The effects of model parsimony and sampling error on the fit of structural equation models
The effects of model parsimony and sampling error on the fit of structural equation models
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DOI：
10.1177/109442810143004
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发表时间：
2001-07-01
影响因子：
9.5
通讯作者：
Rensvold, RB
中科院分区：
文献类型：
--
作者：
Cheung, GW;Rensvold, RB
结构方程模型和数据集之间的拟合可操作为拟合优度指数的值。估计值和表示完美拟合的值之间的差异有三个来源：错误指定、模型描述中的理论简约性引起的误差（简约性误差）和抽样误差。错误设定，代表了“真实世界”的关系和模型中的关系之间的差异，是研究人员最重要的错误来源。然而，除非考虑到简约误差和抽样误差，否则无法准确评估。当排除次要关系时，度量模型会出现简约误差。次要关系在这里被定义为次要因子载荷和误差项相关性，它们的值很小，没有理论基础，也没有实质意义。进行了模拟，以检查简约误差的影响，一个完美的测量模型，并建立适当的标准模型拟合时，简约误差。
The fit between a structural equation model and a data set is operationalized as the value of goodness-of-fit indices. The discrepancy between the estimated value and the value indicating perfect fit has three sources: misspecification, error arising from theoretical parsimony in the description of the model (parsimony error), and sampling error. Misspecification, which represents a disparity between "real-world" relationships and relationships in the model, is the most important source of error for researchers. It cannot be accurately assessed, however, unless parsimony error and sampling error are taken into account. Parsimony error occurs in measurement models when secondary relationships are excluded. Secondary relationships are defined here as secondary factor loadings and error term correlations that have small values, no theoretical bases, and no substantive meaning. A simulation was conducted to examine the effects of parsimony error an perfect measurement models and to establish appropriate criteria for model fit when parsimony error is present.