Critical comments on applying covariance structure modeling
批评了研究者常用协方差结构模型（结构方程模型）来开发测量和理论模型的做法，认为这难以推动科学进步，并提出了更有效的替代方法。
Abstract Covariance structure modeling, also known as structural equation modeling or causal modeling, appears increasingly popular. Such techniques can be used to conduct tests of complex theory on empirical data. To conduct such tests, researchers need measures of known factor structure and the knowledge of structural relations among the constructs of interest. Researchers typically have neither the required measures nor the knowledge of structural relations. Instead of conducting tests of theory, researchers use covariance structure models to develop measurements and theoretical models. This paper discusses why such use of covariance structure models is unlikely to produce scientific progress and proposes some alternative procedures thought to be more fruitful.