Bagozzi R P, Fornell C, Larcker D F
Multivariate Behav Res. 1981 Oct 1;16(4):437-54. doi: 10.1207/s15327906mbr1604_2.
Canonical correlation analysis is commonly considered to be a general model for most parametric bivariate and multivariate statistical methods. Because of its capability for handling multiple criteria and multiple predictors simultaneously, canonical correlation analysis has a great deal of appeal and has also enjoyed increasing application in the behavioral sciences. However, it has also been plagued by several serious shortcomings. In particular, researchers have been unable to determine the statistical significance of individual parameter estimates or to relax assumptions of the canonical model that are inconsistent with theory and/or observed data. As a result, canonical correlation analysis has found more application in exploratory research than in theory testing. This paper illustrates how these problems can be resolved by expressing canonical correlation as a special case of a linear structural relations model.
典型相关分析通常被认为是大多数参数双变量和多变量统计方法的通用模型。由于其能够同时处理多个标准和多个预测变量，典型相关分析具有很大的吸引力，并且在行为科学中的应用也越来越广泛。然而，它也受到了几个严重缺点的困扰。特别是，研究人员无法确定单个参数估计的统计显著性，也无法放宽与理论和/或观测数据不一致的典型模型假设。因此，典型相关分析在探索性研究中的应用比在理论检验中更多。本文说明了如何通过将典型相关表示为线性结构关系模型的一种特殊情况来解决这些问题。
