。在可互换方法方面，本文提出了一个多水平验证性因素模型。对于结构上不同的方法，推荐相关性状相关（方法-1）模型。最后，作者演示了如何适当地分析数据的MTMM设计，同时使用可互换的和结构不同的方法。所有模型都应用于经验数据，以说明其正确使用。MTMM数据建模的一些影响和指导方针进行了讨论。
The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past., attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods used) should Guide the choice of the statistical model to analyze the data. In this respect, the authors distinguish between (a) interchangeable methods, (b) structurally different methods, and (c) the combination of both kinds of methods. The authors present an appropriate model for each type of method. All models allow separating measurement error from trait influences and trait-specific method effects. With respect to interchangeable methods, a multilevel confirmatory factor model is presented. For structurally different methods, the correlated trait-correlated (method-1) model is recommended. Finally, the authors demonstrate how to appropriately analyze data from MTMM designs that simultaneously use interchangeable and structurally different methods. All models are applied to empirical data to illustrate their proper use. Some implications and guidelines for modeling MTMM data are discussed.