Hu L T, Bentler P M, Kano Y
Department of Psychology, University of California, Los Angeles 90024-1563.
Psychol Bull. 1992 Sep;112(2):351-62. doi: 10.1037/0033-2909.112.2.351.
Covariance structure analysis uses chi 2 goodness-of-fit test statistics whose adequacy is not known. Scientific conclusions based on models may be distorted when researchers violate sample size, variate independence, and distributional assumptions. The behavior of 6 test statistics is evaluated with a Monte Carlo confirmatory factor analysis study. The tests performed dramatically differently under 7 distributional conditions at 6 sample sizes. Two normal-theory tests worked well under some conditions but completely broke down under other conditions. A test that permits homogeneous nonzero kurtoses performed variably. A test that permits heterogeneous marginal kurtoses performed better. A distribution-free test performed spectacularly badly in all conditions at all but the largest sample sizes. The Satorra-Bentler scaled test statistic performed best overall.
协方差结构分析使用卡方拟合优度检验统计量，但其充分性尚不清楚。当研究人员违反样本量、变量独立性和分布假设时，基于模型得出的科学结论可能会被扭曲。通过一项蒙特卡洛验证性因子分析研究评估了6种检验统计量的表现。在6种样本量下的7种分布条件下，这些检验的表现差异很大。两种正态理论检验在某些条件下效果良好，但在其他条件下则完全失效。一种允许同质非零峰度的检验表现不一。一种允许异质边际峰度的检验表现更好。一种无分布检验在除最大样本量之外的所有条件下表现都非常差。总体而言，萨托拉-本特勒尺度检验统计量表现最佳。
