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Scaling the Variance of a Latent Variable While Assuring Constancy of the Model

机译:缩放潜在变量的方差,同时保证模型的恒定

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This paper investigates how the major outcome of a confirmatory factor investigation is preserved when scaling the variance of a latent variable by the various scaling methods. A constancy framework, based upon the underlying factor analysis formula that enables scaling by modifying components through scalar multiplication, is described; a proof is included to demonstrate the constancy property of the framework. It provides the basis for a scaling method that enables the comparison of the contribution of different latent variables of the same confirmatory factor model to observed scores, as for example, the contributions of trait and method latent variables. Furthermore, it is shown that available scaling methods are in line with this constancy framework and that the criterion number included in some scaling methods enables modifications. The impact of the number of manifest variables on the scaled variance parameter can be modified and the range of possible values. It enables the adaptation of scaling methods to the requirements of the field of application.
机译:本文调查如何在通过各种缩放方法缩放潜伏变量的方差时保留确认因子调查的主要结果。描述了一种恒定的框架,基于通过标量乘法来通过修改组件来实现缩放的底层因子分析公式;包含证据以演示框架的常量属性。它为缩放方法提供了基础,该缩放方法能够比较相同的确认因子模型的不同潜变量的贡献来观察到得分,例如,特征和方法潜变量的贡献。此外,示出了可用的缩放方法符合该恒定框架,并且某些缩放方法中包含的标准号能够进行修改。可以修改缩放方差参数上的清单变量数的影响以及可能的值范围。它可以使缩放方法适应应用领域的应用。

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