Multivariate Behav Res. 1990 Apr 1;25(2):137-55. doi: 10.1207/s15327906mbr2502_1.
The purpose of this article is to present a strategy for the evaluation and modification of covariance structure models. The approach makes use of recent developments in estimation under non-standard conditions and unified asymptotic theory related to hypothesis testing. Factors affecting the evaluation and modification of these models are reviewed in terms of nonnormality, missing data, specification error, and sensitivity to large sample size. Alternative model evaluation and specification error search strategies are also reviewed. The approach to covariance structure modeling advocated in this article utilizes the LISREL modification index for assessing statistical power, and the expected parameter change statistic for guiding specification error searches. It is argued that the common approach of utilizing alternative fit indices does not allow the investigator to rule out plausible explanations for model misfit. The approach advocated in this article allows one to determine the extent of sample size sensitivity and the effects of specification error by relying on existing statistical theory underlying covariance structure models.
本文旨在提出一种协方差结构模型的评估和修正策略。该方法利用了非标准条件下估计和与假设检验相关的统一渐近理论的最新进展。本文从非正态性、缺失数据、规范误差和对大样本量的敏感性等方面，综述了影响这些模型评估和修正的因素。还回顾了替代模型评估和规范误差搜索策略。本文所倡导的协方差结构建模方法利用 LISREL 修改指数来评估统计功效，利用预期参数变化统计量来指导规范误差搜索。本文认为，利用替代拟合指数的常见方法不允许研究人员排除模型不拟合的合理解释。本文所倡导的方法允许人们通过依赖协方差结构模型的现有统计理论来确定样本量敏感性的程度和规范误差的影响。
