THE USE OF CAUSAL INDICATORS IN COVARIANCE STRUCTURE MODELS - SOME PRACTICAL ISSUES
THE USE OF CAUSAL INDICATORS IN COVARIANCE STRUCTURE MODELS - SOME PRACTICAL ISSUES
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DOI：
10.1037/0033-2909.114.3.533
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发表时间：
1993-11-01
影响因子：
22.4
通讯作者：
BROWNE, MW
中科院分区：
文献类型：
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作者：
MACCALLUM, RC;BROWNE, MW
在协方差结构模型的传统表示中，指标被定义为潜在变量加上误差的线性函数。在另一种表示中，构造可以被定义为它们的指标（称为因果指标）加上误差项的线性函数。这些结构不是潜在变量，而是复合变量。他们没有传统意义上的指标。在某些情况下，模型中复合变量的存在会导致模型参数识别的问题。此外，因果指标的使用可以产生模型，意味着许多测量变量之间的零相关性，这个问题只有通过包括潜在的大量额外参数才能解决。这些现象证明了一个例子，并讨论了它们背后的一般原理。对补救措施进行了说明，以便能够对包含因果指标的模型进行评价。
In conventional representations of covariance structure models, indicators are defined as linear functions of latent variables, plus error. In an alternative representation, constructs can be defined as linear functions of their indicators, called causal indicators, plus an error term. Such constructs are not latent variables but composite variables. and they have no indicators in the conventional sense. The presence of composite variables in a model can, in some situations, result in problems with identification of model parameters. Also, the use of causal indicators can produce models that imply zero correlation among many measured variables, a problem resolved only by the inclusion of a potentially large number of additional parameters. These phenomena are demonstrated with an example, and general principles underlying them are discussed. Remedies are described so as to allow for the evaluation of models that contain causal indicators.