ABSTRACT

Latent variables in structural equation models do not have an observable scale. Hence, researchers resort to scaling methods, such as fixed marker, effects coding, or fixed factor, to assign scales to the latent variables. The use of such procedures results in numerically different estimates, in spite of a single underlying population model. In this paper, we provide a framework which not only allows for a translation between estimates obtained under different scaling methods, but also helps to explore the relation between the underlying population parameters and their estimates, thus providing a basis for the interpretation of estimated parameters. Additionally, the framework proves useful for demonstrating that the choice of scaling method affects the power of the Wald test for testing parameters’ significance.

摘要

结构方程模型中的潜在变量没有可观察的尺度。因此，研究人员诉诸于缩放方法，例如固定标记，效果编码或固定因子，以将缩放比例分配给潜在变量。尽管有一个基本的人口模型，但使用这些程序会导致在数值上不同的估计。在本文中，我们提供了一个框架，该框架不仅允许在使用不同缩放方法获得的估计值之间进行转换，而且还有助于探索底层总体参数与其估计值之间的关系，从而为解释估计的参数提供了基础。此外，该框架对于证明缩放比例方法的选择会影响Wald检验对检验参数的重要性的功效很有用。