Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
指出常用卡方检验在结构方程模型中的缺陷，如样本量增大时仍可能犯第二类错误，并开发了基于共享方差的新检验系统来评估模型解释力。
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.