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Testing Structural Equation Models or Detection of Misspecifications?

机译：测试结构方程模型还是检测错误规格？

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Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the "size" of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test.
机译：评估结构方程模型的正确性对于避免从经验研究中得出错误的结论至关重要。过去，建议使用卡方检验来评估模型的正确性，但由于该检验对样本量的敏感性而受到批评。作为一种反应，已经开发出大量的拟合指数。这些发展的结果是，结构方程建模软件包现在产生了大量的拟合度量。有人会认为，这种进展已导致对模型错误指定方面的评估模型有了清晰的了解。在本文中，我们质疑基于整体拟合优度指标的模型评估方法的有效性。反对这种用法的观点是，它们不能充分说明模型错误指定的“大小”。也就是说，它们随与模型中的错误指定无关的附带参数的值发生巨大变化。使用简单但基本的模型对此进行了说明。作为模型评估的替代方法，我们建议结合修改指数（MI）和MI测试的功能来使用预期的参数更改。

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